Job posting optimization system, job posting optimization server, program, and job posting optimization method

Abstract

This invention provides a job posting optimization system, job posting optimization server, program, and job posting optimization method that can efficiently create job postings that can improve the hiring success rate. [Solution] The system includes an information acquisition unit 131 that acquires job posting source information from job seekers, a requirements extraction unit 132 that extracts job requirements based on the job posting source information, a requirements optimization unit 133 that analyzes the extracted job requirements and generates optimized recommended job requirements, and a job posting editing unit 134 that presents the job requirements and the recommended job requirements to the job seeker and accepts editing of the job requirements.

Description

Technical Field

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[0001] The present invention relates to a job offer optimization system, a job offer optimization server, a program, and a job offer optimization method.

Background Art

[0002] In recent years, job matching services have played a role in efficiently connecting the needs of companies (job offerers) and job seekers. Job site operating companies are strengthening comprehensive matching support by leveraging AI (Artificial Intelligence) and digital technologies, or expanding supplementary services such as attentive support by agents and direct scouting.

[0003] The functions required for job matching services can be broadly classified into those for the job offer side and the job seeker side. Among them, for the job offer side, a function to streamline the creation of job offers is required. For example, in Patent Document 1, a technology is described in which a job offerer's input of the conditions of the human resources they seek is received as an input of human resource conditions, related information associated with the input human resource conditions is acquired, the acquired related information is input into a first artificial intelligence, and a job offer corresponding to the human resources indicated by the related information is output.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] By the way, in job matching, the target population, which is the group of people who meet the job requirements (personnel requirements) of the job posting, is determined once the job requirements (personnel requirements) of the job posting are set. Currently, the quantity and quality of information entered by employers to create job postings is often insufficient (the conditions of the job postings are lax), and even if a function is provided to automatically create job postings based on information obtained from employers, as in Patent Document 1, there is variation in the quality of the job postings that are created. When low-quality job postings are created, it becomes difficult to form a target population, so the creation and revision of job postings takes time and effort, and ultimately, coordination and adjustments with recruiting advisors (people) are done manually.

[0006] Furthermore, the gap in recruitment difficulty is widening between large companies, which tend to have accumulated know-how and a track record in recruitment activities, and small and medium-sized enterprises (SMEs), which tend not to have these resources. There is a lack of efficient support functions for creating and revising job postings for these disadvantaged companies.

[0007] Furthermore, while it is possible to expand the pool of job seekers by linking with the unique job seeker databases of recruitment agencies other than the operator of the job matching service, this is not being utilized to its fullest extent, resulting in lost recruitment opportunities for both employers and job seekers.

[0008] Therefore, this disclosure aims to provide a job posting optimization system, a job posting optimization server, a program, and a job posting optimization method that can efficiently create high-quality and uniform job postings. [Means for solving the problem]

[0009] The job posting optimization system according to the present invention comprises: an information acquisition unit that acquires job posting source information from employers; a requirements extraction unit that extracts job requirements based on the job posting source information; a requirements optimization unit that analyzes the extracted job requirements and generates optimized recommended job requirements; and a job posting editing unit that presents the job requirements and the recommended job requirements to the employer and accepts editing of the job requirements.

[0010] The job posting optimization server according to the present invention comprises: an information acquisition unit that acquires job posting source information from employers; a requirements extraction unit that extracts job requirements based on the job posting source information; a requirements optimization unit that analyzes the extracted job requirements and generates optimized recommended job requirements; and a job posting editing unit that presents the job requirements and the recommended job requirements to the employer and accepts editing of the job requirements.

[0011] Furthermore, the program according to the present invention causes a computer to execute the following steps: an information acquisition step of acquiring job posting source information from a recruiter; a requirements extraction step of extracting job requirements based on the job posting source information; a requirements optimization step of analyzing the extracted job requirements and generating optimized recommended job requirements; and a job posting editing step of presenting the job requirements and recommended job requirements to the recruiter and accepting editing of the job requirements.

[0012] The job posting optimization method according to the present invention comprises: an information acquisition step of acquiring job posting source information from an employer; a requirements extraction step of extracting job requirements based on the job posting source information; a requirements optimization step of analyzing the extracted job requirements and generating optimized recommended job requirements; and a job posting editing step of presenting the job requirements and recommended job requirements to the employer and accepting editing of the job requirements. [Effects of the Invention]

[0013] This disclosure provides a job posting optimization system, a job posting optimization server, a program, and a job posting optimization method that can efficiently create high-quality and uniform job postings. [Brief explanation of the drawing]

[0014] [Figure 1] This is a schematic diagram showing a job posting optimization system according to an embodiment of the present invention. [Figure 2] This is a functional block diagram of a job posting optimization server according to an embodiment of the present invention. [Figure 3] This figure shows an example of information stored in the storage unit of a job posting optimization server according to an embodiment of the present invention. [Figure 4] An example of an instruction sentence according to an embodiment of the present invention is shown. [Figure 5] It is a functional block diagram of a user terminal according to an embodiment of the present invention. [Figure 6] It is a diagram showing a specific example of a screen of a job offer optimization service displayed on a user terminal according to an embodiment of the present invention. [Figure 7] It is a diagram showing a specific example of a screen of a job offer optimization service displayed on a user terminal according to an embodiment of the present invention. [Figure 8] It is a diagram showing a specific example of a screen of a job offer optimization service displayed on a user terminal according to an embodiment of the present invention. [Figure 9] It is a flowchart showing a job offer creation process by a job offer optimization system according to an embodiment of the present invention. [Figure 10] It is a flowchart showing a job offer review process by a job offer optimization system according to an embodiment of the present invention. [Figure 11] It is a block diagram showing the basic configuration of a computer.

Mode for Carrying Out the Invention

[0015] Hereinafter, the job offer optimization system 1 according to an embodiment of the present invention will be described with reference to the drawings. In all the drawings for explaining the embodiment, the same reference numerals are given to common components, and repeated explanations are omitted.

[0016] <System Configuration> FIG. 1 is a schematic diagram showing a job offer optimization system 1 according to an embodiment of the present invention.

