Program, method, information processing device, and system

Abstract

To enable the acquisition of preferences related to a user's job-related interests.SOLUTION: A program is executed by a computer equipped with a processor and a storage unit. The processor executes: a first user information acquisition step of acquiring first user information including transmission information related to a first user or event information in which the first user participated; a prompt generation step of generating a prompt for outputting job preference information indicating a first user's job-related interests to a large language model, based on the first user information acquired in the first user information acquisition step; and a job preference acquisition step of acquiring the job preference information included in the output data output by the large language model by inputting the prompt generated in the prompt generation step to the large language model.SELECTED DRAWING: Figure 13

Description

[Technical Field]

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

[0002] 2. Description of the Related Art Techniques for providing job information to users are known. Patent Document 1 discloses an information providing system that efficiently provides only job information that matches a user. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-121209 Summary of the Invention [Problem to be solved by the invention]

[0004] There is a problem in that it is not possible to obtain the user's preferences related to their occupational interests. Therefore, the present disclosure has been made to solve the above problem, and its purpose is to provide a technology for acquiring preferences related to a user's occupational interests. [Means for solving the problem]

[0005] A program to be executed by a computer having a processor and a memory unit, the program executing the following steps: a first user information acquisition step in which the processor acquires first user information including transmitted information about a first user or information about an event in which the first user has participated; a prompt generation step in which, based on the first user information acquired in the first user information acquisition step, a prompt is generated to cause a large-scale language model to output career preference information indicating interests related to the first user's career; and a career preference acquisition step in which the prompt generated in the prompt generation step is input into the large-scale language model to acquire career preference information included in output data output from the large-scale language model. [Effects of the Invention]

[0006] According to the present disclosure, preferences related to a user's occupational interests can be acquired. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 2 is a block diagram showing the functional configuration of the system 1. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of the server 10. [Figure 3] 2 is a block diagram showing the functional configuration of a first user terminal 20. FIG. [Figure 4] 3 is a block diagram showing the functional configuration of a second user terminal 30. FIG. [Figure 5] FIG. 10 is a diagram showing the data structure of a user table 1012. [Figure 6] FIG. 10 is a diagram showing the data structure of an article table 1013. [Figure 7] FIG. 10 is a diagram showing the data structure of an event table 1014. [Figure 8] FIG. 10 is a diagram showing the data structure of a job preference table 1015. [Figure 9] FIG. 10 is a diagram showing the data structure of a resume table 1016. [Figure 10] FIG. 10 is a diagram showing the data structure of a curriculum vitae table 1017. [Figure 11] FIG. 10 is a diagram showing the data structure of a prompt table 1021. [Figure 12] 10 is a flowchart showing the operation of a career preference process. [Figure 13] 10 is a screen example showing the operation of a career preference process. [Figure 14] FIG. 2 is a block diagram showing the basic hardware configuration of a computer 90. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all drawings describing the embodiments, common components are designated by the same reference numerals, and repeated description will be omitted. Note that the following embodiments do not unduly limit the content of the present disclosure described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present disclosure. Furthermore, each drawing is a schematic diagram and is not necessarily a precise illustration.

[0009] <System 1 Configuration> The system 1 in the present disclosure is an information processing system that provides an information processing service that processes information related to a user's job. The system 1 includes an information processing device including a server 10, a first user terminal 20, a second user terminal 30, and a large-scale language model 50, all of which are connected via a network N. FIG. 1 is a block diagram showing the functional configuration of the system 1. As shown in FIG. FIG. 2 is a block diagram showing the functional configuration of the server 10. As shown in FIG. FIG. 3 is a block diagram showing the functional configuration of the first user terminal 20. As shown in FIG. FIG. 4 is a block diagram showing the functional configuration of the second user terminal 30. As shown in FIG.

[0010] Each information processing device is configured by a computer equipped with an arithmetic unit and a storage device. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by the hardware configuration will be described later. For each of the server 10, the first user terminal 20, the second user terminal 30, and the large-scale language model 50, descriptions that overlap with the basic hardware configuration and basic functional configuration of the computer will be omitted.

[0011] <Server 10 configuration> The server 10 is an information processing device that provides an information processing service for processing information related to the user's job. The server 10 includes a storage unit 101 and a control unit 104 .

[0012] <Configuration of the storage unit 101 of the server 10> The storage unit 101 of the server 10 includes an application program 1011 , a user table 1012 , an article table 1013 , an event table 1014 , a career preference table 1015 , a resume table 1016 , a curriculum vitae table 1017 , and a prompt table 1021 .

[0013] The application program 1011 is a program for causing the control unit 104 of the server 10 to function as each functional unit. Application programs 1011 include applications such as a web browser application.

[0014] User table 1012 is a table that stores and manages information about member users (hereinafter, "users") who use the service. When a user registers to use the service, the user's information is stored in a new record in user table 1012. This allows the user to use the service according to the present disclosure. The user table 1012 is a table having columns of user ID, user name, and user data, with the user ID as the primary key. FIG. 5 is a diagram showing the data structure of the user table 1012. As shown in FIG.

[0015] The user ID is an item that stores user identification information for identifying a user. The user identification information is an item that is set with a unique value for each user. The user name is an item for storing the name of the user. The user name may be set to any character string such as a nickname instead of a name. The user data includes information specific to the individual user and attribute information relating to the user's characteristics and background. The user's unique information includes information unique to the user, such as the user's date of birth (age), sex, and the like. User attribute information includes information about the user's past experiences, such as the user's educational history (highest level of education, major, graduation year), occupation, and work history. User attribute information also includes information about the user's interests, place of residence, language, etc. User data may be configured to be input by the user and stored in advance, or may be configured to be stored by referencing another database, etc.

[0016] The article table 1013 is a table for storing and managing information related to articles (article information). The article table 1013 can also store and manage information transmitted by users (transmitted information). The transmitted information includes article information. The transmitted information transmitted by users is not limited to information created and transmitted by the users themselves, but also includes information transmitted by third parties regarding a specific user. In this disclosure, the article table 1013 is used to store articles written by the user that are publicly available without viewing restrictions on blogs, homepages, etc. The article table 1013 includes any information posted by the user, such as articles on software collaborative development platforms such as GitHub and GitLab, articles on social networking sites such as Facebook, LinkedIn (registered trademark), and Twitter, articles written by the user on question and answer community (platform) services such as Stackoverflow and Reddit, and slide information on Slide Share and Speaker Deck. The article table 1013 may also include academic papers, technical documents, etc. written by the user. Additionally, the article table 1013 may include any article, such as a portfolio (collection of works) on a personal website or the like made public by the user. The article table 1013 is a table having an article ID as a primary key, and columns of article ID, user ID, article data, and date and time. FIG. 6 is a diagram showing the data structure of the article table 1013.

