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

Abstract

The present invention provides a job advertisement optimization system, a job advertisement optimization server, a program, and a job advertisement optimization method that can efficiently create a job advertisement that can improve the hiring decision rate. [Solution] The system comprises an information acquisition unit 131 that acquires job posting source information from employers, 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 employer and accepts editing of the job requirements.

Description

[Technical field]

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

[0002] In recent years, job matching services have played a role in efficiently connecting the needs of companies (employers) and job seekers. Job site operators are strengthening their comprehensive matching support by utilizing AI (Artificial Intelligence) and digital technology, and by expanding ancillary services such as extensive support from agents and direct scouting.

[0003] The functions required for a job matching service can be broadly divided into those for the employer and those for the job seeker. Among these, the employer is required to have a function that makes it more efficient to create job postings. For example, Patent Document 1 describes a technology that accepts input of the conditions of the personnel required by the employer as input of personnel conditions, acquires related information related to the input personnel conditions, inputs the acquired related information into a first artificial intelligence, and outputs a job posting corresponding to the personnel indicated by the related information. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2024-118394 A Summary of the Invention [Problem to be solved by the invention]

[0005] In job matching, the job requirements (human resource requirements) of a job posting are determined, and thus a target population, which is a group of targets that match the job requirements, is also determined. Currently, the amount and quality of information entered by employers to create a job posting is often insufficient (the conditions of the job posting are lenient), and even if a system is equipped with a function to automatically create a job posting based on information obtained from employers, as in Patent Document 1, the quality of the job postings created varies. If a low-quality job posting is created, it becomes difficult to form a target population, so creating and revising the job posting takes time and effort, and ultimately, adjustments and coordination with a recruiting advisor (person) are manually performed.

[0006] In addition, the gap in recruiting difficulty between large companies, which tend to have know-how and a track record in recruiting activities, and small and medium-sized enterprises, which tend not to have such know-how, is widening. There is no efficient support function for creating and revising job postings for companies at the disadvantage of such a gap.

[0007] Furthermore, it is possible to expand the pool of job seekers by linking to the unique job seeker databases held by recruitment agencies other than the operator of the job matching service. However, this is not being used much, resulting in lost recruitment opportunities for both employers and job seekers.

[0008] Therefore, an object of the present disclosure is to provide a job advertisement optimization system, a job advertisement optimization server, a program, and a job advertisement optimization method that can efficiently create high-quality, uniform job advertisements. [Means for solving the problem]

[0009] The job advertisement optimization system of the present invention comprises an information acquisition unit that acquires original job advertisement information from an employer, a requirements extraction unit that extracts job requirements based on the original job advertisement information, a requirements optimization unit that analyzes the extracted job requirements and generates optimized recommended job requirements, and a job advertisement 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 advertisement optimization server of the present invention comprises an information acquisition unit that acquires original job advertisement information from an employer, a requirements extraction unit that extracts job requirements based on the original job advertisement information, a requirements optimization unit that analyzes the extracted job requirements and generates optimized recommended job requirements, and a job advertisement editing unit that presents the job requirements and the recommended job requirements to the employer and accepts editing of the job requirements.

[0011] In addition, the program of the present invention causes a computer to execute an information acquisition step of acquiring original job posting information from a recruiter, a requirements extraction step of extracting job requirements based on the original job posting 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 recruiter and accepting edits to the job requirements.

[0012] The job posting optimization method of the present invention comprises an information acquisition step of acquiring original job posting information from a recruiter, a requirements extraction step of extracting job requirements based on the original job posting 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 recruiter and accepting edits of the job requirements. Effect of the Invention

[0013] According to the present disclosure, it is possible to provide a job advertisement optimization system, a job advertisement optimization server, a program, and a job advertisement optimization method that can efficiently create high-quality, uniform job advertisements. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a schematic diagram showing a job advertisement optimization system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a functional block diagram of a job advertisement optimization server according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a diagram showing an example of information stored in a storage unit of a job advertisement optimization server according to an embodiment of the present invention. [Figure 4] 1 illustrates an example of an instruction statement according to an embodiment of the present invention. [Diagram 5] FIG. 2 is a functional block diagram of a user terminal according to an embodiment of the present invention. [Figure 6] FIG. 13 is a diagram showing a specific example of a job posting optimization service screen displayed on a user terminal according to an embodiment of the present invention. [Figure 7] FIG. 13 is a diagram showing a specific example of a job posting optimization service screen displayed on a user terminal according to an embodiment of the present invention. [Figure 8] FIG. 13 is a diagram showing a specific example of a job posting optimization service screen displayed on a user terminal according to an embodiment of the present invention. [Figure 9] 1 is a flowchart showing a job advertisement creation process by a job advertisement optimization system according to an embodiment of the present invention. [Figure 10] 1 is a flowchart showing a job advertisement review process by the job advertisement optimization system according to an embodiment of the present invention. [Figure 11] FIG. 1 is a block diagram showing a basic configuration of a computer. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

[0017] The job advertisement optimization system 1 includes a job advertisement optimization server 100 (hereinafter simply referred to as the 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 each of the user terminal 200, the generation AI 300, the external database 400, and the internal system 500 is shown, but there may be more than one. The server 100, the user terminal 200, the generation AI 300, the external database 400, and the internal system 500 are connected to a 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 configured with 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 advertisement optimization service that optimizes job advertisements to user terminals 200 used by multiple users (employers) who access the server 100 via a network NW. Users of the job advertisement optimization service are recruiters who employ people, such as companies, enterprises, corporations, government agencies, etc.

