System

Abstract

An object of a system according to an embodiment is to quantify a work skill and improve matching between a job change applicant and a company.SOLUTION: A system includes a generation AI, a skill quantification unit, and a matching unit. The generated AI quantifies the user's work skills. The skill quantifier provides the skill quantified by the generative AI as a score. The matching unit matches the job change applicant with the company using the score provided by the skill quantifying unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult to quantitatively evaluate work skills, and there have been issues with this, such as insufficient promotion of appropriate human resource matching and reskilling.

[0005] The system according to the embodiment aims to quantify job skills and improve matching between job seekers and companies. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a skill quantification unit, and a matching unit. The generation AI quantifies the user's work skills. The skill quantification unit provides the skills quantified by the generation AI as a score. The matching unit matches job seekers with companies using the score provided by the skill quantification unit. [Effects of the Invention]

[0007] The system according to the embodiment can quantify job skills and improve the matching between job seekers and companies. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The work skill quantification system according to an embodiment of the present invention is a system that uses a generative AI to quantify a user's work skills and provide a score. As a result, the work skill quantification system can improve job-change matching rates, reduce turnover rates, improve the level of in-house human resources, and stimulate reskilling.

[0029] A work skill quantification system according to an embodiment includes a generation AI, a skill quantification unit, and a matching unit. The generation AI quantifies a user's work skills. For example, the generation AI analyzes information about work skills input by a user and quantifies the skills. Furthermore, when a user inputs skills such as "programming skills" or "project management ability," the generation AI generates a test related to the skill and provides it to the user. Furthermore, the generation AI generates the test based on prompts containing instructions on what the user wants the generation AI to do. The skill quantification unit provides the skills quantified by the generation AI as a score. For example, the skill quantification unit provides a score by having the user take a test generated by the generation AI and having the generation AI analyze the results. Furthermore, the skill quantification unit provides a score that clearly indicates the user's skill level. Furthermore, the skill quantification unit uses the quantitative score provided by the generation AI to match job seekers with companies. The matching unit uses the score provided by the skill quantification unit to match job seekers with companies. For example, the matching unit compares the skill set desired by a company with the job seeker's skill score to perform optimal matching. The matching unit also places the right person in the right position to improve the job-change matching rate. Furthermore, the matching unit is able to grasp the skill levels of employees and make appropriate assignments, which is expected to reduce turnover rates. This allows the job skill quantification system according to the embodiment to quantify the user's job skills and improve matching between job seekers and companies. For example, the generation AI quantifies the user's job skills and provides a score, thereby improving the job-change matching rate. Furthermore, the skill quantification unit provides a score that allows the user's skill level to be clearly understood. Furthermore, the matching unit compares the skill set desired by the company with the skill score of the job seeker to achieve optimal matching.

[0030] The generative AI can analyze a user's past work history or project deliverables and reflect this in the quantification of skills. For example, the generative AI can analyze a user's past work history and evaluate their role and achievements in each project. For example, experience as a project manager or development experience using a specific technology can be reflected in the skill score. The generative AI can also analyze the user's project deliverables and evaluate their quality and completeness. For example, it can calculate a skill score based on the content of a completed product or a submitted report. Furthermore, the generative AI can comprehensively analyze the user's work history and project deliverables and reflect this in the quantification of skills. This allows the generative AI to analyze a user's past work history and project deliverables and reflect this in the quantification of skills.

[0031] The generation AI can integrate self-assessment or third-party assessment of a user's work skills to provide a more accurate skill score. For example, the generation AI collects and integrates the user's self-assessment data and third-party assessment data from superiors and colleagues to calculate a skill score. For example, the generation AI adjusts the score taking into account the difference between the self-assessment and third-party assessment. The generation AI also analyzes the self-assessment and third-party assessment data to evaluate the reliability of the skill. For example, if the self-assessment is high but the third-party assessment is low, the reliability of that skill is rated low. Furthermore, the generation AI comprehensively analyzes the self-assessment and third-party assessment data to provide a more accurate skill score. This allows the self-assessment and third-party assessment to be integrated to provide a more accurate skill score.

[0032] The generative AI can suggest new skill sets related to work skills based on the user's hobbies or interests. For example, the generative AI analyzes the user's hobbies and interests and suggests related work skills based on them. For example, for a user whose hobby is programming, it suggests data science or AI-related skills. The generative AI can also analyze the user's interests and suggest new skill sets based on them. For example, for a user who is interested in project management, it suggests leadership and team building skills. Furthermore, the generative AI comprehensively analyzes the user's hobbies and interests and suggests new skill sets related to work skills. This makes it possible to suggest new skill sets based on the user's hobbies and interests.

[0033] Generative AI can compare skill sets from different industries and evaluate the applicability of a user's skills to other industries. For example, generative AI can analyze skill sets from different industries and evaluate how a user's skills can be applied to other industries. For example, it can evaluate the applicability of IT industry skills to the medical industry. Generative AI can also compare skill sets from different industries and evaluate the applicability of a user's skills to other industries. For example, it can evaluate the applicability of manufacturing industry skills to the service industry. Furthermore, generative AI can comprehensively analyze skill sets from different industries and evaluate the applicability of a user's skills to other industries. This makes it possible to evaluate the applicability of a user's skills to other industries.