[0017] The job offer ticket optimization system 1 includes a job offer ticket optimization server 100 (hereinafter simply referred to as server 100), a user terminal 200, a generation AI 300, an external database 400, and an internal system 500. In the system configuration example shown in FIG. 1, only one of the user terminal 200, the generation AI 300, the external database 400, and the internal system 500 is shown, but there may be a plurality of them. The server 100, the user terminal 200, the generation AI 300, the external database 400, and the internal system 500 are connected to the network NW and can communicate with each other via the network NW. The network NW includes the Internet and an intranet. The network NW may include a LAN (Local Area Network) and / or a WAN (Wide Area Network).

[0018] The server 100, the user terminal 200, the generation AI 300, the external database 400, and the internal system 500 are each composed of one or more computers 900. The basic configuration of the computer 900 will be described later.

[0019] The server 100 is an information processing device that provides a job offer ticket optimization service for optimizing job offer tickets for a plurality of users (job offerers) who access via the network NW and use the user terminal 200. The users of the job offer ticket optimization service are job offerers, such as companies, enterprises, corporations, government agencies, etc.

[0020] The user terminal 200 is a terminal device used by job offerers. The functional configuration of the user terminal 200 will be described later.

[0021] The Generative AI 300 is a Generative AI provided by an external provider separate from the operator of Server 100, and is a Large Language Model (LLM) capable of text generation such as conversation, writing, summarizing, translation, information retrieval and collection, and programming. The Generative AI is a technology that, when an instruction sentence (prompt) is input, uses artificial intelligence learned from a vast amount of existing information to output a response that is in line with the context of the instruction sentence. Furthermore, the Generative AI can accept input in a conversational format in real time and interactively, and generate and output output. In this embodiment, Server 100 uses the Generative AI 300 in conjunction with its API (Application Programming Interface), but Server 100 may also use a Generative AI that it has independently built internally.

[0022] External database 400 is an arbitrary job seeker database managed independently by someone other than the operator of server 100, such as a recruitment agency (also called an external agent), and provided to the operator of server 100. Server 100 can receive recommendations for job seeker candidates from external database 400 by sharing (transferring) job postings, as described below, with the external database 400. Server 100 connects to external database 400 indirectly and / or directly. By linking job postings with external database 400, the pool of job seekers can be expanded.

[0023] The internal system 500 includes one or more job seeker databases managed by the operator of the server 100. The internal system 500 may include multiple job seeker databases, for example, depending on the form, type, and / or associated services of the matching service. The form, type, and associated services of the matching service include, for example, offer services, agent services, direct scouting, etc. By default, job postings are configured to automatically link with at least one of the job seeker databases in the internal system 500. For job seeker databases in the internal system 500 that are not linked by default, for example, job seeker databases for which a separate fee is incurred, the settings can be changed based on the server 100's suggestion or at the user's discretion. This allows for expansion of the target population.

[0024] Figure 2 is a functional block diagram of a server 100 according to an embodiment of the present invention. The server 100 comprises a communication unit 110, a storage unit 120, and a processing unit 130.

[0025] The communication unit 110 is a network interface and communicates with the user terminal 200, the generation AI 300, the external database 400, and the internal system 500 via the network NW. The communication unit 110 passes data received from the user terminal 200, the generation AI 300, the external database 400, and the internal system 500 to the processing unit 130, and also transmits data generated by the processing unit 130 to the user terminal 200, the generation AI 300, the external database 400, and the internal system 500.

[0026] The storage unit 120 is configured using a storage device such as a magnetic hard disk or a semiconductor storage device. The storage unit 120 stores an application program 121, an internal database 122, and a mathematical model 123.

[0027] The application program 121 is executed by the processing unit 130 and is primarily a program for realizing each functional unit of the processing unit 130.

[0028] The internal database 122 includes the job posting database (hereinafter referred to as the job posting DB) and the recruitment information database (hereinafter referred to as the recruitment information DB), which will be described later. The recruitment information DB is a database that stores and manages recruitment activity information, including recruitment result information that links past job postings with past job seekers.

[0029] Mathematical model 123 is a model that has learned recruitment activity information. Specifically, for example, it is a model that has learned the correlation between multiple past job postings from multiple employers and the recruitment results information of those postings. More specifically, for example, mathematical model 123 is a model that has learned the relationship between past job posting information and the characteristics of applicants contained in the recruitment results information of those postings. Applicant characteristics include information on the applicant's job requirements, basic information of the applicant, the applicant's personality, and the applicant's selection process. Job requirements are the conditions on the job seeker's side that correspond to the job requirements. The applicant's selection process includes information on the applicant's selection history, acceptance / rejection, withdrawal, or joining the company. Learning may be further performed for each industry or job type, and may also be performed for applicants who were hired. Note that an applicant is a job seeker who applied to that job posting, and includes those who received job offers and those who were hired. Mathematical model 123, given job requirements as input, references a specified (linked) job seeker database and outputs at least one of the following: optimized job requirements, target population, and hiring rate. The specified job seeker database includes external database 400 and internal system 500.

[0030] Figure 3 shows an example of information stored in the internal database 122 of the server 100 according to an embodiment of the present invention.

[0031] As shown in Figure 3, the internal database 122 includes at least a job posting database and a recruitment information database.

[0032] The job posting database is a database that manages job posting information for each employer using the job posting optimization service.

[0033] The job posting information includes the job posting ID, the employer, the publication status, the source information of the job posting, and the results of the analysis of the source information of the job posting.

[0034] The job posting ID is an identifier used to uniquely identify a job posting, and is automatically assigned to each job posting by server 100.

[0035] The "Employer" field is where the employer ID of the person creating the job posting is stored.

[0036] The "Public Status" field indicates whether the job posting is publicly available to external parties (primarily external database 400 and / or internal system 500) from server 100. It stores one of two states: public or private. Immediately after a job posting is created, server 100 is set to private. Afterward, the employer who created the job posting can switch between public and private settings.

[0037] The job posting source information is the information that employers provide to server 100 in order to create job postings.

[0038] The results of the job posting source information analysis include job requirements, potential collaborators, and the hiring success rate. The job posting source information analysis results are managed historically for each analysis run. In Figure 3, for example, the first analysis result is labeled ver1, and the second analysis result is labeled ver2.