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

[0021] The generation AI 300 is a generation AI provided by an external provider different from the operator of the server 100, and is, for example, a large language model (LLM) capable of generating text such as conversation, writing, summarizing, translation, information search and collection, and programming. The generation AI is a technology that, when an instruction (prompt) is input, an answer according to the context of the instruction is output using artificial intelligence that has learned using a huge amount of existing information. In addition, the generation AI can accept input in real time and interactively in a conversational format, and generate and output an output. In this embodiment, the server 100 uses the generation AI 300 in cooperation with the API (Application Programming Interface) of the generation AI 300, but the server 100 may also be equipped with and use a generation AI that is independently constructed within the server 100.

[0022] The external database 400 is any job seeker database that is independently managed outside the server 100 by a party other than the operator of the server 100, such as a recruitment agency (also called an external agent), and provided to the operator of the server 100. The server 100 can receive recommendations of job seeker candidates from the external database 400 by sharing (transferring) a job posting, which will be described later, with the external database 400. The server 100 is indirectly and / or directly connected to the external database 400. By linking the job posting with the external database 400, the population 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 according to the form, type, and / or associated services of the matching service, for example. The form, type, and associated services of the matching service include, for example, an offer service, an agent service, and a direct scout. The job posting is set by default to be automatically linked with at least one of the job seeker databases in the internal system 500. For a job seeker database in the internal system 500 that is not linked by default, for example a job seeker database for which a separate fee is charged, the settings can be changed based on a suggestion by the server 100 or at the discretion of the user. This makes it possible to expand the target population.

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

[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 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, a semiconductor storage device, etc. 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 a program for mainly implementing each functional unit of the processing unit 130.

[0028] The internal database 122 includes a job posting database (hereinafter referred to as a job posting DB) and a recruitment information database (hereinafter referred to as a 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] The mathematical model 123 is a model that has learned about recruitment activity information. Specifically, for example, it is a model that has learned the correlation between multiple past job posting information of multiple recruiters and their job posting result information. More specifically, for example, the mathematical model 123 is a model that has learned the relationship between the past job posting information and the characteristics of applicants included in those job posting result information. The characteristics of applicants include information about the job search requirements of applicants, basic information about applicants, personality of applicants, and the selection process of applicants. The job search requirements are conditions on the job seeker side that correspond to the job search requirements. The selection process of applicants includes information about the applicant's selection history, acceptance / rejection, and withdrawal or joining the company. Learning may be further performed for each industry or job type, or may be performed for applicants who have been selected for employment. Note that an applicant is a job seeker who has applied for the job posting among job seekers, and includes those who have been selected for employment and those who have been selected for employment. The mathematical model 123 receives job requirements as input, references a specified (linked) job seeker database, and outputs at least one of the optimization details of the job requirements, the target population, and the hiring decision rate, which will be described later. The specified job seeker database includes the external database 400 and the internal system 500.

[0030] FIG. 3 is a diagram showing an example of information stored in the internal database 122 of the server 100 according to the embodiment of the present invention.

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

[0032] The job posting DB is a database that manages job posting information managed by each employer who uses the job posting optimization service.

[0033] The job posting information includes a job posting ID, an employer, a publication status, job posting source information, and a job posting source information analysis result.

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

[0035] The "employer" field stores the employer ID of the employer creating the job posting.

[0036] The publication status is an item that indicates whether the job posting is to be made public outside of the server 100 (mainly the external database 400 and / or the internal system 500), and is an item that stores one of two states: public or private. Immediately after creating a job posting, the server 100 sets it to private. After that, the employer who created the job posting can switch between public and private settings.

[0037] The job posting source information is information provided to the server 100 by the recruiter in order to create a job posting.

[0038] The job posting source information analysis results include job requirements, collaboration candidates, and hiring decision rates. The job posting source information analysis results are information that is historically managed for each analysis run. In FIG. 3, for example, the first analysis result is indicated as ver1, and the second analysis result is indicated as ver2.

[0039] The job requirements include a plurality of requirement items. Each requirement item includes an initial content, an optimized content, and a variable setting. The initial content is the content of the job requirements immediately after analysis, extracted by analyzing the job posting source information. The initial content is information that can be edited by the employer on the user terminal 200. The optimized content is the content optimized for the job requirements. If the initial content is appropriate, the optimized content does not need to be generated. The variable setting is an item that stores the tolerance for changes to the content for each job requirement. In this embodiment, the employer can select and set on the user terminal 200 from among changeable, non-changeable, and semi-changeable. The tolerance for changes to the content means that the job requirement is absolutely non-negotiable, that there is some flexibility, and the like. For example, if the job requirement is a qualification and a Class 4 Hazardous Materials Handler qualification is required, select non-changeable, and if a Grade 1 Practical English Proficiency Test qualification is desired but a Grade 1 semi-changeable qualification is acceptable depending on other equivalent qualifications held, select semi-changeable. The variable setting may be set to changeable initially.

