Identification of voice inputs providing credentials

专利摘要:
Systems and processes for identifying of a voice input providing one or more user credentials are provided. In one example process, a voice input can be received. A first character, a phrase identifying a second character, and a word can be identified based on the voice input. In response to the identification, the first character, the second character, and the word can be converted to text. The text can be caused to display, with a display, in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
公开号:DK201770128A1
申请号:DKP201770128
申请日:2017-02-21
公开日:2018-10-09
发明作者:Akbacak Murat；Hansen Bryan；Evermann Gunnar
申请人:Apple Inc.；
IPC主号:

专利说明:

DANMARK (10)
DK 2017 70128 A1
(12)
PATENTANSØGNING
Patent- og Varemærkestyrelsen
Int.CI.: G10L 15/26 (2006.01) G10L 15/22 (2006.01)
Ansøgningsnummer: PA 2017 70128
Indleveringsdato: 2017-02-21
Løbedag:2017-02-21
Aim. tilgængelig: 2017-09-15
Publiceringsdato: 2018-10-09
Prioritet:
2016-03-14 US 62/308,090
2016-09-16 US 15/268,360
Ansøger:
Apple Inc., 1 Infinite Loop Cupertino 95014 CA California, USA
Opfinder:
Murat Akbacak, c/o Apple Inc. 1 Infinite Loop Cupertino C A California, 95014, USA Bryan Hansen, c/o Apple Inc. 1 Infinite Loop Cupertino CA California 95014, USA Gunnar Evermann, c/o Apple Inc. 1 Infinite Loop Cupertino CA California 95014, USA
Fuldmægtig:
COPA COPENHAGEN PATENTS K/S, Rosenørns Allé 1,2. sal, 1970 Frederiksberg C, Danmark
Titel: IDENTIFICATION OF VOICE INPUTS PROVIDING CREDENTIALS
Fremdragne publikationer:
US 2014/0282045 A1
US 7143037 B1
US 2012/0271640 A1
EP 0634042 B1
WO 0126093 A1
EP 1320848 A1
Sammendrag:
Systems and processes for identifying of a voice input providing one or more user credentials are provided. In one example process, a voice input can be received. A first character, a phrase identifying a second character, and a word can be identified based on the voice input. In response to the identification, the first character, the second character, and the word can be converted to text. The text can be caused to display, with a display, in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
Fortsættes...
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DEVICE
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DK 2017 70128 A1
IDENTIFICATION OF VOICE INPUTS PROVIDING CREDENTIALS
Cross-reference to Related Applications [0001] This application claims priority to U.S. Patent Application Serial No. 15/268,360, entitled “IDENTIFICATION OF VOICE INPUTS PROVIDING CREDENTIALS,” filed on September 16, 2016; which claims priority to U.S. Provisional Patent Application Serial No. 62/308,090, entitled “IDENTIFICATION OF VOICE INPUTS PROVIDING CREDENTIALS,” filed on March 14, 2016. The content of each of these applications is hereby incorporated by reference in its entirety for all purposes.
Field [0002] This relates generally to voice recognition and, more specifically, to identification of voice inputs providing credentials.
Background [0003] Conventionally, authentication often requires that a user provide credentials such as a user name and a password. Authentication can be performed with devices such as computers, tablets, mobile devices, televisions, multimedia devices, set-top boxes, and the like. Speech recognition is increasing popular for dictation and controlling devices. Credentials may include a mix of characters, words, phrases, commands, and the like. Such mixed input makes it difficult for accurately recognizing the user speech. Enhancing accurate entry of credentials using speech is thus important.
Summary [0004] Systems and processes for identifying voice inputs providing one or more credentials are provided. In accordance with one or more examples, a method includes, at a user device with a display, one or more processors, and memory, receiving a voice input. The method also
DK 2017 70128 A1 includes identifying, based on the voice input, a first character, a phrase identifying a second character, and a word. The method further includes, in response to the identification, converting the first character, the second character, and the word to text; and causing to display, with the display, the text in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
Brief Description of the Drawings [0005] For a better understanding of the various described embodiments, reference should be made to the Description of Embodiments below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures.
[0006] FIG. 1 is a block diagram illustrating a system and environment for implementing a digital assistant according to various examples.
[0007] FIG. 2A is a block diagram illustrating a portable multifunction device implementing the client-side portion of a digital assistant in accordance with some embodiments.
[0008] FIG. 2B is a block diagram illustrating exemplary components for event handling according to various examples.
[0009] FIG. 3 illustrates a portable multifunction device implementing the client-side portion of a digital assistant according to various examples.
[0010] FIG. 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface according to various examples.
[0011] FIG. 5A illustrates an exemplary user interface for a menu of applications on a portable multifunction device according to various examples.
[0012] FIG. 5B illustrates an exemplary user interface for a multifunction device with a touch-sensitive surface that is separate from the display according to various examples.
[0013] FIG. 6A illustrates a personal electronic device according to various examples.
DK 2017 70128 A1 [0014] FIG. 6B is a block diagram illustrating a personal electronic device according to various examples.
[0015] FIG. 7A is a block diagram illustrating a digital assistant system or a server portion thereof according to various examples.
[0016] FIG. 7B illustrates the functions of the digital assistant shown in FIG. 7 A according to various examples.
[0017] FIG. 7C illustrates a portion of an ontology according to various examples.
[0018] FIGs. 8A-8B illustrate block diagrams of exemplary architectures for devices according to various examples.
[0019] FIG. 9 illustrates a block diagram of an exemplary automatic speech recognition module according to various examples.
[0020] FIGs. 10A-10F illustrate functionality of voice input identification for entry of credentials according to various examples.
[0021] FIGs. 11A-1 ID illustrate functionality of voice input identification for entry of credential according to various examples.
[0022] FIGs. 12A-D illustrates a flow diagram of an exemplary process for identifying voice inputs according to various examples.
[0023] FIG. 13 illustrates a block diagram of an electronic device according to various examples.
Detailed Description [0024] In the following description of the disclosure and embodiments, reference is made to the accompanying drawings, in which it is shown by way of illustration, of specific embodiments that can be practiced. It is to be understood that other embodiments and examples can be practiced and changes can be made without departing from the scope of the disclosure.
DK 2017 70128 A1 [0025] Techniques for identification of voice inputs are desirable. As described herein, techniques for identifying voice inputs are desired for various purposes such as entering credentials that typically include a mix of characters, words, phrases, and/or commands. Such techniques are advantageous by allowing the user to enter the credentials hands-free. Further, such techniques for identifying voice inputs can reduce the user’s effort to manually enter credentials or any other information to devices character-by-character, which could be time-consuming and burdensome.
[0026] Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first character could be termed a second character, and, similarly, a second character could be termed a first character, without departing from the scope of the various described examples. The first character and the second character can both be characters and, in some cases, can be separate and different characters.
[0027] The terminology used in the description of the various described examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various described examples and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
[0028] The term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.
DK 2017 70128 A1
1. System and Environment [0029] FIG. 1 illustrates a block diagram of system 100 according to various examples. In some examples, system 100 can implement a digital assistant. The terms “digital assistant,” “virtual assistant,” “intelligent automated assistant,” or “automatic digital assistant” can refer to any information processing system that interprets natural language input in spoken and/or textual form to infer user intent, and performs actions based on the inferred user intent. For example, to act on an inferred user intent, the system can perform one or more of the following: identifying a task flow with steps and parameters designed to accomplish the inferred user intent, inputting specific requirements from the inferred user intent into the task flow; executing the task flow by invoking programs, methods, services, APIs, or the like; and generating output responses to the user in an audible (e.g., speech) and/or visual form.
[0030] Specifically, a digital assistant can be capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request can seek either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request can be a provision of the requested informational answer, a performance of the requested task, or a combination of the two. For example, a user can ask the digital assistant a question, such as “Where am I right now ” Based on the user’s current location, the digital assistant can answer, “You are in Central Park near the west gate.” The user can also request the performance of a task, for example, “Please invite my friends to my girlfriend’s birthday party next week.” In response, the digital assistant can acknowledge the request by saying “Yes, right away,” and then send a suitable calendar invite on behalf of the user to each of the user’s friends listed in the user’s electronic address book. During performance of a requested task, the digital assistant can sometimes interact with the user in a continuous dialogue involving multiple exchanges of information over an extended period of time. There are numerous other ways of interacting with a digital assistant to request information or performance of various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant can also provide responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc.
DK 2017 70128 A1 [0031] As shown in FIG. 1, in some examples, a digital assistant can be implemented according to a client-server model. The digital assistant can include client-side portion 102 (hereafter “DA client 102”) executed on user device 104 and server-side portion 106 (hereafter “DA server 106”) executed on server system 108. DA client 102 can communicate with DA server 106 through one or more networks 110. DA client 102 can provide client-side functionalities such as user-facing input and output processing and communication with DA server 106. DA server 106 can provide server-side functionalities for any number of DA clients 102 each residing on a respective user device 104.
[0032] In some examples, DA server 106 can include client-facing I/O interface 112, one or more processing modules 114, data and models 116, and I/O interface to external services 118. The client-facing I/O interface 112 can facilitate the client-facing input and output processing for DA server 106. One or more processing modules 114 can utilize data and models 116 to process speech input and determine the user’s intent based on natural language input. Further, one or more processing modules 114 perform task execution based on inferred user intent. In some examples, DA server 106 can communicate with external services 120 through network(s) 110 for task completion or information acquisition. I/O interface to external services 118 can facilitate such communications.
[0033] User device 104 can be any suitable electronic device. For example, user devices can be a portable multifunctional device (e.g., device 200, described below with reference to FIG. 2A), a multifunctional device (e.g., device 400, described below with reference to FIG. 4), or a personal electronic device (e.g., device 600, described below with reference to FIG. 6A-B.) A portable multifunctional device can be, for example, a mobile telephone that also contains other functions, such as PDA and/or music player functions. Specific examples of portable multifunction devices can include the iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, California. Other examples of portable multifunction devices can include, without limitation, laptop or tablet computers. Further, in some examples, user device 104 can be a non-portable multifunctional device. In particular, user device 104 can be a desktop computer, a game console, a television, or a television set-top box. In some examples, user device 104 can include a touch-sensitive surface (e.g., touch screen displays and/or touchpads). Further, user device 104 can optionally include one or more other physical user-interface
DK 2017 70128 A1 devices, such as a physical keyboard, a mouse, and/or a joystick. Various examples of electronic devices, such as multifunctional devices, are described below in greater detail.
[0034] Examples of communication network(s) 110 can include local area networks (LAN) and wide area networks (WAN), e.g., the Internet. Communication network(s) 110 can be implemented using any known network protocol, including various wired or wireless protocols, such as, for example, Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or any other suitable communication protocol.
[0035] Server system 108 can be implemented on one or more standalone data processing apparatus or a distributed network of computers. In some examples, server system 108 can also employ various virtual devices and/or services of third-party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of server system 108.
[0036] In some examples, user device 104 can communicate with DA server 106 via second user device 122. Second user device 122 can be similar or identical to user device 104. For example, second user device 122 can be similar to devices 200, 400, or 600 described below with reference to FIGs. 2A, 4, and 6A-B. User device 104 can be configured to communicatively couple to second user device 122 via a direct communication connection, such as Bluetooth, NFC, BTLE, or the like, or via a wired or wireless network, such as a local Wi-Fi network. In some examples, second user device 122 can be configured to act as a proxy between user device 104 and DA server 106. For example, DA client 102 of user device 104 can be configured to transmit information (e.g., a user request received at user device 104) to DA server 106 via second user device 122. DA server 106 can process the information and return relevant data (e.g., data content responsive to the user request) to user device 104 via second user device 122.
[0037] In some examples, user device 104 can be configured to communicate abbreviated requests for data to second user device 122 to reduce the amount of information transmitted from user device 104. Second user device 122 can be configured to determine supplemental information to add to the abbreviated request to generate a complete request to transmit to DA
DK 2017 70128 A1 server 106. This system architecture can advantageously allow user device 104 having limited communication capabilities and/or limited battery power (e.g., a watch or a similar compact electronic device) to access services provided by DA server 106 by using second user device 122, having greater communication capabilities and/or battery power (e.g., a mobile phone, laptop computer, tablet computer, or the like), as a proxy to DA server 106. While only two user devices 104 and 122 are shown in FIG. 1, it should be appreciated that system 100 can include any number and type of user devices configured in this proxy configuration to communicate with DA server system 106.
[0038] Although the digital assistant shown in FIG. 1 can include both a client-side portion (e.g., DA client 102) and a server-side portion (e.g., DA server 106), in some examples, the functions of a digital assistant can be implemented as a standalone application installed on a user device. In addition, the divisions of functionalities between the client and server portions of the digital assistant can vary in different implementations. For instance, in some examples, the DA client can be a thin-client that provides only user-facing input and output processing functions, and delegates all other functionalities of the digital assistant to a backend server.
2. Electronic Devices [0039] Attention is now directed toward embodiments of electronic devices for implementing the client-side portion of a digital assistant. FIG. 2A is a block diagram illustrating portable multifunction device 200 with touch-sensitive display system 212 in accordance with some embodiments. Touch-sensitive display 212 is sometimes called a “touch screen” for convenience and is sometimes known as or called a “touch-sensitive display system.” Device 200 includes memory 202 (which optionally includes one or more computer-readable storage mediums), memory controller 222, one or more processing units (CPUs) 220, peripherals interface 218, RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, input/output (I/O) subsystem 206, other input control devices 216, and external port 224. Device 200 optionally includes one or more optical sensors 264. Device 200 optionally includes one or more contact intensity sensors 265 for detecting intensity of contacts on device 200 (e.g., a touchsensitive surface such as touch-sensitive display system 212 of device 200). Device 200 optionally includes one or more tactile output generators 267 for generating tactile outputs on
DK 2017 70128 A1 device 200 (e.g., generating tactile outputs on a touch-sensitive surface such as touch-sensitive display system 212 of device 200 or touchpad 455 of device 400). These components optionally communicate over one or more communication buses or signal lines 203.
[0040] As used in the specification and claims, the term “intensity” of a contact on a touchsensitive surface refers to the force or pressure (force per unit area) of a contact (e.g., a finger contact) on the touch-sensitive surface, or to a substitute (proxy) for the force or pressure of a contact on the touch-sensitive surface. The intensity of a contact has a range of values that includes at least four distinct values and more typically includes hundreds of distinct values (e.g., at least 256). Intensity of a contact is, optionally, determined (or measured) using various approaches and various sensors or combinations of sensors. For example, one or more force sensors underneath or adjacent to the touch-sensitive surface are, optionally, used to measure force at various points on the touch-sensitive surface. In some implementations, force measurements from multiple force sensors are combined (e.g., a weighted average) to determine an estimated force of a contact. Similarly, a pressure-sensitive tip of a stylus is, optionally, used to determine a pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and/or changes thereto, the capacitance of the touch-sensitive surface proximate to the contact and/or changes thereto, and/or the resistance of the touch-sensitive surface proximate to the contact and/or changes thereto are, optionally, used as a substitute for the force or pressure of the contact on the touch-sensitive surface. In some implementations, the substitute measurements for contact force or pressure are used directly to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurements). In some implementations, the substitute measurements for contact force or pressure are converted to an estimated force or pressure, and the estimated force or pressure is used to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of a contact as an attribute of a user input allows for user access to additional device functionality that may otherwise not be accessible by the user on a reduced-size device with limited real estate for displaying affordances (e.g., on a touch-sensitive display) and/or receiving user input (e.g., via a touch-sensitive display, a touchsensitive surface, or a physical/mechanical control such as a knob or a button).
DK 2017 70128 A1 [0041] As used in the specification and claims, the term “tactile output” refers to physical displacement of a device relative to a previous position of the device, physical displacement of a component (e.g., a touch-sensitive surface) of a device relative to another component (e.g., housing) of the device, or displacement of the component relative to a center of mass of the device that will be detected by a user with the user’s sense of touch. For example, in situations where the device or the component of the device is in contact with a surface of a user that is sensitive to touch (e.g., a finger, palm, or other part of a user’s hand), the tactile output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in physical characteristics of the device or the component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or trackpad) is, optionally, interpreted by the user as a “down click” or “up click” of a physical actuator button. In some cases, a user will feel a tactile sensation such as an “down click” or “up click” even when there is no movement of a physical actuator button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user’s movements. As another example, movement of the touch-sensitive surface is, optionally, interpreted or sensed by the user as “roughness” of the touch-sensitive surface, even when there is no change in smoothness of the touch-sensitive surface. While such interpretations of touch by a user will be subject to the individualized sensory perceptions of the user, there are many sensory perceptions of touch that are common to a large majority of users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., an “up click,” a “down click,” “roughness”), unless otherwise stated, the generated tactile output corresponds to physical displacement of the device or a component thereof that will generate the described sensory perception for a typical (or average) user.
[0042] It should be appreciated that device 200 is only one example of a portable multifunction device, and that device 200 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown in FIG. 2A are implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application-specific integrated circuits.
DK 2017 70128 A1 [0043] Memory 202 may include one or more computer-readable storage mediums. The computer-readable storage mediums may be tangible and non-transitory. Memory 202 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solidstate memory devices. Memory controller 222 may control access to memory 202 by other components of device 200.
[0044] In some examples, a non-transitory computer-readable storage medium of memory 202 can be used to store instructions (e.g., for performing aspects of process 1200, described below) for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In other examples, the instructions (e.g., for performing aspects of process 1200, described below) can be stored on a non-transitory computer-readable storage medium (not shown) of the server system 108 or can be divided between the non-transitory computer-readable storage medium of memory 202 and the non-transitory computer-readable storage medium of server system 108. In the context of this document, a “non-transitory computer-readable storage medium” can be any medium that can contain or store the program for use by or in connection with the instruction execution system, apparatus, or device.
[0045] Peripherals interface 218 can be used to couple input and output peripherals of the device to CPU 220 and memory 202. The one or more processors 220 run or execute various software programs and/or sets of instructions stored in memory 202 to perform various functions for device 200 and to process data. In some embodiments, peripherals interface 218, CPU 220, and memory controller 222 may be implemented on a single chip, such as chip 204. In some other embodiments, they may be implemented on separate chips.
[0046] RF (radio frequency) circuitry 208 receives and sends RF signals, also called electromagnetic signals. RF circuitry 208 converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry 208 optionally includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver,
DK 2017 70128 A1 one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry 208 optionally communicates with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The RF circuitry 208 optionally includes well-known circuitry for detecting near field communication (NFC) fields, such as by a short-range communication radio. The wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EVDO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.1 In, and/or IEEE 802.1 lac), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document.
[0047] Audio circuitry 210, speaker 211, and microphone 213 provide an audio interface between a user and device 200. Audio circuitry 210 receives audio data from peripherals interface 218, converts the audio data to an electrical signal, and transmits the electrical signal to speaker 211. Speaker 211 converts the electrical signal to human-audible sound waves. Audio circuitry 210 also receives electrical signals converted by microphone 213 from sound waves. Audio circuitry 210 converts the electrical signal to audio data and transmits the audio data to peripherals interface 218 for processing. Audio data may be retrieved from and/or transmitted to memory 202 and/or RF circuitry 208 by peripherals interface 218. In some embodiments, audio circuitry 210 also includes a headset jack (e.g., 312, FIG. 3). The headset jack provides an
DK 2017 70128 A1 interface between audio circuitry 210 and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone).
[0048] I/O subsystem 206 couples input/output peripherals on device 200, such as touch screen 212 and other input control devices 216, to peripherals interface 218. I/O subsystem 206 optionally includes display controller 256, optical sensor controller 258, intensity sensor controller 259, haptic feedback controller 261, and one or more input controllers 260 for other input or control devices. The one or more input controllers 260 receive/send electrical signals from/to other input control devices 216. The other input control devices 216 optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s) 260 are, optionally, coupled to any (or none) of the following: a keyboard, an infrared port, a USB port, and a pointer device such as a mouse. The one or more buttons (e.g., 308, FIG. 3) optionally include an up/down button for volume control of speaker 211 and/or microphone 213. The one or more buttons optionally include a push button (e.g., 306, FIG. 3).
[0049] A quick press of the push button may disengage a lock of touch screen 212 or begin a process that uses gestures on the touch screen to unlock the device, as described in U.S. Patent Application 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed December 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g., 306) may turn power to device 200 on or off. The user may be able to customize a functionality of one or more of the buttons. Touch screen 212 is used to implement virtual or soft buttons and one or more soft keyboards.
[0050] Touch-sensitive display 212 provides an input interface and an output interface between the device and a user. Display controller 256 receives and/or sends electrical signals from/to touch screen 212. Touch screen 212 displays visual output to the user. The visual output may include graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output may correspond to userinterface objects.
DK 2017 70128 A1 [0051] Touch screen 212 has a touch-sensitive surface, sensor, or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen 212 and display controller 256 (along with any associated modules and/or sets of instructions in memory 202) detect contact (and any movement or breaking of the contact) on touch screen 212 and convert the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web pages, or images) that are displayed on touch screen 212. In an exemplary embodiment, a point of contact between touch screen 212 and the user corresponds to a finger of the user.
[0052] Touch screen 212 may use LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies may be used in other embodiments. Touch screen 212 and display controller 256 may detect contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen 212. In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, California.
[0053] A touch-sensitive display in some embodiments of touch screen 212 may be analogous to the multi-touch sensitive touchpads described in the following U.S. Patents: 6,323,846 (Westerman et al.), 6,570,557 (Westerman et al.), and/or 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen 212 displays visual output from device 200, whereas touch-sensitive touchpads do not provide visual output.
[0054] A touch-sensitive display in some embodiments of touch screen 212 may be as described in the following applications: (1) U.S. Patent Application No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. Patent Application No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. Patent Application No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed July 30, 2004; (4) U.S. Patent Application No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed January 31, 2005; (5) U.S.
DK 2017 70128 A1
Patent Application No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed January 18, 2005; (6) U.S. Patent Application No. 11/228,758, “Virtual Input Device Placement On A Touch Screen User Interface,” filed September 16, 2005; (7) U.S. Patent Application No. 11/228,700, “Operation Of A Computer With A Touch Screen Interface,” filed September 16, 2005; (8) U.S. Patent Application No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed September 16, 2005; and (9) U.S. Patent Application No. 11/367,749, “Multi-Functional Hand-Held Device,” filed March 3, 2006. All of these applications are incorporated by reference herein in their entirety.
[0055] Touch screen 212 may have a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user may make contact with touch screen 212 using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen. In some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user.
[0056] In some embodiments, in addition to the touch screen, device 200 may include a touchpad (not shown) for activating or deactivating particular functions. In some embodiments, the touchpad is a touch-sensitive area of the device that, unlike the touch screen, does not display visual output. The touchpad may be a touch-sensitive surface that is separate from touch screen 212 or an extension of the touch-sensitive surface formed by the touch screen.
[0057] Device 200 also includes power system 262 for powering the various components. Power system 262 may include a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices.
[0058] Device 200 may also include one or more optical sensors 264. FIG. 2A shows an optical sensor coupled to optical sensor controller 258 in VO subsystem 206. Optical sensor 264
DK 2017 70128 A1 may include charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor 264 receives light from the environment, projected through one or more lenses, and converts the light to data representing an image. In conjunction with imaging module 243 (also called a camera module), optical sensor 264 may capture still images or video. In some embodiments, an optical sensor is located on the back of device 200, opposite touch screen display 212 on the front of the device so that the touch screen display may be used as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user’s image may be obtained for video conferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor 264 can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor 264 may be used along with the touch screen display for both video conferencing and still and/or video image acquisition.
