Processing device, processing program, and processing method

Abstract

To provide a processing device, a processing program, and a processing method which increase the degree of matching between an employer and a job seeker so as to achieve an employment process leading to improvement in stability of workers.SOLUTION: At least one processor executes processing for conducting an AI interview using a learned interview model on a job seeker so as to extract a candidate for a working destination for the job seeker according to a result of the AI interview.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a processing device, a processing program, and a processing method for providing support to employers and employees regarding their hiring and subsequent employment. [Background technology]

[0002] It has been known that artificial intelligence (AI) is used to match employers with workers and provide support at the time of hiring and after hiring. For example, Patent Document 1 describes a system for improving the retention rate of specific human resources, which includes: a) a pre-employment information input means for inputting at least one of health information obtained at the time of hiring a specific human resource, information on questions and answers at the time of hiring a specific human resource, and information on the practical training situation at the work experience session, b) a hiring decision information providing means for providing hiring decision information for hiring human resources expected to have a high retention rate based on at least one of the judgment criteria of cooperativeness, business manners, and job performance ability, based on the information inputted to the pre-employment information input means; and c) a support information providing means for providing information on employment support and technical support for improving the retention rate of members. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2021-174343 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the system for improving the retention rate of specific human resources described in Patent Document 1, although the result of the interview between the employer and the worker is taken into consideration to determine whether or not to employ the person, matching is mainly done based on written or digitalized information before the interview, so the actual personality and character of the job seeker may not be fully understood. In addition, with such matching, the job seeker may have an abstract impression of the employer or company, and the job seeker may have a different impression at the time of the interview. As a result, it may be found difficult to employ or work in the subsequent interview, and the subsequent interview itself may be a waste of time.

[0005] Furthermore, once actual employment begins after such matching and interviews have been carried out, the employer's impression or evaluation of the employee may change, or the employee's impression or evaluation of the employer may change, resulting in a lower employee retention rate.

[0006] The present disclosure has been made against the above-mentioned background, and provides a processing device, processing program, and processing method that enable a hiring process that improves the degree of matching between employers and job seekers and leads to an improvement in the retention rate of employees. [Means for solving the problem]

[0007] According to one aspect of the present disclosure, there is provided "a processing device having at least one processor, the at least one processor being configured to execute a process for conducting an AI interview with a job seeker using a trained interview model, and extracting potential employment opportunities for the job seeker based on the results of the AI ​​interview."

[0008] According to one aspect of the present disclosure, there is provided a "processing program for causing a computer having at least one processor to function as follows: to conduct an AI interview with a job seeker using a trained interview model, and to extract potential employment opportunities for the job seeker based on the results of the AI ​​interview."

[0009] According to one aspect of the present disclosure, there is provided "a processing method executed by at least one processor in a computer having the at least one processor, the processing method including the steps of conducting an AI interview with a job seeker using a trained interview model, and extracting potential employment opportunities for the job seeker based on the results of the AI ​​interview." Effect of the Invention

[0010] According to the present disclosure, it is possible to provide a processing device, a processing program, and a processing method that enable a hiring process that improves the degree of matching between employers and job seekers and leads to an improved employee retention rate.

[0011] It should be noted that the above effects are merely illustrative for the convenience of explanation and are not limiting. In addition to or instead of the above effects, any effect described in this disclosure or any effect obvious to a person skilled in the art may be achieved. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating an outline of a job matching process in a processing system according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 is a conceptual diagram illustrating a schematic configuration of a processing system 1 according to an embodiment of the present disclosure. [Diagram 3] FIG. 3 is a block diagram illustrating an example of a configuration of the terminal device 100 according to an embodiment of the present disclosure. [Figure 4] FIG. 4 is a block diagram illustrating an example of a configuration of the server device 200 according to an embodiment of the present disclosure. [Figure 5A] FIG. 5A is a diagram conceptually illustrating a job seeker management table stored in server device 200 according to an embodiment of the present disclosure. [Figure 5B] FIG. 5B is a diagram conceptually illustrating an employer management table stored in the server device 200 according to an embodiment of the present disclosure. [Figure 6A]FIG. 6A is a diagram showing a processing sequence executed between each terminal device 100 and the server device 200 according to an embodiment of the present disclosure. [Figure 6B] FIG. 6B is a diagram showing a processing sequence executed between each terminal device 100 and the server device 200 according to an embodiment of the present disclosure. [Figure 6C] FIG. 6C is a diagram showing a processing sequence executed between each terminal device 100 and the server device 200 according to an embodiment of the present disclosure. [Figure 7A] FIG. 7A is a diagram showing a process flow executed in the server device 200 according to an embodiment of the present disclosure. [Figure 7B] FIG. 7B is a diagram showing a process flow executed in the server device 200 according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] The embodiments of the present disclosure will be described with reference to the accompanying drawings, in which the same reference numerals are used to designate the same components.

[0014] 1. Overview of Processing System 1 According to the Present Disclosure As an example, the processing system according to the present disclosure is used to conduct an AI interview using artificial intelligence (AI) for a job seeker taking into account the characteristics of the job seeker, and to match the job seeker with an employer who is recruiting the job seeker based on the results. In particular, the processing system is used to conduct an AI interview using a trained interview model that is machine-learned using information (culture, customs) related to the job seeker's place of origin (country, region, prefecture, etc.), criminal information, fugitive information, tests taken in advance by the job seeker (psychological tests, behavioral tests, aptitude tests, etc.), and information on social networking services (SNS). By conducting such an AI interview, it is possible to perform matching that connects a job seeker and an employer who are likely to have good compatibility before the job seeker and employer meet, thereby realizing a recruitment process that increases the degree of matching between the job seeker and the employer and leads to an improvement in the retention rate of employees.

[0015] Fig. 1 is a diagram showing an outline of the employment matching process of the processing system according to the embodiment of the present disclosure. Specifically, Fig. 1 shows an outline of information collection before making an employment decision on a job seeker, the subsequent employment decision such as employment matching and interviews, and information collection after starting employment in chronological order.

[0016] First, a server device of a business entity that matches a job seeker (first user) with an employer (second user) acquires job seeker information from the job seeker, company information from the employer, and web information from the web before making an employment decision for the job seeker. The server device also administers a test to the job seeker and receives information related to the test results.

