Work history information collection device and method for collecting work history information

Abstract

A work history information collection device (400) comprises an acquisition unit (4011) that acquires format information containing required details for a work history document, a collection unit (4012) that collects work history information related to the work history of a user, and a storage unit that stores a natural language processing algorithm (430). The collection unit (4012) uses the natural language processing algorithm stored in the storage unit (403) to have conversation with the user and collect work history information corresponding to the required details.

Description

Work history information collection device and work history information collection method

[0001] The present disclosure relates to a work history information collection device and a method for collecting work history information.

[0002] Resumes play a very important role as one of the criteria that employers use to determine whether a job seeker meets their hiring requirements. For this reason, it is important that a resume contains all the information that is required of a job seeker.

[0003] Patent Document 1 (JP 2016-212533 ​​A) describes a document analysis device that acquires criteria data regarding the presence or absence of required information corresponding to the type of document to be analyzed, such as a resume, and uses the analyzed data and the criteria data to determine whether required data related to the required information is present in the document to be analyzed.

[0004] JP 2016-212533 ​​A

[0005] In the document analysis device described in Patent Document 1, when there is duplicate information regarding required entries, it is not possible to check for the existence of the duplicate information.

[0006] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to collect work history information so that all necessary information is included without omission and without duplication.

[0007] A work history information collection device relating to a first aspect of the present disclosure includes an acquisition unit that acquires format information containing necessary information to be included in a work history, a collection unit that collects work history information related to a user's work history, and a memory unit that stores a natural language processing algorithm, and the collection unit collects work history information corresponding to the necessary information by interacting with the user using the natural language processing algorithm stored in the memory unit.

[0008] A method according to a second aspect of the present disclosure is a method for collecting work history information, the method including causing a computer to perform the steps of obtaining format information including required information for a resume and collecting work history information related to a user's work history, wherein the collecting step includes collecting the work history information corresponding to the required information by interacting with the user using a natural language processing algorithm.

[0009] According to the present disclosure, work history information can be collected so that all required information is included without omission and without duplication.

[0010] 1 is a block diagram showing an overview of a matching system. FIG. 1 is a block diagram showing the configurations of a sharing server, a recruiter device, and an applicant device. FIG. 2 is a block diagram showing the configuration of a generation server. FIG. 3 is a diagram showing an example of a company database. FIG. 4 is a diagram showing an example of a membership database. FIG. 5 is a diagram showing an example of a community database. FIG. 6 is a diagram showing an example of a job posting database. FIG. 7 is a diagram showing an example of a user database. FIG. 8 is a diagram showing an example of a dialogue between a generation server and a user. FIG. 9 is a diagram showing an example of a dialogue between a generation server and a user. FIG. 10 is a diagram showing an example of a dialogue between a generation server and a user (with dictionary registration). FIG. 11 is a diagram for explaining the function of the generation server in terms of a divergence phase and a convergence phase. FIG. 12 is a diagram showing the functional configuration of the generation server. FIG. 13 is a diagram showing the processing steps of a data import unit. FIG. 14 is a diagram showing the processing steps of an information acquisition unit. FIG. 15 is a diagram showing the processing steps of a dictionary information registration unit. FIG. 16 is a diagram showing the processing steps of a dictionary information display unit. FIG. 17 is a diagram showing the processing steps of an information organization unit. FIG. 18 is a diagram showing the processing steps of a resume registration unit. FIG. 19 is a timing chart showing the processing steps of the sharing server, recruiter device, applicant device, and generation server related to a resume. FIG. 19 is a diagram showing the functional configuration of a generation server related to a modified example. FIG. 19 is a diagram showing another example of a dialogue between the generation server and a user. FIG. 1 is a diagram showing another example of a dialogue between a generation server and a user. FIG. 2 is a diagram showing the functional configuration of a generation server including an IPC classification unit. FIG. 3 is a flowchart showing the processing procedure of the IPC classification unit. FIG. 4 is a diagram showing an example of a resume including an IPC. FIG. 5 is a diagram showing an example of a resume including an IPC. FIG. 6 is a diagram showing an example of a resume including an IPC. FIG. 7 is a diagram showing an example of utilizing RAG technology in the functional configuration of a generation server.

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference numerals, and description thereof will not be repeated.

[0012] [Overall Configuration] Fig. 1 is a block diagram showing an overview of a matching system 1. The matching system 1 is used, for example, for crowdsourcing between companies. Crowdsourcing is generally a process of soliciting contributions from an unspecified number of people to obtain needed services, ideas, or content.

[0013] Many companies are encouraging employees to have side jobs in order to make effective use of their human resources. By using crowdsourcing between companies, the capabilities of employees can be utilized.

[0014] 1, the general configuration of the matching system 1 will be described. The matching system 1 includes a sharing server 100, recruiter devices 200A, 200B, 200C, . . ., applicant devices 300A, 300B, 300C, . . ., and a generating server 400.

[0015] The sharing server 100 provides a matching service to many companies that matches business orders and receipts between companies. Figure 1 shows company A, company B, company C, etc. as examples of companies that use the matching service. Company A, company B, company C, etc. are registered as corporate members of the matching system 1. Employees of company A, company B, company C, etc. who use the matching system 1 are also individually registered as members of the matching system 1.

[0016] The work arranged by the matching system 1 is, for example, temporary work that is expected to be completed within a predetermined period of time. Therefore, a person who accepts a work arranged by the matching system 1 will work in the specific department to which they belong within the company as their main job, and will engage in the work arranged by the matching system 1 as a side job, respectively. Note that in the matching system 1, for example, an applicant from company A can also accept an order for work from company A. Therefore, in the matching system 1, an applicant who belongs to a different department Y of company A is also allowed to accept an order for work from department X of company A.

[0017] Hereinafter, a job for which contractors are being recruited in the matching system 1 may be referred to as a "job being recruited" or a "job being recruited," a person who provides a job being recruited may be referred to as a "recruiter," and a person who applies to receive an order for a job being recruited may be referred to as an "applicant." Applying for a job being recruited may be referred to as an "application for the job being recruited" or an "application for the job being recruited."

[0018] An applicant who accepts a job offer is referred to as the "contractor," and a recruiter who places an order with a contractor is referred to as the "orderer." However, in the following, the term "contractor" may be used to refer to the "applicant," and the term "orderer" may be used to refer to the "contractor."

[0019] The sharing server 100 has a database 120 necessary for the matching service. The database 120 includes various databases in which information necessary for providing the matching service is registered. For example, the database 120 registers information about members and recruitment operations. The sharing server 100 is managed and operated by a company separate from the companies that use the matching service. Any of the companies that use the matching service may manage and operate the sharing server 100.

[0020] The recruiter device 200A is operated by an administrator of company A. The recruiter device 200B is operated by an administrator of company B. The recruiter device 200C is operated by an administrator of company C. Hereinafter, the recruiter devices 200A, 200B, 200C, etc. may be collectively referred to as "recruiter devices 200."

[0021] Applicant device 300A is operated by an applicant from company A. Applicant device 300B is operated by an applicant from company B. Applicant device 300C is operated by an applicant from company C. Hereinafter, applicant devices 300A, 300B, 300C, etc. may be collectively referred to as "applicant devices 300." Although FIG. 1 shows two applicants for each company, the number of applicants is not limited to this. There may be more applicants for each company, or a company may have only one applicant. Sharing server 100 may also accept applicants who do not belong to a company, such as freelancers.

[0022] In this embodiment, the managers of companies A, B, C, etc. will act as recruiters. Therefore, hereinafter, the managers of each company will be referred to as "recruiters." Recruiters can also act as applicants for jobs being recruited by other recruiters. In this case, the recruiter device 200 functions as the applicant device 300. In this embodiment, when a company manager acts as a recruiter, the device that the manager uses to use the matching service will be referred to as the recruiter device 200.

[0023] Company A may have one or more administrators. When company A has administrators, each administrator may be given a recruiter device 200, or one recruiter device 200 may be shared by multiple administrators. The same applies to companies B, C, etc.

[0024] The sharing server 100 and the recruiter device 200 are configured to be able to communicate with each other via the Internet 50, which is an example of a communication network. The sharing server 100 and the applicant device 300 are configured to be able to communicate with each other via the Internet 50.

[0025] The sharing server 100 requires sign-in with input of a member ID and password when accepting access from the recruiter device 200. Similarly, the sharing server 100 requires sign-in with input of a member ID and password when accepting access from the applicant device 300. The sharing server 100 identifies each recruiter and applicant by the member ID notified at the time of sign-in.

[0026] The recruiter device 200 accepts various operations by the recruiter. For example, the recruiter device 200 accepts an operation to input a recruitment case (requested work), an operation to input an evaluation of a contractor who has completed the work, an operation to search for a member of the matching service, and the like.

[0027] The recruiter device 200 communicates with the sharing server 100 in response to each operation on the recruiter device 200. The sharing server 100 registers a recruitment request (requested work) in the database 120 in response to an operation to input the recruitment request, registers an evaluation of the target applicant (contractor) in the database 120 in response to an operation to input an evaluation, and provides member information to the recruiter device 200 in response to an operation to search for members.

[0028] The applicant device 300 accepts various operations by the applicant, such as an operation to search for a job offer, an operation to apply for a job offer, an operation to input work results, and an operation to input an evaluation of the recruiter (orderer).

[0029] The applicant device 300 communicates with the sharing server 100 in response to each operation on the applicant device 300. The sharing server 100 provides the applicant device 300 with appropriate job postings in response to an operation to search for job postings, issues a notice of acceptance or rejection to the applicant device 300 in response to an operation to apply for a job posting, registers the job postings in the database 120 in response to an operation to input job postings, and registers an evaluation of the target recruiter (orderer) in the database 120 in response to an operation to input an evaluation.

[0030] A recruiter belonging to a certain department of company A can employ an applicant belonging to another department of company A as a contractor for a job by using the matching system 1. A recruiter belonging to company A can employ an applicant belonging to company B as a contractor for a job by using the matching system 1.

[0031] Members who use the matching system 1 access the sharing server 100 as recruiters or applicants. Hereinafter, members of the matching system 1 may be referred to as "users." Also, below, the recruiter device 200 and applicant device 300 operated by members may be collectively referred to as "user device 500."

[0032] The sharing server 100 is communicably connected to the generation server 400. The generation server 400 provides the user with an interface to assist in creating a resume. The generation server 400 has a database 420 built therein, which is necessary for providing such an interface to the user. The generation server 400 may be managed by the company that manages the sharing server 100, or may be managed by a company different from the company that manages the sharing server 100. The sharing server 100 may include the functions of the generation server 400.

[0033] The generation server 400 is communicably connected to a user device 500 via the Internet 50. The user device 500 includes a recruiter device 200 and an applicant device 300. A user accesses the generation server 400 using the user device 500.

[0034] Similar to the sharing server 100, the generation server 400 requests sign-in involving input of a member ID and password when accepting access from the user device 500. The generation server 400 identifies the user by the member ID notified at the time of sign-in.