[0059] Device 200 optionally also includes one or more contact intensity sensors 265. FIG. 2A shows a contact intensity sensor coupled to intensity sensor controller 259 in I/O subsystem 206. Contact intensity sensor 265 optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electric force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other intensity sensors (e.g., sensors used to measure the force (or pressure) of a contact on a touch-sensitive surface). Contact intensity sensor 265 receives contact intensity information (e.g., pressure information or a proxy for pressure information) from the environment. In some embodiments, at least one contact intensity sensor is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system 212). In some embodiments, at least one contact intensity sensor is located on the back of device 200, opposite touch screen display 212, which is located on the front of device 200.
[0060] Device 200 may also include one or more proximity sensors 266. FIG. 2A shows proximity sensor 266 coupled to peripherals interface 218. Alternately, proximity sensor 266 may be coupled to input controller 260 in I/O subsystem 206. Proximity sensor 266 may perform as described in U.S. Patent Application Nos. 11/241,839, “Proximity Detector In Handheld Device”; 11/240,788, “Proximity Detector In Handheld Device”; 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; 11/586,862, “Automated
DK 2017 70128 A1
Response To And Sensing Of User Activity In Portable Devices”; and 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen 212 when the multifunction device is placed near the user’s ear (e.g., when the user is making a phone call).
[0061] Device 200 optionally also includes one or more tactile output generators 267. FIG. 2A shows a tactile output generator coupled to haptic feedback controller 261 in I/O subsystem 206. Tactile output generator 267 optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor 265 receives tactile feedback generation instructions from haptic feedback module 233 and generates tactile outputs on device 200 that are capable of being sensed by a user of device 200. In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touchsensitive surface (e.g., touch-sensitive display system 212) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device 200) or laterally (e.g., back and forth in the same plane as a surface of device 200). In some embodiments, at least one tactile output generator sensor is located on the back of device 200, opposite touch screen display 212, which is located on the front of device 200.
[0062] Device 200 may also include one or more accelerometers 268. FIG. 2A shows accelerometer 268 coupled to peripherals interface 218. Alternately, accelerometer 268 may be coupled to an input controller 260 in I/O subsystem 206. Accelerometer 268 may perform as described in U.S. Patent Publication No. 20050190059, “Acceleration-based Theft Detection System for Portable Electronic Devices,” and U.S. Patent Publication No. 20060017692, “Methods And Apparatuses For Operating A Portable Device Based On An Accelerometer,” both of which are incorporated by reference herein in their entirety. In some embodiments, information is displayed on the touch screen display in a portrait view or a landscape view based on an analysis of data received from the one or more accelerometers. Device 200 optionally includes, in addition to accelerometer(s) 268, a magnetometer (not shown) and a GPS (or
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GLONASS or other global navigation system) receiver (not shown) for obtaining information concerning the location and orientation (e.g., portrait or landscape) of device 200.
[0063] In some embodiments, the software components stored in memory 202 include operating system 226, communication module (or set of instructions) 228, contact/motion module (or set of instructions) 230, graphics module (or set of instructions) 232, text input module (or set of instructions) 234, Global Positioning System (GPS) module (or set of instructions) 235, Digital Assistant Client Module 229, and applications (or sets of instructions) 236. Further, memory 202 can store data and models, such as user data and models 231. Furthermore, in some embodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/global internal state 257, as shown in FIGS. 2A and 4. Device/global internal state 257 includes one or more of: active application state, indicating which applications, if any, are currently active;
display state, indicating what applications, views or other information occupy various regions of touch screen display 212; sensor state, including information obtained from the device’s various sensors and input control devices 216; and location information concerning the device’s location and/or attitude.
[0064] Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.
[0065] Communication module 228 facilitates communication with other devices over one or more external ports 224 and also includes various software components for handling data received by RF circuitry 208 and/or external port 224. External port 224 (e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin (e.g., 30-pin) connector that is the same as, or similar to and/or compatible with, the 30-pin connector used on iPod® (trademark of Apple Inc.) devices.
[0066] Contact/motion module 230 optionally detects contact with touch screen 212 (in conjunction with display controller 256) and other touch-sensitive devices (e.g., a touchpad or
DK 2017 70128 A1 physical click wheel). Contact/motion module 230 includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining an intensity of the contact (e.g., the force or pressure of the contact or a substitute for the force or pressure of the contact), determining if there is movement of the contact and tracking the movement across the touchsensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module 230 receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, optionally includes determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations are, optionally, applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch’/multiple finger contacts). In some embodiments, contact/motion module 230 and display controller 256 detect contact on a touchpad.
[0067] In some embodiments, contact/motion module 230 uses a set of one or more intensity thresholds to determine whether an operation has been performed by a user (e.g., to determine whether a user has “clicked” on an icon). In some embodiments, at least a subset of the intensity thresholds are determined in accordance with software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of particular physical actuators and can be adjusted without changing the physical hardware of device 200). For example, a mouse “click” threshold of a trackpad or touch screen display can be set to any of a large range of predefined threshold values without changing the trackpad or touch screen display hardware. Additionally, in some implementations, a user of the device is provided with software settings for adjusting one or more of the set of intensity thresholds (e.g., by adjusting individual intensity thresholds and/or by adjusting a plurality of intensity thresholds at once with a system-level click “intensity” parameter).
[0068] Contact/motion module 230 optionally detects a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns (e.g., different motions, timings, and/or intensities of detected contacts). Thus, a gesture is, optionally, detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes
DK 2017 70128 A1 detecting a finger-down event followed by detecting a finger-up (liftoff) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger-down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (liftoff) event.
[0069] Graphics module 232 includes various known software components for rendering and displaying graphics on touch screen 212 or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual property) of graphics that are displayed. As used herein, the term “graphics” includes any object that can be displayed to a user, including, without limitation, text, web pages, icons (such as user-interface objects including soft keys), digital images, videos, animations, and the like.
[0070] In some embodiments, graphics module 232 stores data representing graphics to be used. Each graphic is, optionally, assigned a corresponding code. Graphics module 232 receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller 256.
[0071] Haptic feedback module 233 includes various software components for generating instructions used by tactile output generator(s) 267 to produce tactile outputs at one or more locations on device 200 in response to user interactions with device 200.
[0072] Text input module 234, which may be a component of graphics module 232, provides soft keyboards for entering text in various applications (e.g., contacts 237, email 240, IM 241, browser 247, and any other application that needs text input).
[0073] GPS module 235 determines the location of the device and provides this information for use in various applications (e.g., to telephone 238 for use in location-based dialing; to camera 243 as picture/video metadata; and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets).
[0074] Digital assistant client module 229 can include various client-side digital assistant instructions to provide the client-side functionalities of the digital assistant. For example, digital
DK 2017 70128 A1 assistant client module 229 can be capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., microphone 213, accelerometer(s) 268, touch-sensitive display system 212, optical sensor(s) 229, other input control devices 216, etc.) of portable multifunction device 200. Digital assistant client module 229 can also be capable of providing output in audio (e.g., speech output), visual, and/or tactile forms through various output interfaces (e.g., speaker 211, touch-sensitive display system 212, tactile output generator(s) 267, etc.) of portable multifunction device 200. For example, output can be provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above. During operation, digital assistant client module 229 can communicate with DA server 106 using RF circuitry 208.
[0075] User data and models 231 can include various data associated with the user (e.g., user-specific vocabulary data, user preference data, user-specified name pronunciations, data from the user’s electronic address book, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant. Further, user data and models 231 can includes various models (e.g., speech recognition models, statistical language models, natural language processing models, ontology, task flow models, service models, etc.) for processing user input and determining user intent.
[0076] In some examples, digital assistant client module 229 can utilize the various sensors, subsystems, and peripheral devices of portable multifunction device 200 to gather additional information from the surrounding environment of the portable multifunction device 200 to establish a context associated with a user, the current user interaction, and/or the current user input. In some examples, digital assistant client module 229 can provide the contextual information or a subset thereof with the user input to DA server 106 to help infer the user’s intent. In some examples, the digital assistant can also use the contextual information to determine how to prepare and deliver outputs to the user. Contextual information can be referred to as context data.
[0077] In some examples, the contextual information that accompanies the user input can include sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some examples, the contextual information can also
DK 2017 70128 A1 include the physical state of the device, e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion patterns, cellular signals strength, etc. In some examples, information related to the software state of DA server 106, e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., and of portable multifunction device 200 can be provided to DA server 106 as contextual information associated with a user input.
[0078] In some examples, the digital assistant client module 229 can selectively provide information (e.g., user data 231) stored on the portable multifunction device 200 in response to requests from DA server 106. In some examples, digital assistant client module 229 can also elicit additional input from the user via a natural language dialogue or other user interfaces upon request by DA server 106. Digital assistant client module 229 can pass the additional input to DA server 106 to help DA server 106 in intent deduction and/or fulfillment of the user’s intent expressed in the user request.
[0079] A more detailed description of a digital assistant is described below with reference to FIGs. 7A-C. It should be recognized that digital assistant client module 229 can include any number of the sub-modules of digital assistant module 726 described below.
[0080] Applications 236 may include the following modules (or sets of instructions), or a subset or superset thereof:
• Contacts module 237 (sometimes called an address book or contact list);
• Telephone module 238;
• Video conference module 239;
• Email client module 240;
• Instant messaging (IM) module 241;
• Workout support module 242;
• Camera module 243 for still and/or video images;
DK 2017 70128 A1 • Image management module 244;
• Video player module;
• Music player module;
• Browser module 247;
• Calendar module 248;
• Widget modules 249, which may include one or more of: weather widget 249-1, stocks widget 249-2, calculator widget 249-3, alarm clock widget 249-4, dictionary widget 2495, and other widgets obtained by the user, as well as user-created widgets 249-6;
• Widget creator module 250 for making user-created widgets 249-6;
• Search module 251;
• Video and music player module 252, which merges video player module and music player module;
• Notes module 253;
• Map module 254; and/or • Online video module 255.
[0081] Examples of other applications 236 that may be stored in memory 202 include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication.
[0082] In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, contacts module 237 may be used to manage an address book or contact list (e.g., stored in application internal state 292 of contacts module 237 in memory 202 or memory 470), including: adding name(s) to the address book;
DK 2017 70128 A1 deleting name(s) from the address book; associating telephone number(s), email address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or email addresses to initiate and/or facilitate communications by telephone 238, video conference module 239, email 240, or IM 241; and so forth.
[0083] In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, telephone module 238 may be used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in contacts module 237, modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As noted above, the wireless communication may use any of a plurality of communications standards, protocols, and technologies.
[0084] In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, optical sensor 264, optical sensor controller 258, contact/motion module 230, graphics module 232, text input module 234, contacts module 237, and telephone module 238, video conference module 239 includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions.
[0085] In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, email client module 240 includes executable instructions to create, send, receive, and manage email in response to user instructions. In conjunction with image management module 244, email client module 240 makes it very easy to create and send emails with still or video images taken with camera module 243.
[0086] In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, the instant messaging module 241 includes executable instructions to enter a sequence of characters corresponding to an instant message, to modify previously entered characters, to transmit a
DK 2017 70128 A1 respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages, and to view received instant messages. In some embodiments, transmitted and/or received instant messages may include graphics, photos, audio files, video files and/or other attachments as are supported in an MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internetbased messages (e.g., messages sent using XMPP, SIMPLE, or IMPS).
[0087] In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, map module 254, and music player module, workout support module 242 includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals);
communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store, and transmit workout data.
[0088] In conjunction with touch screen 212, display controller 256, optical sensor(s) 264, optical sensor controller 258, contact/motion module 230, graphics module 232, and image management module 244, camera module 243 includes executable instructions to capture still images or video (including a video stream) and store them into memory 202, modify characteristics of a still image or video, or delete a still image or video from memory 202.
[0089] In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and camera module 243, image management module 244 includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images.
[0090] In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, browser module 247 includes executable instructions to browse the Internet in accordance with user instructions,
DK 2017 70128 A1 including searching, linking to, receiving, and displaying web pages or portions thereof, as well as attachments and other files linked to web pages.
[0091] In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, email client module 240, and browser module 247, calendar module 248 includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, todo lists, etc.) in accordance with user instructions.
[0092] In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, widget modules 249 are mini-applications that may be downloaded and used by a user (e.g., weather widget 249-1, stocks widget 249-2, calculator widget 249-3, alarm clock widget 249-4, and dictionary widget 249-5) or created by the user (e.g., user-created widget 249-6). In some embodiments, a widget includes an HTMF (Hypertext Markup Fanguage) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XMF (Extensible Markup Fanguage) file and a JavaScript file (e.g., Yahoo! Widgets).
[0093] In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, the widget creator module 250 may be used by a user to create widgets (e.g., turning a userspecified portion of a web page into a widget).
[0094] In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, search module 251 includes executable instructions to search for text, music, sound, image, video, and/or other files in memory 202 that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions.
[0095] In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, and browser module 247, video and music player module 252 includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file
DK 2017 70128 A1 formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen 212 or on an external, connected display via external port 224). In some embodiments, device 200 optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.).
[0096] In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, notes module 253 includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions.
[0097] In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, and browser module 247, map module 254 may be used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions, data on stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions.
[0098] In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, text input module 234, email client module 240, and browser module 247, online video module 255 includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port 224), send an email with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module 241, rather than email client module 240, is used to send a link to a particular online video.
Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed June 20, 2007, and U.S. Patent Application No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed December 31, 2007, the contents of which are hereby incorporated by reference in their entirety.
DK 2017 70128 A1 [0099] Each of the above-identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules may be combined or otherwise rearranged in various embodiments. For example, video player module may be combined with music player module into a single module (e.g., video and music player module 252, FIG. 2A). In some embodiments, memory 202 may store a subset of the modules and data structures identified above. Furthermore, memory 202 may store additional modules and data structures not described above.
[0100] In some embodiments, device 200 is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device 200, the number of physical input control devices (such as push buttons, dials, and the like) on device 200 may be reduced.
[0101] The predefined set of functions that are performed exclusively through a touch screen and/or a touchpad optionally include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device 200 to a main, home, or root menu from any user interface that is displayed on device 200. In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad.
[0102] FIG. 2B is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., in operating system 226) and a respective application 236-1 (e.g., any of the aforementioned applications 237-251, 255, 480-490).
[0103] Event sorter 270 receives event information and determines the application 236-1 and application view 291 of application 236-1 to which to deliver the event information. Event sorter 270 includes event monitor 271 and event dispatcher module 274. In some embodiments, application 236-1 includes application internal state 292, which indicates the current application
DK 2017 70128 A1 view(s) displayed on touch-sensitive display 212 when the application is active or executing. In some embodiments, device/global internal state 257 is used by event sorter 270 to determine which application(s) is (are) currently active, and application internal state 292 is used by event sorter 270 to determine application views 291 to which to deliver event information.
[0104] In some embodiments, application internal state 292 includes additional information, such as one or more of: resume information to be used when application 236-1 resumes execution, user interface state information that indicates information being displayed or that is ready for display by application 236-1, a state queue for enabling the user to go back to a prior state or view of application 236-1, and a redo/undo queue of previous actions taken by the user.
[0105] Event monitor 271 receives event information from peripherals interface 218. Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display 212, as part of a multi-touch gesture). Peripherals interface 218 transmits information it receives from I/O subsystem 206 or a sensor, such as proximity sensor 266, accelerometer(s) 268, and/or microphone 213 (through audio circuitry 210). Information that peripherals interface 218 receives from I/O subsystem 206 includes information from touch-sensitive display 212 or a touch-sensitive surface.
[0106] In some embodiments, event monitor 271 sends requests to the peripherals interface 218 at predetermined intervals. In response, peripherals interface 218 transmits event information. In other embodiments, peripherals interface 218 transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration).
[0107] In some embodiments, event sorter 270 also includes a hit view determination module 272 and/or an active event recognizer determination module 273.
[0108] Hit view determination module 272 provides software procedures for determining where a sub-event has taken place within one or more views when touch-sensitive display 212 displays more than one view. Views are made up of controls and other elements that a user can see on the display.
DK 2017 70128 A1 [0109] Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected may correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected may be called the hit view, and the set of events that are recognized as proper inputs may be determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture.
[0110] Hit view determination module 272 receives information related to sub events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module 272 identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module 272, the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view.
[0111] Active event recognizer determination module 273 determines which view or views within a view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module 273 determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module 273 determines that all views that include the physical location of a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views.
[0112] Event dispatcher module 274 dispatches the event information to an event recognizer (e.g., event recognizer 280). In embodiments including active event recognizer determination module 273, event dispatcher module 274 delivers the event information to an event recognizer determined by active event recognizer determination module 273. In some embodiments, event
DK 2017 70128 A1 dispatcher module 274 stores in an event queue the event information, which is retrieved by a respective event receiver 282.
[0113] In some embodiments, operating system 226 includes event sorter 270. Alternatively, application 236-1 includes event sorter 270. In yet other embodiments, event sorter 270 is a stand-alone module, or a part of another module stored in memory 202, such as contact/motion module 230.
[0114] In some embodiments, application 236-1 includes a plurality of event handlers 290 and one or more application views 291, each of which includes instructions for handling touch events that occur within a respective view of the application’s user interface. Each application view 291 of the application 236-1 includes one or more event recognizers 280. Typically, a respective application view 291 includes a plurality of event recognizers 280. In other embodiments, one or more of event recognizers 280 are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application 236-1 inherits methods and other properties. In some embodiments, a respective event handler 290 includes one or more of: data updater 276, object updater 277, GUI updater 278, and/or event data 279 received from event sorter 270. Event handler 290 may utilize or call data updater 276, object updater 277, or GUI updater 278 to update the application internal state 292. Alternatively, one or more of the application views 291 include one or more respective event handlers 290. Also, in some embodiments, one or more of data updater 276, object updater 277, and GUI updater 278 are included in a respective application view 291.
[0115] A respective event recognizer 280 receives event information (e.g., event data 279) from event sorter 270 and identifies an event from the event information. Event recognizer 280 includes event receiver 282 and event comparator 284. In some embodiments, event recognizer 280 also includes at least a subset of: metadata 283, and event delivery instructions 288 (which may include sub-event delivery instructions).
[0116] Event receiver 282 receives event information from event sorter 270. The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch, the event information
DK 2017 70128 A1 may also include speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device attitude) of the device.
[0117] Event comparator 284 compares the event information to predefined event or subevent definitions and, based on the comparison, determines an event or sub event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator 284 includes event definitions 286. Event definitions 286 contain definitions of events (e.g., predefined sequences of sub-events), for example, event 1 (287-1), event 2 (287-2), and others. In some embodiments, sub-events in an event (287) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event 1 (287-1) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event 2 (287-2) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display 212, and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers 290.
[0118] In some embodiments, event definition 287 includes a definition of an event for a respective user-interface object. In some embodiments, event comparator 284 performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display 212, when a touch is detected on touch-sensitive display 212, event comparator 284 performs a hit test to determine which of the three user-interface objects is associated with the touch (subevent). If each displayed object is associated with a respective event handler 290, the event comparator uses the result of the hit test to determine which event handler 290 should be activated. For example, event comparator 284 selects an event handler associated with the subevent and the object triggering the hit test.
DK 2017 70128 A1 [0119] In some embodiments, the definition for a respective event (287) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer’s event type.
[0120] When a respective event recognizer 280 determines that the series of sub-events do not match any of the events in event definitions 286, the respective event recognizer 280 enters an event impossible, event failed, or event ended state, after which it disregards subsequent subevents of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture.
[0121] In some embodiments, a respective event recognizer 280 includes metadata 283 with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate how event recognizers may interact, or are enabled to interact, with one another. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate whether subevents are delivered to varying levels in the view or programmatic hierarchy.
[0122] In some embodiments, a respective event recognizer 280 activates event handler 290 associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer 280 delivers event information associated with the event to event handler 290. Activating an event handler 290 is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer 280 throws a flag associated with the recognized event, and event handler 290 associated with the flag catches the flag and performs a predefined process.
[0123] In some embodiments, event delivery instructions 288 include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated
DK 2017 70128 A1 with the series of sub-events or with actively involved views receive the event information and perform a predetermined process.
[0124] In some embodiments, data updater 276 creates and updates data used in application 236-1. For example, data updater 276 updates the telephone number used in contacts module 237, or stores a video file used in video player module. In some embodiments, object updater 277 creates and updates objects used in application 236-1. For example, object updater 277 creates a new user-interface object or updates the position of a user-interface object. GUI updater 278 updates the GUI. For example, GUI updater 278 prepares display information and sends it to graphics module 232 for display on a touch-sensitive display.
