Reviewer Search System

Abstract

To provide a reviewer search system capable of searching for a reviewer candidate matching the document tendencies of a review subject document creator.SOLUTION: A document tendency appropriateness calculation part 43 executes, based upon reviewer candidate information D1 at a reviewer search request D0, document tendency appropriateness calculation processing using data groups DB1 to DB4 for search, stored in a database 5. Through the execution of the document tendency appropriateness calculation processing, reviewer candidate appropriateness information D43 is obtained which shows document tendency appropriateness D3 between a plurality of respective reviewer candidates that the reviewer candidate information D1 shows and a review subject document creator. The document tendency appropriateness D3 is a value on which a degree of matching in review detection kind between the reviewer candidates and the review subject document creator is reflected.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present disclosure relates to a reviewer search system and a reviewer search method for obtaining reviewer candidate appropriateness information in response to a reviewer search request.

Background Art

[0002] As a method for ensuring the quality of a created document, a method in which a person other than the creator of the document serves as a reviewer and reviews the document by the reviewer is widely used. Note that "review" means "an activity for confirming the appropriateness, validity, effectiveness, and consistency with related documents of the content of a created document". Also, "reviewer" means "a person who conducts a review".

[0003] When reviewing a document describing specialized matters in a certain field such as a technical book, there is a technical background in which the effect of ensuring the quality of the document content cannot be sufficiently obtained unless the person has specialized knowledge about the specific field being described is not a reviewer.

[0004] Under such a technical background, conventional reviewer search systems generally automatically rank reviewers using the knowledge field as a criterion. As such a reviewer search system, for example, there is an individual search system disclosed in Patent Document 1.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] In the conventional reviewer search system disclosed in Patent Document 1, the workload is aggregated using data on each reviewer candidate's past review work, and the review experience in the same knowledge field as the document under review is used as an indicator value. Based on this indicator value, each reviewer candidate is quantitatively evaluated, and a search is performed to rank individuals who are suitable for reviewing the document under review.

[0007] However, the types of issues detected during a review of a document include, for example, "errors in the document" and "insufficient information." "Errors in the document" include typographical errors and misuse of technical terms, while "insufficient information" includes insufficient explanation of technical matters.

[0008] Thus, review detection types include factors with low relevance to knowledge areas, such as typographical errors. Hereafter, review detection types with low relevance to knowledge areas may be specifically referred to as "document tendency detection types."

[0009] Thus, review detection types vary in their likelihood of occurring depending on the characteristics of the document creator, while document tendency detection types are those that are more likely to occur depending on the characteristics of the document creator.

[0010] On the other hand, conventional reviewer search systems use the reviewer's review experience in their area of ​​expertise as an indicator, which means they cannot search for reviewers that take into account the characteristics of the document creator and the resulting tendency towards document quality.

[0011] This disclosure was made to solve the above-mentioned problems and aims to provide a reviewer search system that can search for reviewer candidates that are suitable for the writing style of the document creator of the document to be reviewed. [Means for solving the problem]

[0012] The reviewer search system relating to this disclosure is a reviewer search system that obtains reviewer candidate suitability information in response to a reviewer search request, and comprises a database that stores a set of search data including a plurality of document data indicating a plurality of documents created in the past, and a plurality of review data indicating that each of the plurality of documents has reviewed one of the plurality of documents, and a reviewer search processing unit that, in response to the reviewer search request, accesses the database and performs a database search process using the set of search data to obtain the reviewer candidate suitability information, wherein each of the plurality of document data includes the document, the author of the document, and knowledge related to the document. The field and the type of review detection detected during the review are specified, and each of the multiple review data sets specifies the document to be reviewed, the reviewer, and the type of review detection detected during the review, and the reviewer search request indicates at least the author of the document to be reviewed, and the reviewer search processing unit includes a document trend suitability calculation unit that, based on the reviewer search request, performs a document trend suitability calculation process using the search data set to obtain the reviewer candidate suitability information indicating the document trend suitability between the reviewer candidate and the author of the document to be reviewed, and the document trend suitability calculation process includes (a) The database search process includes the steps of: (a) obtaining author document trend information indicating the type of review detection detected during a review of documents previously created by the author of the document to be reviewed; (b) obtaining reviewer document trend information indicating the type of review detection detected during a review of documents previously reviewed by the candidate reviewer; and (c) calculating the suitability of the document trend for the candidate reviewer, reflecting the degree of agreement between the review detection types of the reviewer document trend information and the author document trend information, and obtaining the candidate reviewer suitability information indicating the candidate reviewer's suitability for the document trend, wherein the database search process includes the document trend suitability calculation process. [Effects of the Invention]

[0013] In the reviewer search system of this disclosure, the document trend suitability calculation unit performs a document trend suitability calculation process to obtain reviewer candidate suitability information indicating the degree of suitability for the document trend.

[0014] The document tendency suitability score indicated by the reviewer candidate suitability information reflects the degree of agreement between the reviewer's document tendency information and the author's document tendency information in terms of the type of review detected. Therefore, reviewer candidates with a high degree of agreement with the document tendencies of the author of the document being reviewed tend to have a relatively high document tendency suitability score.

[0015] As a result, the reviewer search system of this disclosure can search for reviewer candidates that are suited to the writing style of the document author being reviewed, based on information on the suitability of the reviewer candidates. [Brief explanation of the drawing]

[0016] [Figure 1] This is a schematic diagram illustrating an example configuration of a review support system, including a server which is the reviewer search system of this embodiment. [Figure 2] This is a schematic diagram illustrating the internal configuration of the server that serves as the reviewer search system. [Figure 3] This is a schematic diagram illustrating the data exchange relationships of the main components of a server. [Figure 4] This flowchart shows the processing steps for the database search process, which is primarily executed by the reviewer search processing unit within the server. [Figure 5] Figure 4 is a flowchart detailing the process for calculating knowledge field experience. [Figure 6] Figure 4 is a flowchart detailing the document trend appropriateness calculation process. [Figure 7] Figure 4 is a flowchart detailing the recommendation score calculation process. [Figure 8] This is an explanatory diagram showing an example of how user data sets are stored in a table format. [Figure 9] This is an explanatory diagram showing an example of how review data sets are stored in a tabular format. [Figure 10] It is an explanatory diagram showing an example of storing document data groups in tabular form. [Figure 11] It is an explanatory diagram showing an example of storing comment data groups in tabular form. [Figure 12] It is a block diagram showing the configuration of a processing circuit corresponding to the main components in the server. [Figure 13] It is a block diagram showing another configuration example of the processing circuit corresponding to the main components in the server.

Embodiments for Carrying Out the Invention

[0017] <Embodiment> FIG. 1 is an explanatory diagram schematically showing a configuration example of a review support system including a server 10 which is a reviewer search system in an embodiment of the present disclosure.

[0018] In FIG. 1, the server 10 functions as a reviewer search system and is configured using a PC (Personal Computer) or the like. A plurality of clients 2i (i = 1 to n (> 1)) are connected to the server 10 via a network 11. In FIG. 1, two clients 21 and 22 are illustrated as the clients 2i.

