System

Abstract

An object of the system according to the embodiment is to provide an appropriate reskilling material based on the background and the current abilities of a job change candidate.SOLUTION: A system according to an embodiment includes an information collection unit, an analysis unit, and a teaching material generation unit. An information collection part collects the background, the current ability and the background of the job change candidate. The analysis unit analyzes the information collected by the information collection unit. The teaching material generation unit generates a teaching material for an reskilling on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to provide appropriate reskilling materials based on the background and current abilities of job-change candidates.

[0005] The system according to the embodiment aims to provide appropriate reskilling materials based on the background and current abilities of job-change candidates. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a learning material generation unit. The information collection unit collects backgrounds, current abilities, and backgrounds of job-change candidates. The analysis unit analyzes the information collected by the information collection unit. The learning material generation unit generates learning materials for reskilling based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate reskilling materials based on the background and current abilities of job-change candidates. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A job change support system according to an embodiment of the present invention is a system that investigates the background, current abilities, and background of job change candidates in advance and automatically generates reskilling materials that match those by using AI. As a result, the job change support system provides job change candidates with optimal reskilling materials, enabling them to efficiently acquire the necessary skills.

[0029] A job change support system according to an embodiment includes an information collection unit, an analysis unit, and a teaching material generation unit. The information collection unit collects the background, current abilities, and background of job change candidates. For example, the information collection unit collects information such as the candidate's resume, job history, skill sheet, and online profile. The information collection unit can also collect the candidate's past project reports and work diaries. The information collection unit can also analyze the candidate's social media accounts and extract trends in interests and concerns from past posts and comments. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit analyzes the candidate's past work experience, skills, educational background, qualifications, interests, and the like. The analysis unit can also analyze the candidate's past projects and work content in detail to evaluate their specific skill set and work performance ability. The analysis unit can also use an emotion estimation function to analyze the candidate's emotional reactions to their past work experience and evaluate their stress tolerance and motivation. The teaching material generation unit generates reskilling teaching materials based on the results of the analysis by the analysis unit. For example, if a candidate wishes to change jobs in the IT field, the teaching material generation unit generates teaching materials covering everything from the basics to advanced applications of programming languages. Also, if a candidate wishes to change jobs in the marketing field, the teaching material generation unit can generate teaching materials on digital marketing and data analysis. The teaching material generation unit can also analyze the candidate's learning style and pace and generate individually optimized teaching materials. This allows the job change support system according to the embodiment to automatically generate optimal reskilling teaching materials for job change candidates.

[0030] The information collection unit can collect information such as candidates' resumes, work histories, skill sheets, and online profiles. For example, the information collection unit can collect candidates' resumes to obtain information such as educational background, work history, and skill sets. The information collection unit can also collect work histories to obtain information such as past job content, achievements, and skills. The information collection unit can also collect skill sheets to obtain information such as technical skills, soft skills, and qualifications. The information collection unit can also collect online profiles to obtain information such as LinkedIn profiles and portfolio sites. This makes it possible to collect detailed information about candidates.

[0031] The analysis unit can analyze a candidate's past projects and work details in detail to evaluate their specific skill sets and work performance capabilities. For example, the analysis unit collects a candidate's past project reports and work diaries, which AI analyzes. For example, it extracts information such as the scale of the project, the technologies used, and the results achieved, and evaluates the candidate's skill sets. The analysis unit can also analyze the details of the work content and evaluate the tasks responsibilities, technologies used, results, etc. The analysis unit can also clarify the specific content and evaluation criteria of the skill sets and evaluate technical skills, soft skills, qualifications, etc. This makes it possible to evaluate a candidate's specific skill sets and work performance capabilities.

[0032] The analysis unit can analyze a candidate's social media activities and comments in online communities to identify trends in their interests. For example, the analysis unit can analyze a candidate's social media accounts and extract trends in their interests from past posts and comments. For example, it can evaluate the frequency and content of posts related to specific technologies or industries. The analysis unit can also analyze comments in online communities to identify trends in a candidate's interests. For example, it can evaluate the communities in which the candidate participates and the activities they engage in. The analysis unit can also clarify the specific content and scope of comments and evaluate the content of posts, comments, replies, etc. This makes it possible to identify trends in a candidate's interests.

[0033] The analysis unit can analyze candidates' health data and lifestyle information and make suggestions about job aptitude and work styles. For example, the analysis unit collects candidates' health data and uses AI to analyze it. For example, it can make suggestions about job aptitude and work styles based on data from fitness trackers and health apps. The analysis unit can also collect lifestyle information and analyze diet, exercise habits, sleep patterns, etc. For example, it can combine health data and lifestyle information to make suggestions about job aptitude and work styles. The analysis unit can also clarify specific evaluation criteria and measurement methods for job aptitude and evaluate using aptitude tests and feedback. This makes it possible to make suggestions about job aptitude and work styles for candidates.