[0125] In some embodiments, event handler(s) 290 includes or has access to data updater 276, object updater 277, and GUI updater 278. In some embodiments, data updater 276, object updater 277, and GUI updater 278 are included in a single module of a respective application 236-1 or application view 291. In other embodiments, they are included in two or more software modules.
[0126] It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices 200 with input devices, not all of which are initiated on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs; movement of the device; oral instructions; detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub-events which define an event to be recognized.
[0127] FIG. 3 illustrates a portable multifunction device 200 having a touch screen 212 in accordance with some embodiments. The touch screen optionally displays one or more graphics within user interface (UI) 300. In this embodiment, as well as others described below, a user is enabled to select one or more of the graphics by making a gesture on the graphics, for example, with one or more fingers 302 (not drawn to scale in the figure) or one or more styluses 303 (not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the gesture
DK 2017 70128 A1 optionally includes one or more taps, one or more swipes (from left to right, right to left, upward and/or downward), and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device 200. In some implementations or circumstances, inadvertent contact with a graphic does not select the graphic. For example, a swipe gesture that sweeps over an application icon optionally does not select the corresponding application when the gesture corresponding to selection is a tap.
[0128] Device 200 may also include one or more physical buttons, such as “home” or menu button 304. As described previously, menu button 304 may be used to navigate to any application 236 in a set of applications that may be executed on device 200. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen 212.
[0129] In one embodiment, device 200 includes touch screen 212, menu button 304, push button 306 for powering the device on/off and locking the device, volume adjustment button(s) 308, subscriber identity module (SIM) card slot 310, headset jack 312, and docking/charging external port 224. Push button 306 is, optionally, used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device 200 also accepts verbal input for activation or deactivation of some functions through microphone 213. Device 200 also, optionally, includes one or more contact intensity sensors 265 for detecting intensity of contacts on touch screen 212 and/or one or more tactile output generators 267 for generating tactile outputs for a user of device 200.
[0130] FIG. 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device 400 need not be portable. In some embodiments, device 400 is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child’s learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device 400 typically includes one or more processing units (CPUs) 410, one or more network or other communications interfaces 460, memory 470, and one or more communication buses 420 for
DK 2017 70128 A1 interconnecting these components. Communication buses 420 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device 400 includes input/output (I/O) interface 430 comprising display 440, which is typically a touch screen display. I/O interface 430 also optionally includes a keyboard and/or mouse (or other pointing device) 450 and touchpad 455, tactile output generator 457 for generating tactile outputs on device 400 (e.g., similar to tactile output generator(s) 267 described above with reference to FIG. 2A), sensors 459 (e.g., optical, acceleration, proximity, touchsensitive, and/or contact intensity sensors similar to contact intensity sensor(s) 265 described above with reference to FIG. 2A). Memory 470 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 470 optionally includes one or more storage devices remotely located from CPU(s) 410. In some embodiments, memory 470 stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory 202 of portable multifunction device 200 (FIG. 2A), or a subset thereof. Furthermore, memory 470 optionally stores additional programs, modules, and data structures not present in memory 202 of portable multifunction device 200. For example, memory 470 of device 400 optionally stores drawing module 480, presentation module 482, word processing module 484, website creation module 486, disk authoring module 488, and/or spreadsheet module 490, while memory 202 of portable multifunction device 200 (FIG. 2A) optionally does not store these modules.
[0131] Each of the above-identified elements in FIG. 4 may be stored in one or more of the previously mentioned memory devices. Each of the above-identified modules corresponds to a set of instructions for performing a function described above. The above-identified modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules may be combined or otherwise rearranged in various embodiments. In some embodiments, memory 470 may store a subset of the modules and data structures identified above. Furthermore, memory 470 may store additional modules and data structures not described above.
DK 2017 70128 A1 [0132] Attention is now directed towards embodiments of user interfaces that may be implemented on, for example, portable multifunction device 200.
[0133] FIG. 5A illustrates an exemplary user interface for a menu of applications on portable multifunction device 200 in accordance with some embodiments. Similar user interfaces may be implemented on device 400. In some embodiments, user interface 500 includes the following elements, or a subset or superset thereof:
[0134] Signal strength indicator(s) 502 for wireless communication(s), such as cellular and Wi-Fi signals;
• Time 504;
• Bluetooth indicator 505;
• Battery status indicator 506;
• Tray 508 with icons for frequently used applications, such as:
o Icon 516 for telephone module 238, labeled “Phone,” which optionally includes an indicator 514 of the number of missed calls or voicemail messages;
o Icon 518 for email client module 240, labeled “Mail,” which optionally includes an indicator 510 of the number of unread emails;
o Icon 520 for browser module 247, labeled “Browser;” and o Icon 522 for video and music player module 252, also referred to as iPod (trademark of Apple Inc.) module 252, labeled “iPod;” and • Icons for other applications, such as:
o Icon 524 for IM module 241, labeled “Messages;” o Icon 526 for calendar module 248, labeled “Calendar;” o Icon 528 for image management module 244, labeled “Photos;”
DK 2017 70128 A1 o Icon 530 for camera module 243, labeled “Camera;” o Icon 532 for online video module 255, labeled “Online Video;” o Icon 534 for stocks widget 249-2, labeled “Stocks;” o Icon 536 for map module 254, labeled “Maps;” o Icon 538 for weather widget 249-1, labeled “Weather;” o Icon 540 for alarm clock widget 249-4, labeled “Clock;” o Icon 542 for workout support module 242, labeled “Workout Support;” o Icon 544 for notes module 253, labeled “Notes;” and o Icon 546 for a settings application or module, labeled “Settings,” which provides access to settings for device 200 and its various applications 236.
[0135] It should be noted that the icon labels illustrated in FIG. 5A are merely exemplary. For example, icon 522 for video and music player module 252 may optionally be labeled “Music” or “Music Player.” Other labels are, optionally, used for various application icons. In some embodiments, a label for a respective application icon includes a name of an application corresponding to the respective application icon. In some embodiments, a label for a particular application icon is distinct from a name of an application corresponding to the particular application icon.
[0136] FIG. 5B illustrates an exemplary user interface on a device (e.g., device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tablet or touchpad 455, FIG. 4) that is separate from the display 550 (e.g., touch screen display 212). Device 400 also, optionally, includes one or more contact intensity sensors (e.g., one or more of sensors 457) for detecting intensity of contacts on touch-sensitive surface 551 and/or one or more tactile output generators 459 for generating tactile outputs for a user of device 400.
[0137] Although some of the examples which follow will be given with reference to inputs on touch screen display 212 (where the touch-sensitive surface and the display are combined), in
DK 2017 70128 A1 some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in FIG. 5B. In some embodiments, the touch-sensitive surface (e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) that corresponds to a primary axis (e.g., 553 in FIG. 5B) on the display (e.g., 550). In accordance with these embodiments, the device detects contacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface 551 at locations that correspond to respective locations on the display (e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570). In this way, user inputs (e.g., contacts 560 and 562, and movements thereof) detected by the device on the touch-sensitive surface (e.g., 551 in FIG. 5B) are used by the device to manipulate the user interface on the display (e.g., 550 in FIG. 5B) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods are, optionally, used for other user interfaces described herein.
[0138] Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a contact) followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is, optionally, replaced with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse and finger contacts are, optionally, used simultaneously.
[0139] FIG. 6A illustrates exemplary personal electronic device 600. Device 600 includes body 602. In some embodiments, device 600 can include some or all of the features described with respect to devices 200 and 400 (e.g., FIGS. 2A-4B). In some embodiments, device 600 has touch-sensitive display screen 604, hereafter touch screen 604. Alternatively, or in addition to touch screen 604, device 600 has a display and a touch-sensitive surface. As with devices 200 and 400, in some embodiments, touch screen 604 (or the touch-sensitive surface) may have one or more intensity sensors for detecting intensity of contacts (e.g., touches) being applied. The one or more intensity sensors of touch screen 604 (or the touch-sensitive surface) can provide
DK 2017 70128 A1 output data that represents the intensity of touches. The user interface of device 600 can respond to touches based on their intensity, meaning that touches of different intensities can invoke different user interface operations on device 600.
[0140] Techniques for detecting and processing touch intensity may be found, for example, in related applications: International Patent Application Serial No. PCT/US2013/040061, titled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application,” filed May 8, 2013, and International Patent Application Serial No. PCT/US2013/069483, titled “Device, Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships,” filed November 11, 2013, each of which is hereby incorporated by reference in their entirety.
[0141] In some embodiments, device 600 has one or more input mechanisms 606 and 608. Input mechanisms 606 and 608, if included, can be physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, device 600 has one or more attachment mechanisms. Such attachment mechanisms, if included, can permit attachment of device 600 with, for example, hats, eyewear, earrings, necklaces, shirts, jackets, bracelets, watch straps, chains, trousers, belts, shoes, purses, backpacks, and so forth. These attachment mechanisms may permit device 600 to be worn by a user.
[0142] FIG. 6B depicts exemplary personal electronic device 600. In some embodiments, device 600 can include some or all of the components described with respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612 that operatively couples I/O section 614 with one or more computer processors 616 and memory 618. I/O section 614 can be connected to display 604, which can have touch-sensitive component 622 and, optionally, touch-intensity sensitive component 624. In addition, I/O section 614 can be connected with communication unit 630 for receiving application and operating system data, using Wi-Fi, Bluetooth, near field communication (NFC), cellular, and/or other wireless communication techniques. Device 600 can include input mechanisms 606 and/or 608. Input mechanism 606 may be a rotatable input device or a depressible and rotatable input device, for example. Input mechanism 608 may be a button, in some examples.
DK 2017 70128 A1 [0143] Input mechanism 608 may be a microphone, in some examples. Personal electronic device 600 can include various sensors, such as GPS sensor 632, accelerometer 634, directional sensor 640 (e.g., compass), gyroscope 636, motion sensor 638, and/or a combination thereof, all of which can be operatively connected to I/O section 614.
[0144] Memory 618 of personal electronic device 600 can be a non-transitory computerreadable storage medium, for storing computer-executable instructions, which, when executed by one or more computer processors 616, for example, can cause the computer processors to perform the techniques described below, including process 1200 (FIGs. 12A-D). The computerexecutable instructions can also be stored and/or transported within any non-transitory computerreadable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this document, a “non-transitory computerreadable storage medium” can be any medium that can tangibly contain or store computerexecutable instructions for use by or in connection with the instruction execution system, apparatus, or device. The non-transitory computer-readable storage medium can include, but is not limited to, magnetic, optical, and/or semiconductor storages. Examples of such storage include magnetic disks, optical discs based on CD, DVD, or Blu-ray technologies, as well as persistent solid-state memory such as flash, solid-state drives, and the like. Personal electronic device 600 is not limited to the components and configuration of FIG. 6B, but can include other or additional components in multiple configurations.
[0145] As used here, the term “affordance” refers to a user-interactive graphical user interface object that may be displayed on the display screen of devices 200, 400, and/or 600 (FIGS. 2, 4, and 6). For example, an image (e.g., icon), a button, and text (e.g., link) may each constitute an affordance.
[0146] As used herein, the term “focus selector” refers to an input element that indicates a current part of a user interface with which a user is interacting. In some implementations that include a cursor or other location marker, the cursor acts as a “focus selector” so that when an input (e.g., a press input) is detected on a touch-sensitive surface (e.g., touchpad 455 in FIG. 4 or
DK 2017 70128 A1 touch-sensitive surface 551 in FIG. 5B) while the cursor is over a particular user interface element (e.g., a button, window, slider or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations that include a touch screen display (e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212 in FIG. 5A) that enables direct interaction with user interface elements on the touch screen display, a detected contact on the touch screen acts as a “focus selector” so that when an input (e.g., a press input by the contact) is detected on the touch screen display at a location of a particular user interface element (e.g., a button, window, slider, or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations, focus is moved from one region of a user interface to another region of the user interface without corresponding movement of a cursor or movement of a contact on a touch screen display (e.g., by using a tab key or arrow keys to move focus from one button to another button); in these implementations, the focus selector moves in accordance with movement of focus between different regions of the user interface. Without regard to the specific form taken by the focus selector, the focus selector is generally the user interface element (or contact on a touch screen display) that is controlled by the user so as to communicate the user’s intended interaction with the user interface (e.g., by indicating, to the device, the element of the user interface with which the user is intending to interact). For example, the location of a focus selector (e.g., a cursor, a contact, or a selection box) over a respective button while a press input is detected on the touch-sensitive surface (e.g., a touchpad or touch screen) will indicate that the user is intending to activate the respective button (as opposed to other user interface elements shown on a display of the device).
[0147] As used in the specification and claims, the term “characteristic intensity” of a contact refers to a characteristic of the contact based on one or more intensities of the contact. In some embodiments, the characteristic intensity is based on multiple intensity samples. The characteristic intensity is, optionally, based on a predefined number of intensity samples, or a set of intensity samples collected during a predetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10 seconds) relative to a predefined event (e.g., after detecting the contact, prior to detecting liftoff of the contact, before or after detecting a start of movement of the contact, prior to detecting an end of the contact, before or after detecting an increase in intensity of the contact, and/or before or after detecting a decrease in intensity of the contact). A characteristic intensity
DK 2017 70128 A1 of a contact is, optionally based on one or more of: a maximum value of the intensities of the contact, a mean value of the intensities of the contact, an average value of the intensities of the contact, a top 10 percentile value of the intensities of the contact, a value at the half maximum of the intensities of the contact, a value at the 90 percent maximum of the intensities of the contact, or the like. In some embodiments, the duration of the contact is used in determining the characteristic intensity (e.g., when the characteristic intensity is an average of the intensity of the contact over time). In some embodiments, the characteristic intensity is compared to a set of one or more intensity thresholds to determine whether an operation has been performed by a user. For example, the set of one or more intensity thresholds may include a first intensity threshold and a second intensity threshold. In this example, a contact with a characteristic intensity that does not exceed the first threshold results in a first operation, a contact with a characteristic intensity that exceeds the first intensity threshold and does not exceed the second intensity threshold results in a second operation, and a contact with a characteristic intensity that exceeds the second threshold results in a third operation. In some embodiments, a comparison between the characteristic intensity and one or more thresholds is used to determine whether or not to perform one or more operations (e.g., whether to perform a respective operation or forgo performing the respective operation) rather than being used to determine whether to perform a first operation or a second operation.
[0148] In some embodiments, a portion of a gesture is identified for purposes of determining a characteristic intensity. For example, a touch-sensitive surface may receive a continuous swipe contact transitioning from a start location and reaching an end location, at which point the intensity of the contact increases. In this example, the characteristic intensity of the contact at the end location may be based on only a portion of the continuous swipe contact, and not the entire swipe contact (e.g., only the portion of the swipe contact at the end location). In some embodiments, a smoothing algorithm may be applied to the intensities of the swipe contact prior to determining the characteristic intensity of the contact. For example, the smoothing algorithm optionally includes one or more of: an unweighted sliding-average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and/or an exponential smoothing algorithm. In some circumstances, these smoothing algorithms eliminate narrow spikes or dips in the intensities of the swipe contact for purposes of determining a characteristic intensity.
DK 2017 70128 A1 [0149] The intensity of a contact on the touch-sensitive surface may be characterized relative to one or more intensity thresholds, such as a contact-detection intensity threshold, a light press intensity threshold, a deep press intensity threshold, and/or one or more other intensity thresholds. In some embodiments, the light press intensity threshold corresponds to an intensity at which the device will perform operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform operations that are different from operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, when a contact is detected with a characteristic intensity below the light press intensity threshold (e.g., and above a nominal contact-detection intensity threshold below which the contact is no longer detected), the device will move a focus selector in accordance with movement of the contact on the touch-sensitive surface without performing an operation associated with the light press intensity threshold or the deep press intensity threshold. Generally, unless otherwise stated, these intensity thresholds are consistent between different sets of user interface figures.
[0150] An increase of characteristic intensity of the contact from an intensity below the light press intensity threshold to an intensity between the light press intensity threshold and the deep press intensity threshold is sometimes referred to as a “light press” input. An increase of characteristic intensity of the contact from an intensity below the deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a “deep press” input. An increase of characteristic intensity of the contact from an intensity below the contactdetection intensity threshold to an intensity between the contact-detection intensity threshold and the light press intensity threshold is sometimes referred to as detecting the contact on the touchsurface. A decrease of characteristic intensity of the contact from an intensity above the contactdetection intensity threshold to an intensity below the contact-detection intensity threshold is sometimes referred to as detecting liftoff of the contact from the touch-surface. In some embodiments, the contact-detection intensity threshold is zero. In some embodiments, the contact-detection intensity threshold is greater than zero.
[0151] In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a respective press input or in response to detecting
DK 2017 70128 A1 the respective press input performed with a respective contact (or a plurality of contacts), where the respective press input is detected based at least in part on detecting an increase in intensity of the contact (or plurality of contacts) above a press-input intensity threshold. In some embodiments, the respective operation is performed in response to detecting the increase in intensity of the respective contact above the press-input intensity threshold (e.g., a “down stroke” of the respective press input). In some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the press-input threshold (e.g., an “up stroke” of the respective press input).
[0152] In some embodiments, the device employs intensity hysteresis to avoid accidental inputs sometimes termed “jitter,” where the device defines or selects a hysteresis intensity threshold with a predefined relationship to the press-input intensity threshold (e.g., the hysteresis intensity threshold is X intensity units lower than the press-input intensity threshold or the hysteresis intensity threshold is 75%, 90%, or some reasonable proportion of the press-input intensity threshold). Thus, in some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the hysteresis intensity threshold that corresponds to the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the hysteresis intensity threshold (e.g., an “up stroke” of the respective press input). Similarly, in some embodiments, the press input is detected only when the device detects an increase in intensity of the contact from an intensity at or below the hysteresis intensity threshold to an intensity at or above the press-input intensity threshold and, optionally, a subsequent decrease in intensity of the contact to an intensity at or below the hysteresis intensity, and the respective operation is performed in response to detecting the press input (e.g., the increase in intensity of the contact or the decrease in intensity of the contact, depending on the circumstances).
[0153] For ease of explanation, the descriptions of operations performed in response to a press input associated with a press-input intensity threshold or in response to a gesture including
DK 2017 70128 A1 the press input are, optionally, triggered in response to detecting either: an increase in intensity of a contact above the press-input intensity threshold, an increase in intensity of a contact from an intensity below the hysteresis intensity threshold to an intensity above the press-input intensity threshold, a decrease in intensity of the contact below the press-input intensity threshold, and/or a decrease in intensity of the contact below the hysteresis intensity threshold corresponding to the press-input intensity threshold. Additionally, in examples where an operation is described as being performed in response to detecting a decrease in intensity of a contact below the press-input intensity threshold, the operation is, optionally, performed in response to detecting a decrease in intensity of the contact below a hysteresis intensity threshold corresponding to, and lower than, the press-input intensity threshold.
3. Digital Assistant System [0154] FIG. 7A illustrates a block diagram of digital assistant system 700 in accordance with various examples. In some examples, digital assistant system 700 can be implemented on a standalone computer system. In some examples, digital assistant system 700 can be distributed across multiple computers. In some examples, some of the modules and functions of the digital assistant can be divided into a server portion and a client portion, where the client portion resides on one or more user devices (e.g., devices 104, 122, 200, 400, or 600) and communicates with the server portion (e.g., server system 108) through one or more networks, e.g., as shown in FIG. 1. In some examples, digital assistant system 700 can be an implementation of server system 108 (and/or DA server 106) shown in FIG. 1. It should be noted that digital assistant system 700 is only one example of a digital assistant system, and that digital assistant system 700 can have more or fewer components than shown, may combine two or more components, or may have a different configuration or arrangement of the components. The various components shown in FIG. 7A can be implemented in hardware, software instructions for execution by one or more processors, firmware, including one or more signal processing and/or application specific integrated circuits, or a combination thereof.
[0155] Digital assistant system 700 can include memory 702, one or more processors 704, input/output (I/O) interface 706, and network communications interface 708. These components can communicate with one another over one or more communication buses or signal lines 710.
DK 2017 70128 A1 [0156] In some examples, memory 702 can include a non-transitory computer-readable medium, such as high-speed random access memory and/or a non-volatile computer-readable storage medium (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).
[0157] In some examples, I/O interface 706 can couple input/output devices 716 of digital assistant system 700, such as displays, keyboards, touch screens, and microphones, to user interface module 722. I/O interface 706, in conjunction with user interface module 722, can receive user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and processes them accordingly. In some examples, e.g., when the digital assistant is implemented on a standalone user device, digital assistant system 700 can include any of the components and I/O communication interfaces described with respect to devices 200, 400, or 600 in FIGs. 2A, 4, 6AB, respectively. In some examples, digital assistant system 700 can represent the server portion of a digital assistant implementation, and can interact with the user through a client-side portion residing on a user device (e.g., devices 104, 200, 400, or 600).
[0158] In some examples, the network communications interface 708 can include wired communication port(s) 712 and/or wireless transmission and reception circuitry 714. The wired communication port(s) can receive and send communication signals via one or more wired interfaces, e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry 714 can receive and send RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications can use any of a plurality of communications standards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. Network communications interface 708 can enable communication between digital assistant system 700 with networks, such as the Internet, an intranet, and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN), and/or a metropolitan area network (MAN), and other devices.
[0159] In some examples, memory 702, or the computer-readable storage media of memory 702, can store programs, modules, instructions, and data structures including all or a subset of: operating system 718, communications module 720, user interface module 722, one or more
DK 2017 70128 A1 applications 724, and digital assistant module 726. In particular, memory 702, or the computerreadable storage media of memory 702, can store instructions for performing process 1200, described below. One or more processors 704 can execute these programs, modules, and instructions, and reads/writes from/to the data structures.
[0160] Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X, WINDOWS, or an embedded operating system such as VxWorks) can include various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communications between various hardware, firmware, and software components.
[0161] Communications module 720 can facilitate communications between digital assistant system 700 with other devices over network communications interface 708. For example, communications module 720 can communicate with RF circuitry 208 of electronic devices such as devices 200, 400, and 600 shown in FIG. 2A, 4, 6A-B, respectively. Communications module 720 can also include various components for handling data received by wireless circuitry 714 and/or wired communications port 712.
[0162] User interface module 722 can receive commands and/or inputs from a user via I/O interface 706 (e.g., from a keyboard, touch screen, pointing device, controller, and/or microphone), and generate user interface objects on a display. User interface module 722 can also prepare and deliver outputs (e.g., speech, sound, animation, text, icons, vibrations, haptic feedback, light, etc.) to the user via the I/O interface 706 (e.g., through displays, audio channels, speakers, touch-pads, etc.).
[0163] Applications 724 can include programs and/or modules that are configured to be executed by one or more processors 704. For example, if the digital assistant system is implemented on a standalone user device, applications 724 can include user applications, such as games, a calendar application, a navigation application, or an email application. If digital assistant system 700 is implemented on a server, applications 724 can include resource management applications, diagnostic applications, or scheduling applications, for example.