[0019] The network 11 connects the server 10, the client 21, and the client 22 and can communicate using a network protocol such as HTTP (Hypertext Transfer Protocol).

[0020] By receiving an HTTP request via the network 11, the server 10 accesses an internal database 5 to execute a database search process and responds with the reviewer search result obtained internally as an HTTP response. This HTTP response becomes the external output information.

[0021] Client 2i uses browser software such as Internet Explorer®, Microsoft Edge®, Google Chrome®, Mozilla Firefox®, and Safari® to send HTTP requests to Server 10 via Network 11 and receive HTTP responses sent from Server 10 via Network 11.

[0022] As shown in Figure 1, users of the review support system can use client 2i to send an HTTP request containing a reviewer search request D0 to server 10 via a browser, and obtain the reviewer search results from the HTTP response, which is external output information.

[0023] Figure 2 is a schematic diagram illustrating the internal configuration of server 10 shown in Figure 1. Figure 3 is a schematic diagram illustrating the data exchange relationships of the main parts of server 10.

[0024] As shown in Figure 2, the server 10 includes an operating system 1, a communication processing unit 2, a database management system 3, a reviewer search processing unit 4, a database 5, and a network interface 6 as its main components.

[0025] Operating system 1 can send and receive data with network 11 connected to server 10 via network interface 6. Communication processing unit 2 adds network protocol data to the data it receives a transmission instruction for and then instructs operating system 1 to send the data to network 11. Examples of network protocols include TCP / IP and Ethernet®.

[0026] Furthermore, the communication processing unit 2 separates network protocol data from the data received from the operating system 1, using the network protocol data as network metadata, and the remaining data after removing the network protocol metadata as payload data. As a result, payload data can be exchanged between the communication processing unit 2 and the reviewer search processing unit 4.

[0027] Thus, the external input unit, which includes the operating system 1, the communication processing unit 2, and the network interface 6, receives the reviewer search request D0 as payload data from an external source and assigns the reviewer search request D0 to the reviewer search processing unit 4.

[0028] The reviewer search request D0 includes the author of the document under review, the subject area of ​​review, and the maximum number of potential reviewers XS.

[0029] Database 5 stores the search data sets DB1 to DB4, which include user data set DB1, review data set DB2, document data set DB3, and comment data set DB4. Document data set DB3 contains multiple document data, each representing multiple documents created in the past, and review data set DB2 contains multiple review data, each representing the fact that one of multiple documents was reviewed.

[0030] In the search data sets DB1 to DB4, each of the multiple document data within the document data set DB3 contains information that identifies the document, the document's author, the knowledge field associated with the document, and the review detection type.

[0031] Furthermore, each of the multiple review data entries within the review data group DB2 contains information that identifies the document being reviewed and the reviewer. Additionally, the type of review detected by the reviewer can be identified from the comment data group DB4, which shows the details of the comment data in the review data group DB2.

[0032] Thus, in the search data sets DB1 to DB4, each of the multiple document data entries identifies the document, the author of the document, the knowledge field related to the document, and the type of review detection detected during the review. Similarly, each of the multiple review data entries identifies the document being reviewed, the reviewer, and the type of review detection detected during the review.

[0033] Furthermore, the user data database DB1 stores information that identifies the user name and review experience knowledge area, associated with the user ID. Additionally, the comment data database DB4 stores information corresponding to the comment data in the document data database DB3. The comment data provides detailed information about the review detection type. Note that users include the creator of the document being reviewed and potential reviewers.

[0034] The reviewer search processing unit 4 can access the database management system 3 via the database management system 3 to perform database search processing using the search data sets DB1 to DB4 within the database 5, and to perform data processing including adding, deleting, and modifying stored data within the database 5.

[0035] Thus, the database search process performed by the reviewer search processing unit 4 is carried out via the database management system 3, which is a data retrieval mechanism.

[0036] In response to the reviewer search request D0, the reviewer search processing unit 4 executes a database search using the search data sets DB1 to DB4 to obtain reviewer candidate suitability information D43, reviewer candidate recommendation information D44, and final reviewer candidate information D4, etc.

[0037] The reviewer search processing unit 4 mainly includes a reviewer candidate management unit 41, a knowledge field experience calculation unit 42, a document tendency suitability calculation unit 43, a recommendation calculation unit 44, a parameter management unit 45, and an HTTP processing unit 46.

[0038] The reviewer candidate management unit 41 controls the exchange and execution timing of data with the knowledge field experience calculation unit 42, the document tendency suitability calculation unit 43, and the recommendation calculation unit 44, and manages information on users who are candidates for reviewers as working reviewer candidate information.

[0039] Furthermore, the reviewer candidate management unit 41 executes a reviewer candidate search process using the search data sets DB1 to DB4 in the database 5, based on the reviewer search request D0.

[0040] The reviewer candidate management unit 41 obtains reviewer candidate information D1, which indicates multiple reviewer candidates, each of whom has past experience reviewing documents related to the knowledge domain of the document to be reviewed, by performing a reviewer candidate search process.

[0041] Therefore, all multiple reviewer candidates acquired by the Reviewer Candidate Management Department 41 are knowledge experience reviewers who have previously reviewed documents in the knowledge field being reviewed. Note that the authors of the documents being reviewed are not included among the multiple reviewer candidates.

[0042] Theoretically, when the reviewer candidate search process is executed, if no reviewer candidates are found, it is possible that only one reviewer candidate will be found. For the sake of explanation, in the following, we will assume that multiple reviewer candidates, each with their own knowledge and experience, have been found.

[0043] Thus, the reviewer candidate management unit 41 functions as a reviewer candidate search unit that executes the reviewer candidate search process. Since the database search process includes the reviewer candidate search process, the reviewer candidate search process is performed on the database 5 via the database management system 3.

[0044] Furthermore, the reviewer candidate management unit 41 performs additional data management processing within the reviewer search processing unit 4. The data management area C41 shown in Figure 3 virtually represents the data management area managed by the reviewer candidate management unit 41. Working reviewer candidate information is managed on this data management area C41.

[0045] Therefore, the reviewer candidate management unit 41 can provide the necessary data to the knowledge field experience calculation unit 42, the document tendency suitability calculation unit 43, and the recommendation rate calculation unit 44, and manage the data output from the knowledge field experience calculation unit 42, the document tendency suitability calculation unit 43, and the recommendation rate calculation unit 44 in the data management area C41.

[0046] Furthermore, the reviewer candidate management unit 41 also performs execution start control, which issues instructions to start the execution of the knowledge field experience calculation unit 42, the document tendency suitability calculation unit 43, and the recommendation calculation unit 44, respectively.

[0047] The knowledge field experience calculation unit 42 obtains reviewer candidate experience information D42, which shows the reviewer knowledge field experience D2 for each of the multiple reviewer candidates, by performing a knowledge field experience calculation process using the search data sets DB1 to DB4 based on the reviewer candidate information D1.