[0039] The job requirements include multiple requirement items. Each requirement item includes an initial definition, an optimized definition, and variable settings. The initial content consists of the job requirements immediately after analysis, extracted from the source job posting information. This initial content is editable by the employer on user terminal 200. The optimized content is tailored to the specific job requirements. Note that if the initial content is appropriate, the optimized content does not need to be generated. The variable setting is an item that stores the degree to which changes to the content are permissible for each job requirement. In this embodiment, the employer can select and set from the following options on the user terminal 200: changeable, not changeable, or semi-changeable. The degree to which changes are permissible means that this job requirement is absolutely non-negotiable, or that there is some flexibility, etc. For example, if the job requirement is a qualification, and a Class B Hazardous Materials Handler license (Category 4) is mandatory as a qualification, then "not changeable" is selected. If the applicant desires a Grade 1 Practical English Proficiency Test qualification, but if Grade 1 Pre-1 is acceptable depending on other equivalent qualifications, then semi-changeable is selected. The variable setting may be initially set to "changeable".

[0040] The list of potential partners includes initial partners, initial population, optimized partners, and optimized population. Initial partners are a list of potential partners immediately after analysis, obtained from the analysis of the source job posting information, and optimized partners are a list of potential partners obtained for the optimization of the job requirements. Each potential partner included in the initial partners and optimized partners includes selected / unselected status information. Initially, they are set to selected, but the employer can change the selected status on the user terminal 200 (see list D233 in Figure 6, described later). Initial population is the target population immediately after analysis, obtained from the analysis of the source job posting information, and optimized population is the target population obtained for the optimization of the job requirements. The target population will be described later.

[0041] The hiring rate includes the initial hiring rate and the optimized hiring rate. The initial hiring rate is calculated based on the initial content of all job requirements immediately after analysis, obtained from the analysis of the source job posting information, while the optimized hiring rate is calculated based on the optimized content of all job requirements.

[0042] The recruitment information database is a database that manages information that links past job postings and their corresponding recruitment results for multiple current or past users (employers) of the job posting optimization service.

[0043] Returning to Figure 2, we will continue the explanation of the processing unit 130 of the server 100. The processing unit 130 comprises, as functional units, an information acquisition unit 131, a requirements extraction unit 132, a requirements optimization unit 133, a job posting editing unit 134, a linkage unit 135, and an improvement suggestion unit 136. The processing unit 130 realizes each of these functional units by executing the application program 121 stored in the storage unit 120, and executes each step of the job posting creation process and the job posting review process. Details of the job posting creation process and the job posting review process will be described later.

[0044] The information acquisition unit 131 acquires job posting source information to be used to create job postings from the user terminal 200. The job posting source information may be in any format, such as text, an electronic file, or reference information such as a URL (Uniform Resource Locator) accessible via a network, or a combination thereof.

[0045] The information acquisition unit 131 provides the user terminal 200 with a job posting creation screen as a user interface for acquiring job posting source information. An example of the job posting creation screen will be described later.

[0046] Furthermore, if the acquired job posting source information is not in text format, the information acquisition unit 131 converts it into text format so that it can be edited by the server 100 and / or the job seeker.

[0047] The requirements extraction unit 132 extracts job requirements based on the job posting source information. Specifically, the requirements extraction unit 132 analyzes the job posting source information acquired by the information acquisition unit 131 and extracts the initial content of the job requirements for each predetermined requirement item of the job posting. The predetermined requirement items are, for example, job description, contract period, probationary period, place of work, working hours, break time, holidays, overtime work, wages, insurance coverage, name or title of the employer, employment type, status of passive smoking prevention measures, job title, number of hires, job description, application qualifications / requirements, annual salary, working hours, job title / position, etc., and are pre-stored in the storage unit 120. The requirements extraction unit 132 stores the extracted initial content of the job requirements in the job posting DB of the storage unit 120. The initial content of the job requirements obtained by analyzing the job posting source information is stored as the result of the first analysis (ver1).

[0048] The requirements extraction unit 132 may use the generation AI 300 to extract job requirements. That is, the requirements extraction unit 132 may generate a job requirement extraction instruction statement, which is an instruction statement (prompt) for extracting job requirements corresponding to pre-set requirement items from the job posting source information, and input the job requirement extraction instruction statement and the job posting source information to the generation AI 300 to extract the initial content of the job requirements for each requirement item. Figure 4 shows a portion of an example of a job requirements extraction instruction. The job information extraction instruction may be generated according to the format in which the job posting source information is obtained (text, file, URL, etc.).

[0049] The requirements extraction unit 132 may identify job requirements that could not be extracted from the job posting source information and conduct interviews to obtain them from the employer. The interviews may be conducted using a chat-based interface (question and answer format). The requirements extraction unit 132 may also determine whether the extracted job requirements are effective for recruitment activities, and if they are not effective, it may conduct interviews. For example, if the job requirement "place of work" is extracted as "head office or branch office," it may conduct an interview to ask, "Does 'branch office' include foreign branches?" Similarly, if a second job requirement related to the first job requirement is extracted, but it is not effective for recruitment activities, the requirements extraction unit 132 may conduct interviews. For example, even if the job requirements "Job Description" include "Web Engineer" and the "Qualifications" include "Programming Language," if the qualifications listed are only "Programming Language" and not sufficient for the job description "Web Engineer," then a question such as "Please specify the Web programming language" may be asked. The questions for this questioning may be generated using a learning model that has learned the correlation between the job requirements of past job postings and the job requirements that were effective (most likely to lead to hiring) in the hiring activities based on those job postings.

[0050] The requirements optimization unit 133 analyzes the initial content of the job requirements extracted by the requirements extraction unit 132 using a mathematical model 123 and generates optimized content as recommended job requirements. The requirements optimization unit 133 stores the generated recommended job requirements (optimized content) in the job posting database of the storage unit 120.

[0051] The requirements optimization unit 133 may re-analyze the edited original job requirements and update the optimized content as recommended job requirements if the original job requirements have been edited by the employer.

