Computer program, information processing device, and information processing method

Abstract

To provide a computer program, an information processing apparatus, and an information processing method.SOLUTION: A computer executes the processes of: acquiring information transmitted by a user; calculating priority of knowledge data to be presented to the user, based on the acquired transmission information, by referring to information stored in a knowledge database which stores multiple pieces of knowledge data including a problem in a specific field and solutions for the problem in association with evaluation of the knowledge data; and outputting the knowledge data read from the knowledge database on the basis of the calculated priority.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present invention relates to a computer program, an information processing device, and an information processing method. [Background technology]

[0002] In the past, in response to inquiries from users, computers would search knowledge sources such as manuals and past cases to automatically respond, or the respondent would use the searched knowledge to provide an answer. When a computer searches for knowledge, it acquires keywords by analyzing the meaning of sentences and words contained in the inquiry, and then performs a full-text search of the knowledge. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-189837 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when a computer performs a full-text search of knowledge, it is possible that a considerable amount of unnecessary knowledge will be included, making it difficult to narrow down the knowledge to be provided to the user.

[0005] The present invention has been made in view of the above circumstances, and has an object to provide a computer program, an information processing device, and an information processing method that are capable of identifying knowledge to be presented to a user based on priority. [Means for solving the problem]

[0006] The computer program disclosed herein is a computer program that causes a computer to execute a process of acquiring information posted by a user, calculating a priority of knowledge data to be presented to the user based on the acquired information by referring to the contents of a knowledge database that stores multiple knowledge data including problems in a specific field and solutions to the problems, and an evaluation of each knowledge data, and outputting the knowledge data read from the knowledge database based on the calculated priority. [Effects of the Invention]

[0007] According to the present disclosure, knowledge to be presented to a user can be identified based on priority. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is an explanatory diagram illustrating an overview of an information processing system according to a first embodiment. [Figure 2] 2 is a block diagram showing the internal configuration of a management server according to the first embodiment. FIG. [Figure 3] FIG. 2 is a block diagram showing the internal configuration of a user terminal. [Figure 4] FIG. 2 is a conceptual diagram showing an example of the configuration of a knowledge DB. [Figure 5] 10 is a flowchart illustrating a procedure for creating a knowledge DB. [Figure 6] FIG. 10 is an explanatory diagram illustrating a method for extracting a speech portion. [Figure 7] 4 is a flowchart illustrating a procedure for presenting knowledge data in the first embodiment. [Figure 8] FIG. 10 is a schematic diagram showing a display example on a user terminal. [Figure 9] FIG. 10 is a schematic diagram illustrating an example of an evaluation input screen. [Figure 10] FIG. 10 is a block diagram showing the internal configuration of a management server according to a second embodiment. [Figure 11] FIG. 2 is a conceptual diagram showing an example of the configuration of a first knowledge DB and a second knowledge DB. [Figure 12] 10 is a flowchart illustrating a procedure for presenting knowledge data in the second embodiment. [Figure 13] 13 is a flowchart illustrating a procedure for presenting knowledge data in the third embodiment. [Figure 14] 10 is a flowchart illustrating a procedure for introducing knowledge from different fields. [Figure 15] FIG. 10 is a conceptual diagram illustrating an example of an attribute table. [Figure 16] 13 is a flowchart illustrating a procedure of a process executed by a management server according to a fifth embodiment. [Figure 17] FIG. 10 is a schematic diagram showing an example of display of user information. [Figure 18] FIG. 1 is a schematic diagram showing a first display example of a knowledge graph. [Figure 19] FIG. 10 is a schematic diagram showing a second display example of a knowledge graph. DETAILED DESCRIPTION OF THE INVENTION

[0009] The present invention will now be described in detail with reference to the drawings showing embodiments thereof. (Embodiment 1) Fig. 1 is an explanatory diagram illustrating an overview of an information processing system according to the first embodiment. The information processing system according to the first embodiment includes a management server 10 and a user terminal 20. These servers and terminals are communicably connected to each other via a communication network NW. In the configuration example of Fig. 1, for simplicity, there is one user terminal 20, but multiple user terminals 20 may be connected to the communication network NW.

[0010] The management server 10 includes a knowledge DB 121 (see FIG. 2) described later. The knowledge DB 121 is a database that stores a plurality of knowledge data including problems in a specific field and solutions to the problems, as well as evaluations of each knowledge data in association with each other.

[0011] When the management server 10 acquires user information transmitted from the user terminal 20 via the communication network NW, it calculates the priority of the knowledge data to be presented to the user by referring to the contents stored in the knowledge DB 121, and returns the knowledge data read from the knowledge DB 121 to the user terminal 20 based on the calculated priority.

[0012] An example of the configuration of an information processing system will be described below. 2 is a block diagram showing the internal configuration of management server 10 according to embodiment 1. Management server 10 is, for example, a general-purpose or dedicated server computer, and includes a control unit 11, a storage unit 12, a communication unit 13, an operation unit 14, a display unit 15, etc.

[0013] The control unit 11 includes, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The CPU of the control unit 11 controls the operation of the various hardware components described above by loading various programs stored in advance in the ROM or the storage unit 12 into the RAM and executing them, causing the entire device to function as the information processing device (computer) of the present application.

[0014] The control unit 11 is not limited to the above configuration, and may be an arithmetic circuit or control circuit including one or more GPUs (Graphics Processing Units), FPGAs (Field Programmable Gate Arrays), volatile or non-volatile memories, etc. The control unit 11 may also have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when an instruction to start measurement is given until when an instruction to end measurement is given, and a counter that counts numbers.