[0048] The knowledge field experience calculation process is initiated under the control of the reviewer candidate management unit 41. Furthermore, since the database search process includes the knowledge field experience calculation process, the knowledge field experience calculation process is performed on the database 5 via the database management system 3.

[0049] The document tendency suitability calculation unit 43 performs a document tendency suitability calculation process using the search data groups DB1 to DB4 based on the reviewer search request D0 and the reviewer candidate information D1. By performing the document tendency suitability calculation process, reviewer candidate suitability information D43 is obtained, which shows the document tendency suitability D3 between each of the multiple reviewer candidates indicated in the reviewer candidate information D1 and the author of the document under review.

[0050] The document trend suitability calculation process performed by the document trend suitability calculation unit 43 includes the following steps (a) to (c).

[0051] (a) A step to obtain author document trend information D31 indicating the type of review detected during the review of documents previously created by the author of the document to be reviewed, (b) A step to obtain reviewer document trend information D32 that indicates the review detection type detected during the review of documents previously reviewed by each of the multiple reviewer candidates, and (c) For each of the multiple reviewer candidates, calculate a document tendency suitability score D3 that reflects the degree of agreement between the author document tendency information D31 and the reviewer document tendency information D32 in terms of the type of review detected, and obtain reviewer candidate suitability information D43 that shows the document tendency suitability score D3 for each of the multiple reviewer candidates.

[0052] The document trend suitability calculation process performed by the document trend suitability calculation unit 43 is initiated under the control of the reviewer candidate management unit 41. Furthermore, the database search process includes the document trend suitability calculation process, which is performed on the database 5 via the database management system 3.

[0053] The recommendation score calculation unit 44 performs a recommendation score calculation process using the search data sets DB1 to DB4 based on the reviewer candidate information D1, the reviewer candidate experience level information D42, and the reviewer candidate suitability level information D43. By performing the recommendation score calculation process, the recommendation score VR for each of the multiple reviewer candidates indicated in the reviewer candidate information D1 is calculated, and reviewer candidate recommendation information D44 showing the recommendation score VR for each of the multiple reviewer candidates is obtained.

[0054] Each of the multiple reviewer candidates has a recommendation rating VR that has two numerical characteristics: a first numerical characteristic in which the value increases as the document tendency suitability D3, indicated by the reviewer candidate suitability information D43, increases; and a second numerical characteristic in which the value increases as the reviewer knowledge field experience D2, indicated by the reviewer candidate experience information D42, increases.

[0055] The recommendation score calculation process by the recommendation score calculation unit 44 is initiated under the control of the reviewer candidate management unit 41. Furthermore, since the database search process includes the recommendation score calculation process, the recommendation score calculation process is performed on the database 5 via the database management system 3.

[0056] The reviewer candidate management unit 41 creates the final reviewer candidate information D4 by performing data formatting on the reviewer candidate recommendation information D44. The final reviewer candidate information D4 is a list-formatted information that associates recommendation levels with multiple reviewer candidates and arranges them in descending order of recommendation level within the maximum search count XS. In this way, the reviewer candidate management unit 41 also functions as a data formatting unit that performs data formatting.

[0057] As described above, the HTTP processing unit 46 and the parameter management unit 45 retrieve and store the reviewer search request D0 from the HTTP request via the HTTP processing unit 46 from the data received from the communication processing unit 2. In this way, the HTTP processing unit 46 can retrieve the reviewer search request D0 from the HTTP request received from the parameter management unit 45.

[0058] Furthermore, the HTTP processing unit 46 formats the final reviewer candidate information D4 received from the reviewer candidate management unit 41 to match the JSON (JavaScript® Object Notation) data format to create reviewer search results. In addition, it creates HTTP response data that includes the created reviewer search results in the response body and sends the HTTP response via the communication processing unit 2.

[0059] Then, via the network interface 6, the search results data, including the final reviewer candidate information D4 obtained from the reviewer search processing unit 4, is output externally, including to the client 2i, via the network 11.

[0060] In other words, the operating system 1, the communication processing unit 2, and the network interface 6 function as external output units that output search result data, including the final reviewer candidate information D4, to external clients 21 and 22.

[0061] (Processing circuit) Figure 12 is a block diagram showing the configuration of the processing circuit 90 corresponding to the main components within the server 10 shown in Figure 2. The main components include the operating system 1, communication processing unit 2, database management system 3, and reviewer search processing unit 4 within the server 10. In other words, the main components are the parts of the server 10 excluding the database 5 and network interface 6. The functions of the main components are realized by the processing circuit 90 shown in Figure 12. That is, the processing circuit 90 functions as a circuit that includes the main components.

[0062] If the processing circuit 90 is dedicated hardware, the processing circuit 90 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a circuit combining these. Each of the main components' functions 1 to 4 (41 to 46) may be implemented individually by multiple processing circuits or collectively by a single processing circuit.

[0063] Figure 13 is a block diagram showing another example configuration of a processing circuit corresponding to the main components. As shown in the figure, the processing circuit 90 includes a processor 91, a memory 92, and a bus 96 that serves as a data transfer path between the processor 91 and the memory 92. The processor 91 executes a program stored in the memory 92, thereby realizing the functions of each of the main components 1 to 4 (41 to 46). For example, the functions of each of the components 1 to 4 are realized when software or firmware written as a program is executed by the processor 91. In other words, the main components of the server 10 include a memory 92 for storing programs, a processor 91 for executing those programs, and a bus 96 for data transfer between the memory 92 and the processor 91.

[0064] The program instructs the computer to execute the processing procedures or methods for each of the main components, parts 1-4 (41-46).

[0065] For the processor 91, examples include a central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, DSP (Digital Signal Processor), etc. For the memory 92, examples include non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), and EEPROM (Electrically Erasable Programmable Read Only Memory). Alternatively, for the memory 92, any storage medium that will be used in the future, such as magnetic disks, flexible disks, optical disks, compact disks, minidiscs, DVDs, etc., may be used.

[0066] Furthermore, database 5 can be implemented using memory 92 or an external storage device (not shown) connected to bus 96. The search data sets DB1 to DB4 are the data stored in database 5.

[0067] Each of the functions of the main components 1 to 4 described above may be partially implemented by dedicated hardware and partially implemented by software or firmware. In this way, the processing circuit 90 implements the above functions by hardware, software, firmware, or a combination thereof.

[0068] The network interface 6 consists of hardware that is connected via wired or wireless connections.

[0069] (Reviewer search process) Figure 4 is a flowchart showing the processing procedure of the viewer search process mainly executed by the reviewer search processing unit 4 in the server 10, which is the reviewer search system of this embodiment. Figure 5 is a flowchart showing the details of the knowledge field experience level calculation process shown in step S3 of Figure 4, Figure 6 is a flowchart showing the details of the document tendency suitability level calculation process shown in step S4 of Figure 4, and Figure 7 is a flowchart showing the details of the recommendation level calculation process shown in step S5 of Figure 4.

[0070] The following describes the processing procedure for the reviewer search process performed by the server 10, which is the reviewer search system, with reference to Figure 4 and explanations of Figures 5 to 7 as appropriate.