[0034] Generative AI can analyze users' test results in real time and provide instant feedback. For example, generative AI builds a system that analyzes users' test results in real time and provides instant feedback. For example, it displays scores and areas for improvement immediately after the test is completed. Generative AI also develops algorithms that analyze users' test results in real time and provide instant feedback. For example, it calculates scores in real time during the test and provides instant feedback. Furthermore, generative AI designs an interface for analyzing users' test results in real time and providing instant feedback. For example, it displays test results in real time and provides instant feedback to users. This makes it possible to analyze users' test results in real time and provide instant feedback.

[0035] The generative AI can compare the user's test results with past data and evaluate the skill progress. The generative AI, for example, builds a system that compares the user's test results with past data and evaluates the skill progress. For example, it compares past test results with current results to display progress. The generative AI also develops an algorithm that compares the user's test results with past data and evaluates the skill progress. For example, it compares past scores with current scores to evaluate progress. Furthermore, the generative AI designs an interface for comparing the user's test results with past data and evaluating the skill progress. For example, it visually compares past data with current data to evaluate progress. This makes it possible to compare the user's test results with past data and evaluate the skill progress.

[0036] Generative AI can suggest the next skill or training program to be learned based on the user's test results. For example, generative AI analyzes the user's test results and suggests the next skill or training program to be learned. For example, for a user who has learned the basics of programming, it suggests the next advanced skill to be learned. Generative AI can also suggest the next skill set to be learned based on the user's test results. For example, for a user who has learned project management skills, it suggests leadership and team building skills. Furthermore, generative AI comprehensively analyzes the user's test results and suggests the next skill or training program to be learned. This makes it possible to suggest the next skill or training program to be learned based on the user's test results.

[0037] The generative AI can provide different test formats and perform a multifaceted evaluation of skills. The generative AI, for example, provides a simulation test to evaluate the user's skills from multiple angles. For example, it evaluates problem-solving ability in a virtual environment. The generative AI can also provide practical tests to evaluate the user's skills through actual work. For example, it can evaluate the user's skills through tests that involve performing actual work. Furthermore, the generative AI can comprehensively provide different test formats and perform a multifaceted evaluation of the user's skills. This allows the provision of different test formats and a multifaceted evaluation of skills.

[0038] The matching unit can compare the skill set required by the company with the user's skill score in detail to achieve optimal matching. The matching unit, for example, compares the skill set required by the company with the user's skill score in detail to achieve optimal matching. For example, it compares the programming skills required by the company with the user's skill score and calculates the degree of compatibility. The matching unit also compares the skill set required by the company with the user's skill score in detail to achieve the placement of the right person in the right position. For example, it compares the project management skills required by the company with the user's skill score to achieve optimal placement. Furthermore, the matching unit comprehensively compares the skill set required by the company with the user's skill score to achieve optimal matching. This allows the skill set required by the company to be compared in detail with the user's skill score to achieve optimal matching.

[0039] The generation AI can analyze a user's past job change history or work history and predict the success rate of a job change. The generation AI, for example, analyzes a user's past job change history and predicts the success rate of a job change. For example, it calculates the success rate based on the number of previous job changes and the length of employment at the previous workplace. The generation AI also analyzes the user's work history and predicts the success rate of a job change. For example, it calculates the success rate based on the past job content and duties. Furthermore, the generation AI comprehensively analyzes the user's past job change history and work history and predicts the success rate of a job change. In this way, it is possible to analyze a user's past job change history and work history and predict the success rate of a job change.

[0040] The generation AI can analyze job listings in different industries or regions and suggest the most suitable job to the user. For example, the generation AI can analyze job listings in different industries and suggest the most suitable job to the user. For example, it can analyze job listings in the IT industry and suggest positions that suit the user's skills. The generation AI can also analyze job listings in different regions and suggest the most suitable job to the user. For example, it can analyze job listings in urban areas and suggest positions that suit the user's desired work location. Furthermore, the generation AI can comprehensively analyze job listings in different industries and regions and suggest the most suitable job to the user. This allows it to analyze job listings in different industries and regions and suggest the most suitable job to the user.

[0041] Generative AI can suggest future career paths based on the user's skill scores. Generative AI, for example, analyzes the user's skill scores and suggests future career paths. For example, it suggests the next position or industry to aim for based on the user's current skill set. Generative AI also suggests career path steps based on the user's skill scores. For example, it suggests promotion steps and the order in which skills should be acquired. Furthermore, generative AI comprehensively analyzes the user's skill scores and suggests future career paths. This makes it possible to suggest future career paths based on the user's skill scores.

[0042] Generative AI can analyze employees' skill scores and work performance and make appropriate placements. Generative AI can, for example, analyze employees' skill scores and work performance and make appropriate placements. For example, employees with high skill scores can be assigned to important projects. Generative AI can also analyze employees' work performance and make appropriate placements. For example, employee placements can be determined based on performance evaluations and KPIs. Generative AI can also comprehensively analyze employees' skill scores and work performance and make appropriate placements. This allows for the analysis of employees' skill scores and work performance and makes appropriate placements.

[0043] The generation AI can evaluate an employee's suitability for a job based on their skill score and assign them appropriate work. For example, the generation AI analyzes an employee's skill score and evaluates their suitability for a job. For example, it prioritizes the allocation of work in fields with high skill scores. The generation AI also evaluates an employee's suitability for a job and assigns them appropriate work. For example, it determines employee placement based on job suitability. Furthermore, the generation AI comprehensively analyzes an employee's skill score and job suitability and assigns them appropriate work. This makes it possible to evaluate an employee's suitability for a job based on their skill score and assign them appropriate work.