[0034] The analysis unit can analyze candidates' hobbies and special skills to discover new skills and abilities related to the job. For example, the analysis unit collects information about candidates' hobbies and special skills, and AI analyzes it. For example, it makes suggestions for applying skills related to hobby activities and special skills to the job. The analysis unit can also clarify the specific content and scope of hobbies and special skills, and evaluate sports, art, music, etc. The analysis unit can also clarify the specific content and evaluation criteria of new skills, and evaluate technical skills, soft skills, qualifications, etc. This makes it possible to discover new skills and abilities from candidates' hobbies and special skills.

[0035] The teaching material generation unit can analyze the candidate's learning style and pace and generate individually optimized teaching materials. The teaching material generation unit, for example, analyzes the candidate's past learning history and learning style and generates individually optimized teaching materials. For example, the format of video lectures or text teaching materials is customized. The teaching material generation unit can also clarify the specific content and evaluation criteria of learning styles and evaluate visual, auditory, experiential, etc. The teaching material generation unit can also clarify the specific evaluation criteria and measurement method for pace and evaluate learning speed, progress, etc. This makes it possible to generate optimal teaching materials tailored to the candidate's learning style and pace.

[0036] The teaching material generation unit can analyze the candidate's past learning history and propose effective learning methods and teaching material formats. For example, the teaching material generation unit can analyze the candidate's past learning history and propose effective learning methods and teaching material formats. For example, the teaching material can be customized based on learning methods that have been successful in the past. The teaching material generation unit can also clarify the specific content and scope of the learning history and evaluate past learning content, learning outcomes, study time, etc. The teaching material generation unit can also clarify the specific content and criteria of effective learning methods and propose active learning, flipped classroom, project-based learning, etc. This makes it possible to propose effective learning methods and teaching material formats based on the candidate's past learning history.

[0037] The teaching material generation unit can combine teaching materials from different industries and fields to enhance a candidate's skill set in a multifaceted manner. For example, the teaching material generation unit can combine teaching materials from different industries and fields to enhance a candidate's skill set in a multifaceted manner. For example, it can provide a combination of IT and marketing teaching materials. The teaching material generation unit can also clarify the specific content and scope of different industries and evaluate the IT industry, manufacturing industry, service industry, etc. The teaching material generation unit can also clarify the specific content and scope of fields and evaluate the technical field, business field, creative field, etc. This allows a candidate's skill set to be enhanced in a multifaceted manner.

[0038] The teaching material generation unit can update the teaching material content in real time according to the candidate's learning progress, and provide the latest knowledge and skills. The teaching material generation unit, for example, monitors the candidate's learning progress in real time and updates the teaching material content. For example, new skills and knowledge can be instantly reflected. The teaching material generation unit can also clarify specific evaluation criteria and measurement methods for learning progress, and evaluate study time, degree of achievement of learning content, etc. The teaching material generation unit can also clarify specific implementation methods and techniques in real time, and evaluate using real-time data analysis, real-time feedback, etc. This makes it possible to provide the latest knowledge and skills according to the candidate's learning progress.

[0039] The system can analyze a candidate's learning history and automatically generate an optimal learning schedule. For example, the system analyzes a candidate's past learning history and automatically generates an optimal learning schedule. For example, the system adjusts the schedule based on past learning patterns and progress. The system can also clarify the specific content and scope of the learning history and evaluate past learning content, learning outcomes, study time, etc. The system can also clarify the specific content and criteria for an optimal learning schedule and evaluate the allocation of study time, the order of learning content, etc. This makes it possible to automatically generate an optimal learning schedule based on the candidate's learning history.

[0040] The system can provide a community function on the online platform that promotes interaction and information sharing among candidates. For example, the system adds a community function to the online platform to promote interaction and information sharing among candidates. For example, the system can provide a forum or chat function. The system can also clarify the specific content and functions of the online platform and evaluate a learning management system, community function, etc. The system can also clarify the specific content and format of the interaction and provide a discussion forum, chat function, etc. This makes it possible to provide a community function that promotes interaction and information sharing among candidates.

[0041] The system can provide opportunities for actual work experience and internships according to the candidate's learning progress. For example, the system analyzes the candidate's learning progress and provides opportunities for actual work experience and internships. For example, the system collaborates with companies related to the learning content. The system can also clarify specific evaluation criteria and measurement methods for learning progress and evaluate study time, degree of achievement of learning content, etc. The system can also clarify the specific content and format of work experience and provide internships, practical training, etc. This makes it possible to provide opportunities for actual work experience and internships according to the candidate's learning progress.

[0042] The system can analyze the candidate's learning outcomes in detail and provide feedback on specific areas for improvement and reinforcement. The system can, for example, analyze the candidate's learning outcomes in detail and provide feedback on specific areas for improvement and reinforcement. For example, the system can evaluate the level of comprehension of the learning content and the skills acquired. The system can also clarify the specific evaluation criteria and measurement methods for the learning outcomes, and evaluate test results, project completion, etc. The system can also clarify the specific content and evaluation criteria for the areas for improvement, and provide feedback on skill deficiencies, shallow understanding, etc. This makes it possible to analyze the candidate's learning outcomes in detail and provide feedback on specific areas for improvement and reinforcement.