[0071] First, in step S1, server 10 receives reviewer search request D0. That is, server 10 starts the reviewer candidate search process triggered by the receipt of an HTTP request containing reviewer search request D0. This HTTP request becomes the external request information.

[0072] As mentioned above, the HTTP request is received into the reviewer search processing unit 4 via a combination of the external input unit, the operating system 1, the network interface 6, and the communication processing unit 2.

[0073] In this way, the external input unit provides the reviewer search processing unit 4 with an HTTP request, which is external request information including the reviewer search request D0.

[0074] When the reviewer search processing unit 4 starts the reviewer search process, the parameter management unit 45 extracts and stores the reviewer search request D0 from the payload data received from the communication processing unit 2 via the HTTP processing unit 46.

[0075] As a result, the reviewer search processing unit 4 can execute a database search process based on the reviewer search request D0. As mentioned above, the database search process includes a reviewer candidate search process, a knowledge field experience calculation process, a document tendency suitability calculation process, and a recommendation calculation process.

[0076] Furthermore, the parameter management unit 45 holds the network metadata received from the communication processing unit 2 as response destination information.

[0077] Subsequently, in step S2, the reviewer candidate management unit 41 within the reviewer search processing unit 4 executes a reviewer candidate search process using the search data groups DB1 to DB4 based on the reviewer search request D0. The execution of the reviewer candidate search process yields reviewer candidate information D1, which indicates multiple reviewer candidates.

[0078] Each of the multiple reviewer candidates is a knowledge experience reviewer with experience reviewing documents related to the knowledge area being reviewed, as indicated by reviewer search request D0. Note that the author of the document being reviewed is not included among the multiple reviewer candidates.

[0079] The reviewer candidate search process performed in step S2 is carried out by the reviewer candidate management unit 41 to the database 5 via the database management system 3. The combination of the reviewer search request D0 and the reviewer candidate information D1 is managed as working reviewer candidate information in the data management area C41.

[0080] Subsequently, in step S3, the knowledge field experience calculation unit 42 obtains reviewer candidate experience information D42 by performing a knowledge field experience calculation process using the search data group DB1 to DB4 based on the reviewer candidate information D1.

[0081] The knowledge field experience calculation process in step S3 is initiated by a knowledge field experience calculation instruction from the reviewer candidate management unit 41. The knowledge field experience calculation process performed in step S3 is performed by the knowledge field experience calculation unit 42 to the database 5 via the database management system 3.

[0082] The following explanation of the knowledge field experience calculation process performed by the knowledge field experience calculation unit 42 will be given with reference to Figure 5.

[0083] First, in step S31, the reviewer candidates to be searched are set. The reviewer candidate set in step S31 is one of the multiple reviewer candidates shown in the reviewer candidate information D1.

[0084] Subsequently, in step S32, all past review data of the reviewer candidates set in step S31 is searched using the knowledge field to be reviewed, as indicated in the reviewer search request D0, as the search key.

[0085] Next, in step S33, the number of documents related to the knowledge field being reviewed among the documents reviewed in the past is calculated as the number of knowledge-matching documents NY from the review data retrieved in step S32. Since the review data contains information that identifies the documents being reviewed and the reviewers, and the document data contains information that identifies the knowledge field in the documents, the knowledge field experience calculation unit 42 can calculate the number of knowledge-matching documents NY by executing the knowledge field experience calculation process.

[0086] Figures 8 to 11 are explanatory diagrams in tabular format showing specific examples of the search data sets DB1 to DB4 stored in database 5. Figure 8 shows an example of the storage of user data set DB1, Figure 9 shows an example of the storage of review data set DB2, Figure 10 shows an example of the storage of document data set DB3, and Figure 11 shows an example of the storage of comment data set DB4.

[0087] As shown in Figure 8, the user data database DB1 contains user ID, username, and review experience knowledge area information. Thus, the user data database DB1 contains username and review experience knowledge area information in a format corresponding to the user ID that identifies the user. In Figure 8, the review experience knowledge area information is shown as "TCP / IP", "HTTP", "Broadcasting Standards", "Electronic Circuits", "SD Standards", "Financial Law", and "Software".

[0088] As shown in Figure 9, the review data database DB2 contains review ID, review name, review date and time, review time, review target document ID, participating user ID, and comment data. Thus, the review data database DB2 contains review name, review date and time, review time, review target document ID, participating user ID, and comment data in a format corresponding to the review ID, which indicates that a review was performed.

[0089] The participating user IDs indicate the IDs of the users who participated in the review, and all participating users except the document creator are designated as reviewers. The comment data is linked to the minutes of the meeting related to the review discussions.

[0090] As shown in Figure 10, the document data database DB3 contains document ID, document name, creation date, creator user ID, knowledge field information, and document trend information. Thus, the document data database DB3 contains document name, creation date, creator user ID, knowledge field information, and document trend information in a format corresponding to the document ID that identifies the document.

[0091] The document trend information shows the review detection types detected during review for documents identified by their document ID. In the example shown in Figure 10, for document 4 with document ID value DC4, the review detection type detected during review is "Class Mistake (1)". The number in parentheses indicates the number of detections.

[0092] As shown in Figure 11, the comment data database DB4 contains the comment user ID, the location of the issue, the content of the issue, the responding user ID, the response content, and the detection type. Note that the comment data database DB4 represents the data corresponding to "Meeting Minutes 4" in the comment data of the review data database DB2. The "Comment User ID" indicates the ID of the reviewer who made the comment corresponding to the meeting minutes.

[0093] As shown in Figure 11, the "Points of Issue" section shows the "Document ID, Page Number, and Diagram of the Issue," and the "Content of the Issue" section in the first row of Figure 11 specifically shows the content of the review detection type, such as "There is no interface to set value B in class A." In the examples shown in the first and second rows of Figure 11, document 4 with document ID value DC4 is the target of the issue.

[0094] As shown in Figure 11, the "Responding User ID" indicates the user ID of the user who responded to the "Points of Concern," and typically the responder is the document creator of the document in question. In the example shown in Figure 11, User A, whose user ID is "U1" and who created Document 4, is shown as the responding user.

[0095] The "Response Content" shows the specific response from the respondent with the corresponding user ID to the point raised. In the first line of Figure 11, the "Response Content" states that "the point raised is correct and will be corrected," and the "Review Detection Type" is indicated as "Class Incorrect." On the other hand, in the second line of Figure 11, the "Response Content" states that "Class X is correct," so the "Point Raised" is not affirmed, and the "Review Detection Type" is not indicated.

[0096] Thus, the comment data set DB4 contains the location of the issue, the content of the issue, the responding user ID, the content of the response, and the detection type in a format corresponding to the commenting user ID.

[0097] As described above, the document data set DB3 contains information for each of the multiple document data that identifies the document, the author of the document, the knowledge field related to the document, and the type of review detection detected during the review.

[0098] Furthermore, the review data database DB2 contains the documents under review and the reviewers for each of the multiple review data sets. Additionally, the review detection type detected by the reviewer during the review can be identified from both the review data database DB2 and the comment data database DB4. Therefore, in the search data databases DB1-DB4, the documents under review, the reviewers, and the review detection type detected during the review are identified for each of the multiple review data sets.