[0044] Generative AI can propose career development plans based on employees' skill scores. Generative AI, for example, analyzes employees' skill scores and proposes career development plans. For example, it proposes career paths in fields with high skill scores. Generative AI also proposes career development steps based on employees' skill scores. For example, it proposes promotion steps and the order in which skills should be acquired. Furthermore, generative AI comprehensively analyzes employees' skill scores and proposes career development plans. This makes it possible to propose career development plans based on employees' skill scores.

[0045] The generation AI can suggest a transfer to a different department or project based on an employee's skill score. For example, the generation AI analyzes an employee's skill score and suggests a transfer to a different department or project. For example, it suggests a transfer to a department in a field with a high skill score. The generation AI also evaluates the suitability of the transfer based on the employee's skill score. For example, it compares the work content of the transfer destination with the employee's skill score to evaluate suitability. Furthermore, the generation AI comprehensively analyzes the employee's skill score and suggests a transfer to a different department or project. This makes it possible to suggest a transfer to a different department or project based on the employee's skill score.

[0046] The generative AI can propose individual training programs based on an employee's skill score. For example, the generative AI analyzes an employee's skill score and proposes individual training programs. For example, it proposes training programs in areas where the skill score is low. The generative AI also determines training priorities based on the employee's skill score. For example, it provides training in areas where the skill score is low preferentially. Furthermore, the generative AI comprehensively analyzes an employee's skill score and proposes individual training programs. This makes it possible to propose individual training programs based on the employee's skill score.

[0047] Generative AI can identify skill gaps based on an employee's skill scores and provide training to fill those gaps. For example, generative AI analyzes an employee's skill scores and identifies skill gaps. For example, if a specific skill is lacking, it suggests training to strengthen that skill. Generative AI also provides training programs to fill skill gaps based on an employee's skill scores. For example, it provides training in areas where the skill score is low. Furthermore, generative AI comprehensively analyzes an employee's skill scores, identifies skill gaps, and provides training to fill those gaps. This makes it possible to identify an employee's skill gaps and provide training to fill those gaps.

[0048] The generative AI can suggest different tasks or projects for employees to participate in based on their skill scores. For example, the generative AI can analyze an employee's skill score and suggest different tasks or projects for them to participate in. For example, it can suggest participation in projects in fields where the employee has a high skill score. The generative AI can also evaluate the employee's suitability for a task based on their skill score. For example, it can determine employee placement based on task suitability. Furthermore, the generative AI can comprehensively analyze an employee's skill score and suggest different tasks or projects for them to participate in. This makes it possible to suggest different tasks or projects for employees to participate in based on their skill scores.

[0049] Generative AI can propose an in-house mentoring system based on employees' skill scores and promote the sharing of skills. For example, generative AI can analyze employees' skill scores and propose an in-house mentoring system. For example, it can appoint employees with high skill scores as mentors and promote the sharing of skills. Generative AI can also propose how to implement a mentoring system based on employees' skill scores. For example, it can suggest criteria for selecting mentors and the frequency of mentoring. Furthermore, generative AI can comprehensively analyze employees' skill scores and propose an in-house mentoring system and promote the sharing of skills. This makes it possible to propose an in-house mentoring system based on employees' skill scores and promote the sharing of skills.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The work skill quantification system not only quantifies a user's skills, but can also suggest new career paths based on the user's skill set. For example, it can suggest the next skills a user should learn or the qualifications they should obtain based on their current skill set. It can also analyze the user's skill set and suggest future career path steps. It can also comprehensively analyze the user's skill set and evaluate the possibility of changing jobs to a different industry or occupation. This allows users to find a new career path that utilizes their skills.

[0052] The work skill quantification system not only quantifies a user's skills, but also suggests appropriate training programs based on the user's skill set. For example, it suggests specific training programs for areas in which the user has a low skill score. It also analyzes the user's skill set, identifies skill gaps, and provides training to fill those gaps. It also comprehensively analyzes the user's skill set and suggests individual training programs. This allows the user to receive appropriate training to improve their skills.

[0053] The work skill quantification system not only quantifies a user's skills, but also evaluates the applicability of the user's skills to different industries based on the user's skill set. For example, it analyzes the user's skill set and evaluates its applicability in different industries. It also analyzes the user's skill set and suggests career paths in different industries. It also comprehensively analyzes the user's skill set and evaluates the possibility of changing jobs to different industries. This allows users to build careers in different industries by utilizing their skills.

[0054] The work skill quantification system not only quantifies a user's skills, but can also suggest appropriate project participation based on the user's skill set. For example, it analyzes a user's skill set and suggests appropriate project participation. It also analyzes a user's skill set and evaluates project suitability. It also comprehensively analyzes a user's skill set and suggests participation in different projects. This allows users to participate in appropriate projects that utilize their skills.