[0015] The storage unit 12 includes a storage device using a hard disk, a flash memory, etc. The storage unit 12 stores computer programs executed by the control unit 11, various data acquired from the outside, various data generated within the device, etc. In this embodiment, the storage unit 12 includes a knowledge DB 121 that stores knowledge data. The configuration of the knowledge DB 121 will be described in detail later.

[0016] The communication unit 13 includes a communication interface for connecting to the communication network NW. The interface included in the communication unit 13 is a communication interface conforming to wireless communication standards such as WiFi (registered trademark), 3G, 4G, 5G, LTE (Long Term Evolution), etc. The communication unit 13 transmits various information to be notified to the outside and receives various information transmitted from the outside to its own device.

[0017] The operation unit 14 is equipped with input devices such as a keyboard and a mouse, and receives input of various information. The control unit 11 performs appropriate control based on the information input from the operation unit 14, and stores the input information in the storage unit 12 as necessary.

[0018] The display unit 15 includes a display device such as a liquid crystal display panel or an organic EL display panel, and displays information to be notified to the administrator based on a control signal output from the control unit 11.

[0019] The management server 10 does not have to be a single computer, but may be a computer system made up of multiple computers and peripheral devices. Furthermore, the management server 10 may be a virtual machine that is virtually constructed by software.

[0020] Furthermore, in this embodiment, the management server 10 is configured to include the knowledge DB 121, but the knowledge DB 121 may be provided outside the management server 10. For example, the knowledge DB 121 may be provided so as to be accessible from the management server 10 via the communication network NW.

[0021] 3 is a block diagram showing the internal configuration of the user terminal 20. The user terminal 20 is a terminal device such as a smartphone, a tablet terminal, or a personal computer, and includes a control unit 21, a storage unit 22, a communication unit 23, a voice input unit 24, an operation unit 25, and a display unit 26.

[0022] The control unit 21 includes, for example, a CPU, a ROM, a RAM, etc. The CPU of the control unit 21 controls the operation of the various hardware components described above by loading various programs stored in advance in the ROM or the storage unit 22 into the RAM and executing them, thereby causing the entire device to function as the user terminal of the present application.

[0023] Note that the control unit 21 is not limited to the above configuration, and may be configured as a single piece of hardware (SoC: System On a Chip) that integrates a processor, memory, storage, communication interface, etc. Furthermore, the control unit 21 may have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when an instruction to start measurement is given until when an instruction to end measurement is given, and a counter that counts numbers.

[0024] The storage unit 22 includes a storage device using a flash memory, a hard disk, etc. The storage unit 22 stores various computer programs executed by the control unit 21, data necessary for executing the computer programs, and the like.

[0025] The communication unit 23 includes a communication interface for connecting to the communication network NW. The communication interface included in the communication unit 23 is a communication interface conforming to a wireless communication standard such as WiFi (registered trademark), 3G, 4G, 5G, LTE, etc. The communication unit 23 transmits various information to be notified to the outside, and receives various information transmitted from the outside to its own device.

[0026] The audio input unit 24 includes an audio input device such as a microphone. The audio input unit 24 amplifies an analog audio signal input through the microphone, converts it into digital audio data, and then outputs it to the storage unit 22. The audio data output from the audio input unit 24 is stored in the storage unit 22.

[0027] The operation unit 25 is equipped with input devices such as a touch panel and operation buttons, and receives various types of operation information and setting information. The control unit 21 performs appropriate control based on the operation information input from the operation unit 25, and stores the setting information in the storage unit 22 as necessary.

[0028] The display unit 26 includes a display device such as a liquid crystal display panel or an organic EL display panel, and displays information to be notified to the user based on a control signal output from the control unit 21.

[0029] The configuration of the knowledge DB 121 will be described below. 4 is a conceptual diagram showing an example of the configuration of the knowledge DB 121. The knowledge DB 121 stores knowledge data, the number of views for each piece of knowledge data, and user ratings for each piece of knowledge data in association with each other. Each piece of knowledge data includes a situation in a specific field, a problem that may arise in that situation, and a solution to the problem.

[0030] The knowledge DB 121 shown as an example in FIG. 4 is a database that accumulates knowledge data in the field of digital solutions. The knowledge data stored in this knowledge DB 121 includes, for example, a situation in which "when proposing a digital solution for the first time," where there is an issue that "the customer representative is not good at digital," and a solution to this issue that "we will provide an invitation to a DX study group." Another example of knowledge data includes a situation in which "when considering the introduction of a specific solution," where there is an issue that "there are concerns about security, and the internal approval process for the customer is not progressing," and solutions to this issue that "we will introduce security support measures" and "we will introduce case studies from other companies." The knowledge DB 121 stores a large amount of knowledge data that includes such situations, issues, and solutions.

[0031] The number of views indicates the number of times the knowledge data has been viewed by a user. The management server 10 counts the number of times that knowledge data has been provided to the user terminal 20 for each piece of knowledge data, and registers the count value in the knowledge DB 121 as the number of views of the knowledge data. The evaluation of each piece of knowledge data indicates the average value of the evaluations by users who have used the knowledge data. When providing knowledge data to a user, the management server 10 accepts the user's evaluation (for example, a five-point evaluation), calculates the average value based on the evaluations previously accepted, and registers it in the knowledge DB 121.

[0032] 4, the knowledge data is data including a situation, a problem, and a solution, but it may be data including only a problem and a solution without including a situation.Furthermore, the knowledge data may be data including only a problem without including a situation and a solution.

[0033] The knowledge data registered in the knowledge DB 121 may be automatically extracted by the management server 10 based on the dialogue data between users.

