Information processing device and information processing method

Abstract

In one aspect of this present invention, a plurality of documents created by each of a plurality of users are stored in a storage unit in association with user identification information and creation date / time information. In this state, when a keyword related to a problem is received from a counselee, a relevance index with the keyword is obtained for each of the plurality of documents, an evaluation value for each of the users is obtained on the basis of the relevance indexes corresponding to the plurality of documents created by the user, a counseling center user is selected from the plurality of users on the basis of the evaluation values, and counseling center information showing the result is notified to the counselee.

Description

Information processing device and information processing method

[0001] One aspect of the present invention relates to an information processing device and an information processing method that are used to support the selection of a person to consult with when a user consults with another user about a work-related problem during remote work, for example.

[0002] In remote work, it is difficult to have natural conversations between employees and other users compared to face-to-face work, and there are also fewer opportunities to see and hear about other users' work. As a result, when a work-related problem occurs, it is difficult for users to determine who has the knowledge to solve the problem, and they have to spend time searching for someone to consult, which is one of the causes of reduced work efficiency and quality. Therefore, there is a growing need for work support technology that can quickly resolve problems by smoothly pairing the user with someone who has knowledge of the problem.

[0003] In this situation, a technology has been proposed in which business documents created by employees are stored and managed in association with their creators, and employees who want to find someone to consult can search through the business documents using specific keywords related to their problem, obtaining information about business documents and their creators that are highly relevant to the keywords, and using this information to provide consultation to resolve the problem (see, for example, non-patent document 1).

[0004] Masayuki Tamura, Knowledge Management, "Using documents as a catalyst to promote collaboration that will spark innovation," Business Communication, 2023 Vol. 60 No. 1, Internet <URL: https: / / www.bcm.co.jp / site / 2023 / 01 / ntt-data / 2301-ntt-data-01-05.pdf>

[0005] However, with the technology described in Non-Patent Document 1, the consultant must determine, based on a selected, one-off business document, whether the creator of that business document is likely to have the knowledge necessary to solve the problem the consultant is facing. This makes selecting a consultant time-consuming and prone to misselection. Furthermore, because the consultant is selected based on the relevance index with the search keywords obtained for each business document, a specific business document with a high relevance index with the keyword significantly influences the selection of a consultant, potentially leading to the wrong selection of a consultant.

[0006] The present invention has been made in light of the above circumstances, and aims to provide a technique that enables a user to quickly and accurately select a consultation center that has knowledge related to the problem.

[0007] In order to solve the above problems, a first aspect of an information processing device or method according to the present invention stores a plurality of documents created by a plurality of users in a storage unit in association with the user's identification information and creation date and time information. In this state, when a keyword related to a problem is received from a person seeking advice, a relevance index with the keyword is calculated for each of the plurality of documents, an evaluation value for each user is calculated based on the relevance index corresponding to the plurality of documents created by that user, a user to consult with is selected from the plurality of users based on the evaluation value, and consultation information indicating the result is notified to the person seeking advice.

[0008] In a second aspect of the present invention, at least one of an inter-document similarity calculation process that calculates the similarity between documents created by each of the multiple users and a keyword inclusion relationship determination process that determines the inclusion relationship of the keywords between the documents is performed, the value of the relevance index of the document is corrected based on at least one of the similarity calculated by the inter-document similarity calculation process and information indicating the inclusion relationship of the keywords determined by the keyword inclusion relationship determination process, and an evaluation value for the user is obtained based on the corrected relevance index.

[0009] According to the first aspect of the present invention, a consultant is selected for each user based on a comprehensive evaluation of multiple documents created by that user. This makes it possible to select a user with more extensive knowledge of the problem as a consultant, without being influenced by a specific document, compared to when a consultant is selected based on the evaluation of a single document.

[0010] According to a second aspect of the present invention, at least one of a process of calculating the similarity between documents created by a plurality of users and a process of determining the inclusion relationship of the keywords between the documents is performed, and the relevance index corresponding to the target document is corrected based on at least one of the calculated similarity between the documents and the determination result of the inclusion relationship. Therefore, the relevance index corresponding to a document that is in an inclusion relationship with another document, such as a document created by referring to or reusing another document, is corrected, thereby making it possible to reduce the problem of users with little knowledge about the problem being mistakenly selected as consultation partners.