[0055] The work skill quantification system not only quantifies a user's skills, but can also suggest appropriate mentoring programs based on the user's skill set. For example, it analyzes the user's skill set and suggests appropriate mentors. It also analyzes the user's skill set and suggests how to implement a mentoring program. It also analyzes the user's skill set comprehensively and suggests mentoring programs to promote skill sharing. This allows users to utilize their own skills and share them with other employees.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The generative AI quantifies the user's work skills. For example, it analyzes information about work skills entered by the user and quantifies those skills. If the user enters skills such as "programming skills" or "project management ability," it generates a test related to that skill and provides it to the user. The generative AI then generates the test based on prompts containing instructions on what the user wants the generative AI to do. Step 2: The skill quantification unit provides the skills quantified by the generation AI as a score. For example, the user takes a test generated by the generation AI, and the generation AI analyzes the results to provide a score. The skill quantification unit also provides a score that allows the user's skill level to be clearly understood. Step 3: The Matching Department uses the scores provided by the Skill Quantification Department to match job seekers with companies. For example, it compares the skill sets required by companies with the skill scores of job seekers to make the optimal match. The Matching Department also places the right people in the right positions to improve the job-changing matching rate. Furthermore, by understanding the skill levels of employees and making appropriate placements, it is expected that turnover rates will improve.

[0058] (Example 2) The work skill quantification system according to an embodiment of the present invention is a system that uses a generative AI to quantify a user's work skills and provide a score. As a result, the work skill quantification system can improve job-change matching rates, reduce turnover rates, improve the level of in-house human resources, and stimulate reskilling.

[0059] A work skill quantification system according to an embodiment includes a generation AI, a skill quantification unit, and a matching unit. The generation AI quantifies a user's work skills. For example, the generation AI analyzes information about work skills input by a user and quantifies the skills. Furthermore, when a user inputs skills such as "programming skills" or "project management ability," the generation AI generates a test related to the skill and provides it to the user. Furthermore, the generation AI generates the test based on prompts containing instructions on what the user wants the generation AI to do. The skill quantification unit provides the skills quantified by the generation AI as a score. For example, the skill quantification unit provides a score by having the user take a test generated by the generation AI and having the generation AI analyze the results. Furthermore, the skill quantification unit provides a score that clearly indicates the user's skill level. Furthermore, the skill quantification unit uses the quantitative score provided by the generation AI to match job seekers with companies. The matching unit uses the score provided by the skill quantification unit to match job seekers with companies. For example, the matching unit compares the skill set desired by a company with the job seeker's skill score to perform optimal matching. The matching unit also places the right person in the right position to improve the job-change matching rate. Furthermore, the matching unit is able to grasp the skill levels of employees and make appropriate assignments, which is expected to reduce turnover rates. This allows the job skill quantification system according to the embodiment to quantify the user's job skills and improve matching between job seekers and companies. For example, the generation AI quantifies the user's job skills and provides a score, thereby improving the job-change matching rate. Furthermore, the skill quantification unit provides a score that allows the user's skill level to be clearly understood. Furthermore, the matching unit compares the skill set desired by the company with the skill score of the job seeker to achieve optimal matching.

[0060] The generative AI can analyze a user's past work history or project deliverables and reflect this in the quantification of skills. For example, the generative AI can analyze a user's past work history and evaluate their role and achievements in each project. For example, experience as a project manager or development experience using a specific technology can be reflected in the skill score. The generative AI can also analyze the user's project deliverables and evaluate their quality and completeness. For example, it can calculate a skill score based on the content of a completed product or a submitted report. Furthermore, the generative AI can comprehensively analyze the user's work history and project deliverables and reflect this in the quantification of skills. This allows the generative AI to analyze a user's past work history and project deliverables and reflect this in the quantification of skills.

[0061] The generation AI can integrate self-assessment or third-party assessment of a user's work skills to provide a more accurate skill score. For example, the generation AI collects and integrates the user's self-assessment data and third-party assessment data from superiors and colleagues to calculate a skill score. For example, the generation AI adjusts the score taking into account the difference between the self-assessment and third-party assessment. The generation AI also analyzes the self-assessment and third-party assessment data to evaluate the reliability of the skill. For example, if the self-assessment is high but the third-party assessment is low, the reliability of that skill is rated low. Furthermore, the generation AI comprehensively analyzes the self-assessment and third-party assessment data to provide a more accurate skill score. This allows the self-assessment and third-party assessment to be integrated to provide a more accurate skill score.

[0062] The generation AI uses its emotion estimation function to analyze the confidence or anxiety the user feels when inputting a skill, and can reflect that emotion in the skill score. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate the user's confidence or anxiety when inputting a skill. For example, if the user's confidence is high, the skill score is revised upward, and if the user's anxiety is high, the skill score is revised downward. The generation AI also analyzes the user's self-reported data to evaluate the level of confidence or anxiety. For example, if the user self-reports that they are highly confident, the skill score is revised upward. Furthermore, the generation AI analyzes the user's psychological test results to evaluate the level of confidence or anxiety. For example, if the psychological test determines that the user is highly anxious, the skill score is revised downward. This allows the user's confidence or anxiety to be analyzed and their emotions to be reflected in the skill score.

[0063] The generative AI can suggest new skill sets related to work skills based on the user's hobbies or interests. For example, the generative AI analyzes the user's hobbies and interests and suggests related work skills based on them. For example, for a user whose hobby is programming, it suggests data science or AI-related skills. The generative AI can also analyze the user's interests and suggest new skill sets based on them. For example, for a user who is interested in project management, it suggests leadership and team building skills. Furthermore, the generative AI comprehensively analyzes the user's hobbies and interests and suggests new skill sets related to work skills. This makes it possible to suggest new skill sets based on the user's hobbies and interests.