[0040] The linking candidates include initial candidates, an initial population, optimized candidates, and an optimized population. The initial candidates are a list of linking candidates obtained immediately after analysis by analyzing the job posting source information, and the optimized candidates are a list of linking candidates obtained for the optimized content of the job requirements. Each linking candidate included in the initial candidates and optimized candidates includes selected / unselected status information and is initially set to a selected state, but the employer can change the selected state on the user terminal 200 (see list D233 in FIG. 6 described later). The initial population is a target population obtained immediately after analysis by analyzing the job posting source information, and the optimized population is a target population obtained for the optimized content of the job requirements. The target population will be described later.

[0041] The hiring decision rate includes an initial hiring decision rate and an optimized hiring decision rate. The initial hiring decision rate is a hiring decision rate calculated based on the initial contents of all job requirements immediately after analysis obtained by analyzing the job posting source information, and the optimized hiring decision rate is a hiring decision rate calculated based on the optimized contents of all job requirements.

[0042] The recruitment information DB is a database that manages information that associates multiple past job posting information with the recruitment result information for multiple current or past users (employers) of the job posting optimization service.

[0043] Returning to Fig. 2, the explanation of the processing unit 130 of the server 100 will continue. The processing unit 130 includes, as functional units, an information acquisition unit 131, a requirements extraction unit 132, a requirements optimization unit 133, a job advertisement editing unit 134, a collaboration unit 135, and an improvement proposal unit 136. The processing unit 130 realizes each of these functional units by executing an application program 121 stored in the memory unit 120, and executes each step of the job advertisement creation process and the job advertisement review process. The details of the job advertisement creation process and the job advertisement review process will be described later.

[0044] The information acquisition unit 131 acquires job advertisement source information used to create a job advertisement from the user terminal 200. The job advertisement source information may be in any format, such as text, an electronic file, or reference information such as a uniform resource locator (URL) accessible via a network, or a combination of these.

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

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

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

[0048] The requirements extraction unit 132 may extract job requirements using the generation AI 300. That is, the requirements extraction unit 132 may generate a job requirements extraction prompt, which is a prompt for extracting job requirements corresponding to preset requirement items from the job advertisement source information, and input the job requirements extraction prompt and the job advertisement source information to the generation AI 300 to extract the initial contents of the job requirements for each requirement item. 4 shows a part of an example of a job requirement extraction instruction text. The job requirement extraction instruction text may be generated with contents according to the acquisition form (text, file, URL, etc.) of the job posting source information.

[0049] The requirements extraction unit 132 may identify job requirements that could not be extracted from the job posting source information, and may execute a hearing to obtain the requirements from the employer. The hearing may be a chat-style (question-and-answer) interface. The requirements extraction unit 132 may also determine whether the extracted job requirements are effective for recruiting activities, and execute a hearing if they are not effective. For example, when the content "head office or branch office" is extracted as the job requirement "workplace", the requirement extraction unit 132 may execute a hearing as to "does the branch office include overseas branches?". Similarly, the requirements extraction unit 132 may execute a hearing if a second job requirement related to the first job requirement that has been extracted is not effective for recruiting activities. For example, even if the content "WEB engineer" can be extracted as the job requirement "job content" and the content "programming language" can be extracted as the job requirement "application qualifications," if the content of the application qualifications for the job content "WEB engineer" is not effective for the recruiter's recruitment activities if only the content of "programming language" is not effective for the recruiter's recruitment activities, a hearing may be executed asking "Please specify the specific web programming language." The questions in the hearing may be generated using a learning model that has learned the correlation between the recruitment requirements in past job postings and the recruitment requirements that were effective (likely to lead to recruitment) in the recruitment activities based on those job postings.

[0050] The requirements optimization unit 133 analyzes the initial contents of the job requirements extracted by the requirements extraction unit 132 using the mathematical model 123, and generates optimized contents that are optimized with respect to the initial contents as recommended job requirements. The requirements optimization unit 133 stores the generated recommended job requirements (optimized contents) in the job posting DB of the storage unit 120.

[0051] When the initial content of the recruiting requirements is edited by the recruiter, the requirements optimization unit 133 may reanalyze the edited initial content of the recruiting requirements and update the optimized content as the recommended recruiting requirements.

[0052] When the improvement suggestion unit 136 described later determines that the job posting needs to be revised based on the selection status of the job posting (progress of recruiting activities), the requirements optimization unit 133 may reanalyze the initial content of the job requirements at that time and update the recommended job requirements, collaboration candidates, and / or hiring decision 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 advertisement editing unit 134 presents the initial contents of the multiple job requirements extracted by the requirements extraction unit 132 as a job advertisement to the user terminal 200 used by the employer, and accepts editing of the initial contents of the job requirements. The job advertisement editing unit 134 stores the edited contents in the memory unit 120 under version management. In addition, the job advertisement editing unit 134 presents a combination of the initial contents of the job requirements and the optimized contents of the recommended job requirements to the user terminal 200 used by the employer. Note that for requirement items for which optimized contents were not generated, display of the optimized contents is omitted.

