• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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    • 专利摘要:
    The invention relates to a system comprising a computer-readable storage medium storing at least one program and a method (500) for finding experts based on records of solutions to problems. In exemplary embodiments, the method may include extracting multiple topics from records to solutions to problems and using the extracted topics to model the relationship between experts and received user queries to identify experts with the most relevant expertise in user queries. The method may further include presenting an expert selection interface containing a list of the identified experts and information about the experts to assist users in the expert selection decision.
    公开号:CH710619A2
    申请号:CH00021/16
    申请日:2016-01-07
    公开日:2016-07-15
    发明作者:Aurushi Paul Sharoda
    申请人:Gen Electric；
    IPC主号:
    专利说明:

    TECHNICAL AREA
    The subject matter disclosed herein relates to data processing. In particular, the exemplary embodiments may relate to methods of finding experts to solve problems encountered in industrial facilities.
    BACKGROUND
    Employees of a company often need to find experts within the company to get information on a topic, to find someone with special skills for projects, or to find advice on solving a particular problem. With the proliferation of mobile devices in the company, technicians performing equipment maintenance (e.g. during a scheduled interruption in a power plant) often need to troubleshoot in real time with remote experts. A key aspect of joint troubleshooting is to find the right expert on a given problem.
    A common method for identifying experts is the candidate-based approach, which involves creating a profile for a candidate expert based on the relevance of the information generated by the candidate expert about a user query. Another commonly used approach is the document-based approach, in which all documents relevant to a user query are retrieved and links between the documents and the authors of the documents are discovered.
    SUMMARY OF THE INVENTION
    A first aspect of the present invention provides a method. The method has access to a corpus of recorded data on problem statements, the recorded data on problem statements having multiple records of solutions to problems that are assigned to multiple experts, each record being assigned to a specific expert for the solution of a problem of the multiple records of problem solutions which is assigned to a plurality of experts and has textual data regarding a problem which has arisen in an industrial area in the past. The method further comprises extracting a plurality of topics from the problem record data and creating an expert word matrix using the plurality of topics extracted from the problem record data, the expert word matrix for each expert of the several experts shows a probability that the expert utters every word belonging to a vocabulary of words in the problem solution data. The method further comprises receiving from a client device a user search query describing a problem that has arisen in the industrial sector and, for each expert of the plurality of experts, determining a likelihood that the expert has an expertise on the problem described by the user search query based on information in the expert word matrix. The method further comprises causing an expert selection interface to be displayed on the client device, the expert selection interface containing a list of a subset of the plurality of experts, the list being ranked according to the respective likelihood of each expert having the expertise on the problem which is described by the user search query.
    In the aforementioned method, the textual data may include a submitter's name, an assignee's name, a description of the previous problem, and solution information.
    In one embodiment, the extraction of the plurality of topics may be based on a text mining analysis of the text data.
    [0007] In a further embodiment, the extraction of the plurality of topics can additionally or alternatively include performing topic modeling on the text data of the recording data relating to problems.
    In the latter embodiment, performing topic modeling on the text data of the problem record data can model the text data associated with each problem of the plurality of problems as a mixture of topics and make each topic as a probability distribution about Words is represented.
    In any of the methods described above, creating the expert word matrix may include calculating the probability for each expert of the plurality of experts that the expert will utter the words in the search query.
    In one embodiment, the representation of the subset of the plurality of experts can contain availability information for each expert, the availability information including an online status, a local time, a location, a list of related problems and a current workload of problems.
    Additionally or as an alternative to this, the representation of the subset of the plurality of experts may include a representation of the expertise of each expert in the subset of experts.
    In particular, the representation of the expertise of each expert can be a sunbeam graphic, wherein the sunbeam graphic can have a plurality of colored areas, each colored area of the plurality of colored areas corresponding to an area of expertise
    Further in addition or as a further alternative, the representation of the subset of the plurality of experts may contain one or more graphical elements that are operable to receive and display a ranking for each expert of the subset of experts.
    Still further in addition or as yet another alternative to this, the representation of the subset of the plurality of experts can contain a word cloud that corresponds to each expert, the word cloud having several words that the expert will most likely utter from the corpus of the problem statements.
    Another aspect of the present invention provides a system. The system has a machine-readable medium, a topic modeling functional unit (topic modeling engine) and an interface module. The machine-readable medium stores corpus record data on problems, the record data on problem statements having multiple records of solutions to problems that are assigned to multiple experts, each record for solving a problem of the multiple problem-solving records being assigned to a specific one of the multiple experts and Has textual data relating to a past problem encountered in an industrial area. The topic modeling functional unit has one or more processors that are configured to extract multiple topics from the problem record data, wherein the topic modeling functional unit is further configured to build an expert word matrix using the multiple topics that can be extracted from the recorded data on problems, the expert word matrix for each expert of the plurality of experts having a probability that the expert utters each word in a vocabulary of words in the solution data on the problem, the topic modeling functional unit also doing this is set up to determine, on the basis of information in the expert-word matrix, a probability for each expert of the plurality of experts that the expert has expertise on a problem which is described by a user search query. The interface module is set up to receive the user search query that describes the problem, the interface module further being set up to cause a display of an expert selection interface on the client device, the expert selection interface containing a list of the plurality of experts, the list is ranked according to the respective likelihood of each expert having the expertise on the problem described by the user search query.
    The aforementioned system can further comprise a ranking classification module, which is set up to determine a ranking of the plurality of experts according to the respective probability of each expert that this has the knowledge of the problem described by the user search query.
    In any of the systems described above, the text data may include a submitter name, assignee name, description of the previous problem, and solution information.
    In one embodiment, the topic modeling engine can be configured to extract the plurality of topics based on a text mining analysis of the text data.
    Additionally or as an alternative to this, the topic modeling engine can be configured to extract the multiple topics by performing latent Dirichlet Allocation (LDA) modeling on the text data of the problem record data.
    In particular, performing LDA modeling on the text data of the problem record data may include modeling text data associated with the plurality of problems as a mixture of topics and causing each topic to be represented as a probability distribution of words .