DK 2017 70128 A1 [0164] Memory 702 can also store digital assistant module 726 (or the server portion of a digital assistant). In some examples, digital assistant module 726 can include the following submodules, or a subset or superset thereof: input/output processing module 728, speech-to-text (STT) processing module 730, natural language processing module 732, dialogue flow processing module 734, task flow processing module 736, service processing module 738, and speech synthesis module 740. Each of these modules can have access to one or more of the following systems or data and models of the digital assistant module 726, or a subset or superset thereof: ontology 760, vocabulary index 744, user data 748, task flow models 754, service models 756, and ASR systems.
[0165] In some examples, using the processing modules, data, and models implemented in digital assistant module 726, the digital assistant can perform at least some of the following: converting speech input into text; identifying a user’s intent expressed in a natural language input received from the user; actively eliciting and obtaining information needed to fully infer the user’s intent (e.g., by disambiguating words, games, intentions, etc.); determining the task flow for fulfilling the inferred intent; and executing the task flow to fulfill the inferred intent.
[0166] In some examples, as shown in FIG. 7B, RO processing module 728 can interact with the user through RO devices 716 in FIG. 7A or with a user device (e.g., devices 104, 200, 400, or 600) through network communications interface 708 in FIG. 7A to obtain user input (e.g., a speech input) and to provide responses (e.g., as speech outputs) to the user input. I/O processing module 728 can optionally obtain contextual information associated with the user input from the user device, along with or shortly after the receipt of the user input. The contextual information can include user-specific data, vocabulary, and/or preferences relevant to the user input. In some examples, the contextual information also includes software and hardware states of the user device at the time the user request is received, and/or information related to the surrounding environment of the user at the time that the user request was received. In some examples, I/O processing module 728 can also send follow-up questions to, and receive answers from, the user regarding the user request. When a user request is received by I/O processing module 728 and the user request can include speech input, RO processing module 728 can forward the speech input to STT processing module 730 (or speech recognizer) for speech-to-text conversions.
DK 2017 70128 A1 [0167] STT processing module 730 can include one or more ASR systems. The one or more ASR systems can process the speech input that is received through I/O processing module 728 to produce a recognition result. Each ASR system can include a front-end speech pre-processor. The front-end speech pre-processor can extract representative features from the speech input. For example, the front-end speech pre-processor can perform a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors. Further, each ASR system can include one or more speech recognition models (e.g., acoustic models and/or language models) and can implement one or more speech recognition engines. Examples of speech recognition models can include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of speech recognition engines can include the dynamic time warping based engines and weighted finite-state transducers (WFST) based engines. The one or more speech recognition models and the one or more speech recognition engines can be used to process the extracted representative features of the front-end speech pre-processor to produce intermediate recognitions results (e.g., phonemes, phonemic strings, and sub-words), and ultimately, text recognition results (e.g., words, word strings, or sequence of tokens). In some examples, the speech input can be processed at least partially by a third-party service or on the user’s device (e.g., device 104, 200, 400, or 600) to produce the recognition result. Once STT processing module 730 produces recognition results containing a text string (e.g., words, or sequence of words, or sequence of tokens), the recognition result can be passed to natural language processing module 732 for intent deduction. An exemplary ASR system is described in more detail in FIG. 9.
[0168] More details on the speech-to-text processing are described in U.S. Utility Application Serial No. 13/236,942 for “Consolidating Speech Recognition Results,” filed on September 20, 2011, the entire disclosure of which is incorporated herein by reference.
[0169] In some examples, STT processing module 730 can include and/or access a vocabulary of recognizable words via phonetic alphabet conversion module 731. Each vocabulary word can be associated with one or more candidate pronunciations of the word represented in a speech recognition phonetic alphabet. In particular, the vocabulary of recognizable words can include a word that is associated with a plurality of candidate
DK 2017 70128 A1 pronunciations. For example, the vocabulary may include the word “tomato” that is associated with the candidate pronunciations of /ta'meiroo/ and /ta'matoo/. Further, vocabulary words can be associated with custom candidate pronunciations that are based on previous speech inputs from the user. Such custom candidate pronunciations can be stored in STT processing module 730 and can be associated with a particular user via the user’s profile on the device. In some examples, the candidate pronunciations for words can be determined based on the spelling of the word and one or more linguistic and/or phonetic rules. In some examples, the candidate pronunciations can be manually generated, e.g., based on known canonical pronunciations.
[0170] In some examples, the candidate pronunciations can be ranked based on the commonness of the candidate pronunciation. For example, the candidate pronunciation /ta'meiroo/ can be ranked higher than /ta'matoo/, because the former is a more commonly used pronunciation (e.g., among all users, for users in a particular geographical region, or for any other appropriate subset of users). In some examples, candidate pronunciations can be ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, custom candidate pronunciations can be ranked higher than canonical candidate pronunciations. This can be useful for recognizing proper nouns having a unique pronunciation that deviates from canonical pronunciation. In some examples, candidate pronunciations can be associated with one or more speech characteristics, such as geographic origin, nationality, or ethnicity. For example, the candidate pronunciation /ta'meiroo/ can be associated with the United States, whereas the candidate pronunciation /ta'matoo/ can be associated with Great Britain. Further, the rank of the candidate pronunciation can be based on one or more characteristics (e.g., geographic origin, nationality, ethnicity, etc.) of the user stored in the user’s profile on the device. For example, it can be determined from the user’s profile that the user is associated with the United States. Based on the user being associated with the United States, the candidate pronunciation /ta'meiroo/ (associated with the United States) can be ranked higher than the candidate pronunciation /ta'matoo/ (associated with Great Britain). In some examples, one of the ranked candidate pronunciations can be selected as a predicted pronunciation (e.g., the most likely pronunciation).
[0171] When a speech input is received, STT processing module 730 can be used to determine the phonemes corresponding to the speech input (e.g., using an acoustic model), and
DK 2017 70128 A1 then attempt to determine words that match the phonemes (e.g., using a language model). For example, if STT processing module 730 can first identify the sequence of phonemes /ta'meiroo/ corresponding to a portion of the speech input, it can then determine, based on vocabulary index 744, that this sequence corresponds to the word “tomato.” [0172] In some examples, STT processing module 730 can use approximate matching techniques to determine words in a voice input. Thus, for example, the STT processing module 730 can determine that the sequence of phonemes /ta'meiroo/ corresponds to the word “tomato,” even if that particular sequence of phonemes is not one of the candidate sequence of phonemes for that word.
[0173] Natural language processing module 732 (“natural language processor”) of the digital assistant can take the sequence of words or tokens (“token sequence”) generated by STT processing module 730, and attempt to associate the token sequence with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” can represent a task that can be performed by the digital assistant, and can have an associated task flow implemented in task flow models 754. The associated task flow can be a series of programmed actions and steps that the digital assistant takes in order to perform the task. The scope of a digital assistant’s capabilities can be dependent on the number and variety of task flows that have been implemented and stored in task flow models 754, or in other words, on the number and variety of “actionable intents” that the digital assistant recognizes. The effectiveness of the digital assistant, however, can also be dependent on the assistant’s ability to infer the correct “actionable intent(s)” from the user request expressed in natural language.
[0174] In some examples, in addition to the sequence of words or tokens obtained from STT processing module 730, natural language processing module 732 can also receive contextual information associated with the user request, e.g., from I/O processing module 728. The natural language processing module 732 can optionally use the contextual information to clarify, supplement, and/or further define the information contained in the token sequence received from STT processing module 730. The contextual information can include, for example, user preferences, hardware, and/or software states of the user device, sensor information collected before, during, or shortly after the user request, prior interactions (e.g., dialogue) between the
DK 2017 70128 A1 digital assistant and the user, and the like. As described herein, contextual information can be dynamic, and can change with time, location, content of the dialogue, and other factors.
[0175] In some examples, the natural language processing can be based on, e.g., ontology 760. Ontology 760 can be a hierarchical structure containing many nodes, each node representing either an “actionable intent” or a “property” relevant to one or more of the “actionable intents” or other “properties.” As noted above, an “actionable intent” can represent a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” can represent a parameter associated with an actionable intent or a sub-aspect of another property. A linkage between an actionable intent node and a property node in ontology 760 can define how a parameter represented by the property node pertains to the task represented by the actionable intent node.
[0176] In some examples, ontology 760 can be made up of actionable intent nodes and property nodes. Within ontology 760, each actionable intent node can be linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node can be linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, as shown in FIG. 7C, ontology 760 can include a “restaurant reservation” node (i.e., an actionable intent node). Property nodes “restaurant,” “date/time” (for the reservation), and “party size” can each be directly linked to the actionable intent node (i.e., the “restaurant reservation” node).
[0177] In addition, property nodes “cuisine,” “price range,” “phone number,” and “location” can be sub-nodes of the property node “restaurant,” and can each be linked to the “restaurant reservation” node (i.e., the actionable intent node) through the intermediate property node “restaurant.” For another example, as shown in FIG. 7C, ontology 760 can also include a “set reminder” node (i.e., another actionable intent node). Property nodes “date/time” (for setting the reminder) and “subject” (for the reminder) can each be linked to the “set reminder” node. Since the property “date/time” can be relevant to both the task of making a restaurant reservation and the task of setting a reminder, the property node “date/time” can be linked to both the “restaurant reservation” node and the “set reminder” node in ontology 760.
DK 2017 70128 A1 [0178] An actionable intent node, along with its linked concept nodes, can be described as a “domain.” In the present discussion, each domain can be associated with a respective actionable intent, and refers to the group of nodes (and the relationships there between) associated with the particular actionable intent. For example, ontology 760 shown in FIG. 7C can include an example of restaurant reservation domain 762 and an example of reminder domain 764 within ontology 760. The restaurant reservation domain includes the actionable intent node “restaurant reservation,” property nodes “restaurant,” “date/time,” and “party size,” and sub-property nodes “cuisine,” “price range,” “phone number,” and “location.” Reminder domain 764 can include the actionable intent node “set reminder,” and property nodes “subject” and “date/time.” In some examples, ontology 760 can be made up of many domains. Each domain can share one or more property nodes with one or more other domains. For example, the “date/time” property node can be associated with many different domains (e.g., a scheduling domain, a travel reservation domain, a movie ticket domain, etc.), in addition to restaurant reservation domain 762 and reminder domain 764.
[0179] While FIG. 7C illustrates two example domains within ontology 760, other domains can include, for example, “find a movie,” “initiate a phone call,” “find directions,” “schedule a meeting,” “send a message,” and “provide an answer to a question,” “read a list,” “providing navigation instructions,” “provide instructions for a task” and so on. A “send a message” domain can be associated with a “send a message” actionable intent node, and may further include property nodes such as “recipient(s),” “message type,” and “message body.” The property node “recipient” can be further defined, for example, by the sub-property nodes such as “recipient name” and “message address.” [0180] In some examples, ontology 760 can include all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some examples, ontology 760 can be modified, such as by adding or removing entire domains or nodes, or by modifying relationships between the nodes within the ontology 760.
[0181] In some examples, nodes associated with multiple related actionable intents can be clustered under a “super domain” in ontology 760. For example, a “travel” super-domain can include a cluster of property nodes and actionable intent nodes related to travel. The actionable
DK 2017 70128 A1 intent nodes related to travel can include “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest,” and so on. The actionable intent nodes under the same super domain (e.g., the “travel” super domain) can have many property nodes in common. For example, the actionable intent nodes for “airline reservation,” “hotel reservation,” “car rental,” “get directions,” and “find points of interest” can share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.” [0182] In some examples, each node in ontology 760 can be associated with a set of words and/or phrases that are relevant to the property or actionable intent represented by the node. The respective set of words and/or phrases associated with each node can be the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node can be stored in vocabulary index 744 in association with the property or actionable intent represented by the node. For example, returning to FIG. 7B, the vocabulary associated with the node for the property of “restaurant” can include words such as “food,” “drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” and so on. For another example, the vocabulary associated with the node for the actionable intent of “initiate a phone call” can include words and phrases such as “call,” “phone,” “dial,” “ring,” “call this number,” “make a call to,” and so on. The vocabulary index 744 can optionally include words and phrases in different languages.
[0183] Natural language processing module 732 can receive the token sequence (e.g., a text string) from STT processing module 730, and determine what nodes are implicated by the words in the token sequence. In some examples, if a word or phrase in the token sequence is found to be associated with one or more nodes in ontology 760 (via vocabulary index 744), the word or phrase can “trigger” or “activate” those nodes. Based on the quantity and/or relative importance of the activated nodes, natural language processing module 732 can select one of the actionable intents as the task that the user intended the digital assistant to perform. In some examples, the domain that has the most “triggered” nodes can be selected. In some examples, the domain having the highest confidence value (e.g., based on the relative importance of its various triggered nodes) can be selected. In some examples, the domain can be selected based on a combination of the number and the importance of the triggered nodes. In some examples,
DK 2017 70128 A1 additional factors are considered in selecting the node as well, such as whether the digital assistant has previously correctly interpreted a similar request from a user.
[0184] User data 748 can include user-specific information, such as user-specific vocabulary, user preferences, user address, user’s default and secondary languages, user’s contact list, and other short-term or long-term information for each user. In some examples, natural language processing module 732 can use the user-specific information to supplement the information contained in the user input to further define the user intent. For example, for a user request “invite my friends to my birthday party,” natural language processing module 732 can be able to access user data 748 to determine who the “friends” are and when and where the “birthday party” would be held, rather than requiring the user to provide such information explicitly in his/her request.
[0185] Other details of searching an ontology based on a token string is described in U.S. Utility Application Serial No. 12/341,743 for “Method and Apparatus for Searching Using An Active Ontology,” filed December 22, 2008, the entire disclosure of which is incorporated herein by reference.
[0186] In some examples, once natural language processing module 732 identifies an actionable intent (or domain) based on the user request, natural language processing module 732 can generate a structured query to represent the identified actionable intent. In some examples, the structured query can include parameters for one or more nodes within the domain for the actionable intent, and at least some of the parameters are populated with the specific information and requirements specified in the user request. For example, the user may say “Make me a dinner reservation at a sushi place at 7.” In this case, natural language processing module 732 can be able to correctly identify the actionable intent to be “restaurant reservation” based on the user input. According to the ontology, a structured query for a “restaurant reservation” domain may include parameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some examples, based on the speech input and the text derived from the speech input using STT processing module 730, natural language processing module 732 can generate a partial structured query for the restaurant reservation domain, where the partial structured query includes the parameters {Cuisine = “Sushi”} and {Time = “7pm”}. However, in this example, the user’s
DK 2017 70128 A1 speech input contains insufficient information to complete the structured query associated with the domain. Therefore, other necessary parameters such as {Party Size} and {Date} may not be specified in the structured query based on the information currently available. In some examples, natural language processing module 732 can populate some parameters of the structured query with received contextual information. For example, in some examples, if the user requested a sushi restaurant “near me,” natural language processing module 732 can populate a {location} parameter in the structured query with GPS coordinates from the user device.
[0187] In some examples, natural language processing module 732 can pass the generated structured query (including any completed parameters) to task flow processing module 736 (“task flow processor”). Task flow processing module 736 can be configured to receive the structured query from natural language processing module 732, complete the structured query, if necessary, and perform the actions required to “complete” the user’s ultimate request. In some examples, the various procedures necessary to complete these tasks can be provided in task flow models 754. In some examples, task flow models 754 can include procedures for obtaining additional information from the user and task flows for performing actions associated with the actionable intent.
[0188] As described above, in order to complete a structured query, task flow processing module 736 may need to initiate additional dialogue with the user in order to obtain additional information, and/or disambiguate potentially ambiguous speech inputs. When such interactions are necessary, task flow processing module 736 can invoke dialogue flow processing module 734 to engage in a dialogue with the user. In some examples, dialogue flow processing module 734 can determine how (and/or when) to ask the user for the additional information and receives and processes the user responses. The questions can be provided to and answers can be received from the users through I/O processing module 728. In some examples, dialogue flow processing module 734 can present dialogue output to the user via audio and/or visual output, and receives input from the user via spoken or physical (e.g., clicking) responses. Continuing with the example above, when task flow processing module 736 invokes dialogue flow processing module 734 to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” dialogue flow processing module 734 can
DK 2017 70128 A1 generate questions such as “For how many people ” and “On which day ” to pass to the user. Once answers are received from the user, dialogue flow processing module 734 can then populate the structured query with the missing information, or pass the information to task flow processing module 736 to complete the missing information from the structured query.
[0189] Once task flow processing module 736 has completed the structured query for an actionable intent, task flow processing module 736 can proceed to perform the ultimate task associated with the actionable intent. Accordingly, task flow processing module 736 can execute the steps and instructions in the task flow model according to the specific parameters contained in the structured query. For example, the task flow model for the actionable intent of “restaurant reservation” can include steps and instructions for contacting a restaurant and actually requesting a reservation for a particular party size at a particular time. For example, using a structured query such as: {restaurant reservation, restaurant = ABC Café, date = 3/12/2012, time = 7pm, party size = 5], task flow processing module 736 can perform the steps of: (1) logging onto a server of the ABC Café or a restaurant reservation system such as OPENTABFE®, (2) entering the date, time, and party size information in a form on the website, (3) submitting the form, and (4) making a calendar entry for the reservation in the user’s calendar.
[0190] In some examples, task flow processing module 736 can employ the assistance of service processing module 738 (“service processing module”) to complete a task requested in the user input or to provide an informational answer requested in the user input. For example, service processing module 738 can act on behalf of task flow processing module 736 to make a phone call, set a calendar entry, invoke a map search, invoke or interact with other user applications installed on the user device, and invoke or interact with third-party services (e.g., a restaurant reservation portal, a social networking website, a banking portal, etc.). In some examples, the protocols and application programming interfaces (API) required by each service can be specified by a respective service model among service models 756. Service processing module 738 can access the appropriate service model for a service and generate requests for the service in accordance with the protocols and APIs required by the service according to the service model.
DK 2017 70128 A1 [0191] For example, if a restaurant has enabled an online reservation service, the restaurant can submit a service model specifying the necessary parameters for making a reservation and the APIs for communicating the values of the necessary parameter to the online reservation service. When requested by task flow processing module 736, service processing module 738 can establish a network connection with the online reservation service using the web address stored in the service model, and send the necessary parameters of the reservation (e.g., time, date, party size) to the online reservation interface in a format according to the API of the online reservation service.
[0192] In some examples, natural language processing module 732, dialogue flow processing module 734, and task flow processing module 736 can be used collectively and iteratively to infer and define the user’s intent, obtain information to further clarify and refine the user intent, and finally generate a response (i.e., an output to the user, or the completion of a task) to fulfill the user’s intent. The generated response can be a dialogue response to the speech input that at least partially fulfills the user’s intent. Further, in some examples, the generated response can be output as a speech output. In these examples, the generated response can be sent to speech synthesis module 740 (e.g., speech synthesizer) where it can be processed to synthesize the dialogue response in speech form. In yet other examples, the generated response can be data content relevant to satisfying a user request in the speech input.
[0193] Speech synthesis module 740 can be configured to synthesize speech outputs for presentation to the user. Speech synthesis module 740 synthesizes speech outputs based on text provided by the digital assistant. For example, the generated dialogue response can be in the form of a text string. Speech synthesis module 740 can convert the text string to an audible speech output. Speech synthesis module 740 can use any appropriate speech synthesis technique in order to generate speech outputs from text, including, but not limited, to concatenative synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, articulatory synthesis, hidden Markov model (HMM) based synthesis, and sinewave synthesis. In some examples, speech synthesis module 740 can be configured to synthesize individual words based on phonemic strings corresponding to the words. For example, a phonemic string can be associated with a word in the generated dialogue response. The phonemic string can be stored in metadata associated with the word. Speech synthesis model
DK 2017 70128 A1
740 can be configured to directly process the phonemic string in the metadata to synthesize the word in speech form.
[0194] In some examples, instead of (or in addition to) using speech synthesis module 740, speech synthesis can be performed on a remote device (e.g., the server system 108), and the synthesized speech can be sent to the user device for output to the user. For example, this can occur in some implementations where outputs for a digital assistant are generated at a server system. And because server systems generally have more processing power or resources than a user device, it can be possible to obtain higher quality speech outputs than would be practical with client-side synthesis.
[0195] Additional details on digital assistants can be found in the U.S. Utility Application No. 12/987,982, entitled “Intelligent Automated Assistant,” filed January 10, 2011, and U.S. Utility Application No. 13/251,088, entitled “Generating and Processing Task Items That Represent Tasks to Perform,” filed September 30, 2011, the entire disclosures of which are incorporated herein by reference.
4. Exemplary Architecture of a Device [0196] FIG. 8A illustrates a block diagram of an exemplary architecture for device 800 according to various examples of the disclosure. In some examples, device 800 and remote 810, can implement a digital assistant system (e.g., digital assistant system 700). In some examples, device 800, remote 810, and a server (e.g., server 108) can implement a digital assistant system (e.g., digital assistant system 700). In the embodiment of FIG. 8A, media or other content is optionally received by device 800 via network interface 802, which is optionally a wireless or wired connection. The one or more processors 804 can optionally execute any number of programs stored in memory 806 or storage, which optionally includes instructions to perform one or more of the methods and/or processes described herein (e.g., process 1200).
[0197] In some embodiments, display controller 808 causes one or more user interfaces to be displayed on display 814. Further, input to device 800 is optionally provided by remote 810 via remote interface 812 using a wireless or a wired connection. It is understood that the embodiment of FIG. 8A is not meant to limit the features of the device of the disclosure, and that
DK 2017 70128 A1 other components to facilitate other features described in the disclosure are optionally included in or omitted from the architecture of FIG. 8A as well. In some embodiments, device 800 optionally corresponds to one or more of portable multifunction device 200 in FIGs. 2A and 3 and device 400 in Fig. 4; network interface 802 optionally corresponds to one or more of RF circuitry 208, external port 224, and peripherals interface 218 in FIGs. 2A and 4, and network communications interface 460 in FIG. 4; processor 804 optionally corresponds to one or more of processor(s) 220 in FIG. 2A and CPU(s) 410 in Fig. 4; display controller 808 optionally corresponds to one or more of display controller 256 in FIG. 2A and I/O interface 430 in FIG. 4; memory 806 optionally corresponds to one or more of memory 202 in FIG. 2A and memory 470 in FIG. 3; remote interface 812 optionally corresponds to one or more of peripherals interface 218, and I/O subsystem 206 (and/or its components) in FIG. 2A, and I/O interface 430 in FIG. 4; remote 812 optionally corresponds to and or includes one or more of speaker 211, touch-sensitive display system 212, microphone 213, optical sensor(s) 264, contact intensity sensor(s) 265, tactile output generator(s) 267, other input control devices 216, accelerometer(s) 268, proximity sensor 266, and I/O subsystem 206 in FIG. 2A, and keyboard/mouse 450, touchpad 455, tactile output generator(s) 457, and contact intensity sensor(s) 459 in FIG. 4, and touch-sensitive surface 551 in FIG. 5B; and, display 814 optionally corresponds to one or more of touch-sensitive display system 212 in FIGs. 2A and 3, and display 440 in FIG. 4.