[0011] That is, according to the first and second aspects of the present invention, it is possible to provide a technique that enables a user to quickly and accurately select a consultation destination that has knowledge related to the problem.

[0012] FIG. 1 is a diagram showing an example of the configuration of a system for realizing a consultation destination selection support service according to an embodiment of the present invention. FIG. 2 is a block diagram showing an example of the hardware configuration of an information processing device that forms the core of the system shown in FIG. 1. FIG. 3 is a block diagram showing an example of the software configuration of the information processing device that forms the core of the system shown in FIG. 1. FIG. 4 is a flowchart showing an example of the procedure and content of a consultation destination selection support process executed by a control unit of the information processing device shown in FIG. 3. FIG. 5 is a flowchart showing an example of the procedure and content of a process for correcting a search keyword relevance index in the consultation destination selection support process shown in FIG. 4. FIG. 6 is a diagram showing an example of a document selected in the correction process shown in FIG. 5. FIG. 7A is a diagram showing a first example of a calculation result of the inter-document similarity calculation process in the correction process shown in FIG. 5. FIG. 7B is a diagram showing a second example of a calculation result of the inter-document similarity calculation process in the correction process shown in FIG. 5. FIG. 8A is a diagram showing an example of a document that is the target of the inter-document search keyword inclusion relationship determination process in the correction process shown in FIG. 5. FIG. 8B is a diagram showing an inclusion pattern P1 between the documents shown in FIG. 8A. FIG. 8C is a diagram showing an inclusion pattern P2 between the documents shown in FIG. 8A. Fig. 8D is a diagram showing an inclusion pattern P3 between the documents shown in Fig. 8A. Fig. 8E is a diagram showing an inclusion pattern P4 between the documents shown in Fig. 8A. Fig. 9 is a diagram showing an example of a correction result by the correction process shown in Fig. 5. Fig. 10 is a diagram showing an example of a consultation destination selection result.

[0013] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0014] [One Embodiment] (Configuration Example) (1) System FIG. 1 is a diagram showing an example of the configuration of a system that realizes a consultation destination selection support service according to one embodiment of the present invention.

[0015] The system of one embodiment includes an information processing device SV that executes a consultation destination selection support process, and enables data communication via a network NW between this information processing device SV and multiple user terminals UT1 to UTn used by users such as employees.

[0016] The user terminals UT1 to UTn are, for example, personal computers equipped with a browser or a mailer. Note that the user terminals UT1 to UTn may also be mobile terminals such as smartphones and tablet terminals.

[0017] The network NW comprises, for example, a wide area network centered on the Internet and an access network for accessing this wide area network. Examples of the access network include, but are not limited to, a public communication network using wired or wireless connections, a local area network (LAN) using wired or wireless connections, and a cable television (CATV) network. Note that, if the service provision area is limited to a company's business establishment or office, the network NW may be configured solely with a LAN or wireless LAN.

[0018] (2) Information Processing Device SV The information processing device SV is configured by, for example, a server computer installed on the cloud or the Web. Note that the information processing device SV may also be configured by, for example, a personal computer used by a system administrator.

[0019] 2 and 3 are block diagrams showing an example of the hardware configuration and software configuration of the information processing device SV, respectively.

[0020] The information processing device SV has a control unit 1 that uses a hardware processor such as a central processing unit (CPU), and this control unit 1 is connected via a bus 5 to a storage unit having a program storage unit 2 and a data storage unit 3, and a communication interface (hereinafter, the interface will be referred to as I / F) unit 4.

[0021] The communication I / F unit 4 transmits and receives data to and from the user terminals UT1 to UTn using a communication protocol defined in the network NW. The communication I / F unit 4 also transmits data to and from an administrator terminal (not shown) used by a system administrator or the like.

[0022] The program storage unit 2 is configured by combining, for example, a nonvolatile memory such as a solid-state drive (SSD) as a storage medium that can be written to and read from at any time, and a nonvolatile memory such as a read-only memory (ROM), and stores middleware such as an operating system (OS), as well as application programs required to execute various controls according to an embodiment. Hereinafter, the OS and each application program will be collectively referred to as the program.

[0023] The data storage unit 3 is, for example, a combination of a non-volatile memory such as an SSD that can be written to and read from at any time as a storage medium, and a volatile memory such as a RAM (Random Access Memory), and its storage area includes a business document data storage unit 31, a relevance index storage unit 32, and a consultation information storage unit 33.