[0043] The system can compare a candidate's learning history with job market trends and propose the optimal career path. For example, the system can analyze a candidate's learning history and compare it with job market trends. For example, the system can propose a career path based on in-demand skills and job types. The system can also clarify the specific content and scope of the learning history and evaluate past learning content, learning outcomes, study time, etc. The system can also clarify the specific content and evaluation criteria of job market trends and evaluate in-demand skills, industry trends, etc. This makes it possible to compare a candidate's learning history with job market trends and propose the optimal career path.

[0044] The system can compare a candidate's learning outcomes with other candidates and provide a relative evaluation. For example, the system can compare a candidate's learning outcomes with other candidates and provide a relative evaluation. For example, the system can create a ranking based on learning outcomes in the same field. The system can also clarify the specific evaluation criteria and measurement methods for learning outcomes and evaluate test results, project completion, etc. The system can also clarify the specific content and criteria for relative evaluation and provide comparisons and rankings with other candidates. This makes it possible to compare a candidate's learning outcomes with other candidates and provide a relative evaluation.

[0045] The system can provide actual work simulations and case studies according to the candidate's learning progress. For example, the system analyzes the candidate's learning progress and provides actual work simulations and case studies. For example, it performs simulations related to the learning content. The system can also clarify specific evaluation criteria and measurement methods for learning progress and evaluate learning time, achievement level of learning content, etc. The system can also clarify the specific content and format of the work simulation and provide virtual projects, simulation software, etc. This makes it possible to provide actual work simulations and case studies according to the candidate's learning progress.

[0046] The system can provide optimal job information in real time based on the candidate's new skill set. For example, the system analyzes the candidate's new skill set and provides optimal job information in real time. For example, the system filters job information using a skill matching algorithm. The system can also clarify the specific content and evaluation criteria of the new skill set and evaluate technical skills, soft skills, qualifications, etc. The system can also clarify the specific content and format of the job information and provide job details, salary, work location, etc. This makes it possible to provide optimal job information in real time based on the candidate's new skill set.

[0047] The system can analyze a candidate's application documents and suggest specific areas for improvement and strengthening. For example, the system can analyze a candidate's application documents and suggest specific areas for improvement and strengthening. For example, the system can evaluate the content of a resume or work history and make improvement suggestions. The system can also clarify the specific content and format of application documents and evaluate resumes, work history, cover letters, etc. The system can also clarify the specific content and evaluation criteria for areas for improvement and suggest areas such as lack of skills and insufficient understanding. This makes it possible to analyze a candidate's application documents and suggest specific areas for improvement and strengthening.

[0048] The system can suggest the possibility of changing jobs to a different industry based on the candidate's new skill set. For example, the system can analyze the candidate's new skill set and suggest the possibility of changing jobs to a different industry. For example, the system can evaluate the versatility of skills and suggest how to use them in a different industry. The system can also clarify the specific content and evaluation criteria of the new skill set and evaluate technical skills, soft skills, qualifications, etc. The system can also clarify the specific content and scope of the different industry and suggest a job change, for example, from the IT industry to the manufacturing industry. This makes it possible to suggest the possibility of changing jobs to a different industry based on the candidate's new skill set.

[0049] The system can provide a matching service with mentors and coaches to support candidates in their job hunting activities. For example, the system can analyze the candidate's skill set and career goals and provide a matching service with appropriate mentors and coaches. For example, the system can introduce candidates with experience in the same industry or job type. The system can also clarify the specific content and scope of the job hunting activities and provide support for collecting job information, preparing application documents, preparing interviews, etc. The system can also clarify the specific roles and selection criteria of mentors and coaches and evaluate industry experience, teaching experience, expertise, etc. This makes it possible to provide a matching service with mentors and coaches to support candidates in their job hunting activities.

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

[0051] The analysis department can suggest new fields and job types that candidates should try based on their past work experience. For example, if a candidate has experience in project management, the analysis department can use that experience to suggest a career path as a project manager or team leader. In addition, if a candidate has technical skills, the analysis department can use those skills to suggest a career path as a technical consultant or technical leader. Furthermore, the analysis department can analyze a candidate's past work experience in detail and identify new fields and job types that the candidate should try.

[0052] The information gathering department can collect information about candidates' hobbies and special skills and use that information to suggest new career paths. For example, if a candidate programs as a hobby, the information gathering department can collect that information and suggest a career path in the IT field. Also, if a candidate has design as a special skill, that information can be used to suggest a design-related job. Furthermore, the information gathering department can collect detailed information about candidates' hobbies and special skills and use that information to suggest new career paths.

[0053] The analysis department can suggest new skills and knowledge that candidates should learn based on their past work experience. For example, if a candidate has past experience in marketing, the analysis department can leverage that experience and suggest that they learn digital marketing or data analysis skills. Also, if a candidate has past experience in sales, the analysis department can leverage that experience and suggest that they learn sales strategy or customer relationship management skills. Furthermore, the analysis department can perform a detailed analysis of a candidate's past work experience to identify new skills and knowledge that the candidate should learn.