[0034] The procedure for creating the knowledge DB 121 will be described below. 5 is a flowchart illustrating the procedure for creating the knowledge DB 121. The management server 10 acquires user dialogue data through the communication unit 13 (step S101). In this embodiment, the dialogue data is, for example, voice data, which is input from the voice input unit 24 of the user terminal 20 while the user is interacting with a customer and stored in the storage unit 22. The management server 10 acquires the user dialogue data in real time or at an appropriate timing after the end of the dialogue by communicating with the user terminal 20 via the communication network NW. The management server 10 stores the acquired dialogue data in the storage unit 12.

[0035] The control unit 11 of the management server 10 analyzes the acquired dialogue data and extracts from the dialogue data portions of the utterances that include the situation, the problem, and the solution (step S102). The control unit 11 can extract from the dialogue data portions of the utterances that include the situation, the problem, and the solution, for example, by estimating the emotions of the user (including the customer who is the dialogue partner) based on the dialogue data.

[0036] FIG. 6 is an explanatory diagram illustrating a method for extracting speech portions. The horizontal axis of the graph in FIG. 6 represents time, and the vertical axis represents the tension of the speech. The control unit 11 of the management server 10 can estimate the tension of the speech, for example, by using a learning model for emotion estimation. The learning model for emotion estimation is a known model such as a Support Vector Machine (SVM), and is configured to output information regarding the level of tension when dialogue data (voice data) is input. Such a learning model is generated by learning according to a predetermined learning algorithm using, as training data, a data set including the user's speech data and tension (ground truth data) identified by vital data such as heart rate and pulse rate. The generated learning model may be stored in the storage unit 12 of the management server 10 or in an external server device.

[0037] When the control unit 11 of the management server 10 acquires user dialogue data through the communication unit 13, it inputs the acquired dialogue data into a trained learning model to estimate the tension of the utterance. The control unit 11 distinguishes between sections with relatively high conversation tension and sections with relatively low conversation tension by referring to the calculation results of the learning model. In the example of FIG. 6, section C indicates a section identified as a section with relatively high conversation tension as a result of calculation by the learning model. The control unit 11 extracts the utterance data of section C as a section of speech containing a solution. The control unit 11 also extracts the utterance data of section B, which is connected to section C and has an increasing tendency of speech tension, as a section of speech containing a problem. Furthermore, the control unit 11 extracts the utterance data of section A, which is connected to section B and has a relatively low speech tension, as a section of speech containing a situation.

[0038] The control unit 11 converts the dialogue data of the dialogue portion including the situation, the dialogue data of the dialogue portion including the problem, and the dialogue data of the dialogue portion including the solution into text data (step S103). For converting the dialogue data (voice data) into text data (character string data), known methods such as a hidden Markov model or a statistical method can be used.

[0039] After converting the data into text data, the control unit 11 associates the text data of the dialogue portion including the situation, the text data of the dialogue portion including the problem, and the text data of the dialogue portion including the solution with each other, and registers the data as knowledge data in the knowledge DB 121 (step S104). When registering the knowledge data in the knowledge DB 121, the control unit 11 may perform morphological analysis, contextual analysis, etc., and may perform processing such as formatting the text to be registered or deleting unnecessary parts. Immediately after registration, the number of views of the knowledge data is registered as "0", and the evaluation is registered as "none".

[0040] The management server 10 acquires user speech data from the user terminal 20 at any time, not only before the system starts operation but also after the system starts operation, and collects knowledge data and registers it in the knowledge DB 121 by extracting speech parts that include the situation, problem, and solution from the speech data.

[0041] In this embodiment, the tension of the speech is estimated from the voice data, and the dialogue parts corresponding to the situation, problem, and solution are extracted based on the estimated tension. However, the voice data may be converted into text data, and then the situation, problem, and solution may be extracted from the text data by understanding the user's intention using morphological analysis, contextual analysis, etc.

[0042] Furthermore, the data of speech between users is not limited to voice data, but may be text data contained in chats or emails. In this case, the control unit 11 may extract situations, issues, and solutions from the text data by understanding the user's intentions using morphological analysis, context analysis, etc., and register the extracted text data indicating the situations, issues, and solutions in the knowledge DB 121 as knowledge data.

[0043] The management server 10 may be configured to extract situations, issues, and solutions from text data corresponding to speech between users using natural language processing models such as BERT (Bidirectional Encoder Representations from Transformers) and GPT-3 (Generative Pre-Training-3). In this case, when text data is input, the situations, issues, and solutions may be extracted from the text data using a first natural language processing model tuned to output sentences indicating the situation, a second natural language processing model tuned to output sentences indicating the problem, and a third natural language processing model tuned to output sentences indicating the solution.

[0044] Furthermore, users from whom dialogue data is acquired may be limited according to user attributes. For example, if the knowledge DB 121 stores knowledge in the field of digital solutions, dialogue data may be acquired only from users who perform work related to digital solutions, and knowledge extracted from the dialogue data may be registered in the knowledge DB 121. For this reason, user authentication may be performed in advance, and dialogue data may be acquired only if it is determined that the work of the authenticated user is related to digital solutions.

[0045] The following describes the processing that the management server 10 executes after the knowledge DB 121 is created and the system starts operating.

[0046] 7 is a flowchart illustrating the procedure for presenting knowledge data in the first embodiment. The control unit 11 of the management server 10 acquires the transmitted information transmitted from the user through the communication unit 13 (step S121). The transmitted information transmitted from the user is any information transmitted by a user (e.g., a user registered in advance) who wishes to receive knowledge data. The transmitted information is input from the voice input unit 24 or the operation unit 25 of the user terminal 20 and transmitted to the management server 10 via the communication network NW.