[0198] FIG. 8B illustrates an exemplary structure for remote 810 according to various examples of the disclosure. Remote 810 optionally includes touch-sensitive surface 551. In some embodiments, touch-sensitive surface 551 is edge-to-edge (e.g., touch-sensitive surface 551 extends to the opposite edges of remote 810, such that little or no surface of remote 810 exists between the touch-sensitive surface 551 and one or more edges of remote 810, as illustrated in FIG. 8B). Touch-sensitive surface 551 is optionally able to sense contacts as well as contact intensities (e.g., clicks of touch-sensitive surface 551) in accordance with examples described herein. Remote 810 also optionally includes buttons 816, 818, 820, 822, 824, and 826. Buttons 816, 818, 820, 822, 824, and 826 are optionally mechanical buttons or mechanical button alternatives that are able to sense contact with, or depression of, such buttons to initiate corresponding action(s) on, for example, device 800. In some embodiments, selection of “menu” button 816 by a user navigates device 800 backwards in a currently-executing application or currently-displayed user interface (e.g., back to a user interface that was displayed previous to
DK 2017 70128 A1 the currently-displayed user interface), or navigates device 800 to a one-higher-level user interface than the currently-displayed user interface. In some embodiments, selection of “home” button 818 by a user navigates device 800 to a main, home, or root user interface from any user interface displayed on display 814 of device 800 (e.g., to a home screen of device 800 that optionally includes one or more applications accessible on device 800). In some embodiments, selection of “play/pause” button 820 by a user toggles between playing and pausing a currentlyplaying content item on device 800 (e.g., if a content item is playing on device 800 when “play/pause” button 820 is selected, the content item is optionally paused, and if a content item is paused on device 800 when “play/pause” button 820 is selected, the content item is optionally played). In some embodiments, selection of “+” 822 or 824 buttons by a user increases or decreases, respectively, the volume of audio reproduced by device 800 (e.g., the volume of a content item currently-playing on device 800). In some embodiments, selection of “voice input” button 826 by a user allows a digital assistant (e.g., digital assistant system 700) to receive a voice input of the user. Remote 810 can also transmit the received voice input or a representation thereof to another device (e.g., device 800 or a server) for further processing. In some embodiments, remote 810 includes one or more accelerometers for detecting information about the motion of the remote; the accelerometers are optionally utilized in the manners provided in the descriptions of embodiments set forth below.
[0199] FIG. 9 illustrates an exemplary schematic block diagram of an automatic speech recognition (ASR) system 900 in accordance with some embodiments. In some examples, ASR system 900 can correspond to ASR system(s) 731 of digital assistant system 700. In some embodiments, ASR system 900 can be implemented using one or more multifunction devices, including but not limited to devices 200, 400, and 800 (FIGS. 2A, 4, and 8A). The multifunctional devices can include devices such as servers, personal computers, mobile device, remote, or the like. In particular, ASR system 900 can be implemented in the memory (e.g., memory 202 or 470) and/or processor(s) (e.g., processor(s) 220 or CPUs 410) of one or more devices. ASR system 900 can be included in a digital assistant and can provide speech recognition capabilities in a multifunctional device. In particular, ASR system 900 can be configured to perform any of the processes or methods described below (e.g., process 1200). The ASR system 900 may include a feature extractor 902 and a recognition engine 904, described further below.
DK 2017 70128 A1 [0200] Generally, the ASR system 900 may receive a voice input (e.g., a speech input); identify characters, phrases, commands, words, or a combination thereof (collectively ’’content”) based on the voice input; and convert the identified content of the voice input to text. ASR system 900 can further cause the text to be displayed in a sequence corresponding to an order of the content included in the voice input.
[0201] In some embodiments, a character can include a letter, a numerical digit, a control character, or a space. A letter can be, for example, any lower case or upper case letter of an alphabet (e.g., “a”, “b”,. . . “z”, or “A”, “B”,. . . “Z”). A numerical digit can be, for example, any numerical digit having a value between 0 and 9 (“0”, “1”, “2”, . . . “9”). A control character can be a punctuation mark (e.g., “@”, “#”, “%”, “!”) character. In some examples, a punctuation mark may be identified by a punctuation word. For example, a punctuation mark “@” can be identified by a punctuation word “at”; a punctuation mark “#” can be identified by a punctuation word “pound-sign”; a punctuation mark “%” can be identified by a punctuation word “percent” or “percent sign”; and a punctuation mark “!” can be identified by a punctuation word “exclamation-point”. Other examples of punctuation marks and their corresponding punctuation words may include, for instance, a punctuation mark identified by a punctuation word “slash”, a punctuation mark identified by a punctuation word “hyphen” or “dash”, a punctuation mark identified by a punctuation word “period” or “dot”, a punctuation mark identified by a punctuation word “comma”, a punctuation mark identified by a punctuation word “colon”, a punctuation mark identified by a punctuation word “semicolon”, a punctuation mark “ ” identified by a punctuation word “question-mark”, a punctuation mark identified by a punctuation word “apostrophe”, a punctuation mark “(” identified by a punctuation word “open-parenthesis”, a punctuation mark “)” identified by a punctuation word “close-parenthesis”, a punctuation mark “$” identified by a punctuation word “dollar-sign”, a punctuation mark identified by a punctuation word “asterisk”, a punctuation mark identified by a punctuation word “ampersand”, and the like. A control character can also be a non-punctuation mark character. For example, a control character can be a carriage return, a tab, or the like.
[0202] A phrase may be used to identify a corresponding character and may, for instance, include a spelling word used for identification of the character. For example, the spelling word for identifying the letter “a” may be “apple,” “alpha,” or the like. Similarly, the spelling word
DK 2017 70128 A1 for identifying the letter “b” may be “boy,” “bravo,” or the like. In some examples, a phrase for identifying a corresponding character may have a certain format such as “a as in apple,” “b as in boy,” “n like Nancy,” and “m like Mary.” It will be appreciated that any spelling word may be used to identify a corresponding character and a phrase for identifying the corresponding character can have any desired format.
[0203] A command may enable editing of displayed text and/or performing one or more other functions with respect to the displayed text. Exemplary commands include “upper case,” “lower case,” “insert,” “delete,” “replace,” copy,” “paste,” “select,” search,” or the like. A command can include one or more accompanying parameters. For example, the command “insert” may be accompanied by parameters indicating the character to be inserted (e.g., “insert c”), the place of the insertion, the number of times of the insertion, or the like. A command may further be used in combination with other commands in some examples. The command “insert” may for instance be used in combination with the command “upper case” (e.g., “insert upper case c”).
[0204] A word can include a plurality of characters. In some examples, a word can include any combination of the plurality of characters, such as natural language words “Lincoln” and “center.” In some circumstances, a word may include a pre-determined character combination. By way of example, a word following a punctuation mark “@” may include a pre-determined character combination forming a domain extension (e.g., “com,” “net,” “org”), an acronym (e.g., FBI, IRS), or the like. This type of word may have a pre-determined number of character combinations.
[0205] In an example operation of the ASR system 900, upon receiving a voice input, the feature extractor 902 can pre-process the voice input and provide the pre-processed voice input to the recognition engine 904. In some examples, pre-processing the voice input may include extracting one or more acoustic features from the voice input. Accordingly, feature extractor 902 can extract acoustic features from the voice input and provide the extracted acoustic features to the recognition engine 904. Upon receiving the pre-processed voice input, recognition engine 904 can identify the content of the voice input. Recognition engine 904 may identify the content using, for example, one or more of vocabulary 912, speech recognition models 914, contextual data 916, and recognized credential models 918 (collectively, knowledge repository 910). For
DK 2017 70128 A1 example, recognition engine 904 can compare content of the voice input with the characters, phrases, commands, and/or words (collectively as known types) stored in knowledge repository 910. If one or more matches are found, recognition engine 904 can identify the content as any combination of known types. Recognition engine 904 can identify the content of the voice input as including a first character (e.g., the letter “a”), a second character identified by a phrase (e.g., the letter “b” identified by the phrase “b as in boy”), and/or a word (e.g., “Lincoln”), for example.
[0206] Once content has been identified, recognition engine 904 can convert the content of the voice input to text and cause the text to be displayed in a sequence corresponding to an order of the content of the voice input. For example, the order of the text displayed may be “a” “b” “r,” which corresponds to the order the characters in the voice input (FIGs. 10A-10C).
[0207] In some examples, identifying content of a voice input may include identifying one or more utterances included in the voice input. As described, acoustic features extracted by the feature extractor 902 may be provided to the recognition engine 904 for content identification. Based on the acoustic features, the recognition engine may identify one or more utterances in a corresponding voice input. In some instances, acoustic features may be used to determine the beginning and end of one or more utterances. As an example, recognition engine 904 may determine that one or more acoustic features correspond to (e.g., match) a prolonged silence or pause between utterances and thus identify separate or individual utterances. As another example, a frequency spectrum of a pause or silence may be different from the frequency spectrum of an utterance of a character, a phrase, a command, or a word. As a result, analysis of the frequency spectrum can be used for identification of one or more utterances within the voice input.
[0208] In some embodiments, identifying content of a voice input may include determining types of the identified utterances. For instance, for each identified utterance, the recognition engine 904 can determine a type of the utterance. The recognition engine 904 can determine, for instance, whether the type of an utterance corresponds to a character, a phrase for identifying a corresponding character, a command, or a word. In some examples, previously recognized characters, phrases, words, and commands can be stored in one or more portions of knowledge repository 910. As an example, characters, phrases, commands, and words stored in knowledge
DK 2017 70128 A1 repository 910 may include commonly used characters, phrases, commands, and words and/or those characters, phrases, and commands received by recognition engine 904 in one or more previous voice inputs. Knowledge repository 910 may be stored on device 800, a server (e.g., server 108), a plurality of servers (e.g., a server farm), or a combination thereof.
[0209] As described above, knowledge repository 910 may include vocabulary 912, speech recognition models 914, contextual data 916, and/or recognized credential models 918.
Vocabulary 912 can include commonly used characters (e.g., the 26 alphabetical characters), phrases (e.g., phrases including the NATO’s phonetic alphabet), commands (e.g., editing commands), and words (e.g., natural language words, domain extensions, and/or acronyms). As described above, speech recognition models 914 can include, for example, acoustic models and/or language models, and can implement one or more speech recognition engines. Examples of speech recognition models can include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of speech recognition engines can include the dynamic time warping based engines and weighted finite-state transducers (WFST) based engine.
[0210] In some embodiments, speech recognition models 914 can be user specific and can be generated by speech recognition and language model generator 913 using user-specific data. Speech recognition and language model generator 913 can be configured to receive or obtain user-specific data, such as user input, user usage data, user profile information, or the like. Speech recognition and language model generator 913 can further be configured to generate one or more user-specific speech recognition models 914 using the user-specific data.
[0211] Contextual data 916 can include, for example, user preferences, user speech profile, hardware and/or software states, sensor information collected, prior interactions between the digital assistant and the user, and the like. Contextual data 916 can also include information indicating the natural of the secured link or device to which a user is providing the credentials. For example, contextual data 916 may indicate that the user is providing credentials to a web email account (e.g., Yahoo, Gmail), a cloud (e.g., Apple iCloud) account, or a device.
[0212] Recognized credential models 918 can include commonly used credential models and/or credential models based on the user’s past interaction with the digital assistant. For
DK 2017 70128 A1 example, recognized credential models 918 may include a user name model and a password model. Exemplary user name models may include firstname.lastname©domain _name.com (e.g., Abraham.Lincoln@Lincolncenter.com), firstinital.lastname@domain_name.com (e.g., ALincoln@Lincolncenter.com), firstnamelastname_numerical_digits@domain_name.com (e.g., AbrahamLincoln_123@Lincolncenter.com), or the like.
[0213] As described, the recognition engine 904 can determine a type of an utterance of the voice input, for instance, based on acoustic features provided by the feature extractor 902. In some examples, the recognition engine 904 can identify intermediate recognition results (e.g., phonemes, phonemic strings, and sub-words) corresponding to one or more identified utterances of the voice input (e.g., using an acoustic model). Intermediate recognition results may, for instance, be used to determine a type of each utterance. The recognition engine 904 can determine whether the type of utterance corresponds to a character, phrase, command, or word in some embodiments. For example, recognition engine 904 can compare the intermediate recognition results (e.g., using a language model) to known types in knowledge repository 910.
[0214] In some examples, the recognition engine 904 can compare a sequence of phonemes (e.g., a pronunciation of a character or word) of the utterance with one or more sequences of phonemes of the known types included in knowledge repository 910. A sequence of phonemes can be identified for an utterance (e.g., using acoustic features). If the sequence of phonemes of the utterance matches (e.g., substantially matches) the phoneme sequence of a known type in knowledge repository 910, recognition engine 904 determines the type of the utterance accordingly. The degree by which phoneme sequences match may be used to determine a confidence level in some examples. By way of example, if the phoneme sequences are a complete match, the confidence level may be 100%. As another example, if 5 of 6 of the phonemes of the sequences match, the confidence level may be 90%. If the sequence of phonemes matches (e.g., substantially matches) a character, a phrase, a command, or a word, the type of the utterance is determined to be a character, a phrase, a command, or a word, respectively.
[0215] In some examples, in accordance with a determination that a type of an utterance of a voice input correspond to a character, phrase, word, or a combination thereof, recognition engine
DK 2017 70128 A1
904 can obtain a representation of the voice input content corresponding to the utterance. The representation may be an audio representation, a textual representation, a spatial/vector representation, or a combination thereof.
[0216] In some examples, if the sequence of phonemes matches more than one type in knowledge repository 910, recognition engine 904 may determine a confidence level for each matched type and select the type with the highest confidence level. For example, if the sequence of phonemes match both a word (e.g., the word “insert”) and a command (e.g., the command “insert”), recognition engine 904 may determine a confidence level for the type as a word and a confidence level for the type as a command. In some embodiments, the confidence level can be determined based on additional content in the voice input or other context related to the voice input. For example, a command may be accompanied by one or more parameters for performing actions according to the command. As a result, if recognition engine 904 identifies one or more parameters associated with the command in the voice input, it may determine that the confidence level for a type of command is higher than that for a type of word. In some examples, recognition engine 904 can determine the confidence level based on contextual data 916. In some examples, if no match is found, recognition engine 904 may be configured to determine that the utterance corresponds to a word (e.g., a natural language word), as a default configuration. In some examples, if no match is found, recognition engine 904 may also be configured to determine one or more candidate types. Candidate types can be, for example, determined based on the confidence levels of each type. In some examples, candidate types may be used for obtaining candidate representations of the content of voice input. Candidate representations may be in the form of audio representations, textual representations, spatial/vector representations, or a combination thereof. Candidate representations may be presented to a user for selection.
[0217] It will be appreciated that the determination of the type of utterance can be performed in any order or any combination with respect to character, phrase, word, or command. For example, recognition engine 904 can determine first whether the type of utterance corresponds to a character. If the type of utterance does not correspond to a character, it further determines whether the type corresponds to a phrase, a word, and so forth. As another example, recognition engine 904 can also determine first whether the type of utterance corresponds to a phrase. If the
DK 2017 70128 A1 type of utterance does not correspond to a phrase, it further determines whether the type corresponds to a character, a word, and so forth. The determination can also be performed in parallel with respect to character, phrase, word, or command.
[0218] Upon obtaining a representation, recognition engine 904 can convert the representation to text. Upon the conversion to text, recognition engine 904 can cause the text to be displayed.
[0219] In some examples, identifying the content of the voice input can include determining a confidence level. A confidence level may, for instance, be indicative of the probability that content of the voice input is accurately identified. In some examples, a confidence level may be associated with the identified content of the voice input. As described, in some examples, identifying the content of the voice input may include identifying an utterance of the voice input, determining a type of the utterance, and/or obtaining a representation of the content of the voice input, described in further detail below. Accordingly, a confidence level may thus be associated with identification of an utterance, a determined type of the utterance, an obtained representation of the content of the voice input, or a combination thereof. For instance, recognition engine 904 may recognize content as including an utterance of a particular type if an associated confidence level is greater than or equal to a threshold. Conversely, the recognition engine 904 may not recognize content as including an utterance of any type if an associated confidence level is not greater than or equal to the threshold. Instead, the recognition engine 904 may ignore the identified utterance type, and provide one or more candidate types of the utterance, an error message, or a combination thereof. Candidate types can be, for example, determined based on the confidence levels of each type. Candidate types may be provided to the user in a visual form, an auditory form, or a combination thereof. In some examples, recognition engine 904 can also cause an error message to be provided to the user (e.g., “Sorry, I missed that. Can you say it again ”).
[0220] In some examples, recognition engine 904 can obtain a confidence level for the representation of the voice input content. As previously described, a confidence level indicates a degree of certainty that a determination of a type of an utterance is accurate. In accordance with a determination that the confidence level is greater than or equal to a threshold, recognition
DK 2017 70128 A1 engine 904 converts the representation to text. In some examples, in accordance with a determination that the confidence level is less than the threshold, recognition engine 904 can identify one or more candidate types obtain corresponding candidate representations. Candidate representations can be provided to the user for selection. In some examples, recognition engine 904 can provide candidate representations, an error message (e.g., “I do not recognize what you said, please say a letter or a phrase such as “a as in apple”), or a combination thereof. In some examples, candidate representations can be generated based on a match between the intermediate recognition results (e.g., phonemes, phonemic strings, and sub-words) and the known types (e.g., using a language model) stored in knowledge repository 910.
[0221] In some examples, recognition engine 904 can identify a command based on the voice input. As described above, in some embodiments, recognition engine 904 can first identify an utterance of the voice input, and determine whether a type of the utterance corresponds to a command. In accordance with a determination that the type of an utterance corresponds to a command, recognition engine 904 can cause an action to be initiated according to the command. For example, recognition engine 904 can cause previously displayed text to be edited (e.g., insert or delete characters).
[0222] In some examples, recognition engine 904 may also cause one or more utterances to be stored for learning and/or training purposes. Utterances may be stored, for instance, in a memory (e.g., memory 202) and/or a database. The one or more utterances correspond to the characters, phrases identifying the corresponding characters, commands, and/or words identified by the recognition engine 904 in some examples. These utterances (e.g., the acoustic features of the utterances) may be used to update at least a portion of knowledge repository 910 such that the type of subsequent user utterances can be more accurately determined.
5. Voice Input Identification Examples [0223] FIGs. 10A-10F illustrate functionality of voice input identification according to various examples. In accordance with one or more examples described herein, an electronic device, such as device 800 of FIG. 8A and/or remote 810, may be controlled using spoken user inputs (e.g., voice inputs, speech inputs) provided by a user. Device 800, remote 810, a server (e.g., server 108), or a combination thereof, may implement a digital assistant system (digital
DK 2017 70128 A1 assistant system 700). With reference to FIGs. 10A-10F, in some examples, device 800 may receive one or more voice inputs, such as 1024A-F, from a user 1030. User 1030 may provide the voice inputs for the purpose of, for example, dictating or otherwise entering user credentials (e.g., a user name and/or a password). Display 814 may be used to prompt and/or allow a user 1030 to enter a user name and a password for authentication purposes. A user may, for instance, be prompted to enter credentials to log into a device, an account, a service, or the like. Device 800 may receive the voice inputs directly from user 1030 or indirectly through another device, such as remote 810. Voice inputs 1024A-F may be received via a microphone (e.g., microphone 213) of device 800 or remote 810. In some examples, device 800 may identify content (e.g., a character, a phrase identifying a character, a command, a word, or a combination thereof) of voice inputs 1024A-F in accordance with examples described herein. The identification of the content of voice inputs 1024A-F can be performed by, for example, a digital assistant including an automatic speech recognition system (e.g., ASR system 900).
[0224] With reference to FIG. 10A, in some examples, the device 800 can identify a character based on a voice input. By way of example, upon receiving voice input 1024A, device 800 can identify one or more utterances of voice input 1024A. As illustrated, an identified utterance of the voice input 1024A may include the character “a.” [0225] Upon identifying the one or more utterances, the device 800 can determine a type of the utterance. In some embodiments, the determination can be based on characters stored in a knowledge repository, such as commonly used alphabetical characters, numerical digits, and control characters, and/or characters device 800 received previously from user 1030. The characters included in the knowledge repository 910 may be stored on device 800 or a server (e.g., server 108). For example, device 800 can determine whether the type of the utterance corresponds to a character by matching a sequence of phonemes (e.g., a pronunciation of a character) of the utterance to the phoneme sequence of a known character.
[0226] In some examples, in accordance with a determination that the type of utterance corresponds to a character, device 800 identifies the character as included in the content of voice input 1024A (e.g., character “a”) and converts the character to text.
DK 2017 70128 A1 [0227] In response to the conversion to text, device 800 can cause the text of the content of voice input 1024A (e.g., text of character “a”) to be displayed, for instance using the display 814.
[0228] With reference to FIG. 10B, in some examples, device 800 can identify a character that is identified by a phrase. By way of example, upon receiving voice input 1024B, device 800 can identify one or more utterances of voice input 1024B. As illustrated, voice input 1024B may include a phrase (e.g., “b as in boy”) for identifying a corresponding character of the phrase (e.g., “b”).
[0229] Upon identifying one or more utterances of voice input 1024B, device 800 can determine a type of the utterance. In some embodiments, the determination can be based on phrases stored in a knowledge repository, such as commonly used phrases. Commonly used phrases for identifying characters may use spelling words as specified in the NATO’s phonetic alphabet (e.g., Alpha, Bravo, Charlie, Delta, Echo, Foxtrot, Golf, Hotel, India, Juliet, Kilo, Fima, Mike, November, Oscar, Papa, Quebec, Romeo, Sierra, Tango, Uniform, Victor, Whiskey, X-ray, Yankee, Zulu).
[0230] In some embodiments, the phrases in the knowledge repository may also include previously received phrases. For example, user 1030 may not use the NATO’s phonetic alphabet as spelling words for identifying characters. Instead, user 1030 may have previously used a customized phrase such as “a as in Aaron” or “1 like Lincoln.” Device 800 can also store such customized phrases that the user 1030 previously provided in the knowledge repository. As a result, if user 1030 utters the customized phrase that was previous used, device 800 can also determine that the type of the utterance corresponds to a phrase for identifying a corresponding character. The phrases for identifying corresponding characters in the knowledge repository may be stored on device 800 or a server (e.g., server 108).
[0231] As described, a type of an utterance can be determined in response to a user providing a voice input. For example, device 800 can determine whether a type of an utterance corresponds to a phrase by matching the sequence of phonemes (e.g., a pronunciation of a phrase) of the utterance to the phoneme sequence of a phrase in the knowledge repository.
DK 2017 70128 A1 [0232] In some examples, in accordance with a determination that that the type of utterance corresponds to a phrase for identifying a corresponding character, device 800 identifies the phrase as included in the content of voice input 1024B and converts the character identified by the phrase to text. In response to the conversion to text, device 800 can cause the text of the content of voice input 1024B (e.g., text of character “b”) to be displayed, for instance using the display 814.
[0233] With reference to FIG. 10C, in some examples, device 800 can identify additional characters and phrases based on additional voice input. By way of example, upon receiving voice input 1024C, device 800 can identify one or more utterances of voice input 1024C. As illustrated, voice input 1024C may include additional characters (e.g., “r,” “a,” “h,” and “a”) and an additional phrase (e.g., “m as in Mary”). As described, in response to receiving voice input 1024C, device 800 can identify the one or more utterances of voice input 1024C, determine the type of the utterances, and convert the identified characters of content of voice input 1024C to text for displaying on display 814.
[0234] In some examples, device 800C can identify a punctuation word based on a voice input. Upon receiving voice input 1024C, for instance, device 800 can identify one or more utterances of voice input 1024C. As illustrated, voice input 1024C may include a punctuation word “at” identifying (e.g., corresponding to) a punctuation mark [0235] Upon identifying the one or more utterances of voice input 1024C, device 800 can determine a type of the utterance. In some embodiments, the determination can be based on punctuation words stored in a knowledge repository, such as commonly used punctuation words (e.g., “at,” “pound-sign,” “dollar-sign,” or the like) and/or customized punctuation words device 800 previously received from user 1030. For example, instead of the punctuation word “asterisk,” which corresponds to the punctuation mark user 1030 may have previously uttered the punctuation word “star,” intending it to also correspond to the punctuation mark Such customized punctuation words can also be stored in the knowledge repository. The punctuation words included in the knowledge repository may be stored on device 800 or a server (e.g., server 108).
DK 2017 70128 A1 [0236] As described, the type of the utterance can be determined in response to a user providing a voice input. For example, device 800 can determine whether the type of the utterance corresponds to a punctuation word by matching the sequence of phonemes (e.g., a pronunciation of a character) of the utterance to the phoneme sequence of a punctuation word included in knowledge repository 910. Under some circumstances, there may be more than one substantial match. Accordingly, device 800 may disambiguate matches based on context of the punctuation word and/or using confidence levels. As an example, the device 800 may determine which match is appropriate based on one or more other utterances identified from the voice input provided by user 1030. As another example, device 800 may determine a confidence level for each matched type and select the type with the highest confidence level. For example, if the sequence of phonemes match both a punctuation word (e.g., the punctuation word “at”) and a word (e.g., a natural language word “at”), device 800 can determine a confidence level for the type as a punctuation word and for the type as a word. In some embodiments, the confidence level can be determined based on additional utterances in voice input 1024C. For example, using recognized credential models, device 800 may determine that the user is intending to provide an email address as a user name in voice input 1024C. Accordingly, device 800 may determine that the confidence level for a type of a punctuation word is higher than that for a type of a natural language word.