[0064] Generative AI can compare skill sets from different industries and evaluate the applicability of a user's skills to other industries. For example, generative AI can analyze skill sets from different industries and evaluate how a user's skills can be applied to other industries. For example, it can evaluate the applicability of IT industry skills to the medical industry. Generative AI can also compare skill sets from different industries and evaluate the applicability of a user's skills to other industries. For example, it can evaluate the applicability of manufacturing industry skills to the service industry. Furthermore, generative AI can comprehensively analyze skill sets from different industries and evaluate the applicability of a user's skills to other industries. This makes it possible to evaluate the applicability of a user's skills to other industries.

[0065] The generation AI can use the emotion estimation function to analyze the motivation felt by the user when inputting a skill and provide feedback to increase motivation. For example, the generation AI can use the emotion estimation function to analyze the motivation felt by the user when inputting a skill and provide feedback based on that data. For example, if motivation is low, an encouraging message is displayed. The generation AI also analyzes the user's self-reported data and evaluates the level of motivation. For example, if the user self-reports that their motivation is low, an encouraging message is displayed. The generation AI also analyzes the user's psychological test results and evaluates the level of motivation. For example, if the psychological test determines that motivation is low, an encouraging message is displayed. This makes it possible to analyze the user's motivation and provide feedback.

[0066] Generative AI can analyze users' test results in real time and provide instant feedback. For example, generative AI builds a system that analyzes users' test results in real time and provides instant feedback. For example, it displays scores and areas for improvement immediately after the test is completed. Generative AI also develops algorithms that analyze users' test results in real time and provide instant feedback. For example, it calculates scores in real time during the test and provides instant feedback. Furthermore, generative AI designs an interface for analyzing users' test results in real time and providing instant feedback. For example, it displays test results in real time and provides instant feedback to users. This makes it possible to analyze users' test results in real time and provide instant feedback.

[0067] The generative AI can compare the user's test results with past data and evaluate the skill progress. The generative AI, for example, builds a system that compares the user's test results with past data and evaluates the skill progress. For example, it compares past test results with current results to display progress. The generative AI also develops an algorithm that compares the user's test results with past data and evaluates the skill progress. For example, it compares past scores with current scores to evaluate progress. Furthermore, the generative AI designs an interface for comparing the user's test results with past data and evaluating the skill progress. For example, it visually compares past data with current data to evaluate progress. This makes it possible to compare the user's test results with past data and evaluate the skill progress.

[0068] The generation AI can use the emotion estimation function to analyze the stress or concentration level the user feels during the test and reflect those emotions in the score. For example, the generation AI can use the emotion estimation function to analyze the stress and concentration level the user feels during the test and reflect that data in the score. For example, if stress is high, the score is adjusted. The generation AI also analyzes the user's self-reported data and evaluates the level of stress and concentration. For example, if the user self-reports that stress is high, the score is adjusted. The generation AI also analyzes the user's psychological test results and evaluates the level of stress and concentration. For example, if the psychological test determines that the user's concentration level is high, the score is revised upward. This makes it possible to analyze the user's stress and concentration level and reflect those emotions in the score.

[0069] Generative AI can suggest the next skill or training program to be learned based on the user's test results. For example, generative AI analyzes the user's test results and suggests the next skill or training program to be learned. For example, for a user who has learned the basics of programming, it suggests the next advanced skill to be learned. Generative AI can also suggest the next skill set to be learned based on the user's test results. For example, for a user who has learned project management skills, it suggests leadership and team building skills. Furthermore, generative AI comprehensively analyzes the user's test results and suggests the next skill or training program to be learned. This makes it possible to suggest the next skill or training program to be learned based on the user's test results.

[0070] The generative AI can provide different test formats and perform a multifaceted evaluation of skills. The generative AI, for example, provides a simulation test to evaluate the user's skills from multiple angles. For example, it evaluates problem-solving ability in a virtual environment. The generative AI can also provide practical tests to evaluate the user's skills through actual work. For example, it can evaluate the user's skills through tests that involve performing actual work. Furthermore, the generative AI can comprehensively provide different test formats and perform a multifaceted evaluation of the user's skills. This allows the provision of different test formats and a multifaceted evaluation of skills.

[0071] The generation AI can use the emotion estimation function to analyze the motivation felt by the user during the test and provide feedback to increase motivation. For example, the generation AI can use the emotion estimation function to analyze the motivation felt by the user during the test and provide feedback based on that data. For example, if motivation is low, an encouraging message can be displayed. The generation AI can also analyze the user's self-reported data and evaluate the level of motivation. For example, if the user self-reports that their motivation is low, an encouraging message can be displayed. The generation AI can also analyze the user's psychological test results and evaluate the level of motivation. For example, if the psychological test determines that motivation is low, an encouraging message can be displayed. This makes it possible to analyze the user's motivation and provide feedback.

[0072] The matching unit can compare the skill set required by the company with the user's skill score in detail to achieve optimal matching. The matching unit, for example, compares the skill set required by the company with the user's skill score in detail to achieve optimal matching. For example, it compares the programming skills required by the company with the user's skill score and calculates the degree of compatibility. The matching unit also compares the skill set required by the company with the user's skill score in detail to achieve the placement of the right person in the right position. For example, it compares the project management skills required by the company with the user's skill score to achieve optimal placement. Furthermore, the matching unit comprehensively compares the skill set required by the company with the user's skill score to achieve optimal matching. This allows the skill set required by the company to be compared in detail with the user's skill score to achieve optimal matching.