[0047] The control unit 11 extracts tasks from the acquired user's transmitted information (step S122). The same method as described above is used to extract the tasks. That is, if the user's transmitted information is voice data, emotion estimation can be used to identify sections of the voice data where tension is gradually increasing, thereby extracting the speech portion containing the task. Furthermore, if the user's transmitted information is text data, morphological analysis, context analysis, etc. can be used to understand the user's intention, thereby extracting the speech portion corresponding to the task.

[0048] The control unit 11 calculates the priority of the knowledge data to be presented to the user by referring to the contents stored in the knowledge DB 121 using the extracted task as a search key (step S123). The control unit 11 calculates the similarity between the task extracted in step S122 and the tasks registered in the knowledge DB 121, and determines the priority depending on the level of similarity.

[0049] For example, the control unit 11 counts the frequency of occurrence of words included in the assignment, generates a document vector having the occurrence frequency as an element, and calculates the similarity between the generated document vectors to determine the similarity between the assignments. For example, if the assignment registered in the knowledge DB 121 is "The customer representative is not good at digital technology," the control unit 11 generates a document vector having elements of 1, 1, 1 because the words "customer representative," "digital," and "not good at" each appear once. On the other hand, if the assignment extracted in step S122 is "The customer representative is not good at digital technology," the control unit 11 generates a document vector having elements of 0, 1, 1 because the words "customer representative" appear 0 times and "digital" and "not good at" appear once each. The control unit 11 can determine the similarity between the assignments by calculating the similarity between the two generated document vectors. Note that a known method such as cosine similarity is used to calculate the similarity between document vectors.

[0050] Furthermore, since the knowledge DB 121 includes user evaluations of each piece of knowledge data, the control unit 11 may calculate the priority by taking into consideration the evaluation of each piece of knowledge data. If the similarity between tasks is X and the evaluation of the knowledge data is Y, the control unit 11 can calculate the priority by, for example, w1×X+w2×Y, where w1 and w2 are weights that are set appropriately.

[0051] The control unit 11 reads out knowledge data from the knowledge DB 121 based on the priority calculated in step S123 (step S124). For example, the control unit 11 reads out only the knowledge data with the highest priority from the knowledge DB 121. Alternatively, the control unit 11 may set a threshold value for the priority and read out one or more pieces of knowledge data having a priority exceeding the threshold value from the knowledge DB 121. Alternatively, the control unit 11 may read out a predetermined number of pieces of knowledge data from the knowledge DB 121 in descending order of priority.

[0052] The control unit 11 outputs the knowledge data read in step S124 (step S125). Specifically, the control unit 11 transmits the knowledge data read from the knowledge DB 121 to the user terminal 20 via the communication unit 13. The transmitted knowledge data may be only a solution to the problem, or may include the situation, the problem, and the solution. The control unit 11 also accesses the knowledge DB 121 and increases the number of views of the knowledge data provided to the user by +1.

[0053] When the user terminal 20 receives knowledge data transmitted from the management server 10 via the communication unit 23, the user terminal 20 displays the received knowledge data on the display unit 26. Fig. 8 is a schematic diagram showing an example of a display on the user terminal 20. The display screen 200 shown as an example in Fig. 8 includes a problem display field 201 that displays the user's problem, a solution display field 202 that displays solutions provided by the management server 10, a problem selection field 203, a send button 204, and an end button 205.

[0054] 8, in response to a user's issue (muttering) that "I'm not good at digital," multiple solutions proposed by the management server 10, such as "We'll send you an invitation to a DX study group," "We'll introduce you to related books," ..., and "We'll introduce you to a support center," are displayed in the solution display field 202. These solutions may be displayed in order of priority calculated by the management server 10.

[0055] The problem selection field 203 accepts the selection of a solution by the user. When the send button 204 is pressed, the number of the solution selected by the user (n numbers from 1 to n in the example of FIG. 8) is notified to the management server 10. When the end button 205 is pressed, the display screen 200 is closed without notifying the solution number. Note that the management server 10 may execute processing to solve the user's problem according to the solution number notified from the user terminal 20. For example, when solution 1 is selected on the display screen 200 of FIG. 8, an invitation to a DX study group may be notified to the user terminal 20 by email or SNS (Short Messaging Service).

[0056] The control unit 11 of the management server 10 requests the user to rate the knowledge data at an appropriate timing after providing the knowledge data to the user (step S126). Specifically, the control unit 11 transmits screen data of a rating input screen to the user terminal 20 via the communication unit 13 and displays the rating input screen on the display unit 26 of the user terminal 20, thereby requesting a rating from the user. FIG. 9 is a schematic diagram showing an example of the rating input screen. The rating input screen 210 shown as an example in FIG. 9 includes rating acceptance fields 211-1, 211-2, ..., 211-n for accepting ratings for each piece of knowledge data, a send button 212 for sending the accepted rating to the management server 10, and an exit button 213 for closing the rating input screen 210 without sending the rating. The user's rating for each piece of knowledge data presented in step S125 is input into the rating acceptance fields 211-1, 211-2, ..., 211-n, for example, on a five-point scale. When the send button 212 is pressed, the evaluation by the user is sent from the user terminal 20 to the management server 10.

[0057] In this embodiment, the management server 10 is configured to accept evaluations for all knowledge data presented to the user terminal 20, but the management server 10 may be configured to accept evaluations only for the solution selected by the user.

