System

Abstract

An object of the system according to the embodiment is to objectively evaluate the skill, aptitude, and personality of a candidate and to quickly find a person most suitable for the needs of a company.SOLUTION: In one embodiment, a system comprises a generation AI, a biometric data collector, an evaluator, and a matcher. The generation AI performs a skill test and problem solving of the candidate. The biometric data collection unit collects biometric data in the course of a skill test or problem solving by the generated AI. The evaluation part analyzes the biometrics data collected by the biometrics data collection part to evaluate the skill, aptitude, and personality of the candidate. The matching unit matches the evaluation result of the candidate evaluated by the evaluation unit with the needs of the company.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 makes it difficult to objectively and comprehensively evaluate candidates' skills, aptitude, and personality, making it difficult to quickly find the talent that best suits a company's needs.

[0005] The system according to the embodiment aims to objectively evaluate the skills, aptitude, and personality of candidates and quickly find the personnel who best suit the needs of a company. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, a biometric data collection unit, an evaluation unit, and a matching unit. The generation AI conducts skill tests and problem-solving for candidates. The biometric data collection unit collects biometric data during the skill tests and problem-solving process by the generation AI. The evaluation unit analyzes the biometric data collected by the biometric data collection unit to evaluate the skills, aptitude, and personality of candidates. The matching unit matches the evaluation results of candidates evaluated by the evaluation unit with the needs of companies. [Effects of the Invention]

[0007] The system according to the embodiment objectively evaluates the skills, aptitude, and personality of candidates, enabling companies to quickly find the best candidates to meet their needs. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A talent recruitment platform according to an embodiment of the present invention is a system that combines generative AI and biometric technology to objectively evaluate candidates' skills, aptitude, and personality, and quickly and effectively recruit talent that best suits a company's needs. This allows the talent recruitment platform to comprehensively and unbiasedly evaluate candidates' skills, aptitude, and personality, and quickly and effectively recruit talent that best suits a company's needs.

[0029] A recruitment platform according to an embodiment includes a generation AI, a biometric data collection unit, an evaluation unit, and a matching unit. The generation AI conducts skill tests and problem-solving for candidates. For example, the generation AI assigns candidates a task to create a specific program and evaluates the accuracy and efficiency of the code. The generation AI can also assign scenario-based tasks to evaluate the candidate's problem-solving ability. The generation AI can also provide real-time feedback on the candidate's answers and point out areas for improvement. The biometric data collection unit collects biometric data during the skill tests and problem-solving process using the generation AI. For example, the biometric data collection unit collects the candidate's heart rate, facial expressions, voice tone, etc. The biometric data collection unit can also analyze heart rate fluctuations and facial expressions to evaluate the candidate's stress level and concentration level. The biometric data collection unit can also evaluate the candidate's health and fitness level and predict their long-term performance. The evaluation unit analyzes the biometric data collected by the biometric data collection unit to evaluate the candidate's skills, aptitude, and personality. For example, the evaluation unit evaluates the candidate's stress tolerance and concentration. The evaluation unit can also evaluate the candidate's emotional state to evaluate their emotional stability and motivational sustainability. The evaluation unit can also analyze the candidate's learning history and grades to evaluate their learning ability and growth potential. The matching unit matches the evaluation results of the candidate evaluated by the evaluation unit with the company's needs. For example, the matching unit analyzes the company's job information and lists candidates who meet the requirements. The matching unit can also analyze the company's past recruitment data and optimize the matching algorithm based on successful recruitment patterns. The matching unit can also evaluate the candidate's evaluation results based on the company's long-term strategy and goals and predict their future contribution. As a result, the recruitment platform according to the embodiment can comprehensively and bias-freely evaluate the candidate's skills, aptitude, and personality, enabling the company to quickly and effectively recruit talent best suited to its needs.For example, companies can select the best candidates based on their long-term potential, making the recruitment process more efficient.

[0030] Generative AI can analyze a candidate's past projects and achievements and provide customized tasks based on that. For example, generative AI can analyze a candidate's past project data and provide tasks based on similar projects. For example, it can analyze the code of an application the candidate developed in the past and provide a task to create a new application with similar functions. Generative AI can also customize tasks to strengthen specific skill sets based on the candidate's performance data. For example, if a candidate has previously worked on a data analysis project, it can provide more advanced data analysis tasks. Generative AI can also analyze a candidate's resume and portfolio and provide individually optimized tasks based on that. For example, if a candidate has worked on many design projects, it can provide tasks to evaluate their design skills. This allows for more accurate skill assessment by providing appropriate tasks based on the candidate's past achievements.

[0031] Generative AI can provide real-time feedback on candidates' answers and point out areas for improvement. For example, generative AI can analyze a candidate's code in real time and point out errors and inefficiencies. For example, it can detect bugs in a program written by the candidate and suggest ways to fix them. Generative AI can also provide immediate feedback on candidates' answers, indicating specific areas for improvement. For example, it can suggest more effective ways to structure and write the content of a report submitted by the candidate. Generative AI can also monitor the candidate's answering process in real time and provide hints and advice at the appropriate time. For example, if a candidate gets stuck, it can provide advice on the next step. This allows candidates to receive feedback in real time, quickly identify areas for improvement, and improve their skills.

[0032] Based on a candidate's skill test results, generative AI can perform comparative analysis with other candidates to assess their relative skill level. For example, generative AI stores the candidate's skill test results in a database and compares them with the results of other candidates. For example, it evaluates the relative skill level based on the time and accuracy of answering the same task. It also statistically analyzes the candidate's skill test results to clarify the performance difference with other candidates. For example, it identifies the candidate's strengths and weaknesses by comparing them with the top 10% of candidates. Generative AI also compares the candidate's skill test results with industry standards and benchmarks. For example, it evaluates whether the candidate exceeds the industry average in a specific skill set. This makes it possible to compare the candidate's skill level with other candidates and perform a relative evaluation.

[0033] Generative AI can customize the content of skill tests for each industry and conduct evaluations specialized for that specific industry. For example, generative AI creates skill tests specialized for a specific industry and administers them to candidates. For example, it provides programming tasks for the IT industry and data analysis tasks for the financial industry. The content of skill tests is also customized according to the needs of each industry. For example, candidates for the medical industry are given tasks related to analyzing medical data and developing diagnostic support systems. Generative AI also provides skill tests that reflect the latest trends and technological developments in the industry. For example, in industries where AI technology is advancing, it provides tasks related to building and analyzing AI models. This enables more appropriate skill evaluations by conducting evaluations specialized for each industry.