[0073] The generation AI can analyze a user's past job change history or work history and predict the success rate of a job change. The generation AI, for example, analyzes a user's past job change history and predicts the success rate of a job change. For example, it calculates the success rate based on the number of previous job changes and the length of employment at the previous workplace. The generation AI also analyzes the user's work history and predicts the success rate of a job change. For example, it calculates the success rate based on the past job content and duties. Furthermore, the generation AI comprehensively analyzes the user's past job change history and work history and predicts the success rate of a job change. In this way, it is possible to analyze a user's past job change history and work history and predict the success rate of a job change.

[0074] The generation AI can use its emotion estimation function to analyze the expectations or anxieties a user feels when changing jobs and reflect those emotions in matching. For example, the generation AI can use its emotion estimation function to analyze the expectations or anxieties a user feels when changing jobs and reflect that data in matching. For example, if expectations are high, it will prioritize positive matching. The generation AI also analyzes the user's self-reported data and evaluates the level of expectations and anxieties. For example, if the user self-reports that their expectations are high, it will prioritize positive matching. Furthermore, the generation AI analyzes the user's psychological test results and evaluates the level of expectations and anxieties. For example, if the psychological test indicates high anxiety, it will carefully consider matching. This makes it possible to analyze the user's expectations and anxieties and reflect those emotions in matching.

[0075] The generation AI can analyze job listings in different industries or regions and suggest the most suitable job to the user. For example, the generation AI can analyze job listings in different industries and suggest the most suitable job to the user. For example, it can analyze job listings in the IT industry and suggest positions that suit the user's skills. The generation AI can also analyze job listings in different regions and suggest the most suitable job to the user. For example, it can analyze job listings in urban areas and suggest positions that suit the user's desired work location. Furthermore, the generation AI can comprehensively analyze job listings in different industries and regions and suggest the most suitable job to the user. This allows it to analyze job listings in different industries and regions and suggest the most suitable job to the user.

[0076] Generative AI can suggest future career paths based on the user's skill scores. Generative AI, for example, analyzes the user's skill scores and suggests future career paths. For example, it suggests the next position or industry to aim for based on the user's current skill set. Generative AI also suggests career path steps based on the user's skill scores. For example, it suggests promotion steps and the order in which skills should be acquired. Furthermore, generative AI comprehensively analyzes the user's skill scores and suggests future career paths. This makes it possible to suggest future career paths based on the user's skill scores.

[0077] The generation AI can use the emotion estimation function to analyze the motivation a user feels when they want to change jobs and provide feedback to increase their motivation. For example, the generation AI can use the emotion estimation function to analyze the motivation a user feels when they want to change jobs and provide feedback based on that data. For example, if motivation is low, an encouraging message can be displayed. The generation AI can also analyze the user's self-reported data and evaluate their motivation level. For example, if the user self-reports that their motivation is low, an encouraging message can be displayed. The generation AI can also analyze the user's psychological test results and evaluate their motivation level. For example, if the psychological test determines that their motivation is low, an encouraging message can be displayed. This makes it possible to analyze the user's motivation and provide feedback.

[0078] Generative AI can analyze employees' skill scores and work performance and make appropriate placements. Generative AI can, for example, analyze employees' skill scores and work performance and make appropriate placements. For example, employees with high skill scores can be assigned to important projects. Generative AI can also analyze employees' work performance and make appropriate placements. For example, employee placements can be determined based on performance evaluations and KPIs. Generative AI can also comprehensively analyze employees' skill scores and work performance and make appropriate placements. This allows for the analysis of employees' skill scores and work performance and makes appropriate placements.

[0079] The generation AI can evaluate an employee's suitability for a job based on their skill score and assign them appropriate work. For example, the generation AI analyzes an employee's skill score and evaluates their suitability for a job. For example, it prioritizes the allocation of work in fields with high skill scores. The generation AI also evaluates an employee's suitability for a job and assigns them appropriate work. For example, it determines employee placement based on job suitability. Furthermore, the generation AI comprehensively analyzes an employee's skill score and job suitability and assigns them appropriate work. This makes it possible to evaluate an employee's suitability for a job based on their skill score and assign them appropriate work.

[0080] The generation AI can use its emotion estimation function to analyze the satisfaction or stress that employees feel while working and reflect those emotions in their placement. The generation AI can, for example, use its emotion estimation function to analyze the satisfaction or stress that employees feel while working and reflect that data in their placement. For example, it can prioritize assignments that result in high satisfaction. The generation AI can also analyze employees' self-reported data and evaluate their levels of satisfaction and stress. For example, it can prioritize assignments that employees self-report as being highly satisfying. The generation AI can also analyze employees' psychological test results and evaluate their levels of satisfaction and stress. For example, it can avoid tasks that are determined to be highly stressful in the psychological test. This makes it possible to analyze the satisfaction and stress that employees feel while working and reflect those emotions in their placement.

[0081] Generative AI can propose career development plans based on employees' skill scores. Generative AI, for example, analyzes employees' skill scores and proposes career development plans. For example, it proposes career paths in fields with high skill scores. Generative AI also proposes career development steps based on employees' skill scores. For example, it proposes promotion steps and the order in which skills should be acquired. Furthermore, generative AI comprehensively analyzes employees' skill scores and proposes career development plans. This makes it possible to propose career development plans based on employees' skill scores.