[0035] The generation server 400 provides the user with an interface for assisting in the creation of a career history. As a result, a career history creation tool is displayed on the screen 550 of the user device 500. The generation server 400 is an example of a career history information collection device.

[0036] The generation server 400 has a function of collecting all the work history information necessary for creating a resume from the user while interacting with the user by displaying a question box 551 and an answer box 552 on a screen 550. The generation server 400 creates a resume in a standardized format based on the collected work history information. The generation server 400 displays the created resume on the screen 550, giving the user an opportunity to check the resume. The generation server 400 stores the resume after the user has checked it in the generation server 400. When a user applies for a job posting, the generation server 400 cooperates with the sharing server 100 and transmits the resume to the recruiter device 200.

[0037] The background to the introduction of the generation server 400 in the matching system 1 is the "complexity of creating a resume" and the "difficulty of standardizing resumes." Each of these will be described in detail below.

[0038] [The complexity of creating a resume] In order to efficiently utilize in-house human resources, it is effective to clarify the skills and work experience possessed by each individual. To clarify the skills and work experience possessed by each individual, it is necessary to collect as much personal information as possible without omissions and compile it into a standard document format.

[0039] However, recalling the skills and work experience possessed and then compiling them into a standardized document format is a very cumbersome task for job seekers.

[0040] Of course, those who wish to officially change jobs will be able to maintain a certain level of motivation for the tedious process described above, as they will have to prepare for rigorous document screening and interviews. On the other hand, employees who apply for internal projects or wish to take on side jobs outside the company will find it difficult to maintain motivation for the tedious process described above. This is because, in addition to not involving a formal job change, there is no requirement for rigorous document screening and multifaceted interviews.

[0041] In this way, in so-called "closed circle" cases, applicants have little motivation to go through the troublesome process described above. In such cases, there is a problem in that job seekers cannot thoroughly collect information on the skills and work experience possessed by individuals and compile the collected information into a standard document format.

[0042] To create a high-quality resume, more information is needed regarding an individual's skills and experience. However, in private cases, even if a lot of information is available, many job seekers are not motivated to write a resume and find it troublesome (cumbersome) to write about their skills and experience. In such cases, the amount of information included in the resume tends to be small. Therefore, even if a job seeker has a lot of information available, they are unable to organize that information into a standardized document format, which presents a challenge.

[0043] [Difficulty in Standardizing Resumes] Consistent accuracy of resumes makes it easier for evaluators to evaluate each employee's work history. Furthermore, consistent accuracy of resumes allows companies to group employees' work histories for effective talent management. However, because resumes are typically written in free format, resume styles vary depending on the creator. Even if creators are required to write in a standard format, it is difficult to standardize the accuracy of resumes due to differences in individual document writing ability. There is also a risk that duplicate entries may be mixed into a resume due to the creator's carelessness. As a result, evaluators spend a lot of time evaluating each employee's work history based on their resumes. Furthermore, it is difficult for companies to group employees' work histories based on their resumes.

[0044] Based on the background described above, the generation server 400 is introduced into the matching system 1. The generation server 400 collects all work history information from the user in an interactive format and creates a resume in a standardized format. The created resume contains all work history information related to required information without omission or duplication.

[0045] According to this embodiment, it is possible to support a user so that the user can create a resume that includes all the necessary information and does not contain any duplicate information. In addition, according to this embodiment, it is possible to support a user so that the user can create a resume that properly reflects the user's work history, regardless of the user's document creation ability. According to this embodiment, it is possible to collect work history information so that the necessary information is included without omission and without duplication.

[0046] FIG. 2 is a block diagram showing the configurations of the sharing server 100, the recruiter device 200, and the applicant device 300.

[0047] [Configuration of Sharing Server 100 ] The sharing server 100 includes a processor 101 , a memory 102 , a storage 103 , and a communication interface 104 .

[0048] The memory 102 may include a random access memory (RAM), a read only memory (ROM), a flash memory, or any other suitable memory system. The memory 102 stores programs required for the arithmetic processing of the processor 101, temporary data calculated in the arithmetic processing, and the like.

[0049] The storage 103 is configured with a hard disk drive, a solid state drive, etc. A database 120 is stored in the storage 103. The database 120 includes multiple types of databases. The multiple types of databases include a company database (company DB) 121, a member database (member DB) 122, a community database (community DB) 123, and a job posting database (job posting DB) 124.

[0050] Some of these multiple types of databases may be stored in storage provided separately from the sharing server 100. For example, the sharing server 100 may be connected to a cloud service separate from the sharing server 100, and some of the multiple types of databases shown in Fig. 2 may be stored on that cloud. In this case, the sharing server 100 can access the necessary databases by communicating with that cloud via the Internet 50.

[0051] Processor 101 connects to Internet 50 via communication interface 104 in accordance with a program stored in memory 102. Processor 101 connects to Internet 50 and communicates with recruiter device 200 and applicant device 300. Processor 101 accesses database 120 and executes processes such as extracting necessary data, registering new data in database 120, and updating data registered in database 120.

[0052] [Configuration of Recruiter Device 200] The recruiter device 200 includes a processor 201, a memory 202, a communication interface 203, an input / output interface 204, a display 205, and an operation unit 206. The operation unit 206 includes a mouse, a keyboard, and the like.

[0053] The memory 202 may include a random access memory (RAM), a read only memory (ROM), a flash memory, or any other suitable memory system. The memory 202 stores programs required for the arithmetic processing of the processor 201, temporary data calculated in the arithmetic processing, and the like.

[0054] The processor 201 connects to the Internet 50 via the communication interface 203 in accordance with a program stored in the memory 202. The processor 201 connects to the Internet 50 and communicates with the sharing server 100. The processor 201 communicates with the sharing server 100 and executes processes such as sending a job offer, displaying information about applicant members on the display 205, placing an order for work with a contractor selected from the applicants, and sending the contents of an evaluation of the contractor entered by the recruiter to the sharing server 100.

[0055] Information input by operating the operation unit 206 is notified to the processor 201 via the input / output interface 204 .

[0056] [Configuration of Applicant Device 300] Applicant device 300 comprises a processor 301, memory 302, a communication interface 303, an input / output interface 304, a display 305, and an operation unit 306. Operation unit 306 is made up of a mouse, a keyboard, and the like.

[0057] The memory 302 may include a random access memory (RAM), a read only memory (ROM), a flash memory, or any other suitable memory system. The memory 302 stores programs required for the arithmetic processing of the processor 301, temporary data calculated in the arithmetic processing, and the like.

[0058] The processor 301 connects to the Internet 50 via the communication interface 303 in accordance with a program stored in the memory 302. The processor 301 connects to the Internet 50 and communicates with the sharing server 100. The processor 301 communicates with the sharing server 100 and executes processes such as applying for a job offer, displaying on the display 305 a notification of whether the applicant has been selected or not for the job offer, transmitting the performance of the work order to the sharing server 100, and transmitting the contents of the evaluation of the recruiter entered by the applicant to the sharing server 100.

[0059] Information input by operating the operation unit 306 is notified to the processor 301 via the input / output interface 304 .

[0060] [Database 120] The database 120 will now be described. The company database 121 stores information about companies that are members of the matching system 1. The member database 122 stores information about members who use the matching system 1. Many of the members are employees of companies that are members of the matching system 1.

[0061] Members registered in the member database 122 can act as recruiters (orderers) or applicants (recipients of orders) by using the matching system 1. Members may include employees of companies registered in the company database 121 as well as individuals (freelancers) who are not affiliated with a company.

[0062] The community database 123 stores information for identifying companies that belong to a community. A community is formed by agreement between companies. Therefore, multiple communities can be formed depending on how the companies agree. The number of companies that belong to one community can also be set in various ways. Companies that have a community relationship form a relationship of trust within the scope determined by how they agree to form the community. The community database 123 registers information for each community that identifies companies that belong to the community.

[0063] Jobs (jobs) for which contractors are being recruited are registered in the job database 124. Employees of each company can work in their main job in their own department at the company, and at the same time, as members of the matching system 1, can accept orders for jobs from other departments of their own company or jobs from other companies that are registered in the job database 124. In this case, the members accept orders for jobs from other departments of their own company or jobs from other companies as a side job.

[0064] 3 is a block diagram showing the configuration of the generation server 400. The generation server 400 includes a processor 401, a memory 402, a storage 403, and a communication interface 404.

[0065] The memory 402 may include a random access memory (RAM), a read only memory (ROM), a flash memory, or any other suitable memory system. The memory 402 stores programs required for the arithmetic processing of the processor 401, temporary data calculated in the arithmetic processing, and the like.

[0066] The storage 403 is configured with a hard disk drive, a solid state drive, etc. The storage 403 stores a database 420 and a large language model (LLM) 430. The large language model 430 is an example of a natural language processing algorithm. The storage 403 is an example of a storage unit that stores a natural language processing algorithm.

[0067] The large-scale language model 430 is a language model (trained language model) that has been trained in advance by machine learning. A huge amount of text data is used to train the large-scale language model 430. The large-scale language model 430 is formed as an autoregressive model that uses a transformer such as a GPT (Generative Pre-trained Transformer). The large-scale language model 430 according to this embodiment may include Bard and the like in addition to GPT (GPT-2, GPT-3, GPT-4).

[0068] The database 420 includes a plurality of types of databases, including a user database (user DB) 421, a dialogue information database (dialogue information DB) 422, and a dictionary database (dictionary DB) 423.

[0069] The memory 402 stores a program (algorithm) 410. The processor 401 executes the program (algorithm) 410 to utilize a large-scale language model 430 and create a resume.

[0070] The program 410 includes an interaction program 411, a confirmation program 412, and a creation program 413. By executing the interaction program 411, the processor 401 utilizes a large-scale language model 430 to interact with the user. As a result, the processor 401 acquires a large amount of work history information from the user. By executing the confirmation program 412, the processor 401 utilizes the large-scale language model 430 to confirm that all work history information necessary for creating a career history has been acquired. By executing the creation program 413, the processor 401 utilizes the large-scale language model 430 to create a career history in accordance with a predetermined format.

[0071] [Database 420] The database 420 will be described below. In the user database 421, "work history information" and "career history" are registered for each user's membership ID. In this disclosure, "work history information" refers to information used to create a "career history." For example, the generation server 400 can receive work history information in file format from the user via the user device 500 before interacting with the user. When the generation server 400 receives work history information from the user, it registers the work history information in the user database 421 for each user's membership ID.

[0072] If the work history information registered in the user database 421 includes all the information necessary to create a work history, the generation server 400 creates the work history without interacting with the user.