[0099] The following describes an example of the processing content in step S33 of Figure 5, referring to Figures 8 to 11. For example, if the reviewer candidate is user E with user ID value U5, the review data group DB2 shows that user E participated in review 4 of document 4, which has document ID value DC4 and is indicated by review ID value R4. Although user E also participated in review 3 of document 3 with review ID value R3, document 3 is excluded because it is a document with document ID value DC3 created by user E himself.

[0100] The document data set DB3 indicates that the knowledge domain of document 4, indicated by document ID value DC4, is {"TCP / IP", "HTTP", and "Software"}. Therefore, if the knowledge domain to be reviewed is any of "TCP / IP", "HTTP", or "Software", then document 4 becomes a knowledge-matching document that matches the knowledge domain to be reviewed. The number of knowledge-matching documents NY is then calculated by the number of documents previously reviewed by the reviewer candidate that match the knowledge domain to be reviewed.

[0101] Returning to Figure 5, in step S34, the number of knowledge-matching documents NY is calculated as the reviewer's knowledge domain experience level D2. Therefore, the larger the number of knowledge-matching documents NY, the larger the value of the reviewer's knowledge domain experience level D2. Alternatively, the value obtained by multiplying the number of knowledge-matching documents NY by a weighting coefficient β other than "1" may also be used as the reviewer's knowledge domain experience level D2.

[0102] Subsequently, in step S35, it is confirmed whether (YES) or (NO) all of the multiple reviewer candidates indicated in reviewer candidate information D1 have been included in the search, and the processes in steps S31 to S34 are repeatedly executed until step S35 is YES. In other words, the processes in steps S31 to S34 are executed until the reviewer knowledge field experience level D2 is calculated for all of the multiple reviewer candidates indicated in reviewer candidate information D1.

[0103] If the answer in step S35 is YES, the knowledge field experience calculation process is completed, and reviewer candidate experience information D42 is obtained, which shows the reviewer knowledge field experience D2 for each of the multiple reviewer candidates indicated by reviewer candidate information D1.

[0104] Reviewer candidate experience level information D42 is managed on the data management area C41 by the reviewer candidate management unit 41. The combination of reviewer search request D0, reviewer candidate information D1, and reviewer candidate experience level information D42 on the data management area C41 becomes the working reviewer candidate information after the execution of step S3.

[0105] Returning to Figure 4, in step S4, the document trend suitability calculation unit 43 performs a document trend suitability calculation process using the search data groups DB1 to DB4 based on the reviewer search request D0 and the reviewer candidate information D1. By performing the document trend suitability calculation process, reviewer candidate suitability information D43 is obtained, which shows the document trend suitability D3 for each of the multiple reviewer candidates indicated in the reviewer candidate information D1.

[0106] The document trend suitability calculation process in step S4 is triggered by a document trend suitability calculation instruction from the reviewer candidate management unit 41. The document trend suitability calculation process performed in step S4 is performed by the document trend suitability calculation unit 43 on the database 5 via the database management system 3.

[0107] As mentioned above, Figure 6 is a flowchart detailing the document trend suitability calculation process in step S4 shown in Figure 4. The processing details of the document trend suitability calculation process will be explained below with reference to the same figure.

[0108] First, in step S41, the creator's past document data, which consists of document data previously created by the creator of the document under review, is searched.

[0109] Next, in step S42, creator document trend information D31 is obtained based on the creator's past document data retrieved in step S41.

[0110] The following describes an example of the processing content in step S42 with reference to Figures 8 to 11. For example, if the creator of the document to be reviewed is user A with user ID value U1, then the search data sets DB1 to DB4 can be used to recognize the review detection type detected during the review, which indicates that "there was one class error" for document 4 with document ID value DC4 created by user A. This review detection type represents the document tendencies of user A, the creator of the document to be reviewed.

[0111] Thus, steps S41 and S42 are steps to obtain author document trend information D31, which indicates the type of review detection that has been previously reviewed in documents created by the author of the document to be reviewed.

[0112] Subsequently, in step S43, the reviewer candidates to be searched are set. The reviewer candidate set in step S43 is one of the multiple reviewer candidates indicated in the reviewer candidate information D1.

[0113] Then, in step S44, the system searches for past review data, which consists of review data previously reviewed by the reviewer candidates set in step S43.

[0114] Next, in step S45, reviewer document trend information D32 is obtained based on the past review data retrieved in step S44.

[0115] The following describes an example of the processing content in step S45 with reference to Figures 8 to 11. For example, if the reviewer candidate is user B with user ID value U2, by referring to the document data group DB3 and the comment data group DB4, it is possible to recognize the review detection type detected during the review, such as "One class error detected," from the content of the first line of the comment data group DB4 regarding document 4 with document ID value DC4, which was reviewed by user B. This review detection type represents the document tendency of user B, the reviewer candidate.

[0116] Thus, steps S44 and S45 are steps to obtain reviewer document trend information D32, which indicates the type of review detected in documents previously reviewed by the reviewer candidate.

[0117] Next, in step S46, the document tendency suitability score D3 of the reviewer candidate is calculated. The document tendency suitability score D3 is a value that reflects the degree of agreement between the reviewer document tendency information D32 obtained in step S42 and the author document tendency information D31 obtained in step S45 in terms of the type of review detected.

[0118] (Execution details of steps S41-S46 under virtual conditions) The following explains how steps S41 to S46 are executed under the hypothetical conditions shown below.

[0119] The review detection types found in document data I and document data J, which were previously created by the PA (Document Author) of the documents under review, are as follows: (K1) and (K2).

[0120] (K1) Document data I review detection types: Sequence error (number detected = 1), Missing sequence (number detected = 1), Inter-document inconsistency (number detected = 3) (K2) Document data J review detection types: Sequence error (number of detections = 1), Missing class (number of detections = 1), Inter-document inconsistency (number of detections = 1)

[0121] On the other hand, the contents of the reviewer document trend information D32 obtained by executing step S45 are the following hypothetical results (Kb).

[0122] (Kb) Review detection types for the reviewer candidate PB shown in reviewer document trend information D32: class mismatch (number of detections = 5), missing class (number of detections = 7), inter-document inconsistency (number of detections = 4)

[0123] In addition, "sequence" refers to a sequence diagram that describes the system's overview, specifications, and processing flow; "missing sequence" indicates that there was a gap in the flow shown in the sequence diagram; and "error in sequence" indicates that there was an error in part of the flow shown in the sequence diagram.

[0124] Furthermore, "class" refers to a class diagram that represents the elements and relationships that make up a system, "missing class" indicates that there was a omission in the elements shown in the class diagram, and "incorrect class" indicates that there was an error in some of the elements shown in the class diagram.

[0125] "Inter-document inconsistencies" indicate inconsistencies in terminology, etc., between different documents. Other review detection types include, for example, "inter-diagram inconsistencies," which indicate inconsistencies between multiple diagrams.