[0237] In some examples, in accordance with a determination that that the type of utterance corresponds to a punctuation word, device 800 identifies the punctuation word of the content of voice input 1024C (e.g., the word “at”) and converts the punctuation word to text of the corresponding punctuation mark. For example, device 800 can convert the punctuation word “at” to text of the punctuation mark “@.” With reference to FIG. 10C, in response to the conversion, device 800 can cause the text of the content of voice input 1024C (e.g., text of punctuation mark “@”) to be displayed, for instance, using display 814.
[0238] With reference to FIG. 10D, in some examples, device 800 can identify one or more words based on a voice input, for instance, by identifying one or more utterances of voice input 1024D. As illustrated, voice input 1024D may include one or more words, each of which can include any combination of a plurality of characters. A word may be, for example, a natural language word such as “Lincoln” and “center.”
DK 2017 70128 A1 [0239] Upon identifying the utterances of voice input 1024D, device 800 can determine a type of the utterances. In some examples, the determination can be based on words stored in a knowledge repository, such as commonly used word for entering a credential (e.g., “yahoo”, “gmail,” and “aol”) and/or words that user 1030 uttered previously. For example, user 1030 may have uttered the words “Lincoln” and “center” previously for entering the user name. Thus, the words “Lincoln” and “center” are stored in the knowledge repository. The words included in the knowledge repository may be stored on device 800 or a server (e.g., server 108).
[0240] Device 800 can determine whether the type of the utterance corresponds to a word by matching the sequence of phonemes (e.g., a pronunciation of a word) of the utterance to the phoneme sequence of a word included in the knowledge repository.
[0241] In some examples, device 800 can determine whether the type of utterance corresponds to a word based on a pre-determined configuration. For example, device 800 may be preconfigured to determine the type of utterance such that the default type of an utterance corresponds to a word. For example, upon receiving a voice input, device 800 may identify one or more utterances and determine whether the type of each identified utterance corresponds to a character (e.g., a letter, a numerical digit, or a control character such as a punctuation mark), a phrase identifying a corresponding character, or a command. If device 800 determines that the type of the utterance does not correspond to any of these types, device 800 may be configured to determine, by default, that the type of utterance corresponds to a word.
[0242] In some examples, in accordance with a determination that that the type of utterance corresponds to a word, device 800 identifies the word (e.g., the word “Lincoln”) and converts the identified word to text. With reference to FIG. 10D, upon the conversion to text, device 800 can cause the text of the content of voice input 1024D (e.g., text of word “Lincoln”) to be displayed using display 814.
[0243] With reference to FIG. 10E, in some examples, device 800 can identify additional punctuation words, for instance, by identifying one or more utterances in voice input 1024E. As illustrated, voice input 1024E may include an additional punctuation word (e.g., punctuation word “dot”). In response to receiving voice input 1024E, device 800 can identify the utterance,
DK 2017 70128 A1 determine the type of the utterance as a punctuation word, and convert the utterance to text for displaying on display 814.
[0244] In some examples, device 800 can also identify a word having a pre-determined character combination based on a voice input. By way of example, upon receiving voice input 1024E, device 800 can identify an utterance of voice input 1024E. As illustrated, voice input 1024E may include a word having a pre-determined character combination (e.g., a domain extension). As described, a word having a pre-determined character combination may include a plurality of characters forming, for instance, a domain extension (e.g., “com,” “net,” and “org”), an acronym (e.g., FBI, IRS), or the like.
[0245] Upon identifying the utterance based on voice input 1024E, device 800 can determine a type of the utterance. In some examples, the determination can be based on words previously recognized and stored in a knowledge repository (e.g., knowledge repository 910). As an example, these words may include some commonly used words for entering a credential (e.g., a domain extension “com,” “net,” and “org”). As another example, these words may include words that user 1030 uttered previously, such as the acronyms “FBI,” “IRS,” and “USPTO.” Thus, words that were uttered previously may also be stored in the knowledge repository. The words included in the knowledge repository may be stored on device 800 or a server (e.g., server 108).
[0246] As described, in response to a user providing a voice input, the type of the utterance can be determined. For example, as described above, device 800 can determine whether the type of the utterance corresponds to a word having a pre-determined character combination by matching the sequence of phonemes of the utterance to the phoneme sequence of a word in the knowledge repository.
[0247] In some examples, in accordance with a determination that that the type of utterance corresponds to a word having a pre-determined character combination, device 800 identifies the word having a pre-determined character combination (e.g., “com”) and converts the word to text. With reference to FIG. 10E, in response to the conversion to text, device 800 can cause the text of the content of voice input 1024E (e.g., text of word “com”) to be displayed using display 814.
DK 2017 70128 A1 [0248] With reference to FIG. 10F, in some examples, device 800 can identify additional characters, punctuation words, and phrases. Device 800 can also identify a command. By way of examples, upon receiving voice input 1024F, device 800 can identify one or more utterances in voice input 1024F. As illustrated, voice input 1024F may comprise a mix of characters, phrases, and commands (e.g., a character “a,” a punctuation word “asterisk,” a character “b,” a command “upper case,” a character “c,” a punctuation word “dollar sign,” numerical digits “1,” “2,” and “3,” and a punctuation word “percent-sign”). In response to receiving voice input 1024F, device 800 can identify the utterances of the character “a,” the punctuation word “asterisk,” the character “b,” the punctuation word “dollar sign,” the numerical digits “1,” “2,” and “3,” and the punctuation word “percent sign.” Device 800 can also determine the types of these utterances, and convert these utterances to text for displaying on display 814.
[0249] With reference to FIG. 10F, device 800 can also identify a command based on a voice input, for instance, by identifying an utterance in voice input 1024F. As illustrated, voice input 1024F may include a command (e.g., the command “upper case”). A command enables editing of displayed text or performing any desired functions with respect to the displayed text. Exemplary commands include “upper case,” “lower case,” “insert,” “delete,” “replace,” copy,” “paste,” “select,” search,” or the like.
[0250] Upon identifying the utterance based on voice input 1024F, device 800 can determine a type of the utterance. In some examples, the determination can be based on commands previously recognized and stored in a knowledge repository (e.g., knowledge repository 910). In some embodiments, these commands may include commonly used commands such as “insert,” “delete,” “upper case,” “lower case,” or the like. These commands may also include commands device 800 received previously from user 1030. The commands included in the knowledge repository may be stored on device 800 or a server (e.g., server 108). Device 800 can determine whether the type of the utterance corresponds to a command by matching the sequence of phonemes (e.g., a pronunciation of a command) of the utterance to the phoneme sequence of a command in the knowledge repository.
[0251] In some examples, in accordance with a determination that that the type of utterance corresponds to a command, device 800 can cause an action to be initiated according to the
DK 2017 70128 A1 command to be initiated. For example, with reference to FIG. 10F, in accordance with the determination that the type of the utterance of “upper case” corresponds to a command, device 800 can cause the content of voice input 1024F (e.g., the utterance of character “c”) to be converted to text and an action converting the text of “c” to its upper case form “C” to be initiated.
[0252] FIGs. 11A-1 IE illustrate functionality of voice input identification according to various examples. With reference to FIG. 11 A, in some examples, device 800 can identify additional characters and phrases. Upon receiving voice input 1144A, for instance, device 800 can identify one or more utterances in voice input 1144A. As illustrated, voice input 1144A may include a mix of characters, phrases, and commands (e.g., a character “a,” a phrase “b as in boy,” characters “r,” “a,” and “h”). As described, in response to receiving voice input 1144A, device 800 can identify the utterances of characters “a,” “r,” and “a,” and the phrase “b as in boy,” converting them to text, and causing the text on display 814 to be displayed.
[0253] Device 800 can also identify additional commands based on a voice input. By way of example, device 800 may mistakenly identify the character “h” as the character “s” and therefore causes to display the character “s” instead of the character “h” on display 814. As illustrated in FIG. 11A, user 1130 may wish to correct such error and thus, in voice input 1144A, utter “delete,” intending to delete the character “s.” In some examples, user 1130 may alternatively utter “h as in Henry,” to more explicitly emphasize the user’s intended input.
[0254] In some examples, in accordance with a determination that that the type of an utterance in voice input 1144A corresponds to a command, device 800 can initiate an action according to the command. For example, with reference to FIG. 11 A, in accordance with the determination that the type of the utterance in voice input 1144A corresponds to a command (e.g., a command “delete”), device 800 can cause a current character or an immediate preceding character displayed on display 814 to be deleted (e.g., the character “s”). In some examples, if more than one “delete” command is identified, device 800 can cause a current character and one or more preceding characters displayed on display 814 to be deleted. For example, if voice input 1144A includes two consecutive commands “delete,” device 800 may cause the two immediately preceding characters (e.g., the characters “s” and “a”) to be deleted; and if voice input 1144A
DK 2017 70128 A1 includes three consecutive commands “delete,” device 800 may cause the three immediately preceding characters (e.g., the characters “s,” “a,” and “r”) to be deleted, and so forth.
[0255] In some examples, after providing a voice input including the command “delete,” user 1030 may provide additional voice input to clarify the user’s intention using, for example, a phrase (e.g., “h as in Henry”). Upon receiving voice input 1144A, device 800 can identify the correct character (e.g., “h”) based on the voice input 1144A. For example, device 800 can identify the utterance of the phrase, convert the correct character to text based on the identification, and cause display of the text on display 814. As illustrated in FIG. 11A, device 800 can cause display 814 to display the text of the content of voice input 1144A as a result of the performance of an action according to the command (e.g., deleting “s” and displaying “h”).
[0256] With reference to FIG. 1 IB, in some examples, device 800 can identify additional commands. By way of example, user 1130 may desire to insert the character “a” and thus may utter “insert ‘a.’” [0257] In some examples, in accordance with a determination that that the type of utterance corresponds to a command (e.g., “insert”), device 800 can cause an action to be initiated according to the command. For example, as illustrated in FIG. 1 IB, in accordance with the determination that the type of the utterance of voice input 1144B corresponds to a command, device 800 causes the character (e.g., “a”) following the command “insert” to be inserted at a position immediately preceding the current character displayed on display 814 (e.g., preceding the character “m”). As illustrated in FIG. 1 IB, device 800 can cause the text of voice input 1144B to be displayed (e.g., using display 814) as a result of the performance of an action according to the command (e.g., inserting character “a” before character “m”).
[0258] With reference to FIG. 11C, in some examples, commands may be provided in combination with one or more parameters. By way of example, user 1130 may not recognize an improper entry until after most parts of voice input 1144C have been provided by user 1130. For example, the user 1130 may not realize that the character “a” is missing until after user 1030 utters “dot” “com” in attempting to enter the user name. As a result, the user 1130 may desire to insert the character “a” between letter “h” and “m” in the word “abraham.” Accordingly, the user 1130 may provide a voice input 1144C including a command “insert” and further specifying
DK 2017 70128 A1 “‘a’ between ‘h’ and ‘nT before ‘at.’” In some examples, voice input 1144C may include a command such as “insert ‘a’ between ‘h’ and ‘nT in the word ‘abraham.’” As described, upon receiving voice input 1144C, device 800 can identify the utterances in voice input 1144C.
[0259] Upon identifying the utterances, device 800 can also determine whether the types of the utterances correspond to a command (e.g., “insert”) and one or more associated parameters. In some examples, device 800 can first determine whether one of the utterances corresponds to a command (e.g., “insert”). In accordance with a determination that that the type of an utterance corresponds to a command, device 800 can further determine whether the command is accompanied by one or more parameters for performing an action according to the command. Such parameters may include, for example, the character to be inserted or deleted (e.g., “a”), and the position of the editing (e.g., “‘between’ ‘h’ and ‘nT before ‘at,’” “between ‘h’ and ‘nT in the word ‘abraham’). In some examples, the determination of whether the command is accompanied by one or more parameters may enable actions to be initiated according to the user’s intent, rather than according to a default configuration. For example, as described below, device 800 can cause an action to be initiated according to the command and the parameters, rather than according to the command and one or more default configurations (e.g., default configurations to delete the immediate preceding character, to insert after the current character, or the like).
[0260] In accordance with a determination that a command is accompanied by one or more parameters, device 800 can cause an action to be initiated corresponding to the command and the parameters. For example, as illustrated in FIG. 11C, in accordance with the determination that the type of the utterance of “insert” corresponds to a command and the determination that there are parameters accompanying the “insert” command, device 800 can cause an action to be initiated according to the command (e.g., to insert the corresponding character such as letter “a”) and the parameters (e.g., between “h” and “m” in the word “abraham”). With reference to FIG. 11C, device 800 can cause the text of the content of voice input 1144C to be displayed (e.g., using display 814) as a result of the performance of the action according to the command (e.g., inserting character “a” in the correct position) and the parameters.
[0261] With reference to FIG. 1 ID, device 800 can identify one or more additional commands. By way of example, user 1130 may repeat a previous character. For example, in
DK 2017 70128 A1 voice input 1144D, intending to entering the character “b” twice in a password, user 1130 may utter “b as in boy,” followed by a command “repeat.” [0262] As described, in accordance with a determination that that the type of utterance corresponds to a command, device 800 can cause an action to be initiated according to the command. For example, as illustrated in FIG. 1 ID, device 800 can cause an action to be initiated according to the command (e.g., to repeat the immediate preceding character). With reference to FIG. 1 ID, device 800 can cause the text of the content of voice input 1144D to be displayed as a result of the performance of the action according to the command (e.g., displaying an additional character “b”).
[0263] It is appreciated that while FIGs. 10A-10Fand 11 A-l ID are described using device 800 as an example, one or more of the digital assistant functions and processes described herein can alternatively be performed by a server (e.g., server 108) or a combination of device 800 and a server. For example, upon receiving one or more of voice inputs 1024A-F, device 800 can provide data representing the voice inputs 1024A-F to server 108, and server 108 can identify the content of the voice inputs 1024A-F. As an example, server 108 can identify one or more utterances, determine the types of the one or more utterances, and obtain representations of the content of the voice inputs 1024A-F. In some examples, server 108 can provide the representations of the content of voice inputs 1024A-F to device 800. Upon receiving the representations of the content of voice input 1024A-F from server 108, device 800 can convert the obtained representations to text and cause the text to be displayed on display 814. In some examples, server 108 can convert the representations of voice inputs 1024A-F to text and provide the text to device 800. Device 800 can thereafter cause the text to be displayed on display 814.
6. Process for Identifying a Voice Input [0264] FIGs. 12A-D illustrate a flow diagram of an exemplary process 1200 for voice input identification in accordance with some embodiments. Process 1200 may be performed using one or more devices 104, 108, 200, 400, 600, 800, or 810, (FIGs. 1, 2A, 4, 6A-B, or 8A-8B).
Operations in process 1200 are, optionally, combined or split and/or the order of some operations is, optionally, changed.
DK 2017 70128 A1 [0265] With reference to FIG. 12A, at block 1202, a voice input is received. The voice input may include one or more utterances corresponding to characters, phrases, commands, and/or words. The voice input can correspond to at least one of a first user credential representing a user name or a second user credential representing a password.
[0266] At block 1204, a first character, a phrase identifying a second character, and a word are identified based on the voice input. In some examples, the first character includes at least one of: a letter, a numerical digit, a control character, or a space. A control character can include a punctuation mark identified by a punctuation word, or a non-punctuation mark character (e.g., a tab, a carriage return). The phrase identifying the second character can include a spelling word for assisting the identification of the second character. The word may include any combination of a plurality of characters representing, for example a natural language word. The word may also include a pre-determined character combination representing, for example, a domain extension and/or an acronym.
[0267] In some examples, the first character, the phrase identifying a second character, and the word based on the voice is identified based on at least one of contextual data associated with a user or one or more recognized credential models. For example, the contextual data may include the user’s calendar, contacts, a user speech profile, or any other user-specific data. In some examples, the recognized credential models may represent recognized credential formats such as a user name format (e.g., firstname_lastname@domain_name.com, firstinitial_lastname@domain_name.org), or a password format such as a mixture of letters, cases, control characters, and numerical digits.
[0268] In some examples, the first character, the phrase identifying a second character, and the word based on the voice is identified based on gaze detection. For example, while receiving the voice input from the user, it can be determined (e.g., by using a camera) whether the user is viewing a certain direction or area. If it is determined that the direction or area corresponds to a keyboard or key for inputting the credentials, the information of the user’s viewing direction or area can assist in identifying first character, the phrase identifying a second character, and the word.
DK 2017 70128 A1 [0269] At block 1206, the content of the voice input can be identified based one or more speech recognition models stored in a user device (e.g., device 800 in FIGs 10A-10F and 11A-1 ID). In some examples, the identification can be performed on the user device without communicating with a server (e.g., server 108).
[0270] At block 1208, in some examples, the identification of the content of the voice input can be performed using a user device and a server. For example, the identification can include providing data representing the voice input from a user device to a server. The server can perform the identification of the at least one of the first character, the phrase identifying the second character, and the word based on one or more language models stored in the server. The user device then receives, from the server, data representing at least one of the identified first character, the identified phrase identifying the second character, or the identified word.
[0271] With reference to FIG. 12B, at block 1212, the identification of the content of the voice input includes identifying an utterance of the voice input. At block 1214, the identification of the first character, the phrase identifying the second character, and the word includes determining the type of the utterance.
[0272] At block 1216, it is determined whether the type of the utterance corresponds to a recognized character. At block 1218, it is determined, for the utterance, whether the utterance corresponds to a control character. At block 1220, in accordance with a determination that the utterance corresponds to a control character, it is determined, for the utterance, whether the utterance corresponds to a punctuation word.
[0273] At block 1221, in accordance with a determination that the utterance corresponds to a punctuation word, a representation of a punctuation mark corresponding to the punctuation word is obtained. At block 1222, in accordance with a determination that the utterance does not corresponds to a punctuation word, a representation of the control character other than a punctuation word is obtained.
[0274] At block 1224, it is determined whether the type of the utterance corresponds to a recognized phrase identifying the second character. At block 1226, it is determined whether the type of the utterance corresponds to a word.
DK 2017 70128 A1 [0275] At block 1228, based on the determined type of the utterance, a representation of the first character, it is obtained the second character identified by the phrase, or the word corresponding to the utterance. In some examples, the representation may include an audio representation, a textual representation, a spatial/vector representation, or a combination thereof.
[0276] At block 1230, a confidence level for the representation is obtained. A confidence level indicates the degree of accuracy of the identification. At block 1232, it is determined whether the confidence level is greater than or equal to a threshold. At block 1234, in accordance with a determination that the confidence level is greater than or equal to the threshold, the representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance is converted to text. At block 1236, in accordance with a determination that the confidence level is less than the threshold, one or more candidate representations, an error message or a combination thereof can be provided.
[0277] At block 1238, at least one command can be identified. The command enables editing of the displayed text. At block 1240, for identifying the command, at least one utterance is identified. At block 1242, it is determined whether a type of the utterance corresponds to a recognized command. At block 1244, in accordance with a determination that the type of the utterance corresponds to a recognized command, an action is caused to initiate according to the recognized command for editing the displayed text.
[0278] At block 1246, the utterance corresponding to the at least one command and information associated with the utterance corresponding to the at least one command are stored. The information associated with the utterance corresponding to the at least one command can include, for example, the number of times the user utter the command “delete.” The information may indicate the degree of accuracy of identification.
[0279] At block 1248, in response to the identification, the first character, the second character, and the word are converted to text. At block 1250, the text is displayed, on a display, in a sequence corresponding to an order of the first character, the second character, and the word in the voice input. For example, the voice input may be provided in the order of the first character, followed by the word, followed by the phrase identifying the second character. The
DK 2017 70128 A1 text displayed is thus in the order of the first character, followed by the word, followed by the second character.
[0280] At block 1252, one or more identified utterances of the voice input, the one or more identified utterances corresponding to the first characters, the phrase identifying the second character, and the word can be stored. Storing the utterances can enable the learning and training of the digital assistant to improve the accuracy of identification.
7. Electronic Device [0281] FIG. 13 shows a functional block diagram of electronic device 1300 configured in accordance with the principles of the various described examples, including those described with reference to FIGs. 9, 10A-10F, and 11 A-l ID. The functional blocks of the device can be optionally implemented by hardware, software, or a combination of hardware and software to carry out the principles of the various described examples. It is understood by persons of skill in the art that the functional blocks described in FIG. 13 can be optionally combined or separated into sub-blocks to implement the principles of the various described examples. Therefore, the description herein optionally supports any possible combination, separation, or further definition of the functional blocks described herein.
[0282] As shown in FIG. 10, electronic device 1300 can include a microphone 1302 and processing unit 1308. In some examples, processing unit 1308 includes receiving unit 1310, identifying unit 1312, converting unit 1314, and causing unit 1316, and optionally, providing unit 1318, determining unit 1320, obtaining unit 1322, and storing unit 1324.
[0283] The processing unit 1308 is configured to: receive (e.g., with the receiving unit 1310) a voice input; and identify (e.g., with the identifying unit 1312), based on the voice input, a first character, a phrase identifying a second character, and a word. The processing unit is further configured to, in response to the identification, convert (e.g., with the converting unit 1314) the first character, the second character, and the word to text; and cause (e.g., with the causing unit 1316) to display, with the display, the text in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
DK 2017 70128 A1 [0284] In some examples, the first character includes at least one of a letter, a numerical digit, a control character, or a space.
[0285] In some examples, the control character includes a punctuation mark identified by a punctuation word.
[0286] In some examples, the phrase includes a spelling word for assisting the identification of the second character.
[0287] In some examples, the word includes a plurality of characters.
[0288] In some examples, the plurality of characters represent at least one of a domain extension or an acronym.
[0289] In some examples, the identifying of the first character, the phrase identifying the second character, and the word includes identifying (e.g., with the identifying unit 1312) at least one of the first character, the phrase identifying the second character, or the word based on one or more speech recognition models stored in the user device.
[0290] In some examples, the identifying of the first character, the phrase identifying the second character, and the word includes providing (e.g., with the providing unit 1318) data representing the voice input to a server; and receiving (e.g., with the receiving unit 1310), from the server, data representing at least one of the identified first character, the identified phrase identifying the second character, or the identified word.
[0291] In some examples, the identifying of the first character, the phrase identifying the second character, and the word includes identifying (e.g., with the identifying unit 1312) at least one of the first character, the phrase identifying the second character, or the word based on one or more language models stored in the server.
[0292] In some examples, the identifying, based on the voice input of, the first character, the phrase identifying the second character, and the word includes identifying (e.g., with the identifying unit 1312) an utterance of the voice input; determining (e.g., with the determining unit 1320) a type of the utterance; and obtaining (e.g., with the obtaining unit 1322), based on the
DK 2017 70128 A1 determined type of the utterance, a representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance.
[0293] In some examples, determining the type of the utterance comprises performing at least one of: determining (e.g., with the determining unit 1320) whether the type of the utterance corresponds to a recognized character; determining (e.g., with the determining unit 1320) whether the type of the utterance corresponds to a recognized phrase identifying the second character; and determining (e.g., with the determining unit 1320) whether the type of the utterance corresponds to a word.
[0294] In some examples, determining whether the type of utterance corresponds to a recognized character comprises: determining (e.g., with the determining unit 1320), for the utterance, whether the utterance corresponds to a control character; and, in accordance with a determination that the utterance corresponds to a control character, determining (e.g., with the determining unit 1320), for the utterance, whether the utterance corresponds to a punctuation word.
[0295] In some examples, obtaining, based on the determined type of the utterance, the representation of the character comprises: in accordance with a determination that the utterance corresponds to a punctuation word, obtaining (e.g., with the obtaining unit 1322) a representation of a punctuation mark corresponding to the punctuation word, in accordance with a determination that the utterance does not corresponds to a punctuation word, obtaining (e.g., with the obtaining unit 1322) a representation of the control character other than a punctuation word.