[0073] The generation server 400 interacts with the user to acquire work history information from the user. The "work history information" acquired through the interaction is registered in the interaction information database 422 for each member ID. The generation server 400 creates a "work history" using the "work history information" registered in the interaction information database 422. If "work history information" is registered in the user database 421, the generation server 400 creates a "work history" using the "work history information" registered in the user database 421 and the "work history information" registered in the interaction information database 422. The generation server 400 registers the created "work history" in the user database 421. The configuration of the user database 421 will be described in detail later using FIG. 8.

[0074] The dictionary database 423 stores terms and their meanings extracted during the dialogue between the generation server 400 and the user. The generation server 400 associates terms registered in the dictionary from the resume with the dictionary database 423. The generation server 400 (or the sharing server 100) displays the resume on the user device 500. When the user clicks with the mouse on a term registered in the dictionary from the resume, the generation server 400 (or the sharing server 100) displays the meaning of the term on the user device 500.

[0075] The large-scale language model 430 or part of the multiple types of databases may be stored in storage provided separately from the generation server 400. For example, the generation server 400 may be connected to a cloud service separate from the generation server 400, and part of the multiple types of databases shown in FIG. 3 or the large-scale language model 430 may be stored on that cloud. In this case, the generation server 400 can access the necessary database or large-scale language model 430 by communicating with the cloud via the Internet 50.

[0076] The processor 401 connects to the Internet 50 via the communication interface 404 in accordance with a program stored in the memory 402. The processor 401 connects to the Internet 50 and communicates with the user device 500 (see FIG. 1). The processor 401 accesses the database 420 and executes processes such as extracting necessary data, registering new data in the database 420, and updating data registered in the database 420.

[0077] The processor 401 communicates with the sharing server 100. For example, in response to a request from the sharing server 100, the processor 401 transmits the resume of the member (user) registered in the user database 421 to the sharing server 100.

[0078] [Company Database 121] Fig. 4 is a diagram showing an example of the company database 121. In the company database 121, a company ID for identifying the company, a company name, a company address, etc. are registered for each company. In this embodiment, members are permitted to apply for jobs recruited by various companies and departments and to receive orders for those jobs.

[0079] [Member Database 122] Fig. 5 is a diagram showing an example of the member database 122. Various information about members is registered in the member database 122. The various information about members includes a member ID for identifying the member, the ID of the company to which the member belongs, the member name, the member's authority, and the department to which the member belongs.

[0080] The types of member authority include administrator and applicant. A member with administrator authority is given the authority to use the matching system 1 as both a recruiter and an applicant. A member with applicant authority is given the authority to use the matching system 1 as an applicant, but is not given the authority to use the matching system 1 as a recruiter. A department head within a company is given administrator authority to manage the side job status of subordinates within the department. A manager with administrator authority is given the authority to approve applications from their subordinate applicants. Therefore, a manager functions as an approver.

[0081] [Community Database 123] Fig. 6 is a diagram showing an example of the community database 123. Information on communities formed between companies is registered in the community database 123. The community information includes a community ID for identifying the community, a community name, and a list of IDs of companies belonging to the community. Each company can form various communities by agreeing with other companies. A company belonging to a community can change the companies that belong to the community by agreeing with the other companies.

[0082] [Recruitment request database 124] Fig. 7 is a diagram showing an example of the recruitment request database 124. Information on recruitment requests is registered in the recruitment request database 124. The recruitment request information includes a request ID for identifying the recruitment request, the ID of the company to which the recruiter who registered the recruitment request belongs, a list of non-disclosure company IDs, a disclosure level, a request title, an estimated number of man-hours, an estimated period, and a request content.

[0083] The non-disclosure company ID list registers the IDs of companies that prohibit the disclosure of job postings. The disclosure level can be set to one of three levels: "Company," "Within the community," or "All." If the disclosure level is set to "All," applicants outside the community will also be disclosed.

[0084] 7, the IDs of companies that can view the recruitment requests are shown on the right side of the recruitment request database 124. For example, the disclosure level for the recruitment request corresponding to request ID=001 is set to "our company." In this case, only members belonging to the company that registered the recruitment request (company ID=00A) can view the recruitment request corresponding to request ID=001.

[0085] Hereinafter, using the case ID, the recruiting cases corresponding to each case ID may be referred to as case 001, case 002, case 003, etc. Similarly, using the community ID, the communities corresponding to each community ID may be referred to as community 01, community 02, community 03, etc., and using the member ID, the members corresponding to each member ID may be referred to as member P1, member P2, member P3, etc. Also, hereinafter, using part of the company ID, the companies corresponding to each company ID may be referred to as company A, company B, company C, etc.

[0086] The disclosure level for case 002 is set to "within the community." According to the community database 123 shown in Fig. 6, the companies that have a community relationship with company A, which registered case 002, are company B and company C. Therefore, as shown in Fig. 7, only members belonging to company A, company B, or company C can view case 002.

[0087] Case 003 has the same registered companies and disclosure level as case 002. However, case 003 has "00B" registered in the non-disclosure company ID list. Therefore, as shown in Figure 7, only members belonging to either company A or company C can view case 003, and members belonging to company B are not authorized to view case 003.

[0088] If the disclosure level of a job posting is set to "all," all members can view the job posting. This is the case for job posting 005 shown in Figure 7. If one or more company IDs are registered in the non-disclosure company ID list for job posting 005, members belonging to the companies with those company IDs will not be authorized to view job posting 005.

[0089] The estimated man-hours and estimated period are used by applicants and the matching system 1 to estimate the time it will take to process a recruitment request.

[0090] Each of the company database 121, the member database 122, and the community database 123 may be shared between the sharing server 100 and the generating server 400. The recruiting request database 124 may register the member ID of the recruiter corresponding to the recruiting request.

[0091] [User Database] FIG. 8 is a diagram showing an example of the user database 421. As shown in FIG. 8, the user database 421 registers the "career history information" and "resume" of each member (user) for each member ID. As described above, the "career history information" refers to information used to create the "resume." The "resume" is created based on the "career history information." In this embodiment, the "career history information" may be acquired in advance before the user interacts with the generation server 400, or may be acquired through the user interacting with the generation server 400.

[0092] The format of the resume is specified in advance by the designer, and includes the types of information that must be included and the order in which the information must be included.

[0093] The required information to be included in a resume includes "formal items" and "substantive items." For example, the formal items include "name," "age," "gender," "work history summary," "work history (duration and content)," "qualifications and skills," and "self-promotion." As shown in FIG. 8 , these formal items are arranged in the following order in the resume: "name," "age," "gender," "work history summary," "work history (duration and content)," "qualifications and skills," and "self-promotion." In the resume created by the generation server 400, the "formal items" shown in FIG. 8 are listed in the order shown in FIG. 8 . Note that the types and order of the "formal items" shown in FIG. 8 are merely examples.

[0094] A resume contains content corresponding to each of the "formal items." The content to be included in a resume includes predetermined "substantial items." In this embodiment, an item called "STAR" is introduced as an example of a "substantial item."

[0095] "STAR" is generally known as one of the methods used by interviewers to effectively interview job seekers. "STAR" is a coined word created by combining the initials of "S (Situation)," "T (Task)," "A (Action)," and "R (Result)." The generation server 400 acquires work history information from the user and creates a resume using the acquired work history information so that the user's work history items related to each of the four items intended by "STAR" are included.

[0096] In addition to the resumes (including content) created for each user, the user database 421 also stores resume format information. This format information includes the necessary information (formal and substantive information) to be included in the resume. The generation server 400 references the formats registered in the user database 421 and executes processes related to resume creation support.

[0097] The work history information registered in the user database 421 is information that serves as the basis for creating a work history, similar to the work history information obtained through the dialogue between the generation server 400 and the user. If work history information is not registered in the user database 421, the generation server 400 creates a work history based on the "work history information" obtained through the dialogue with the user. Therefore, in the present disclosure, it is not essential that work history information be registered in the user database 421. The user database 421 is an example of a work history database in which work history information collected before the dialogue by the collection unit is registered.

[0098] The "work history information" is, for example, information about the name, department, job description, work experience, and employment period of the company where the user has previously worked, and includes information about the above-mentioned "STAR." Alternatively, the "work history information" is information about the qualifications held by the user.

[0099] "Work history information" includes fragmentary information about the user's work history. Fragmentary information is, for example, information about one of the multiple required items included in a work history. One of the multiple required items is, for example, information about a qualification held by the user. Alternatively, fragmentary information is information that constitutes part of the required items. For example, if a user holds three qualifications, fragmentary information is information about one of the three qualifications.

[0100] The user database 421, together with the dialogue information database 422 and the dictionary database 423, constitutes the database 420. Here, the user database 421 out of the databases 420 has been described in detail using the drawings. The dialogue information database 422 and the dictionary database 423 have already been described in a manner that allows the reader to understand their respective configurations, and therefore the description of the dialogue information database 422 and the dictionary database 423 will not be repeated here.

[0101] 9 and 10 show an example of a dialogue between the generation server 400 and a user. The user accesses the generation server 400 using a user device 500. The generation server 400 uses the large-scale language model 430 to dialogue with the user in the following procedure. In particular, the generation server 400 diverges the dialogue with the user so that all of the user's work history is acquired without omission from the perspective of STAR (Situation, Task, Action, Result) related to the required information. In this way, the generation server 400 acquires all of the user's work history information necessary for creating a resume.

[0102] Here, an example will be described in which the generation server 400 interacts with the user using the screen of the user device 500. That is, the generation server 400 inquires of the user about their work history by displaying a request or question on the screen of the user device 500. The user responds to the inquiry by inputting text using a keyboard or the like of the user device 500. The generation server 400 may also interact with the user by voice.

[0103] First, the generation server 400 displays a request message on the screen saying, "Please enter your work history" (step S1). The user responds to the request in step S1 by saying, "I was a lead designer at XX company" (step S2).

[0104] Based on the response in step S2, the generation server 400 displays the query "What was the work environment like?" on the screen (step S3). The user responds to the query in step S3 by saying, "The team was short-staffed and had a large number of backlogged cases. The account manager set an unreasonable deadline, which caused stress for the team and led to low morale" (step S4).

[0105] Based on the response in step S4, the generating server 400 displays on the screen the query, "Please explain the responsibilities and roles you played in the situation and task at that time" (step S5). In response to the query in step S5, the user responds, "My role as a team leader was not only to ensure that the team worked within the deadline, but also to communicate the team's processing capabilities to other departments and maintain the team's motivation" (step S6).

[0106] In the same manner, the generation server 400 communicates with the user in a chat format to acquire all of the work history information necessary to create a resume from the user. The generation server 400 then creates a resume based on the work history information. The generation server 400 displays a request message on the screen stating, "I have created a work history. Please check it." (Step S7). The generation server 400 then presents the created resume to the user (Step S8). The user checks the resume on the screen of the user device 500.

[0107] In particular, the generation server 400 diverges the interaction between the generation server 400 and the user so that the entire work history of the user is acquired without omission from the perspective of STAR (Situation, Task, Action, Result).