[0054] The analysis unit can analyze a candidate's health data and lifestyle information and make suggestions about job aptitude and work styles. For example, it can collect a candidate's health data and make suggestions about job aptitude and work styles based on data from fitness trackers and health apps. The analysis unit can also collect lifestyle information and analyze diet, exercise habits, sleep patterns, etc. For example, it can combine health data and lifestyle information to make suggestions about job aptitude and work styles. The analysis unit can also clarify specific evaluation criteria and measurement methods for job aptitude and evaluate using aptitude tests and feedback. This makes it possible to make suggestions about job aptitude and work styles for a candidate.

[0055] The analysis unit can analyze candidates' hobbies and special skills to discover new skills and abilities related to the job. For example, information about candidates' hobbies and special skills is collected and analyzed by AI. For example, it can suggest how skills related to hobby activities and special skills can be applied to the job. The analysis unit can also clarify the specific content and scope of hobbies and special skills, and evaluate sports, art, music, etc. The analysis unit can also clarify the specific content and evaluation criteria of new skills, and evaluate technical skills, soft skills, qualifications, etc. This makes it possible to discover new skills and abilities from candidates' hobbies and special skills.

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

[0057] Step 1: The information gathering department collects information about the candidate's background, current capabilities, and background, for example, by analyzing the candidate's resume, CV, skill sheet, online profile, past project reports, work diary, and social media accounts. Step 2: The analysis unit analyzes the information collected by the information collection unit, such as the candidate's past work experience, skills, educational background, qualifications, interests, specific skill sets and job performance abilities, and emotional responses using an emotion estimation function to assess stress tolerance and motivation. Step 3: The material generation unit generates reskilling materials based on the results of the analysis by the analysis unit. For example, it generates materials according to the candidate's desired field (such as programming languages ​​in the IT field, or digital marketing and data analysis in the marketing field) and provides materials optimized for the candidate's learning style and pace.

[0058] (Example 2) A job change support system according to an embodiment of the present invention is a system that investigates the background, current abilities, and background of job change candidates in advance and automatically generates reskilling materials that match those by using AI. As a result, the job change support system provides job change candidates with optimal reskilling materials, enabling them to efficiently acquire the necessary skills.

[0059] A job change support system according to an embodiment includes an information collection unit, an analysis unit, and a teaching material generation unit. The information collection unit collects the background, current abilities, and background of job change candidates. For example, the information collection unit collects information such as the candidate's resume, job history, skill sheet, and online profile. The information collection unit can also collect the candidate's past project reports and work diaries. The information collection unit can also analyze the candidate's social media accounts and extract trends in interests and concerns from past posts and comments. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit analyzes the candidate's past work experience, skills, educational background, qualifications, interests, and the like. The analysis unit can also analyze the candidate's past projects and work content in detail to evaluate their specific skill set and work performance ability. The analysis unit can also use an emotion estimation function to analyze the candidate's emotional reactions to their past work experience and evaluate their stress tolerance and motivation. The teaching material generation unit generates reskilling teaching materials based on the results of the analysis by the analysis unit. For example, if a candidate wishes to change jobs in the IT field, the teaching material generation unit generates teaching materials covering everything from the basics to advanced applications of programming languages. Also, if a candidate wishes to change jobs in the marketing field, the teaching material generation unit can generate teaching materials on digital marketing and data analysis. The teaching material generation unit can also analyze the candidate's learning style and pace and generate individually optimized teaching materials. This allows the job change support system according to the embodiment to automatically generate optimal reskilling teaching materials for job change candidates.

[0060] The information collection unit can collect information such as candidates' resumes, work histories, skill sheets, and online profiles. For example, the information collection unit can collect candidates' resumes to obtain information such as educational background, work history, and skill sets. The information collection unit can also collect work histories to obtain information such as past job content, achievements, and skills. The information collection unit can also collect skill sheets to obtain information such as technical skills, soft skills, and qualifications. The information collection unit can also collect online profiles to obtain information such as LinkedIn profiles and portfolio sites. This makes it possible to collect detailed information about candidates.

[0061] The analysis unit can analyze a candidate's past projects and work details in detail to evaluate their specific skill sets and work performance capabilities. For example, the analysis unit collects a candidate's past project reports and work diaries, which AI analyzes. For example, it extracts information such as the scale of the project, the technologies used, and the results achieved, and evaluates the candidate's skill sets. The analysis unit can also analyze the details of the work content and evaluate the tasks responsibilities, technologies used, results, etc. The analysis unit can also clarify the specific content and evaluation criteria of the skill sets and evaluate technical skills, soft skills, qualifications, etc. This makes it possible to evaluate a candidate's specific skill sets and work performance capabilities.

[0062] The analysis unit can analyze a candidate's social media activities and comments in online communities to identify trends in their interests. For example, the analysis unit can analyze a candidate's social media accounts and extract trends in their interests from past posts and comments. For example, it can evaluate the frequency and content of posts related to specific technologies or industries. The analysis unit can also analyze comments in online communities to identify trends in a candidate's interests. For example, it can evaluate the communities in which the candidate participates and the activities they engage in. The analysis unit can also clarify the specific content and scope of comments and evaluate the content of posts, comments, replies, etc. This makes it possible to identify trends in a candidate's interests.

