Information processing device, method, program, and system

Abstract

To enable provision of assistance in considering questions to be used during recruiting interviews.SOLUTION: An aspect of a program disclosed herein causes a computer to function as means of acquiring recruitment information regarding recruitment by a company, means of acquiring anticipated questions to be used by the company to ask job applicants in recruiting interviews based on the recruitment information, and means of outputting the anticipated questions.SELECTED DRAWING: Figure 4

Description

[Technical field]

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

[0002] In order for a company to properly evaluate a job seeker during the selection process, it is effective to ask appropriate questions during the job interview. However, the appropriateness of the questions varies depending not only on the content of the job offer (e.g., the desired personality type) but also on the job seeker, so the burden of considering questions for the company is large, especially when interviewing a large number of job seekers.

[0003] On the other hand, it is also effective for job seekers to prepare in advance for possible questions and answers to be asked in a job interview in order to be properly evaluated. However, as the possible questions to be asked in a job interview may differ depending on the content of the job offer and the company offering the job, job seekers may be burdened with some consideration, especially when applying to multiple companies offering jobs.

[0004] Patent Document 1 discloses a technical idea intended to realize a novel questionnaire method for marketing research. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2007-272567 A Summary of the Invention [Problem to be solved by the invention]

[0006] The technical idea of ​​Patent Document 1 is directed to questionnaires for marketing research, and cannot be immediately applied to the consideration of questions in job interviews.

[0007] The purpose of this disclosure is to aid in the consideration of questions to be used in job interviews. [Means for solving the problem]

[0008] A program of one embodiment of the present disclosure causes a computer to function as a means for acquiring job information regarding job openings held by a company, a means for acquiring anticipated questions to be asked in a job interview that the company will ask applicants for the job based on the job information, and a means for outputting the anticipated questions. [Brief description of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a configuration of an information processing system according to an embodiment of the present invention; [Diagram 2] FIG. 2 is a block diagram showing a configuration of a client device according to the present embodiment. [Diagram 3] FIG. 2 is a block diagram showing a configuration of a server according to the present embodiment. [Figure 4] FIG. 1 is an explanatory diagram of one aspect of the present embodiment. [Diagram 5] FIG. 2 is a diagram showing an overall flow of information processing according to the present embodiment. [Figure 6] 11A to 11C are diagrams illustrating examples of screens displayed in the information processing of the present embodiment. [Figure 7] FIG. 13 is a diagram showing an overall flow of information processing in the first modified example. [Figure 8] 13A and 13B are diagrams showing examples of screens displayed in information processing according to the first modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In the drawings for explaining the embodiment, the same components are generally designated by the same reference numerals, and the repeated description will be omitted.

[0011] (1) Information Processing System Configuration The configuration of the information processing system will be described below. Fig. 1 is a block diagram showing the configuration of the information processing system according to the present embodiment.

[0012] As shown in FIG. 1, the information processing system 1 includes a client device 10 and a server 30. The client device 10 and the server 30 are connected via a network (eg, the Internet or an intranet) NW.

[0013] The server 30 provides a response to a request transmitted from the client device 10 to the client device 10. The server 30 is an example of an information processing device. The server 30 is, for example, a server computer.

[0014] The server 30 can manage a talent matching service (which can also be called a "talent matching platform") in which job seekers and recruiting companies participate. The talent matching service corresponds to a place that connects job seekers and recruiting companies. In this specification, a job seeker includes a person who is changing jobs or (re)hunting jobs, a person who is interested in these activities, or a person who intends to seek work. For example, a job seeker can include a person who registers his / her information on the talent matching service or prepares to register (e.g., uploads a resume file or a curriculum vitae file, or inputs his / her information). Similarly, a recruiting company includes a corporation or individual who intends to hire. For example, a recruiting company can include a person who uses the talent matching service to carry out recruitment activities (e.g., sends scouting documents, registers or prepares to register job information, notifies an offer, etc.).

[0015] The client device 10 transmits a request to the server 30. The client device 10 is an example of an information processing device. The client device 10 is, for example, a smartphone, a tablet terminal, or a personal computer.

[0016] The user of the client device 10 is typically a person who belongs to the company offering a job (for example, a recruiter at the company offering a job). Here, the recruiter at the company offering a job is not limited to a person in the human resources department, but may be, for example, a person in a department that is looking to hire someone through the job offer. The user of the client device 10 may be a job seeker, but for convenience, the following description of this embodiment will be based on the assumption that the user is a person who belongs to the company offering a job, unless otherwise specified.

[0017] Here, an example of the behavior of the server 30 with regard to the management of the human resources matching service will be described. Specifically, the server 30 makes the profile information provided by the job seeker viewable (for example, published anonymously and searchable) by, for example, the recruiting staff of the hiring company. The recruiting staff can create and send scouting documents to job seekers who are deemed suitable for their company. Alternatively, the server 30 publishes the job information provided by the hiring company in, for example, a state that can be viewed by job seekers. The job seeker can create a resume and apply for a job at a hiring company that he or she deems suitable for him or her. In this way, hiring companies and job seekers can be linked together.