[0034] The biometric data collection unit can evaluate a candidate's health condition and fitness level and predict their long-term performance. The biometric data collection unit analyzes biometric data such as the candidate's heart rate and blood pressure to evaluate their health condition. For example, the candidate's stress tolerance and concentration are evaluated based on the stability of their heart rate. The biometric data collection unit also analyzes the candidate's exercise data and physical activity level to evaluate their fitness level. For example, the candidate's physical strength and endurance are evaluated based on the candidate's number of steps and exercise time. The biometric data can also be used to predict the candidate's long-term performance. For example, a candidate with a high health condition and fitness level is evaluated as having the potential to perform well in the long term. This makes it possible to predict a candidate's long-term performance by evaluating their health condition and fitness level.

[0035] The biometric data collection unit can compare the collected biometric data with the candidate's past data to analyze fluctuations in stress tolerance and concentration. The biometric data collection unit, for example, compares the candidate's past biometric data with current data to analyze fluctuations in stress tolerance. For example, if there is less fluctuation in heart rate compared to past data, the candidate is evaluated as having high stress tolerance. In addition, past data is compared with current data to analyze fluctuations in the candidate's concentration. For example, if the candidate's concentration is sustained compared to past data, the candidate is evaluated as having high concentration. In addition, based on the collected biometric data, fluctuations in the candidate's stress tolerance and concentration are analyzed in detail. For example, responses to specific tasks are compared to evaluate fluctuations in stress tolerance and concentration. In this way, fluctuations in stress tolerance and concentration can be analyzed in detail by comparing with the candidate's past data.

[0036] The biometric data collection unit analyzes the biometric data in association with the candidate's work environment and lifestyle habits, and can propose the optimal work environment. For example, the biometric data collection unit uses a generation AI to analyze the candidate's biometric data in association with the work environment and propose the optimal work environment. For example, it identifies an environment in which the candidate has a low stress level and proposes that environment. It also analyzes the candidate's lifestyle data in association with the biometric data and proposes the optimal work environment. For example, it identifies an environment in which the candidate can relax and proposes that environment. It also makes proposals to optimize the candidate's work environment based on the biometric data. For example, it identifies an environment in which the candidate can easily concentrate and proposes that environment. In this way, it is possible to propose the optimal work environment by analyzing the biometric data in association with the candidate's work environment and lifestyle habits.

[0037] The biometric data collection unit can integrate the collected biometric data with the data of other candidates to evaluate collective health and stress levels. For example, the generative AI in the biometric data collection unit integrates the biometric data of multiple candidates to evaluate collective health. For example, it analyzes the average heart rate and stress levels of all candidates. It also integrates this data with the data of other candidates to evaluate collective stress levels. For example, it compares the stress responses of all candidates to a specific task to evaluate collective stress levels. It also performs detailed analysis of collective health and stress levels based on the collected biometric data. For example, it evaluates trends in health and stress levels based on the data of all candidates. This makes it possible to evaluate collective health and stress levels by integrating the data of multiple candidates.

[0038] The matching unit can analyze a company's past recruitment data and optimize the matching algorithm based on successful recruitment patterns. In the matching unit, for example, the generation AI analyzes a company's past recruitment data and identifies successful recruitment patterns. For example, it analyzes the skill sets and aptitudes of candidates hired in the past and extracts the factors for success. The generation AI also optimizes the matching algorithm based on the past recruitment data. For example, it develops an algorithm that reflects successful recruitment patterns to improve the accuracy of matching candidates and companies. The matching algorithm can also be analyzed to improve the accuracy of matching candidates and companies based on successful recruitment patterns. For example, it can develop an algorithm that preferentially recommends candidates with specific skill sets and aptitudes. This allows the accuracy of matching candidates and companies to be improved by analyzing a company's past recruitment data and optimizing the matching algorithm based on successful recruitment patterns.

[0039] The matching department can customize the candidate evaluation results based on the company's team dynamics and culture to achieve optimal matching. For example, the generation AI analyzes the company's team dynamics and culture and customizes the candidate evaluation results based on that. For example, it may recommend candidates who match the team's communication style and values. The candidate evaluation results can also be customized based on the company's culture to achieve optimal matching. For example, it may preferentially recommend candidates who sympathize with the company's mission and vision. The generation AI can also analyze the company's team dynamics and achieve optimal matching based on the candidate evaluation results. For example, it may recommend candidates who match the team's skill balance and division of roles. In this way, optimal matching can be achieved by customizing the candidate evaluation results based on the company's team dynamics and culture.

[0040] The matching unit is able to update a company's needs in real time and match candidates based on the latest job information. For example, the generation AI collects a company's job information in real time and matches candidates based on the latest needs. For example, when new job information is added, the candidate list is instantly updated. A system is also built that updates a company's needs in real time and matches candidates based on the latest job information. For example, if a company's requirements change, the candidate list is automatically adjusted. The generation AI also analyzes a company's needs in real time and recommends the most suitable candidates based on the latest job information. For example, it instantly lists candidates suitable for a company's new project. This makes it possible to update a company's needs in real time and match candidates based on the latest job information, enabling fast and appropriate recruitment.

[0041] The matching unit evaluates the candidate's evaluation results based on the company's long-term strategy and goals, and can predict their future contribution. For example, the matching unit uses a generation AI to analyze a company's long-term strategy and goals, and customize the candidate's evaluation results based on them. For example, it may preferentially recommend candidates who can contribute to the company's growth strategy. It also evaluates the candidate's evaluation results based on the company's long-term goals, and predicts their future contribution. For example, it evaluates whether the candidate fits the company's future needs based on their growth potential. It also analyzes the company's long-term strategy, and predicts their future contribution based on the candidate's evaluation results. For example, it evaluates whether the candidate can contribute to the company's new business. In this way, it is possible to predict their future contribution by evaluating the candidate's evaluation results based on the company's long-term strategy and goals.

[0042] The evaluation unit can analyze the candidate's learning history and grades to evaluate their learning ability and growth potential. In the evaluation unit, for example, the generation AI analyzes the candidate's learning history to evaluate their learning ability. For example, it evaluates their learning ability based on the courses the candidate has taken in the past and the qualifications they have obtained. The generation AI also evaluates their growth potential based on the candidate's grade data. For example, it analyzes the candidate's trend toward improvement in grades and evaluates their future growth potential. The generation AI also analyzes the candidate's learning history and grades in detail to evaluate their learning ability and growth potential. For example, it evaluates the speed and efficiency at which the candidate learns new skills. In this way, it is possible to evaluate a candidate's learning ability and growth potential by analyzing their learning history and grades.