[0108] Step S3 is an example of an inquiry for obtaining work history information corresponding to a "Situation" from the user. Step S5 is an example of an inquiry for obtaining work history information corresponding to a "Task" from the user. Although not illustrated in FIGS. 9 and 10, an "Action" relates to the method by which the user overcame the "Situation" or "Task." A "Result" relates to the outcome obtained by the user's "Action."

[0109] The dialogue program 411 includes parameters for diverging the dialogue in terms of STAR (Situation, Task, Action, Result). Such parameters are designed by, for example, the system administrator of the generation server 400.

[0110] In addition, in addition to, or instead of, chat-style dialogue, the generation server 400 may allow the user to send a CSV (Comma Separated Value) file or the like related to work history information from the user device 500 to the generation server 400.

[0111] Some examples of "venting" are as follows. For example, when information acquired from a user is expanded into a sentence, a situation may arise in which the number of characters in the sentence does not meet the number of characters specified in the required information. More specifically, when asking the question "What is the work environment like?", the answer obtained may not reach the specified number of characters (e.g., 100 characters or more). In such a case, the generation server 400 may ask the user an additional question such as "How was your relationship with your direct supervisor?" or "How were your relationships with your colleagues?"

[0112] Alternatively, if the answer obtained from the user is not specific enough, the generation server 400 asks the user an additional question. For example, if the reason is not clear from the user's answer, the generation server 400 asks the user an additional question. For example, if the user answers "The team is short-staffed..." but the user's answer does not contain a "keyword" or the like indicating the reason for the shortfall, the generation server 400 asks the user an additional question such as "Why were there a shortage of staff?"

[0113] If the answer obtained from the user does not contain enough information about the "reason," the generation server 400 may present the user with options for the reason after organizing the information using the large-scale language model 430. For example, suppose the answer obtained from the user is, "The team was short-staffed and had a large number of backlogged cases. The account manager set an unreasonable deadline, which caused stress for the team and lowered morale."

[0114] In this case, the generation server 400 determines, for example, that (a) the reason for the lack of personnel and (b) the reason why the user has a large number of unprocessed cases are insufficient, and presents the user with options to obtain each reason.

[0115] To obtain "(a) Reason for personnel shortage," the generation server 400 may, for example, ask the user, "I understand that there was a personnel shortage. Could the reason be one of the following? (Multiple choices allowed)," and may also present the user with the following options: "A: Even after hiring, we were unable to find enough people," "B: There was a sudden increase in orders," "C: Many people are quitting," and "D: Other (free description)."

[0116] To obtain "(b) the reason why the user ended up with a large number of unprocessed cases," the generation server 400 may, for example, ask, "I understand that you had a large number of unprocessed cases. Could one of the following be the reasons? (Multiple choices allowed)," and may also present the user with the following options: "A: There was a lack of information sharing between sales and development, and too many cases were accepted compared to the number of developers," "B: The account manager lacked the management skills," "C: The development staff lacked the skills," and "D: Other (free description)."

[0117] The user may select three options, A, B, and C, as answers to (a) above, and two options, A and B, as answers to (b) above.

[0118] As a result, the generation server 400 can extract more information from the user that is more comprehensive about the reasons for (a) and (b) above. For example, before asking the user additional questions, the information obtained from the user might be, "The team was short-staffed and had a large backlog of work. The account manager had set an unreasonable deadline, which caused stress for the team and low morale."

[0119] In response to this, by asking additional questions regarding (a) and (b) above, the generation server 400 can extract information from the user such as, for example, "There was a lack of information coordination between sales and development, the number of projects was excessive and too rapid compared to the number of developers, and the account managers lacked the management skills to handle all the projects. There were many projects and working hours tended to be excessive, so many people quit, and the staff shortage worsened. Furthermore, the company was looking to hire mid-career employees who could contribute immediately, but they were unable to find enough people."

[0120] 11 and 12 are diagrams showing an example of a dialogue (with dictionary registration) between the generation server 400 and a user. Here, the operation of the generation server 400 when the generation server 400 detects a term to be registered in the dictionary database 423 in the dialogue shown in FIGS. 9 and 10 will be described.

[0121] 11, the generating server 400 detects "lead designer" from the input in step S2 as a term that is likely to be misunderstood. In this case, the generating server 400 displays on the screen a query sentence such as "Does lead designer mean XXXXXX?" (step S2a).

[0122] In response to the inquiry in step S2a, the user replies, "No, I mean XXXXXX." (step S2b). Based on the reply in step S2b, the generation server 400 displays the sentence, "I understand." on the screen (step S2c). Furthermore, in step S2c, the generation server 400 registers the term "lead designer" and its meaning as a set in the dictionary database 423.

[0123] The generation server 400 then continues to interact with the user and acquires all of the work history information necessary to create the resume from the user. The generation server 400 then creates the resume based on the work history information. The generation server 400 displays a request message on the screen saying, "Your work history has been created. Please confirm it." (Step S7). The generation server 400 then presents the created resume to the user (Step S8).

[0124] As shown in FIG. 12 , the term "lead designer" is underlined in the resume. The user understands that the underlined term is linked to the dictionary. When the user clicks on the term displayed on the screen with a mouse or the like, the meaning of the term registered in the dictionary database 423 is displayed on the screen. The user checks the resume, which includes a link to the dictionary, on the screen of the user device 500. The generation server 400 links the resume approved by the user to the user's membership ID and registers it in the user database 421.

[0125] The resume registered in the user database 421 is transmitted to the recruiter device 200 at the request of the user. The recruiter device 200 displays the resume including a link to the dictionary on the recruiter device 200. When the recruiter clicks a term displayed on the screen with a mouse or the like, the meaning of the term registered in the dictionary database 423 is displayed on the screen. This allows the recruiter to accurately understand the terms written in the resume.

[0126] [Functions of the Generation Server] FIG. 13 is a diagram for explaining the content of the processing executed by the generation server 400 from the viewpoint of the divergence phase and the convergence phase.

[0127] The generation server 400 has a large-scale language model 430 and a program 410 (a dialogue program 411, a confirmation program 412, and a creation program) that executes processing to support the creation of a resume using the large-scale language model 430. The processing to support the creation of a resume is divided into a divergence phase and a convergence phase, as shown in FIG.

[0128] The dialogue program 411 and the large-scale language model 430 are combined to form a "dialogue AI (Artificial Intelligence) 4110." The dialogue AI 4110 acquires from the user a large amount of work history information necessary for creating a resume. Furthermore, the dialogue AI 4110 inquires of the user whether the created resume meets the user's intentions.

[0129] The "confirmation AI 4120" is configured by combining the confirmation program 412 and the large-scale language model 430. The confirmation AI 4120 confirms that all work history information corresponding to the necessary items required for creating a resume has been obtained without omission.

[0130] The "creation (summarization) AI 4130" is configured by combining the creation program 413 and the large-scale language model 430. The creation (summarization) AI 4130 creates a resume in accordance with a predefined resume format.

[0131] In the divergence phase, the dialogue AI 4110 chats with the user and acquires from the user work history information related to the necessary items to be included in the resume. At this time, the dialogue AI 4110 controls the dialogue so that the dialogue diverges from the perspective of "STAR (Situation, Task, Action, Result)" according to parameters designed by the system administrator.

[0132] In the divergence phase, the confirmation AI 4120 reads the resume format. The resume format contains the necessary information required to create a resume. The confirmation AI 4120 verifies that all work history information corresponding to the necessary information required to create a resume has been obtained without omission. If work history information is missing, the confirmation AI 4120 instructs the dialogue AI 4110 to continue the dialogue regarding the missing work history information.

[0133] The dialogue AI 4110 stores the dialogue history. Therefore, when the dialogue AI 4110 is instructed by the confirmation AI 4120 to continue the dialogue, it can continue the dialogue from where it left off without having to restart the dialogue that has already been completed.

[0134] In the divergence phase, divergent dialogue by the dialogue AI 4110 and confirmation by the confirmation AI 4120 are repeated until all the work history information corresponding to the necessary items to be written in creating a resume is obtained without omission.

[0135] After completing the divergence phase, the generation server 400 moves to the convergence phase. In the convergence phase, the creation AI (summarization AI) 4130 has the function of arranging the acquired information into a predetermined format. More specifically, the creation AI 4130 creates a resume that conforms to a pre-defined resume format. The dialogue AI 4110 presents the created resume to the user. The user checks the resume. If the user determines that the resume does not meet the user's intentions, the dialogue AI 4110 instructs the dialogue AI 4110 to make corrections. In this case, the creation AI 4130 corrects the resume based on the user's correction instructions. The dialogue AI 4110 presents the corrected resume to the user. The user checks the resume.

[0136] In the convergence phase, the creation (modification) of the resume by the creation AI 4130 and the dialogue between the dialogue AI 4110 and the user are repeated until the resume intended by the user is created.

[0137] Fig. 14 is a diagram showing the functional configuration of the generation server 400. As shown in Fig. 14, the generation server 400 includes a control unit 4010. The control unit 4010 is realized by the processor 401, memory 402, and communication interface 404 shown in Fig. 3.

[0138] The control unit 4010 can function as the dialogue AI 4110, the confirmation AI 4120, and the creation AI 4130 shown in Fig. 13 by utilizing the large-scale language model 430. The control unit 4010 accesses the database 420 to execute processing related to resume creation support.

[0139] The control unit 4010 includes a data import unit 4011 , an information acquisition unit 4012 , a dictionary information registration unit 4013 , a dictionary information display unit 4014 , an information organization unit 4015 , and a resume registration unit 4016 .

[0140] The data import unit 4011 imports work history information already owned by the user into the dialogue AI 4110. For example, the data import unit 4011 can receive work history information in a predetermined file format from the user via the user device 500. The control unit 4010 uses the imported work history information to create a resume. In this way, by utilizing the work history information already owned by the user, the dialogue cost of the dialogue AI 4110 can be reduced.

[0141] The information acquisition unit 4012 acquires work history information from the user using the dialogue AI 4110. The information acquisition unit 4012 uses the confirmation AI 4120 to confirm that all work history information corresponding to the necessary items to be written in creating a resume has been acquired without omission.

[0142] The dictionary information registration unit 4013 registers unique terms obtained during the dialogue between the user and the dialogue AI 4110 in the dictionary database 423. Here, the unique terms may be, for example, information unique to an organization such as a company.

[0143] If a term registered in the dictionary database 423 is included in the resume, the dictionary information display unit 4014 displays the term in the resume in the form of a link. The information organizing unit 4015 uses the creation AI 4130 to create the resume in a predetermined format.

[0144] The resume registration unit 4016 stores the created resume in the user database 421. In particular, when a term registered in the dictionary database 423 is written in the resume, the resume registration unit 4016 generates a link that associates the term written in the resume with the meaning of the term registered in the dictionary database 423, and then stores the work history information in the user database 421.