[0017] The job seeker information obtained from the job seeker includes basic personal information of the job seeker, such as the job seeker's country of origin, region of origin, age, gender, etc. The job seeker information also includes personal characteristic information of the job seeker, such as the job seeker's skills, experience, or information listed on his / her resume. Here, the job seeker may be either Japanese or foreign.

[0018] In contrast, company information obtained from employers includes basic employer information related to the employer's name, location, business area, business scale, turnover rate, age ratio, ratio of foreign nationals, ratio of men to women, ratio of disabled people, and job information (job type, work location, job content, etc.). Here, a general company (enterprise) is assumed as the employer, but the employer may also be a sole proprietor, a union, an organization, or another legal entity.

[0019] Furthermore, web information obtained from the web includes information on national or local culture, criminal information, fugitive information, and posts or comments by job seekers on SNS, etc. In particular, information related to the SNS may be obtained by crawling.

[0020] The server device uses the acquired information to perform machine learning for the trained interview model for the AI ​​interview and the trained matching model for matching between job seekers and employers, as well as additional machine learning. This makes it possible to conduct a preliminary AI interview (first interview) with the job seeker using the trained interview model, and optimal questions selected based on various information are asked. In addition, based on the results of the AI ​​interview and other information, it becomes possible to match the job seeker with the employer with high accuracy using the trained matching model, thereby achieving optimal matching for both parties. Note that the trained interview model for the AI ​​interview and the trained matching model for matching between job seekers and employers are not limited to being separate models, and may be a single model capable of AI interviews and matching.

[0021] Thereafter, when the hiring decision stage begins, the job seeker operates his / her own terminal device to access the server device and takes the first interview (AI interview) using the trained interview model for AI interviews. During the AI ​​interview, the server device converts responses made during the interview into text and stores the text information in memory. The server device also performs processing related to simultaneous language interpretation for foreign job seekers. This processing may be performed by the trained interview model for AI interviews, or by other translation programs, etc.

[0022] In addition, during the AI ​​interview, the server device may receive status information related to the question and answer session of the job seeker and determine whether or not there is any deception in the job seeker's words and actions. For example, the server device may receive photographic information of the job seeker as status information and analyze the eye movement and body movement of the job seeker from the photographic information. Specifically, if the job seeker's eyes wander or his / her limbs move and are restless, it may be determined that the job seeker is lying. The analysis may be performed by a trained interview model for AI interviews, or by a program dedicated to the analysis or another trained model.

[0023] Next, the server device uses a trained matching model for matching between job seekers and employers to extract employers who are suitable for the job seeker who has undergone the AI ​​interview. For this extraction, not only the result information of the AI ​​interview but also the job seeker information acquired before the AI ​​interview and the information related to the test results may be used. Then, the server device transmits the job seeker information to the extracted employers and transmits the employer information of the extracted employers to the job seeker, and asks each of them whether or not to conduct an interview (second interview). When the server device receives a communication from both the job seeker and the employer that they wish to conduct an interview, it coordinates the holding of the interview. Furthermore, the server device acquires the results of the interview (information related to hiring or not hiring) and stores it in memory, and performs additional machine learning on the trained interview model for the AI ​​interview and the trained matching model for matching between job seekers and employers.

[0024] After that, after a certain period of time has passed since the employment of the job seeker is confirmed and the job seeker starts working as an employee at the employer's company, the server device acquires working environment information from the employee and acquires evaluation information of the employee from the employer. The server device then stores the acquired information in a memory and performs additional machine learning on a trained interview model for an AI interview and a trained matching model for matching the job seeker with the employer. Here, the working environment information may include, for example, the employee's evaluation of the employer, the evaluation of the work content, the evaluation of the working conditions, and the results of engagement scoring. Meanwhile, the evaluation information of the employee may include promotion / demotion, work attitude, attendance time, resignation, personnel evaluation, etc.

[0025] In this way, the server device performs additional machine learning on the trained interview model and trained matching model using various judgment results related to hiring judgment, making it possible to update the models according to information related to the hiring process and information related to post-hiring evaluation, etc. Therefore, by accumulating hiring experience, it is possible to further improve the accuracy of AI interviews and matching between job seekers and employers.

[0026] In this disclosure, even if a description such as "first" or "second" is used, it does not mean that the description is limited to only the two elements to which the description is attached. Naturally, the description may include "third," "fourth," and more elements.

[0027] 2. Configuration of Processing System 1 Fig. 2 is a conceptual diagram illustrating a schematic configuration of a processing system 1 according to an embodiment of the present disclosure. According to Fig. 2, a first user terminal device 100-1 operated by a first user who is a job seeker looking for employment, a second user terminal device 100-2 operated by a second user who is an employer looking for workers, and a server device 200 that conducts AI interviews and matching are connected to each other so as to be able to communicate with each other via a network. The network is configured by wireless, wired, or a combination thereof.

[0028] 2, only one each of the first user terminal device 100-1 and the second user terminal device 100-2 is shown, but it is possible to include two or more of each device. Also, the server device 200 is shown as a single device, but it is possible to distribute the components and processes of the server device 200 to multiple server devices or cloud server devices.

[0029] 2, the first user terminal device 100-1 and the second user terminal device 100-2 are described separately from each other, but they may be collectively referred to as the terminal device 100. However, even in such a case, each terminal device is merely collectively referred to, and does not mean that the first user terminal device 100-1 and the second user terminal device 100-2 have the same processing and configuration.

[0030] 3. Configuration of the Terminal Device 100 Fig. 3 is a block diagram showing an example of the configuration of a terminal device 100 according to an embodiment of the present disclosure. The terminal device 100 does not need to include all of the components shown in Fig. 3, and may have a configuration in which some components are omitted, or may include other components.

[0031] The terminal device 100 is typically a terminal device capable of wireless communication, such as a smartphone, but is not limited to this device. For example, the terminal device may be a feature phone, a personal digital assistant, a PDA, a laptop computer, a desktop computer, a portable game machine, a stationary game machine, or any other device capable of executing the program according to the present disclosure. As described above, the terminal device 100 includes a first user terminal device 100-1 and a second user terminal device 100-2, but these terminal devices do not always need to be the same type or the same terminal device, and may be different types of terminal devices. As described above, the first user terminal device 100-1 and the second user terminal device 100-2 in the processing system 1 may each include multiple terminal devices, but the terminal devices do not need to be the same type of terminal device, and may be different types of terminal devices.