[0055] The job posting editing unit 134 may present to the recruiter 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 editing unit 134 may obtain a selection input from the user terminal 200 used by the recruiter to select changeable, non-changeable, or semi-changeable as a variable setting for each requirement item of the job requirements. The job posting editing unit 134 may also present a change in the hiring effect when non-changeable or semi-changeable is changed to changeable, specifically, a change in the target population and / or hiring decision rate.

[0057] The collaboration unit 135 extracts one or more collaboration candidates from multiple job seeker databases based on the job requirements. Specifically, the collaboration unit 135 analyzes the initial contents and the optimized contents of the job requirements using the mathematical model 123, and extracts each of the collaboration candidates as the initial candidates. Furthermore, the linking unit 135 uses the mathematical model 123 to generate a target population when linking with linkage candidates extracted based on the initial contents of the job requirements as an initial population, and generates a target population when linking with linkage candidates extracted based on the optimization contents as an optimized population, and stores them in the storage unit 120. The target population is a population of job seekers in the job seeker database who match the job requirements at a predetermined rate or more. The target population also varies depending on the job seeker database to be linked. Furthermore, the collaboration unit 135 calculates the hiring decision rate using the mathematical model 123 based on the job requirements. Specifically, the collaboration unit 135 calculates the initial hiring decision rate based on the initial contents of the job requirements, and the optimized hiring decision rate based on the optimized contents. The collaboration unit 135 may also calculate the initial hiring decision rate based on the initial contents of the job requirements and the initial population and / or initial candidates. Similarly, the collaboration unit 135 may calculate the optimized hiring decision rate based on the optimized contents and the optimized population and / or optimized candidates.

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

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

[0060] The improvement suggestion unit 136 acquires selection events related to the job posting from the user terminal 200 used by the recruiter, and judges whether or not the job posting needs to be reviewed based on the selection status (progress of recruitment activities). Selection events include, for example, an application from a job seeker, an occurrence of an exchange with an applicant, a decision to not hire an applicant, an applicant's withdrawal, etc. The judgment of whether or not a review is necessary occurs each time a selection event is acquired and / or for each preset period. The measurement of the preset period may be reset when a selection event occurs. The judgment of whether or not a review is necessary may be based on a single selection event, may be based on multiple selection events, or may be based on the number of times a judgment of whether or not a review is necessary has been made based on a preset period. The preset period may be managed by the server 100, or may be changed by the recruiter.

[0061] 5 is a functional block diagram of a user terminal 200 according to an embodiment of the present invention. The user terminal 200 includes 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, the storage unit 220, the control unit 230, the display unit 240, and the 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, a PC, or the like.

[0062] <Screen example> Next, a screen example of the job advertisement optimization service provided by the server 100 will be described.

[0063] 6, 7, and 8 are diagrams showing screens D100, D200, and D300, which are example screens of a job advertisement optimization service that the server 100 according to an embodiment of the present invention displays on the user terminal 200.

[0064] A screen D100 shown in FIG. 6 is an example of a job posting creation screen, and is a user interface through which an employer inputs job posting source information.

[0065] Screen D100 is arranged with a title area D110 showing the title of the screen, an input area D120 for easily entering existing information as job posting source information, a button D130 for confirming the entered content and sending it to server 100, and an input area D140 for the employer to manually enter text as the job posting source information.

[0066] In the title area D110, the character string "Job posting creation" is placed.

[0067] In the input field D120, the character string "Automatically create using existing information" and three input fields D121, D122, and D124 are arranged. Above the input area D121 is the text "Read other companies' media or your own website," and inside is the text "Enter or copy and paste the URL." The text "Load file" is placed above the input area D122, and the text "Drag and drop the file or select the file from the Browse button" is placed inside the area D122. Also, a "Browse" button D123 for selecting a file is placed on the right inside the input area D122. The text "Automatically generate using AI" is placed above the input area D124, and the text "Please enter" is placed inside.

[0068] In the input field D140, the character string "Create it yourself" and a plurality of input fields D141, D142, D143, D144, . . . are arranged. Item numbers and explanations of the items are arranged above the input fields D141, D142, D143, D144, . . . and the character string "Please enter" is arranged inside each of them.

[0069] 7 is an example of a job posting edit screen, and is a user interface that displays one or more recruitment requirements extracted by server 100 based on the job posting source information entered on screen D100 on user terminal 200 used by the recruiter and accepts editing. Screen D200 is displayed on user terminal 200 after button D130 is selected (clicked or tapped) on screen D100.

[0070] Screen D200 is arranged with a title area D210 that shows the title of the screen, an area D220 that displays each job requirement in the job posting and accepts editing, an area D230 that displays a list of services that can be linked to the job posting and accepts input for selection or non-selection, and a button D240 for confirming the content and sending it to server 100.

[0071] In the title area D210, the character string "Job Voucher Editing" is placed.

[0072] Inside area D220, there are arranged the text "There are items we recommend you review" in the upper left corner, multiple input areas D222, D223, D225, ..., and a button D221 for reevaluating (reanalyzing) the job requirements all at once. 5. Item numbers and item descriptions are arranged above the input fields D222, D223, D225, . . . similar to the input fields D141, D142, D143, D144, . Inside the input fields D222, D223, D225, ..., text corresponding to the initial content of the job requirements extracted for each requirement item from the original job advertisement information entered by the employer on the job advertisement input screen of Figure 5 is automatically entered and displayed in an editable format. The button D221 is a button for allowing the recruiter to select (click or tap) after editing one or more, particularly multiple, input fields D222, D223, D225, ..., to collectively reanalyze all edited content. The button D221 may be relocated to the bottom of the page of the screen D200.