[0058] The control unit 11 of the management server 10 receives the evaluation sent from the user terminal 20 (step S127) and updates the evaluation of the knowledge data in the knowledge DB 121 (step S128). At this time, the control unit 11 calculates an average value of the evaluation based on the evaluations received in the past, and registers the calculated average value of the evaluation in the knowledge DB 121.

[0059] In this embodiment, when information transmitted from the user terminal 20 is received, the user's problem is extracted by emotion estimation, context analysis, etc. However, the configuration may also be such that a problem from the user is directly accepted and the accepted problem is transmitted to the management server 10. In this case, the management server 10 may refer to the knowledge DB 121 based on the problem received from the user terminal 20 and search for a corresponding solution.

[0060] As described above, in the first embodiment, knowledge data that matches the user's problem can be extracted from the knowledge DB 121 and presented to the user, and the knowledge DB 121 can be updated based on the user's evaluation of the presented knowledge data. The optimization of the knowledge DB 121 progresses based on the user's evaluation, and the progress in optimization of the knowledge DB can increase the number of users and the frequency of use by the users.

[0061] (Embodiment 2) In the second embodiment, a configuration including knowledge DBs for multiple fields will be described.

[0062] 10 is a block diagram showing the internal configuration of a management server 10 according to embodiment 2. The management server 10 is, for example, a general-purpose or dedicated server computer, and includes a control unit 11, a storage unit 12, a communication unit 13, an operation unit 14, a display unit 15, etc. The configuration of each of these hardware units is the same as in embodiment 1.

[0063] In the second embodiment, a configuration will be described in which the management server 10 includes a knowledge DB 121 that accumulates knowledge data in the digital solution field and a knowledge DB 122 in the e-commerce field. In the following description, the knowledge DB 121 will be referred to as a first knowledge DB 121, and the knowledge DB 122 will be referred to as a second knowledge DB 122.

[0064] 11 is a conceptual diagram showing an example of the configuration of the first knowledge DB 121 and the second knowledge DB 122. The first knowledge DB 121 stores knowledge data in the digital solution field, the number of views for each piece of knowledge data, and user ratings for each piece of knowledge data, in association with each other. The second knowledge DB 122 stores knowledge data in the e-commerce field, the number of views for each piece of knowledge data, and user ratings for each piece of knowledge data, in association with each other. The management server 10 can create the first knowledge DB 121 and the second knowledge DB 122 using a method similar to that of the first embodiment.

[0065] When creating the first knowledge DB121, it is sufficient to obtain dialogue data limited to users engaged in digital solution-related work, and register the knowledge extracted from that dialogue data in the first knowledge DB121. When creating the second knowledge DB122, it is sufficient to obtain dialogue data limited to users engaged in e-commerce-related work, and register the knowledge extracted from that dialogue data in the second knowledge DB122.

[0066] The following describes the processing that the management server 10 executes after the first knowledge DB 121 and the second knowledge DB 122 are created and the operation of the system is started.

[0067] 12 is a flowchart illustrating the procedure for presenting knowledge data in embodiment 2. The control unit 11 of the management server 10 acquires transmitted information transmitted from a user through the communication unit 13 (step S201), and extracts a problem from the transmitted information (step S202).

[0068] Next, the control unit 11 determines the knowledge DB to be searched (step S203). For example, the control unit 11 may extract a specific keyword from the acquired transmitted information or task, and identify a field containing the extracted keyword, thereby determining the knowledge DB to be searched. Alternatively, the control unit 11 may perform user authentication in advance, and determine the knowledge DB to be searched based on the user attributes identified at the time of authentication. In this embodiment, either the first knowledge DB 121 or the second knowledge DB 122 is determined.

[0069] The control unit 11 refers to the knowledge DB (for example, the first knowledge DB 121) determined in step S203, and calculates the priority of the knowledge data to be presented in the same procedure as in the first embodiment (step S204).

[0070] Next, the control unit 11 determines whether to provide knowledge in a different field (step S205). For example, when the control unit 11 is unable to extract a predetermined number or more (e.g., one or more) of knowledge items having a priority higher than a set value from the first knowledge DB 121, the control unit 11 determines to provide knowledge data from the second knowledge DB 122 in which knowledge data in a different field is registered. Alternatively, the control unit 11 may determine to provide knowledge in a different field if the number of knowledge data registered in the knowledge DB determined in step S203 is not sufficiently large (i.e., if it is less than the set number). When it is determined not to provide knowledge in a different field (S205: NO), the control unit 11 executes the following processing from step S207 onwards.

[0071] If it is determined that knowledge from a different field is to be provided (S205: YES), the control unit 11 refers to a knowledge DB from the different field (for example, the second knowledge DB 122) and calculates the priority of the knowledge data to be presented (step S206) in the same procedure as in embodiment 1. That is, the control unit 11 calculates the similarity between the problem extracted in step S202 and the problem registered in the knowledge DB from the different field, and determines the priority depending on the level of similarity.

[0072] The control unit 11 reads the knowledge data from the knowledge DB based on the calculated priority of the knowledge data (step S207). The control unit 11 may read the knowledge data from both the first knowledge DB 121 and the second knowledge DB 122, or may read the knowledge data from either one of them. The control unit 11 outputs the read knowledge data to the outside (step S208).

[0073] The control unit 11 requests the user to rate the knowledge data at an appropriate timing after providing the knowledge data to the user (step S209). The control unit 11 receives the rating of the knowledge data transmitted from the user terminal 20 (step S210), and updates the knowledge DB based on the received rating (step S211).