[0032] 3, the terminal device 100 includes an output interface 111, a processor 112, a memory 113 including a RAM, a ROM, or a non-volatile memory (HDD in some cases), a communication interface 114 including a communication processing circuit and an antenna, a camera 115, and an input interface 116 including a touch sensor and a hard key. These components are electrically connected to each other via control lines and data lines.

[0033] The output interface 111 functions as an output unit that outputs images captured by the camera 115 and various displays output by executing the program according to the present disclosure to devices such as a display or a printer in response to an instruction from the processor 112. Note that such a display is, for example, a liquid crystal display, an organic EL display, or electronic paper.

[0034] The processor 112 is composed of a CPU (microcomputer) and functions as a control unit that controls other components connected thereto based on various programs stored in the memory 113. Specifically, the processor 112 reads out from the memory 113 a program for executing an application according to the present disclosure and a program for executing an OS, and executes the program. In the present disclosure, the processor 112 particularly executes each process and the like described in the process sequences of Figs. 6A and 6C (details of the process are described in Figs. 6A and 6C). The processor 112 may be composed of a single CPU, or may be composed of a combination of multiple CPUs and GPUs.

[0035] The memory 113 is composed of a ROM, a RAM, a non-volatile memory, a HDD, etc., and functions as a storage unit. The ROM stores instructions for executing the application and the OS according to the present disclosure as a program. The RAM is used for writing and reading data while the program stored in the ROM is being processed by the processor 112. The non-volatile memory is a memory into which data is written and read by the execution of the program, and the data written therein is saved even after the execution of the program is terminated. In the present disclosure, the memory 113 particularly stores a program for executing each process etc. described in the process sequences of FIG. 6A and FIG. 6C (details of the process will be described in FIG. 6A and FIG. 6C).

[0036] The communication interface 114 functions as a communication unit that transmits and receives information to and from the server device 200 and other terminal devices that are installed remotely via a communication processing circuit and an antenna. The communication processing circuit performs processing for transmitting and receiving information from the server device 200 and other terminal devices according to the progress of processing of programs and various information used in the processing system 1.

[0037] The communication processing circuit processes based on a wideband wireless communication system such as the LTE system, but can also process based on a narrowband wireless communication system or a contactless wireless communication system such as wireless LAN such as IEEE802.11 or Bluetooth (registered trademark). Also, wired communication can be used instead of or in addition to wireless communication.

[0038] The camera 115 is, for example, a general web camera or video camera, and constantly captures the status of the job seeker or the employer. In particular, the camera 115 is used during the first interview, which is an AI interview, or when the second interview is conducted online.

[0039] The input interface 116 is composed of a touch sensor, hard keys, etc., and functions as an input unit that accepts instruction inputs related to the execution of the program according to the present disclosure and operation inputs for registering various information. The touch sensor is arranged to cover the output interface 111, and transmits position coordinate information corresponding to image data output from the output interface 111 to the display to the processor 112. As the touch sensor method, known methods such as a resistive film method, a capacitive coupling method, and an ultrasonic surface acoustic wave method can be used. In the present disclosure, the touch sensor detects a swipe operation or a tap operation on each icon displayed on the output interface 111 by an indicator. In the present disclosure, the input interface 116 provided in the terminal device 100 is used, but it is also possible to use an input interface 116 connected wirelessly or by wire to a main body equipped with the processor 112, such as a mouse.

[0040] 4. Configuration of Server Device 200 Fig. 4 is a block diagram showing an example of the configuration of a server device 200 according to an embodiment of the present disclosure. The server device 200 does not need to include all of the components shown in Fig. 4, and may have a configuration in which some components are omitted, or other components may be added. In addition, the server device 200 does not need to include the components shown in Fig. 4 in a single housing, and each component and process of the server device 200 may be distributed to multiple server devices or cloud server devices.

[0041] 4, the server device 200 includes a memory 211 including a RAM, a ROM, a non-volatile memory, an HDD, etc., a processor 212 including a CPU, etc., and a communication interface 213. These components are electrically connected to each other via control lines and data lines.

[0042] The memory 211 is composed of a ROM, a RAM, a non-volatile memory, a HDD, and the like, and functions as a storage unit. The ROM stores instructions for executing the application and the OS according to the present disclosure as a program. Such a program is loaded and executed by the processor 212. The RAM is used to write and read data while the program stored in the ROM is being processed by the processor 212. The non-volatile memory is a memory into which data is written and read by the execution of the program, and the data written therein is saved even after the execution of the program is terminated. In the present disclosure, the memory 211 particularly stores programs for "a process of conducting an AI interview with a job seeker using a trained interview model," "a process of extracting job candidates for the job seeker according to the results of the AI ​​interview," and the like. The memory 211 also stores various information described in the job seeker management table (FIG. 5A) and the employer management table (FIG. 5B).

[0043] Processor 212 is composed of a CPU (microcomputer) and functions as a control unit for controlling other connected components based on various programs stored in memory 211. Specifically, processor 212 reads out from memory 211 a program for executing an application related to the present disclosure and a program for executing an OS, and executes them. In the present disclosure, processor 212 particularly executes "processing for conducting an AI interview with a job seeker using a trained interview model," "processing for extracting potential employment destinations for the job seeker based on the results of the AI ​​interview," and the like. Processor 212 may be composed of a single CPU, or may be composed of a combination of multiple CPUs and GPUs.

[0044] The communication interface 213 functions as a communication unit that transmits and receives information to and from other remotely installed server devices and terminal devices 100 via a communication processing circuit and an antenna. The communication processing circuit performs processing for transmitting and receiving information to and from other server devices and terminal devices 100 according to the progress of processing of programs and various information used in the processing system 1.

[0045] The communication processing circuit processes based on a wideband wireless communication system such as the LTE system, but can also process based on a narrowband wireless communication system or a contactless wireless communication system such as wireless LAN such as IEEE802.11 or Bluetooth (registered trademark). Also, wired communication can be used instead of or in addition to wireless communication.