[0052] If the requirements optimization unit 133 determines, based on the selection status of the job posting (progress of recruitment activities) as described later by the improvement suggestion unit 136, it may re-analyze the original content of the job requirements at that time and update the recommended job requirements, collaborating candidates, and / or hiring rate.

[0053] The requirements optimization unit 133 may analyze the job requirements based on variable settings of the job requirements. For example, weighting according to the variable settings may be applied to the analysis.

[0054] The job posting editing unit 134 presents the initial content of multiple job requirements extracted by the requirements extraction unit 132 as a job posting to the user terminal 200 used by the employer, and accepts edits to the initial content of the job requirements. The job posting editing unit 134 stores the edited content in the storage unit 120 under version control. The job posting editing unit 134 also presents the combination of the initial content of the job requirements and the optimized content of the recommended job requirements to the user terminal 200 used by the employer. Note that for requirement items for which optimized content was not generated, the display of the optimized content is omitted.

[0055] The job posting editorial department 134 may present the employer with a combination of a target population formed based on the initial content of the job requirements and a target population formed based on the optimized content of the recommended job requirements.

[0056] The job posting editorial department 134 may obtain a selection input from the user terminal 200 used by the employer, which allows the user to select one of the following as variable settings for each requirement item of the job requirements: changeable, unchangeable, or semi-changeable. The job posting editorial department 134 may also present the changes in recruitment effectiveness, specifically the changes in the target population and / or the hiring rate, when the setting is changed from unchangeable or semi-changeable to changeable.

[0057] The collaboration unit 135 extracts one or more potential collaborators from multiple job seeker databases based on the job requirements. Specifically, the collaboration unit 135 uses a mathematical model 123 to analyze the initial and optimized content of the job requirements and extracts initial collaborators for each. Furthermore, the linking unit 135 uses the mathematical model 123 to generate a target population as the initial population when linking to linking candidates extracted based on the initial content of the job requirements, and generates a target population as the optimized population when linking to linking candidates extracted based on the optimization content, and stores them in the storage unit 120. The target population is the population of job seekers in the job seeker database that matches the job requirements in a predetermined proportion or more. The target population also changes depending on the job seeker database being linked. Furthermore, the collaboration unit 135 calculates the hiring rate using the mathematical model 123 based on the job requirements. Specifically, it calculates the initial hiring rate based on the initial job requirements and the optimized hiring rate based on the optimized content. The collaboration unit 135 may also calculate the initial hiring rate based on the initial job requirements and the initial population and / or initial candidates. Similarly, the collaboration unit 135 may calculate the optimized hiring rate based on the optimized content and the optimized population and / or optimized candidates.

[0058] The collaboration unit 135 may automatically connect with the collaboration candidates extracted based on the job requirements.

[0059] The collaboration unit 135 may, if the initial content of the job requirements is edited, update at least one of the target population, collaborating candidates, and hiring rate based on the edited initial content of the job requirements and display it to the job seeker. The display update may be in real time, or it may be at the time a request for a display update is received from the user terminal 200.

[0060] The improvement suggestion unit 136 acquires selection events related to job postings from the user terminal 200 used by the employer and determines whether the job posting needs to be revised based on the selection status (progress of recruitment activities). Selection events include, for example, applications from job seekers, interactions with applicants, decisions to reject applicants, and applicants withdrawing their applications. The determination of whether revision is necessary is made each time a selection event is acquired and / or at predetermined intervals. The measurement of the predetermined interval may be reset when a selection event occurs. The determination of whether revision is necessary may be based on a single selection event, on multiple selection events, or on the number of times the determination of whether revision is necessary has been made based on a predetermined interval. The predetermined interval may be managed by the server 100 or changed by the employer.

[0061] Figure 5 is a functional block diagram of a user terminal 200 according to an embodiment of the present invention. The user terminal 200 comprises a communication unit 210, a storage unit 220, a control unit 230, a display unit 240, and an operation unit 250. The communication unit 210, storage unit 220, control unit 230, display unit 240, and operation unit 250 are electrically connected to each other via a communication bus 260. The user terminal 200 is, for example, a smartphone, a tablet terminal, or a PC.

[0062] <Screen example> Next, we will explain an example of the screen for the job posting optimization service provided by server 100.

[0063] Figures 6, 7, and 8 show, respectively, screen D100, screen D200, and screen D300, which are examples of screens for the job posting optimization service displayed on a user terminal 200 by a server 100 according to an embodiment of the present invention.

[0064] The screen D100 shown in Figure 6 is an example of a job posting creation screen, and is a user interface where the employer inputs the source information for the job posting.

[0065] Screen D100 includes a title area D110 that displays the screen title, an input area D120 for easily entering existing information as job posting source information, a button D130 for confirming the input content and sending it to the server 100, and an input area D140 for the job seeker to manually enter text as job posting source information.

[0066] The title area D110 contains the text "Job Posting Creation".

[0067] Input field D120 contains the text "Automatically create using existing information," along with three input fields D121, D122, and D124. Above input field D121 is the text "Load another company's media or your own website," and inside it is the text "Please enter or copy and paste the URL." Above input area D122 is the text "Load File," and inside it is the text "Drag and drop your file or select a file using the Browse button." Additionally, to the right of the inside of input area D122 is the "Browse" button D123 for selecting a file. The text "Automatically generated by AI" is placed above input field D124, and the text "Please enter information" is placed inside it.

[0068] Input field D140 contains the string "Create by yourself," along with multiple input fields D141, D142, D143, D144, ... Above each input field D141, D142, D143, D144, ... are the item number and a description of the item, and inside each field is the text "Please enter information".

[0069] The screen D200 shown in Figure 7 is an example of a job posting editing screen. Based on the job posting source information entered on screen D100, the server 100 extracts one or more job requirements, which are then displayed on the user terminal 200 used by the job seeker, and the user is allowed to edit them. Screen D200 is displayed on the user terminal 200 after selecting (clicking or tapping) button D130 on screen D100.

[0070] Screen D200 includes a title area D210 that displays the screen's title, an area D220 that displays each job requirement of the job posting and accepts editing, an area D230 that displays candidates for services linked to the job posting and accepts selection / deselection input, and a button D240 for confirming the content and sending it to server 100.