[0074] As described above, in the second embodiment, in cases where a solution to a problem in a specific field (for example, the digital solutions field) is not registered in the knowledge database of that field, knowledge data can be searched from a knowledge database of a different field and provided to the user.

[0075] (Embodiment 3) In the third embodiment, a configuration will be described in which knowledge data is searched across knowledge DBs in a plurality of fields and provided to the user.

[0076] The management server 10 according to the third embodiment includes a first knowledge DB 121 and a second knowledge DB 122, similar to the second embodiment.

[0077] 13 is a flowchart illustrating a procedure for presenting knowledge data in embodiment 3. The control unit 11 of the management server 10 acquires transmitted information transmitted from a user through the communication unit 13 (step S301), and extracts a problem from the transmitted information (step S302).

[0078] The control unit 11 refers to a knowledge DB of a specific field (for example, the first knowledge DB 121) and calculates the priority of the knowledge data to be presented in the same procedure as in embodiment 1 (step S303). The control unit 11 reads out the knowledge data from the first knowledge DB 121 based on the calculated priority of the knowledge data (step S304).

[0079] Next, the control unit 11 reads out knowledge data related to the read knowledge data in the specific field from a knowledge DB in a different field (for example, the second knowledge DB 122) (step S305). The relatedness of knowledge data is derived based on the similarity of the problems or solutions. For example, the control unit 11 can find the similarity between problems or solutions by counting the frequency of appearance of words included in the problems or solutions, generating document vectors having the frequency of appearance as elements, and calculating the similarity between the generated document vectors. The control unit 11 can identify knowledge data with a high similarity between problems or solved problems as related knowledge data.

[0080] The control unit 11 outputs the knowledge data read out in steps S304 and S305 to the outside (step S306).

[0081] The control unit 11 requests the user to rate the knowledge data at an appropriate timing after providing the knowledge data to the user (step S307). The control unit 11 receives the rating of the knowledge data transmitted from the user terminal 20 (step S308), and updates the knowledge DB based on the received rating (step S309).

[0082] As described above, in embodiment 3, in addition to knowledge data extracted from a knowledge database in a specific field (for example, the digital solutions field), knowledge data in a different field (for example, the e-commerce field) that is related to the knowledge data can also be provided to the user.

[0083] In this embodiment, knowledge data from a specific field and knowledge data from a different field that are related to the knowledge data from the specific field are provided to the user together, but knowledge data from a different field that are not related to the knowledge data from the specific field may also be output together. In this case, in step S305 of the above-mentioned flowchart, knowledge data with a low degree of similarity to the problem or problem-to-be-solved may be identified as related knowledge data.

[0084] (Fourth embodiment) In the fourth embodiment, a configuration will be described in which knowledge data from a different field is introduced into a knowledge DB in a specific field.

[0085] 14 is a flowchart illustrating the procedure for introducing knowledge from different fields. At an appropriate timing after creating a knowledge DB in a specific field, the control unit 11 counts the number of pieces of knowledge data stored in the knowledge DB (step S401).

[0086] The control unit 11 compares the counted accumulated number with a set value and determines whether the accumulated number is less than the set value (step S402). The set value is set appropriately by an administrator or the like.

[0087] If it is determined that the accumulated number is less than the set value (S402: YES), the control unit 11 introduces the knowledge data registered in the knowledge DB of the different field into the knowledge DB of the specific field (step S403). At this time, the control unit 11 reads out part or all of the knowledge data registered in the knowledge DB of the different field and registers the read knowledge data in the knowledge DB of the specific field as knowledge data of the specific field. When reading out part of the knowledge data registered in the knowledge DB of the different field, the control unit 11 may, for example, extract knowledge data from the different field that is related to the knowledge data of the specific field and register it in the knowledge DB as knowledge data of the specific field. Furthermore, the control unit 11 may extract knowledge data from the knowledge DB of the different field that has an evaluation higher than a set value and register it in the knowledge DB as knowledge data of the specific field.

[0088] In step S402, if it is determined that the accumulated number is equal to or greater than the set value and that a sufficient amount of knowledge data is registered in the knowledge DB (S402: NO), the control unit 11 terminates the processing according to this flowchart without introducing knowledge data from a different field.

[0089] As described above, in the fourth embodiment, knowledge data from a different field is introduced into a knowledge DB of a specific field that has a small number of registered knowledge data and may not be fully utilized. As a result, for example, knowledge data from a completely different field, such as the medical field, may be introduced into a knowledge DB of the financial field, which may provide stimulating solutions to users in the financial field and encourage them to use the knowledge DB.

[0090] In this embodiment, the number of accumulated knowledge data is used as a judgment index to determine whether to introduce knowledge data from a different field, but the judgment index is not limited to the accumulated number, and may be the accumulation rate or accumulation acceleration. For example, the control unit 11 may calculate the number of accumulated knowledge data per predetermined period as the accumulation rate, and if this is less than a set value, determine to introduce knowledge data from a different field. Furthermore, the control unit 11 may calculate the accumulation rate of knowledge data per predetermined period as the accumulation acceleration, and if this is less than a set value, determine to introduce knowledge data from a different field.

[0091] Furthermore, the use frequency of knowledge data in a specific field may be used as an index to determine whether to introduce knowledge data from a different field. The control unit 11 may refer to the knowledge DB of the specific field, count the number of times that knowledge data has been provided to a user as the use frequency, and if this is less than a set value, determine to introduce knowledge data from a different field.

[0092] (Embodiment 5) In the fifth embodiment, a configuration for prompting a user to register knowledge data will be described.