[0063] The analysis unit can use the emotion estimation function to analyze the candidate's emotional responses to their past work experience and evaluate their stress tolerance and motivation. The analysis unit, for example, analyzes interviews and questionnaires regarding the candidate's past work experience and evaluates their emotional responses using the emotion estimation function. For example, it identifies work experiences with a high proportion of positive emotions. The analysis unit can also use the emotion estimation function to evaluate the candidate's stress tolerance and motivation. For example, it performs the evaluation based on an emotion score. The analysis unit can also clarify specific evaluation criteria and measurement methods for emotional responses and use emotion scores, feedback, etc. to evaluate the candidate's stress tolerance and motivation. This makes it possible to evaluate the candidate's stress tolerance and motivation.

[0064] The analysis unit can analyze candidates' health data and lifestyle information and make suggestions about job aptitude and work styles. For example, the analysis unit collects candidates' health data and uses AI to analyze it. For example, it can make suggestions about job aptitude and work styles based on data from fitness trackers and health apps. The analysis unit can also collect lifestyle information and analyze diet, exercise habits, sleep patterns, etc. For example, it can combine health data and lifestyle information to make suggestions about job aptitude and work styles. The analysis unit can also clarify specific evaluation criteria and measurement methods for job aptitude and evaluate using aptitude tests and feedback. This makes it possible to make suggestions about job aptitude and work styles for candidates.

[0065] The analysis unit can analyze candidates' hobbies and special skills to discover new skills and abilities related to the job. For example, the analysis unit collects information about candidates' hobbies and special skills, and AI analyzes it. For example, it makes suggestions for applying skills related to hobby activities and special skills to the job. The analysis unit can also clarify the specific content and scope of hobbies and special skills, and evaluate sports, art, music, etc. The analysis unit can also clarify the specific content and evaluation criteria of new skills, and evaluate technical skills, soft skills, qualifications, etc. This makes it possible to discover new skills and abilities from candidates' hobbies and special skills.

[0066] The analysis unit uses the emotion estimation function to identify the work environment and work content that the candidate feels most positive about, and can suggest an appropriate job. The analysis unit, for example, uses the emotion estimation function to identify the work environment and work content that the candidate feels most positive about. For example, the analysis unit evaluates the candidate based on emotion scores for past work experience. The analysis unit can also clarify the specific content and scope of the work environment and evaluate the office environment, remote work environment, etc. The analysis unit can also clarify the specific content and scope of the work content and evaluate the job responsibilities, technology used, results, etc. This makes it possible to identify the work environment and work content that the candidate feels most positive about, and suggest an appropriate job.

[0067] The teaching material generation unit can analyze the candidate's learning style and pace and generate individually optimized teaching materials. The teaching material generation unit, for example, analyzes the candidate's past learning history and learning style and generates individually optimized teaching materials. For example, the format of video lectures or text teaching materials is customized. The teaching material generation unit can also clarify the specific content and evaluation criteria of learning styles and evaluate visual, auditory, experiential, etc. The teaching material generation unit can also clarify the specific evaluation criteria and measurement method for pace and evaluate learning speed, progress, etc. This makes it possible to generate optimal teaching materials tailored to the candidate's learning style and pace.

[0068] The teaching material generation unit can analyze the candidate's past learning history and propose effective learning methods and teaching material formats. For example, the teaching material generation unit can analyze the candidate's past learning history and propose effective learning methods and teaching material formats. For example, the teaching material can be customized based on learning methods that have been successful in the past. The teaching material generation unit can also clarify the specific content and scope of the learning history and evaluate past learning content, learning outcomes, study time, etc. The teaching material generation unit can also clarify the specific content and criteria of effective learning methods and propose active learning, flipped classroom, project-based learning, etc. This makes it possible to propose effective learning methods and teaching material formats based on the candidate's past learning history.

[0069] The teaching material generation unit uses the emotion estimation function to analyze fluctuations in stress and motivation felt by candidates while they are studying, and can provide encouragement and advice at appropriate times. The teaching material generation unit, for example, uses the emotion estimation function to analyze fluctuations in stress and motivation felt by candidates while they are studying. For example, it analyzes facial expressions and voices during studying and calculates an emotion score. The teaching material generation unit also clarifies specific evaluation criteria and measurement methods for stress, and can evaluate using stress scores, self-assessments, etc. The teaching material generation unit also clarifies specific evaluation criteria and measurement methods for motivation, and can evaluate using motivation scores, feedback, etc. This makes it possible to analyze fluctuations in stress and motivation felt by candidates while they are studying, and provide encouragement and advice at appropriate times.

[0070] The teaching material generation unit can combine teaching materials from different industries and fields to enhance a candidate's skill set in a multifaceted manner. For example, the teaching material generation unit can combine teaching materials from different industries and fields to enhance a candidate's skill set in a multifaceted manner. For example, it can provide a combination of IT and marketing teaching materials. The teaching material generation unit can also clarify the specific content and scope of different industries and evaluate the IT industry, manufacturing industry, service industry, etc. The teaching material generation unit can also clarify the specific content and scope of fields and evaluate the technical field, business field, creative field, etc. This allows a candidate's skill set to be enhanced in a multifaceted manner.