[0073] Between the input area D223 and the input area D225, a message area D224 for presenting recommended job requirements is arranged. The message area D224 has an arrow pointing to the input area D225 so that it can be seen that the message area D224 is a recommended job requirement for the requirement item in the input area D225. Inside the message area D224, an icon for calling attention, a character string indicating a notification message saying "It seems difficult to hire with these conditions", a character string of "Estimated number of applicants: 0" as reference information regarding the notification message, and a character string of "Recommended value (average annual salary of applicants): 5 to 6 million yen" indicating the optimized content (optimized content) for item 3, which is a requirement item in the input area D225, are arranged. In addition, in the message area D224, a button D226 for reflecting the optimized content in the input area D225 is arranged to the right of the character string indicating the optimized content.

[0074] Furthermore, a button D227 for reevaluating (reanalyzing) is provided in the input area D225 that is the target of the message area D224. After editing the input area D225, the recruiter can select (click or tap) the button D227 to perform analysis based on the edited content of the input area D225 on a requirement item basis. In this manner, the button D227 for reevaluating may be temporarily displayed only in the input area of ​​the requirement item that is the target of the presentation of recommended recruiting requirements.

[0075] Inside area D230, there are the following text in the upper left corner: "By linking (opening) your services to our partner agent services, you can increase the pool of candidates and improve your hiring decision rate."; area D231 showing the size of the target pool; area D232 showing the hiring decision rate; and list D233 displaying a list of potential partners. Area D231 shows the number of people in the target population (12 people) calculated from the current content of the job posting (initial content of the job conditions) and the number of people in the target population (23 people) calculated when the content of the job posting is changed to optimized content. Area D232 shows the estimated hiring decision rate (35%) calculated from the current job posting content (initial job conditions) and the estimated minimum hiring decision rate (48%) calculated if the job posting content is changed to optimized content. List D233 displays a combination of the names of the collaboration candidates and check boxes indicating their selected / unselected states. In the example shown in Fig. 6, the initial candidates (our service 1, our service 2, agent 1, agent 2, agent 3) are selected by default, and the optimized candidate (agent 4) is unselected by default with its name highlighted (e.g., underlined) to indicate that it is an optimized candidate. The optimized candidates that are unselected by default may be automatically changed to selected by the server 100 when the employer edits the initial content of the recruitment requirements to improve the match with the recommended recruitment requirements. The employer can select or deselect the collaboration candidates using the check boxes in list D233. Furthermore, when the selection state of the collaboration candidate is changed in the list D233, the target population shown in the area D231 and the estimated adoption decision rate shown in the area D232 are updated and displayed.

[0076] The button D240 displays the text "Confirm edited content and make it public." Additionally, the button D211 has been rearranged above the button D240.

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

[0078] A screen D300 shown in FIG. 8 is an example of a screen listing applicants who applied to a job posting, and is a user interface that displays details of an applicant selected from the list in a pop-up window.

[0079] On the screen D300, there are arranged a title area D310 showing the title of the screen, an area D320 displaying a list of the applicants, and a pop-up window area D330. In area D320, an overview of each applicant is arranged in list form. In the pop-up window area D330, a face photo and basic information of the applicant selected from the list, the selection status (result of the selection event), and a button D331 are arranged. The button D331 displays the text "Why not review the job posting?". By selecting (clicking or tapping) the button D331 on the screen D300, the employer can transition to a job posting edit screen shown on screen D200 in FIG. 6. In this case, the job posting edit screen presents the initial content of the recruitment requirements at the time of transition (current / latest) along with optimized content obtained by reanalyzing the initial content and the results of the selection event for that job posting. Similarly, the results of an analysis of the current initial content, taking into account the results of the selection event, are presented for the target population and hiring decision rate.

[0080] <System Operation> Next, the operation of the job advertisement creation process and job advertisement review process of the job advertisement optimization system 1 having the above-mentioned configuration and functions will be described with reference to a flowchart. The job posting creation process is a series of processes including a job posting source information acquisition process, a job requirement extraction process, a job requirement optimization process, a linking process, a job posting presentation process, an edited content acquisition process, and a job posting confirmation process.

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

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

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

[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 requirement 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 step).

[0086] In step S105, the job advertisement 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 collaboration candidates and target population extracted in step S104 as a job advertisement in an editable manner on the user terminal 200 (job advertisement presentation process). The job advertisement editing unit 134 presents the job advertisement, for example, through the user interface of a job advertisement editing screen shown in FIG.

[0087] In step S106, the recruiter can edit the job posting presented to the user terminal 200 in step S105, if necessary. What can be edited is the initial content of the job requirements, and the selection or non-selection of collaboration candidates. If the recruiter has input edits to the job posting in the user terminal 200, the process proceeds to step S107. On the other hand, if the recruiter has not input edits to the job posting in the user terminal 200, the process proceeds to step S111.

[0088] In step S107, the user terminal 200 accepts an operation input for confirming the edited content from the recruiter, and transmits the edited content to the server 100. The server 100 acquires the edited content from the user terminal 200 (edited content acquisition step).