[0093] The management server 10 according to the fifth embodiment includes an attribute table 120 in the storage unit 12, which stores user attribute information. FIG. 15 is a conceptual diagram showing an example of the attribute table 120. The attribute table 120 stores user attribute information, such as a user ID, password, name, email address, department, field, and frequency of use, in association with each other. Here, the ID and password are authentication information used when the management server 10 performs user authentication. The management server 10 can perform user authentication using the ID and password when using this system. The field indicates the field for which the user wishes to receive knowledge data. The frequency of use indicates the number of times the user has received knowledge data from the management server 10. The attribute information is not limited to the above, and may also include information indicating whether the user is expected to provide good solutions.

[0094] 16 is a flowchart illustrating the procedure of processing executed by the management server 10 according to the fifth embodiment. The control unit 11 of the management server 10 uses an internal timer to measure the time elapsed since the knowledge data was last registered in the knowledge DB 121 (step S501), and determines whether the set time has elapsed (step S502). If the set time has not elapsed (S502: NO), the control unit 11 returns the processing to step S501.

[0095] If it is determined that a set time has elapsed since the last time knowledge data was registered (S502: YES), the control unit 11 refers to the attribute table 120 and identifies users who should be encouraged to register knowledge data (step S503). If the registration target is the knowledge DB 121 in the digital solution field, the control unit 11 searches the attribute table 120 for users who belong to the digital solution field, and identifies, among the searched users, for example, the user with the highest usage frequency as a user who should be encouraged to register knowledge data. The control unit 11 may also identify users who are expected to provide good solutions as users who should be encouraged to register knowledge data.

[0096] The control unit 11 requests the specified user to register the knowledge data (step S504). Specifically, the control unit 11 refers to the attribute table 120, reads out the email address of the requested user, and sends an email addressed to the read email address to request the registration of the knowledge data.

[0097] After requesting the user to register knowledge data, the control unit 11 determines whether or not the knowledge data transmitted from the user terminal 20 of the user has been received (step S505). If the knowledge data has not been received (S505: NO), the control unit 11 waits until the knowledge data is received.

[0098] When the knowledge data is received, the control unit 11 registers the situation, problem, and solution contained in the received knowledge data in a knowledge DB of the relevant field (for example, the knowledge DB 121) (step S506).

[0099] As described above, in the fifth embodiment, users who should be encouraged to register are identified based on the user's attribute information, and the identified users are encouraged to register knowledge data, so that the knowledge DB can be updated successively. Furthermore, successively updating the knowledge data increases the frequency of use by users.

[0100] In this embodiment, if knowledge data is not received from a user who has been prompted to register, the system waits until knowledge data is received from that user. However, instead of waiting, another user may be searched for and the searched user may be prompted to register knowledge data.

[0101] In this embodiment, the configuration is such that users who should be encouraged to register are identified based on the user's attribute information, but it is also possible to ask each user whether or not they are able to register knowledge data, and obtain knowledge data from users who respond that they are able to register and register it in the knowledge DB.

[0102] (Embodiment 6) In the sixth embodiment, a configuration for displaying information about users who use the knowledge DB will be described.

[0103] When the management server 10 according to the sixth embodiment receives a request from the user terminal 20, it reads out user information from the attribute table 120 and provides the read out information to the user terminal 20. At this time, the management server 10 may aggregate the information read out from the attribute table 120 and provide the aggregated information to the user terminal 20.

[0104] Fig. 17 is a schematic diagram showing an example of a display of user information. The display screen 220 shown in Fig. 17 is an example of a screen displayed on the display unit 26 of the user terminal 20. The display screen 220 shows a list of which departments have a high frequency of use of knowledge data for each field. The management server 10 can generate the display screen 220 shown in Fig. 17 by referring to the attribute table 120 and aggregating the frequency of use of knowledge data for each department for each field.

[0105] FIG. 17 shows an example of the display screen 220 that displays the frequency of use for each department, which is calculated for each field. However, the display screen 220 may also display the frequency of use for each user, which is calculated for each field.

[0106] As described above, in the sixth embodiment, information on users who use the knowledge DB is displayed, so that the browsing behavior of users who use the knowledge DB frequently can induce browsing behavior of other users.

[0107] (Embodiment 7) In the seventh embodiment, a configuration for displaying a knowledge graph will be described.

[0108] FIG. 18 is a schematic diagram showing a first display example of a knowledge graph. The display screen 230 shown as the first display example in FIG. 18 includes a knowledge graph for the digital solutions field and a knowledge graph for the e-commerce field. Nodes in each knowledge graph represent words extracted from knowledge data in the knowledge DB. In the example of FIG. 18, white circles represent nodes in the digital solutions field, and black circles represent nodes in the e-commerce field. The size of a node indicates the number of views. A larger node indicates a higher number of views, and a smaller node indicates a lower number of views. Nodes that are related to each other are connected by edges (also called links). The length of the edge indicates the degree of relatedness. A shorter edge indicates a higher relatedness, and a longer edge indicates a lower relatedness. Furthermore, the arrow pointing from the node in the e-commerce field to the node in the digital solutions field indicates that knowledge data has been introduced from the e-commerce field to the digital solutions field using the cross-field knowledge introduction function.

[0109] Furthermore, the nodes may be highlighted by blinking or changing their display color depending on the rate of increase in the number of views over a certain period of time. Furthermore, the thickness of the edges may be changed depending on the rating of the knowledge data.

[0110] Furthermore, it is possible to identify areas with low node density and prompt the user to register knowledge data in order to increase the number of nodes in the identified areas. In the example of Figure 18, there is an area with low node density in the knowledge graph of the e-commerce field, so the user is prompted to register knowledge data in that area.