[0296] In some examples, identifying, based on the voice input, the first character, the phrase identifying the second character, and the word further comprises: obtaining (e.g., with the obtaining unit 1322) a confidence level for the representation; determining (e.g., with the determining unit 1320) whether the confidence level is greater than or equal to a threshold.
[0297] In some examples, converting the first character, the second character, and the word to text comprises: in accordance with a determination that the confidence level is greater than or equal to the threshold, converting (e.g., with the converting unit 1314) the representation of the first character, the second character identified by the phrase, or the word corresponding to the
DK 2017 70128 A1 utterance to text; in accordance with a determination that the confidence level is less than the threshold, providing (e.g., with the providing unit 1318) one or more candidate representations, an error message or a combination thereof.
[0298] In some examples, processing unit 1308 is further configured to identify (e.g., with the identifying unit 1312) at least one command, wherein the at least one command enables editing of the displayed text.
[0299] In some examples, identifying the at least one command comprises: identifying (e.g., with the identifying unit 1312) at least one utterance; determining (e.g., with the determining unit 1320) whether a type of the utterance corresponds to a recognized command; and in accordance with a determination that the type of the utterance corresponds to a recognized command, causing (e.g., with the causing unit 1316) an action to be initiated according to the recognized command for editing the displayed text.
[0300] In some examples, processing unit 1308 is further configured to storing (e.g., with the storing unit 1324) the utterance corresponding to the at least one command and storing (e.g., with the storing unit 1324) information associated with the utterance corresponding to the at least one command.
[0301] In some examples, identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on at least one of contextual data associated with a user or one or more recognized credential models.
[0302] In some examples, the context data associated with the user comprises a user speech profile.
[0303] In some examples, the one or more recognized credential models represent one or more recognized credential formats.
[0304] In some examples, identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on gaze detection.
[0305] In some examples, the voice input corresponds to at least one of a first user credential representing a user name or a second user credential representing a password.
DK 2017 70128 A1 [0306] In some examples, processing unit 1308 is further configured to storing (e.g., with the storing unit 1324) one or more utterances of the voice input, the one or more utterances corresponding to the first characters, the phrase identifying the second character, and the word.
[0307] The operation described above with respect to FIG. 13 is, optionally, implemented by components depicted in FIGs. 1, 2A, 4, 6A-B, 7A, 8A-8B, or 9. For example, receiving operation 1310, identifying operation 1312, converting operation 1314, and causing operation 1316 are optionally implemented by processor(s) 220. It would be clear to a person of ordinary skill in the art how other processes can be implemented based on the components depicted in FIGs. 1, 2A, 4, 6A-B, 7A, 8A-8B, or 9.
[0308] It is understood by persons of skill in the art that the functional blocks described in FIG. 13 are, optionally, combined or separated into sub-blocks to implement the principles of the various described embodiments. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein. For example, processing unit 1308 can have an associated “controller” unit that is operatively coupled with processing unit 1308 to enable operation. This controller unit is not separately illustrated in FIG. 13 but is understood to be within the grasp of one of ordinary skill in the art who is designing a device having a processing unit 1308, such as device 1300. As another example, one or more units, such as the receiving unit 1310, may be hardware units outside of processing unit 1308 in some embodiments. The description herein thus optionally supports combination, separation, and/or further definition of the functional blocks described herein.
[0309] Exemplary methods, non-transitory computer-readable storage media, systems and electronic devices are set out in the following items:
1. A method for identifying of a voice input providing one or more user credentials, comprising:
at a user device with a display, one or more processors, and memory: receiving a voice input;
identifying, based on the voice input, a first character, a phrase identifying a second character, and a word;
DK 2017 70128 A1 in response to the identification, converting the first character, the second character, and the word to text; and causing to display, with the display, the text in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
2. The method of item 1, wherein the first character includes at least one of a letter, a numerical digit, a control character, or a space.
3. The method of item 2, wherein the control character comprises a punctuation mark identified by a punctuation word.
4. The method of any items 1-3, wherein the phrase comprises a spelling word for assisting the identification of the second character.
5. The method of any items 1-4, wherein the word comprises a plurality of characters.
6. The method of item 5, wherein the plurality of characters represent at least one of a domain extension or an acronym.
7. The method of any items 1-6, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
identifying at least one of the first character, the phrase identifying the second character, or the word based on one or more speech recognition models stored in the user device.
8. The method of any items 1-7, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
providing data representing the voice input to a server; and receiving, from the server, data representing at least one of the first character, the phrase identifying the second character, or the word,
DK 2017 70128 A1 wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises identifying at least one of the first character, the phrase identifying the second character, or the word based on one or more language models stored in the server.
9. The method of any items 1-8, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
identifying an utterance of the voice input;
determining a type of the utterance; and obtaining, based on the determined type of the utterance, a representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance.
10. The method of item 9, wherein determining the type of the utterance comprises performing at least one of:
determining whether the type of the utterance corresponds to a character;
determining whether the type of the utterance corresponds to a phrase identifying a character; and determining whether the type of the utterance corresponds to a word.
11. The method of item 10, wherein determining whether the type of utterance corresponds to a character comprises:
determining, for the utterance, whether the utterance corresponds to a control character;
in accordance with a determination that the utterance corresponds to a control character, determining, for the utterance, whether the utterance corresponds to a punctuation word; and wherein obtaining, based on the determined type of the utterance, the representation of the character comprises:
DK 2017 70128 A1 in accordance with a determination that the utterance corresponds to a punctuation word, obtaining a representation of a punctuation mark corresponding to the punctuation word, in accordance with a determination that the utterance does not corresponds to a punctuation word, obtaining a representation of the control character other than a punctuation word.
12. The method of any items 9-11, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word further comprises:
obtaining a confidence level for the representation;
determining whether the confidence level is greater than or equal to a threshold; and wherein converting the first character, the second character, and the word to text comprises:
in accordance with a determination that the confidence level is greater than or equal to the threshold, converting the representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance to text;
in accordance with a determination that the confidence level is less than the threshold, providing one or more candidate representations, an error message or a combination thereof.
13. The method of any items 1-12, further comprising:
identifying at least one command based on the voice input, wherein the at least one command enables editing of the displayed text.
14. The method of item 13, wherein identifying the at least one command comprises: identifying at least one utterance;
determining whether a type of the utterance corresponds to a command; and
DK 2017 70128 A1 in accordance with a determination that the type of the utterance corresponds to a command, causing to initiate an action corresponding to the command for editing the displayed text.
15. The method of item 14, further comprising:
storing the utterance corresponding to the at least one command and storing information associated with the utterance corresponding to the at least one command.
16. The method of any items 1-15, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on at least one of contextual data associated with a user or one or more recognized credential models.
17. The method of item 16, wherein the contextual data associated with the user comprises a user speech profile.
18. The method of any items 16 and 17, wherein the one or more recognized credential models represent one or more recognized credential formats.
19. The method of any items 1-18, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on gaze detection.
20. The method of any items 1-19, wherein the voice input corresponds to at least one of a first user credential representing a user name or a second user credential representing a password.
21. The method of any items 1-20, further comprising:
storing one or more utterances of the voice input, the one or more utterances corresponding to the first characters, the phrase identifying the second character, and the word recognized based on the identification.
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22. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to:
receive a voice input;
identify, based on the voice input, a first character, a phrase identifying a second character, and a word;
in response to the identification, convert the first character, the second character, and the word to text; and cause to display, with the display, the text in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
23. An electronic device, comprising:
one or more processors;
a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
receiving a voice input;
identifying, based on the voice input, a first character, a phrase identifying a second character, and a word;
in response to the identification, converting the first character, the second character, and the word to text; and causing to display, with the display, the text in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
24. An electronic device, comprising:
means for receiving a voice input;
means for identifying, based on the voice input, a first character, a phrase identifying a second character, and a word;
DK 2017 70128 A1 in response to the identification, means for converting the first character, the second character, and the word to text; and means for causing to display, with the display, the text in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
25. An electronic device comprising:
one or more processors;
a memory; and one or more programs stored in memory, the one or more programs including instructions for performing the methods of any one of items 1-21.
26. A computer-readable storage medium comprising one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions which, when executed by the one or more processors, cause the electronic device to perform the methods of any one of items 1-21.
27. An electronic device, comprising:
means for performing any of the methods of items 1-21.
28. An electronic device comprising:
a processing unit configured to:
receive a voice input;
identify based on the voice input, a first character, a phrase identifying a second character, and a word;
in response to the identification, convert the first character, the second character, and the word to text; and cause to display the text in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
DK 2017 70128 A1
29. The electronic device of item 28, wherein the first character includes at least one of a letter, a numerical digit, a control character, or a space.
30. The electronic device of item 29, wherein the control character comprises a punctuation mark identified by a punctuation word.
31. The electronic device of any items 28-30, wherein the phrase comprises a spelling word for assisting the identification of the second character.
32. The electronic device of any items 28-31, wherein the word comprises a plurality of characters.
33. The electronic device of item 32, wherein the plurality of characters represent at least one of a domain extension or an acronym.
34. The electronic device of any items 28-33, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
identifying at least one of the first character, the phrase identifying the second character, or the word based on one or more speech recognition models stored in the user device.
35. The electronic device of any items 28-34, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
providing data representing the voice input to a server; and receiving, from the server, data representing at least one of the first character, the phrase identifying the second character, or the word, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises
DK 2017 70128 A1 identifying at least one of the first character, the phrase identifying the second character, or the word based on one or more language models stored in the server.
36. The electronic device of any items 28-35, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
identifying an utterance of the voice input;
determining a type of the utterance; and obtaining, based on the determined type of the utterance, a representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance.
37. The electronic device of item 36, wherein determining the type of the utterance comprises performing at least one of:
determining whether the type of the utterance corresponds to a character;
determining whether the type of the utterance corresponds to a phrase identifying a character; and determining whether the type of the utterance corresponds to a word.
38. The electronic device of item 37, wherein determining whether the type of utterance corresponds to a character comprises:
determining, for the utterance, whether the utterance corresponds to a control character;
in accordance with a determination that the utterance corresponds to a control character, determining, for the utterance, whether the utterance corresponds to a punctuation word; and wherein obtaining, based on the determined type of the utterance, the representation of the character comprises:
in accordance with a determination that the utterance corresponds to a punctuation word, obtaining a representation of a punctuation mark corresponding to the punctuation word,
DK 2017 70128 A1 in accordance with a determination that the utterance does not corresponds to a punctuation word, obtaining a representation of the control character other than a punctuation word.
39. The electronic device of any items 36-38, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word further comprises:
obtaining a confidence level for the representation;
determining whether the confidence level is greater than or equal to a threshold; and wherein converting the first character, the second character, and the word to text comprises:
in accordance with a determination that the confidence level is greater than or equal to the threshold, converting the representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance to text;
in accordance with a determination that the confidence level is less than the threshold, providing one or more candidate representations, an error message or a combination thereof.
40. The electronic device of any items 28-39, wherein the processing unit is further configured to:
identify at least one command based on the voice input, wherein the at least one command enables editing of the displayed text.
41. The electronic device of item 40, wherein identifying the at least one command comprises:
identifying at least one utterance;
determining whether a type of the utterance corresponds to a command; and in accordance with a determination that the type of the utterance corresponds to a command, causing to initiate an action corresponding to the command for editing the displayed text.
DK 2017 70128 A1
42. The electronic device of item 41, wherein the processing unit is further configured to:
store the utterance corresponding to the at least one command and storing information associated with the utterance corresponding to the at least one command.
43. The electronic device of any items 28-42, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on at least one of contextual data associated with a user or one or more recognized credential models.
44. The electronic device of item 43, wherein the contextual data associated with the user comprises a user speech profile.
45. The electronic device of any items 43 and 44, wherein the one or more recognized credential models represent one or more recognized credential formats.
46. The electronic device of any items 28-45, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on gaze detection.
47. The electronic device of any items 28-46, wherein the voice input corresponds to at least one of a first user credential representing a user name or a second user credential representing a password.
48. The electronic device of any items 28-47, further comprising:
storing one or more utterances of the voice input, the one or more utterances corresponding to the first characters, the phrase identifying the second character, and the word recognized based on the identification.
DK 2017 70128 A1 [0310] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various embodiments with various modifications as are suited to the particular use contemplated.
[0311] Although the disclosure and examples have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims.
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权利要求:
Claims (44)
[1] WHAT IS CLAIMED IS:
1. A method for identifying of a voice input providing one or more user credentials, comprising:
at a user device with a display, one or more processors, and memory:
receiving a voice input;
identifying, based on the voice input, a first character, a phrase identifying a second character, and a word;
in response to the identification, converting the first character, the second character, and the word to text; and causing to display, with the display, the text in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
[2] 2. The method of claim 1, wherein the first character includes at least one of a letter, a numerical digit, a control character, or a space.
[3] 3. The method of claim 2, wherein the control character comprises a punctuation mark identified by a punctuation word.
[4] 4. The method of any claims 1-3, wherein the phrase comprises a spelling word for assisting the identification of the second character.
[5] 5. The method of any claims 1-4, wherein the word comprises a plurality of characters.
[6] 6. The method of claim 5, wherein the plurality of characters represent at least one of a domain extension or an acronym.
[7] 7. The method of any claims 1-6, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
identifying at least one of the first character, the phrase identifying the second character, or the word based on one or more speech recognition models stored in the user device.
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[8] 8. The method of any claims 1-7, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
providing data representing the voice input to a server; and receiving, from the server, data representing at least one of the first character, the phrase identifying the second character, or the word, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises identifying at least one of the first character, the phrase identifying the second character, or the word based on one or more language models stored in the server.
[9] 9. The method of any claims 1-8, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
identifying an utterance of the voice input;
determining a type of the utterance; and obtaining, based on the determined type of the utterance, a representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance.
[10] 10. The method of claim 9, wherein determining the type of the utterance comprises performing at least one of:
determining whether the type of the utterance corresponds to a character;
determining whether the type of the utterance corresponds to a phrase identifying a character; and determining whether the type of the utterance corresponds to a word.
[11] 11. The method of claim 10, wherein determining whether the type of utterance corresponds to a character comprises:
determining, for the utterance, whether the utterance corresponds to a control character;
in accordance with a determination that the utterance corresponds to a control character, determining, for the utterance, whether the utterance corresponds to a punctuation word; and
102
DK 2017 70128 A1 wherein obtaining, based on the determined type of the utterance, the representation of the character comprises:
in accordance with a determination that the utterance corresponds to a punctuation word, obtaining a representation of a punctuation mark corresponding to the punctuation word, in accordance with a determination that the utterance does not corresponds to a punctuation word, obtaining a representation of the control character other than a punctuation word.
[12] 12. The method of any claims 9-11, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word further comprises:
obtaining a confidence level for the representation;
determining whether the confidence level is greater than or equal to a threshold; and wherein converting the first character, the second character, and the word to text comprises:
in accordance with a determination that the confidence level is greater than or equal to the threshold, converting the representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance to text;
in accordance with a determination that the confidence level is less than the threshold, providing one or more candidate representations, an error message or a combination thereof.
[13] 13. The method of any claims 1-12, further comprising:
identifying at least one command based on the voice input, wherein the at least one command enables editing of the displayed text.
[14] 14. The method of claim 13, wherein identifying the at least one command comprises: identifying at least one utterance;
determining whether a type of the utterance corresponds to a command; and in accordance with a determination that the type of the utterance corresponds to a command, causing to initiate an action corresponding to the command for editing the displayed text.
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[15] 15. The method of claim 14, further comprising:
storing the utterance corresponding to the at least one command and storing information associated with the utterance corresponding to the at least one command.
[16] 16. The method of any claims 1-15, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on at least one of contextual data associated with a user or one or more recognized credential models.
[17] 17. The method of claim 16, wherein the contextual data associated with the user comprises a user speech profile.
[18] 18. The method of any claims 16 and 17, wherein the one or more recognized credential models represent one or more recognized credential formats.
[19] 19. The method of any claims 1-18, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on gaze detection.
[20] 20. The method of any claims 1-19, wherein the voice input corresponds to at least one of a first user credential representing a user name or a second user credential representing a password.
[21] 21. The method of any claims 1-20, further comprising:
storing one or more utterances of the voice input, the one or more utterances corresponding to the first characters, the phrase identifying the second character, and the word recognized based on the identification.
[22] 22. An electronic device comprising:
one or more processors;
a memory; and one or more programs stored in memory, the one or more programs including instructions for performing the methods of any one of claims 1-21.
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[23] 23. A computer-readable storage medium comprising one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions which, when executed by the one or more processors, cause the electronic device to perform the methods of any one of claims 1-21.
[24] 24. An electronic device comprising:
a processing unit configured to:
receive a voice input;
identify based on the voice input, a first character, a phrase identifying a second character, and a word;
in response to the identification, convert the first character, the second character, and the word to text; and cause to display the text in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.
[25] 25. The electronic device of claim 24, wherein the first character includes at least one of a letter, a numerical digit, a control character, or a space.
[26] 26. The electronic device of claim 25, wherein the control character comprises a punctuation mark identified by a punctuation word.
[27] 27. The electronic device of any claims 24-26, wherein the phrase comprises a spelling word for assisting the identification of the second character.
[28] 28. The electronic device of any claims 24-27, wherein the word comprises a plurality of characters.
[29] 29. The electronic device of claim 28, wherein the plurality of characters represent at least one of a domain extension or an acronym.
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[30] 30. The electronic device of any claims 24-29, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
identifying at least one of the first character, the phrase identifying the second character, or the word based on one or more speech recognition models stored in the user device.
[31] 31. The electronic device of any claims 24-30, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
providing data representing the voice input to a server; and receiving, from the server, data representing at least one of the first character, the phrase identifying the second character, or the word, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises identifying at least one of the first character, the phrase identifying the second character, or the word based on one or more language models stored in the server.
[32] 32. The electronic device of any claims 24-31, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word comprises:
identifying an utterance of the voice input;
determining a type of the utterance; and obtaining, based on the determined type of the utterance, a representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance.
[33] 33. The electronic device of claim 32, wherein determining the type of the utterance comprises performing at least one of:
determining whether the type of the utterance corresponds to a character;
determining whether the type of the utterance corresponds to a phrase identifying a character; and determining whether the type of the utterance corresponds to a word.
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[34] 34. The electronic device of claim 33, wherein determining whether the type of utterance corresponds to a character comprises:
determining, for the utterance, whether the utterance corresponds to a control character;
in accordance with a determination that the utterance corresponds to a control character, determining, for the utterance, whether the utterance corresponds to a punctuation word; and wherein obtaining, based on the determined type of the utterance, the representation of the character comprises:
in accordance with a determination that the utterance corresponds to a punctuation word, obtaining a representation of a punctuation mark corresponding to the punctuation word, in accordance with a determination that the utterance does not corresponds to a punctuation word, obtaining a representation of the control character other than a punctuation word.
[35] 35. The electronic device of any claims 32-34, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word further comprises:
obtaining a confidence level for the representation;
determining whether the confidence level is greater than or equal to a threshold; and wherein converting the first character, the second character, and the word to text comprises:
in accordance with a determination that the confidence level is greater than or equal to the threshold, converting the representation of the first character, the second character identified by the phrase, or the word corresponding to the utterance to text;
in accordance with a determination that the confidence level is less than the threshold, providing one or more candidate representations, an error message or a combination thereof.
[36] 36. The electronic device of any claims 24-35, wherein the processing unit is further configured to:
identify at least one command based on the voice input, wherein the at least one command enables editing of the displayed text.
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[37] 37. The electronic device of claim 36, wherein identifying the at least one command comprises:
identifying at least one utterance;
determining whether a type of the utterance corresponds to a command; and in accordance with a determination that the type of the utterance corresponds to a command, causing to initiate an action corresponding to the command for editing the displayed text.
[38] 38. The electronic device of claim 37, wherein the processing unit is further configured to: store the utterance corresponding to the at least one command and storing information associated with the utterance corresponding to the at least one command.
[39] 39. The electronic device of any claims 24-38, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on at least one of contextual data associated with a user or one or more recognized credential models.
[40] 40. The electronic device of claim 39, wherein the contextual data associated with the user comprises a user speech profile.
[41] 41. The electronic device of any claims 39 and 40, wherein the one or more recognized credential models represent one or more recognized credential formats.
[42] 42. The electronic device of any claims 24-41, wherein identifying, based on the voice input, the first character, the phrase identifying the second character, and the word is based on gaze detection.
[43] 43. The electronic device of any claims 24-42, wherein the voice input corresponds to at least one of a first user credential representing a user name or a second user credential representing a password.
[44] 44. The electronic device of any claims 24-43, further comprising:
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1/30