[0018] (1-1) Client device configuration The configuration of the client device will now be described with reference to Fig. 2, which is a block diagram showing the configuration of the client device of this embodiment.

[0019] 2, the client device 10 includes a storage device 11, a processor 12, an input / output interface 13, and a communication interface 14. The client device 10 is connected to a display 21.

[0020] The storage device 11 is configured to store programs and data. The storage device 11 is, for example, a combination of a Read Only Memory (ROM), a Random Access Memory (RAM), and a storage (for example, a flash memory or a hard disk).

[0021] The programs include, for example, the following programs: ·OS (Operating System) programs · Programs for applications that process information (e.g. web browsers or dedicated applications)

[0022] The data includes, for example, the following data: Databases referenced in information processing Data obtained by performing information processing (i.e., the results of performing information processing)

[0023] The processor 12 is a computer that realizes the functions of the client device 10 by running a program stored in the storage device 11. The processor 12 is, for example, at least one of the following: ·CPU(Central Processing Unit) ·GPU(Graphic Processing Unit) ·ASIC(Application Specific Integrated Circuit) ·FPGA(Field Programmable Gate Array)

[0024] The input / output interface 13 is configured to obtain information (eg, a user's instruction) from an input device connected to the client device 10, and to output information (eg, an image) to an output device connected to the client device 10.

[0025] The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof. The output device is, for example, a display 21, a speaker, or a combination thereof.

[0026] The communication interface 14 is configured to control communications between the client device 10 and an external device (eg, a server 30).

[0027] The display 21 is configured to display an image (still image or moving image). The display 21 is, for example, a liquid crystal display or an organic EL display.

[0028] (1-2) Server configuration The configuration of the server will now be described with reference to Fig. 3, which is a block diagram showing the configuration of the server according to this embodiment.

[0029] As shown in FIG. 3, the server 30 includes a storage device 31, a processor 32, an input / output interface 33, and a communication interface .

[0030] The storage device 31 is configured to store programs and data, and is, for example, a combination of a ROM, a RAM, and a storage (for example, a flash memory or a hard disk).

[0031] The programs include, for example, the following programs: -OS programs Application programs that perform information processing

[0032] The data includes, for example, the following data: Databases referenced in information processing Results of information processing

[0033] The processor 32 is a computer that realizes the functions of the server 30 by starting a program stored in the storage device 31. The processor 32 is, for example, at least one of the following. ·CPU GPU ·ASIC FPGA

[0034] The input / output interface 33 is configured to obtain information (eg, a user's instruction) from an input device connected to the server 30, and to output information (eg, an image) to an output device connected to the server 30.

[0035] The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof. The output device is, for example, a display.

[0036] The communication interface 34 is configured to control communications between the server 30 and external devices (eg, the client device 10).

[0037] (2) One aspect of the embodiment An embodiment of the present invention will now be described with reference to Fig. 4, which is an explanatory diagram of an embodiment of the present invention.

[0038] As shown in Figure 4, a user US1, corresponding to a recruiter at a company offering a job, uses the information processing system 1 to receive assistance in considering questions (hereinafter referred to as "anticipated questions") that may be asked to a job seeker during a recruitment interview conducted with the person who applies for a job offered by the company offering a job.

[0039] Specifically, the user US1 operates the client device 10 to input or specify information about a job offer corresponding to a job interview for which expected questions are to be considered (hereinafter referred to as “job offer information”). The client device 10 transmits information that can identify the job offer information (e.g., a uniform resource locator (URL), identification information, etc.) to the server 30.

[0040] The server 30 acquires job information based on information received from the client device 10. The server 30 generates model input information for the natural language model LM5 based on at least the job information. The server 30 inputs the generated model input information to the natural language model LM5, thereby acquiring the anticipated question generated by the natural language model LM5.

[0041] Here, the natural language model LM5 is a trained model that performs natural language processing according to, for example, an instruction sentence (an example of "model input information") and generates a response sentence. In this example, the model input information is text based on job information. The natural language model LM5 may be an interactive (also called chat or conversational) model that alternates between accepting model input information and generating a response sentence. The natural language model LM5 may be a large-scale language model trained with a large amount of text data, or a model that is obtained by transfer learning or fine tuning the large-scale language model. The natural language model LM5 may also be constructed in a system external to the information processing system 1.

[0042] The server 30 transmits the acquired assumed questions to the client device 10. The client device 10 presents the assumed questions received from the server 30 to the user US1.

[0043] This allows user US1 to obtain anticipated questions to be asked in a hiring interview for a desired job offer without the user US1 having to consider appropriate questions by himself / herself in light of the purpose of the job offer at the hiring company. User US1 can use the anticipated questions as they are in the hiring interview, or can refine questions to be used in the hiring interview based on the anticipated questions. In this way, information processing system 1 can support consideration of questions to be used in a hiring interview.