[0024] The business document data storage unit 31 stores a plurality of business documents created by a plurality of users in association with the identification information (user ID) of the creators and information indicating the creation date and time.

[0025] The relevance index storage unit 32 is used to store the relevance index calculated for each business document between the business document and the search keyword related to the problem for a period until the series of processes for selecting a consultation destination is completed.

[0026] The consultation information storage unit 33 is used to store information indicating the result of the selection of a consultation destination determined by the control unit 1 .

[0027] The control unit 1 includes the following processing functions necessary to implement one embodiment of the present invention: a business document management processing unit 11, a search keyword relevance index calculation processing unit 12, an inter-document similarity calculation processing unit 13, an inter-document search keyword inclusion relationship determination processing unit 14, a relevance index correction processing unit 15, a consultation destination suitability evaluation processing unit 16, and a consultation destination information notification processing unit 17.

[0028] The processing units 11 to 17 are all realized by causing a hardware processor in the control unit 1 to execute an application program stored in the program storage unit 2. Note that some or all of the processing units 11 to 17 may be realized using hardware such as an LSI (Large Scale Integration) or an ASIC (Application Specific Integrated Circuit).

[0029] The business document management processing unit 11 acquires data of multiple business documents created by multiple users from the user terminals UT1 to UTn, respectively, and stores the acquired data of each business document in the business document data storage unit 31 in association with the user ID of the creating user and information representing the creation date and time.

[0030] When a search keyword relevance index calculation processing unit 12 receives a search keyword related to a problem from a user terminal UT1 to UTn of a user who is to seek advice, it calculates an index indicating the relevance of each business document stored in the business document data storage unit 31 to the search keyword, and stores the calculated relevance index in the relevance index storage unit 32 in association with the identification information (document ID) of the business document.

[0031] The inter-document similarity calculation processing unit 13 calculates, between users, the inter-document similarity between a plurality of business documents stored in the business document data storage unit 31. An example of a method for calculating the inter-document similarity will be described in the operation example.

[0032] The inter-document search keyword inclusion relationship determination processing unit 14 determines the inclusion relationship of search keywords between users among multiple business documents stored in the business document data storage unit 31, that is, whether or not a group of keywords included in a business document created by one user are all included in a business document created by another user. An example of this process of determining the inclusion relationship of search keywords will also be described in the operation example.

[0033] The relevance index correction processing unit 15 corrects the relevance index corresponding to the business document stored in the relevance index storage unit 32, based on the inter-document similarity calculated by the inter-document similarity calculation processing unit 13 and the determination result of the inclusion relationship of the search keywords between documents by the inter-document search keyword inclusion relationship determination processing unit 14. An example of this relevance index correction processing will also be described in the operation example.

[0034] The consultation destination suitability evaluation processing unit 16 calculates an evaluation value representing suitability as a consultation destination for each user who is a candidate for consultation, by combining the search keyword relevance indices corresponding to multiple business documents created by the user, using the search keyword relevance indices corresponding to each business document corrected by the relevance index correction processing unit 15. The consultation destination suitability evaluation processing unit 16 then selects a consultation destination based on the calculation result of the evaluation value, and stores information representing the selected consultation destination in the consultation destination information storage unit 33 in association with the user ID of the user who is the source of the consultation.

[0035] Each time consultation information for a person making a request is generated, the consultation information notification processing unit 17 reads out the consultation information from the consultation information storage unit 33 and transmits the read consultation information from the communication I / F unit 4 to the user terminals UT1 to UTn used by the user making the request.

[0036] (Example of Operation) Next, an example of operation of the information processing device SV configured as above will be described.

[0037] (I) Management of Business Document Data Under the control of the business document management processing unit 11, each time an employee (user) creates a business document, the control unit 1 of the information processing device SV acquires data of the created business document from the user terminal UT1 to UTn via the communication I / F unit 4. The business document management processing unit 11 then associates the acquired business document data with the user ID of the user who created the document and information indicating the creation date and time, and stores the data in the business document data storage unit 31 in the data storage unit 3.

[0038] (II) Support for Selection of Consultant FIG. 4 is a flowchart illustrating an example of the procedure and content of a process for supporting selection of a consultant executed by the control unit 1 of the information processing device SV.