[0071] The teaching material generation unit can update the teaching material content in real time according to the candidate's learning progress, and provide the latest knowledge and skills. The teaching material generation unit, for example, monitors the candidate's learning progress in real time and updates the teaching material content. For example, new skills and knowledge can be instantly reflected. The teaching material generation unit can also clarify specific evaluation criteria and measurement methods for learning progress, and evaluate study time, degree of achievement of learning content, etc. The teaching material generation unit can also clarify specific implementation methods and techniques in real time, and evaluate using real-time data analysis, real-time feedback, etc. This makes it possible to provide the latest knowledge and skills according to the candidate's learning progress.

[0072] The learning material generation unit can use the emotion estimation function to identify the learning material content that the candidate is most interested in and customize it to increase their motivation to learn. The learning material generation unit can, for example, use the emotion estimation function to identify the learning material content that the candidate is most interested in. For example, it can analyze facial expressions and voices during learning to identify themes of high interest. The learning material generation unit can also clarify the specific content and range of the learning material content that the candidate is interested in and evaluate specific topics, learning formats, types of learning materials, etc. The learning material generation unit can also clarify specific evaluation criteria and measurement methods for motivation to learn and evaluate using motivation scores, feedback, etc. This makes it possible to identify the learning material content that the candidate is most interested in and customize it to increase their motivation to learn.

[0073] The system can analyze a candidate's learning history and automatically generate an optimal learning schedule. For example, the system analyzes a candidate's past learning history and automatically generates an optimal learning schedule. For example, the system adjusts the schedule based on past learning patterns and progress. The system can also clarify the specific content and scope of the learning history and evaluate past learning content, learning outcomes, study time, etc. The system can also clarify the specific content and criteria for an optimal learning schedule and evaluate the allocation of study time, the order of learning content, etc. This makes it possible to automatically generate an optimal learning schedule based on the candidate's learning history.

[0074] The system uses the emotion estimation function to analyze the candidate's emotional state while studying and can suggest breaks and refreshment at appropriate times. The system, for example, uses the emotion estimation function to analyze the candidate's emotional state while studying. For example, it analyzes facial expressions and voice while studying to detect stress and fatigue. The system can also clarify specific evaluation criteria and measurement methods for the emotional state and evaluate using emotion scores, feedback, etc. The system can also clarify the specific content and timing of breaks and suggest break times, types of breaks, etc. This makes it possible to analyze the candidate's emotional state while studying and suggest breaks and refreshment at appropriate times.

[0075] The system can provide a community function on the online platform that promotes interaction and information sharing among candidates. For example, the system adds a community function to the online platform to promote interaction and information sharing among candidates. For example, the system can provide a forum or chat function. The system can also clarify the specific content and functions of the online platform and evaluate a learning management system, community function, etc. The system can also clarify the specific content and format of the interaction and provide a discussion forum, chat function, etc. This makes it possible to provide a community function that promotes interaction and information sharing among candidates.

[0076] The system can provide opportunities for actual work experience and internships according to the candidate's learning progress. For example, the system analyzes the candidate's learning progress and provides opportunities for actual work experience and internships. For example, the system collaborates with companies related to the learning content. The system can also clarify specific evaluation criteria and measurement methods for learning progress and evaluate study time, degree of achievement of learning content, etc. The system can also clarify the specific content and format of work experience and provide internships, practical training, etc. This makes it possible to provide opportunities for actual work experience and internships according to the candidate's learning progress.

[0077] The system uses the emotion estimation function to identify an environment in which the candidate can learn most relaxedly and optimize the learning environment. The system, for example, uses the emotion estimation function to identify an environment in which the candidate can learn most relaxedly. For example, the system analyzes facial expressions and voice during learning to evaluate the degree of relaxation. The system also clarifies specific evaluation criteria and measurement methods for relaxation, and can evaluate using a relaxation score, feedback, etc. The system also clarifies specific methods and criteria for optimizing the learning environment, and can suggest things like the design of learning spaces and the selection of learning tools. This makes it possible to identify an environment in which the candidate can learn most relaxed and optimize the learning environment.

[0078] The system can analyze the candidate's learning outcomes in detail and provide feedback on specific areas for improvement and reinforcement. The system can, for example, analyze the candidate's learning outcomes in detail and provide feedback on specific areas for improvement and reinforcement. For example, the system can evaluate the level of comprehension of the learning content and the skills acquired. The system can also clarify the specific evaluation criteria and measurement methods for the learning outcomes, and evaluate test results, project completion, etc. The system can also clarify the specific content and evaluation criteria for the areas for improvement, and provide feedback on skill deficiencies, shallow understanding, etc. This makes it possible to analyze the candidate's learning outcomes in detail and provide feedback on specific areas for improvement and reinforcement.