[0046] 5. Information stored in the memory 211 of the server device 200 5A is a diagram conceptually illustrating a job applicant management table stored in the server device 200 according to an embodiment of the present disclosure. Of the information stored in the job applicant management table, the job applicant ID and individual basic information are generated each time a new registration request is received from the first user terminal device 100-1, and other information is updated as the processing progresses.

[0047] The job seeker management table is additionally generated row by row for each newly generated job seeker ID. In FIG. 5A, "basic personal information," "personal characteristic information," "AI interview information," "work performance information," and "work desire information" are stored in association with the "job seeker ID." The "job seeker ID" is a unique identifier given to each job seeker who is a first user who desires employment. The "job seeker ID" may be, for example, a serial number, may be composed of any letters and numbers set by the job seeker, or may be an email address or a part thereof. For this reason, the "job seeker ID" may be freely set as long as it can identify the job seeker.

[0048] The "basic personal information" includes information that does not depend on abilities, personality, etc., among the personal information of the job seeker. For example, the "basic personal information" may include name, sex, age, place of origin, nationality, current address, past addresses, work history, etc. In contrast, the "personal characteristic information" includes information that depends on abilities, personality, etc., among the personal information of the job seeker. For example, the "personal characteristic information" may include the results of a general psychological test, behavioral test, technical test, language test, or aptitude test. These tests are not limited to those conducted by the server device 200, and may be tests that the job seeker takes separately and independently. In addition, these tests may be tests that the job seeker can take unconsciously by playing a general game. In particular, a game may be played in a metaverse, which is a three-dimensional virtual space constructed on the Internet, and a behavioral test of the job seeker may be performed by the game on the metaverse.

[0049] The "AI interview information" is information related to the results of an AI interview that the server device 200 provides to a job seeker before the matching process. For example, the "AI interview information" may include text information on the content of the question and answer session between the job seeker and the AI ​​during the AI ​​interview. That is, data such as what kind of answer the job seeker gives to the AI's questions is stored. The "AI interview information" may also include the movement and gestures of the job seeker during the AI ​​interview, and the analysis results thereof. The movement and gestures may include the movement of the eyes and mouth, and other body movements. The analysis results may also include whether the job seeker is calm, whether there is a possibility of suspicious behavior, etc. The "AI interview information" may also include the analysis results of whether or not the question and answer session contains lies based on the analysis results.

[0050] "Work performance information" is information related to the place of employment when a job seeker is offered employment, and information related to the employer's evaluation of the worker after the worker has started working. Therefore, if the place of employment has not been determined, no information will be stored in the relevant information column. Examples of information related to the employer's evaluation of the worker may be information related to work attitude, attendance hours, resignation, personnel evaluation, and promotion / demotion.

[0051] "Work preference information" is information about the job seeker's desired working conditions, place of employment, working environment, working time, etc. This information is stored when the job seeker is registered (i.e., when the job seeker ID is generated), but may be updated each time desired conditions are received from the job seeker.

[0052] 5B is a diagram conceptually illustrating an employer management table stored in the server device 200 according to an embodiment of the present disclosure. Of the information stored in the employer management table, the employer ID and the employer basic information are generated each time a new registration request is received from the second user terminal device 100-2, and other information is updated as the processing progresses.

[0053] The employer management table is additionally generated row by row for each newly generated employer ID. In FIG. 5B, "employer basic information," "working conditions information," "recruitment record information," and "employee evaluation information" are stored in association with the "employer ID." The "employer ID" is a unique identifier given to each employer who is a second user searching for employees. The "employer ID" may be, for example, a serial number, or may be composed of any letters and numbers set by the employer, or may be an email address or a part thereof. For this reason, the "employer ID" may be freely set as long as it can identify the employer.

[0054] "Basic employer information" includes publicly available information about the employer company, etc., and non-public information obtained from the employer. For example, "basic employer information" may include the company's name, location, base, business area, number of employees, turnover rate, age ratio, ratio of disabled employees, ratio of foreign employees, etc. "Basic employer information" may also include a score (company score) calculated based on this information and evaluation information by employees.

[0055] "Working conditions information" is information related to various conditions when a job seeker is hired. For example, "working conditions information" may include information such as salary and other benefits, work location, working hours, and holidays. "Recruitment history information" is information related to the employer's past hiring record. For example, "recruitment history information" may include the number of people hired in the past and the job seeker IDs of those who were hired in the past.

[0056] "Employee evaluation information" is information in which a worker evaluates an employer after a specified period of time has passed since the job seeker started working as an employee, based on the worker's previous work experience, etc. Therefore, if no evaluation has been made by the worker, no information will be stored in the relevant information column. Examples of information related to the worker's evaluation of the employer may include the results of engagement scoring, the worker's subjective impression, the work environment, differences from the initial working conditions, etc.

[0057] 6. Processing sequence performed by processing system 1 (A) Processing up to the AI ​​interview Fig. 6A is a diagram showing a processing sequence executed between each terminal device 100 and the server device 200 according to an embodiment of the present disclosure. Specifically, Fig. 6A is a diagram showing a processing sequence executed between each terminal device 100 and the server device 200 from the login process by the first user terminal device 100-1 until the result of the AI ​​interview is registered in the server device 200.

[0058] According to FIG. 6A, first, the processor 112 of the first user terminal device 100-1 accepts operational input by the job seeker via the input interface 116 and generates login information (S11). The login information includes the job seeker's ID and password. More specifically, the job seeker's operational input is when the job seeker operates the first user terminal device 100-1 to launch an application for employment support services, and inputs the user's ID and password via the input interface 116 on the startup screen of the application. The processor 112 then transmits the generated login information via the communication interface 114 to the server device 200, together with a login request (T11) indicating an intention to start employment support.

[0059] When the login information and login request generated by the first user terminal device 100-1 are received by the server device 200 via the communication interface 213, the processor 212 of the server device 200 performs authentication processing for login based on the login information (S12). Specifically, the processor 212 of the server device 200 compares the received login information with the job seeker information stored in the memory 211, and confirms whether the person making the login request is a registered job seeker. If the processor 212 of the server device 200 confirms the registration, it approves the job seeker's login as a result of the authentication processing (T12). On the other hand, if the processor 212 of the server device 200 cannot confirm the registration, it requests the first user terminal device 100-1 to re-enter the job seeker ID and password.