[0126] Thus, the review detection types include the aforementioned "missing sequences," "incorrect sequences," "missing classes," "incorrect classes," "inter-document inconsistencies," and "inter-diagram inconsistencies." These review detection types are classified as document trend detection types with low relevance to knowledge domains.

[0127] When step S42 is executed under the above hypothetical conditions (K1) and (K2), the author document tendency information D31 will be the hypothetical result shown in (Ka) below.

[0128] (Ka) Document tendencies of the PA, the author of the reviewed document, as shown in the author document tendency information D31: {Sequence errors (number detected = 2), missing sequences (number detected = 1), inter-document inconsistencies (number detected = 4), missing classes (number detected = 1)}

[0129] Based on the above hypothetical results (Ka) and (Kb), when step S46 is executed, the document tendency suitability score D3 will be as shown in (KX) below.

[0130] (KX) Document Tendency Suitability D3:23 = {7 × 1 (missing class) + 4 × 4 (inconsistencies between documents)}

[0131] In this way, the document tendency suitability score D3 can be obtained by summing the number of detections where the review detection type matches between the author document tendency information D31 and the reviewer document tendency information D32.

[0132] Regarding the "missing class" shown in the virtual result (KX), the number of detections for the document author PA under review is "1" and the number of detections for the candidate reviewer PB is "7", so adding these two together gives "7". Regarding the "inter-document inconsistencies" shown in the virtual result (KX), the number of detections for the document author PA under review is "4" and the number of detections for the candidate reviewer PB is "4", so adding these two together gives "16". As a result, the final sum of "7" and "16" is "23", which is the document tendency suitability score D3.

[0133] Subsequently, in step S47, it is confirmed whether (YES) or (NO) all of the multiple reviewer candidates indicated in reviewer candidate information D1 were included in the search, and the processes in steps S43 to S46 are repeatedly executed until step S45 is YES. In other words, the processes in steps S43 to S46 are executed until the document tendency suitability score D3 for all of the multiple reviewer candidates indicated in reviewer candidate information D1 is calculated.

[0134] If the answer in step S47 is YES, then we can obtain reviewer candidate suitability information D43, which shows the document tendency suitability D3 for each of the multiple reviewer candidates indicated by reviewer candidate information D1.

[0135] Reviewer candidate suitability information D43 is managed by the reviewer candidate management unit 41 on the data management area C41. The combination of reviewer search request D0, reviewer candidate information D1, reviewer candidate experience level information D42, and reviewer candidate suitability information D43 becomes the working reviewer candidate information managed on the data management area C41.

[0136] Returning to Figure 4, in step S5, the recommendation calculation unit 44 performs a recommendation calculation process using the search data sets DB1 to DB4 based on the reviewer candidate information D1, reviewer candidate experience information D42, and reviewer candidate suitability information D43. By performing the recommendation calculation process, reviewer candidate recommendation information D44 is obtained, which shows the recommendation VR for each of the multiple reviewer candidates.

[0137] The recommendation score calculation process in step S5 is triggered by a recommendation score calculation instruction from the reviewer candidate management unit 41. The recommendation score calculation process performed in step S5 is performed by the recommendation score calculation unit 44 to the database 5 via the database management system 3.

[0138] As mentioned above, Figure 7 is a flowchart detailing the recommendation score calculation process in step S5 shown in Figure 4. The following explanation of the recommendation score calculation process will refer to this figure.

[0139] First, in step S51, normalization is performed on the reviewer knowledge field experience level D2 for all reviewer candidates. All reviewer candidates refer to all of the multiple reviewer candidates indicated in the reviewer candidate information D1.

[0140] Note that "normalization" refers to "a scaling method that sets the minimum value to "0" and the maximum value to "1". Here, the reviewer's knowledge domain experience level D2 after normalization will be referred to as "normalized reviewer's knowledge domain experience level RD2".

[0141] Next, in step S52, the document tendency suitability score D3 is normalized for all reviewer candidates. Here, the document tendency suitability score D3 after normalization is referred to as "normalized document tendency suitability score RD3".

[0142] Subsequently, in step S53, the normalized document tendency suitability score RD3 for all reviewer candidates is multiplied by a weight coefficient α other than "1" to obtain the weighted document tendency suitability score WD3. The weight coefficient α is usually set to a value greater than "1".

[0143] After steps S51 to S53 are executed, the normalized reviewer knowledge domain experience score RD2 and the weighted document tendency aptitude score WD3 are obtained for all reviewer candidates.

[0144] Subsequently, in step S54, the reviewer candidates to be included in the recommendation score calculation are set. The reviewer candidate set in step S54 is one of the multiple reviewer candidates indicated in the reviewer candidate information D1.

[0145] Next, in step S55, the recommendation score VR is calculated for the reviewer candidates set in step S54. The recommendation score VR is obtained as the sum of the normalized reviewer knowledge field experience score RD2 and the weighted document tendency suitability score WD3. That is, {VR = RD2 + RD3}.

[0146] The recommendation score VR reflects the weighted document tendency suitability score WD3, and has a first numerical characteristic in that the higher the document tendency suitability score D3 indicated by the reviewer candidate suitability information D43, the larger the value. In addition, the recommendation score VR reflects the normalized reviewer knowledge area experience score RD2, and has a second numerical characteristic in that the higher the reviewer knowledge area experience score D2, the larger the value.

[0147] Subsequently, in step S56, it is confirmed whether (YES) or (NO) all of the multiple reviewer candidates indicated in reviewer candidate information D1 were included in the search, and the processes in steps S54 and S55 are repeatedly executed until step S56 is YES. In other words, the processes in steps S54 and S55 are executed until the recommendation score VR for all of the multiple reviewer candidates indicated in reviewer candidate information D1 is calculated.

[0148] If the answer in step S56 is YES, the recommendation score calculation process is completed, and reviewer candidate recommendation information D44 is obtained, which shows the recommendation score VR for each of the multiple reviewer candidates indicated by reviewer candidate information D1. Reviewer candidate recommendation information D44 is managed by the reviewer candidate management unit 41.

[0149] After the execution of step S5, which includes steps S51 to S55, the combination of reviewer candidate information D1 and reviewer candidate recommendation information D44 becomes the working reviewer candidate information managed on the data management area C41.

[0150] Returning to Figure 4, after step S5 is executed, in step S6, the reviewer candidate management unit 41 creates the final reviewer candidate information D4 by performing data formatting processing on the reviewer candidate recommendation information D44. The final reviewer candidate information D4 is a list-format information in which multiple reviewer candidates are assigned recommendation levels and arranged in descending order of recommendation level.

[0151] When creating the final reviewer candidate information D4, a filtering process is also performed to narrow down the results to the maximum search count XS specified by the reviewer search request D0. In other words, the final reviewer candidate information D4 will be a list of reviewer candidates arranged in descending order of recommendation score VR within the maximum search count XS. If the maximum search count XS is greater than or equal to the number of multiple reviewer candidates included in the reviewer candidate information D1, the filtering process described above will not be performed, and all of the multiple reviewer candidates indicated in the reviewer candidate information D1 will be arranged in descending order of recommendation score VR.