[0043] The evaluation unit can analyze a candidate's past career path and develop an algorithm that predicts their future career path. For example, the evaluation unit uses a generation AI to analyze a candidate's past career path and develop an algorithm that predicts their future career path. For example, the generation AI predicts their future career path based on the candidate's work history and job change history. The generation AI also predicts their future career path based on the candidate's past career data. For example, the generation AI predicts their future career path based on the job content and positions that the candidate has held in the past. The generation AI also analyzes the candidate's career path in detail and develops an algorithm that predicts their future career path. For example, it predicts what positions and duties the candidate is likely to hold in the future. In this way, the generation AI can analyze a candidate's past career path and develop an algorithm that predicts their future career path, thereby predicting the candidate's future career path.

[0044] The evaluation unit evaluates whether a candidate's potential abilities can be transferred to different industries or occupations, and can suggest career diversification. For example, the generative AI analyzes a candidate's skill set and evaluates whether it can be transferred to different industries or occupations. For example, it evaluates whether a candidate with IT skills can be successful in other industries. It also evaluates whether a candidate's potential abilities can be transferred to different industries or occupations based on the candidate's potential, and suggests career diversification. For example, it evaluates whether a candidate's skills can be applied to other occupations. It also analyzes a candidate's skill set in detail and evaluates whether it can be transferred to different industries or occupations. For example, it suggests career paths in other industries or occupations based on the candidate's skills. In this way, it is possible to suggest career diversification by evaluating whether a candidate's potential abilities can be transferred to different industries or occupations.

[0045] The evaluation unit can evaluate the candidate's future potential by linking it to the company's growth strategy and market trends. In the evaluation unit, for example, the generation AI evaluates the candidate's future potential by linking it to the company's growth strategy. For example, it prioritizes the evaluation of candidates who can contribute to the company's growth strategy. It also evaluates the candidate's future potential by linking it to market trends. For example, it evaluates whether the candidate's skills match future market needs. It also evaluates the candidate's future potential by linking it to the company's growth strategy and market trends. For example, it evaluates whether the candidate can contribute to the company's new business in the future. In this way, by evaluating the candidate's future potential by linking it to the company's growth strategy and market trends, it is possible to evaluate candidates who match the company's future needs.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] Generative AI can provide real-time feedback on candidates' answers and point out areas for improvement. For example, it can analyze a candidate's code in real time and point out errors and inefficient parts. It can detect bugs in programs written by candidates and suggest ways to fix them. It can also provide immediate feedback on candidates' answers, indicating specific areas for improvement. It can suggest more effective ways to structure and write the content of reports submitted by candidates. It can also monitor the candidate's answering process in real time and provide hints and advice at the appropriate time. If a candidate gets stuck, it can provide advice showing the next step. This allows candidates to receive feedback in real time, quickly identify areas for improvement, and improve their skills.

[0048] Generative AI can analyze a candidate's past projects and achievements and provide customized tasks based on that. For example, it can analyze a candidate's past project data and provide tasks based on similar projects. It can analyze the code of an application the candidate has previously developed and provide a task to create a new application with similar functionality. It can also customize tasks to strengthen specific skill sets based on the candidate's performance data. If a candidate has previously worked on data analysis projects, it can provide more advanced data analysis tasks. It can also analyze a candidate's resume and portfolio and provide individually optimized tasks based on that. If a candidate has worked on many design projects, it can provide tasks to evaluate their design skills. This allows for more accurate skill assessment by providing appropriate tasks based on the candidate's past achievements.

[0049] Generative AI can evaluate a candidate's relative skill level by performing comparative analysis with other candidates based on their skill test results. For example, the candidate's skill test results can be stored in a database and compared with the results of other candidates. Relative skill level can be evaluated based on the time and accuracy required to answer the same task. Statistical analysis of the candidate's skill test results can also be performed to clarify performance differences with other candidates. The candidate's strengths and weaknesses can be identified by comparing them with the top 10% of candidates. Furthermore, the candidate's skill test results can be compared with industry standards and benchmarks to evaluate whether the candidate exceeds the industry average for a specific skill set. This allows a relative evaluation to be made by comparing the candidate's skill level with other candidates.

[0050] Generative AI can customize the content of skill tests for each industry, allowing for evaluations tailored to specific industries. For example, skill tests tailored to specific industries can be created and administered to candidates. Programming tasks for the IT industry and data analysis tasks for the financial industry can be provided. The content of skill tests can also be customized according to the needs of each industry. Candidates for the medical industry can be given tasks related to analyzing medical data and developing diagnostic support systems. Skill tests that reflect the latest trends and technological developments in the industry are also provided. In industries where AI technology is advancing, tasks related to building and analyzing AI models can be provided. This allows for more appropriate skill evaluations by conducting evaluations tailored to each industry.

[0051] The biometric data collection unit can evaluate a candidate's health condition and fitness level and predict their long-term performance. For example, it analyzes biometric data such as the candidate's heart rate and blood pressure to evaluate their health condition. It evaluates the candidate's stress tolerance and concentration based on the stability of their heart rate. It also analyzes the candidate's exercise data and physical activity level to evaluate their fitness level. It evaluates their physical strength and endurance based on the candidate's number of steps and exercise time. It also predicts the candidate's long-term performance based on the biometric data. Candidates with high health conditions and fitness levels are evaluated as having the potential to perform well in the long term. This makes it possible to predict a candidate's long-term performance by evaluating their health condition and fitness level.

[0052] The biometric data collection unit can compare the collected biometric data with the candidate's past data to analyze fluctuations in stress tolerance and concentration. For example, the candidate's past biometric data is compared with the current data to analyze fluctuations in stress tolerance. If there is less fluctuation in heart rate compared to the past data, the candidate is evaluated as having high stress tolerance. Furthermore, past data is compared with current data to analyze fluctuations in the candidate's concentration. If the candidate's concentration is sustained compared to the past data, the candidate is evaluated as having high concentration. Furthermore, based on the collected biometric data, fluctuations in the candidate's stress tolerance and concentration are analyzed in detail. Responses to specific tasks are compared to evaluate fluctuations in stress tolerance and concentration. This allows for a detailed analysis of fluctuations in stress tolerance and concentration by comparing with the candidate's past data.