[0044] (3) Information processing The information processing of this embodiment will be described below. Fig. 5 is a diagram showing an overall flow of the information processing of this embodiment. Fig. 6 is a diagram showing an example of a screen displayed in the information processing of this embodiment.

[0045] The process of FIG. 5 may be started, for example, by the client device 10 accessing a talent matching service and receiving an instruction from a user (e.g., a recruiter at a recruiting company) to start creating anticipated questions for a recruitment interview for a job offered by the recruiting company.

[0046] As shown in FIG. 5, the server 30 executes information acquisition (S130). Specifically, the server 30 receives information used to generate the model input information or information capable of identifying the information from the client device 10. The server 30 acquires the information used to generate the model input information based on the received information.

[0047] The information used to generate the model input information includes at least a job posting. The job posting may include information regarding at least one of the following: Information about job openings Information about job requirements (required of applicants) Information about the benefits offered in the job offer Information about companies hiring

[0048] The information about the job type being recruited may be, for example, information about the job type, the position (e.g., manager), development orientation (contract development or in-house development), the work content, or the attractiveness of the job.

[0049] The information on the job requirements may be, for example, the skills to be used, the required skills, the welcome skills, the required experience (for example, whether a certain number of years of work experience is required or not), the welcome experience, or the information on the desired personality.

[0050] The information regarding benefits may be, for example, information regarding the development environment, annual salary, or working conditions (which may include factors such as whether remote work is possible or how often work is required in the office).

[0051] The information about the recruiting company may be, for example, the type and size of the company's industry, or cultural information (such as MVV (Mission Vision Value)).

[0052] As a first example of obtaining job information, the user inputs job information from the company to which the user belongs that corresponds to the employment interview that is the subject of expected questions, into an object that accepts information arranged on a UI (User Interface) screen of the human resources matching service that is displayed on the display 21 of the client device 10. The client device 10 transmits the content input into this object to the server 30 as job information.

[0053] As a second example of obtaining job information, the user selects one corresponding to the employment interview to be the subject of anticipated questions from a list of job information from the company to which the user belongs, on the UI screen of the human resources matching service displayed on the display 21 of the client device 10. The client device 10 transmits information identifying the specified information (for example, a job ID identifying the job corresponding to the specified job information, or a URL at which the specified job information can be viewed) to the server 30 as information identifying the job information.

[0054] As a third example of acquiring job information, the server 30 may acquire job information by crawling the Web.

[0055] The information used to generate the model input information may include information other than the job listing. The information other than the job listing may include, for example, at least one of the following: ·Job applicant information Rating-related information ·Prohibited items information History information ·Interviewee information However, it is also possible to interpret that at least one of the evaluation-related information, history information, or interviewee information is included in the job information.

[0056] The job seeker information may include information regarding at least one of the personal attributes, career history, skills, preferences, or job-seeking intentions of a specific job seeker to whom the anticipated questions are to be asked (however, the specific job seeker is not limited to an actual person but may be a fictitious person such as a anticipated applicant).

[0057] Information regarding the personal attributes of a job seeker (hereinafter referred to as "personal attribute information") may include, for example, at least one of the job seeker's name, gender, age, place of residence, or current annual income.

[0058] Information regarding the job seeker's career (hereinafter referred to as "career information") may include, for example, at least one of the job seeker's job type, job title, place of employment, work history (which may include, for example, length of employment, development track record, job types experienced, or an overview of projects experienced), or educational background.

[0059] Information regarding the job seeker's skills (hereinafter referred to as "skill information") may include, for example, at least one of the job seeker's technical skills, business skills, skills, language ability (e.g., English proficiency), technology used (e.g., programming language used), and aptitude test results.

[0060] Information regarding the job seeker's preferences (hereinafter referred to as "preference information") may include, for example, at least one of the job seeker's preferences (e.g., whether they prefer in-house development or contract development), advantages, or disadvantages.

[0061] Information regarding a job seeker's job search intentions (hereinafter referred to as "job search intention information") may include, for example, at least one of the strength of the desire to change jobs, the strength of the desire to quickly change jobs, what the job seeker would like to do in the future (specifically, what is desired in the next job, such as desired job type, desired duties, desired work location, desired remote work or frequency of coming into the office, or desired career), what the job seeker does not want to do in the future, or desired income.

[0062] As a first example of acquiring job seeker information, the user inputs, for example, by copying and pasting, information about the job seeker into an object for accepting information arranged on the UI screen of the human resources matching service displayed on the display 21 of the client device 10. The client device 10 transmits the content input into this object to the server 30 as job seeker information.

[0063] As a second example of acquiring job seeker information, a user specifies information of a desired job seeker on a UI screen of a talent matching service displayed on the display 21 of the client device 10. For example, in the talent matching service, job seeker information is registered by the job seeker so that it can be viewed, and a user (a person in charge at a recruiting company) can specify information of a job seeker with whom the recruiting company plans to have a job interview. Alternatively, if the talent matching service provided by the information processing system 1 also has a recruitment management function, a user may specify information of a job seeker with whom a job interview is scheduled from information of job seekers who have applied for a job at a recruiting company, which is managed by the function. The client device 10 transmits information identifying the specified job seeker information (for example, a user ID identifying a job seeker corresponding to the specified job seeker information, or a URL at which the specified job seeker information can be viewed) to the server 30 as information that can identify the job seeker information.