[0071] The title area D210 contains the text "Job Posting Editing".

[0072] Inside area D220, in the upper left corner, there is the text "There are items that we recommend reviewing," along with multiple input areas D222, D223, D225, ... and a button D221 for re-evaluating (re-analyzing) the job requirements all at once. Above input areas D222, D223, D225, ..., the same item numbers and descriptions as in input areas D141, D142, D143, D144, ... in Figure 5 are placed. Inside input areas D222, D223, D225, ..., text corresponding to the initial content of the job requirements extracted for each requirement item from the job posting source information entered by the employer in the job posting input screen shown in Figure 5 is automatically entered and displayed in an editable format. Button D221 is a button that, after a job seeker has edited one or more input fields, particularly multiple input fields D222, D223, D225, ..., can be selected (clicked or tapped) to perform a batch re-analysis of all edited content. Button D221 may also be repositioned to the bottom of the page on screen D200.

[0073] Between input areas D223 and D225 is a message area D224, which displays recommended job requirements. Message area D224 has an arrow pointing to input area D225 to indicate that it is a recommended job requirement for the requirements item in input area D225. Inside message area D224 are an icon to draw attention, a string of text indicating a notification that "It seems difficult to hire under these conditions," the string of text indicating "Estimated number of applicants: 0" as reference information for the notification, and the string of text indicating optimized content (optimized content) for item 3, which is a requirement item in input area D225, which is "Recommended value (average annual salary of applicants): 5 to 6 million yen." In addition, to the right of the string of text indicating the optimized content, there is a button D226 in message area D224 to apply the optimized content to input area D225.

[0074] Furthermore, the input area D225, which is the target of the message area D224, has a button D227 for re-evaluation (re-analysis). After editing the input area D225, the job seeker can select (click or tap) button D227 to perform an analysis based on the edited content of input area D225, on a per-requirement-item basis. Thus, the re-evaluation button D227 may be temporarily displayed only in the input area of ​​the requirement item that is the target of the recommended job requirements presentation.

[0075] Inside area D230, in the upper left corner, there is the text "By linking (making public) our services and partner agent services, it will lead to an increase in the candidate pool and a rise in the hiring success rate," along with area D231 which shows the size of the target candidate pool, area D232 which shows the hiring success rate, and list D233 which displays a list of potential partners. Area D231 shows the number of target candidates calculated from the current job posting content (initial job requirements) (12 people) and the number of target candidates calculated when the job posting content is changed to the optimized content (23 people). Area D232 shows the estimated hiring rate (35%) calculated from the current job posting content (initial job requirements) and the estimated minimum hiring rate (48%) calculated when the job posting content is changed to an optimized version. List D233 displays a combination of candidate names for the collaboration candidates and checkboxes indicating their selected or unselected status. In the example shown in Figure 6, the initial candidates (Our Service 1, Our Service 2, Agent 1, Agent 2, Agent 3) are selected by default, while the optimized candidate (Agent 4) is unselected by default, with its name highlighted (e.g., underlined) to indicate that it is an optimized candidate. Optimized candidates that are unselected by default may be automatically selected by Server 100 when the recruiter edits the initial job requirements to improve their match with the recommended job requirements. Job seekers can change whether they select or deselect potential collaborators using the checkboxes in list D233. Furthermore, in list D233, when the selection status of a collaborating candidate changes, the target population shown in area D231 and the estimated acceptance rate shown in area D232 are updated and displayed.

[0076] Button D240 displays the text "Confirm edits and publish". Additionally, button D211 has been repositioned above button D240.

[0077] The job seeker can confirm the content edited on screen D200 and send the edited content to server 100 by selecting (clicking or tapping) button D240 on screen D200 displayed on user terminal 200.

[0078] The screen D300 shown in Figure 8 is an example of a list screen of applicants who have applied for a job posting, and it is a user interface that displays the details of the applicant selected from the list in a pop-up window.

[0079] Screen D300 contains a title area D310 that displays the screen's title, an area D320 that displays a list of each applicant, and a pop-up window area D330. In area D320, each applicant's profile is displayed in a list format. The pop-up window area D330 contains the facial photograph and basic information of the applicant selected from the list, the selection status (results of the selection event), and button D331. Button D331 displays the text, "Why not review your job posting?". By selecting (clicking or tapping) button D331 on screen D300, the employer can transition to the job posting editing screen shown on screen D200 in Figure 6. In this case, the job posting editing screen presents the initial content of the job requirements for the current / latest version, along with optimized content obtained by re-analyzing that initial content and the selection event results for that job posting. Similarly, the target population and hiring rate are also presented, showing the results of an analysis that incorporates the selection event results into the current initial content.

[0080] <System Operation> Next, the operation of the job posting creation process and job posting review process of the job posting optimization system 1 having the above-described configuration and functions will be explained using a flowchart. The job posting creation process is a series of processes that include the acquisition of source job posting information, the extraction of job requirements, the optimization of job requirements, the integration process, the presentation of the job posting, the acquisition of edited content, and the finalization of the job posting.

[0081] Figure 9 is a flowchart showing the job posting creation process by the job posting optimization system 1 according to an embodiment of the present invention.

[0082] In step S101, the user terminal 200 receives job posting source information from the employer using, for example, the user interface of the job posting creation screen shown in Figure 5, and transmits the job posting source information to the server 100. The information acquisition unit 131 of the server 100 acquires the job posting source information from the user terminal 200 and stores it in the storage unit 120 (job posting source information acquisition step).

[0083] In step S102, the requirements extraction unit 132 of the server 100 analyzes the job posting source information acquired in step S101 and extracts the job requirements (job requirements extraction step). The requirements extraction unit 132 stores the extracted job requirements in the storage unit 120 as the initial content.

[0084] In step S103, the requirements optimization unit 133 of the server 100 analyzes the job requirements extracted in step S102 and generates optimized recommended job requirements (job requirements optimization step).

[0085] In step S104, the collaboration unit 135 of the server 100 extracts collaboration candidates based on the job requirements extracted in step S102, and generates a target population based on the extracted collaboration candidates (collaboration process).