[0053] The biometric data collection unit analyzes the biometric data in association with the candidate's work environment and lifestyle habits, and can propose the optimal work environment. For example, the generative AI analyzes the candidate's biometric data in association with the work environment and proposes the optimal work environment. It identifies an environment where the candidate's stress level is low and proposes that environment. It also analyzes the candidate's lifestyle data in association with the biometric data and proposes the optimal work environment. It identifies an environment where the candidate can relax and proposes that environment. Furthermore, it makes proposals to optimize the candidate's work environment based on the biometric data. It identifies an environment where the candidate can easily concentrate and proposes that environment. In this way, by analyzing the data in association with the candidate's work environment and lifestyle habits, it is possible to propose the optimal work environment.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The generative AI conducts skill tests and problem-solving tasks on the candidate. For example, the generative AI assigns the candidate a task to create a specific program and evaluates the accuracy and efficiency of the code. The generative AI can also pose scenario-based tasks to assess the candidate's problem-solving abilities. Furthermore, the generative AI can provide real-time feedback on the candidate's answers and suggest areas for improvement. Step 2: The biometric data collection unit collects biometric data during the skill test and problem-solving process using the generative AI. For example, the biometric data collection unit collects the candidate's heart rate, facial expression, and voice tone. The biometric data collection unit can also analyze heart rate variability and facial expression to assess the candidate's stress level and concentration. It can also evaluate the candidate's health and fitness level and predict their long-term performance. Step 3: The evaluation unit analyzes the biometric data collected by the biometric data collection unit to evaluate the candidate's skills, aptitude, and personality. For example, the evaluation unit evaluates the candidate's stress tolerance and concentration. The evaluation unit can also evaluate the candidate's emotional state and evaluate their emotional stability and motivational sustainability. Furthermore, the evaluation unit can analyze the candidate's learning history and grades to evaluate their learning ability and growth potential. Step 4: The matching department matches the evaluation results of candidates evaluated by the evaluation department with the company's needs. For example, the matching department analyzes a company's job information and lists candidates who meet the requirements. The matching department can also analyze the company's past recruitment data and optimize the matching algorithm based on successful recruitment patterns. Furthermore, the matching department can evaluate the candidate evaluation results based on the company's long-term strategy and goals and predict their future contribution.

[0056] (Example 2) A talent recruitment platform according to an embodiment of the present invention is a system that combines generative AI and biometric technology to objectively evaluate candidates' skills, aptitude, and personality, and quickly and effectively recruit talent that best suits a company's needs. This allows the talent recruitment platform to comprehensively and unbiasedly evaluate candidates' skills, aptitude, and personality, and quickly and effectively recruit talent that best suits a company's needs.

[0057] A recruitment platform according to an embodiment includes a generation AI, a biometric data collection unit, an evaluation unit, and a matching unit. The generation AI conducts skill tests and problem-solving for candidates. For example, the generation AI assigns candidates a task to create a specific program and evaluates the accuracy and efficiency of the code. The generation AI can also assign scenario-based tasks to evaluate the candidate's problem-solving ability. The generation AI can also provide real-time feedback on the candidate's answers and point out areas for improvement. The biometric data collection unit collects biometric data during the skill tests and problem-solving process using the generation AI. For example, the biometric data collection unit collects the candidate's heart rate, facial expressions, voice tone, etc. The biometric data collection unit can also analyze heart rate fluctuations and facial expressions to evaluate the candidate's stress level and concentration level. The biometric data collection unit can also evaluate the candidate's health and fitness level and predict their long-term performance. The evaluation unit analyzes the biometric data collected by the biometric data collection unit to evaluate the candidate's skills, aptitude, and personality. For example, the evaluation unit evaluates the candidate's stress tolerance and concentration. The evaluation unit can also evaluate the candidate's emotional state to evaluate their emotional stability and motivational sustainability. The evaluation unit can also analyze the candidate's learning history and grades to evaluate their learning ability and growth potential. The matching unit matches the evaluation results of the candidate evaluated by the evaluation unit with the company's needs. For example, the matching unit analyzes the company's job information and lists candidates who meet the requirements. The matching unit can also analyze the company's past recruitment data and optimize the matching algorithm based on successful recruitment patterns. The matching unit can also evaluate the candidate's evaluation results based on the company's long-term strategy and goals and predict their future contribution. As a result, the recruitment platform according to the embodiment can comprehensively and bias-freely evaluate the candidate's skills, aptitude, and personality, enabling the company to quickly and effectively recruit talent best suited to its needs.For example, companies can select the best candidates based on their long-term potential, making the recruitment process more efficient.

[0058] Generative AI can analyze a candidate's past projects and achievements and provide customized tasks based on that. For example, generative AI can analyze a candidate's past project data and provide tasks based on similar projects. For example, it can analyze the code of an application the candidate developed in the past and provide a task to create a new application with similar functions. Generative AI can also customize tasks to strengthen specific skill sets based on the candidate's performance data. For example, if a candidate has previously worked on a data analysis project, it can provide more advanced data analysis tasks. Generative AI can also analyze a candidate's resume and portfolio and provide individually optimized tasks based on that. For example, if a candidate has worked on many design projects, it can provide tasks to evaluate their design skills. This allows for more accurate skill assessment by providing appropriate tasks based on the candidate's past achievements.

[0059] Generative AI can provide real-time feedback on candidates' answers and point out areas for improvement. For example, generative AI can analyze a candidate's code in real time and point out errors and inefficiencies. For example, it can detect bugs in a program written by the candidate and suggest ways to fix them. Generative AI can also provide immediate feedback on candidates' answers, indicating specific areas for improvement. For example, it can suggest more effective ways to structure and write the content of a report submitted by the candidate. Generative AI can also monitor the candidate's answering process in real time and provide hints and advice at the appropriate time. For example, if a candidate gets stuck, it can provide advice on the next step. This allows candidates to receive feedback in real time, quickly identify areas for improvement, and improve their skills.

[0060] The generative AI uses emotion estimation to evaluate the candidate's emotional state when working on a task, allowing for evaluations that take into account fluctuations in stress and motivation. For example, the generative AI analyzes the candidate's facial expressions and tone of voice to evaluate the candidate's emotional state when working on a task. For example, if the candidate is feeling stressed, it identifies the cause and provides appropriate support. It also uses emotion estimation to monitor fluctuations in the candidate's motivation in real time and reflect this in the evaluation. For example, if a candidate maintains high motivation, their efforts will be taken into account in the evaluation. The generative AI also adjusts the difficulty of the task, taking into account the candidate's emotional state. For example, if a candidate is feeling excessively stressed, it will lower the difficulty to provide an appropriate evaluation. This allows for more accurate skill evaluations by taking the candidate's emotional state into account in the evaluation.