[0064] As a third example of acquiring job seeker information, the server 30 may acquire job seeker information based on information accumulated in an external system by a job seeker using the external system. Specifically, the server 30 may acquire job seeker information from an external system using account information (e.g., account name, login information, or a combination thereof) of the job seeker in a service provided by the external system (hereinafter referred to as an "external service"). The server 30 may acquire job seeker information using an application programming interface (API) of the external service, or may acquire job seeker information by crawling a web page of the external service. The server 30 may acquire information on content posted by the job seeker, log information indicating the activity of the job seeker in an issue management service, or information on the profile registered by the job seeker in the external service. The external service may include, but is not limited to, Qiita (registered trademark), Zenn (registered trademark), LinkedIn (registered trademark), note (registered trademark), Slide Share, or Speaker Deck.

[0065] Furthermore, the information that can identify the job seeker information may be a resume prepared by the job seeker, or a memo recording what the job seeker said during the selection process at the hiring company. In this case, the server 30 may extract text that corresponds to, for example, personal attribute information, career information, skill information, preference information, or job-seeking intention information from the acquired information (document). A large-scale language model may be used for such processing.

[0066] The job seeker information may include information that the user (the recruiter at the company offering a job) is not permitted to view. For example, job-seeking intention information may be information that is not permitted to be viewed by anyone other than the job seeker.

[0067] The evaluation-related information is information on at least one of the evaluation viewpoints or evaluation criteria for a recruiting company to determine whether or not to accept an applicant for a job corresponding to a hypothetical question. For example, the evaluation-related information may include checkpoints, points added, points subtracted, cutoff scores, or pass / fail criteria. The evaluation-related information may be determined in advance for each industry of the hiring company, for each hiring company, for each job type targeted by the hiring, or for each job offer, or may be specified by the user each time.

[0068] The prohibited matter information is information about matters that should not be asked in a job interview. For example, the prohibited matter information may include the birthplace, family, assets, or beliefs of a job applicant.

[0069] The prohibited item information may be predetermined for each business type of a hiring company, for each hiring company, for each job type targeted by the hiring, for each job posting, or may be common to all companies. Alternatively, the user may specify the prohibited item information each time.

[0070] The history information is information about a selection process that a recruiting company has already conducted for a specific job seeker who applied for a job corresponding to the expected question. For example, the history information may include information about at least one of the questions and answers in an interview conducted before the employment interview that is the subject of the expected question, or information about matters handed over from a person in charge of the selection process conducted before the employment interview.

[0071] The interviewer information is information about the interviewer who will conduct the job interview that will be the subject of the anticipated questions. The interviewer information may include, for example, the job title of the interviewer, or the expectations that the interviewer has of the job seeker, etc.

[0072] After step S130, the server 30 executes generation of model input information (S131). Specifically, the server 30 generates model input information for causing the natural language model LM5 to generate a hypothetical question based on the information acquired in step S130. As an example, the server 30 generates text based on the information acquired in step S130 as the model input information. The text may correspond to an instruction sentence for the natural language model LM5.

[0073] The model input information may include information corresponding to constraints on aspects (eg, output format) of the anticipated question (ie, the response sentence generated by the natural language model LM5). For example, the constraints may be at least one of the following: -Constraints on what you want to follow when generating potential questions -Constraints on what you do not want to follow when generating hypothetical questions

[0074] In short, the model input information may be a sentence (instruction sentence) that instructs the natural language model LM5 to create anticipated questions for a job interview that a recruiting company will ask to applicants for a job based on the information acquired in step S130 and so as to satisfy the constraint conditions. Also, information (template) that is the source of the model input information may be stored in the storage device 31 of the server 30, and the server 30 may generate the model input information by applying the information acquired in step S130 to the template. The constraint conditions may be embedded in the template in advance.

[0075] As a first example of generating the model input information (S131), the server 30 generates the model input information based on the job information and not based on the job seeker information, which enables the server 30 to obtain general-purpose assumed questions that are not directed to a specific job seeker.

[0076] As a second example of generating model input information (S131), the server 30 generates the model input information based on the job information and the job seeker information. This enables the server 30 to obtain expected questions suitable for a specific job seeker (i.e., a job seeker corresponding to the job seeker information). Note that in this example, the server 30 may replace the personal information included in the job seeker information with information that has been anonymized or pseudonymized. The server 30 generates the model input information further based on the history information, thereby enabling it to obtain expected questions that are more suitable for a specific job seeker (taking into account interactions during the selection process that has already been carried out).

[0077] In each of the above examples, the server 30 may generate the model input information to include information that constrains the output format of the natural language model LM5 based on at least one of the career information and the skill information among the job seeker information, in particular. This allows the server 30 to obtain the anticipated questions in a format that allows identification of the relevance of the anticipated questions to at least one of the career information and the skill of a specific job seeker.