    In a further embodiment, the topic modeling functional unit can be set up to build an expert word matrix by performing operations that include calculating, for each expert of the plurality of experts, a probability that the expert will use the words in the search query.
    In any of the above-mentioned systems, the expert selection interface can contain availability information for each expert, the availability information including an online status, a local time, a location and a current workload due to problems.
    In addition or as an alternative to this, the representation of the subset of the plurality of experts can contain a word cloud which corresponds to each expert, the word cloud comprising several words from the corpus of the problems which the expert will most likely utter.
    Yet another aspect of the present invention provides a non-transitory machine readable storage medium. The non-transitory machine-readable storage medium contains instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising: accessing a corpus of problem record data, the problem record data having multiple records of solutions of problems that are assigned to a plurality of experts, each record for solving a problem of the plurality of records for solving problems is assigned to a particular one of the plurality of experts and has text data relating to a problem that has arisen in an industrial sector in the past; Extracting multiple topics from the problem record data; Creating an expert word matrix using the multiple topics extracted from the problem record data, the expert word matrix for each expert of the multiple experts having a probability for each expert of the multiple experts that the expert will each Expresses a word that belongs to a vocabulary of words in the problem solution data; Receiving, from a client device, a user search query describing a problem that has arisen in the industry; Determining, based on information in the expert word matrix, a likelihood for each expert of the plurality of experts that the expert is knowledgeable about the problem described by the user search query; and causing an expert selection interface to be displayed on the client device, the expert selection interface including a list of the plurality of experts, the list being ranked according to the respective likelihood of each expert having the expertise on the problem raised by the user search query is described.
    BRIEF DESCRIPTION OF THE FIGURES
    Various of the accompanying drawings represent only exemplary embodiments of the present inventive subject matter and cannot be viewed as limiting its scope of protection. 1 is a network architecture diagram showing an enterprise network system having a client-server architecture configured to exchange data over a network, in accordance with some example embodiments. 2 is a block diagram illustrating various functional components of an expert identification application provided as part of the enterprise network system, according to some example embodiments. 3 is an interaction diagram showing example exchanges between the functional components of the expert identification application while they are involved in a skill identification process, in accordance with some example embodiments. 4 is a flowchart illustrating a method for determining topic-based expertise of experts based on problem solving data in accordance with some embodiments. 5 is a flow diagram of a method for identifying experts with expertise relevant to a user query, according to some example embodiments. 6 is an interface diagram illustrating an expert selection interface in accordance with an exemplary embodiment. 7 is an interface diagram illustrating an expert selection interface in accordance with an alternative exemplary embodiment. 8 is a schematic illustration of a machine in the exemplary form of a computer system within which a set of instructions for causing the machine to perform any or more of the procedures described herein may be executed.
    DETAILED DESCRIPTION
    In what follows, reference is made in detail to specific exemplary embodiments for implementing the subject matter according to the invention. Examples of these specific embodiments are shown in the accompanying drawings, and specific details are set forth in the description below in order to provide a thorough understanding of the subject matter. It should be understood that these examples are not intended to limit the scope of the claims to the illustrated embodiments. On the contrary, it is intended that they cover such alternatives, modifications, and equivalents as can be included within the scope of the disclosure.
    Aspects of the present disclosure relate to methods for expert identification and presentation. Exemplary embodiments include systems and methods that are convenient for users, such as Equipment technicians, enable them to find experts relevant to a given problem by leveraging existing records of problem solving. In particular, the system extracts issues from the records of solutions to problem sharing previously submitted by technicians and resolved by experts. The system can then use the extracted topics to model the relationship between experts and received user queries in order to identify the experts with the most relevant expertise regarding the user queries. In this manner, aspects of the present disclosure can provide the technical effect of reducing the resources required to maintain traditional registers and organizational profiles by leveraging existing data generated as part of the formal job responsibilities of experts. In addition, aspects of the present disclosure can offer the additional technical effect of reducing the cycle time for problem solving by allowing technicians to quickly find the correct expert at the location of the problem, and thus reducing the downtime of failed or malfunctioning facilities.
    Exemplary embodiments include a user interface operable to receive a user request that includes one or more keywords that describe a problem that a user is encountering. The system can then identify experts with the most relevant expertise on the problem by text mining the records for solutions to problems. The user interface in turn presents a ranked list of the relevant experts together with additional information about the experts to aid in an expert selection decision by the user. The additional information includes, for example, a taxonomy-based representation of a subject expertise, information regarding experience with related problems, accessibility information and availability indicators.
    FIG. 1 is a network architecture diagram showing a corporate network environment 100 that includes a client-server architecture configured to exchange data over a network 102, according to some example embodiments. In order to avoid obscuring the subject matter of the invention due to unnecessary details, various functional components (e.g. modules and engines (functional units)), which are not relevant for conveying an understanding of the subject matter of the invention, have been omitted from FIG. However, a person skilled in the art will readily recognize that various additional functional components can be supported by the corporate network environment 100 to enable additional functionality that is not specifically described here. Further, while FIG. 1 provides an exemplary architecture consistent with some embodiments, the present subject matter is not limited to the architecture illustrated in FIG. 1, and it can, for example, be used in an event-driven, distributed, or peer-to-peer Architectural system application. It should also be recognized that, although various components of corporate network environment 100 are described in the singular, multiple instances of one or more of the various functional components may be used.
    The corporate network environment 100 includes an enterprise system 104 that is in communication with a client device 106 over the network 102. The enterprise system 104 communicates and exchanges data within the enterprise network environment 100 concerning various functions and aspects related to the enterprise network environment 100 and its users. The enterprise system 104 may provide server-side functionality to the client device 106 over the network 102 (e.g., the Internet). The client device 106 can be operated by a user of the corporate network environment 100 in order to exchange data via the network 102. The users of the corporate network environment 100 may include, for example, engineers, technicians, or experts with machines or facilities that are used within the company or other industrial sector. The data exchanges may include transmission, reception, and processing of data to and from the users, and about the content, users, and assets of the corporate network environment 100.