[0145] 15 to 20, an example of the processing procedure of the control unit 4010 included in the generation server 400 will be described. Here, as the processing procedure of the control unit 4010, the processing procedures of the data import unit 4011, the information acquisition unit 4012, the dictionary information registration unit 4013, the dictionary information display unit 4014, the information organization unit 4015, and the resume registration unit 4016 will be described.

[0146] 15 is a flowchart showing the processing procedure of the data import unit 4011. First, the data import unit 4011 acquires, from the user database 421, format information for the resume and work history information registered before the dialogue between the dialogue AI 4110 and the user (step S11). Step S11 is an example of a collection unit that collects work history information related to the user's work history. Furthermore, step S11 is an example of an acquisition unit that acquires format information including necessary information to be written in the resume.

[0147] The "work history information registered before the dialogue between the dialogue AI 4110 and the user" refers to the "work history information" shown in Figure 8. In step S11, the data import unit 4011 may read the user's work history information from a specified local file. The format information for the work history includes the necessary information to be included in the work history, the order in which the necessary information is to be included, etc.

[0148] Next, the data import unit 4011 inputs the work history information acquired in step S11 to the dialogue AI 4110 as prior information (step S12). Next, the data import unit 4011 determines whether the work history information acquired in step S11 includes all the information necessary to create a resume (step S13). The data import unit 4011 calls the confirmation AI 4120 and makes the determination in step S13 by referring to the format information of the resume.

[0149] If the work history information acquired in step S11 contains all the information necessary to create a curriculum vitae, no dialogue between the dialogue AI 4110 and the user is required. Therefore, if the work history information acquired in step S11 contains all the information necessary to create a curriculum vitae, the data import unit 4011 passes the process to the information organization unit 4015. The information organization unit 4015 creates the curriculum vitae.

[0150] If the work history information acquired in step S11 does not include all the information necessary to create a work history, the data import unit 4011 passes the process to the information acquisition unit 4012.

[0151] 16 is a flowchart showing the processing procedure of the information acquisition unit 4012. First, the information acquisition unit 4012 uses the large-scale language model 430 to call the dialogue AI 4110 and the confirmation AI 4120 (step S21).

[0152] Next, the information acquisition unit 4012 uses the dialogue AI 4110 to dialogue with the user (step S22). As a result, the information acquisition unit 4012 acquires dialogue information (including work history information) from the user. Step S22 is an example of a collection unit that collects work history information related to the user's work history. Furthermore, step S22 is an example of a dialogue unit that collects work history information by dialogue with the user. Next, the information acquisition unit 4012 stores the dialogue information in the dialogue information database 422 (step S23). The dialogue information is information that indicates the history of dialogue with the user.

[0153] Next, the information acquisition unit 4012 determines whether all of the work history information corresponding to the required items necessary for creating a curriculum vitae has been acquired without omission, based on the dialogue information (step S24). At this time, the information acquisition unit 4012 makes the determination of step S24 by referring to the format information acquired in step S11. Note that the information acquisition unit 4012 may acquire the format information from the user database 421 in a step separate from step S11. If all of the work history information corresponding to the required items has not been acquired without omission, the information acquisition unit 4012 returns the process to step S22.

[0154] When all work history information corresponding to the required fields has been acquired without omission, the information acquisition unit 4012 confirms with the user whether there is other work history information (step S25). If there is other work history information, the information acquisition unit 4012 returns the process to step S22. If there is no other work history information, the information acquisition unit 4012 passes the process to the information organization unit 4015. The information organization unit 4015 creates a work history.

[0155] 17 is a flowchart showing the processing procedure of the dictionary information registration unit 4013. First, the dictionary information registration unit 4013 detects words to be registered in the dictionary during a dialogue between the dialogue AI 4110 and the user (step S31). Words to be registered in the dictionary include proper nouns and polysemous word information. Words to be registered in the dictionary may be, for example, information specific to an organization such as a company. Next, the dictionary information registration unit 4013 confirms the meaning of the word with the user (step S32).

[0156] Next, the dictionary information registration unit 4013 acquires the user's answer (step S33). Next, the dictionary information registration unit 4013 registers the word detected in step S31 and its meaning based on the user's answer as dictionary information in the dictionary database 423 (step S34), and ends the processing based on this flowchart. Step S34 is an example of a registration unit that registers the meaning of the term included in the work history information in the dictionary database.

[0157] 18 is a flowchart showing the processing steps of the dictionary information display unit 4014. First, the dictionary information display unit 4014 acquires the user's career history from the user database 421 (step S41). Next, the dictionary information display unit 4014 acquires terms present in the career history from the dictionary database 423 (step S42). Next, the dictionary information display unit 4014 displays the career history with a link on the screen of the user device 500 (step S43).

[0158] Next, the dictionary information display unit 4014 displays the dictionary information corresponding to the link on the screen of the user device 500 in response to the user's operation (step S44), and ends the processing based on this flowchart. The dictionary information display unit 4014 is an example of a display unit that displays the resume on a display device. The user device 500 is an example of a display device. Note that the dictionary information display unit 4014 may display the resume on a display device provided in the generation server 400 instead of or in addition to the user device 500.

[0159] 19 is a flowchart showing the processing procedure of the information organizing unit 4015. First, the information organizing unit 4015 calls the creation AI 4130 using the large-scale language model 430 (step S51).

[0160] Next, the information organizing unit 4015 reads the dialogue information from the dialogue information database 422 (step S52). Furthermore, if pre-registered work history information exists in the user database 421, the information organizing unit 4015 reads the work history information from the user database 421 in step S52.

[0161] Next, the information organizing unit 4015 creates a curriculum vitae from the dialogue information (step S53). If the information organizing unit 4015 has read the career history information from the user database 421, the information organizing unit 4015 creates a curriculum vitae from the career history information and the dialogue information in step S53. Step S53 is an example of a creation unit that creates a curriculum vitae.

[0162] The information organizing unit 4015 references the format information acquired in step S11 and creates a resume in a format that conforms to the format information. Note that the information acquiring unit 4012 may acquire the format information from the user database 421 in a step separate from step S11. Next, the information organizing unit 4015 displays the resume on the screen of the user device 500 (step S54).

[0163] Next, the information organizing unit 4015 determines whether or not an approval operation from the user has been received (step S55). If an approval operation from the user has been received, the information organizing unit 4015 ends the processing based on this flowchart.

[0164] If the information organizing unit 4015 has not received an approval operation from the user, it determines whether or not a non-approval operation from the user has been received (step S56). If the information organizing unit 4015 has not received a non-approval operation from the user, it returns the process to step S55. If the information organizing unit 4015 has received a non-approval operation from the user, it passes the process to the information acquisition unit 4012. The information acquisition unit 4012 again calls the dialogue AI 4110 and acquires new dialogue information from the user by interacting with the user.

[0165] The information organizing unit 4015 may not only acquire new dialogue information from the user, but also re-engage in a dialogue with the user without changing the conditions before the dialogue, because a large-scale language model may generally generate multiple answers (sentences) with different nuances for the same question and conditions.

[0166] 20 is a flowchart showing the processing steps of the resume registration unit 4016. First, the resume registration unit 4016 acquires the user's resume from the user database 421 (step S61). Next, the resume registration unit 4016 references the dictionary database 423 to generate a link between the resume and dictionary information (step S62). Next, the resume registration unit 4016 stores the linked resume in the user database 421 (step S63).

[0167] Next, the resume registration unit 4016 receives a resume output request from the user (step S64). Next, the resume registration unit 4016 outputs the resume to the user device 500 (step S65), and ends the processing based on this flowchart. Note that the user device 500 may display the resume on the screen of the user device 500. Alternatively, the user device 500 may store the resume data in memory until instructed by the user.

[0168] 21 is a timing chart showing the processing procedures related to a curriculum vitae performed by the sharing server 100, the recruiter device 200, the applicant device 300, and the generation server 400. The processing flow related to a curriculum vitae performed by the matching system 1 will be described using the timing chart shown in FIG.

[0169] First, the applicant accesses the generation server using the applicant device 300 and creates a curriculum vitae (step S101). The generation server 400 registers the created curriculum vitae in the user database 421 (step S102). The detailed processing procedures of steps S101 and S102 have already been described using Figures 15 to 20, and therefore will not be repeated here.

[0170] Next, the applicant accesses the sharing server 100 using the applicant device 300, searches for the job opening, and then decides where to apply (step S103). Next, the applicant operates the applicant device 300 to send a command requesting the applicant's resume from the applicant device 300 to the sharing server 100 (step S104). Upon receiving the command, the sharing server 100 identifies the applicant (step S105). More specifically, the sharing server 100 identifies the applicant's member ID.

[0171] Next, the sharing server 100 transmits a command requesting transmission of the resume to the generation server 400 (step S106). This command includes the applicant's membership ID. The generation server 400 searches the user database 421 for the applicant's resume based on the membership ID included in the command (step S107).

[0172] Next, the generation server 400 transmits the resume found as a result of the search to the sharing server 100 (step S108). As a result, the sharing server 100 acquires the applicant's resume (step S109). Next, the sharing server 100 transmits the acquired resume to the applicant device 300 (step S110).

[0173] The applicant device 300 displays the received resume on the screen of the applicant device 300 (step S111). If the resume contains a term registered in the dictionary database 423, the resume including a link to the dictionary is displayed on the screen.

[0174] The applicant confirms that there are no problems with the resume displayed on the screen. For example, the applicant confirms that there is no information that needs to be updated in the resume. If there are no problems with the resume, the applicant performs an approval operation using a keyboard, etc. If there are problems with the resume, the applicant performs a disapproval operation using a keyboard, etc.

[0175] The applicant device 300 determines whether an approval operation has been detected (step S112). If the applicant device 300 detects a non-approval operation rather than an approval operation, the process returns to step S101. In this case, the process of creating a resume is executed again. However, it is preferable that the generation server 400 not execute the process of creating a resume from the beginning, but rather interact with the applicant regarding items that need to be corrected and correct the contents of the resume.

[0176] When the applicant device 300 detects the approval operation, it transmits a command to the sharing server 100 to instruct the sending of the resume (step S113). The sharing server 100 identifies the recruiter to whom the resume should be sent (step S114). For example, the sharing server 100 identifies the recruiter to whom the resume should be sent based on the application destination determined in step S103.

[0177] Next, the sharing server 100 transmits the resume to the recruiter device 200 (step S115). The recruiter device 200 displays the received resume on the screen of the recruiter device 200 (step S116). If the resume contains a term registered in the dictionary database 423, the resume including a link to the dictionary is displayed on the screen. The recruiter uses the resume to understand the applicant's work history. When the applicant clicks with the mouse on a term registered in the dictionary in the resume, the meaning of the term is displayed on the recruiter device 200. Therefore, if the resume contains a term that the applicant does not understand, such as internal jargon of the company where the applicant works, the recruiter can understand the meaning of the term.

[0178] 21, the sharing server 100 and the generation server 400 may be configured as a single server. That is, the sharing server 100 may be provided with the functions of the generation server 400, or the generation server 400 may be provided with the functions of the sharing server 100. Steps S110 and S115 are an example of an output unit configured to output the resume created by the creation unit to a user device.