[0078] In each of the above examples, the server 30 may generate model input information further based on the evaluation-related information to acquire anticipated questions in line with the evaluation viewpoints. As an example, the server 30 may generate the model input information based on the evaluation-related information so as to include information that constrains the output format of the natural language model LM5. This allows the server 30 to acquire anticipated questions in a format that allows identification of the association between the anticipated questions and the evaluation viewpoints.

[0079] In each of the above examples, the server 30 may generate model input information to include information that constrains the output format of the natural language model based on the prohibited matter information, thereby enabling the server 30 to obtain anticipated questions that do not deal with matters that should not be asked in a job interview.

[0080] In each of the above examples, the server 30 can generate model input information further based on the interviewee information to obtain hypothetical questions appropriate to the interviewee's position.

[0081] After step S131, the server 30 acquires anticipated questions (S132). Specifically, the server 30 inputs the model input information generated in step S131 into the natural language model LM5, thereby acquiring the assumed question (text) generated by the natural language model LM5.

[0082] After step S132, the server 30 executes output of a hypothetical question (S133). Specifically, the server 30 outputs the anticipated question acquired in step S133. As an example, the server 30 presents the anticipated question to the user via the client device 10. That is, the server 30 transmits the anticipated question to the client device 10. The client device 10 displays the received anticipated question on the display 21. For example, the client device 10 may display the screen of FIG. 6 on the display 21.

[0083] 6 includes an object J20. The object J20 displays the assumed questions acquired in step S132. If multiple assumed questions have been acquired, the object J20 may display them in a list format.

[0084] The output of the anticipated questions (S133) may be performed by presentation on an app screen or a website screen, or by transmission of an email or other message (e.g., a Social Networking Service (SNS) message, a Short Message Service (SMS) message, etc.). The output of the anticipated questions (S133) may also include a notice by posting a message on an electronic bulletin board, chat tool, or SNS in a format viewable by the user (and further by a legitimate authority such as the user's supervisor). The output of the anticipated questions (S133) may also include provision as a file in, for example, CSV format.

[0085] (4) Summary As described above, the server 30 acquires job information on a job offered by a company, acquires expected questions to be asked in a job interview that the company will ask to applicants for the job based on the job information, and outputs the expected questions. This allows the user to obtain expected questions to be asked in a job interview for the job offer without the user having to consider appropriate questions by himself / herself in consideration of the purpose of the desired job offer at the company. In other words, the server 30 can assist in the consideration of questions to be used in the job interview. In other words, if the user is a recruiter at the company, the user can use the expected questions as they are in the job interview, or can refine questions to be used in the job interview based on the expected questions. On the other hand, if the user is a job seeker, the user can organize in advance his / her own answers to questions that are likely to be asked in the job interview at the company.

[0086] The server 30 may acquire anticipated questions by inputting model input information based on job information into the natural language model. This makes it possible to acquire appropriate anticipated questions without defining rules for generating anticipated questions.

[0087] The server 30 may acquire job seeker information on a specific job seeker, and acquire anticipated questions by inputting model input information based on the job seeker information and job information into the natural language model. This makes it possible to acquire anticipated questions suitable for a specific job seeker. In particular, when a user matches a specific job seeker, the user can prepare in advance his or her own answers to questions that are likely to be asked in a job interview at a company offering a job.

[0088] The model input information may be based on job information and information in which personal information included in the job seeker information has been anonymized or pseudonymized, thereby protecting the personal information of the job seeker.

[0089] The job seeker information may include information on at least one of the job seeker's personal attributes, career history, skills, preferences, or job-seeking intentions. This makes it possible to acquire hypothetical questions that take into account the job seeker's personal attributes, career history, skills, preferences, or job-seeking intentions.

[0090] The job seeker information may include information on at least one of the career or skills of a specific job seeker. The server 30 may obtain the anticipated questions in a format that allows identification of the association between the anticipated questions and at least one of the career or skills of a specific job seeker by inputting information that constrains the output format of the natural language model based on the job seeker information and model input information based on the job information to the natural language model. This makes it possible to obtain anticipated questions related to the career or skills of a specific job seeker.

[0091] The server 30 may acquire anticipated questions in a format that allows identification of the relationship between the anticipated questions and the evaluation viewpoints by inputting model input information based on the job information and information that restricts the output format of the natural language model based on evaluation-related information related to evaluation viewpoints used by the company to determine whether or not to hire applicants for a job. This makes it possible to acquire anticipated questions related to the evaluation viewpoints for hiring.

[0092] The server 30 may acquire prohibited matter information on matters that should not be asked in a job interview, and acquire anticipated questions by inputting information that restricts the output format of the natural language model based on the prohibited matter information and model input information based on the job information to the natural language model. This makes it possible to prevent the acquisition of anticipated questions with inappropriate content.