[0060] When the result of the authentication process (approval result) by the server device 200 is received by the first user terminal device 100-1 via the communication interface 114, the employment support service is started in the first user terminal device 100-1. When the service is started, the processor 112 of the first user terminal device 100-1 accepts the job seeker's operation input via the input interface 116 and generates personal basic information and employment preference information (S13). Here, the personal basic information includes the job seeker's name, sex, age, birthplace, nationality, current address, past addresses, and work history. The employment preference information includes information such as the job seeker's desired working conditions, place of employment, working environment, and employment time. Then, the processor 112 of the first user terminal device 100-1 transmits a registration request to the server device 200 via the communication interface 114 together with the generated personal basic information and employment preference information (T13).

[0061] Next, when the processor 212 of the server device 200 receives the registration request, the individual basic information, and the employment desire information, it starts an external information acquisition process (S14). Specifically, the processor 212 of the server device 200 identifies the job seeker based on the received individual basic information, and acquires information related to the job seeker from outside the server device 200. For example, the processor 212 of the server device 200 may perform tracking on SNS and the web, and acquire information such as posts on SNS by the job seeker, information on related parties such as friends connected on SNS (i.e., information on the presence or absence of connections with criminals), and posts on various Internet sites. The processor 212 of the server device 200 may also inquire of an institution in the country where the job seeker resides that the job seeker is not a criminal or a fugitive, or may acquire information on criminals or fugitives in the relevant country from an external database. Furthermore, the processor 212 of the server device 200 may acquire information on criminals or fugitives from related parties as analog information such as paper, digitize the analog information, and store it in the memory 211.

[0062] Next, the processor 212 of the server device 200 transmits a request to participate in the game to the first user terminal device 100-1 via the communication interface 213 (T14). Specifically, the processor 212 of the server device 200 requests the job seeker to participate in the game as an example of a pre-test (psychological test, aptitude test, behavioral test) for the job seeker before the AI ​​interview. The reason for conducting a game as an example of a test is to enable the job seeker to take the test in a natural state without being conscious of the test, and to obtain the potential characteristics of the job seeker.

[0063] When the first user terminal device 100-1 receives the game participation request sent from the server device 200 via the communication interface 114, the first user terminal device 100-1 and the server device 200 perform game processing as a test for the job seeker (S15). Specifically, the processor 212 of the server device 200 reads out a program for the game from the memory 211 and provides an environment in which the first user terminal device 100-1 can play the game. The processor 112 of the first user terminal device 100-1 then accepts operation inputs by the job seeker via the input interface 116 to progress the game. That is, the game is provided to the first user terminal device 100-1 from the server device 200 via a communication line such as the Internet, and progresses sequentially according to the operation of the job seeker. Note that the test of the job seeker is not limited to a game, and general psychological tests, behavioral tests, aptitude tests, language tests, and technical tests may be performed instead of a game.

[0064] When the game ends, processor 212 of server device 200 stores the received individual basic information, job preference information, and game results (i.e., test results) in memory 211 (S16). More specifically, processor 212 stores each piece of information in the corresponding location in the job seeker management table shown in FIG. 5A.

[0065] Next, the processor 212 of the server device 200 transmits a request to conduct an AI interview to the first user terminal device 100-1 via the communication interface 213 (T15). Specifically, the processor 212 of the server device 200 requests the job seeker to conduct an AI interview as a first interview in order to understand the characteristics of the job seeker before matching the job seeker with an employer. By conducting a first interview using an AI interview, it becomes possible to understand the compatibility between the job seeker and the employer before they meet in person, and to perform optimal matching so that the direct interview between the job seeker and the employer is not wasted.

[0066] When the first user terminal device 100-1 receives the request to conduct an AI interview sent from the server device 200 via the communication interface 114, an AI interview as a first interview with the job seeker is conducted in the first user terminal device 100-1 and the server device 200 (S17). Specifically, the processor 212 of the server device 200 reads out the trained interview model from the memory 211, and provides an environment in which the AI ​​interview can be conducted in the first user terminal device 100-1. The processor 112 of the first user terminal device 100-1 then accepts operation input and voice of the job seeker via the input interface 116, and transmits the voice, etc. to the server device 200. The trained interview model of the server device 200 executes a process of corresponding to the received voice, etc., and conducting the next question and answer session, etc. These processes are repeated to progress the AI ​​interview. The processor 212 of the server device 200 or the trained interview model may perform simultaneous interpretation or convert the conversation into text for foreigners, etc., and transmit the interpretation information or the text information to the first user terminal device 100-1. The processor 212 of the server device 200 or the trained interview model may also perform and analyze the movements and gestures of the job seeker during the AI ​​interview. That is, the processor 212 of the server device 200 or the trained interview model may analyze whether the job seeker is calm or not, and whether there is a possibility of suspicious behavior, from the movement of the job seeker's eyes and mouth, or other body movements, and analyze whether there are lies in the questions and answers during the interview.

[0067] When the AI ​​interview is completed, the processor 212 of the server device 200 registers the result of the AI ​​interview (S18). Specifically, the processor 212 of the server device 200 stores the information obtained by the AI ​​interview in the AI ​​interview information column of the job applicant management table shown in Fig. 5A. In addition, if the job applicant's personal characteristic information or employment preference information is obtained in the AI ​​interview, the processor 212 of the server device 200 may add this information to the personal characteristic information column or the employment preference information column of the job applicant management table shown in Fig. 5A.

[0068] (B) Processing related to matching job seekers with employers 6B is a diagram showing a processing sequence executed between each terminal device 100 and the server device 200 according to an embodiment of the present disclosure. Specifically, it is a diagram showing a processing sequence executed between each terminal device 100 and the server device 200 from matching between a job seeker and an employer to execution of additional machine learning of the trained interview model and the trained matching model based on pass / fail information after the second interview is conducted.