[0061] Based on a candidate's skill test results, generative AI can perform comparative analysis with other candidates to assess their relative skill level. For example, generative AI stores the candidate's skill test results in a database and compares them with the results of other candidates. For example, it evaluates the relative skill level based on the time and accuracy of answering the same task. It also statistically analyzes the candidate's skill test results to clarify the performance difference with other candidates. For example, it identifies the candidate's strengths and weaknesses by comparing them with the top 10% of candidates. Generative AI also compares the candidate's skill test results with industry standards and benchmarks. For example, it evaluates whether the candidate exceeds the industry average in a specific skill set. This makes it possible to compare the candidate's skill level with other candidates and perform a relative evaluation.

[0062] Generative AI can customize the content of skill tests for each industry and conduct evaluations specialized for that specific industry. For example, generative AI creates skill tests specialized for a specific industry and administers them to candidates. For example, it provides programming tasks for the IT industry and data analysis tasks for the financial industry. The content of skill tests is also customized according to the needs of each industry. For example, candidates for the medical industry are given tasks related to analyzing medical data and developing diagnostic support systems. Generative AI also provides skill tests that reflect the latest trends and technological developments in the industry. For example, in industries where AI technology is advancing, it provides tasks related to building and analyzing AI models. This enables more appropriate skill evaluations by conducting evaluations specialized for each industry.

[0063] Using its emotion estimation function, the generative AI can monitor the emotional state of candidates in real time as they work on tasks and provide advice to elicit positive emotions. For example, the generative AI can analyze the candidate's facial expressions and tone of voice in real time and provide advice to elicit positive emotions. For example, if the candidate is nervous, it can suggest breathing techniques to help them relax. The generative AI can also use its emotion estimation function to monitor the candidate's emotional state and provide feedback to help them maintain positive emotions. For example, it can display an encouraging message when the candidate succeeds. The generative AI can also adjust the progress of tasks according to the candidate's emotional state. For example, if the candidate is feeling positive, it can present more difficult tasks to encourage them to take on the challenge. This makes it possible to improve a candidate's performance by monitoring their emotional state in real time and eliciting positive emotions.

[0064] The biometric data collection unit can evaluate a candidate's health condition and fitness level and predict their long-term performance. The biometric data collection unit analyzes biometric data such as the candidate's heart rate and blood pressure to evaluate their health condition. For example, the candidate's stress tolerance and concentration are evaluated based on the stability of their heart rate. The biometric data collection unit also analyzes the candidate's exercise data and physical activity level to evaluate their fitness level. For example, the candidate's physical strength and endurance are evaluated based on the candidate's number of steps and exercise time. The biometric data can also be used to predict the candidate's long-term performance. For example, a candidate with a high health condition and fitness level is evaluated as having the potential to perform well in the long term. This makes it possible to predict a candidate's long-term performance by evaluating their health condition and fitness level.

[0065] The biometric data collection unit can compare the collected biometric data with the candidate's past data to analyze fluctuations in stress tolerance and concentration. The biometric data collection unit, for example, compares the candidate's past biometric data with current data to analyze fluctuations in stress tolerance. For example, if there is less fluctuation in heart rate compared to past data, the candidate is evaluated as having high stress tolerance. In addition, past data is compared with current data to analyze fluctuations in the candidate's concentration. For example, if the candidate's concentration is sustained compared to past data, the candidate is evaluated as having high concentration. In addition, based on the collected biometric data, fluctuations in the candidate's stress tolerance and concentration are analyzed in detail. For example, responses to specific tasks are compared to evaluate fluctuations in stress tolerance and concentration. In this way, fluctuations in stress tolerance and concentration can be analyzed in detail by comparing with the candidate's past data.

[0066] The biometric data collection unit can use the emotion estimation function to perform a detailed analysis of a candidate's emotional state and evaluate the impact of emotional fluctuations on performance. For example, the biometric data collection unit uses a generative AI to analyze a candidate's facial expressions and tone of voice and perform a detailed evaluation of their emotional state. For example, if a candidate has positive emotions, it evaluates that their performance is likely to improve. The emotion estimation function also monitors a candidate's emotional fluctuations in real time and evaluates their impact. For example, if a candidate has negative emotions, it evaluates that their performance is likely to decline. The biometric data collection unit can also perform a detailed analysis of a candidate's emotional state and evaluate the impact of emotional fluctuations on performance. For example, if a candidate is feeling stressed, it evaluates that their performance is likely to decline. This makes it possible to evaluate the impact of emotional fluctuations on performance by performing a detailed analysis of a candidate's emotional state.

[0067] The biometric data collection unit analyzes the biometric data in association with the candidate's work environment and lifestyle habits, and can propose the optimal work environment. For example, the biometric data collection unit uses a generation AI to analyze the candidate's biometric data in association with the work environment and propose the optimal work environment. For example, it identifies an environment in which the candidate has a low stress level and proposes that environment. It also analyzes the candidate's lifestyle data in association with the biometric data and proposes the optimal work environment. For example, it identifies an environment in which the candidate can relax and proposes that environment. It also makes proposals to optimize the candidate's work environment based on the biometric data. For example, it identifies an environment in which the candidate can easily concentrate and proposes that environment. In this way, it is possible to propose the optimal work environment by analyzing the biometric data in association with the candidate's work environment and lifestyle habits.

[0068] The biometric data collection unit can integrate the collected biometric data with the data of other candidates to evaluate collective health and stress levels. For example, the generative AI in the biometric data collection unit integrates the biometric data of multiple candidates to evaluate collective health. For example, it analyzes the average heart rate and stress levels of all candidates. It also integrates this data with the data of other candidates to evaluate collective stress levels. For example, it compares the stress responses of all candidates to a specific task to evaluate collective stress levels. It also performs detailed analysis of collective health and stress levels based on the collected biometric data. For example, it evaluates trends in health and stress levels based on the data of all candidates. This makes it possible to evaluate collective health and stress levels by integrating the data of multiple candidates.