[0179] As described above, according to this embodiment, it is possible to support a user in creating a resume that includes all the necessary information and does not contain any duplicate information. In addition, according to this embodiment, it is possible to support a user in creating a resume that properly reflects the user's work history, regardless of the user's document creation ability. Furthermore, according to this embodiment, the following effects are achieved.

[0180] a. It is possible to provide a user with a resume that adequately expresses the user's skills and experience, regardless of the user's document creation ability.

[0181] b) It is possible to thoroughly collect a wide variety of work history information of the user. c) By providing the user with an interactive interface, it is possible to reduce the burden on the user in appropriately extracting work history information.

[0182] d) It reduces the burden on users when creating a resume. e) Because resumes are created in a standardized format, it is possible to improve the efficiency of managing and searching a large number of resumes.

[0183] f. It is possible to provide an interface for creating a high-quality resume to users who find it difficult to stay motivated to complete the cumbersome task of creating a resume, such as employees applying for an internal project or employees looking to take on a side job outside the company.

[0184] g. If the resume contains a term registered in the dictionary database 423, the resume will be displayed on the screen with a link to the dictionary, so that if the resume contains a term that the recruiter does not understand, they can understand the meaning of the term.

[0185] Note that part of the configuration of the generation server 400 may be configured by a device separate from the generation server 400. For example, at least one of the multiple databases included in the database 420 and the large-scale language model 430 may be located on a cloud separate from the generation server 400. In this case, it is sufficient that the generation server 400 and the cloud are connected to each other via a network so as to be able to communicate with each other.

[0186] [Modification] In the present embodiment, an example has been shown in which the generation server 400 is arranged in the matching system 1. However, a generation server that creates a curriculum vitae may be constructed independently of the matching system 1. An example of a generation server that creates a curriculum vitae independently of the matching system 1 will be described.

[0187] 22 is a diagram showing the functional configuration of a generation server 400A according to a modified example. Similar to the generation server 400, the generation server 400A has components such as a processor, a memory, a communication interface, and storage, and includes a control unit 4010A realized by these components.

[0188] The generation server 400A is communicably connected to a user device 500A used by a user via the Internet or the like.

[0189] The generation server 400A includes a satisfying requirement database (satisfying requirement DB) 426, a raw information storage database (raw information storage DB) 427, a generative format database (generative format DB) 428, and large-scale language models 430A and 430B. These databases and large-scale language models are configured in the storage of the generation server 400A.

[0190] Like the large-scale language model 430, the large-scale language models 430A and 430B are natural language processing models trained using large amounts of text data. In the present disclosure, BERT (registered trademark) (LaMBDA) by Google, GPT-4 by OpenAI, or the like may be adopted as the large-scale language model. In the present disclosure, the large-scale language model is an example of a natural language processing algorithm. A language model other than the large-scale language model may be adopted as the natural language processing algorithm. A model generated by machine learning such as pattern matching may be adopted as the natural language processing algorithm. Furthermore, instead of using two models, the large-scale language models 430A and 430B, a single large-scale language model may be used as the large-scale model. For example, the large-scale language model 430 may be used.

[0191] As shown in FIG. 22, the control unit 4010A includes an input unit 4021, a content sufficiency determination unit 4022, an input request unit 4023, a raw information storage unit 4024, a generation unit 4025, a confirmation request unit 4026, and an output unit 4027.

[0192] The input unit 4021 accepts information input by the user using the user device 500. The information input by the user includes work history information. The input unit 4021 may accept work history information in chat format, for example. The input unit 4021 may also accept work history information in audio file format and other file formats. When using GPT-3 or the like as a large-scale language model and a chat-style UI such as ChatGPT, a file may be attached to the chat. In this case, the generation server 400 may be provided with a function for converting the file into a chat-style document.

[0193] The output unit 4027 outputs various information to the user device 500. The output unit 4027 may output not only chat-style information but also other files such as audio to the user device 500. When the control unit 4010A interacts with the user in chat style, the input of information to the input unit 4021 and the output of information from the output unit 4027 are repeatedly executed.

[0194] The requirements fulfillment database 426 stores information about the requirements (requirements fulfillment) that the large-scale language model 430A uses to determine whether all of the work history information corresponding to the necessary entries required for creating a resume has been acquired without omission.

[0195] Information regarding multiple types of fulfillment requirements may be registered in the fulfillment requirement database 426. In this case, the control unit 4010A may transmit multiple types of fulfillment requirements to the user device 500A and allow the user to select the fulfillment requirement of their choice. Alternatively, the control unit 4010A may accept fulfillment requirements set by the user via the user device 500A.

[0196] The control unit 4010A may also generate a trained model that functions as an AI using the contents of a large number of fulfillment requirements as training data. In this case, the generated trained model may be used instead of the large-scale language model. The fulfillment requirements may also be used in the prompts of an existing large-scale language model 430A. The fulfillment requirements may include specific information, such as the number of companies the user wants to include in their resume.

[0197] The content sufficiency determination unit 4022 determines whether the information obtained by the input unit 4021 satisfies the sufficiency requirements using the large-scale language model 430. If the content sufficiency determination unit 4022 determines that the information obtained by the input unit 4021 does not satisfy the sufficiency requirements, it instructs the input request unit 4023 to acquire additional information. If the content sufficiency determination unit 4022 determines that the information obtained by the input unit 4021 satisfies the sufficiency requirements, it outputs the information obtained by the input unit 4021 to the raw information storage unit 4024.

[0198] Based on the instruction of the content sufficiency determination unit 4022, the input request unit 4023 instructs the large-scale language model 430A to continue the dialogue with the user so that additional information (missing information) can be obtained from the user. When multiple pieces of additional information are required, the large-scale language model 430A may dialogue with the user so that the multiple pieces of additional information are acquired all at once, or may dialogue with the user so that each piece of additional information is acquired sequentially. For example, when information A, B, and C are missing, the large-scale language model 430A may first inquire about information A from the user.

[0199] The large-scale language model 430A may automatically generate a dialogue rule for prompting the user to input additional information, or may select a dialogue rule from preset options.

[0200] The raw information storage unit 4024 temporarily stores work history information that is the basis for generating a career history. The raw information storage unit 4024 outputs the temporarily stored work history information to the raw information storage database 427. The raw information storage database 427 registers the work history information. The generation unit 4025 creates a career history using the work history information registered in the raw information storage database 427. Note that the generation unit 4025 may have a function of creating a career history without waiting for a determination by the content sufficiency determination unit 4022.

[0201] For example, if the user has a draft of a resume, the user may only want to have the resume finalized or formatted. In this case, the input unit 4021 may accept data of the draft of the resume from the user via the user device 500. Furthermore, the input unit 4021 may register the accepted data in the raw information storage database 427. The generation unit 4025 may create the resume based on the draft data registered in the raw information storage database 427.

[0202] The generation format database 428 stores the formats required for the large-scale language model 430B to generate work histories.

[0203] Note that multiple types of formats may be registered in the generation format database 428. In this case, the control unit 4010A may transmit multiple types of formats to the user device 500A and allow the user to select a format of their choice. Alternatively, the control unit 4010A may accept a format set by the user via the user device 500A.

[0204] The control unit 4010A may also generate a trained model that functions as an AI using multiple formats as training data. In this case, the generated trained model may be used instead of the large-scale language model. Alternatively, a format may be used for the prompt of an existing large-scale language model 430A.

[0205] The generation unit 4025 generates a resume using the large-scale language model 430B. The format registered in the generation format database 428 is input to the large-scale language model 430B. The generation unit 4025 references the raw information storage database 427 and creates a resume that reflects the career history information in a predefined format. Note that the large-scale language model 430B may create the resume instead of the generation unit 4025. The resume generated by the generation unit 4025 is output to the confirmation request unit 4026.

[0206] The confirmation request unit 4026 requests the user to confirm the resume. The confirmation request unit 4026 transmits the resume to the user device 500 via the output unit 4027. The user confirms the content of the resume displayed on the user device 500. The confirmation request unit 4026 may divide the content of the resume into multiple items and have the user confirm the content of the resume for each item.

[0207] For example, if the resume contains entries for Company A and Company B, the confirmation request unit 4026 may have the user confirm the entry for Company A, and then have the user confirm the entry for Company B. Of course, the confirmation request unit 4026 may have the user confirm all of the contents of the resume at once. The large-scale language model 430B may automatically generate a dialogue rule for having the user confirm the resume, or may select a dialogue rule from preset options.

[0208] Note that some of the components of the generation server 400A may be configured separately from the generation server 400A. For example, at least one of the satisfying requirement database 426, the raw information storage database 427, the generation format database 428, the large-scale language model 430A, and the large-scale language model 430B may be configured separately from the generation server 400A. In this case, it is sufficient that the device configured separately from the generation server 400A and the generation server 400A are connected to each other via a network so as to be able to communicate with each other.

[0209] [Variations Related to the Use of IPC Classification] Generally, an engineer's resume contains a detailed description of the skills the engineer possesses. However, with the rapid development of IT-related technology, previously little-known new technologies are emerging one after another, and technological diversification is progressing rapidly. On the other hand, the granularity of the skills possessed by users varies from field to field. As a result, consistency in the descriptions of skills in resumes is disappearing. This is hindering recruiters when analyzing applicants' resumes.

[0210] Similar problems exist within companies as well. It is important for those in a position to evaluate employees to understand and systematically organize the skills possessed by employees. This is thought to enable the company to make the most of its technical resources. However, due to the circumstances described above, even if each employee is required to submit a resume, it is difficult to uniformly manage each employee's skills based on a set standard.

[0211] Therefore, an index that can classify various technical skills using uniform criteria is needed. One possible index to achieve this is the International Patent Classification (IPC). The IPC is an internationally unified technology classification for classifying inventions that have been applied for patents. By using the IPC, technologies can be classified in detail by dividing them into hierarchical levels such as "section," "subsection," "class," "subclass," "main group," and "subgroup."

[0212] IPC is originally an index used to classify patent documents. However, by using IPC as an index to classify engineer skills, it is thought that it would be possible to classify a wide variety of engineer skills, including the latest technologies, using uniform standards. However, because IPC is a highly specialized classification index used in the patent industry, it is difficult for users who are unfamiliar with IPC to accurately select an IPC that corresponds to their skills from the many IPC options.

[0213] Therefore, we propose a system that allows a user to select an appropriate IPC that corresponds to their skills through a dialogue between the generation server 400 and the user. Here, an IPC is assumed as an example of the required information or format information for a resume, and the user's career information required to obtain an IPC that corresponds to the user's skills is assumed as an example of work history information. The generation server 400 collects the user's work history information required to obtain the IPC by dialogue with the user using a natural language processing algorithm.