[0152] Thus, the reviewer candidate management unit 41, which performs the data formatting process in step S6, also functions as a data formatting unit.

[0153] After step S6 is executed, in step S7, the reviewer candidate management unit 41 transmits the final reviewer candidate information D4 to an external source.

[0154] The reviewer candidate management unit 41 retrieves the response destination information from the parameter management unit 45 and instructs the HTTP processing unit 46 to send the final reviewer candidate information D4 according to the response destination information. Upon receiving the transmission instruction, the HTTP processing unit 46 formats the final reviewer candidate information D4 to match the JSON data format and creates the reviewer search results.

[0155] Next, the HTTP processing unit 46 creates HTTP response data that includes the created reviewer search results in the response body, and sends the HTTP response to the network interface 6 via the communication processing unit 2, specifying the destination information as the destination.

[0156] As a result, an HTTP response containing final reviewer candidate information D4 can be sent from network interface 6 to an external source, including client 2i, via network 11. This HTTP response becomes the external output information.

[0157] Thus, the external output unit, which includes the operating system 1, the communication processing unit 2, and the network interface 6, outputs the reviewer search results, including the final reviewer candidate information D4, to the server 10.

[0158] (effect) In the server 10, which is the reviewer search system of this embodiment, the document tendency suitability calculation unit 43 included in the reviewer search processing unit 4 performs a document tendency suitability calculation process to obtain reviewer candidate suitability information D43 indicating the document tendency suitability D3.

[0159] Since the document tendency suitability score D3 reflects the degree of agreement between the reviewer's document tendency information D32 and the author's document tendency information D31 in terms of the type of review detected, reviewer candidates who have a high degree of match with the document tendencies of the author of the document being reviewed will have a relatively high document tendency suitability score D3.

[0160] As a result, the server 10 of this embodiment can obtain reviewer candidate suitability information D43, which has a high document tendency suitability score D3 for reviewer candidates that match the document tendencies of the document author of the document to be reviewed. The document tendencies of the document author of the document to be reviewed are the tendencies of the review detection types detected in documents previously created by the document author of the document to be reviewed.

[0161] Furthermore, the recommendation score calculation unit 44 included in the reviewer search processing unit 4 performs a recommendation score calculation process to obtain reviewer candidate recommendation information D44.

[0162] In the reviewer candidate recommendation information D44, the recommendation level VR for each of the multiple reviewer candidates indicated in the reviewer candidate information D1 has a first numerical characteristic in which the value increases as the document tendency suitability D3 indicated in the reviewer candidate suitability information D43 increases. Therefore, the recommendation level calculation unit 44 can increase the recommendation level VR value of reviewer candidates that match the document tendencies of the author of the document to be reviewed.

[0163] As a result, the server 10 of this embodiment can obtain reviewer candidate recommendation information D44, which has a high recommendation rating VR for reviewer candidates that are well-suited to the document tendencies of the document creator of the document to be reviewed.

[0164] The reviewer candidate information obtained through the reviewer candidate search process of the reviewer candidate management unit 41 indicates multiple reviewer candidates, each of whom is a knowledgeable and experienced reviewer.

[0165] Therefore, the server 10, which is the reviewer search system in this embodiment, can pre-select reviewer candidates to be knowledgeable and experienced reviewers.

[0166] Therefore, the recommendation calculation unit 44 of the server 10 in this embodiment can obtain reviewer candidate recommendation information D44 in which the recommendation value VR of reviewer candidates is high, and the reviewer candidates have past experience reviewing documents in the knowledge field to be reviewed and are suited to the document tendencies of the document creator to be reviewed.

[0167] Furthermore, regarding the reviewer candidate recommendation information D44, in addition to the first numerical characteristic, the recommendation score VR of each of the multiple reviewer candidates has a second numerical characteristic in which the higher the reviewer's knowledge field experience level D2, the larger the value.

[0168] Since the recommendation score VR of each of the multiple reviewer candidates has the first and second numerical characteristics described above, the server 10, which is the reviewer search system of this embodiment, can assign a large recommendation score VR to reviewer candidates who have a high document tendency suitability score D3 and a high reviewer knowledge field experience score D2.

[0169] As a result, the server 10 of this embodiment can obtain reviewer candidate recommendation information D44 that shows a recommendation score VR of a value that matches the combination of document tendency suitability score D3 and reviewer knowledge field experience score D2. Furthermore, by increasing the weight coefficient α, the ratio of the first numerical characteristic between the first and second numerical characteristics can be increased.

[0170] The server 10 of this embodiment further includes an external input unit whose main components are an operating system 1, a communication processing unit 2, and a network interface 6, so that in response to an HTTP request, which is external request information including a reviewer search request D0 received from the outside, the reviewer search processing unit 4 can execute a reviewer candidate search process.

[0171] Furthermore, the server 10, which is the reviewer search system of this embodiment, obtains final reviewer candidate information D4 in list format, which is arranged in order of recommendation level within the maximum number of searches XS, by the reviewer candidate management unit 41, which functions as a data formatting unit.

[0172] The reviewer candidate recommendation information D44 is formatted into final reviewer candidate information D4, and then sent outside the server 10 as an HTTP response containing the final reviewer candidate information D4 by the external output unit. Here, the external output unit includes the operating system 1, the communication processing unit 2, and the network interface 6.

[0173] Therefore, a user who sends an HTTP request containing a reviewer search request D0 to the server 10 from an external source can easily identify suitable reviewer candidates that are narrowed down to the maximum number of searches XS and are appropriate for the reviewer search request D0 by referring to the final reviewer candidate information D4 included in the HTTP response from the client 2i.

[0174] In the server 10 of this embodiment, the database search process performed by the reviewer search processing unit 4 is carried out via the database management system 3, which is a dedicated data search mechanism, thus enabling a reduction in search time.

[0175] (How to search for reviewers) The following reviewer search method can be executed using the server 10 of this embodiment. The reviewer search method is a method of obtaining reviewer candidate suitability information D43 by accessing the database 5 in response to a reviewer search request D0 and performing a database search process using the search data groups DB1 to DB4.

[0176] The reviewer search method of this embodiment includes the following steps (a).

[0177] (a) Based on the reviewer search request D0, a database search process is performed on database 5 to obtain reviewer candidate suitability information D43 that shows the suitability D3 for the document tendencies of the reviewer candidate and the author of the document to be reviewed.

[0178] Note that step (a) above corresponds to the processing of step S4 shown in Figure 4. In addition, the reviewer candidates include the knowledge and experience reviewers shown in the reviewer candidate information D1.

[0179] Furthermore, step (a) above includes the following steps (a-1) to (a-3).

[0180] (a-1) A step to obtain author document trend information D31 that indicates the type of review detected during the review of documents previously created by the author of the document to be reviewed, (a-2) A step to obtain reviewer document trend information D32 that indicates the review detection type detected during the review of documents previously reviewed by the reviewer candidate, and (a-3) For the reviewer candidates, calculate the document tendency suitability score D3, which reflects the degree of agreement between the reviewer document tendency information D32 and the author document tendency information D31 in terms of the review detection type, and obtain reviewer candidate suitability information D43 that shows the document tendency suitability score D3 of the reviewer candidates.