[0086] In step S105, the job posting editing unit 134 of the server 100 presents (displays) the job requirements extracted in step S102, the recommended job requirements generated in step S103, and the collaborating candidates and target population extracted in step S104 as an editable job posting to the user terminal 200 (job posting presentation step). The job posting editing unit 134 presents the job posting, for example, using the user interface of the job posting editing screen shown in Figure 6.

[0087] In step S106, the employer can edit the job posting presented to the user terminal 200 in step S105, if necessary. The editable parts are the initial job requirements and the selection / deselection of potential collaborators. If the employer makes edits to the job posting on the user terminal 200, the process proceeds to step S107. If the employer does not make edits to the job posting on the user terminal 200, the process proceeds to step S111.

[0088] In step S107, the user terminal 200 receives an input from the job seeker confirming the edited content and sends the edited content to the server 100. The server 100 retrieves the edited content from the user terminal 200 (edited content retrieval step).

[0089] In step S108, the requirements optimization unit 133 and the linkage unit 135 of the server 100 re-analyze the edited content obtained in step S107 and update the job posting.

[0090] In step S109, the job posting editing unit 134 of the server 100 presents (displays) the job posting updated in step S108 to the user terminal 200 in an editable format. The job posting editing unit 134 presents the updated job posting, for example, through the user interface of the job posting editing screen shown in Figure 6.

[0091] In step S110, the employer can edit the job posting presented to the user terminal 200 in step S109 if necessary. The editable parts are the initial job requirements and the selection / deselection of potential collaborators. If the employer makes edits to the job posting on the user terminal 200, the process returns to step S107. If the employer does not make any edits to the job posting on the user terminal 200, the process proceeds to step S111.

[0092] In step S111, the user terminal 200 receives an input from the recruiter to confirm the job posting and notifies the server 100 of the confirmation. The server 100 receives the confirmation notification from the user terminal 200 (job posting confirmation process).

[0093] In step S112, the server 100 confirms the updated job posting in the storage unit 120, changes the publication status of the job posting from private to public, and terminates the flow.

[0094] Figure 10 is a flowchart showing the job posting review process by the job posting optimization system 1 according to an embodiment of the present invention. The job posting review process is executed at a predetermined timing after the job posting has been finalized and published. The job posting review process includes a selection event acquisition step, a selection analysis step, a review determination step, a review notification step, and an edit acceptance step.

[0095] In step S201, the user terminal 200 sends selection event information related to the job posting to the server 100. The server 100 retrieves the selection event information from the user terminal 200 (selection event retrieval step).

[0096] In step S202, the improvement suggestion unit 136 of the server 100 analyzes the selection event information acquired in step S201 (selection analysis step).

[0097] In step S203, the improvement suggestion unit 136 of the server 100 determines whether or not a review of the job posting linked to the selection event information is necessary based on the analysis results in step S202 (review determination step). If the determination result is true (YES), i.e., a review of the job posting is necessary, the process proceeds to step S204. On the other hand, if the determination result is false (NO), i.e., a review of the job posting is not necessary, the process ends.

[0098] In step S204, the improvement suggestion unit 136 of the server 100 presents (displays) a notification suggesting improvements to the user terminal 200 used by the job seeker of the job posting (revision notification step).

[0099] In step S205, the recruiter can choose to agree to or disagree with the notification proposing improvements presented in step S204. If the recruiter chooses to agree to the proposal on the user terminal 200, the process proceeds to step S206. On the other hand, if the user terminal 200 chooses to disagree with the proposal, the flow ends.

[0100] In step S206, that is, in step S205, if the employer chooses to agree to the proposal, the user terminal 200 sends a notification to the server 100 requesting a review. The server 100 receives the notification requesting a review from the user terminal 200 and proceeds to step S105 of the job posting creation process (jumping from number 1 in Figure 10 to number 1 in Figure 9), and processes the job posting to accept editing (editing acceptance process).

[0101] <Basic Computer Configuration> Figure 11 is a block diagram showing the basic hardware configuration of computer 900. Computer 900 includes a control unit 901, a storage unit 902, a communication unit 903, an input unit 904, and an output unit 905. The control unit 901, storage unit 902, communication unit 903, input unit 904, and output unit 905 are electrically connected to each other via a communication bus 910.

[0102] The control unit 901 includes a CPU (Central Processing Unit, also called a processor) and controls various parts of the computer 900, as well as reading and executing various programs stored in the storage unit 902.

[0103] The memory unit 902 includes a main memory such as DRAM (Dynamic Random Access Memory) and an auxiliary memory such as a hard disk, and is a device that stores various programs for running the operating system and various applications of the computer 900, as well as data used by these programs. The processes shown in the flowchart above are realized by the control units 901 of each computer constituting the server 100, user terminal 200, generation AI 300, external database 400, and internal system 500 executing the programs stored in their respective memory units 902.

[0104] The communication unit 903 is a device for communicating with external devices and sends and receives data according to instructions from the control unit 901. Each computer that makes up the server 100, user terminal 200, generation AI 300, external database 400, and internal system 500 uses this communication unit 903 to communicate with other devices, including the network NW shown in Figure 1.

[0105] The input unit 904 is a device that receives input from an external source and supplies it to the control unit 901, and includes, for example, a keyboard, mouse, touch panel, and camera. The output unit 905 is a device that outputs the processing results of the control unit 901 to the outside, and includes, for example, a display and speaker. <Program> Here, we will describe the programs for realizing each functional unit of the server 100 according to this embodiment.

[0106] Server 100 is implemented in computer 900. The operation of each component of server 100 is stored in the auxiliary storage device of storage unit 902 in the form of a program. Control unit 901 reads the program from the auxiliary storage device of storage unit 902, loads it into the main memory of storage unit 902, and executes the above processing according to the program. Also, control unit 901 reserves a storage area in the main memory of storage unit 902 corresponding to the storage unit 120 described above, according to the program.

[0107] Specifically, the program is a program to be executed by a computer 900 which is equipped with a processor and a memory unit, and the program causes the computer to execute an information acquisition step of acquiring job posting source information from a job seeker, a requirements extraction step of extracting job requirements based on the job posting source information, a requirements optimization step of analyzing the extracted job requirements and generating optimized recommended job requirements, and a job posting editing step of presenting the job requirements and the recommended job requirements to the job seeker and accepting editing of the job requirements.