[0214] 23 and 24 are diagrams showing another example of a dialogue between the generation server 400 and a user. In Fig. 23 and Fig. 24, an example is shown in which the generation server 400 acquires an appropriate IPC corresponding to the user's skills through a dialogue with the user.

[0215] First, the generation server 400 displays a request message on the screen saying, "Please enter your work history." (Step S201). The user responds to the request in Step S1 with their own work history (Step S202). In Step S202, for example, the user responds with their work history related to the development of lithium-ion secondary batteries.

[0216] Based on the answer in step S2, the generation server 400 organizes the information using the large-scale language model 430 and presents to the user IPCs that may correspond to the user's skills (step S203). In step S203, for example, one or more "IPCs" corresponding to each of the information obtained through the dialogue, such as "research and development of lithium ion secondary batteries," "development of new positive electrode materials," "development of lithium cobalt nickel manganese oxide," "development of silicon negative electrodes," "development of electrolytes that suppress degradation," and "development and practical application of lithium ion secondary batteries with lithium titanate negative electrodes," are presented to the user.

[0217] Next, the generation server 400 presents several IPCs from the IPCs presented in step S203 to the user as selection candidates (step S204). At this time, the generation server 400 may present the selection candidates along with check boxes, as shown in FIG. 24 . The user considers whether there is an IPC that seems appropriate among the options. If there is an IPC that seems appropriate among the options, the user responds by checking the check box corresponding to that IPC. In this case, a button 601 as shown in FIG. 24 may be displayed on the screen of the user device 500. After checking the check box, the user clicks the button 601. When the generation server 400 detects the click of the button 601, it determines the IPC to be included in the user's resume based on the user's response.

[0218] In this way, the generation server 400 suggests to the user IPCs that are considered relevant from among a large number of IPCs based on the work history acquired through correspondence. Therefore, even if the user does not have specialized knowledge of IPCs, they can select IPCs that are relevant to their own skills. While the example shown here uses "subgroups," higher-level symbols such as "subclasses" or "groups" may be used depending on the desired granularity of classification. Furthermore, descriptions such as "subclasses," "groups," or "main groups" may be added to the description of "subgroups," or buttons such as question marks may be added to allow the user to check the contents of higher levels as needed.

[0219] Although an example has been given here in which the user is prompted to select an IPC related to his or her own technical field, the IPC presented to the user may be any of "section," "subsection," "class," "subclass," "main group," and "subgroup." For example, a subclass may be presented to the user, such as "H01: Electrical element." Furthermore, together with the classification symbol, the name of the technical field classified by that classification symbol may also be presented to the user.

[0220] If there is no suitable IPC among the options proposed in step S205, the user can request the generation server 400 to present other candidates or to start over with entering the work history. In this case, buttons 602 and 603 as shown in FIG. 24 may be displayed on the screen of the user device 500.

[0221] When the generation server 400 detects a click operation on the button 602, it selects an IPC different from the options presented in step S204 from the IPCs presented in step S203 and presents the selected IPC to the user. When the generation server 400 detects a click operation on the button 603, it returns the process to step S204 and prompts the user to input their work history.

[0222] Furthermore, IPC candidate generation may be performed automatically multiple times. The generation server 400 may generate different answers to the same sentence (or the same question). For this reason, before proposing IPC candidates in step S204, the IPC generation from step S202 may be performed automatically multiple times to widen the range of IPC candidates. This increases the likelihood that the user will be able to select an IPC that is suitable for them.

[0223] As described above, the generation server 400 supports the user's actions to identify IPCs corresponding to the user's skills through a dialogue between the generation server 400 and the user. Note that the processing of step S203 is not essential in this embodiment. That is, the generation server 400 may detect multiple IPCs that are thought to correspond to the user's skills through the dialogue, and then present some of the detected multiple IPCs to the user in step S204 without performing the processing of step S203.

[0224] The generation server 400 may present other candidates to the user autonomously, not only when receiving a request from the user to present other candidates again. For example, the generation server 400 may execute the process of generating option candidates multiple times and present multiple candidates to the user. In general, answers obtained from a large-scale language model in response to a query may differ even if the content of the first query and the content of the second query are the same. Therefore, by having the generation server 400 execute the process of generating option candidates multiple times, the generation server 400 may derive multiple candidates from different perspectives. By obtaining the user's selection for each of these multiple candidates, the generation server 400 can obtain a wider range of answers regarding IPC from the user.

[0225] FIG. 25 is a diagram showing the functional configuration of the generation server 400, including an IPC classification unit 4017. In this modification, the IPC classification unit 4017 is added to the control unit 4010 of the generation server 400 shown in FIG. 14. The IPC classification unit 4017 determines the user's IPCs to be included in the resume. The IPC classification unit 4017 can present the user with IPCs that are considered to correspond to the user's skills using the user's work history information obtained through dialogue. The IPC classification unit 4017 can also present the user with IPCs that are considered to correspond to the user's skills using the work history information imported by the data import unit 4011. The IPC classification unit 4017 presents the user with multiple IPCs and then determines, through dialogue, an IPC that is appropriate for the user's skills. The determined IPC is imported into the information organization unit 4015. The information organization unit 4015 uses the creation AI 4130 to create a resume including the IPCs in a predetermined format. The curriculum vitae registration unit 4016 stores the created curriculum vitae in the user database 421 .

[0226] 26 is a flowchart showing the processing procedure of the IPC classification unit 4017. First, the IPC classification unit 4017 calls the dialogue AI 4110 using the large-scale language model 430 (step S71).

[0227] Next, the IPC classification unit 4017 interacts with the user using the interaction AI 4110 (step S72). As a result, the IPC classification unit 4017 acquires interaction information (including work history information) from the user. Step S72 is an example of a collection unit that collects work history information related to the user's work history. Furthermore, step S72 is an example of a dialogue unit that collects work history information by interacting with the user. Information related to IPCs may be stored in the dictionary database 423. In this case, the IPC classification unit 4017 may refer to the dictionary database 423 to determine the IPC to be presented to the user.

[0228] Next, the IPC classification unit 4017 presents to the user several IPCs that are considered to correspond to the user's skills (step S73). Next, the IPC classification unit 4017 determines whether the user has performed an operation to select an IPC from the presented IPCs (step S74).

[0229] If the user has not performed an operation to select an IPC, the IPC classification unit 4017 determines the user's request (step S76). More specifically, if the user has requested the re-creation of an IPC, the IPC classification unit 4017 returns the process to step S73, changes the IPC to be presented, and presents several IPCs to the user again. This process is executed, for example, when a click on button 602 shown in FIG. 24 is detected. If the user has requested a redo of the dialogue, the IPC classification unit 4017 returns the process to step S72 and engages in dialogue with the user again. This process is executed, for example, when a click on button 603 shown in FIG. 24 is detected.

[0230] For example, if the IPC classification unit 4017 detects a click on button 601 shown in FIG. 24 , it determines that the user is selecting an IPC. In this case, the IPC classification unit 4017 confirms the IPC to be included in the resume based on the user's operation (step S75) and terminates the processing based on this flowchart. The generation server 400 registers the IPC confirmed in step S75 in the user database 421 as part of the work history information. Therefore, the work history information includes the IPC. In this flowchart, steps S72 to S75 are an example of a collection unit that collects patent classification information related to the user's work based on information acquired from the user through dialogue. As in steps S72 to S75, the collection unit presents the user with multiple IPCs related to the user's work based on the information acquired from the user through dialogue, and then confirms the IPC selected by the user as the user's work history information.

[0231] Next, several examples of resumes will be described with reference to Figures 27 to 30. Figures 27 to 30 are diagrams showing examples of resumes including IPC classifications. The resumes shown in Figures 27 to 30 include basic information, job content, and IPCs related to the job content. In particular, Figure 27 shows a resume format in which IPC items are listed separately from job content items. In contrast, Figure 28 shows a resume format in which the corresponding IPCs are incorporated into the description of job content.

[0232] A format may be adopted in which IPC items are listed separately from job content items, and the corresponding IPC is incorporated into the job content description. Figure 29 shows an example of a resume created using such a format.

[0233] If the user is listed as an inventor in a patent application, the resume may include a patent section, as shown in Figure 30. The patent section may include the patent information along with the corresponding IPC. By listing the user's patent information along with the IPC in the resume, the user can more effectively demonstrate their expertise.

[0234] In this way, IPCs corresponding to the user's skills can be entered in the resume in various formats. The generation server 400 stores information regarding job content, work experience, and employment period as work history information in the user database 421, linking the information with the IPC. When the generation server 400 acquires information regarding the user's patents, as shown in FIG. 30, the generation server 400 stores the patent information as work history information in the user database 421, linking the information with the IPC. Furthermore, the generation server 400 stores the resumes illustrated in FIGS. 27 to 30 in the user database 421. The generation server 400 may allow the user to select the style of the resume through a dialogue. Note that the dialogue is a concept that includes expressions of intent (such as questions and answers) exchanged between the generation server 400 and the user.

[0235] Here, the patent classification has been described as an example of format information containing the necessary information for a resume. The patent classification is not limited to IPC, and F-terms, FI-terms, CPC (Cooperative Patent Classification), etc. may also be used.

[0236] [Other Modifications] Fig. 31 is a diagram showing an example in which RAG (Retrieval-Augmented Generation) technology is utilized in the functional configuration of the generation server 400. RAG is a technology in which a large-scale language model is configured to access a knowledge source containing up-to-date, accurate information, and the large-scale language model generates answers based on the knowledge source. As is well known, large-scale language models may occasionally output inaccurate or misleading information, which is called hallucination. RAG can compensate for such imperfect operation of the large-scale language model and improve the quality of answers generated by the large-scale language model.

[0237] To improve the accuracy of IPC selection, an IPC database 429 storing IPCs, patent information, etc. may be provided in the generation server 400, as shown in Fig. 31. The large-scale language model 430 acquires IPCs from the IPC database 429, and identifies IPC candidates that are thought to correspond to the user's skills based on the acquired IPCs.

[0238] This prevents the large-scale language model 430 from outputting an answer that corresponds to hallucination. As a result, the accuracy and reliability of the large-scale language model 430 can be improved. Furthermore, the system administrator can easily access the information source from which the large-scale language model 430 derived an answer. This allows the system administrator to easily determine the accuracy of the answer obtained from the large-scale language model 430.

[0239] In general, by adopting RAG technology, it is possible to create an interactive AI that can specialize in the desired function and obtain more precise answers. For example, by including internal company confidential information, it is possible to create an interactive AI that is intended for use exclusively within the company. It is also possible to input large amounts of information according to the purpose into the large-scale language model 430. Another advantage is that the type of large-scale language model 430 can be easily switched.

[0240] In general, in order to obtain appropriate answers from generative AI, it is considered important to improve the quality of prompts input by a user when interacting with the generative AI. Prompt engineering is known as a technique for improving the quality of such prompts. Prompt engineering is a technique for developing and optimizing prompts to be provided to a large-scale language model in order to efficiently use the large-scale language model. Prompt engineering is a method for optimizing output for a specific task, assuming the use of an existing model.