[0082] The generation AI can suggest a transfer to a different department or project based on an employee's skill score. For example, the generation AI analyzes an employee's skill score and suggests a transfer to a different department or project. For example, it suggests a transfer to a department in a field with a high skill score. The generation AI also evaluates the suitability of the transfer based on the employee's skill score. For example, it compares the work content of the transfer destination with the employee's skill score to evaluate suitability. Furthermore, the generation AI comprehensively analyzes the employee's skill score and suggests a transfer to a different department or project. This makes it possible to suggest a transfer to a different department or project based on the employee's skill score.

[0083] The generation AI can use its emotion estimation function to analyze the motivation employees feel while working and provide feedback to increase their motivation. For example, the generation AI can use its emotion estimation function to analyze the motivation employees feel while working and provide feedback based on that data. For example, if motivation is low, it can display an encouraging message. The generation AI can also analyze employees' self-reported data and evaluate their motivation level. For example, if an employee self-reports that their motivation is low, it can display an encouraging message. The generation AI can also analyze employees' psychological test results and evaluate their motivation level. For example, if the psychological test determines that their motivation is low, it can display an encouraging message. This makes it possible to analyze employees' motivation and provide feedback.

[0084] The generative AI can propose individual training programs based on an employee's skill score. For example, the generative AI analyzes an employee's skill score and proposes individual training programs. For example, it proposes training programs in areas where the skill score is low. The generative AI also determines training priorities based on the employee's skill score. For example, it provides training in areas where the skill score is low preferentially. Furthermore, the generative AI comprehensively analyzes an employee's skill score and proposes individual training programs. This makes it possible to propose individual training programs based on the employee's skill score.

[0085] Generative AI can identify skill gaps based on an employee's skill scores and provide training to fill those gaps. For example, generative AI analyzes an employee's skill scores and identifies skill gaps. For example, if a specific skill is lacking, it suggests training to strengthen that skill. Generative AI also provides training programs to fill skill gaps based on an employee's skill scores. For example, it provides training in areas where the skill score is low. Furthermore, generative AI comprehensively analyzes an employee's skill scores, identifies skill gaps, and provides training to fill those gaps. This makes it possible to identify an employee's skill gaps and provide training to fill those gaps.

[0086] The generation AI can use its emotion estimation function to analyze the satisfaction or stress that employees feel during training and reflect those emotions in the training program. For example, the generation AI can use its emotion estimation function to analyze the satisfaction or stress that employees feel during training and reflect that data in the training program. For example, it can prioritize providing training content that results in high satisfaction. The generation AI can also analyze employees' self-reported data and evaluate their satisfaction and stress levels. For example, it can prioritize providing training content that employees self-reported as highly satisfying. The generation AI can also analyze employees' psychological test results and evaluate their satisfaction and stress levels. For example, it can avoid training content that is determined to be highly stressful in the psychological test. This makes it possible to analyze the satisfaction and stress that employees feel during training and reflect those emotions in the training program.

[0087] The generative AI can suggest different tasks or projects for employees to participate in based on their skill scores. For example, the generative AI can analyze an employee's skill score and suggest different tasks or projects for them to participate in. For example, it can suggest participation in projects in fields where the employee has a high skill score. The generative AI can also evaluate the employee's suitability for a task based on their skill score. For example, it can determine employee placement based on task suitability. Furthermore, the generative AI can comprehensively analyze an employee's skill score and suggest different tasks or projects for them to participate in. This makes it possible to suggest different tasks or projects for employees to participate in based on their skill scores.

[0088] Generative AI can propose an in-house mentoring system based on employees' skill scores and promote the sharing of skills. For example, generative AI can analyze employees' skill scores and propose an in-house mentoring system. For example, it can appoint employees with high skill scores as mentors and promote the sharing of skills. Generative AI can also propose how to implement a mentoring system based on employees' skill scores. For example, it can suggest criteria for selecting mentors and the frequency of mentoring. Furthermore, generative AI can comprehensively analyze employees' skill scores and propose an in-house mentoring system and promote the sharing of skills. This makes it possible to propose an in-house mentoring system based on employees' skill scores and promote the sharing of skills.

[0089] The generation AI can use its emotion estimation function to analyze the motivation employees feel during training and provide feedback to increase their motivation. For example, the generation AI can use its emotion estimation function to analyze the motivation employees feel during training and provide feedback based on that data. For example, if motivation is low, it can display an encouraging message. The generation AI can also analyze employees' self-reported data and evaluate their motivation level. For example, if an employee self-reports that their motivation is low, it can display an encouraging message. The generation AI can also analyze employees' psychological test results and evaluate their motivation level. For example, if the psychological test determines that their motivation is low, it can display an encouraging message. This makes it possible to analyze the motivation employees feel during training and provide feedback.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The work skill quantification system not only quantifies a user's skills, but can also suggest new career paths based on the user's skill set. For example, it can suggest the next skills a user should learn or the qualifications they should obtain based on their current skill set. It can also analyze the user's skill set and suggest future career path steps. It can also comprehensively analyze the user's skill set and evaluate the possibility of changing jobs to a different industry or occupation. This allows users to find a new career path that utilizes their skills.