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Memory 202
I Operating System
I Communication Module iContact/Motion Module Graphics Module Haptic Feedback Module Text Input Module____________
I GPS Module_______________
Digital Assistant Client Module User Data and Models________
Applications___________________
226
Portable Multifunction Device 200
Applications (continued)
Contacts Module___________
Telephone Module_________
Video Conference Module
E-mail Client Module
Instant Messaging Module Workout Support Module Camera Module
Image Management Module Video & Music Player Module Notes Module
230
232
233
234
235
229
231
236
237
238
239
240
241
242
243
244
252
253
Map Module
Browser Module
Calendar Module get Modules
Weather Widget(s) Stocks Widget Calculator Widget Alarm Clock Widget Dictionary Widget « &
User-Created Widget(s)
Widget Creator Module Search Module
Online Video Module -------------------------------------------------Device/Global Internal State [Power System .....Éxtemai Port
203 ²⁰l
262
224
204
21R
203____

Controller

Peripherals Interface >203
Processor(s)
203
RF Circuitry 208
Audio
236
254
247
248
249
249-1
249-2
249-3
249-4
249-5
249-6
250
251
255
257 >203
-—Circuitry

Proximity Sensor

Microphone
213
266
Speaker 211
268
206 iP203
I/O Subsystem
Display Controller 256 Optical Sensor(s) IntensitySensor(s) Haptic Feedback Other Input Controller(s) k Contra ler 258 Centro ler 259 Controller 261 Δ jO 1 Γ203 Ηθ3 -----------------,1 Ηθ3 1^203 Ηθ3 TouchSensitiveDisplay System212 Optical Sensor(s) 264 Contact Intensity Sensor(s) 265 Tactile Output Generator(s) 267 Other Input Control Devices 216

类似技术:

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公开号 | 公开日 | 专利标题

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AU2017101401A4|2017-11-23|Identification of voice inputs providing credentials

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AU2018101496A4|2018-11-08|Intelligent automated assistant

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AU2017234428B2|2020-04-09|Identification of voice inputs providing credentials

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US20180330714A1|2018-11-15|Machine learned systems

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US9865280B2|2018-01-09|Structured dictation using intelligent automated assistants

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US10636424B2|2020-04-28|Multi-turn canned dialog

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US10127220B2|2018-11-13|Language identification from short strings

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US10403278B2|2019-09-03|Methods and systems for phonetic matching in digital assistant services

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DK202070455A1|2020-07-16|User-specific acoustic models

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DK201770421A1|2018-12-07|Detecting a trigger of a digital assistant

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EP3424046B1|2020-07-08|User-specific acoustic models

同族专利:

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公开号 | 公开日

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DE202017000991U1|2017-06-19|

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CN107195306A|2017-09-22|

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CN107195306B|2021-08-13|

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AU2017101401A4|2017-11-23|

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AU2017100229A4|2017-03-30|

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US10446143B2|2019-10-15|

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US20170263249A1|2017-09-14|

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法律状态:
2018-10-09| PAT| Application published|Effective date: 20170915 |

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2019-06-14| PHB| Application deemed withdrawn due to non-payment or other reasons|Effective date: 20190521 |

优先权:

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申请号 | 申请日 | 专利标题

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US201662308090P| true| 2016-03-14|2016-03-14|

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US62/308,090|2016-03-14|

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US15/268,360|2016-09-16|

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US15/268,360|US10446143B2|2016-03-14|2016-09-16|Identification of voice inputs providing credentials|PCT/US2017/019332| WO2017160487A1|2016-03-14|2017-02-24|lDENTIFICATION OF VOICE INPUTS PROVIDING CREDENTIALS|

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EP17767149.2A| EP3394852A4|2016-03-14|2017-02-24|lDENTIFICATION OF VOICE INPUTS PROVIDING CREDENTIALS|

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AU2017234428A| AU2017234428B2|2016-03-14|2017-02-24|Identification of voice inputs providing credentials|

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JP2018541383A| JP6725672B2|2016-03-14|2017-02-24|Identifying voice input that provides credentials|

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KR1020187023746A| KR102190856B1|2016-03-14|2017-02-24|Identification of voice inputs that provide credentials|