    The client device 106, which can be any of a variety of types of devices (e.g., a smartphone, a tablet computer, a personal digital assistant (PDA), a personal navigation device (PND), a portable computer, A desktop computer, laptop or netbook, portable computing device, global positioning system (GPS) device, data-sharing book reader, or video game system console may be connected to communication network 102 via a port. Any of a variety of types of interconnection and communication networks 102 may be employed depending on the form of the client device 106. In various embodiments, network 102 may e.g. contain one or more wireless access points connected to a local area network (LAN), wide area network (WAN), the Internet, or other packet switched data network. In some embodiments, network 102 itself may be a LAN, WAN, the Internet, or other packet-switched data network. Accordingly, a variety of different configurations are expressly contemplated.
    In various embodiments, the data exchanged within the corporate network environment 100 may depend on user-selected functions that are available through one or more customer or user interfaces (UIs). The UIs can, for example, be assigned in particular to a web client 108 (e.g. a browser) which is executed on the client device 106 and is in communication with the company system 104. The UIs may also be associated with an application 110 running on the client device 106, such as a client application designed to interact with enterprise system 104. For example, the application 110 may enable users to create and submit search queries to identify experts who can assist in solving problems encountered in the field.
    With particular reference to the enterprise system 104, an API server 112 and a web server 114 are connected (e.g., via wired or wireless interfaces) to an application server 116 and provide a programmatic and web interface, respectively, to it. For example, the application server 116 can accommodate one or more applications, such as an expert identification application 118. The expert identification application 118 assists users in identifying experts for solving problems encountered in the field. To this end, the expert identification application 118 is designed to receive a user request describing a problem and to return a list of experts with expertise pertaining to the problem.
    As illustrated in FIG. 1, the application server 116 is coupled to a database server 120, which enables access to a database 122. However, in some exemplary embodiments, the application server 116 can access the database 122 directly without the need for the database server 120. Further, the database 122 may include multiple databases that may be internal or external to the enterprise system 104. The database 122 stores data pertaining to various functions and aspects associated with the corporate network environment 100 and its users. For example, the database 122 may store and maintain multiple user records for users of the corporate network environment 100 (e.g., engineers, technicians, or experts). The user records can contain information about the users, such as e.g. a name, a title, a location, a number of years of experience, assigned problems, solved problems, availability information and accessibility information.
    The database 122 also stores problem solution data. The problem solution data has multiple existing problem solution records that were previously prepared by the technicians. Each record of a solution to a problem corresponds to a problem that includes a problem from the past encountered in an industrial area such as in the context of a power plant. Each problem has a corresponding expert to whom the problem was assigned. Each of the records on problem statements contains text information that relates to the solution of the problem by the assigned expert. Furthermore, each problem record may contain one or more taxonomy problem markers to aid in indexing and finding problems in the system.
    FIG. 2 is a block diagram illustrating various functional components of the expert identification application 118 created as part of the enterprise system 104 in accordance with some example embodiments. As one skilled in the relevant computer and Internet related arts will understand, the modules and engines (functional units) illustrated in Figure 2 represent a set of executable software instructions and the associated hardware (e.g., memory and processor) for executing the instructions. In addition, the various functional components shown in FIG. 2 can be present in a single computer (e.g. a server) or can be arranged across several computers in different arrangements, e.g. in cloud-based architectures. Additionally, it should be understood that while the functional components (e.g., modules and tools) of Figure 2 are described in the singular, in other embodiments multiple instances of one or more modules may be used.
    As illustrated in Fig. 2, the expert identification application 118 includes a topic modeling engine or functional unit 200, a ranking classification engine or functional unit 202 and an interface module 204, all of which are set up (e.g. via a bus, a shared memory, a network 102 or a switch) to be in communication with one another in order to enable information to be exchanged between the functional components, or to enable the functional components to share and access common data. In addition, each of the functional components illustrated in FIG. 2 can access and retrieve data from the database 122, and each of the functional components can be able to communicate with the other components of the corporate network environment 100 (e.g., the client device 106) .
    The topic modeling engine 200 may be configured to determine how likely it is that a given candidate (e.g., an expert who previously solved a problem) is an expert on a received user search query. In accordance with some embodiments, topic modeling engine 200 may make this determination based on the likelihood that candidate ca is an expert on input request q, which is labeled P (ca∣q) for the purposes of the following discussion. P (ca∣q) can be calculated according to Bayes' theorem as follows:
    where P (ca∣q) is the probability that a candidate ca generates the query g, P (ca) is the earlier probability for the candidate ca and P (q) is the probability for the query q. Taking into account that P (q) is constant and P (ca) is a uniform distribution over all candidates, P (ca∣q) depends primarily on P (q∣ca).
    To compute P (q∣ca), the topic modeling engine 200 can use topics as hidden variables to model the relationship between the user queries and experts. In accordance with some embodiments, the topic modeling engine 200 may use a topic modeling technique (e.g., Latent Dirichlet Allocation (LDA)) that models documents as a mixture of topics and represents each topic as a probability distribution across words. The topic modeling engine 200 can use the extracted topics to model the relationship between the candidates and words in the query. In this way, any problem that may arise in the field, as expressed by a received user request, can be viewed as a mixture of topics. Accordingly, the topic modeling engine 200 can calculate the probability that a candidate ca generate the query as follows:
    Here P (w∣t) is the probability that a word w belongs to a topic t, and P (t∣ca) is the probability that the expert ca creates a topic t.
    The ranking classification engine 202 may be configured to rank the candidates according to the likelihood that the candidate is an expert on the received user request, as determined by the topic modeling engine 200. The ranking classification engine 202 may also select a subset of the identified experts (e.g., the experts with the top three ranks) for presentation in response to receiving the user search query.