[0039] (1) Calculation of Search Keyword Relevance Index Suppose a user has a business problem and sends a search keyword related to the problem from his / her user terminal (e.g., UTi) along with a request to select a consultant. In response to this, the control unit 1 of the information processing device SV, upon receiving the request in step S10, executes a calculation process of a search keyword relevance index for each business document data stored in the business document data storage unit 31 under the control of the search keyword relevance index calculation processing unit 12 as follows:

[0040] That is, first, in step S11, the search keyword relevance index calculation processing unit 12 acquires, via the communication I / F unit 4, a search keyword related to the problem transmitted from the user terminal UTi of the client.

[0041] Next, in step S12, the search keyword relevance index calculation processing unit 12 sequentially reads out all business documents excluding the business document created by the client from the business document data storage unit 31. Then, the search keyword relevance index calculation processing unit 12 searches each of the read business documents to determine whether it contains a search keyword related to the problem, and calculates a search word relevance index from the search results.

[0042] For example, the number of search keywords in each business document is counted, and the counted value is used as the relevance index of the search keywords to the business document. The relevance index may be the ratio of the search keywords to all words included in the business document, the frequency of appearance of the search keywords, or the like.

[0043] The search keyword relevance index calculation processing unit 12 stores the calculated relevance index of the search keyword in the relevance index storage unit 32 in association with the business document identification ID.

[0044] (2) Correction of search keyword relevance index Next, in each of steps S131 to S133 of step S13, the control unit 1 of the information processing device SV executes the process of correcting the search keyword relevance index corresponding to each of the business documents under the control of the inter-document similarity calculation processing unit 13, the inter-document search keyword inclusion relationship determination processing unit 14, and the relevance index correction processing unit 15 as follows.

[0045] FIG. 5 is a flowchart showing an example of the processing procedure and processing contents of a series of processes executed by the inter-document similarity calculation processing unit 13, the inter-document search keyword inclusion relationship determination processing unit 14, and the relevance index correction processing unit 15.

[0046] In the following explanation, we will use as an example a case where, as shown in Figure 6, the inter-document similarity is calculated between a business document created by employee A and business documents created by other employees B, C, ... (for simplicity, Figure 6 shows only employees A and B), the keyword inclusion relationship between the documents is determined, and the search keyword relevance index corresponding to the document created by employee A is corrected based on the results of both processes.

[0047] (2-1) Calculation of Inter-Document Similarity Inter-document similarity is used to determine the possibility that one business document created by different creators was created by referring to or reusing another business document. For example, if the inter-document similarity is above a threshold, it is determined that there is a possibility that a business document created by employee A was created by referring to or reusing a business document created by employee B.

[0048] First, in step S20, the inter-document similarity calculation processing unit 131 selects one business document, for example, created by employee A, from all business documents excluding those created by the consultant that are stored in the business document data storage unit 31. Then, in step S21, the inter-document similarity calculation processing unit 131 calculates the inter-document similarity between the selected business document of employee A (hereinafter referred to as the target document) and all business documents created by all employees other than employee A who created the target document (in this example, employees B and C), and saves the results in a storage area (not shown) in the data storage unit 3.

[0049] The similarity between documents can be calculated, for example, by evaluating the degree of correspondence between character strings in business documents using natural language processing technology. Other calculation methods include analyzing the structure of the XML files of business documents to evaluate the structural similarity of the documents, and using image processing technology to evaluate the similarity of images.

[0050] 7A and 7B show examples of the calculation results of the similarity between documents. A-1 is the target document, and this document Doc A-1 and Employee B's business document Doc B-1 and Employee C's business document Doc C-1 7B shows an example of calculating the similarity of character strings between employee A's business document Doc and A-2 is the target document, and this document Doc A-2 and Employee B's business document Doc B-2 and Employee C's business document Doc C-2 10 shows an example of calculating the similarity between the image images.

[0051] The calculation of the inter-document similarity may be performed not only when a problem occurs, but also whenever a new business document is acquired before the problem occurs. In this case, the calculated value indicating the inter-document similarity is stored in the data storage unit 3 in association with the document ID of the target document.

[0052] (2-2) Determining the inclusion relationship of inter-document search keywords Similar to the inter-document similarity described above, the inclusion relationship of inter-document search keywords is used to determine the possibility that one business document created by different creators was created with reference to or by reusing another business document.