[0093] The server 30 may acquire anticipated questions based on evaluation-related information related to at least one of the evaluation viewpoints or evaluation criteria for a company to determine whether or not to hire an applicant for a job. This makes it possible to acquire anticipated questions that take into account the evaluation viewpoints or evaluation criteria for employment.

[0094] The server 30 may acquire anticipated questions based on history information regarding a selection process that has already been carried out by a company for a specific job seeker who applied for a job. This makes it possible to acquire anticipated questions that take into account the selection process that has already been carried out.

[0095] The history information may be related to at least one of the contents of questions and answers in an interview conducted before the job interview, or matters handed over from a person in charge of a selection process conducted before the job interview. This makes it possible to obtain anticipated questions taking into account the contents of questions and answers or matters handed over in a selection process that has already been conducted.

[0096] The job information may include interviewer information about the interviewer who will conduct the job interview. The server 30 may acquire anticipated questions based on the interviewer information. This makes it possible to acquire anticipated questions suitable for the interviewer.

[0097] The job information may include information about the job type, employment conditions, or benefits, or about the company, so that appropriate anticipated questions can be obtained.

[0098] (5) Variations A modification of this embodiment will now be described.

[0099] (5-1) Variation 1 Variation 1 will be described. Variation 1 is an example in which model answers to assumed questions are further acquired. In Variation 1, the user of the client device 10 is typically a job seeker. However, Variation 1 is also applicable even if the user is a person who belongs to a company that is hiring.

[0100] The following describes information processing of Modification 1. Fig. 7 is a diagram showing an overall flow of information processing of Modification 1. Fig. 8 is a diagram showing an example of a screen displayed in the information processing of Modification 1.

[0101] The process of FIG. 7 may be initiated, for example, by the client device 10 accessing a talent matching service and receiving an instruction from a user (e.g., a job seeker) to begin creating anticipated questions to be asked in a recruitment interview for a job offered by a company hiring, and model answers to those anticipated questions.

[0102] As shown in FIG. 7, the server 30 executes the processes of obtaining information (S130), generating model input information (S131), and obtaining anticipated questions (S132) in the same manner as in FIG.

[0103] After step S132, the server 30 executes generation of model input information (S234). Specifically, the server 30 generates model input information for causing the natural language model LM5 to generate a model answer based on the information acquired in step S130 (particularly, job seeker information) and the expected question acquired in step S132. As an example, the server 30 generates text based on the information acquired in step S130 and the expected question acquired in step S132 as the model input information. The text may correspond to an instruction sentence for the natural language model LM5. Note that, when information different from the information acquired in step S130 (e.g., job information) (e.g., job seeker information, history information, etc.) is required in this step, the server 30 may acquire the information.

[0104] The model input information may include information corresponding to constraints on aspects (eg, output format) of model answers (ie, response sentences generated by the natural language model LM5). For example, the constraints may be at least one of the following: -Constraints on what should be followed when generating model answers -Constraints on what should not be followed when generating model answers

[0105] In short, the model input information may be a sentence (instruction sentence) instructing the natural language model LM5 to create a model answer to the anticipated question based on the information acquired in step S130 and the anticipated question acquired in step S132 so as to satisfy the constraint conditions. Furthermore, information (template) that is the source of the model input information may be stored in the storage device 31 of the server 30, and the server 30 may generate the model input information by applying the information acquired in step S130 and the anticipated question acquired in step S132 to the template. The constraint conditions may be embedded in the template in advance.

[0106] As an example, the server 30 generates model input information based on the job seeker information and the expected questions. This allows the server 30 to obtain model answers to the expected questions that take into consideration the personal attributes, career history, skills, preferences, or job-seeking intentions of a specific job seeker (a job seeker corresponding to the job seeker information). Note that in this example, the server 30 may replace personal information included in the job seeker information with information that has been anonymized or pseudonymized. The server 30 generates model input information based further on history information, thereby allowing it to obtain model answers that are more suitable for a specific job seeker (taking into consideration interactions during the selection process that has already been carried out).

[0107] After step S234, the server 30 acquires a model answer (S235). Specifically, the server 30 inputs the model input information generated in step S234 into the natural language model LM5, thereby acquiring the model answer (text) generated by the natural language model LM5.

[0108] After step S235, the server 30 executes output of assumed questions and model answers (S236). Specifically, the server 30 outputs the expected question acquired in step S132 and the model answer acquired in step S236. As an example, the server 30 presents the expected question and model answer to the user via the client device 10. That is, the server 30 transmits the expected question and model answer to the client device 10. The client device 10 displays the received expected question and model answer on the display 21. For example, the client device 10 may display the screen of FIG. 8 on the display 21.

[0109] 8 includes an object J21. The object J21 displays the expected question obtained in step S132 and the model answer obtained in step S235. If multiple sets of expected questions and model answers have been obtained, the object J21 may display them in a question-and-answer format.