[0017] The article ID is an item that stores article identification information for identifying an article. The article identification information is an item in which a unique value is set for each piece of article information. The user ID is an item for storing user identification information for identifying a user. Article data is an item that stores information about articles. It may also be configured to store information about users (transmission data). Transmission data includes article data. Transmission data about users is not limited to articles created and transmitted by the users themselves, but also includes articles transmitted by third parties about the users. In addition, transmission data transmitted by users may also include articles that are referenced or mentioned by third parties about the users. For example, an interview article written by a third party about a specific user is included in the transmission data of that specific user. Specifically, the article data includes the article title, the article author (name, occupation, contact information, etc.), date information such as publication date and update date, category information such as technology, career, project experience, text information related to the article body, tags such as technology stack and project name, and other references and reference links to external blog articles, external documents, etc. Technology is information about the technology or technology stack included in the article, such as front-end development, back-end development, machine learning, cloud infrastructure, etc. Careers includes advice and information on professional growth, career choices, and career paths included in the article. Project experience is information about insights and lessons learned based on specific projects or work experiences included in the article, such as contributing to open source projects, practical project management, etc. The technology stack is information that indicates the specific technologies and tools included in the article. For example, it includes information such as React, Node.js, and Docker. The technology stack also includes information that indicates the programming language. For example, the programming language is the name of the programming language included in the article, and includes information such as Python, Java, and Go. Project Name includes the name of the software project or development initiative included in the article. The date and time is an item for storing the date and time when a record is newly stored or updated in the article table.

[0018] The event table 1014 is a table for storing and managing information relating to events (event information). The event table 1014 is a table having an event ID as a primary key, and columns of an event ID, a user ID, event data, and a date and time. FIG. 7 is a diagram showing the data structure of the event table 1014. As shown in FIG.

[0019] The event ID is an item that stores event identification information for identifying an event. The event identification information is an item that is set with a unique value for each piece of event information. The user ID is an item for storing user identification information for identifying a user. Event data is an item that stores information about an event. Specifically, the event data includes information such as the event title, an overview of the event, the event agenda (session titles, descriptions, time, etc.), the event speakers (names, affiliations, positions, profiles, etc.), the event organizer (names, organizations, contact information, etc.), the event location, the event date and time, and any other notes about the event. Additionally, if the user is a speaker at an event, the event data may include information similar to the article data in article table 1013 regarding the content of the presentation by the user. The date and time is an item for storing the date and time when a new record is stored or updated in the event table.

[0020] The job preference table 1015 is a table for storing and managing information relating to job preferences (job preference information). The job preference table 1015 is a table having a job preference ID as a primary key and columns of a job preference ID, a user ID, job preference data, and a date and time. FIG. 8 is a diagram showing the data structure of the job preference table 1015.

[0021] The career preference ID is an item for storing career preference identification information for identifying career preferences. The career preference identification information is an item for which a unique value is set for each career preference information. The user ID is an item for storing user identification information for identifying a user. The job preference data is an item for storing information about the user's job preference (job preference information). Specifically, the career preference data includes information on technology stacks, industries, industries, job types, job duties, etc. that the user is interested in. The career preference data may also include information on technology stacks, industries, industries, job types, job duties, etc. that the user is not interested in. In other words, the career preference data may include information on technology stacks, industries, industries, job types, job duties, etc. that the user likes, and information on technology stacks, industries, industries, job types, job duties, etc. that the user dislikes. An industry is a group of companies that produce and provide similar products or services within a classification of economic activity. An industry represents the broad framework of the entire economy and indicates the range of areas in which companies operate. An industry is a further sub-category or group within an industry, and refers to a group of companies that offer a specific type of product or service. Occupations are a category or group of jobs in the labor market that are classified based on a specific skill set or job content. Occupations are a category or group of occupations that have similar job content, required skills, or duties. Job duties are the specific work content and responsibilities expected of an individual position. Job duties indicate the specific tasks and activities that an individual must perform in that position. The date and time is an item for storing the date and time when a new record is stored or updated in the career preference table.

[0022] The resume table 1016 is a table for storing and managing information relating to resumes (resume information). The resume table 1016 is a table having a resume ID as a primary key, and columns of resume ID, user ID, resume data, and date and time. FIG. 9 is a diagram showing the data structure of the resume table 1016.

[0023] The resume ID is an item that stores resume identification information for identifying a resume. The resume identification information is an item that has a unique value set for each piece of resume information. The user ID is an item for storing user identification information for identifying a user. The resume data is an item that stores information about the user's resume. Specifically, resume data includes information such as the user's basic information (name, contact details, etc.), self-introduction, technical skills (technology name, proficiency, years of experience, etc.), work history (company name, job title, period of employment, duties and responsibilities, achievements and results), educational background (school name, degree, major, period of study), qualifications and certifications (qualification name, issuing institution, date of acquisition), languages ​​(programming language name, proficiency, etc.), project experience (project name, role, technology used, project overview, deliverables and results). The date and time is an item for storing the date and time when a record is newly stored or updated in the resume table.

[0024] The curriculum vitae table 1017 is a table for storing and managing information relating to curriculum vitae (curriculum vitae information). The curriculum vitae table 1017 is a table having a curriculum vitae ID as a primary key, and columns of curriculum vitae ID, user ID, curriculum vitae data, and date and time. FIG. 10 is a diagram showing the data structure of the curriculum vitae table 1017.

[0025] The resume ID is an item for storing resume identification information for identifying a resume. The resume identification information is an item for which a unique value is set for each piece of resume information. The user ID is an item for storing user identification information for identifying a user. The curriculum vitae data is an item that stores information about the user's curriculum vitae. Specifically, the resume data includes information such as work history (company name, job title, length of employment, duties and responsibilities, achievements and results), project experience (project name, role, technology used, project overview, deliverables and results), etc. The date and time is an item for storing the date and time when a record is newly stored or updated in the resume table.

[0026] The prompt table 1021 is a table for storing and managing information relating to prompts (prompt information). The prompt table 1021 is a table having a prompt ID as a primary key and columns of a prompt ID, a user ID, a prompt, answer data, and a date and time. FIG. 11 is a diagram showing the data structure of the prompt table 1021. As shown in FIG.

[0027] The prompt ID is an item that stores prompt identification information for identifying a prompt. The prompt identification information is an item that has a unique value set for each piece of prompt information. The user ID is an item for storing user identification information for identifying a user. The prompt is an item that stores text data of commands called prompts, which are input data to be input into a large-scale language model. Note that the prompt may contain any binary data such as image data, audio data, etc. in addition to text data. The answer data is an item that stores output data that the large-scale language model outputs in response to a prompt input to the large-scale language model. The large-scale language model may output any binary data, such as image data or voice data, in addition to text data. The date and time is an item for storing the date and time when a new record is stored or updated in the prompt table.

[0028] <Configuration of the control unit 104 of the server 10> The control unit 104 of the server 10 includes a user registration control unit 1041. The control unit 104 executes an application program 1011 stored in the storage unit 101, thereby realizing each functional unit.

[0029] The user registration control unit 1041 performs processing to store information about users who wish to use the service according to the present disclosure in the user table 1012. The information stored in the user table 1012 is generated when a user opens a web page operated by a service provider from any information processing terminal, enters information into a predetermined input form, and transmits the information to the server 10. The user registration control unit 1041 stores the received information in a new record in the user table 1012, completing the user registration. This allows the user stored in the user table 1012 to use the service. Before the user registration control unit 1041 registers the user information in the user table 1012, the service provider may conduct a predetermined examination to restrict whether or not the user is permitted to use the service. The user ID may be any character string or number that can identify the user, any character string or number desired by the user, or may be automatically set by the user registration control unit 1041.