[0111] FIG. 19 is a schematic diagram showing a second display example of a knowledge graph. The display screen 240 shown as the second display example in FIG. 19 includes a problem input field 241. When a problem is entered in the problem input field 241, the control unit 11 of the management server 10 performs a search across multiple knowledge databases and creates a knowledge graph based on the search results. That is, the control unit 11 creates the knowledge graph based on the words contained in the problems found by the cross-search and the relationships between the words, regardless of the field. As described above, the size of the node represents the number of views, and the length of the edge represents the degree of relevance. Furthermore, the node may be highlighted by blinking or changing its display color depending on the rate of increase in the number of views over a certain period of time. Furthermore, the thickness of the edge may be changed depending on the evaluation of the knowledge data.

[0112] As described above, in the seventh embodiment, the registration status of knowledge data can be visualized.

[0113] The embodiments disclosed herein should be considered in all respects as illustrative and not restrictive. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0114] 10 Management Server 11 Control section 12 Storage section 13 Communications Department 14 Control section 15 Display 20 User terminal 21 Control section 22 Memory section 23 Communications Department 24 Audio input section 25 Control section 26 Display section 121,122 Knowledge DB

Claims

1. Acquires transmitted information including user utterances; Extracting the user's issues from the acquired transmitted information; a knowledge database that stores a plurality of pieces of knowledge data, including problems and solutions to the problems in a specific field and problems and solutions to the problems in a field different from the specific field, in association with an evaluation of each piece of knowledge data, by using the extracted problem of the user as a search key to refer to the stored contents of the knowledge database; and outputting the knowledge data read from the knowledge database based on the calculated priority; Calculating any one of the number of accumulated knowledge data, accumulation speed, accumulation acceleration, and usage frequency in the specific field; If the calculated value is less than a set value, the knowledge data of the different field is introduced as knowledge data of the specific field. A computer program that causes a computer to execute a process.

2. Accepting an evaluation of the knowledge data; Based on the received evaluation, the contents of the knowledge database are updated. The computer program product according to claim 1, for causing the computer to execute a process.

3. Acquire dialogue data relating to dialogue between users; Extracting issues and solutions to those issues from the acquired dialogue data to generate knowledge data; The generated knowledge data is stored in the knowledge database.

3. A computer program according to claim 1 or 2, for causing a computer to execute a process.

4. Searching for knowledge data in the different field according to the search results for knowledge data in the specific field.

4. A computer program according to claim 1, for causing a computer to execute a process.

5. When outputting the knowledge data of the specific field, the knowledge data of the different field that is related to the knowledge data of the specific field is also output.

5. A computer program according to claim 1, for causing a computer to execute a process.

6. When outputting the knowledge data of the specific field, the knowledge data of the different field that is not related to the knowledge data of the specific field is also output.

5. A computer program according to claim 1, for causing a computer to execute a process.

7. Deriving the relevance between the knowledge data in the specific field and the knowledge data in the different field based on the similarity between the problems or solutions between them.

7. A computer program product according to claim 1, for causing a computer to execute a process.

8. Acquire attribute information of multiple users, Based on the acquired attribute information, users who should be encouraged to register knowledge data are identified, Notify the identified users that they should register knowledge data 8. A computer program according to claim 1, for causing a computer to execute a process.

9. measuring the elapsed time since the registration of the knowledge data; When the set time has elapsed, the specified user is notified that the knowledge data should be registered.

9. A computer program product according to claim 8, for causing the computer to execute a process.

10. Displaying information about users using the knowledge database 10. A computer program product according to claim 1, for causing a computer to execute a process.

11. A plurality of pieces of knowledge data stored in the knowledge database are mapped by nodes indicating each piece of knowledge data and edges indicating the relationships between the nodes.

11. A computer program product according to claim 1, for causing a computer to execute a process.

12. The display mode of the nodes and edges is changed depending on the attributes of each piece of knowledge data, including the strength of the relevance between the pieces of knowledge data, the number of viewers of each piece of knowledge data, and the rate of increase in the number of viewers over a certain period of time.

12. A computer program product according to claim 11, for causing a computer to execute a process.

13. an acquisition unit that acquires transmitted information including a user's utterance; an extraction unit that extracts the user's problem from the acquired transmitted information; a calculation unit that calculates the priority of knowledge data to be presented to the user by referring to the contents of a knowledge database that stores a plurality of knowledge data, including problems and solutions to the problems in a specific field and problems and solutions to the problems in a field different from the specific field, in association with an evaluation of each knowledge data, using the extracted problem of the user as a search key; an output unit that outputs the knowledge data read from the knowledge database based on the calculated priority; Equipped with Calculating any one of the number of accumulated knowledge data, accumulation speed, accumulation acceleration, and usage frequency in the specific field; If the calculated value is less than a set value, the knowledge data of the different field is introduced as knowledge data of the specific field. Information processing device.

14. Acquires transmitted information including user utterances; Extracting the user's issues from the acquired transmitted information; a knowledge database that stores a plurality of pieces of knowledge data, including problems and solutions to the problems in a specific field and problems and solutions to the problems in a field different from the specific field, in association with an evaluation of each piece of knowledge data, by using the extracted problem of the user as a search key to refer to the stored contents of the knowledge database; and outputting the knowledge data read from the knowledge database based on the calculated priority; Calculating any one of the number of accumulated knowledge data, accumulation speed, accumulation acceleration, and usage frequency in the specific field; If the calculated value is less than a set value, the knowledge data of the different field is introduced as knowledge data of the specific field. An information processing method in which processing is carried out by a computer.

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