[0069] The biometric data collection unit can use the emotion estimation function to monitor the candidate's emotional state in real time and suggest relaxation techniques to reduce stress. For example, the biometric data collection unit uses a generative AI to monitor the candidate's emotional state in real time and suggest relaxation techniques to reduce stress. For example, if the candidate is feeling stressed, it can suggest deep breathing or meditation. The emotion estimation function can also be used to analyze the candidate's emotional state and suggest customized relaxation techniques. For example, it can provide music or environmental sounds that help the candidate relax. The biometric data collection unit can also monitor the candidate's emotional state in real time and suggest specific actions to reduce stress. For example, if the candidate is nervous, it can suggest taking a short break. This makes it possible to improve the candidate's performance by monitoring the candidate's emotional state in real time and suggesting relaxation techniques to reduce stress.

[0070] The matching unit can analyze a company's past recruitment data and optimize the matching algorithm based on successful recruitment patterns. In the matching unit, for example, the generation AI analyzes a company's past recruitment data and identifies successful recruitment patterns. For example, it analyzes the skill sets and aptitudes of candidates hired in the past and extracts the factors for success. The generation AI also optimizes the matching algorithm based on the past recruitment data. For example, it develops an algorithm that reflects successful recruitment patterns to improve the accuracy of matching candidates and companies. The matching algorithm can also be analyzed to improve the accuracy of matching candidates and companies based on successful recruitment patterns. For example, it can develop an algorithm that preferentially recommends candidates with specific skill sets and aptitudes. This allows the accuracy of matching candidates and companies to be improved by analyzing a company's past recruitment data and optimizing the matching algorithm based on successful recruitment patterns.

[0071] The matching department can customize the candidate evaluation results based on the company's team dynamics and culture to achieve optimal matching. For example, the generation AI analyzes the company's team dynamics and culture and customizes the candidate evaluation results based on that. For example, it may recommend candidates who match the team's communication style and values. The candidate evaluation results can also be customized based on the company's culture to achieve optimal matching. For example, it may preferentially recommend candidates who sympathize with the company's mission and vision. The generation AI can also analyze the company's team dynamics and achieve optimal matching based on the candidate evaluation results. For example, it may recommend candidates who match the team's skill balance and division of roles. In this way, optimal matching can be achieved by customizing the candidate evaluation results based on the company's team dynamics and culture.

[0072] The matching unit uses an emotion estimation function to evaluate the emotional compatibility between candidates and companies and can recommend candidates who are easy to empathize with emotionally. For example, the matching unit uses a generative AI to analyze the emotional state of candidates and evaluate emotional compatibility that matches the company's culture and team dynamics. For example, it evaluates whether the candidate empathizes with the company's values. It also uses the emotion estimation function to evaluate the emotional compatibility between candidates and companies and recommend candidates who are easy to empathize with. For example, if a candidate empathizes with the company's mission, it will preferentially recommend that candidate. It also analyzes the candidate's emotional state and evaluates emotional compatibility that matches the company's team dynamics. For example, it evaluates whether the candidate can adapt to the team's communication style. In this way, by evaluating the emotional compatibility between candidates and companies, it is possible to recommend candidates who are easy to empathize with emotionally.

[0073] The matching unit is able to update a company's needs in real time and match candidates based on the latest job information. For example, the generation AI collects a company's job information in real time and matches candidates based on the latest needs. For example, when new job information is added, the candidate list is instantly updated. A system is also built that updates a company's needs in real time and matches candidates based on the latest job information. For example, if a company's requirements change, the candidate list is automatically adjusted. The generation AI also analyzes a company's needs in real time and recommends the most suitable candidates based on the latest job information. For example, it instantly lists candidates suitable for a company's new project. This makes it possible to update a company's needs in real time and match candidates based on the latest job information, enabling fast and appropriate recruitment.

[0074] The matching unit evaluates the candidate's evaluation results based on the company's long-term strategy and goals, and can predict their future contribution. For example, the matching unit uses a generation AI to analyze a company's long-term strategy and goals, and customize the candidate's evaluation results based on them. For example, it may preferentially recommend candidates who can contribute to the company's growth strategy. It also evaluates the candidate's evaluation results based on the company's long-term goals, and predicts their future contribution. For example, it evaluates whether the candidate fits the company's future needs based on their growth potential. It also analyzes the company's long-term strategy, and predicts their future contribution based on the candidate's evaluation results. For example, it evaluates whether the candidate can contribute to the company's new business. In this way, it is possible to predict their future contribution by evaluating the candidate's evaluation results based on the company's long-term strategy and goals.

[0075] The matching unit uses the emotion estimation function to monitor the emotional compatibility between candidates and companies in real time and continuously adjust for optimal matching. For example, the matching unit uses a generation AI to monitor the emotional compatibility between candidates and companies in real time and continuously adjust for optimal matching. For example, if the candidate's emotional state changes, the matching results are updated. The emotion estimation function is also used to analyze the emotional compatibility between candidates and companies in real time and achieve optimal matching. For example, it evaluates whether the candidate can adapt to the company's culture. A system is also built that monitors the emotional compatibility between candidates and companies in real time and continuously adjusts the matching results. For example, if the candidate's emotional state changes, the matching results are updated immediately. This makes it possible to monitor the emotional compatibility between candidates and companies in real time and continuously adjust for optimal matching, thereby achieving better matching.

[0076] The evaluation unit can analyze the candidate's learning history and grades to evaluate their learning ability and growth potential. In the evaluation unit, for example, the generation AI analyzes the candidate's learning history to evaluate their learning ability. For example, it evaluates their learning ability based on the courses the candidate has taken in the past and the qualifications they have obtained. The generation AI also evaluates their growth potential based on the candidate's grade data. For example, it analyzes the candidate's trend toward improvement in grades and evaluates their future growth potential. The generation AI also analyzes the candidate's learning history and grades in detail to evaluate their learning ability and growth potential. For example, it evaluates the speed and efficiency at which the candidate learns new skills. In this way, it is possible to evaluate a candidate's learning ability and growth potential by analyzing their learning history and grades.

[0077] The evaluation unit can analyze a candidate's past career path and develop an algorithm that predicts their future career path. For example, the evaluation unit uses a generation AI to analyze a candidate's past career path and develop an algorithm that predicts their future career path. For example, the generation AI predicts their future career path based on the candidate's work history and job change history. The generation AI also predicts their future career path based on the candidate's past career data. For example, the generation AI predicts their future career path based on the job content and positions that the candidate has held in the past. The generation AI also analyzes the candidate's career path in detail and develops an algorithm that predicts their future career path. For example, it predicts what positions and duties the candidate is likely to hold in the future. In this way, the generation AI can analyze a candidate's past career path and develop an algorithm that predicts their future career path, thereby predicting the candidate's future career path.