[0030] <Configuration of First User Terminal 20> The first user terminal 20 is an information processing device operated by a first user who uses the service. In the present disclosure, the first user will be described as a job seeker who is searching for a company, a job, or the like, as an example. The first user terminal 20 may be, for example, a mobile terminal such as a smartphone or a tablet, a desktop personal computer (PC), a laptop PC, or a wearable terminal such as a head mounted display (HMD) or a wristwatch terminal. The first user terminal 20 includes a storage unit 201 , a control unit 204 , an input device 206 , and an output device 208 .

[0031] <Configuration of the storage unit 201 of the first user terminal 20> The storage unit 201 of the first user terminal 20 includes a first user ID 2011 and an application program 2012 .

[0032] The first user ID 2011 is the account ID of the first user. The first user transmits the first user ID 2011 from the first user terminal 20 to the server 10. The server 10 identifies the first user based on the first user ID 2011 and provides the first user with the service according to the present disclosure. The first user ID 2011 includes information such as a session ID temporarily assigned by the server 10 to identify the first user who is using the first user terminal 20.

[0033] The application program 2012 may be stored in advance in the storage unit 201, or may be configured to be downloaded from a web server or the like operated by a service provider via a communication IF. The application programs 2012 include applications such as a web browser application. The application program 2012 includes an interpreted programming language such as JavaScript (registered trademark) that runs on a web browser application stored on the first user terminal 20.

[0034] <Configuration of the control unit 204 of the first user terminal 20> The control unit 204 of the first user terminal 20 includes an input control unit 2041 and an output control unit 2042. The control unit 204 executes an application program 2012 stored in the storage unit 201, thereby realizing each functional unit.

[0035] <Configuration of the input device 206 of the first user terminal 20> The input device 206 of the first user terminal 20 includes a camera 2061 , a microphone 2062 , a position information sensor 2063 , a motion sensor 2064 , and a touch device 2065 .

[0036] <Configuration of the output device 208 of the first user terminal 20> The output device 208 of the first user terminal 20 includes a display 2081 and a speaker 2082 .

[0037] <Configuration of second user terminal 30> The second user terminal 30 is an information processing device operated by a second user who uses the service. In the present disclosure, the second user will be described as an example of a user who is an employee of a specific company or organization that is hiring employees. The second user terminal 30 may be, for example, a mobile terminal such as a smartphone or tablet, a desktop personal computer (PC), a laptop PC, or a wearable terminal such as a head mounted display (HMD) or a wristwatch terminal. The second user terminal 30 includes a storage unit 301 , a control unit 304 , an input device 306 , and an output device 308 .

[0038] <Configuration of the storage unit 301 of the second user terminal 30> The storage unit 301 of the second user terminal 30 includes a second user ID 3011 and an application program 3012 .

[0039] The second user ID 3011 is the account ID of the second user. The second user transmits the second user ID 3011 from the second user terminal 30 to the server 10. The server 10 identifies the second user based on the second user ID 3011 and provides the second user with the service according to the present disclosure. The second user ID 3011 includes information such as a session ID temporarily assigned by the server 10 to identify the second user who is using the second user terminal 30.

[0040] The application program 3012 may be stored in advance in the storage unit 301, or may be configured to be downloaded from a web server or the like operated by a service provider via a communication IF. Application programs 3012 include applications such as a web browser application. The application program 3012 includes an interpreted programming language such as JavaScript (registered trademark) that runs on a web browser application stored in the second user terminal 30.

[0041] <Configuration of the control unit 304 of the second user terminal 30> The control unit 304 of the second user terminal 30 includes an input control unit 3041 and an output control unit 3042. The control unit 304 executes an application program 3012 stored in the storage unit 301, thereby realizing each functional unit.

[0042] <Configuration of the input device 306 of the second user terminal 30> The input device 306 of the second user terminal 30 includes a camera 3061 , a microphone 3062 , a position information sensor 3063 , a motion sensor 3064 , and a touch device 3065 .

[0043] <Configuration of the output device 308 of the second user terminal 30> The output device 308 of the second user terminal 30 includes a display 3081 and a speaker 3082 .

[0044] <Configuration of 50 large-scale language models> The large-scale language model 50 is a type of artificial intelligence system that includes a large-scale language model. The large-scale language model 50 is an information processing system that has the ability to automatically generate content such as text, images, audio, and video. The large-scale language model 50 learns from a huge data set and is capable of generating text and images like a human being and synthesizing natural-sounding speech. Well-known large-scale language models include OpenAI GPT and Google Gemini.

[0045] <System 1 Operation> Each process of the system 1 will be explained below. FIG. 12 is a flowchart showing the operation of the job preference processing. FIG. 13 is an example of a screen showing the operation of the career preference processing.

[0046] <Career Preference Processing> The job preference process is a process related to the user's job-related interests and preferences.

[0047] <Overview of job preference processing> Career preference processing is a series of processes that collect information about the user's articles, event participation information, user experiences, etc., extract information related to the user's career interests, generate prompts to input into a large-scale language model, receive answers as output data by inputting the prompts into the large-scale language model, present the answers, and utilize the answers.

[0048] <Details of job preference processing> The job preference process will be described in detail below.

[0049] <User information collection step> In step S101, the control unit 104 of the server 10 executes a first user information acquisition step of acquiring first user information including article information created by a first user or event information in which the first user has participated. Specifically, the control unit 104 of the server 10 accesses the URLs (websites) of blog sites, homepages, software collaborative development platforms such as GitHub and GitLab, articles on social networking sites such as Facebook, LinkedIn (registered trademark) and Twitter, and question and answer community (platform) services such as Stackoverflow and Reddit (hereinafter referred to as blog sites, etc.) via the Internet, and obtains information (article information) such as HTML of articles published on the websites. Similarly, the control unit 104 of the server 10 accesses the URL (website) of a web service (hereinafter referred to as an event site, etc.) that is hosting a predetermined event, and acquires information such as HTML of the event that is published on the website. Note that the control unit 104 of the server 10 may also access the URL (website) of an event that is held by a business that provides the information processing service according to the present disclosure, and acquire information (event information) such as HTML of the event that is published on the website. For example, the control unit 104 of the server 10 executes any scraping application to collect article information and event information from blog sites, event sites, etc. In this disclosure, the article information, event information, and user data stored in the user table 1012 are referred to as user information related to the user.

[0050] The control unit 104 of the server 10 may obtain article information and event information by constantly or periodically accessing blog sites, event sites, etc., and as an example, a process will be described in which information regarding articles and events that are open to the public without viewing restrictions on blog sites, event sites, etc. is collected in conjunction with the execution of the career preference processing according to the present disclosure.

[0051] The control unit 104 of the server 10 identifies the first user ID by analyzing the acquired article information. For example, the first user ID can be identified by a predetermined HTML tag or the like included in the article information. Alternatively, the control unit 104 of the server 10 may identify the first user ID included in the article information using a machine learning model, a deep learning model, a large-scale language model, or any other artificial intelligence model. Furthermore, the control unit 104 of the server 10 may identify the first user ID based on the first user's name or title. The control unit 104 of the server 10 stores the identified first user ID and article information (for example, HTML data related to the article) in the user ID and article data fields of a new record in the article table 1013 of the server 10.