    The interface module 204 is responsible for presenting the user interfaces to the users. For example, the interface module 204 may transmit a set of instructions to the client device 106 that cause the client device 106 to present one or more user interfaces to a user. The interface module 204 may, for example, present an expert selection interface that is operable to receive a user search query describing a problem. Accordingly, the interface module 204 is also responsible for receiving and processing user input received via any of the user interfaces presented by the interface module 204. The expert selection interface presented by the interface module 204 may further include a presentation of a list of experts identified as having expertise relevant to the problems described by the received user queries. The presentation of the list of experts can also include information about each of the experts to aid in the users' expert selection decision. Further information about the details of the expert selection interface according to exemplary embodiments is explained below with reference to FIGS. 5 and 6.
    3 is an interaction diagram showing example exchanges between the functional components of the expert identification application 118 while engaged in a skill identification process, in accordance with some example embodiments. As shown, the topic modeling engine 200 receives a corpus of problem solution data 300 from the database 122. The problem solution data corpus 300 includes multiple problem solution records generated by multiple technicians 302. Each of the problem solution records contains textual information pertaining to the solution of a problem in an industrial area by an assigned expert. The textual information may include, for example, a name of the technician who submitted the problem (also referred to here as the “Submitter”), a name of an expert who was assigned the problem (also referred to here as the “Assigned”) Reference is made), a description of the problem including, if applicable, an identifier of the facilities that were involved in the problem, and a description of how the problem was resolved.
    As shown, at operation 304, the topic modeling engine 200 searches the textual information contained in the corpus of problem solution data 300 to identify latent topics. At operation 306, a record of each of the extracted topics is stored in database 122 with a link to one or more words and to one or more experts. At operation 308, the topic modeling engine 200 generates an expert word matrix 310 using the extracted topics. The expert word matrix 310 contains the probabilities P (w∣ca) for all experts ca and all words w in the vocabulary , where P (w∣ca) is the probability that a given expert is related to a word in the vocabulary (e.g. uttering it). For the purposes of this disclosure, the term "vocabulary" refers collectively to any unique words contained in a corpus of problem solving data.
    At operation 312, the interface module 204 (not shown) receives a user request 314 from a client device (e.g., client device 106) operated by a technician 316. The received user request 314 describes a problem that has been encountered in an industrial application. In some cases, query 314 may be related to a particular asset that is used in the industry. The asset can be part of a machine or equipment that is responsible for performing one or more functions within the industrial sector. The asset can take a variety of forms including, for example, medical equipment, appliances, power equipment, air transport units, trains, vehicles, wind turbines, gas turbines, or the like. In the example illustrated in FIG. 3, user query 314 relates to “compressor blade” and, more particularly, to “compressor blade damage”.
    After receiving the user query 314 in operation 318, the topic modeling engine 200 accesses the expert word matrix 310 to determine the probability P (q∣ca) for all experts belonging to the corpus of problem solving data 300, where P (q∣ca) is the probability that the expert has knowledge of the problem described by user query 314. The ranking classification engine 202 then sets a ranking for all experts assigned to the corpus of problem solution data 300 according to the likelihood that the expert has an expertise on the problem described by query 314. In operation 320, the interface module 204 (not shown) causes a list of the highest ranked experts to be displayed on the client device 106 operated by the technician 316.
    4 is a flow diagram illustrating a method 400 for determining topic-based expertise from experts based on solution data 300 to problem definitions, in accordance with some embodiments. The method 400 may be embodied in computer readable instructions for execution by a hardware component (e.g., a processor) so that the steps of the method 400 can be performed in part or in full by the functional components of the expert identification application 118, and accordingly, the method 400 is below as a Example described with reference to this. It should be recognized, however, that the method 400 can be used on various other hardware configurations and is not intended to be limited to the expert identification application 118.
    In operation 405, the topic modeling engine 200 accesses a corpus of problem solving data from the database 122. The problem solution data has multiple existing problem solution records that were previously authored by technicians. Every recording of a solution to a problem corresponds to a problem that includes a problem from the past that has been encountered in an industrial sector (e.g. a power plant). Each problem has an associated expert to whom the case has been assigned. Each of the problem-solving records contains textual data containing information relating to the problem-solving by the assigned expert. Accordingly, the text data can contain, for example, a name of the submitter, a name of the assigned person, a description of the problem and information on the solution.
    In operation 410, the topic modeling engine 200 extracts multiple topics from the problem solution data. The topic modeling engine 200 can extract topics from the problem solution data by applying text mining techniques to the text data contained in the plurality of problem solution records that collectively make up the problem solution data. In particular, the topic modeling engine 200 can extract words from each record to solve a problem and map each extracted word to a topic. In this way, each problem record contained in the corpus of problem solution data can be viewed as a mixture of different topics.
    In operation 415, topic modeling engine 200 builds an expert word matrix using the plurality of extracted topics. The expert word matrix contains a row for each expert assigned to the problem solution data, and each entry in the row has the probability that the expert utters a given word of the vocabulary assigned to the problem solution data (e.g. is related to this). Accordingly, the operation for creating the expert word matrix may include calculating a vocabulary for the problem solving data and calculating, for each expert, a probability that the expert will answer a given word of the vocabulary about all of the topics derived from the solution data Problems are derived, assigned (e.g. this expresses), included. The topic modeling engine 200 can build the expert word matrix using the extracted topics as hidden variables to model a relationship between experts and words pertaining to a given topic. The topic modeling engine 200 may, for example, be a generative model for natural language processing, such as use the LDA to model records of problems, each assigned to an expert, as a mixture of topics, and represent each topic as a probability distribution across words.
    5 is a flow chart of a method for identifying experts with expertise relevant to a user query, according to some example embodiments. The method 500 may be embodied in computer readable instructions for execution by a hardware component (e.g., a processor) so that the steps of the method 500 can be performed in part or in full by the functional components of the expert identification application 118, and accordingly, the method 500 is hereinafter referred to as a Example described with reference to this. It should be recognized, however, that the method 500 can be used on various other hardware configurations and is not intended to be limited to the expert identification application 118.