[0078] The evaluation unit can use the emotion estimation function to evaluate the candidate's emotional state and assess their emotional stability and motivational sustainability. For example, the evaluation unit uses the generative AI to analyze the candidate's emotional state and assess their emotional stability. For example, it evaluates how stable the candidate is against stress and pressure. It also uses the emotion estimation function to evaluate the candidate's motivational sustainability. For example, it evaluates whether the candidate can maintain high motivation over a long period of time. It also analyzes the candidate's emotional state in detail to evaluate their emotional stability and motivational sustainability. For example, it evaluates how well the candidate can maintain their motivation when faced with difficult situations. In this way, it is possible to evaluate the candidate's emotional state and assess their emotional stability and motivational sustainability.

[0079] The evaluation unit evaluates whether a candidate's potential abilities can be transferred to different industries or occupations, and can suggest career diversification. For example, the generative AI analyzes a candidate's skill set and evaluates whether it can be transferred to different industries or occupations. For example, it evaluates whether a candidate with IT skills can be successful in other industries. It also evaluates whether a candidate's potential abilities can be transferred to different industries or occupations based on the candidate's potential, and suggests career diversification. For example, it evaluates whether a candidate's skills can be applied to other occupations. It also analyzes a candidate's skill set in detail and evaluates whether it can be transferred to different industries or occupations. For example, it suggests career paths in other industries or occupations based on the candidate's skills. In this way, it is possible to suggest career diversification by evaluating whether a candidate's potential abilities can be transferred to different industries or occupations.

[0080] The evaluation unit can evaluate the candidate's future potential by linking it to the company's growth strategy and market trends. In the evaluation unit, for example, the generation AI evaluates the candidate's future potential by linking it to the company's growth strategy. For example, it prioritizes the evaluation of candidates who can contribute to the company's growth strategy. It also evaluates the candidate's future potential by linking it to market trends. For example, it evaluates whether the candidate's skills match future market needs. It also evaluates the candidate's future potential by linking it to the company's growth strategy and market trends. For example, it evaluates whether the candidate can contribute to the company's new business in the future. In this way, by evaluating the candidate's future potential by linking it to the company's growth strategy and market trends, it is possible to evaluate candidates who match the company's future needs.

[0081] The evaluation unit can use the emotion estimation function to monitor the candidate's emotional state in real time and predict their future emotional stability. For example, the evaluation unit uses a generative AI to monitor the candidate's emotional state in real time and predict their future emotional stability. For example, it evaluates how stable the candidate is against stress. The evaluation unit also uses the emotion estimation function to analyze the candidate's emotional state and predict their future emotional stability. For example, it evaluates whether the candidate can maintain high emotional stability over the long term. The evaluation unit also builds a system that monitors the candidate's emotional state in real time and predicts their future emotional stability. For example, it evaluates how emotionally stable the candidate is when faced with a difficult situation. This makes it possible to evaluate a candidate's long-term performance by monitoring their emotional state in real time and predicting their future emotional stability.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] Generative AI can provide real-time feedback on candidates' answers and point out areas for improvement. For example, it can analyze a candidate's code in real time and point out errors and inefficient parts. It can detect bugs in programs written by candidates and suggest ways to fix them. It can also provide immediate feedback on candidates' answers, indicating specific areas for improvement. It can suggest more effective ways to structure and write the content of reports submitted by candidates. It can also monitor the candidate's answering process in real time and provide hints and advice at the appropriate time. If a candidate gets stuck, it can provide advice showing the next step. This allows candidates to receive feedback in real time, quickly identify areas for improvement, and improve their skills.

[0084] Generative AI can analyze a candidate's past projects and achievements and provide customized tasks based on that. For example, it can analyze a candidate's past project data and provide tasks based on similar projects. It can analyze the code of an application the candidate has previously developed and provide a task to create a new application with similar functionality. It can also customize tasks to strengthen specific skill sets based on the candidate's performance data. If a candidate has previously worked on data analysis projects, it can provide more advanced data analysis tasks. It can also analyze a candidate's resume and portfolio and provide individually optimized tasks based on that. If a candidate has worked on many design projects, it can provide tasks to evaluate their design skills. This allows for more accurate skill assessment by providing appropriate tasks based on the candidate's past achievements.

[0085] The generative AI uses emotion estimation to evaluate the emotional state of candidates as they work on tasks, and can conduct evaluations that take into account fluctuations in stress and motivation. For example, it analyzes the candidate's facial expressions and tone of voice to evaluate their emotional state as they work on tasks. If the candidate is feeling stressed, it identifies the cause and provides appropriate support. It also uses emotion estimation to monitor fluctuations in the candidate's motivation in real time and reflect this in the evaluation. If the candidate maintains high motivation, their efforts are taken into account in the evaluation. Furthermore, the generative AI adjusts the difficulty of the tasks, taking into account the candidate's emotional state. If the candidate is feeling excessively stressed, it lowers the difficulty to provide an appropriate evaluation. This allows for more accurate skill evaluations by conducting evaluations that take into account the candidate's emotional state.

[0086] Generative AI can evaluate a candidate's relative skill level by performing comparative analysis with other candidates based on their skill test results. For example, the candidate's skill test results can be stored in a database and compared with the results of other candidates. Relative skill level can be evaluated based on the time and accuracy required to answer the same task. Statistical analysis of the candidate's skill test results can also be performed to clarify performance differences with other candidates. The candidate's strengths and weaknesses can be identified by comparing them with the top 10% of candidates. Furthermore, the candidate's skill test results can be compared with industry standards and benchmarks to evaluate whether the candidate exceeds the industry average for a specific skill set. This allows a relative evaluation to be made by comparing the candidate's skill level with other candidates.

[0087] Generative AI can customize the content of skill tests for each industry, allowing for evaluations tailored to specific industries. For example, skill tests tailored to specific industries can be created and administered to candidates. Programming tasks for the IT industry and data analysis tasks for the financial industry can be provided. The content of skill tests can also be customized according to the needs of each industry. Candidates for the medical industry can be given tasks related to analyzing medical data and developing diagnostic support systems. Skill tests that reflect the latest trends and technological developments in the industry are also provided. In industries where AI technology is advancing, tasks related to building and analyzing AI models can be provided. This allows for more appropriate skill evaluations by conducting evaluations tailored to each industry.