[0052] The control unit 104 of the server 10 identifies the first user ID by analyzing the acquired event information. For example, the first user ID can be identified by a predetermined HTML tag or the like included in the event information. Alternatively, the control unit 104 of the server 10 may identify the first user ID included in the event information using a machine learning model, a deep learning model, a large-scale language model, or any other artificial intelligence model. Furthermore, the control unit 104 of the server 10 may identify the first user ID based on the first user's name or title. The control unit 104 of the server 10 stores the identified first user ID and event information (for example, HTML data related to the event) in the user ID and event data fields of a new record in the event table 1014 of the server 10.

[0053] The control unit 104 of the server 10 may accept input of identification information such as a first user ID, which is information for identifying a specific user (first user), and may collect and acquire information such as articles and events related to the first user identified by the accepted first user ID from a blog site, event site, etc. For example, the control unit 104 of the server 10 may search for information such as articles and events related to the first user using a search function provided on a blog site, event site, etc., based on information such as the first user's ID, name, and title, and acquire the article information and event information. For example, the control unit 104 of the server 10 may identify article information created by the first user or mentioning the first user as article information related to the first user. The control unit 104 of the server 10 may identify event information in which the first user participates, speaks, or mentions the first user as event information related to the first user. Input of identification information such as the first user ID may be received from the first user, or may be received from another user, a user such as an administrator of the information processing service of the present disclosure, etc. Also, the control unit 104 of the server 10 may identify the user based on the user ID included in the user information stored in the user table 1012. In other words, the control unit 104 of the server 10 may be configured to periodically identify all or some of the users stored in the user table 1012 and periodically execute the job preference processing according to the present disclosure.

[0054] The control unit 104 of the server 10 may execute each step of the job preference processing according to the present disclosure in order for each of one or more predetermined first users. Alternatively, the control unit 104 of the server 10 may execute one or more steps of the job preference processing according to the present disclosure collectively for all or some of the first users stored in the user table 1012. Alternatively, the control unit 104 may execute a combination of these steps.

[0055] <Job-related information extraction step> In step S102, the control unit 104 of the server 10 extracts information about the user's job from the article information and event information collected in step S101. Note that step S102 does not necessarily have to be executed and may be omitted. Since the article information and event information collected in step S101 are usually long HTML documents, including all of the information in the prompt to be input to the large-scale language model 50 would result in the prompt becoming redundant. The number of characters and character length of the prompt that can be input to the large-scale language model 50 are limited, and it is usually not possible to input a prompt that is too long. Furthermore, if information unrelated to work is included in the prompt, the quality of the output data (job preference information) output from the large-scale language model 50 may deteriorate. In step S102, the quality of the job preference information output from the large-scale language model 50 can be improved by generating a prompt that extracts only job-related parts from the article information and event information.

[0056] In step S102, the control unit 104 of the server 10 executes a technology extraction step of extracting technology stack information indicating a technology stack used by the first user based on the first user information acquired in the first user information acquisition step. The technology extraction step executes a step of extracting technology stack information including information identifying the programming language used by the first user. Specifically, the control unit 104 of the server 10 extracts information about a technology stack related to each first user from the user information stored in the article data item of the article table 1013, the event data item of the event table 1014, and the user data item of the user table 1012. Note that the technology stack was explained in the article data item of the article table 1013. For example, the control unit 104 of the server 10 can identify a portion (text) that includes a description about a technology stack from a predetermined HTML tag or the like included in text information such as HTML stored in the item of article data, event data, or user data. Alternatively, the control unit 104 of the server 10 may use a machine learning model, a deep learning model, a large-scale language model, or any other artificial intelligence model to identify a portion (text) that includes a description about a technology stack from the item of article data, event data, or user data.

[0057] In step S102, the control unit 104 of the server 10 executes a job extraction step to extract job information including at least one of the industry, business type, job type, and job position to which the first user belongs, based on the first user information acquired in the first user information acquisition step. Specifically, the control unit 104 of the server 10 extracts, for each first user, information about the industry, business type, occupation, and job to which the first user belongs from the user information stored in the article data item of the article table 1013, the event data item of the event table 1014, and the user data item of the user table 1012. The information about the industry, business type, occupation, and job has been explained in the job preference data item of the job preference table 1015. For example, the control unit 104 of the server 10 can identify portions (text) containing descriptions related to the industry, business type, job type, and job duties from predetermined HTML tags, etc. contained in text information such as HTML stored in the article data, event data, and user data items. Additionally, the control unit 104 of the server 10 may use a machine learning model, a deep learning model, a large-scale language model, or any other artificial intelligence model to identify portions (text) containing descriptions related to the industry, business type, job type, and job duties from the article data, event data, and user data items.

[0058] In step S102, the control unit 104 of the server 10 executes an experience acquisition step of acquiring experience information indicating the job-related experience of the first user. Specifically, the control unit 104 of the server 10 extracts information about the experience of each first user from the user information stored in the article data item of the article table 1013, the event data item of the event table 1014, and the user data item of the user table 1012. Note that the information about experience has been described in the user data item of the user table 1012, the article data item of the article table 1013, the resume data item of the resume table 1016, the resume data item of the resume table 1017, etc. For example, the control unit 104 of the server 10 can identify portions (text) containing descriptions about experiences based on predetermined HTML tags or the like included in text information such as HTML stored in the article data, event data, and user data items. Alternatively, the control unit 104 of the server 10 may use a machine learning model, a deep learning model, a large-scale language model, or any other artificial intelligence model to identify portions (text) containing descriptions about experiences from the article data, event data, and user data items.

[0059] <Prompt generation step> In step S103, the control unit 104 of the server 10 executes a prompt generation step to generate a prompt for outputting job preference information indicating the first user's job-related interests to the large-scale language model based on the first user information acquired in the first user information acquisition step. Specifically, the control unit 104 of the server 10 creates a command statement (sentence) for each first user, called a prompt, to cause the large-scale language model 50 to execute a predetermined process, based on the user information stored in the article data item of the article table 1013, the event data item of the event table 1014, and the user data item of the user table 1012. In the present disclosure, the control unit 104 of the server 10 includes in the prompt a command statement for causing the large-scale language model 50 to output career preference information indicating the first user's career-related interests.