[0241] In contrast to this, there is the concept of fine-tuning. Fine-tuning is a technique for improving the performance of an existing model for a specific task by additional training. In fine-tuning, at least a portion of a trained model generated based on one dataset is additionally trained based on another dataset. This fine-tunes the parameters of the machine learning model for a specific task. In a broad sense, fine-tuning can also be interpreted as a type of transfer learning. However, the two differ in that fine-tuning is a technique for fine-tuning the weights of all layers of a trained model, while transfer learning is a technique for fixing the weights of a trained model and training using only the added layers.

[0242] Fine-tuning has the problem of requiring enormous computational resources because it requires additional training of a large-scale language model with many parameters. Prompt tuning solves this problem by taking a different approach from fine-tuning. In prompt tuning, the prompt itself is the learning target. In other words, in prompt tuning, the parameters corresponding to the prompt are the targets of optimization.

[0243] RLHF (Reinforcement Learning from Human Feedback) is a model learning method that combines "supervised learning," "reinforcement learning," and "inverse reinforcement learning." RLHF allows AI to learn complex tasks such as natural language processing while minimizing the need for human involvement, such as in supervised learning. Therefore, the large-scale language model 430 may be trained using such RLHF.

[0244] In this embodiment, a large-scale language model is used as an example of a natural language processing algorithm. However, algorithms that can be used as natural language processing algorithms are not limited to large-scale language models. For example, instead of large-scale language models, algorithms generated by rule-based techniques such as pattern matching may be used.

[0245] The user devices 500 (recruiter device 200 and applicant device 300) may not only be equipped with all of the processor, memory, communication interface, and input / output interface shown in FIG. 2, but may also be thin client systems using VDI (Virtual Desktop Infrastructure). A thin client system using VDI is a system in which a desktop environment on a server is transferred to a terminal in a remote location for use. The user devices 500 (recruiter device 200, applicant device 300), sharing server 100, and generation server 400 do not necessarily have to be independent devices. When using a thin client system such as the one described above, the functions of the user device 500, sharing server 100, and generation server 400 can be provided on the same aggregation server.

[0246] The databases 120 and 420 are not limited to relational databases, and object-type or NoSQL-type databases may also be used.

[0247] Each of the sharing server 100 and the generation server 400 is an example of a computing device. A computing device may be configured by a server (on-premise server, cloud server, etc.), a serverless system, etc. Here, an on-premise server is a server installed and managed in facilities managed within a company. A cloud server is a server (rented server) provided by another business operator via a network. A serverless system is a system in which computing and memory functions can be used only when necessary, without being aware of the existence of a server. Computing devices include servers and serverless systems. Servers include on-premise servers and cloud servers.

[0248] [Aspects] Aspects of the present disclosure are listed below.

[0249] (Section 1) The work history information collection device (generation server 400, 400A) described in Section 1 includes an acquisition unit (step S11) that acquires format information containing the necessary information to be included in a work history, a collection unit (steps S11, S22) that collects work history information related to the user's work history, and a memory unit (storage 403) that stores a natural language processing algorithm, and the collection unit collects work history information corresponding to the necessary information by interacting with the user using the natural language processing algorithm stored in the memory unit (step S22).

[0250] (Section 2) In addition to the work history information collection device described in Section 1, the work history information collection device described in Section 2 further includes a creation unit (step S53) that creates a work history using the work history information collected by the collection unit, and when the collection unit has completed collecting the work history information related to the necessary information, the creation unit creates the work history in accordance with the format information using a natural language processing algorithm stored in the memory unit (step S53).

[0251] (Section 3) The work history information collection device described in Section 3, in addition to the work history information collection device described in Section 2, further includes a work history database (user database 421) in which work history information already collected before the collection unit interacts, and if the work history information registered in the work history database contains information corresponding to the required information, the creation unit creates a work history based on the work history information registered in the work history database (step S13).

[0252] (Section 4) The work history information collection device described in Section 4, in addition to the work history information collection device described in Section 2 or 3, further includes an output unit configured to output the work history created by the creation unit to a user device (step S110, step S115: the generation server 400 may have the functions of the sharing server 100), and the user device is a recruiter device (200) operated by a recruiter or an applicant device (300) operated by an applicant in a matching system that matches recruiters who are recruiting contractors for work with applicants.

[0253] (Section 5) The work history information collection device described in Section 5, in addition to the work history information collection device described in any one of Sections 1 to 4, further includes a dictionary database (dictionary database 423), a registration unit (step S34) that registers the meanings of terms included in the work history information in the dictionary database, and a display unit (step S43) that displays the work history on a display device (user device 500), and if a term registered in the dictionary database is included in the work history, the display unit displays the meaning of the term on the display device (step S44).

[0254] (Section 6) The work history information collection device described in Section 6 is similar to the work history information collection device described in Section 5, in that the collection unit queries the user about the meaning of terms while interacting with the user (step S32).

[0255] (Section 7) The employment history information collection device described in Section 7 includes, in addition to the employment history information collection device described in any one of Sections 1 to 6, a natural language processing algorithm that includes a large-scale language model (large-scale language models 430, 430A, 430B).

[0256] (Clause 8) The work history information collection device described in clause 8 includes, in addition to the work history information collection device described in any one of clauses 1 to 7, the collection unit collects patent classification information related to the user's work based on information obtained from the user through dialogue (step S75), and the work history information includes the patent classification information.

[0257] (Clause 9) The work history information collection device described in clause 9 includes the same functions as the work history information collection device described in clause 8, in that the collection unit presents the user with multiple patent classification information related to the user's work based on information obtained from the user through dialogue, and then determines the patent classification information selected by the user as work history information (steps S72 to S75).

[0258] (Clause 10) The method described in clause 10 is a method for creating a work history information collection, the method including causing a computer to perform the steps of obtaining format information including required information for a work history document and collecting work history information related to a user's work history, wherein the collecting step includes collecting work history information corresponding to the required information by interacting with the user using a natural language processing algorithm.

[0259] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the description of the above embodiments, and is intended to include all modifications within the meaning and scope of the claims.

[0260] 1 Matching system, 50 Internet, 100 Sharing server, 101 Processor, 102 Memory, 103 Storage, 104 Communication interface, 120 Database (DB), 121 Company database (Company DB), 122 Member database (Member DB), 123 Community database (Community DB), 124 Recruitment job database (Recruitment job DB), 200, 200A, 200B, 200C Recruiter device, 201 Processor, 202 Memory, 203 Communication interface, 204 Input / output interface, 205 Display, 206 Operation unit, 300, 300A, 300B, 300C Applicant device, 301 Processor, 302 Memory, 303 Communication interface, 304 Input / output interface, 305 Display, 306 Operation unit, 400, 400A Generation server, 401 Processor, 402 Memory, 403 Storage, 404 Communication interface, 410 Program, 411 Dialogue program, 412 Confirmation program, 413 Creation program, 420 Database (DB), 421 User database (user DB), 422 Dialogue information database (dialogue information DB), 423 Dictionary database (dictionary DB), 426 Satisfying requirement database (satisfying requirement DB), 427 Original information storage database (original information storage DB), 428 Generative format database (generative format DB), 429 IPC database, 430, 430A, 430B Large-scale language model, 500, 500A User device, 550 Screen, 551 Question box, 552 Answer box, 601 to 603 Buttons, 4010, 4010A Control unit, 4011 Data import unit, 4012 Information acquisition unit, 4013 Dictionary information registration unit, 4014 Dictionary information display unit, 4015 information organization unit, 4016 resume registration unit, 4017 IPC classification unit, 4021 input unit, 4022 content sufficiency determination unit, 4023 input request unit, 4024 original information storage unit, 4025 generation unit, 4026 confirmation request unit, 4027 output unit, 4110 dialogue AI, 4120 confirmation AI, 4130 creation AI.

Claims

1. A work history information collection device, an acquisition unit that acquires format information including necessary information to be included in a resume; a collection unit that collects work history information relating to the user's work history; a storage unit storing a natural language processing algorithm; The collection unit collects work history information corresponding to the required items by interacting with a user using a natural language processing algorithm stored in the storage unit.

2. The system further includes a creation unit that creates a resume using the work history information collected by the collection unit, 2. The work history information collection device according to claim 1, wherein the creation unit creates a work history in accordance with the format information using a natural language processing algorithm stored in the memory unit when the collection unit has completed collection of work history information related to the required items.

3. The system further includes a work history database in which work history information collected before the interaction by the collection unit is registered, 3. The work history information collection device of claim 2, wherein the creation unit creates a work history based on the work history information registered in the work history database if the work history information registered in the work history database includes information corresponding to the required information.

4. An output unit configured to output the resume created by the creation unit to a user device, The work history information collection device of claim 2, wherein the user device is a recruiter device operated by a recruiter who is recruiting contractors for a job, or an applicant device operated by an applicant, in a matching system that matches applicants with recruiters who are recruiting contractors for a job.

5. A dictionary database, a registration unit that registers the meanings of terms included in the work history information in a dictionary database; Further comprising a display unit that displays the resume on a display device, 2. The employment history information collection device according to claim 1, wherein, when a term registered in the dictionary database is included in the employment history, the display unit displays the meaning of the term on the display device.

6. The work history information collection device according to claim 5 , wherein the collection unit inquires of the user about the meaning of the term while interacting with the user.

7. The apparatus of claim 1 , wherein the natural language processing algorithm includes a large-scale language model.

8. the collection unit collects patent classification information related to the user's job based on the information acquired from the user through the dialogue; The employment history information collection device according to claim 1 , wherein the employment history information includes the patent classification information.

9. The work history information collection device according to claim 8, wherein the collection unit presents the user with a plurality of pieces of patent classification information related to the user's work based on information acquired from the user through the dialogue, and then determines the patent classification information selected by the user as the work history information.

10. A creation unit that creates a resume using the work history information collected by the collection unit; a presentation unit that presents the resume created by the creation unit to a user; a reception unit that receives, from the user, an instruction to correct the resume presented by the presentation unit; The employment history information collection device according to claim 1 , wherein the creation unit amends the employment history in accordance with the amendment instruction received by the reception unit.

11. The work history information collection device described in Claim 1, wherein the collection unit determines whether or not all of the work history information related to the required items has been collected by interacting with the user.

12. A creation unit that creates a resume using the work history information collected by the collection unit; The system further includes a work history database in which work history information collected before the interaction by the collection unit is registered, 2. The work history information collection device according to claim 1, wherein the creation unit creates a work history using both the work history information registered in the work history database and the work history information collected by the collection unit.

13. 1. A method of collecting employment history information, comprising: The method includes: A step of obtaining format information including necessary information to be included in a resume; collecting work history information relating to the user's work history; The method, wherein the collecting step includes collecting work history information corresponding to the required entries by interacting with a user using a natural language processing algorithm.