[0089] In step S108, the requirements optimization unit 133 and the collaboration unit 135 of the server 100 reanalyze the edited content acquired in step S107 and update the job posting.

[0090] In step S109, the job advertisement editing unit 134 of the server 100 presents (displays) the job advertisement updated in step S108 in an editable manner on the user terminal 200. The job advertisement editing unit 134 presents the updated job advertisement, for example, through the user interface of a job advertisement editing screen shown in FIG.

[0091] In step S110, the recruiter can re-edit the job posting presented to the user terminal 200 in step S109, if necessary. What can be edited is the initial content of the job requirements and the selection / non-selection of collaboration candidates. If the recruiter has input edits to the job posting in the user terminal 200, the process returns to step S107. On the other hand, if the recruiter has not input edits to the job posting in the user terminal 200, the process proceeds to step S111.

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

[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 ends this flow.

[0094] 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 is finalized and published. The job posting review process includes a selection event acquisition process, a selection analysis process, a review determination process, a review notification process, and an edit acceptance process.

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

[0096] In step S202, the improvement proposing unit 136 of the server 100 analyzes the selected 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 the job posting linked to the selection event information needs to be reviewed based on the analysis result of step S202 (review determination process). If the determination result is true (YES), that is, if the job posting needs to be reviewed, the process proceeds to step S204. On the other hand, if the determination result is false (NO), that is, if the job posting does not need to be reviewed, the flow ends.

[0098] In step S204, the improvement suggesting unit 136 of the server 100 presents (displays) a notification proposing improvement on the user terminal 200 used by the recruiter of the job posting (review notification step).

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

[0100] In step S206, i.e., if the employer selects to agree to the proposal in step S205, the user terminal 200 sends a notification requesting a review to the server 100. 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 (jumps from number 1 in FIG. 10 to number 1 in FIG. 9), and performs a process of accepting edits to the job posting (edit acceptance process).

[0101] <Basic computer configuration> 11 is a block diagram showing a basic hardware configuration of a computer 900. The computer 900 has 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, the storage unit 902, the communication unit 903, the input unit 904, and the 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 each unit of the computer 900, and is a device that reads out and executes various programs stored in the storage unit 902.

[0103] The storage unit 902 includes a main storage device such as a dynamic random access memory (DRAM) and an auxiliary storage device such as a hard disk, and is a device for storing various programs for executing the operating system of the computer 900 and various applications, as well as data used by these programs. The process shown in the above-mentioned flowchart is realized by the control unit 901 of each computer constituting the server 100, the user terminal 200, the generation AI 300, the external database 400, and the internal system 500 executing the programs stored in the respective storage units 902.

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

[0105] The input unit 904 is a device that receives input from the outside and supplies it to the control unit 901, and is configured to include, for example, a keyboard, a mouse, a touch panel, and a camera. The output unit 905 is a device that outputs the processing result of the control unit 901 to the outside, and is configured to include, for example, a display and a speaker. <Program> Here, a program for implementing each functional unit of the server 100 according to this embodiment will be described.

[0106] The server 100 is implemented in a computer 900. The operation of each component of the server 100 is stored in the form of a program in the auxiliary storage device of the storage unit 902. The control unit 901 reads the program from the auxiliary storage device of the storage unit 902, loads it in the main storage device of the storage unit 902, and executes the above-mentioned processing in accordance with the program. The control unit 901 also secures a storage area corresponding to the above-mentioned storage unit 120 in the main storage device of the storage unit 902 in accordance with the program.

[0107] Specifically, the program is a program for causing a computer having a processor and a memory unit to execute in computer 900, and the program causes the computer to execute an information acquisition step of acquiring original job posting information from the employer, a requirements extraction step of extracting job requirements based on the original job posting information, a requirements optimization step of analyzing the extracted job requirements and generating optimized recommended job posting requirements, and a job posting editing step of presenting the job requirements and the recommended job posting requirements to the employer and accepting edits to the job requirements.

[0108] The auxiliary storage device of the storage unit 902 is an example of a non-transient tangible medium. Other examples of non-transient tangible media include a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, and a semiconductor memory connected via the input unit 904. When this program is distributed to the computer 900 via the network NW, the computer 900 that has received the program may load the program into the main storage device of the storage unit 902 and execute the above process.

[0109] The program may be for realizing part of the above-mentioned functions. Furthermore, the program may be a so-called difference file (difference program) that realizes the above-mentioned functions in combination with another program already stored in the auxiliary storage device of the storage unit 902.

[0110] According to the job advertisement optimization system 1 according to the present embodiment described above, high-quality and uniform job advertisements can be efficiently created. By using the generation AI, the server 100 can automatically extract job requirements from the job posting source information. Employers can use the URL of their own website or an electronic file of an existing job posting as the job posting source information, reducing the burden on employers in creating job postings. In other words, job postings can be created / edited quickly and efficiently, making the creation of job postings more efficient. By presenting the content optimized for the recruitment requirements extracted by the server 100 to the employer, even employers without know-how on recruitment can easily revise the recruitment requirements without spending time by referring to the optimized content, without relying on the skills and experience of agents. The revisions improve the quality of the recruitment advertisement, allowing the formation of an appropriate target population and increasing the hiring decision rate. Furthermore, by linking to an external database 400 suitable for job postings, a job seeker database in the internal system 500, and associated services of the internal system 500, it becomes possible to expand the target population and strengthen support for employers.