[0060] The prompt generating step includes generating a prompt for causing the large-scale language model to output career preference information including a technology stack that the first user is interested in. The prompt generating step includes generating a prompt for causing the large-scale language model to output career preference information including a programming language that the first user is interested in. The prompt generating step includes a step of generating a prompt for causing the large-scale language model to output career preference information indicating at least one of industry, business type, occupation, and job position in which the first user is interested. For example, the control unit 104 of the server 10 generates the following prompt: The {article data}, {event data}, and {user data} sections are filled with the article data of the article table 1013, the event data of the event table 1014, and the user data of the user table 1012 for a specific user, respectively. Note that the prompt does not need to include all of the article data, event data, and user data, and may include at least one of them. 〔prompt〕 #command sentence The following article data, event data, and user data are text data related to a specific user. From this data, output career preference information that indicates the user's career-related interests. The career preference information includes information such as technology stacks, industries, industries, job types, and job roles that the first user is interested in. The technology stacks include information indicating technologies, tools, and programming languages ​​related to the first user. The career preference information may include information such as technology stacks, industries, industries, job types, and job roles that the first user likes, and information such as technology stacks, industries, industries, job types, and job roles that the first user dislikes. The career preference information may include information on technology stacks, industries, business types, occupations, job functions, etc. in which the first user has no interest. #Article data {Article data} #Event Data {Event data} #UserData {user data}

[0061] In step S103, the prompt generation step executes a step of generating a prompt based on the technology stack information extracted in the technology extraction step, the job information extracted in the job extraction step, and the experience information acquired in the experience acquisition step. Specifically, the control unit 104 of the server 10 may be configured to include in the prompt part (extracted user data) of the user information (article data, event data, user data) extracted in step S102 as the article data, event data, and user data. The extracted user data consists of technology stack information including information identifying the programming language of the first user, job information including at least one of the industry, business type, job type, and job function to which the first user belongs, and experience information indicating the first user's job-related experience. Specifically, the control unit 104 of the server 10 inserts the extracted user data extracted in step S102 from the article data, event data, and user data into the {article data}, {event data}, and {user data} parts of the prompt, respectively.

[0062] In step S103, the prompt generation step executes a step of generating a prompt based on the publicly available first user information acquired in the first user information acquisition step, without using information directly input from the first user to the computer. Specifically, the control unit 104 of the server 10 may generate a prompt based on article data, event data, and in some cases user data, etc. collected from the Internet, etc. in step S101, without requesting the first user to input information into the information processing system of the present disclosure, and without referring to information directly input by the first user into the information processing system of the present disclosure. The information processing service of the present disclosure can generate prompts based on article information, event information, user data, etc. related to the first user that are publicly available on the Internet, without the first user directly inputting input such as the technology stack, the industry, business type, occupation, and job position to which the first user belongs, and job information such as duties, and experience information indicating the first user's job-related experience, or without referring to directly input information. This saves the first user the trouble of inputting information about his / her occupational interests. Furthermore, without receiving input of information about his / her occupational interests from the first user, the first user's occupational interests preferences can be obtained from the large-scale language model 50 based on the first user's public information.

[0063] In addition, the information processing service disclosed herein may generate a prompt based on input received directly from the first user, such as the technology stack, the industry, business type, occupation, and job information to which the first user belongs, and experience information indicating the first user's work-related experience. For example, if the information processing service disclosed herein is an information processing service that provides job information to job seekers, the prompt may be generated based on job information such as the technology stack, the industry, business type, job type, and job duties to which the first user belongs, and experience information indicating the first user's work-related experience, which the first user has entered into an input form such as a resume or job history.

[0064] The control unit 104 of the server 10 stores the user ID and the generated prompt in the user ID and prompt items of the new record in the prompt table 1021.

[0065] <Prompt sending step> In step S104, the control unit 104 of the server 10 transmits the prompt generated in step S103 as input data to the large-scale language model 50. Specifically, the control unit 104 of the server 10 sends a request including a prompt to an endpoint (URL) provided by the large-scale language model 50.

[0066] <Response receiving step> In step S105, the control unit 104 of the server 10 executes a job preference acquisition step of acquiring job preference information contained in output data output from the large-scale language model by inputting the prompt generated in the prompt generation step into the large-scale language model. Specifically, in response to a request, the large-scale language model 50 transmits output data (text data related to the response) corresponding to the content of the request as a response to the server 10. The control unit 104 of the server 10 receives the output data in response to the prompt output from the large-scale language model 50. The output data may be in text format or may be data in a structured format such as JSON. For example, it is preferable to control the large-scale language model 50 to acquire career preference information as structured information for each item such as technology stack, programming language, framework, library, database, tool, industry, line of business, job type, duties, and work history. For example, by using a function called function calling in the GPT API, it is possible to obtain career preference information in a structured format.

[0067] In the present disclosure, the control unit 104 of the server 10 can receive text data including job preference information indicating the first user's job-related interests from the large-scale language model 50. Details of the information included in the job preference information have been described in the section on job preference data in the job preference table 1015. <Example of output data> { Programming language: [Java, Python, JavaScript], Framework: [React, Node.js], Libraries: [Numpy, Scipy], Database: [MySQL (registered trademark), MongoDB], Tools: [Docker, Git, AWS], Industry: [Software, SaaS], Job type: [Front-end engineer, database engineer], Job: [Project Manager], Work history: [Technologies Innovation Co., Ltd., Project Manager, April 2020 - Present] ] Educational background: [·· University, Master of Computer Science, Major: Software Engineering, Enrolled April 2010 - March 2014] ], }

[0068] The control unit 104 of the server 10 stores the user ID and the received output data in the answer data item of the target record in the prompt table 1021. The control unit 104 of the server 10 stores the user ID and the received output data in the user ID and job preference data items of a new record in the job preference table 1015. Note that the control unit 104 of the server 10 may be configured to store a portion of the output data received from the large-scale language model 50 or data that has been appropriately processed (processed data) in the job preference data item of the job preference table 1015.

[0069] In step S106, the control unit 104 of the server 10 can present the output data received from the large-scale language model 50 to various users. For example, the control unit 104 of the server 10 can present the output data to a first user who is a user of the information processing service according to the present disclosure and is a job seeker looking for a company to work for or change jobs at, or a job, etc. The control unit 104 of the server 10 can present the output data to a second user who is a user of the information processing service according to the present disclosure and is an employee, etc., belonging to a specific company, organization, etc. that is hiring employees, etc. Alternatively, the control unit 104 of the server 10 may present the output data to a user who is an administrator of the information processing service according to the present disclosure.

[0070] <Career Preference Presentation Step (First Embodiment)> In step S106, the control unit 104 of the server 10 executes a job preference presenting step of presenting the job preference information acquired in the job preference acquiring step to the first user. Specifically, the first user operates the input device 206 of the first user terminal 20 to execute a browser application or the like, and opens the career preference presentation page D1 by inputting the URL or the like of a web page (career preference presentation page) for executing the career preference presentation process. The control unit 204 of the first user terminal 20 transmits a request including the first user ID 2011 for opening the career preference presentation page to the server 10. For example, the career preference presentation page may be an input screen for inputting information about the first user's career when the first user creates a resume, a curriculum vitae, or the like in the information processing service of the present disclosure.

[0071] When the server 10 receives the request, it generates a job preference presentation page and transmits it to the first user terminal 20. The control unit 204 of the first user terminal 20 displays the job preference presentation page on the display 2081 of the first user terminal 20 and presents it. 13 is a screen example of a job preference presentation screen in the job preference presentation process. A job preference presentation page D1 is displayed on the display 2081 of the first user terminal 20. The career preference submission page D1 includes input fields for a technology stack D11, work history D12, projects D13, educational history D14, etc., and a submit button D15. Technology stack D11 includes input fields for programming languages, frameworks / libraries, databases, tools, etc. The work history D12 includes input fields for industry, business type, job type, job duties, and the like. Project D13 includes a field for input regarding involvement in various projects, such as the open source community. The educational history D14 includes input fields for inputting the name of the school graduated from, major, etc. The input fields do not necessarily have to be separated by item, but may be configured so that all information can be entered together in one input field, or may be configured as an input form consisting of multiple input fields that are subdivided into smaller input fields.