    In operation 505, the interface module 204 receives a user search request (e.g., from the client device 106) describing a problem encountered in an industrial setting. For example, the user query may be generated by an engineer correcting a technical problem caused by damage to a compressor blade. The user query can contain one or more terms that were used to describe the problem.
    In operation 510, the topic modeling engine 200 determines, for each expert of the plurality of experts associated with a corpus of problem solution data, a likelihood that the expert has an expertise on the problem described by the user query . Topic modeling engine 200 may determine the likelihood of the expert based on a relationship between the one or more terms used to describe the problem and a plurality of topics extracted from the corpus of problem solution data Has expertise in the problem described by the query. Topic modeling engine 200 determines the likelihood that the expert will be knowledgeable about the problem by using topics as hidden variables to model the relationship between user queries and experts. For example, as described above with reference to Figures 2 and 5, the topic modeling engine 200 may use a topic modeling technique such as LDA, to extract topics from the corpus of problem solving data, and use the extracted topics to model the relationship between candidates and terms in a query.
    In operation 515, the ranking classification engine 202 ranks the plurality of experts according to the respective likelihood that each expert is knowledgeable about the problem described by the user query. In operation 520, the interface module 204 selects a subset of the plurality of experts to present to the user who submitted the query. The interface module 204 can, for example, select the experts with the three highest ranks for a presentation.
    In operation 525, the interface module 204 causes a presentation of an expert selection interface on the client device 106 from which the user request was received. The expert selection interface contains graphic representations of the subset of experts (e.g. the experts with the three highest ranks) together with information about the experts who can support the user's expert selection decision. The interface module 204 may cause the expert selection interface to be displayed by providing a set of instructions to the client device 106 that cause the client device 106 to display the expert selection interface to the user. From the perspective of the user of the client device 106, the user submits the request and the expert selection interface is displayed in response.
    6 is an interface diagram illustrating an expert selection interface 600 in accordance with an exemplary embodiment. The expert selection interface 600 may be displayed on the client device 106 that is in communication with the application server 116. In some embodiments, the expert selection interface 600 can be accessed through an appropriate URL using the web client 108. In some embodiments, the expert selection interface 600 may be provided as one of several different interfaces that the application 110 brings with it.
    As shown, the expert selection interface 600 includes a request field 602 for entering a user request to describe a problem encountered in the field. A user (e.g., a technician 316) entering a query into the query field 602 can compose an informal text query or select search terms from a predefined list of frequently used search terms. In response to receiving the user search query, the expert selection interface 600 provides a list of experts with relevant expertise in a window 604. In this way, the expert selection interface 600 allows the user to review the presented experts and select an expert to assist them with the problem. Furthermore, the users can immediately communicate with the selected expert using suitable collaboration tools.
    As shown, the window 604 contains information about each expert, including a name 606, a skill 608, related issues 610, and a rating 612. In this example, each expert's skill 608 includes a taxonomy-based representation of the topic-related skill that are independent of the request. In particular, the skill 608 is illustrated by a sunbeam graphic generated using taxonomy problem labels that are used in problem solution records. The sunbeam graphic has multiple colored areas, each color corresponding to a specific problem label. The size of each area corresponds to that expert's level of expertise in the associated problem label. The related problems 610 provide a query specific experience of the expert by listing a selection of relevant problems solved by the expert along with links to the problem solution records. The rating 612 shows an aggregated user rating of the expert and allows users to give the experts e.g. to rate on a five-star scale.
    Furthermore, the expert selection interface 600 also provides availability information for each expert, including an online status 614, a location 616 and a local time 618. The expert selection interface 600 also contains a link 620 to a profile of each expert in the company's social network where the User can view the expert's organization chart, role, group membership and social media posts. The expert selection interface 600 also contains, for each expert, an indicator of how busy the expert is, e.g. the utilization of problems 622 (e.g. the number of currently assigned problem cases).
    7 is an interface diagram illustrating an expert selection interface 700 in accordance with an alternative exemplary embodiment. The expert selection interface 700 is substantially similar to the expert selection interface 600, except for the representation of the expertise 608 of each expert. In particular, the expertise 608 of each expert is indicated in the expert selection interface 700 by word clouds 702-704. In accordance with some embodiments, word clouds 702-704 provide skill information independent of the query and are generated using the highest scoring words (e.g., top 50 words) through LDA scoring. In some other embodiments, word clouds 702-704 may be generated using problem title titles used in the problem solution records assigned to the expert.
    It should be recognized that the information illustrated in the expert selection interfaces 600 and 700 of Figures 6 and 7 is merely an example of the expert information that may be displayed and, in other embodiments, less or more information may be displayed will. In some embodiments, the expert selection interface 600, 700 may include, for example, a number of years of experience of each expert or the languages that the expert speaks.
    MODULES, COMPONENTS AND LOGIC
    [0061] Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules can either form software modules (for example code embodied on a machine-readable medium or in a transmission signal) or hardware modules. A hardware module is a touchable unit that is capable of performing certain operations, and it can be designed or arranged in a certain way. In exemplary embodiments, one or more computer system (s) (e.g., a stand-alone, client, or server computer system) or one or more hardware module (s) of a computer system (e.g., a processor or group of processors) may contain software (e.g., an application or an application part) can be configured as a hardware module that operates to perform certain operations as described herein.
    In various embodiments, a hardware module can be implemented mechanically or electronically. For example, a hardware module can include dedicated circuitry or logic that is permanently set up (for example as a special purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain To perform operations. A hardware module can also include programmable logic or circuitry (such as contained in a standard processor or other programmable processor) that is temporarily set up by software to perform certain operations. It is recognized that the decision to mechanically implement a hardware module in a dedicated and permanently set up circuit arrangement or in a temporarily set up circuit arrangement (for example set up by software) can be driven by cost and time factors.