[0053] For example, if the search keywords contained in one business document created on a certain date and time are all contained in another business document created on an earlier date and time, it can be determined that the one business document may have been created with reference to or by reusing the other business document.

[0054] In step S22, the inter-document search keyword inclusion relationship determination processing unit 14 determines the inclusion relationships of search keywords between the target document selected in step S20 and all business documents created by users other than the user who created the target document.The inter-document search keyword inclusion relationship determination processing unit 14 then stores the determination results in a storage area (not shown) in the data storage unit 3.

[0055] As shown in FIG. 8A, the business document Doc of employee A A-1 and Employee B's business document Doc B-1 For example, let us consider a case where the inclusion relationship of search keywords between a business document Doc and A-1 The creation date is March 2023, and the target document is a business document Doc. B-1 is created later in September 2023, and the keywords W to be judged are k1, K2, and K3.

[0056] In this case, for example, patterns P1 and P2 shown in FIGS. 8B and 8C are both documents Doc A-1 Set W1 of keywords included in document Doc B-1 There is an inclusion relationship of W2⊆W1 between the set of keywords W2 included in the document Doc. B-1 is document Doc A-1 It is determined that there is a possibility that it was created with reference to or by appropriating the above.

[0057] On the other hand, for example, patterns P3 and P4 shown in FIGS. 8D and 8E are both documents Doc A-1 Set W1 of keywords included in document Doc B-1 The inclusion relationship W2⊆W1 does not hold between the set of keywords W2 included in the document Doc. B-1 is document Doc A-1 It is determined that there is no possibility that it was created with reference to or by appropriating the above.

[0058] (2-3) Correction of Relevance Index After the inter-document similarity calculation process and the inter-document search keyword inclusion relationship determination process for the target document are completed, the relevance index correction processing unit 15 then determines in step S23 whether the search keyword relevance index of the target document is to be corrected. This determination is made based on the result of comparing the inter-document similarity with a threshold and the determination result of the search keyword inclusion relationship. Specifically, if the inter-document similarity is equal to or greater than the threshold and it is determined that there is a search keyword inclusion relationship, it is determined that the search keyword relevance index of the target document is to be corrected.

[0059] Then, when the relevance index correction processing unit 15 determines that the search keyword relevance index of the target document is to be corrected, in step S24, it corrects the value of the search keyword relevance index corresponding to the target document stored in the relevance index memory unit 32.

[0060] FIG. 9 shows the target document Doc of employee A. A-1 and each document Doc of employee B to be judged B-1 ,Doc B-2 ,Doc B-3 , and an example of the determination result of the inter-document similarity and the search keyword inclusion relationship between the target document Doc and the target document Doc, and whether or not correction is performed based on these results, and the correction result. A-1 is a document B-1 The document similarity to document Doc is equal to or greater than the threshold value "0.6" and B-1 Since it is determined that the search keyword relevance index is included in the search keyword relevance index, it is determined that the search keyword relevance index is to be corrected.

[0061] When it is determined that the document is to be corrected, the relevance index correction processing unit 15 A-1 The value of the search keyword relevance index of the target document Doc is corrected, for example, by decreasing it. A-1 A method of setting the search keyword relevance index p to "0" or a method of calculating the corrected relevance index P' by calculating p' = P x (1 - R) based on the maximum similarity R (0 ≤ R ≤ 1) is used.B-1 The search keyword relevance index value may be corrected to be higher.

[0062] The control unit 1 of the information processing device SV determines in step S25 whether all target documents have been selected. If the result of this determination shows that there are still target documents that have not been selected, the process returns to step S20, and the inter-document similarity calculation processing unit 13 selects the next document. Then, the series of processes from steps S21 to S24 described above is executed again for the selected target document. Thereafter, the series of processes from steps S20 to S24 is repeatedly executed for all target documents in the same manner.

[0063] (3) Evaluation of suitability as a consultation destination Once the correction process of the search keyword relevance index is completed, in step S14, the control unit 1 of the information processing device SV, under the control of the consultation destination suitability evaluation processing unit 16, evaluates the suitability as a consultation destination using the search keyword relevance index that reflects the correction.

[0064] For example, the consultation destination suitability evaluation processing unit 16 first reads, for each of multiple users who are candidates for consultation, the corrected search keyword relevance index for each of multiple business documents created by that user from the relevance index storage unit 32. The consultation destination suitability evaluation processing unit 16 then calculates an evaluation value for the user as a consultation destination by combining the corrected search keyword relevance indexes corresponding to each of the read business documents. The evaluation value can be calculated, for example, by calculating the sum or average value of the search keyword relevance indexes corresponding to multiple business documents created by each user.