[0069] First, according to FIG. 6B, the processor 212 of the server device 200 performs a matching process to select potential employers (employers) for the job seeker who has had an AI interview (S21). Specifically, the processor 212 of the server device 200 reads out the trained matching model stored in the memory 211, inputs various information on the job seeker into the trained matching model, and obtains an output of optimal potential employers. At this time, the number of potential employers is not limited to one, and may be multiple. The following describes the case where one potential employer is output.

[0070] Next, when the candidate workplace is obtained, the processor 212 of the server device 200 transmits the output candidate workplace information related to the candidate workplace to the first user terminal device 100-1 of the job seeker via the communication interface 213 (T21). Here, the candidate workplace information includes a request to reply as to whether or not to conduct an interview with the candidate workplace. The job seeker checks the candidate workplace information output from the first user terminal device 100-1 and decides whether or not to conduct a direct interview with the candidate workplace. In response, the processor 212 of the server device 200 transmits the information of the job seeker as candidate worker information to the second user terminal device 100-2 of the output candidate workplace via the communication interface 213 (T22). Here, the candidate worker information includes the job seeker's personal basic information, personal characteristic information, AI interview information, and a request to reply as to whether or not to conduct an interview with the job seeker who is a candidate for employment. For this reason, the employer checks the various pieces of information about the job seeker output from the second user terminal device 100-2 and decides whether or not to conduct a face-to-face interview with the job seeker.

[0071] Thereafter, if the job seeker wishes to interview with a potential employer, the processor 112 of the first user terminal device 100-1 accepts the job seeker's operation input via the input interface 116 and transmits a request for a second interview with the potential employer to the server device 200 (T23). On the other hand, if the employer wishes to interview the job seeker, the processor 112 of the second user terminal device 100-2 accepts the employer's operation input via the input interface 116 and transmits a request for a second interview with the job seeker to the server device 200 (T24). In this way, if the job seeker and the employer's desire to conduct an interview match, the processor 212 of the server device 200 performs a second interview arrangement process (S22). Specifically, the processor 212 of the server device 200 arranges the date, time, location, etc. of the second interview, and supports the job seeker and employer until the second interview is conducted.

[0072] After that, a second interview is conducted between the job seeker and the employer, and when the employer decides whether or not to hire the job seeker, the processor 112 of the second user terminal device 100-2 accepts operational input from the employer via the input interface 116, generates pass / fail information, and transmits the pass / fail information to the first user terminal device 100-1 and the server device 200 via the communication interface 114 (T25, T26).

[0073] Next, when the server device 200 receives the pass / fail information transmitted from the second user terminal device 100-2, the processor 212 of the server device 200 registers the pass / fail information in the memory 211 (S23). Specifically, the processor 212 of the server device 200 registers the received pass / fail information in the employment performance information of the job seeker management table shown in Fig. 5A. Note that the pass / fail information is output via the output interface 111 of the first user terminal device 100-1, allowing the job seeker to know whether or not he or she will be hired.

[0074] Next, the processor 212 of the server device 200 performs additional machine learning of the trained interview model and the trained matching model based on various information related to the job seeker and the employer obtained before the pass / fail decision of the job seeker is made (S24). Specifically, the processor 212 of the server device 200 reads the information added to the job seeker management table and the employer management table, and performs additional machine learning based on the read information. This makes it possible to update each model based on information related to the most recent game test, information related to the AI ​​interview, and information related to the second interview (pass / fail information), thereby further improving the accuracy of the AI ​​interview and matching process from the next time onwards.

[0075] (C) Processing of evaluations after starting employment Fig. 6C is a diagram showing a processing sequence executed between each terminal device 100 and the server device 200 according to an embodiment of the present disclosure. Specifically, Fig. 6C is a diagram showing a processing sequence executed between each terminal device 100 and the server device 200 until an evaluation of the employer and additional machine learning are performed based on information acquired from the former job seeker who has become an employee and information acquired from the employer after a predetermined period has elapsed since the job seeker began working.

[0076] According to Fig. 6C, first, the processor 112 of the first user terminal device 100-1 accepts an operation input from the former job seeker who has become an employee via the input interface 116, and generates working environment information (S31). Here, the working environment information may include, for example, the employee's evaluation of the employer, the evaluation of the work content, the evaluation of the working conditions, and the results of engagement scoring. Then, the processor 112 of the first user terminal device 100-1 transmits the generated working environment information to the server device 200 via the communication interface 114 (T31).

[0077] Next, the processor 112 of the second user terminal device 100-2 accepts the employer's operational input via the input interface 116, and generates worker information (S32). Here, the worker information may include, for example, evaluation information on the worker, such as promotion / demotion, work attitude, attendance time, retirement, and personnel evaluation. The processor 112 of the second user terminal device 100-2 then transmits the generated worker information to the server device 200 via the communication interface 114 (T32).

[0078] Next, the processor 212 of the server device 200 stores the received working environment information and worker evaluation information in the memory 211, and evaluates the employer based on the working environment information and information related to other employers (S33). Specifically, the processor 212 of the server device 200 stores various information included in the received working environment information in "Employee Evaluation Information" and the like in the employer management table of FIG. 5B. The processor 212 of the server device 200 also stores various information included in the received worker evaluation information in "Work Performance Information" and the like in the job seeker management table of FIG. 5A. Furthermore, the processor 212 of the server device 200 reads out and executes a program for the scoring from the memory 211 in order to perform scoring as the evaluation of the employer. In the program, not only the employer information already stored but also the most recently obtained evaluation information by the worker (work environment information) is used to perform the latest scoring that is suited to the actual employer. The processor 212 of the server device 200 may store the scoring result in the employer management table of FIG. 5B, or may transmit the result to the second user terminal device 100-2.

[0079] Next, the processor 212 of the server device 200 performs additional machine learning of the trained interview model and the trained matching model based on the newly added working environment information, the worker evaluation information, and the information related to the employer's scoring (S34). Specifically, the processor 212 of the server device 200 reads the information added to the job seeker management table and the employer management table, and performs additional machine learning based on the read information. This allows each model to be updated based on information related to the evaluation of the worker and the employer after a certain period of time has passed since the start of employment, and the accuracy of the AI ​​interview and matching process from the next time onwards can be further improved. Furthermore, since each model is updated appropriately using information related to other workers and other employers, even if the person or his / her own company has a poor hiring and employment record, he / she can receive services related to the AI ​​interview and matching process using a model with higher accuracy.