[0110] The output of the expected questions and model answers (S236) may be performed by presentation on an app screen or a website screen, or by transmission of an email or other message (e.g., an SNS message, an SMS message, etc.). The output of the expected questions and model answers (S236) may include, for example, a notice by posting a message on an electronic bulletin board, a chat tool, or an SNS in a format viewable by the user (and by a legitimate authority such as the user's supervisor). The output of the expected questions and model answers (S236) may also include provision in the form of a file, for example in CSV format.

[0111] In addition, in the first modification, the generation of model input information (S131) ​​to the acquisition of expected questions (S132) and the generation of model input information (S234) to the acquisition of model answers (S235) may be integrated. In other words, the server 30 may input model input information for generating both expected questions and model answers to the natural language model LM5, thereby acquiring both expected questions and model answers together.

[0112] As described above, the server 30 of the first modification acquires job seeker information on a specific job seeker, acquires model answers to anticipated questions based on the job seeker information, and outputs the model answers. This allows the user to obtain the model answers without having to consider answers to the anticipated questions by himself / herself.

[0113] (6) Other modifications The storage device 11 may be connected to the client device 10 via a network NW. The display 21 may be integrated with the client device 10. The storage device 31 may be connected to the server 30 via the network NW.

[0114] Each step of the above information processing can be executed by either the client device 10 or the server 30. In addition, in the above description, an example in which each step in the information processing is executed in a specific order has been shown, but the execution order of each step is not limited to the example described above as long as there is no dependency between the steps.

[0115] Although the embodiment of the present invention has been described in detail above, the scope of the present invention is not limited to the above embodiment. Furthermore, the above embodiment can be improved or modified in various ways without departing from the spirit of the present invention. Furthermore, the above embodiment and the modified examples can be combined.

[0116] (6) Additional Notes The matters described in the embodiment and the modified examples are supplemented below.

[0117] (Appendix 1) A computer (30), A means for acquiring job information regarding job openings by companies (S130); A means for acquiring anticipated questions to be asked in a job interview to be conducted by a company for a person who applies for the job based on the job information (S132); A means for outputting anticipated questions (S133, S236); A program that functions as a

[0118] (Appendix 2) The means for acquiring anticipated questions includes acquiring anticipated questions by inputting model input information based on job information into a natural language model; 2. The program described in Appendix 1.

[0119] (Appendix 3) The computer is caused to function as a means for acquiring job seeker information relating to a specific job seeker (S130); The means for acquiring anticipated questions acquires anticipated questions by inputting model input information based on job seeker information and job information into a natural language model; 2. The program described in Appendix 1.

[0120] (Appendix 4) The model input information is based on anonymous or pseudonymized personal information contained in job seeker information and job information. 2. The program described in Appendix 3.

[0121] (Appendix 5) The job seeker information includes information on at least one of the job seeker's personal attributes, career history, skills, preferences, or job-seeking intentions; 2. The program described in Appendix 3.

[0122] (Appendix 6) The job applicant information includes information regarding at least one of the background or skills of a particular job applicant; The means for acquiring the anticipated questions includes inputting information that constrains an output format of the natural language model based on the job seeker information and model input information based on the job information into the natural language model, thereby acquiring the anticipated questions in a format that allows identification of a relationship between the anticipated questions and at least one of the career history or skills of a specific job seeker; 2. The program described in Appendix 3.

[0123] (Appendix 7) The means for acquiring anticipated questions includes inputting, into the natural language model, information that constrains the output format of the natural language model based on evaluation-related information related to evaluation viewpoints used by the company to determine whether or not to hire applicants for the job and model input information based on the job information, thereby acquiring anticipated questions in a format that allows identification of the relationship between the anticipated questions and the evaluation viewpoints; 2. The program described in Appendix 2.

[0124] (Appendix 8) The computer is caused to function as a means for acquiring prohibited question information (S130) regarding questions that should not be asked in a job interview; The means for acquiring the anticipated questions acquires the anticipated questions by inputting, to the natural language model, information that restricts an output format of the natural language model based on the prohibited matter information and model input information based on the job information. 2. The program described in Appendix 2.

[0125] (Appendix 9) The means for acquiring anticipated questions acquires anticipated questions based on evaluation-related information regarding at least one of evaluation perspectives or evaluation criteria for the company to determine whether or not to hire an applicant for a job; 2. The program described in Appendix 1.

[0126] (Appendix 10) The means for acquiring anticipated questions includes acquiring anticipated questions based on history information regarding a selection process already conducted by a company for a specific job seeker who applied for a job; 2. The program described in Appendix 1.

[0127] (Appendix 11) The history information includes at least one of the following: the content of questions and answers in an interview conducted prior to the employment interview, or information handed over from a person in charge of the selection process conducted prior to the employment interview; 11. The program according to claim 10.

[0128] (Appendix 12) A computer (30), A means for acquiring job seeker information relating to a specific job seeker (S130); A means for acquiring model answers to anticipated questions based on job seeker information (S235); A means for outputting a model answer (S236); 2. The program described in Appendix 1.

[0129] (Appendix 13) The job information includes interviewer information regarding interviewers who conduct job interviews, The means for acquiring anticipated questions includes acquiring anticipated questions based on interviewee information; 2. The program described in Appendix 1.