[0065] The consultation destination suitability evaluation processing unit 16 then compares the evaluation values ​​between the users, selects the user with the highest evaluation value as the consultation destination, and generates consultation destination information based on the result and stores it in the consultation destination information storage unit 33.

[0066] For example, if the evaluation scores for suitability as a consultation destination obtained for employees A, B, and C are "160," "80," and "120," respectively, as shown in Figure 10, the consultation destination suitability evaluation processing unit 16 selects employee A as the consultation destination. Then, the consultation destination suitability evaluation processing unit 16 generates consultation destination information including attribute information such as contact information for the selected employee A, and stores the generated consultation destination information in the consultation destination information storage unit 33 in association with the user ID of the person making the consultation. Note that the number of users to be consulted is not limited to one, and multiple users with high evaluation scores may be selected as consultation destination candidates.

[0067] (4) Notification of Consultation Destination Information When the consultation destination information is obtained, in step S15, the control unit 1 of the information processing device SV reads out the consultation destination information from the consultation destination information storage unit 33 under the control of the consultation destination information notification processing unit 17. Then, the consultation destination information notification processing unit 17 transmits the read consultation destination information from the communication I / F unit 4 to the user terminal UTi of the user who requested the consultation.

[0068] (Effects) As described above, in one embodiment, when a request for selecting a consultant is sent from a user seeking advice, a relevance index for search keywords related to the user's problem is first calculated for each of multiple business documents by multiple users stored in advance. Next, document similarity is calculated between a target document of a given user and documents by other users among the multiple users, and the inclusion relationship of search keywords between the documents is determined. Then, based on the document similarity and the determination result of the inclusion relationship of search keywords between the documents, it is determined whether correction is necessary for the search keyword relevance index corresponding to the target document. Specifically, if it is determined that the similarity between the target document and other documents is equal to or greater than a threshold and that the target document has an inclusion relationship of search keywords with the other documents, it is determined that correction is necessary. If correction is necessary, the search keyword relevance index for the target document is corrected, for example, by decreasing the value of the search keyword relevance index. Then, the total or average value of the search keyword relevance index reflecting the correction is calculated for the multiple business documents for each user, and the user with the highest total or average value is selected as the consultation destination.

[0069] Therefore, one embodiment provides the following advantages: (1) For each user, a user to whom advice is to be provided is selected based on the sum or average value of the search keyword relevance index calculated for multiple business documents created by that user. Therefore, a user to whom advice is to be provided is selected based on a comprehensive evaluation of multiple business documents created by the user. This makes it possible to select a user to whom advice is to be provided who is more likely to have extensive knowledge of the problem, without being influenced only by a specific business document, compared to when advice is selected based on the evaluation value of a single business document.

[0070] (2) Generally, in actual business situations, when employees create business documents, they rarely review and create all of them on their own, but rather create business documents by referring to or reusing business documents created by other employees. In this way, when there is a reference-referenced relationship between business documents created by employees, it is expected that the relevance index between the business document and keywords will not match the amount of business knowledge the employee possesses regarding the actual document. Even if an employee has business documents with a high relevance index with keywords, they may not be suitable as an actual person to consult, which could lead to the wrong person being selected for consultation.

[0071] In contrast, in one embodiment, the similarity between documents among multiple users is calculated, and the inclusion relationships of search keywords between the documents are determined. Based on these results, it is determined whether the keyword relevance index of the target document needs to be corrected, and if necessary, it is corrected. As a result, the keyword relevance index corresponding to a business document created by referring to or reusing other business documents is corrected to be lower, which reduces the number of users with little knowledge about the problem who are selected as consultation points.

[0072] [Other Embodiments] (1) In one embodiment, the search keyword relevance index corresponding to a business document is corrected based on the determination results of the inter-document similarity and the inclusion relationship of the search keywords between the documents. However, the present invention is not limited to this. For example, if the number of business documents created by each user is sufficiently large, the correction process may be omitted, and a user to be consulted may be selected based on an evaluation value calculated by integrating the search keyword relevance indexes calculated for the plurality of business documents.