[0072] The control unit 104 of the server 10 searches the user ID field in the career preference table 1015 based on the first user ID 2011 and acquires career preference data. The control unit 104 of the server 10 analyzes the career preference data and inputs information related to the first user's technology stack interests into the input fields for the technology stack D11. The control unit 104 of the server 10 analyzes the career preference data and inputs information related to the first user's industry, line of business, occupation, and job interests into the input fields for work history D12, projects D13, etc. The control unit 104 of the server 10 analyzes the career preference data and inputs information related to the first user's work-related experience into the input fields for work history D12, projects D13, education history D14, etc. The allocation of the multiple pieces of information (items) included in the career preference data to the technology stack D11, work history D12, project D13, and educational history D14 may be configured by analyzing character string information and the like included in the career preference data (for example, in the case where the career preference data is structured JSON data, character string information assigned to keys such as "programming language," "framework," "library," "database," "tool," "industry," "business type," "job type," "job position," "work history," and "educational history"), or by using any machine learning model, deep learning model, large-scale language model, or any other artificial intelligence model.

[0073] This allows the first user to view the career preference presentation page D1 with the desired information already entered, without having to enter information into the input fields for technology stack, work history, projects, educational history, etc.

[0074] <Career Preference Presentation Step (Second Embodiment)> In step S106, the control unit 104 of the server 10 identifies job information based on the job preference information acquired in the job preference acquisition step, and executes a job presentation step of presenting the identified job information to the first user. Specifically, the control unit 104 of the server 10 searches a database storing job information (not shown) based on the job preference data, and identifies job information that is highly relevant to the job preference data. For example, the control unit 104 of the server 10 uses the job preference data as a search string to search a search database that stores multiple job postings that describe the technology stack, industry, line of business, job type, duties, experience, etc. of the desired personnel as text. For example, the control unit 104 of the server 10 calculates the similarity between the job preference data and the job postings based on an algorithm for calculating similarity between documents, such as cosine similarity or TFIDF, and identifies job data related to job postings that are determined to have a similarity equal to or greater than a predetermined value as job postings that are preferable to the first user. The control unit 104 of the server 10 transmits the job data related to the identified job advertisement to the first user terminal 20. The control unit 204 of the first user terminal 20 displays the received job data on the display 2081 of the first user terminal 20 and presents it to the first user. This allows the first user to be presented with job information that matches the first user's job interests. The first user can check job information that matches his or her own job interests.

[0075] <Career Preference Presentation Step (Third Embodiment)> In step S106, the control unit 104 of the server 10 executes a job preference specifying step of specifying predetermined job preference information from the plurality of job preference information acquired in the job preference acquiring step, based on the predetermined job information stored in the job information storing step. The control unit 104 of the server 10 executes a job seeker presenting step of presenting information of a predetermined first user corresponding to the predetermined job preference information specified in the job preference specifying step to a hiring company associated with the predetermined job information.

[0076] Specifically, predetermined job information is transmitted to the server 10 in advance by an input operation on an information processing device by an employee of the hiring company (mainly a recruiter, etc.). The control unit 104 of the server 10 executes a job storage step of storing the received predetermined job information in the storage unit 101 of the server 10. In this case, each step of the job preference processing is executed for a plurality of first users, and the job preference data of each of the plurality of first users is stored in the job preference table 1015. The control unit 104 of the server 10 searches the job preference data item in the job preference table 1015 based on the stored job information, and acquires a user ID (predetermined first user ID). Specifically, the control unit 104 identifies a user ID associated with job preference data that is highly relevant to the job information. The identification method is the same as in the job preference presentation step (second embodiment). This makes it possible to identify one or more predetermined first users related to the job information. The control unit 104 of the server 10 transmits information including predetermined profile information of the first user (skills, career history, work experience, educational background, etc.) to the email address or the like of the recruiter of the company seeking employment. The second user, who is the company's recruiter, selects the profile information of the first user received from the server 10 by operating the input device 306 or the like of the second user terminal 30. The control unit 304 of the second user terminal 30 displays and presents the received profile information of the first user on the display 3081 or the like of the second user terminal 30.

[0077] For example, the control unit 104 of the server 10 extracts technology stack information for programming languages ​​(e.g., Python, Java, Go) included in a given job posting. For example, the control unit 104 of the server 10 may extract technology stack information for programming languages ​​using machine learning models, deep learning models, large-scale language models, or other arbitrary artificial intelligence models, as well as morphological analysis. In this case, the control unit 104 of the server 10 identifies job seekers whose technology stack information for programming languages ​​(e.g., Python, Java, Go) extracted from the job preference data is included and presents the job seekers in the job posting. This allows companies to present not only job seekers with past experience with a particular programming language, but also job seekers who, based on various posts such as blogs, have expressed interest or a preference in a particular programming language in the future. The information processing service disclosed herein allows companies to identify job seekers based not only on past experience but also on future skills and motivation.

[0078] Similarly, the control unit 104 of the server 10 may identify one or more companies that created (posted) job postings searched based on the first user's job preference data in the job preference presentation step (second embodiment). Note that the control unit 104 of the server 10 may directly identify one or more companies that are highly relevant to the first user's job preference data by searching a company database (not shown) or the like based on the first user's job preference data. The control unit 104 of the server 10 transmits information including the profile information of the first user (skills, career history, work experience, educational background, etc.) to the email addresses, etc. of the recruiters of the identified one or more companies. The second user, who is the recruiter of the company, selects the profile information of the first user received from the server 10 by operating the input device 306, etc. of the second user terminal 30. The control unit 304 of the second user terminal 30 displays and presents the received profile information of the first user on the display 3081, etc. of the second user terminal 30. This allows the first user to be presented as information about a job seeker to companies that are hiring that match the first user's job interests. For example, the companies can scout the first user or approach them regarding job openings.

[0079] <Steps for utilizing occupational preferences (first embodiment)> In step S107, the control unit 104 of the server 10 executes a job preference input step of accepting an input operation from the first user regarding the job preference information presented in the job preference presentation step. Specifically, the first user can operate the input device 206 of the first user terminal 20 to edit the contents of input fields such as technical stack D11, work history D12, project D13, and educational history D14 on the career preference presentation page D1 displayed on the display 2081 of the first user terminal 20 in the career preference presentation step (first embodiment). For example, in the present disclosure, the input fields of the career preference presentation page D1, such as the technology stack D11, work history D12, project D13, and education history D14, are presented with information about the first user's job pre-entered based on the career preference data, but it is conceivable that the input content may be insufficient or incorrect. In such cases, the first user can add the missing input content or correct the incorrect input content by operating the input device 206 of the first user terminal 20. This allows the first user to complete the input on the career preference presentation page D1 simply by correcting any missing or incorrect information in the input fields, thereby reducing the burden on the first user in creating career preference information.