    Accordingly, the term “hardware module” should be understood to include a touchable unit, be it a unit that is physically constructed, permanently set up. (e.g., hardwired) or temporarily arranged (e.g., programmed) to function in a particular manner and / or perform certain operations described herein. Taking into account embodiments in which hardware modules are temporarily set up (for example programmed), each of the hardware modules need not be set up or instantiated at all times. For example, if the hardware modules include a general purpose processor that is implemented with software, the general purpose processor can be implemented as respective different hardware modules at different times. The software can accordingly configure a processor, for example to form a specific hardware module at one point in time and to form a different hardware module at another point in time.
    Hardware modules can provide information to and receive information from other hardware modules. Correspondingly, the hardware modules described can be viewed as communicatively coupled. If several such hardware modules are present at the same time, communication can be achieved by signal transmission (for example via suitable circuits and buses that connect the hardware modules). In embodiments in which multiple hardware modules are set up or instantiated at different times, communications between such hardware modules can be achieved, for example, by storing and retrieving information in memory structures to which the multiple hardware modules have access. For example, a hardware module can perform an operation and store the output of this operation in a storage device to which it is communicatively coupled. Another hardware module can then access the storage device at a later point in time in order to retrieve and process the stored output. Hardware modules can also initiate communications with input or output devices and can work with a resource (e.g., a collection of information).
    The various operations of example methods described herein may be performed, at least in part, by one or more processors that are temporarily set up (e.g., by software) or permanently set up to perform the relevant operations. Regardless of whether they are temporary or permanent, such processors can represent processor-implemented modules that operate to perform one or more operation (s) or function (s). The modules referred to herein, in some exemplary embodiments, include processor-implemented modules.
    Similarly, the methods described herein can be at least partially processor implemented. For example, at least some of the operations of a method can be performed by one or more processor (s) or processor-implemented modules. The performance of certain operations can be distributed among the one processor or the multiple processors that are not only present within a single machine, but are used across a number of machines. In some example embodiments, the processor or processors can be in a single location (e.g., within a home, office, or server farm), while in other embodiments the processors can be distributed over a number of locations.
    The one or more processors can also work to support the implementation of the relevant operations in a “cloud computing / environment” or as “software as a service” (SaaS). For example, at least some of the operations can be performed by a group of computers (as examples of machines containing processors), which operations are accessed over a network 102 (e.g. the Internet) and via one or more suitable interfaces (e.g. APIs) can be accessed.
    ELECTRONIC DEVICE AND SYSTEM
    Exemplary embodiments may be implemented in digital electronic circuitry, or in computer hardware, firmware, or software, or combinations of these. Exemplary embodiments can be implemented using a computer program product, for example a computer program that can be touched in an information carrier, for example in a machine-readable medium, for execution by a data processing device, for example a programmable processor, a computer or several computers, or for controlling the operation of the data processing device is embodied.
    A computer program can be written in any form of programming languages, including compiled or interpreted languages, and it can be implemented in any form, including a stand-alone program or module, subroutine, or other unit suitable for use is suitable in a computing environment. A computer program can be used to be executed on one computer or on several computers at one location or over several locations and connected to one another via a communication network 102.
    In example embodiments, operations may be performed by one or more programmable processor (s) that execute a computer program to perform functions by processing input data and generating outputs. Method operations can also be performed by special purpose logic circuitry (e.g., an FPGA or an ASIC), and devices of exemplary embodiments can be implemented as special purpose logic circuitry (e.g., an FPGA or an ASIC).
    The computing system can include clients and servers. The client and server are generally located remotely from one another and typically interact with one another via a communication network. The relationship between client and server is created by means of computer programs that run on the respective computers and have a client-server relationship with one another. In embodiments employing a programmable computing system, it should be noted that both hardware and software architectures require consideration. In particular, it will be appreciated that the choice of having a particular functionality in permanently configured hardware (e.g. an ASIC), in temporarily configured hardware (e.g. a combination of software and programmable processor), or in a combination of permanently and temporarily configured hardware is a Construction choice can be. Hardware (for example machine) and software architectures that can be used in various exemplary embodiments are set out below.
    MACHINE ARCHITECTURE AND MACHINE READABLE MEDIUM
    Figure 8 is a schematic illustration of a machine in the exemplary form of a computer system 800 within which a set of instructions may be executed to cause the machine to perform any one or more of the approaches described herein. Computer system 800 may correspond to any of client device 106, application server 116, API server 112, or web server 114, in accordance with some embodiments. The computer system 800 may include instructions that cause the machine to perform one or more of the methodologies discussed herein. In alternative embodiments, the machine operates as a stand-alone device or may be connected (e.g., networked) to other machines. In a networked application, the machine can act as a server or client machine in a server-client network environment or as a peer machine in a peer-to-peer (or distributed) network environment. The machine can be a personal computer (PC), a PDA, a mobile phone, a smartphone (e.g. an iPhone <®>), a tablet computer, a web application, a portable computer, a desktop computer, a laptop or netbook , a set-top box (STB) such as that provided by cable or satellite-based content providers; a wearable computing device such as the Glasses or a wristwatch, a multimedia device integrated into a motor vehicle, a global positioning system (GPS) device, a data-sharing book reader, a video game system console, a network router, a switch or bridge, or any machine that works in is able to execute instructions (sequential or otherwise) specifying actions to be taken by the machine. While only a single machine is shown, the term "machine" is intended to be understood to include any collection of machines that, individually or collectively, execute a set (or sets) of instructions to achieve one or more of the purposes herein the methodologies described.
    The exemplary computer system 800 includes a processor 802 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory 804, and a static memory 806 that communicate with each other over a bus 808. The computer system 800 may also include a video display 810 (e.g., a liquid crystal display (LCD) or a cathode ray tube display (CRT)). The computer system 800 also includes one or more input-output (I / O) devices 812, a location component 814, a drive unit 816, a signal generation device 818 (e.g., a speaker), and a network interface device 820 a keyboard, mouse, keypad, touch-sensitive surface (eg, a touch screen or trackpad), a microphone, a camera, and the like.