[0080] Next, the processor 212 of the server device 200 generates management information for a specific employer using various information in the job seeker management table of FIG. 5A and the employer management table of FIG. 5B (S35). Here, the management information is information that can be utilized for organizational management from success cases of each employer based on information related to recruitment to actual employment stored in the memory 211 of the server device 200. That is, based on cases in which each employer was able to sufficiently recruit and employ job seekers and train each job seeker, information that can be applied to the recruitment to employment of other job seekers is formed. Therefore, even if a company has few success cases, it can receive management information generated based on the success cases of multiple other employers. In other words, each employer can obtain information for utilizing the success cases of other companies in its own management. Specifically, the processor 212 of the server device 200 may generate the management information by reading and executing a program for generating management information from the memory 211, or may generate the management information by using another trained model.

[0081] Then, the processor 212 of the server device 200 transmits the management information to the second user terminal device 100-2 via the communication interface 213. (T33). The employer can use the management information output in the second user terminal device 100-2 to manage the organization. The processor 212 of the server device 200 may also transmit the engagement scoring information described above, which allows the employer to use the management information and the engagement scoring information to more firmly establish the organization as a company.

[0082] Thereafter, the processor 212 of the server device 200 uses the trained matching model that has undergone additional machine learning to execute a matching process with other job seekers and other employers, and performs an output process of the results (S36). Specifically, when there is an inquiry from a new job seeker, the processor 212 of the server device 200 may use the trained matching model to extract a matching employer from among employers around the world, and introduce the job seeker to the employer using a direct message or the like (output process). In addition, when there is an inquiry from an employer about a job offer, the processor 212 of the server device 200 may use the trained matching model to extract a matching job seeker from among job seekers around the world, and introduce the job seeker to the employer using a direct message or the like (output process).

[0083] 7. Processing flow executed in the server device 200 Below, we will specifically explain the processing flow executed by the server device 200 when generating and using the trained interview model and the trained matching model.

[0084] (A) Creation and use of trained estimation models Fig. 7A is a diagram showing a process flow executed in the server device 200 according to an embodiment of the present disclosure. Specifically, Fig. 7A is a diagram showing a process flow relating to the generation to use of a trained interview model according to an embodiment of the present disclosure. Note that the process flow may be executed by the processor 212 of the server device 200, or may be executed by a processor of another processing device.

[0085] According to Fig. 7A, the processor 212 reads out, for example, various information from the job seeker management table of Fig. 5A and various information from the employer management table of Fig. 5B as learning interview data (S111). For example, the processor 212 may extract personal basic information, personal characteristic information, employer basic information, and other related information. Furthermore, the processor 212 also reads out, as learning data, information related to employment records for which a decision on whether to employ has been completed (S112). For example, the processor 212 may extract working record information from the job seeker management table of Fig. 5A and hiring record information from the employer management table of Fig. 5B.

[0086] When the above-mentioned various information is obtained, the processor 212 executes a step of performing machine learning of the trained interview model using the information (S113). As an example, the machine learning is performed by providing a set of the information to a neural network that combines neurons, and repeating learning while adjusting the parameters of each neuron so that the output from the neural network is the same as the correct information. Then, the processor 212 executes a step of acquiring the trained interview model and storing it in the memory 211 (S114). Note that the acquired trained interview model may be stored in another device connected to the server device 200 via a wired or wireless network.

[0087] On the other hand, when processor 212 conducts an AI interview using the trained interview model, it reads out from memory 211 the basic personal information, personal characteristic information, employment preference information, etc. of the job seeker who will be conducting the AI ​​interview as initial input data (S115). Then, processor 212 inputs the input data into the trained estimation model and conducts the AI ​​interview using the trained interview model (S116). After that, processor 212 stores the AI ​​interview results in the job seeker management table of Fig. 5A (S117).

[0088] Thereafter, the processor 212 performs additional machine learning (S113) based on the AI ​​interview results (S117). The processor 212 also performs additional machine learning (S113) based on the work environment information and evaluation information (S118) obtained after a predetermined period of time has elapsed since the job seeker began working as an employee. That is, the processor 212 repeats additional learning (iterative learning) every time the above-mentioned learning interview data, AI interview results, work environment information, and evaluation information are obtained, thereby improving the output accuracy of the trained interview model.

[0089] (B) Creation and use of trained matching models Fig. 7B is a diagram showing a process flow executed in the server device 200 according to an embodiment of the present disclosure. Specifically, Fig. 7B is a diagram showing a process flow related to the generation to use of a trained matching model according to an embodiment of the present disclosure. Note that the process flow may be executed by the processor 212 of the server device 200, or may be executed by a processor of another processing device.

[0090] According to Fig. 7B, the processor 212 reads out, for example, job seeker information from the job seeker management table in Fig. 5A and employer information from the employer management table in Fig. 5B as learning interview data (S211). For example, the processor 212 may extract personal basic information, personal characteristic information, employer basic information, and other related information. Furthermore, the processor 212 also reads out, as learning data, information related to employment records for which a decision on whether to employ has been completed (S212). For example, the processor 212 may extract working record information from the job seeker management table in Fig. 5A and employment record information from the employer management table in Fig. 5B.

[0091] When the above-mentioned various information is obtained, the processor 212 executes a step of performing machine learning of the trained matching model using the information (S213). As an example, the machine learning is performed by providing a set of the information to a neural network that combines neurons, and repeating learning while adjusting the parameters of each neuron so that the output from the neural network is the same as the correct information. Then, the processor 212 executes a step of acquiring the trained matching model and storing it in the memory 211 (S214). Note that the acquired trained matching model may be stored in another device connected to the server device 200 via a wired or wireless network.

[0092] On the other hand, when the processor 212 performs matching processing using a trained matching model, the processor 212 reads job seeker information or employer information as input data from the memory 211 (S215). The processor 212 then inputs the input data into the trained matching model and executes matching determination (S216). The processor 212 then stores the matching results in the job seeker management table and employer management table of FIG. 5A (S217).