[0080] The first user presses the send button D15 by operating the input device 206 of the first user terminal 20. The control unit 204 of the first user terminal 20 transmits the information (updated career preference data) entered in the input fields of the career preference presentation page D1, such as the technology stack D11, work history D12, project D13, and educational history D14, to the server 10. The control unit 104 of the server 10 stores the received updated career preference data in the career preference data field of the target record in the career preference table 1015, overwriting it. This allows the first user to modify the content of the job preference data output from the large-scale language model 50 by himself / herself to more accurate content (updated job preference data).

[0081] In step S107, the control unit 104 of the server 10 executes a document creation step of creating a resume or curriculum vitae for the first user based on the input operation for the career preference information accepted in the career preference input step. The control unit 104 of the server 10 creates resume data based on the job preference data or updated job preference data (hereinafter referred to as job preference data). For example, the control unit 104 of the server 10 creates resume data by applying items included in the job preference data to a specified resume template. Alternatively, the resume data may be created using a machine learning model, a deep learning model, a large-scale language model, or any other artificial intelligence model. The control unit 104 of the server 10 stores the first user ID 2011 and the created resume data in the user ID and resume data fields of a new record in the resume table 1016. The control unit 104 of the server 10 creates resume data based on the career preference data or updated career preference data. For example, the control unit 104 of the server 10 creates the resume data by applying items included in the career preference data to a specified resume template. Alternatively, the resume data may be created using a machine learning model, a deep learning model, a large-scale language model, or any other artificial intelligence model. The control unit 104 of the server 10 stores the first user ID 2011 and the created resume data in the user ID and resume data fields of a new record in the resume table 1017. This allows the first user to create a resume or curriculum vitae by referring to the presented career preference information, thereby reducing the burden on the first user in creating a resume or curriculum vitae.

[0082] <Basic computer hardware configuration> 14 is a block diagram showing the basic hardware configuration of a computer 90. The computer 90 includes at least a processor 901, a main memory device 902, an auxiliary memory device 903, and a communication IF 991 (interface), which are electrically connected to one another by a communication bus 921.

[0083] The processor 901 is hardware for executing an instruction set written in a program, and is composed of an arithmetic unit, registers, peripheral circuits, and the like.

[0084] The main memory device 902 is used to temporarily store programs, data to be processed by the programs, etc. For example, it is a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0085] The auxiliary storage device 903 is a storage device for saving data and programs, such as a flash memory, a hard disk drive (HDD), a magneto-optical disk, a CD-ROM, a DVD-ROM, or a semiconductor memory.

[0086] The communication IF 991 is an interface for inputting and outputting signals for communicating with other computers via a network using wired or wireless communication standards. The network is composed of the Internet, a LAN, various mobile communication systems constructed by wireless base stations, etc. For example, the network includes 3G, 4G, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks (e.g., Wi-Fi (registered trademark)) that can connect to the Internet via a predetermined access point. In the case of a wireless connection, communication protocols include, for example, Z-Wave (registered trademark), ZigBee (registered trademark), and Bluetooth (registered trademark). In the case of a wired connection, the network also includes a direct connection using a USB (Universal Serial Bus) cable, etc.

[0087] It should be noted that the computer 90 can be virtually realized by distributing all or part of each hardware configuration across multiple computers 90 and interconnecting them via a network. In this way, the computer 90 is a concept that includes not only a computer 90 housed in a single housing or case, but also a virtualized computer system.

[0088] <Basic functional configuration of computer 90> The following describes the functional configuration of a computer realized by the basic hardware configuration (FIG. 14) of the computer 90. The computer includes at least the functional units of a control unit, a storage unit, and a communication unit.

[0089] The functional units of the computer 90 can also be realized by distributing all or part of the functional units among multiple computers 90 interconnected via a network. The computer 90 is a concept that includes not only a single computer 90 but also a virtualized computer system.

[0090] The control unit is realized by the processor 901 reading out various programs stored in the auxiliary storage device 903, expanding them in the main storage device 902, and executing processing in accordance with the programs. The control unit can realize functional units that perform various types of information processing depending on the type of program. In this way, the computer is realized as an information processing device that performs information processing.

[0091] The storage unit is realized by a main storage device 902 and an auxiliary storage device 903. The storage unit stores data, various programs, and various databases. Furthermore, the processor 901 can allocate a storage area corresponding to the storage unit in the main storage device 902 or the auxiliary storage device 903 in accordance with the programs. Furthermore, the control unit can cause the processor 901 to execute processes for adding, updating, and deleting data stored in the storage unit in accordance with the various programs.

[0092] A database refers to a relational database, which manages data sets called masters and tables in a tabular format structurally defined by rows and columns, by relating them to each other. In a database, a table is called a table, a master, a column in a table is called a column, and a row in a table is called a record. In a relational database, relationships between tables and masters can be set and associated. Typically, each table and each master has a column set as a primary key to uniquely identify a record, but setting a primary key to a column is not essential. The control unit can cause the processor 901 to add, delete, or update records in specific tables and masters stored in the storage unit according to various programs. Furthermore, by storing data, various programs, and various databases in the storage unit, it can be considered that the information processing device and information processing system according to the present disclosure have been manufactured.

[0093] Note that the databases and masters in this disclosure may include any data structure in which information is structurally defined (such as a list, dictionary, associative array, or object). The data structure also includes data that can be considered as a data structure by combining data with functions, classes, methods, etc. written in any programming language.

[0094] The communication unit is realized by the communication IF 991. The communication unit realizes a function of communicating with other computers 90 via a network. The communication unit can receive information transmitted from other computers 90 and input the information to the control unit. The control unit can cause the processor 901 to execute information processing on the received information in accordance with various programs. In addition, the communication unit can transmit information output from the control unit to other computers 90.

[0095] <Additional Notes> The matters described in the above embodiments will be supplemented below.

[0096] (Appendix 1) A program to be executed by a computer having a processor and a memory unit, the program executing: a first user information acquisition step (S101) in which the processor acquires first user information including transmitted information about a first user or event information in which the first user has participated; a prompt generation step (S103) in which, based on the first user information acquired in the first user information acquisition step, a prompt is generated for outputting career preference information indicating interests related to the first user's career to a large-scale language model; and a career preference acquisition step (S105) in which the prompt generated in the prompt generation step is input into the large-scale language model to acquire career preference information included in output data output from the large-scale language model. This allows the first user's preferences related to professional interests, such as technology stack, industry, business type, job title, and job function, to be acquired without directly receiving input of information related to the interests from the first user. For example, the first user information includes articles that the first user has posted on blogs, question-and-answer sites, and the like that are widely accessible to general users via the Internet. The first user information also includes information on events that the first user has attended.

[0097] (Appendix 2) The program described in Appendix 1, wherein the prompt generation step (S103) is a step of generating a prompt based on the publicly available first user information acquired in the first user information acquisition step, without using information directly input by the first user to the computer. This saves the first user the trouble of inputting information about his / her occupational interests. Also, without receiving input of information about his / her occupational interests from the first user, it is possible to obtain the first user's occupational interests from the first user's public information.

[0098] (Appendix 3) A program described in Appendix 1 or 2, in which a processor executes an experience acquisition step (S102) of acquiring experience information indicating the first user's job-related experience, and a prompt generation step (S103) of generating a prompt based on the experience information acquired in the experience acquisition step. This makes it possible to acquire the first user's preferences regarding his / her occupational interests based on information about the first user's past experiences such as educational history and work history.