[0073] (2) In one embodiment, the similarity between documents between multiple users is calculated and the inclusion relationship of search keywords between the documents is determined, and the search keyword relevance index corresponding to the target document is corrected based on both of these results. However, this invention is not limited to this. For example, the similarity between documents alone may be referenced, and the keyword relevance index corresponding to a business document whose similarity between documents is equal to or greater than a threshold may be corrected. Alternatively, the keyword relevance index corresponding to a business document may be corrected based on the result of the determination of the inclusion relationship of search keywords between documents alone, and if it is determined that an inclusion relationship exists, the keyword relevance index corresponding to the business document may be corrected.

[0074] (3) In one embodiment, the consultation destination selection support process according to the present invention has been described as being executed on a server computer on the cloud or the web. However, the present invention is not limited to this, and the consultation destination selection support process may be executed on a user terminal used by the user.

[0075] (4) In one embodiment, the process of selecting a person to consult among employees within a company has been described as an example. However, this invention can also be applied to other cases, such as when a process of selecting a candidate for consultation is performed between students and teachers at an educational institution such as a school.

[0076] (5) In addition, the functions of the information processing device relating to the consultation destination selection support process, and the processing procedures and processing contents thereof can be modified in various ways without departing from the spirit of the present invention.

[0077] Although the embodiments of the present invention have been described in detail above, the above description is merely an example of the present invention in every respect. It goes without saying that various improvements and modifications can be made without departing from the scope of the present invention. In other words, when implementing the present invention, specific configurations according to the embodiments may be appropriately adopted.

[0078] In short, this invention is not limited to the above-described embodiments, and in the implementation stage, the components can be modified and embodied without departing from the spirit of the invention. Furthermore, various inventions can be formed by appropriately combining multiple components disclosed in the above-described embodiments. For example, some components may be omitted from all the components shown in the embodiments. Furthermore, components from different embodiments may be appropriately combined.

[0079] SV...information processing device UT1 to UTn...user terminal NW...network 1...control unit 2...program storage unit 3...data storage unit 4...communication I / F unit 5...bus 11...business document management processing unit 12...search keyword relevance index calculation processing unit 13...inter-document similarity calculation processing unit 14...inter-document search keyword inclusion relationship determination processing unit 15...relevance index correction processing unit 16...consultation destination suitability evaluation processing unit 17...consultation destination information notification processing unit 31...business document data storage unit 32...relevance index storage unit 33...consultation destination information storage unit

Claims

1. A storage unit that stores a plurality of documents created by a plurality of users in association with user identification information and creation date and time information; a first processing unit that, when receiving a keyword related to a problem from a consulter, obtains a relevance index with respect to each of the plurality of documents; a second processing unit that, for each user, obtains an evaluation value for the user based on the relevance indexes corresponding to the plurality of documents created by the user, and selects a consulting destination user from among the plurality of users based on the evaluation value; and a third processing unit that generates consulting destination information including attribute information related to the selected consulting destination user, and notifies the generated consulting destination information to the consulter. An information processing apparatus comprising:

2. A fourth processing unit that performs at least one of a document similarity calculation process for calculating a similarity between documents created by these users among the plurality of users and a keyword inclusion relationship determination process for determining an inclusion relationship of the keyword in the documents; and a fifth processing unit that corrects a value of the relevance index corresponding to the document based on at least one of the similarity calculated by the document similarity calculation process and information representing the inclusion relationship of the keyword determined by the keyword inclusion relationship determination process. Further provided, the second processing unit obtains an evaluation value for the user based on the relevance index corrected by the fifth processing unit. The information processing apparatus according to claim 1.

3. The information processing apparatus according to claim 2, wherein the fourth processing unit determines whether a keyword group included in a first document created at a first date and time is included in a second document created at a second date and time before the first date and time when determining the inclusion relationship of the keyword in the documents.

4. A process of storing in a storage unit a plurality of documents created by a plurality of users, respectively, in association with user identification information and creation date and time information; a process of obtaining a relevance index with respect to the keyword for each of the plurality of documents when receiving a keyword related to a problem from a consulter; a process of obtaining an evaluation value for each user based on the relevance indexes corresponding to the plurality of documents created by the user, and selecting a consulting destination user from among the plurality of users based on the evaluation value; and a process of generating consulting destination information including attribute information related to the selected consulting destination user and notifying the generated consulting destination information to the consulter.

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