    The location component 814 can be used to determine a location of the computer system 800. In some embodiments, the location component 814 may correspond to a GPS transceiver that may use the network interface device 820 to exchange GPS signals with a GPS satellite. The location component 814 may also be configured to determine a location of the computer system 800 using an Internet Protocol (IP) address lookup or by triangulating a position based on nearby communication towers. The location component 814 may further be configured to store a user-defined location in the main memory 804 or the static memory 806. In some embodiments, a mobile location sharing application may work in conjunction with location component 814 and network interface device 820 to provide the location of computer system 800 to the application server or to a third party server for the purpose of identifying the location of a user who the computer system 800 operates to transmit.
    In some embodiments, the network interface device 820 may correspond to a transceiver device and an antenna. The transceiver device can be set up to both transmit and receive cellular network signals, wireless data signals or other types of signals via the antenna, depending on the type of computer system 800.
    MACHINE-READABLE MEDIUM
    The drive unit 816 includes a machine-readable medium 822 having stored thereon one or more sets of data structures and instructions (e.g., software) 824 that are embodied or used by one or more of the methodologies or functions described herein can / can. The instructions 824, during their execution by the computer system 800, may also be wholly or at least partially within the main memory 804, the static memory 806 and / or the processor 802, the main memory 804, the static memory 806 and the processor 802 also being machine-readable media 822 form.
    [0077] In accordance with some embodiments, instructions 824 may relate to the operations of an operating system (OS). Depending on the specific type of computer system 800, the OS may be, for example, the iOS <©> operating system, the Android <©> operating system, a BlackBerry <©> operating system, the Microsoft <©> Windows <®> Phone operating system, the Symbian <©> OS, or the webOS <®>. Further, in accordance with some embodiments, instructions 824 may relate to operations performed by applications (commonly referred to as "apps"). An example of such an application is a mobile browser application that displays content such as e.g. displays a web page or user interface using a browser.
    While the machine readable medium 822 is illustrated as a single medium in an exemplary embodiment, the term "machine readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database and / or assigned caches and servers), that store the one or more data structures or instructions 824. The term "machine-readable medium" is also intended to be understood to include any tangible medium that is capable of storing, encoding, or carrying instructions (e.g., instructions 824) for execution by the machine, and that the machine cause any one or more of the methodologies of the present disclosure to be performed, or capable of storing, encoding, or carrying data structures used by or assigned to such instructions 824. The term “machine-readable medium” is accordingly intended to be construed as encompassing, but not limited to, solid-state storage and optical and magnetic media. Specific examples of machine-readable media 822 include non-volatile memories including, for example, semiconductor memory devices (e.g., erasable programmable read-only memories (EPROM), electrically erasable programmable read-only memories (EEPROM)) and flash memory devices; magnetic disks such as internal hard drives and removable disks; magneto-optical disks and CD-ROM and DVD-ROM disks
    In addition, the touchable machine-readable medium 822 is non-transitory in that it does not embody a propagating signal. The designation of the touchable machine-readable medium 822 as "non-transitory", however, is not to be interpreted to mean that the medium is incapable of movement - the medium should be viewed as coming from a location in the real world can be transported to another. Since the machine-readable medium 822 is touchable, the medium can also be viewed as a machine-readable device.
    TRANSFER MEDIUM
    The instructions 824 may also be transmitted or received over a network 826 using a transmission medium. Instructions 824 can be transmitted using network interface device 820 and any of a number of well-known communication protocols (e.g., HTTP). Examples of communication networks include a LAN, a WAN, the Internet, cellular telephone networks, simple-aged telephone service (POTS) networks, and wireless data networks (e.g., WiFi and WiMAX networks). The term "transmission medium" is intended to be understood to include any intangible medium that is capable of storing, encrypting, or carrying instructions 824 for execution by the machine and that is not digital or analog communication signals or otherwise contains tangible media to support communication of such software.
    Although the embodiments of the present invention have been described with reference to specific exemplary embodiments, it will be apparent that various modifications and changes can be made to these embodiments without departing from the broader scope of the inventive subject matter. Accordingly, the specification and drawings are to be considered in an illustrative rather than a restrictive sense. The accompanying drawings, which constitute a part hereof, show by way of illustration and not limitation, specific embodiments in which the present subject matter may be practiced. The illustrated embodiments are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments can be used and derived therefrom so that structural and logical substitutions and changes can be made without departing from the scope of this disclosure. It is therefore intended that this detailed description not be taken in a limiting sense, and the scope of the various embodiments is to be defined only by the appended claims, along with a full breadth of equivalents to which such claims are entitled.
    Such embodiments of the subject matter of the invention can be referred to individually and / or together with the term "invention" for the sake of simplicity, without the intention to voluntarily extend the scope of this application to any individual invention or a single special inventive concept if more than one is actually disclosed. Thus, while specific embodiments have been shown and described herein, it should be recognized that any means calculated to accomplish the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments and other embodiments not specifically described herein will occur to those skilled in the art upon reading the above description.
    All publications, patents and patents referenced in this document are incorporated herein by reference in their entirety as if they were individually incorporated by reference. In the event of inconsistent uses between this document and those documents incorporated herein by reference, the use in the incorporated reference document should be regarded as complementary to that in this document; in the event of incompatible inconsistencies, the use in this document has priority.
    In this document, the terms "a" or "an" are used as is customary in patent specifications to include one or more than one, regardless of any other cases or uses of "at least one" or "one or more ». In this document, the term "or" is used to refer to a non-exclusive or, such that "A or B" includes "A but not B", "B but not A" and "A and B", unless otherwise stated. In the appended claims, the terms “contain” and “in which” are used as linguistic equivalents of the corresponding terms “comprise” and “wherein”, respectively. In addition, the terms “contain” and “have” can be expanded in the following claims; that is, a system, apparatus, article, or process that includes item (s) in addition to items listed after such term in a claim are further considered to be within the scope of that claim.
    Aspects of the present disclosure include a system that includes a computer-readable storage medium that stores at least one program, and a method for finding solutions to problems based on records. In exemplary embodiments, the method may include extracting multiple topics from problem resolution records and using the extracted topics to model the relationship between experts and received user requests to identify experts with the most relevant expertise regarding the user requests. The method may further include displaying an expert selection interface that contains a list of the identified experts and information about the experts in order to assist users in the expert selection decision.