[0181] Step (a-1) corresponds to the processing in steps S41 and S42 shown in Figure 6, step (a-2) corresponds to the processing in steps S44 and S45 shown in Figure 6, and step (a-3) corresponds to the processing in step S46 shown in Figure 6.

[0182] The reviewer search method of this embodiment performs step (a) to obtain reviewer candidate suitability information D43 indicating document tendency suitability D3.

[0183] Since the document tendency suitability score D3 reflects the degree of agreement between the reviewer's document tendency information D32 and the author's document tendency information D31 in terms of the type of review detected, reviewer candidates with a high degree of agreement with the document tendencies of the author of the document being reviewed will have a relatively high document tendency suitability score D3.

[0184] As a result, the reviewer search method of this embodiment can search for reviewer candidates that are suitable for the writing style of the document creator of the document to be reviewed, based on the reviewer candidate suitability information D43.

[0185] <Other> The review support system shown in Figure 1, which includes the server 10 of this embodiment, uses HTTP for communication between the server 10 and the client 2i, but it may be configured using other communication protocols besides HTTP as needed.

[0186] Furthermore, the number of servers 10 and clients 2i connected to network 11 shown in Figure 1 is merely an example, and the number of servers 10 and clients 2i connected to network 11 may be increased or decreased. In addition, devices other than servers 10 and clients 2i may also be connected.

[0187] In this embodiment, the database management system 3 and database 5 are built into the server 10. However, a modified configuration is also possible in which the database management system 3 and database 5 are installed on a server separate from server 10 and connected to the network 11. In the modified configuration, database search processing can be performed from server 10 to database 5 via the network 11, and data processing, including adding, deleting, and modifying stored data in database 5, can be executed.

[0188] Furthermore, within the scope of this disclosure, the embodiments may be modified or omitted as appropriate. [Explanation of Symbols]

[0189] 1 Operating system, 2 Communication processing unit, 3 Database management system, 4 Reviewer search processing unit, 5 Database, 6 Network interface, 10 Server, 11 Network, 21, 22, 2i Client, 41 Reviewer candidate management unit, 42 Knowledge field experience calculation unit, 43 Document tendency suitability calculation unit, 44 Recommendation score calculation unit, 45 Parameter management unit, 46 HTTP processing unit.

Claims

1. A reviewer search system that obtains information on the suitability of candidate reviewers in response to a reviewer search request, A database that stores a set of searchable data including multiple document data showing multiple documents created in the past, and multiple review data showing that each of them reviewed one of the multiple documents, The system includes a reviewer search processing unit that, in response to the reviewer search request, accesses the database and performs a database search process using the search data set to obtain the reviewer candidate suitability information. In the aforementioned search data set, each of the multiple document data sets is identified as a document, the author of the document, the knowledge field related to the document, and the type of review detection detected during the review, and each of the multiple review data sets is identified as the document to be reviewed, the reviewer, and the type of review detection detected during the review. The aforementioned reviewer search request must at least indicate the author of the document under review, The reviewer search processing unit is: The system includes a document trend suitability calculation unit that, based on the reviewer search request, performs a document trend suitability calculation process using the search data set to obtain reviewer candidate suitability information indicating the degree of suitability between the reviewer candidate and the author of the document to be reviewed. The aforementioned document trend appropriateness calculation process is: (a) A step of obtaining author document trend information indicating the type of review detected during the review of documents previously created by the author of the document to be reviewed, (b) A step of obtaining reviewer document trend information that indicates the review detection type detected during the review of documents previously reviewed by the reviewer candidate, (c) The steps include: calculating a document trend suitability score for the reviewer candidate that reflects the degree of agreement between the reviewer document trend information and the author document trend information in terms of the review detection type; and obtaining reviewer candidate suitability information that indicates the document trend suitability score of the reviewer candidate. The database search process includes the document trend suitability calculation process. Reviewer search system.

2. A reviewer search system according to claim 1, The reviewer search request further indicates the subject of review, which is the subject of knowledge related to the document under review. The reviewer search processing unit is: The system further includes a reviewer candidate search unit that, based on the reviewer search request, performs a reviewer candidate search process using the search data set to obtain reviewer candidate information indicating knowledge experience reviewers who have previously reviewed documents related to the knowledge field to be reviewed. The aforementioned reviewer candidates include the aforementioned knowledge and experience reviewers, The document trend suitability calculation unit, in addition to the reviewer search request, executes the document trend suitability calculation process using the search data set based on the reviewer candidate information. The database search process includes the reviewer candidate search process. Reviewer search system.

3. A reviewer search system according to claim 2, The reviewer search processing unit is: The system further includes a knowledge field experience calculation unit that obtains reviewer candidate experience information indicating the reviewer knowledge field experience of the reviewer candidate by performing a knowledge field experience calculation process using the search data set based on the reviewer candidate information, wherein the reviewer knowledge field experience of the reviewer candidate becomes larger the number of knowledge matching documents, which is the number of documents related to the knowledge field that matches the knowledge field to be reviewed among the documents reviewed in the past. The reviewer search processing unit is: The system further includes a recommendation calculation unit that calculates the recommendation level of a reviewer candidate by performing a recommendation level calculation process using the search data set based on the reviewer candidate information, the reviewer candidate experience level information, and the reviewer candidate suitability information, and obtains reviewer candidate recommendation information indicating the recommendation level of the reviewer candidate. The recommendation score of the reviewer candidate has a first numerical characteristic which increases as the suitability for the document tendency indicated by the reviewer candidate suitability information increases, and a second numerical characteristic which increases as the experience level in the reviewer's knowledge field indicated by the reviewer candidate experience information increases. The database search process includes the knowledge field experience calculation process and the recommendation calculation process. Reviewer search system.

4. A reviewer search system according to claim 3, The aforementioned reviewer search request further indicates the maximum number of searches, Q: The list of reviewer candidates includes multiple reviewer candidates. For each of the aforementioned multiple reviewer candidates, the document tendency suitability calculation process by the document tendency suitability calculation unit, the knowledge field experience calculation process by the knowledge field experience calculation unit, and the recommendation calculation process by the recommendation calculation unit are performed. The reviewer search processing unit is: The system includes a data formatting unit that performs data formatting on the reviewer candidate recommendation information to associate the recommendation level with each of the multiple reviewer candidates, thereby obtaining final reviewer candidate information in list format, where the multiple reviewer candidates are arranged in descending order of recommendation level within the maximum number of searches. The aforementioned reviewer search system is: An external input unit receives external request information, including the reviewer search request, from an external source and provides the external request information to the reviewer search processing unit. The system further includes an external output unit that outputs external output information, including the aforementioned final reviewer candidate information, to the outside. Reviewer search system.

5. A reviewer search system according to any one of claims 1 to 4, The database search process performed by the reviewer search processing unit is carried out via a data search mechanism. Reviewer search system.

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