[0088] The generative AI can use its emotion estimation function to monitor the emotional state of candidates in real time as they work on tasks and provide advice to elicit positive emotions. For example, it can analyze the candidate's facial expressions and tone of voice in real time and provide advice to elicit positive emotions. If the candidate is nervous, it can suggest breathing techniques to help them relax. It can also use its emotion estimation function to monitor the candidate's emotional state and provide feedback to help them maintain positive emotions. It can also display encouraging messages when the candidate succeeds. Furthermore, the generative AI can adjust the progress of the task according to the candidate's emotional state. If the candidate is feeling positive, it can present more difficult tasks to encourage them to take on the challenge. This makes it possible to improve the candidate's performance by monitoring the candidate's emotional state in real time and eliciting positive emotions.

[0089] The biometric data collection unit can evaluate a candidate's health condition and fitness level and predict their long-term performance. For example, it analyzes biometric data such as the candidate's heart rate and blood pressure to evaluate their health condition. It evaluates the candidate's stress tolerance and concentration based on the stability of their heart rate. It also analyzes the candidate's exercise data and physical activity level to evaluate their fitness level. It evaluates their physical strength and endurance based on the candidate's number of steps and exercise time. It also predicts the candidate's long-term performance based on the biometric data. Candidates with high health conditions and fitness levels are evaluated as having the potential to perform well in the long term. This makes it possible to predict a candidate's long-term performance by evaluating their health condition and fitness level.

[0090] The biometric data collection unit can compare the collected biometric data with the candidate's past data to analyze fluctuations in stress tolerance and concentration. For example, the candidate's past biometric data is compared with the current data to analyze fluctuations in stress tolerance. If there is less fluctuation in heart rate compared to the past data, the candidate is evaluated as having high stress tolerance. Furthermore, past data is compared with current data to analyze fluctuations in the candidate's concentration. If the candidate's concentration is sustained compared to the past data, the candidate is evaluated as having high concentration. Furthermore, based on the collected biometric data, fluctuations in the candidate's stress tolerance and concentration are analyzed in detail. Responses to specific tasks are compared to evaluate fluctuations in stress tolerance and concentration. This allows for a detailed analysis of fluctuations in stress tolerance and concentration by comparing with the candidate's past data.

[0091] The biometric data collection unit can use the emotion estimation function to perform a detailed analysis of a candidate's emotional state and evaluate the impact of emotional fluctuations on performance. For example, the generative AI analyzes a candidate's facial expressions and tone of voice to perform a detailed evaluation of their emotional state. If a candidate has positive emotions, it assesses that their performance is likely to improve. The emotion estimation function also monitors a candidate's emotional fluctuations in real time and evaluates their impact. If a candidate has negative emotions, it assesses that their performance is likely to decline. Furthermore, the candidate's emotional state is analyzed in detail to evaluate the impact of emotional fluctuations on performance. If a candidate is feeling stressed, it assesses that their performance is likely to decline. This makes it possible to evaluate the impact of emotional fluctuations on performance by performing a detailed analysis of a candidate's emotional state.

[0092] The biometric data collection unit analyzes the biometric data in association with the candidate's work environment and lifestyle habits, and can propose the optimal work environment. For example, the generative AI analyzes the candidate's biometric data in association with the work environment and proposes the optimal work environment. It identifies an environment where the candidate's stress level is low and proposes that environment. It also analyzes the candidate's lifestyle data in association with the biometric data and proposes the optimal work environment. It identifies an environment where the candidate can relax and proposes that environment. Furthermore, it makes proposals to optimize the candidate's work environment based on the biometric data. It identifies an environment where the candidate can easily concentrate and proposes that environment. In this way, by analyzing the data in association with the candidate's work environment and lifestyle habits, it is possible to propose the optimal work environment.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The generative AI conducts skill tests and problem-solving tasks on the candidate. For example, the generative AI assigns the candidate a task to create a specific program and evaluates the accuracy and efficiency of the code. The generative AI can also pose scenario-based tasks to assess the candidate's problem-solving abilities. Furthermore, the generative AI can provide real-time feedback on the candidate's answers and suggest areas for improvement. Step 2: The biometric data collection unit collects biometric data during the skill test and problem-solving process using the generative AI. For example, the biometric data collection unit collects the candidate's heart rate, facial expression, and voice tone. The biometric data collection unit can also analyze heart rate variability and facial expression to assess the candidate's stress level and concentration. It can also evaluate the candidate's health and fitness level and predict their long-term performance. Step 3: The evaluation unit analyzes the biometric data collected by the biometric data collection unit to evaluate the candidate's skills, aptitude, and personality. For example, the evaluation unit evaluates the candidate's stress tolerance and concentration. The evaluation unit can also evaluate the candidate's emotional state and evaluate their emotional stability and motivational sustainability. Furthermore, the evaluation unit can analyze the candidate's learning history and grades to evaluate their learning ability and growth potential. Step 4: The matching department matches the evaluation results of candidates evaluated by the evaluation department with the company's needs. For example, the matching department analyzes a company's job information and lists candidates who meet the requirements. The matching department can also analyze the company's past recruitment data and optimize the matching algorithm based on successful recruitment patterns. Furthermore, the matching department can evaluate the candidate evaluation results based on the company's long-term strategy and goals and predict their future contribution.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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, to avoid confusion and 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.

[0161] 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]

[0162] 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. Generative AI and a biometrics data collection unit; an evaluation unit; a matching unit; The generated AI is We conduct skill tests and problem-solving for candidates, The biometric data collection unit Biometric data is collected during skill tests and problem-solving processes using the generative AI. The evaluation unit analyzing the biometric data collected by the biometric data collection unit to evaluate the candidate's skills, aptitude, and personality; The matching unit Matching the evaluation results of candidates evaluated by the evaluation department with the needs of companies A system characterized by:

2. The generated AI is Analyze the candidate's past projects and achievements and provide customized assignments based on that.

2. The system of claim 1.

3. The generated AI is Providing real-time feedback on the candidate's answers and suggesting areas for improvement for the candidate 2. The system of claim 1.

4. The generated AI is Evaluate the candidate's emotional state when working on the task, taking into account fluctuations in stress and motivation 2. The system of claim 1.

5. The generated AI is Based on the candidate's skill test results, a comparative analysis is conducted with the other candidates to assess their relative skill levels.

2. The system of claim 1.

6. The generated AI is The content of the skill test is customized for each industry, and the assessment is specialized for that specific industry.

2. The system of claim 1.

Images