[0099] (Appendix 4) The program according to any one of appendices 1 to 3, wherein the prompt generation step (S103) is a step of generating a prompt for causing a large-scale language model to output career preference information including a technology stack in which the first user is interested. This makes it possible to obtain preference information about technology stacks such as programming languages, software tools, software architectures, and software platforms in which the first user is interested.

[0100] (Appendix 5) A program according to any one of appendices 1 to 4, wherein the prompt generation step (S103) is a step of generating a prompt for causing a large-scale language model to output career preference information including a programming language in which the first user is interested. This makes it possible to obtain preference information about programming languages ​​in which the first user is interested.

[0101] (Appendix 6) A program described in any one of Appendices 1 to 5, wherein the prompt generation step (S103) is a step of generating a prompt for causing a large-scale language model to output career preference information indicating at least one of the industry, business type, occupation, and job position in which the first user is interested. This makes it possible to acquire occupation-related preference information such as the type of business, industry, occupation, and job in which the first user is interested.

[0102] (Appendix 7) A program described in any of Appendices 1 to 5, in which a processor executes a career preference presentation step (S106) of presenting the career preference information acquired in the career preference acquisition step to the first user, a career preference input step (S107) of accepting input operations from the first user regarding the career preference information presented in the career preference presentation step, and a document creation step (S107) of creating a resume or curriculum vitae for the first user based on the input operations regarding the career preference information accepted in the career preference input step. This allows the first user to create a resume or curriculum vitae by referring to the presented career preference information, thereby reducing the burden on the first user in creating a resume or curriculum vitae.

[0103] (Appendix 8) A program described in any of Appendices 1 to 5, in which a processor executes a job presentation step (S106) of identifying job information based on the job preference information acquired in the job preference acquisition step and presenting the identified job information to the first user. This allows the first user to be presented with job information that matches the first user's job interests. The first user can check job information that matches his or her own job interests.

[0104] (Appendix 9) The job preference acquisition step (S105) is a step of acquiring multiple job preference information of multiple first users, and the processor executes a job storage step (S106) of storing specified job information, a job preference identification step (S106) of identifying specified job preference information from the multiple job preference information acquired in the job preference acquisition step based on the specified job information stored in the job storage step, and a job seeker presentation step (S106) of presenting information of the specified first user corresponding to the specified job preference information identified in the job preference identification step to a hiring company associated with the specified job information. This allows the first user to be presented as information about a job seeker to companies offering jobs that match the first user's job interests. For example, a company that is hiring can scout the first user or approach them regarding recruitment.

[0105] (Appendix 10) A computer-implemented method comprising a processor and a memory, wherein the processor performs all of the steps performed in the invention according to any one of appendices 1 to 9. This allows the first user's preferences related to professional interests, such as technology stack, industry, business type, job title, and job function, to be acquired without directly receiving input of information related to the interests from the first user. For example, the first user information includes articles that the first user has posted on blogs, question-and-answer sites, and the like that are widely accessible to general users via the Internet. The first user information also includes information on events that the first user has attended.

[0106] (Appendix 11) An information processing device comprising a control unit and a storage unit, wherein the control unit executes all of the steps executed in the invention according to any one of Supplementary Note 1 to Supplementary Note 9. This allows the first user's preferences related to professional interests, such as technology stack, industry, business type, job title, and job function, to be acquired without directly receiving input of information related to the interests from the first user. For example, the first user information includes articles that the first user has posted on blogs, question-and-answer sites, and the like that are widely accessible to general users via the Internet. The first user information also includes information on events that the first user has attended.

[0107] (Appendix 12) A system comprising means for performing all steps performed in any of the inventions according to any one of appendixes 1 to 9. This allows the first user's preferences related to professional interests, such as technology stack, industry, business type, job title, and job function, to be acquired without directly receiving input of information related to the interests from the first user. For example, the first user information includes articles that the first user has posted on blogs, question-and-answer sites, and the like that are widely accessible to general users via the Internet. The first user information also includes information on events that the first user has attended. [Explanation of symbols]

[0108] 1 System, 10 Server, 101 Memory unit, 104 Control unit, 106 Input device, 108 Output device, 20 First user terminal, 201 Memory unit, 204 Control unit, 206 Input device, 208 Output device, 30 Second user terminal, 301 Memory unit, 304 Control unit, 306 Input device, 308 Output device, 50 Large-scale language model, 501 Memory unit, 504 Control unit, 506 Input device, 508 Output device

Claims

1. A program to be executed by a computer having a processor and a storage unit, the processor: a first user information acquisition step of acquiring first user information including transmitted information related to the first user or event information in which the first user has participated; a prompt generating step of generating a prompt for causing a large-scale language model to output job preference information indicating job-related interests of the first user based on the first user information acquired in the first user information acquiring step; a job preference acquisition step of inputting the prompt generated in the prompt generation step into a large-scale language model to acquire the job preference information included in output data output from the large-scale language model; A program that executes.

2. the prompt generating step is a step of generating the prompt based on the first user information that is publicly available and that is acquired in the first user information acquiring step, without using information directly input from the first user to the computer. The program according to claim 1.

3. the processor: an experience acquisition step of acquiring experience information indicating the first user's work-related experience; Run the prompt generating step includes performing a step of generating the prompt based on the experience information acquired in the experience acquiring step. The program according to claim 1.

4. The prompt generating step is a step of generating a prompt for outputting career preference information including a technology stack in which the first user is interested to a large-scale language model. The program according to claim 1.

5. The prompt generating step is a step of generating a prompt for causing a large-scale language model to output career preference information including a programming language in which the first user is interested. The program according to claim 1.

6. the prompt generation step is a step of generating a prompt for causing a large-scale language model to output career preference information indicating at least one of an industry, a business type, a job type, and a job position in which the first user is interested. The program according to claim 1.

7. the processor: a job preference presentation step of presenting the job preference information acquired in the job preference acquisition step to the first user; a job preference input step of receiving an input operation from the first user regarding the job preference information presented in the job preference presentation step; a document creation step of creating a resume or a curriculum vitae for the first user based on an input operation for the career preference information received in the career preference input step; To execute The program according to claim 1.

8. the processor: a job presentation step of identifying job information based on the job preference information acquired in the job preference acquisition step and presenting the identified job information to the first user; To execute The program according to claim 1.

9. the job preference acquisition step is a step of acquiring a plurality of pieces of job preference information of a plurality of first users, the processor: a job vacancy storing step for storing predetermined job vacancy information; a job preference specifying step of specifying predetermined job preference information from the plurality of job preference information acquired in the job preference acquiring step based on the predetermined job information stored in the job storage step; a job seeker presenting step of presenting information of a predetermined first user corresponding to the predetermined job preference information identified in the job preference identifying step to a recruiting company associated with the predetermined job information; To execute The program according to claim 1.

10. A method implemented on a computer having a processor and a memory, wherein the processor performs all of the steps performed in the invention according to any one of claims 1 to 9.

11. 10. An information processing device comprising a control unit and a storage unit, wherein the control unit executes all of the steps executed in the invention according to any one of claims 1 to 9.

12. A system comprising means for executing all steps performed in any one of the inventions according to claims 1 to 9.

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