[0079] The system can compare a candidate's learning history with job market trends and propose the optimal career path. For example, the system can analyze a candidate's learning history and compare it with job market trends. For example, the system can propose a career path based on in-demand skills and job types. The system can also clarify the specific content and scope of the learning history and evaluate past learning content, learning outcomes, study time, etc. The system can also clarify the specific content and evaluation criteria of job market trends and evaluate in-demand skills, industry trends, etc. This makes it possible to compare a candidate's learning history with job market trends and propose the optimal career path.

[0080] The system can use the emotion estimation function to analyze the candidate's emotional fluctuations during learning and provide feedback to maintain motivation. The system, for example, uses the emotion estimation function to analyze the candidate's emotional fluctuations during learning. For example, the system analyzes facial expressions and voice during learning to evaluate fluctuations in motivation. The system can also clarify specific evaluation criteria and measurement methods for emotional fluctuations, and evaluate using emotion scores, feedback, etc. The system can also clarify specific evaluation criteria and measurement methods for motivation, and evaluate using motivation scores, feedback, etc. This makes it possible to analyze the candidate's emotional fluctuations during learning and provide feedback to maintain motivation.

[0081] The system can compare a candidate's learning outcomes with other candidates and provide a relative evaluation. For example, the system can compare a candidate's learning outcomes with other candidates and provide a relative evaluation. For example, the system can create a ranking based on learning outcomes in the same field. The system can also clarify the specific evaluation criteria and measurement methods for learning outcomes and evaluate test results, project completion, etc. The system can also clarify the specific content and criteria for relative evaluation and provide comparisons and rankings with other candidates. This makes it possible to compare a candidate's learning outcomes with other candidates and provide a relative evaluation.

[0082] The system can provide actual work simulations and case studies according to the candidate's learning progress. For example, the system analyzes the candidate's learning progress and provides actual work simulations and case studies. For example, it performs simulations related to the learning content. The system can also clarify specific evaluation criteria and measurement methods for learning progress and evaluate learning time, achievement level of learning content, etc. The system can also clarify the specific content and format of the work simulation and provide virtual projects, simulation software, etc. This makes it possible to provide actual work simulations and case studies according to the candidate's learning progress.

[0083] The system can use the emotion estimation function to identify the feedback format that evokes the most positive emotions in the candidate and provide appropriate feedback. For example, the system can use the emotion estimation function to identify the feedback format that evokes the most positive emotions in the candidate. For example, the system can evaluate the content and format of the feedback. The system can also clarify specific evaluation criteria and measurement methods for positive emotions and evaluate using emotion scores, feedback, etc. The system can also clarify the specific content and type of feedback format and provide verbal feedback, written feedback, etc. This makes it possible to identify the feedback format that evokes the most positive emotions in the candidate and provide appropriate feedback.

[0084] The system can provide optimal job information in real time based on the candidate's new skill set. For example, the system analyzes the candidate's new skill set and provides optimal job information in real time. For example, the system filters job information using a skill matching algorithm. The system can also clarify the specific content and evaluation criteria of the new skill set and evaluate technical skills, soft skills, qualifications, etc. The system can also clarify the specific content and format of the job information and provide job details, salary, work location, etc. This makes it possible to provide optimal job information in real time based on the candidate's new skill set.

[0085] The system can analyze a candidate's application documents and suggest specific areas for improvement and strengthening. For example, the system can analyze a candidate's application documents and suggest specific areas for improvement and strengthening. For example, the system can evaluate the content of a resume or work history and make improvement suggestions. The system can also clarify the specific content and format of application documents and evaluate resumes, work history, cover letters, etc. The system can also clarify the specific content and evaluation criteria for areas for improvement and suggest areas such as lack of skills and insufficient understanding. This makes it possible to analyze a candidate's application documents and suggest specific areas for improvement and strengthening.

[0086] The system can suggest the possibility of changing jobs to a different industry based on the candidate's new skill set. For example, the system can analyze the candidate's new skill set and suggest the possibility of changing jobs to a different industry. For example, the system can evaluate the versatility of skills and suggest how to use them in a different industry. The system can also clarify the specific content and evaluation criteria of the new skill set and evaluate technical skills, soft skills, qualifications, etc. The system can also clarify the specific content and scope of the different industry and suggest a job change, for example, from the IT industry to the manufacturing industry. This makes it possible to suggest the possibility of changing jobs to a different industry based on the candidate's new skill set.

[0087] The system can provide a matching service with mentors and coaches to support candidates in their job hunting activities. For example, the system can analyze the candidate's skill set and career goals and provide a matching service with appropriate mentors and coaches. For example, the system can introduce candidates with experience in the same industry or job type. The system can also clarify the specific content and scope of the job hunting activities and provide support for collecting job information, preparing application documents, preparing interviews, etc. The system can also clarify the specific roles and selection criteria of mentors and coaches and evaluate industry experience, teaching experience, expertise, etc. This makes it possible to provide a matching service with mentors and coaches to support candidates in their job hunting activities.