[0130] (Appendix 14) Job information includes information about the job type, employment conditions, or benefits, or company information. 2. The program described in Appendix 1.

[0131] (Appendix 15) A means for acquiring job information regarding job openings by companies (S130); A means for acquiring expected questions to be asked in a job interview to be conducted by a company for a person who applies for the job based on the job information (S132); A means for outputting anticipated questions (S133, S236); An information processing device (30) comprising:

[0132] (Appendix 16) A computer (30) A step of acquiring job information regarding job openings by companies (S130); A step of acquiring expected questions to be asked in a job interview to be conducted by a company for a person who applies for the job based on the job information (S132); Steps (S133, S236) for outputting anticipated questions; How to do it.

[0133] (Appendix 17) A system (1) comprising a plurality of computers (10, 30), A means for acquiring job information regarding job openings by companies (S130); A means for acquiring expected questions to be asked in a job interview to be conducted by a company for a person who applies for the job based on the job information (S132); A means for outputting anticipated questions (S133, S236); A system comprising: [Explanation of symbols]

[0134] 1: Information processing system 10: Client device 11:Storage device 12: Processor 13: Input / Output Interface 14: Communication interface 21: Display 30: Server 31: Storage device 32 : Processor 33: Input / Output Interface 34: Communication interface

Claims

1. Computer, A means of obtaining job information regarding job openings by companies; A means for acquiring anticipated questions to be asked in a job interview to be conducted by the company for the applicant for the job based on the job information; means for outputting the assumed question; A program that functions as a

2. The means for acquiring the anticipated questions acquires the anticipated questions by inputting model input information based on the job information into a natural language model. The program according to claim 1.

3. causing the computer to function as a means for acquiring job seeker information relating to a specific job seeker; the means for acquiring the anticipated questions acquires the anticipated questions by inputting model input information based on the job seeker information and the job offer information into a natural language model; The program according to claim 1.

4. The model input information is based on information obtained by anonymizing or pseudonymizing personal information included in the job seeker information and the job information, The program according to claim 3.

5. The job seeker information includes information regarding at least one of the job seeker's personal attributes, career history, skills, preferences, or job-seeking intentions; The program according to claim 3.

6. The job seeker information includes information regarding at least one of a career history or a skill of the particular job seeker; the means for acquiring the anticipated questions acquires the anticipated questions in a format that allows identification of a relationship between the anticipated questions and at least one of the career history or skills of the specific job seeker by inputting information that constrains an output format of the natural language model based on the job seeker information and the model input information based on the job information into the natural language model; The program according to claim 3.

7. the means for acquiring the anticipated questions inputs, into the natural language model, information that constrains the output format of the natural language model based on evaluation-related information related to evaluation criteria used by the company to determine whether or not to hire applicants for the job opening, and the model input information based on the job opening information, thereby acquiring the anticipated questions in a format that allows identification of a relationship between the anticipated questions and the evaluation criteria; The program according to claim 2.

8. The computer is caused to function as a means for acquiring prohibited question information regarding questions that should not be asked in a job interview; the means for acquiring the anticipated questions acquires the anticipated questions by inputting, to the natural language model, information that restricts an output format of the natural language model based on the prohibited matter information and the model input information based on the job information. The program according to claim 2.

9. the means for acquiring the anticipated questions acquires the anticipated questions based on evaluation-related information relating to at least one of evaluation viewpoints or evaluation criteria used by the company to determine whether or not to hire an applicant for the job offer; The program according to claim 1.

10. The means for acquiring the anticipated questions acquires the anticipated questions based on history information regarding a selection process already conducted by the company for a specific job seeker who applied for the job offer. The program according to claim 1.

11. The history information relates to at least one of the content of questions and answers in an interview conducted prior to the employment interview, or the matters handed over from a person in charge of a selection process conducted prior to the employment interview, The program according to claim 10.

12. The computer, A means of obtaining job seeker information about a particular job seeker; A means for acquiring model answers to the anticipated questions based on the job seeker information; The model answer is outputted. The program according to claim 1.

13. The job information includes interviewer information regarding an interviewer who will conduct the job interview, The means for acquiring the anticipated questions acquires the anticipated questions based on the interviewee information. The program according to claim 1.

14. The job information includes information about the job type, job conditions, or benefits of the job, or information about the company, The program according to claim 1.

15. A means for obtaining job information regarding job openings by companies; A means for acquiring anticipated questions to be asked in a job interview to be conducted by the company for applicants for the job based on the job information; A means for outputting the anticipated question; An information processing device comprising:

16. The computer obtaining job information regarding job openings by companies; acquiring, based on the job information, anticipated questions to be asked in a job interview to be conducted by the company for a person who applies for the job; outputting the assumed question; How to do it.

17. A system composed of a plurality of computers, A means for obtaining job information regarding job openings by companies; A means for acquiring anticipated questions to be asked in a job interview to be conducted by the company for applicants for the job based on the job information; A means for outputting the anticipated question; A system comprising:

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