[0108] The auxiliary storage device of the memory unit 902 is an example of a tangible medium that is not temporary. Other examples of tangible mediums that are not temporary include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, and semiconductor memory connected via the input unit 904. Furthermore, if this program is distributed to the computer 900 via the network NW, the computer 900 that receives the program may load it into the main memory of the memory unit 902 and execute the above processing.

[0109] Furthermore, the program may be intended to implement some of the functions described above. In addition, the program may be a so-called differential file (differential program) that implements the functions described above in combination with other programs already stored in the auxiliary storage device of the memory unit 902.

[0110] According to the job posting optimization system 1 of this embodiment described above, high-quality and uniform job postings can be efficiently created. Server 100 can automatically extract job requirements from source job posting information using generation AI. Employers can use their company website URL or existing job posting electronic files as source information, reducing the burden on them in creating job postings. In other words, it enables quick and efficient creation and modification of job postings, streamlining the job posting process. By presenting job seekers with optimized content based on the job requirements extracted by Server 100, even job seekers without recruitment expertise can easily and quickly revise their job requirements based on the optimized content, without relying on the skills or experience of an agent. This revision improves the quality of the job posting, helps to create an appropriate target pool, and increases the hiring rate. Furthermore, by linking with external databases 400 and internal system 500's job seeker databases and ancillary services of internal system 500, it becomes possible to expand the target population and strengthen support for employers.

[0111] Furthermore, the present invention is not limited to the embodiments described above, and various modifications can be adopted.

[0112] For example, in the above embodiment, the hiring rate was calculated based on the initial content of all job requirements, but it may also be calculated based on some of the job requirements selected according to predetermined conditions. The predetermined conditions are, for example, variable settings for the requirement items.

[0113] Furthermore, if multiple optimized requirements are generated as recommended job requirements, they may be ranked according to a predetermined algorithm, and only the top-ranked ones may be presented (displayed) to job seekers, or their ranking may be indicated. The predetermined algorithm may take into account factors such as the impact on the size of the target population and variable settings. This allows for efficient optimization of job postings.

[0114] Furthermore, in the above embodiment, the re-analysis of the edited content in step S108 required the confirmation of the edited content by the job seeker in step S107. However, changes in the selection or non-selection of partner candidates may be updated in real time at the time the on / off status is received. This allows for immediate detection of changes.

[0115] Furthermore, in the above embodiment, the storage unit 120 was used for version control of the job requirements. However, for content that can be edited by the job seeker, the editing history until it is finalized may be temporarily stored, and only the latest finalized content may be stored in the storage unit 120.

[0116] Furthermore, although the generation AI400 in the above embodiment uses a large-scale language model, non-text-based generation models such as image generation models, video generation models, and speech generation models, or multimodal generation models combining these, may also be used.

[0117] (Note) The features of the above-described embodiment are noted below. (Note 1) Information acquisition unit that obtains job posting source information from job seekers, A requirements extraction unit extracts job requirements based on the aforementioned job posting source information, A requirements optimization unit analyzes the extracted job requirements and generates optimized recommended job requirements, A job posting editing department presents the aforementioned job requirements and the aforementioned recommended job requirements to the aforementioned employers and accepts editing of the aforementioned job requirements, A job posting optimization system equipped with the following features. This automatically extracts the necessary job requirements for creating a job posting, allowing employers to use source information in any format they prefer. Furthermore, optimized recommended job requirements are presented, enabling even employers with limited recruitment expertise, experience, or track record to easily create high-quality job postings. (Note 2) The system further includes a linking unit that generates a target population when one or more job seeker databases are selected and linked based on the aforementioned job requirements. The aforementioned job posting editorial department presents the aforementioned employer with combinations of the aforementioned target population based on the aforementioned job requirements and the aforementioned target population based on the aforementioned recommended job requirements. The job posting optimization system described in Appendix 1. This allows for the creation of a target pool that meets job requirements, and by linking with multiple job seeker databases, the target pool can be expanded to improve the hiring rate. (Note 3) The aforementioned linking unit automatically links to the job seeker database extracted based on the job requirements. The job posting optimization system described in Appendix 2. This allows access to a database of job seekers who meet the job requirements. (Note 4) The requirements optimization unit, when the job requirements are edited, re-analyzes the edited job requirements and updates the recommended job requirements. The job posting optimization system described in Appendix 1. This allows you to check the impact of the edits. (Note 5) The aforementioned linking unit updates the respective target populations based on the edited job requirements when the job requirements are edited. The job posting optimization system described in Appendix 2. This allows us to check the impact of the edited content on the target population. (Note 6) The aforementioned job posting editorial department obtains a selection input from the employer, which indicates that the job requirements are changeable, not changeable, or semi-changeable. The requirements optimization unit analyzes the job requirements for each of the variable settings. The job posting optimization system described in Appendix 1. This allows for analysis that takes into account the specific preferences of job seekers. (Note 7) The aforementioned job posting editorial department presents the recruitment effects when the variable setting is changed from "unchangeable" or "semi-changeable" to "changeable". The job posting optimization system described in Appendix 1. This allows us to propose a compromise that addresses the specific requirements of the job seeker. (Note 8) The requirements extraction unit extracts the job requirements using a generation AI. The job posting optimization system described in Appendix 1. This makes the job posting creation process more efficient. (Note 9) The aforementioned linkage unit links to services associated with the job seeker database. The job posting optimization system described in Appendix 2. This allows users to access support services tailored to the specific characteristics of their job postings. (Note 10) The information acquisition unit accepts a URL (Uniform Resource Locator) and / or an electronic file as the source information for the job posting, and provides a user interface to the user terminal used by the job seeker that converts it into editable text. The job posting optimization system described in Appendix 1. This makes the job posting creation process more efficient. (Note 11) The system further includes an improvement suggestion unit that obtains selection events for a job posting created based on the aforementioned job posting source information, and determines whether or not the job posting needs to be revised based on the aforementioned selection events. The job posting optimization system described in Appendix 1. This allows us to suggest a causal relationship between the selection process and the job posting, and to propose revisions to the job posting at an appropriate or necessary time. (Note 12) If the requirements optimization unit determines that the job posting needs to be reviewed, it re-analyzes the job requirements. The job posting optimization system described in Appendix 11. This allows for the content of job postings to be improved in response to changes over time in the balance between the job market and the job seeker market. (Note 13) The requirements extraction unit identifies the job requirements that could not be extracted from the job posting source information and obtains the unextracted job requirements from the employer. The job posting optimization system described in Appendix 1. This makes it easy to create job postings that are complete and accurate. (Note 14) Information acquisition unit that obtains job posting source information from job seekers, A requirements extraction unit extracts job requirements based on the aforementioned job posting source information, A requirements optimization unit analyzes the extracted job requirements and generates optimized recommended job requirements, A job posting editing department presents the aforementioned job requirements and the aforementioned recommended job requirements to the aforementioned employers and accepts editing of the aforementioned job requirements, A job posting optimization server equipped with the following features. (Note 15) The information acquisition step involves obtaining the source information of the job posting from the employer, A requirements extraction step that extracts job requirements based on the aforementioned job posting source information, A requirements optimization step involves analyzing the extracted job requirements and generating optimized recommended job requirements, A job posting editing step that presents the aforementioned job requirements and the aforementioned recommended job requirements to the employer and accepts editing of the aforementioned job requirements, A program that causes a computer to execute something. (Note 16) The information acquisition step involves obtaining the source information of the job posting from the employer, A requirements extraction step that extracts job requirements based on the aforementioned job posting source information, A requirements optimization step involves analyzing the extracted job requirements and generating optimized recommended job requirements, A job posting editing step that presents the aforementioned job requirements and the aforementioned recommended job requirements to the employer and accepts editing of the aforementioned job requirements, A method for optimizing job postings by having a computer perform the optimization. [Explanation of Symbols]