[0111] The present invention is not limited to the above-described embodiment, and various modifications can be made.

[0112] For example, in the above embodiment, the hiring decision rate is calculated based on the initial contents of all job requirements, but it may be calculated based on a part of job requirements selected according to a predetermined condition, for example, a variable setting of the requirement items.

[0113] In addition, when multiple optimization contents are generated as recommended job requirements, they may be ranked according to a predetermined algorithm, and the top ones may be presented (displayed) to the employer, or the ranking may be displayed. The predetermined algorithm may take into account the impact on the size of the target population, variable settings, etc. This allows for efficient optimization of job postings.

[0114] In addition, in the above embodiment, the reanalysis of the edited content in step S108 required the recruiter to confirm the edited content in step S107, but when selecting or deselecting a collaboration candidate, the target population and the hiring decision rate may be updated in real time when the on / off is accepted. This allows changes to be grasped immediately.

[0115] In addition, in the above embodiment, the recruitment requirements were version-managed in the memory unit 120, but for content that can be edited by the employer, the editing history up until the content is confirmed may be temporarily stored, and only the most recently confirmed content may be stored in the memory unit 120.

[0116] In addition, the generation AI 400 in the above embodiment uses a large-scale language model, but it may also use non-text-based generation models such as image generation models, video generation models, and audio generation models, or multi-modal generation models that combine these.

[0117] (Additional Note) The following additional features relate to the above-described embodiment. (Appendix 1) An information acquisition unit that acquires job posting source information from a recruiter; A requirement extraction unit that extracts job requirements based on the job posting source information; A requirement optimization unit that analyzes the extracted job requirements and generates optimized recommended job requirements; 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; A job posting optimization system equipped with: This allows the system to automatically extract job requirements for creating job postings, allowing employers to use the original job posting information in any format. In addition, the system presents recommended job requirements that optimize the extracted job requirements, allowing even employers with little experience or track record in recruiting activities to easily create high-quality job postings. (Appendix 2) A linking unit is further provided for selecting one or more job seeker databases from a plurality of job seeker databases based on the job requirements and linking the selected databases to generate a target population. The job posting editing unit presents to the recruiter a combination of the target population based on the job requirements and the target population based on the recommended job requirements. Attachment 1 describes a job posting optimization system. This allows you to create a target population that matches the job requirements, and by linking with multiple job seeker databases, you can expand the target population and improve your hiring success rate. (Appendix 3) The 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 you to access a job seeker database that matches your job requirements. (Appendix 4) When the job requirements are edited, the requirements optimization unit reanalyzes the edited job requirements to update the recommended job requirements. Attachment 1 describes a job posting optimization system. This allows you to see the impact of your edits. (Appendix 5) When the job requirements are edited, the linking unit updates each of the target populations based on the edited job requirements. The job posting optimization system described in Appendix 2. This allows you to see the impact of your edits on your target population. (Appendix 6) The job posting editing unit acquires a selection input from the employer to select one of changeable, non-changeable, and semi-changeable as a variable setting of the job requirement, The requirements optimization unit analyzes the job requirements for each of the variable settings. Attachment 1 describes a job posting optimization system. This allows analysis to take into account the employer's preferences. (Appendix 7) The job posting editing unit presents the employment effect when the unchangeable or semi-changeable in the variable setting is changed to the changeable setting, Attachment 1 describes a job posting optimization system. This allows us to propose a compromise that meets the employer's requirements. (Appendix 8) The requirement extraction unit extracts the job requirements using a generation AI. Attachment 1 describes a job posting optimization system. This makes the process of creating job postings more efficient. (Appendix 9) The linking unit links to a service associated with the job seeker database. The job posting optimization system described in Appendix 2. This allows you to use support services that are tailored to the characteristics of the job posting. (Appendix 10) The information acquisition unit receives a URL (Uniform Resource Locator) and / or an electronic file as the job posting source information, and provides a user interface for converting the URL and the electronic file into editable text on a user terminal used by the recruiter. Attachment 1 describes a job posting optimization system. This makes the process of creating job postings more efficient. (Appendix 11) An improvement suggestion unit acquires a selection event of a job posting created based on the job posting source information, and determines whether or not the job posting needs to be revised based on the selection event. Attachment 1 describes a job posting optimization system. This makes it possible to suggest a causal relationship between the selection situation and the job posting, while also suggesting a review of the job posting at an appropriate or necessary time. (Appendix 12) The requirements optimization unit reanalyzes the job requirements when it is determined that the job posting needs to be revised. 12. A job posting optimization system as described in Appendix 11. This allows the content of the job posting to be improved in response to changes over time in the balance of the job market and the job-seeking market. (Appendix 13) The requirement extraction unit identifies job requirements that could not be extracted from the job advertisement source information, and acquires the job requirements that could not be extracted from the recruiter. Attachment 1 describes a job posting optimization system. This makes it easy to create a complete job posting. (Appendix 14) An information acquisition unit that acquires job posting source information from a recruiter; A requirement extraction unit that extracts job requirements based on the job posting source information; A requirement optimization unit that analyzes the extracted job requirements and generates optimized recommended job requirements; 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; A job posting optimization server equipped with the above. (Appendix 15) An information acquisition step of acquiring job posting source information from a recruiter; A requirement extraction step of extracting job requirements based on the job posting source information; A requirement optimization step of analyzing the extracted job requirements and generating optimized recommended job requirements; A job posting editing step of presenting the job requirements and the recommended job requirements to the employer and accepting editing of the job requirements; A program that causes a computer to execute the following. (Appendix 16) An information acquisition step of acquiring job posting source information from a recruiter; A requirement extraction step of extracting job requirements based on the job posting source information; A requirement optimization step of analyzing the extracted job requirements and generating optimized recommended job requirements; A job posting editing step of presenting the job requirements and the recommended job requirements to the employer and accepting editing of the job requirements; A job posting optimization method that uses a computer to execute the above. [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 section 131: Information acquisition department 132: Requirements extraction part 133: Requirements Optimization Department 134: Job Posting Editorial Department 135: Collaboration Department 136: Improvement proposal department 200: User terminal 300: Generation AI 400 : External Database 500: Internal system D100, D200, D300: Screen NW: Network