    权利要求:
    Claims (10)
    [1]
    A method, comprising:Accessing a corpus of problematic recording data, the problematic recording data having a plurality of records of solutions to problems assigned to a plurality of experts, each recording for solving a problem of the plurality of problem solving records associated with a particular expert of the plurality of experts and textual data concerning a problem encountered in an industrial field in the past;Extracting several topics from the recording data to problems;Creating an expert word matrix using the plurality of topics extracted from the problem record data, wherein the expert word matrix for each expert of the multiple experts has a probability that the expert will utter each word that belongs to one Vocabulary of words in problem solving data belongs;Receiving a user search request describing a problem that has occurred in the industrial area from a client device;for each expert of the multiple experts, determining a probability that the expert has expertise in the problem described by the user search request based on information in the expert word matrix; andCausing a representation of an expert selection interface on the client device, wherein the expert selection interface includes a list of a subset of the plurality of experts, the list having a ranking corresponding to the respective probability for each expert to have that expertise on the issue identified by the user search request is described, is provided.
    [2]
    2. A method according to claim 1, wherein the text data includes a name of the submitter, a name of the assignee, a description of the previous problem, and solution information; and / or wherein extracting the plurality of topics is based on a text mining analysis of the text data.
    [3]
    The method of claim 1 or 2, wherein extracting the plurality of topics includes performing topic modeling on the text data of the problem record data;wherein performing the topic modeling on the text data of the recording data on problems preferably comprises:Modeling the text data associated with each problem of the multiple problems as a mixture of topics; andHave each topic represented as a probability distribution over words.
    [4]
    4. The method of claim 1, wherein creating the expert word matrix comprises calculating the probability that the expert will utter the words in the search query for each expert of the plurality of experts.
    [5]
    5. The method of claim 1, wherein the representation of the subset of the plurality of experts includes availability information for each expert, wherein the availability information includes an online status, a local time, a location, a list of related problems, and a current workload; and / or wherein the representation of the subset of the plurality of experts includes a word cloud corresponding to each expert, the word cloud having a plurality of words from the corpus of problems that the expert will most likely voice.
    [6]
    The method of any one of the preceding claims, wherein the representation of the subset of the plurality of experts includes a representation of the expertise of each expert in the subset of the experts;wherein the representation of the expertise of each expert is preferably a sunray graphic, the sunray graphic comprising a plurality of colored areas, each colored area of the plurality of colored areas corresponding to a skill area; and orwherein the representation of the subset of the plurality of experts includes one or more graphical elements operable to receive and display a score for each expert of the subset of experts.
    [7]
    7. System comprising:a machine-readable medium storing a corpus with problem-data recording data, the problem-data-recording data having multiple records of solutions to problems assigned to a plurality of experts, each of which resolves a problem of the plurality of problem-solving records to a particular expert associated with multiple experts and having textual data pertaining to a past problem that has occurred in an industrial area;a topic modeling functional unit having one or more processors configured to extract a plurality of topics from the problem recording data, the topic modeling functional unit further configured to construct an expert word matrix using the plurality of topics; extracted from the problem data recording data, the expert word matrix having, for each expert of the plurality of experts, a probability that the expert will utter every word in a vocabulary of words in the solution data to the problem statement; The functional unit is further configured to determine for each expert of the plurality of experts a probability that the expert has expertise in a problem described by a user search request based on information in the expert word matrix; andan interface module configured to receive the user search request describing the problem, wherein the interface module is further configured to cause a representation of an expert selection interface on the client device, wherein the expert selection interface includes a list of the plurality of experts, the list is ranked according to each expert's likelihood of having that expertise to the problem described by the user search request.
    [8]
    8. The system of claim 7 further comprising a ranking classification module configured to set a ranking of the plurality of experts according to the respective probability for each expert having said expertise on the problem described by the user search request.
    [9]
    A system according to claim 7 or 8, wherein the topic modeling functional unit is arranged to extract the plurality of topics by performing a latent Dirichlet Allocation (LDA) modeling on the text data of the problem recording data;wherein performing the LDA modeling on the text data of the recording data on problems preferably comprises:Modeling textual data associated with multiple problems as a mixture of topics; andHave each topic represented as a probability distribution over words.
    [10]
    10. A non-transitory machine-readable storage medium containing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:Accessing a corpus of problematic recording data, the problematic recording data having a plurality of records of solutions to problems assigned to a plurality of experts, each recording for solving a problem of the plurality of problem solving records associated with a particular expert of the plurality of experts and textual data concerning a problem encountered in an industrial field in the past;Extracting several topics from the recording data to problems;Creating an expert word matrix using the plurality of topics extracted from the problem record data, wherein the expert word matrix for each expert of the multiple experts has a probability that the expert will utter each word that belongs to one Vocabulary of words in problem solving data belongs;Receiving a user search request describing a problem that has occurred in the industrial area from a client device;for each expert of the multiple experts, determining a probability that the expert has expertise in the problem described by the user search request based on information in the expert word matrix; andCausing a presentation of an expert selection interface on the client device, wherein the expert selection interface includes a list of the plurality of experts, the list having a ranking corresponding to the probability for each expert to have that expertise on the problem described by the user search request , is provided.
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    同族专利:
    公开号 | 公开日
    US20160203140A1|2016-07-14|
    CN105786960A|2016-07-20|
    JP2016131022A|2016-07-21|
    DE102016100046A1|2016-07-14|
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    法律状态:
    2017-03-15| NV| New agent|Representative=s name: GENERAL ELECTRIC TECHNOLOGY GMBH GLOBAL PATENT, CH |
    2019-04-30| AZW| Rejection (application)|
    优先权:
    申请号 | 申请日 | 专利标题
    US14/597,023|US20160203140A1|2015-01-14|2015-01-14|Method, system, and user interface for expert search based on case resolution logs|
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