[0093] Thereafter, the processor 212 performs additional machine learning based on the matching result (S217) (S213). The processor 212 also performs additional machine learning based on the work environment information and evaluation information (S218) obtained after a predetermined period of time has elapsed since the job seeker began working as an employee (S213). That is, the processor 212 repeats additional learning (iterative learning) every time the above-mentioned learning interview data, matching result, work environment information, and evaluation information are obtained, thereby improving the output accuracy of the trained matching model.

[0094] 8. Variations In the above-described embodiment, the matching process is performed after the AI ​​interview, but this is not limited to the above. That is, the processor 212 of the server device 200 may first perform the matching process when performing the acquired information registration process after the game process. Then, the AI ​​interview with the job seeker may be performed in correspondence with the matching destination.

[0095] The processes and procedures described herein can be realized not only by those explicitly described in this disclosure, but also by software, hardware, or a combination thereof. Specifically, the processes and procedures described herein can be realized by implementing logic corresponding to the processes in a medium such as an integrated circuit, a volatile memory, a non-volatile memory, a magnetic disk, or an optical storage. In addition, the processes and procedures described herein can be implemented as computer programs and executed by various computers including terminal devices and server devices.

[0096] Although the processes and procedures described herein are described as being executed by a single device, software, component, or module, such processes or procedures may be executed by multiple devices, multiple software, multiple components, and / or multiple modules. Furthermore, although the various information described herein is described as being stored in a single memory or storage unit, such information may be stored in multiple memories provided in a single device or multiple memories distributed across multiple devices. Furthermore, the software and hardware elements described herein may be realized by integrating them into fewer components or breaking them down into more components.

[0097] 9. Summary of the embodiments of the present disclosure A processing device according to an embodiment of the present disclosure is a processing device having at least one processor, and the at least one processor is configured to execute processing for conducting an AI interview with a job seeker using a trained interview model, and extracting potential employment opportunities for the job seeker based on the results of the AI ​​interview.

[0098] In the above configuration, the at least one processor may conduct the AI ​​interview by taking into account at least one of the job seeker's personal basic information and personal characteristic information. In this case, the job seeker's personal characteristic information may include the results of a test conducted in advance on the job seeker. Furthermore, in this case, the job seeker's personal basic information may include information on the job seeker's hometown.

[0099] In the above configuration, the at least one processor may analyze the attitude and behavior of the job seeker during the AI ​​interview and analyze whether there is any lie in the job seeker's answers.

[0100] In the above configuration, the at least one processor may convert the exchange in the AI ​​interview into text and store it in the memory. In this case, the trained interview model may be subjected to additional machine learning based on the converted exchange.

[0101] In the above configuration, the trained interview model may be subjected to machine learning using personal characteristic information of individuals who are judged to be unsuitable as workers as training interview data.

[0102] In the above configuration, the at least one processor may extract the potential employers using a trained matching model trained by machine learning based on job seeker information, employer information, and employment history. In this case, the at least one processor may obtain post-employment work environment information from the job seeker whose employer was determined through the AI ​​interview, and perform additional machine learning of the trained interview model and the trained matching model based on the work environment information. In addition, in the above case, the at least one processor may obtain post-employment evaluation information from the employer whose employer was determined through the AI ​​interview, and perform additional machine learning of the trained interview model and the trained matching model based on the evaluation information.

[0103] A processing program according to an embodiment of the present disclosure causes a computer having at least one processor to function in such a way that an AI interview is conducted with a job seeker using a trained interview model, and candidate employment opportunities for the job seeker are extracted based on the results of the AI ​​interview.

[0104] A processing method according to an embodiment of the present disclosure is a processing method executed by at least one processor in a computer having the at least one processor, and includes a step of conducting an AI interview with a job seeker using a trained interview model, and a step of extracting potential employment opportunities for the job seeker based on the results of the AI ​​interview. [Explanation of symbols]

[0105] 100 Terminal Equipment 100-1 First user terminal device 100-2 Second user terminal device 200 Server device

Claims

1. A processing device comprising at least one processor, The at least one processor: Conduct AI interviews with job seekers using a trained interview model, Extracting employment candidates for the job seeker based on the results of the AI ​​interview; 2. A processing device configured to perform processing for:

2. The at least one processor conducts the AI ​​interview by taking into account at least one of the job seeker's personal basic information or personal characteristic information. The processing device of claim 1 .

3. The personal characteristic information of the job seeker includes the results of a test previously conducted on the job seeker. The processing device according to claim 2 .

4. The basic personal information of the job seeker includes information related to the job seeker's hometown, The processing device according to claim 3 .

5. The at least one processor: Analyzing the attitude and behavior of the job seeker during the AI ​​interview and analyzing whether there is a lie in the answer of the job seeker; The processing device of claim 1 .

6. The at least one processor: The exchange in the AI ​​interview is converted into text and stored in memory. The processing device of claim 1 .

7. The trained interview model has undergone additional machine learning based on the transcribed interactions. The processing device of claim 6.

8. The trained interview model is machine-learned using personal characteristic information of individuals who are judged to be unsuitable as workers as training interview data. The processing device of claim 1 .

9. The at least one processor: Extracting the potential employers using a machine-learned matching model based on job seeker information, employer information, and hiring history; The processing device of claim 1 .

10. The at least one processor: Obtaining working environment information after employment from the job seeker whose employment has been decided through the AI ​​interview, Perform additional machine learning of a trained interview model and a trained matching model based on the working environment information. The processing device of claim 9.

11. The at least one processor: Obtaining evaluation information of the job seeker after employment from the place of employment determined through the AI ​​interview, Based on the evaluation information, additional machine learning is performed on a trained interview model and a trained matching model. The processing device of claim 9.

12. In a computer having at least one processor, the at least one processor is Conduct AI interviews with job seekers using a trained interview model, Extracting employment candidates for the job seeker based on the results of the AI ​​interview; A processing program that makes it function as such.

13. A processing method executed by at least one processor in a computer having the at least one processor, comprising: A step of conducting an AI interview with a job seeker using the trained interview model; Extracting employment candidates for the job seeker based on the results of the AI ​​interview; A processing method comprising:

Images