Claims

1. An information acquisition unit that acquires job posting source information from a recruiter; A requirement extraction unit that extracts job requirements based on the job posting source information; a requirement optimization unit that analyzes the extracted job requirements and generates recommended job requirements that are optimized based on a hiring decision rate; 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; A linking unit that generates a target population when one or more job seeker databases are selected and linked from a plurality of job seeker databases that can be selected by an employer to be linked; Equipped with The job posting editing unit presents the target population based on the job requirements and the target population based on the recommended job requirements to the employer. Job posting optimization system.

2. The linking unit automatically links to the job seeker database extracted based on the job requirements. The job posting optimization system according to claim 1 .

3. When the job requirements are edited, the requirements optimization unit reanalyzes the edited job requirements to update the recommended job requirements. The job posting optimization system according to claim 1 .

4. When the job requirements are edited, the linking unit updates the target population based on the edited job requirements. The job posting optimization system according to claim 1 .

5. The job posting editing unit acquires a selection input from the employer to select one of changeable, non-changeable, and semi-changeable as a variable setting of the job requirement, The requirements optimization unit analyzes the job requirements for each of the variable settings. The job posting optimization system according to claim 1 .

6. The job posting editing unit presents a change in the target population and / or the hiring decision rate when the unchangeable or semi-changeable in the variable setting is changed to the changeable setting, The job posting optimization system according to claim 5.

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

8. The linking unit links to a service associated with the job seeker database. The job posting optimization system according to claim 1 .

9. The information acquisition unit receives a URL (Uniform Resource Locator) and / or an electronic file as the job posting source information, and provides a user interface for converting the received URL and the electronic file into editable text on a user terminal used by the recruiter. The job posting optimization system according to claim 1 .

10. An improvement suggestion unit acquires selection event information of the job posting created based on the job posting source information, and determines whether or not the job posting needs to be revised based on the selection event information. The job posting optimization system according to claim 1 .

11. The requirements optimization unit reanalyzes the job requirements when it is determined that the job posting needs to be revised. The job posting optimization system according to claim 10.

12. The requirement extraction unit identifies job requirements that could not be extracted from the job advertisement source information, and acquires the job requirements that could not be extracted from the recruiter. The job posting optimization system according to claim 1 .

13. An information acquisition unit that acquires job posting source information from a recruiter; A requirement extraction unit that extracts job requirements based on the job posting source information; a requirement optimization unit that analyzes the extracted job requirements and generates recommended job requirements that are optimized based on a hiring decision rate; 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; A linking unit that generates a target population when one or more job seeker databases are selected and linked from a plurality of job seeker databases that can be selected by an employer to be linked; Equipped with The job posting editing unit presents the target population based on the job requirements and the target population based on the recommended job requirements to the employer. Job posting optimization server.

14. An information acquisition step of acquiring job posting source information from a recruiter; A requirement extraction step of extracting job requirements based on the job posting source information; A requirement optimization step of analyzing the extracted job requirements and generating recommended job requirements optimized based on a hiring decision rate; A job posting editing step of presenting the job requirements and the recommended job requirements to the employer and accepting editing of the job requirements; A linking step of generating a target population when linking one or more job seeker databases selected from a plurality of job seeker databases that can be selected by the employer; A program for causing a computer to execute the following: The job posting editing step presents the target population based on the job requirements and the target population based on the recommended job requirements to the recruiter, program.

15. An information acquisition step of acquiring job posting source information from a recruiter; A requirement extraction step of extracting job requirements based on the job posting source information; A requirement optimization step of analyzing the extracted job requirements and generating recommended job requirements optimized based on a hiring decision rate; A job posting editing step of presenting the job requirements and the recommended job requirements to the employer and accepting editing of the job requirements; A linking step of generating a target population when linking one or more job seeker databases selected from a plurality of job seeker databases that can be selected by the employer; A job posting optimization method for causing a computer to execute the following: The job posting editing step presents the target population based on the job requirements and the target population based on the recommended job requirements to the recruiter, How to optimize your job posting.

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