[0118] 1: Job posting optimization system 100: Job posting optimization server 110: Communications Department 120: Storage section 123: Mathematical Models 130: Processing Unit 131: Information acquisition department 132: Requirements extraction part 133: Requirements Optimization Department 134: Job Posting Editorial Department 135: Liaison Department 136: Improvement proposal department 200: User terminal 300: Generation AI 400: External database 500: Internal Systems D100, D200, D300: Screen NW: Network

Claims

1. Information acquisition unit that obtains job posting source information from job seekers, A requirements extraction unit extracts job requirements based on the aforementioned job posting source information, A requirements optimization unit analyzes the extracted job requirements and generates optimized recommended job requirements, A job posting editing department presents the aforementioned job requirements and the aforementioned recommended job requirements to the aforementioned employers and accepts editing of the aforementioned job requirements, A job posting optimization system equipped with the following features.

2. The system further includes a linking unit that generates a target population when one or more job seeker databases are selected and linked based on the aforementioned job requirements. The aforementioned job posting editorial department presents the aforementioned target population based on the aforementioned job requirements and the aforementioned target population based on the aforementioned recommended job requirements to the aforementioned employer. The job posting optimization system according to claim 1.

3. The aforementioned linking unit automatically links to the job seeker database extracted based on the job requirements. The job posting optimization system according to claim 2.

4. The requirements optimization unit, when the job requirements are edited, re-analyzes the edited job requirements and updates the recommended job requirements. The job posting optimization system according to claim 1.

5. The aforementioned linkage unit updates the target population based on the edited job requirements when the job requirements are edited. The job posting optimization system according to claim 2.

6. The aforementioned job posting editorial department obtains a selection input from the employer, which indicates that the job requirements are changeable, not changeable, or semi-changeable. The requirements optimization unit analyzes the job requirements for each of the variable settings. The job posting optimization system according to claim 1.

7. The aforementioned job posting editorial department presents the recruitment effects when the variable setting is changed from "unchangeable" or "semi-changeable" to "changeable". The job posting optimization system according to claim 6.

8. The requirements extraction unit extracts the job requirements using generation AI. The job posting optimization system according to claim 1.

9. The aforementioned linkage unit links to services associated with the job seeker database. The job posting optimization system according to claim 2.

10. The information acquisition unit accepts a URL (Uniform Resource Locator) and / or an electronic file as the source information for the job posting, and provides a user interface to the user terminal used by the job seeker that converts it into editable text. The job posting optimization system according to claim 1.

11. The system further includes an improvement suggestion unit that obtains selection events for a job posting created based on the aforementioned job posting source information, and determines whether or not the job posting needs to be revised based on the aforementioned selection events. The job posting optimization system according to claim 1.

12. If the requirements optimization unit determines that the job posting needs to be reviewed, it re-analyzes the job requirements. The job posting optimization system according to claim 11.

13. The requirements extraction unit identifies the job requirements that could not be extracted from the job posting source information and obtains the unextracted job requirements from the employer. The job posting optimization system according to claim 1.

14. Information acquisition unit that obtains job posting source information from job seekers, A requirements extraction unit extracts job requirements based on the aforementioned job posting source information, A requirements optimization unit analyzes the extracted job requirements and generates optimized recommended job requirements, A job posting editing department presents the aforementioned job requirements and the aforementioned recommended job requirements to the aforementioned employers and accepts editing of the aforementioned job requirements, A job posting optimization server equipped with the following features.

15. The information acquisition step involves obtaining the source information of the job posting from the employer, A requirements extraction step that extracts job requirements based on the aforementioned job posting source information, A requirements optimization step involves analyzing the extracted job requirements and generating optimized recommended job requirements, A job posting editing step that presents the aforementioned job requirements and the aforementioned recommended job requirements to the employer and accepts editing of the aforementioned job requirements, A program that causes a computer to execute something.

16. The information acquisition step involves obtaining the source information of the job posting from the employer, A requirements extraction step that extracts job requirements based on the aforementioned job posting source information, A requirements optimization step involves analyzing the extracted job requirements and generating optimized recommended job requirements, A job posting editing step that presents the aforementioned job requirements and the aforementioned recommended job requirements to the employer and accepts editing of the aforementioned job requirements, A method for optimizing job postings by having a computer perform the optimization.

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