[0092] The work skill quantification system not only quantifies a user's skills, but also suggests appropriate training programs based on the user's skill set. For example, it suggests specific training programs for areas in which the user has a low skill score. It also analyzes the user's skill set, identifies skill gaps, and provides training to fill those gaps. It also comprehensively analyzes the user's skill set and suggests individual training programs. This allows the user to receive appropriate training to improve their skills.

[0093] The work skill quantification system not only quantifies a user's skills, but also evaluates the applicability of the user's skills to different industries based on the user's skill set. For example, it analyzes the user's skill set and evaluates its applicability in different industries. It also analyzes the user's skill set and suggests career paths in different industries. It also comprehensively analyzes the user's skill set and evaluates the possibility of changing jobs to different industries. This allows users to build careers in different industries by utilizing their skills.

[0094] The work skill quantification system not only quantifies a user's skills, but can also suggest appropriate project participation based on the user's skill set. For example, it analyzes a user's skill set and suggests appropriate project participation. It also analyzes a user's skill set and evaluates project suitability. It also comprehensively analyzes a user's skill set and suggests participation in different projects. This allows users to participate in appropriate projects that utilize their skills.

[0095] The work skill quantification system not only quantifies a user's skills, but can also suggest appropriate mentoring programs based on the user's skill set. For example, it analyzes the user's skill set and suggests appropriate mentors. It also analyzes the user's skill set and suggests how to implement a mentoring program. It also analyzes the user's skill set comprehensively and suggests mentoring programs to promote skill sharing. This allows users to utilize their own skills and share them with other employees.

[0096] The work skill quantification system uses an emotion estimation function to analyze the motivation felt by the user when entering skills and can provide feedback based on that data. For example, if motivation is low, an encouraging message can be displayed. The system also analyzes the user's self-reported data to evaluate their motivation level. Furthermore, it analyzes the user's psychological test results to evaluate their motivation level. This allows the system to analyze the user's motivation and provide feedback.

[0097] The work skill quantification system uses an emotion estimation function to analyze the stress and concentration levels felt by the user when entering skills, and can adjust the score based on that data. For example, if stress is high, the score is revised downward, and if concentration is high, the score is revised upward. The system also analyzes the user's self-reported data to evaluate the level of stress and concentration. Furthermore, it analyzes the user's psychological test results to evaluate the level of stress and concentration. This allows the system to analyze the user's stress and concentration levels and reflect that data in the score.

[0098] The work skill quantification system uses an emotion estimation function to analyze the confidence or anxiety felt by the user when entering skills, and can adjust the score based on that data. For example, if confidence is high, the score is revised upward, and if anxiety is high, the score is revised downward. The system also analyzes the user's self-reported data to evaluate their confidence and anxiety levels. Furthermore, it analyzes the user's psychological test results to evaluate their confidence and anxiety levels. This allows the system to analyze the user's confidence and anxiety and reflect that data in the score.

[0099] The work skill quantification system uses an emotion estimation function to analyze the expectations and anxieties felt by users when entering their skills, and can adjust matching based on this data. For example, if expectations are high, it prioritizes positive matching, and if anxiety is high, it performs more cautious matching. It also analyzes users' self-reported data to evaluate their levels of expectations and anxiety. It also analyzes users' psychological test results to evaluate their levels of expectations and anxiety. This allows it to analyze users' expectations and anxieties and reflect that data in matching.

[0100] The work skill quantification system uses its emotion estimation function to analyze the satisfaction and stress felt by the user when entering skills, and can provide feedback based on that data. For example, if satisfaction is high, positive feedback is provided, and if stress is high, advice on how to relax is provided. The system also analyzes the user's self-reported data to evaluate their satisfaction and stress levels. Furthermore, it analyzes the user's psychological test results to evaluate their satisfaction and stress levels. This allows the system to analyze the user's satisfaction and stress levels and provide feedback based on that data.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The generative AI quantifies the user's work skills. For example, it analyzes information about work skills entered by the user and quantifies those skills. If the user enters skills such as "programming skills" or "project management ability," it generates a test related to that skill and provides it to the user. The generative AI then generates the test based on prompts containing instructions on what the user wants the generative AI to do. Step 2: The skill quantification unit provides the skills quantified by the generation AI as a score. For example, the user takes a test generated by the generation AI, and the generation AI analyzes the results to provide a score. The skill quantification unit also provides a score that allows the user's skill level to be clearly understood. Step 3: The Matching Department uses the scores provided by the Skill Quantification Department to match job seekers with companies. For example, it compares the skill sets required by companies with the skill scores of job seekers to make the optimal match. The Matching Department also places the right people in the right positions to improve the job-changing matching rate. Furthermore, by understanding the skill levels of employees and making appropriate placements, it is expected that turnover rates will improve.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that quantifies users' work skills using generative AI, a skill quantification unit that provides the skills quantified by the generation AI as a score; a matching unit that matches job seekers with companies using the scores provided by the skill quantification unit. A system characterized by:

2. The generated AI is Analyze the confidence or anxiety that users feel when entering skills, and reflect that emotion in the skill score.

2. The system of claim 1.

3. The generated AI is Compare skill sets across industries and assess the applicability of your skills to other industries 2. The system of claim 1.

4. The generated AI is Analyze the stress or concentration the user feels during the test and reflect that emotion in the score.

2. The system of claim 1.

5. The generated AI is Analyzing the expectations or anxieties that users have when looking for a new job and reflecting those emotions in matching 2. The system of claim 1.

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