[0088] The system can use the emotion estimation function to identify the application documents and interview preparation that a candidate can be most confident in and provide appropriate support. For example, the system can use the emotion estimation function to identify the application documents and interview preparation that a candidate can be most confident in. For example, the system can evaluate the content of application documents and interview practice. The system can also clarify the specific content and evaluation criteria of application documents that a candidate can be confident in and evaluate resumes, job history documents, cover letters, etc. The system can also clarify the specific content and methods of interview preparation and provide mock interviews, interview question sets, etc. This makes it possible to identify the application documents and interview preparation that a candidate can be most confident in and provide appropriate support.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The analysis department can suggest new fields and job types that candidates should try based on their past work experience. For example, if a candidate has experience in project management, the analysis department can use that experience to suggest a career path as a project manager or team leader. In addition, if a candidate has technical skills, the analysis department can use those skills to suggest a career path as a technical consultant or technical leader. Furthermore, the analysis department can analyze a candidate's past work experience in detail and identify new fields and job types that the candidate should try.

[0091] The information gathering department can collect information about candidates' hobbies and special skills and use that information to suggest new career paths. For example, if a candidate programs as a hobby, the information gathering department can collect that information and suggest a career path in the IT field. Also, if a candidate has design as a special skill, that information can be used to suggest a design-related job. Furthermore, the information gathering department can collect detailed information about candidates' hobbies and special skills and use that information to suggest new career paths.

[0092] The analysis department can suggest new skills and knowledge that candidates should learn based on their past work experience. For example, if a candidate has past experience in marketing, the analysis department can leverage that experience and suggest that they learn digital marketing or data analysis skills. Also, if a candidate has past experience in sales, the analysis department can leverage that experience and suggest that they learn sales strategy or customer relationship management skills. Furthermore, the analysis department can perform a detailed analysis of a candidate's past work experience to identify new skills and knowledge that the candidate should learn.

[0093] The analysis unit can analyze a candidate's health data and lifestyle information and make suggestions about job aptitude and work styles. For example, it can collect a candidate's health data and make suggestions about job aptitude and work styles based on data from fitness trackers and health apps. The analysis unit can also collect lifestyle information and analyze diet, exercise habits, sleep patterns, etc. For example, it can combine health data and lifestyle information to make suggestions about job aptitude and work styles. The analysis unit can also clarify specific evaluation criteria and measurement methods for job aptitude and evaluate using aptitude tests and feedback. This makes it possible to make suggestions about job aptitude and work styles for a candidate.

[0094] The analysis unit can analyze candidates' hobbies and special skills to discover new skills and abilities related to the job. For example, information about candidates' hobbies and special skills is collected and analyzed by AI. For example, it can suggest how skills related to hobby activities and special skills can be applied to the job. The analysis unit can also clarify the specific content and scope of hobbies and special skills, and evaluate sports, art, music, etc. The analysis unit can also clarify the specific content and evaluation criteria of new skills, and evaluate technical skills, soft skills, qualifications, etc. This makes it possible to discover new skills and abilities from candidates' hobbies and special skills.

[0095] The analysis unit can use the emotion estimation function to identify the work environment and work content that the candidate feels most positive about and suggest appropriate jobs. For example, the emotion estimation function can be used to identify the work environment and work content that the candidate feels most positive about. For example, the evaluation can be based on emotion scores for past work experience. The analysis unit can also clarify the specific content and scope of the work environment and evaluate the office environment, remote work environment, etc. The analysis unit can also clarify the specific content and scope of the work content and evaluate the responsibilities, technology used, results, etc. This makes it possible to identify the work environment and work content that the candidate feels most positive about and suggest appropriate jobs.

[0096] The analysis unit can use the emotion estimation function to analyze the candidate's emotional responses to their past work experience and evaluate their stress tolerance and motivation. For example, it can analyze interviews and questionnaires about the candidate's past work experience and use the emotion estimation function to evaluate their emotional responses. For example, it can identify work experiences with a high proportion of positive emotions. The analysis unit can also use the emotion estimation function to evaluate the candidate's stress tolerance and motivation. For example, it can perform an evaluation based on an emotion score. The analysis unit can also clarify specific evaluation criteria and measurement methods for emotional responses and use emotion scores, feedback, etc. to evaluate the candidate's stress tolerance and motivation. This makes it possible to evaluate the candidate's stress tolerance and motivation.

[0097] The analysis unit can use the emotion estimation function to identify an environment in which the candidate can study most relaxedly and optimize the learning environment. For example, the emotion estimation function can be used to identify an environment in which the candidate can study most relaxedly. For example, facial expressions and voice during study are analyzed to evaluate the degree of relaxation. The analysis unit can also clarify specific evaluation criteria and measurement methods for relaxation, and evaluate using a relaxation score, feedback, etc. The analysis unit can also clarify specific methods and criteria for optimizing the learning environment, and propose the design of learning spaces, selection of learning tools, etc. This makes it possible to identify an environment in which the candidate can study most relaxed and optimize the learning environment.

[0098] The analysis unit can use the emotion estimation function to identify the learning material content that the candidate is most interested in and customize it to increase their motivation to learn. For example, the emotion estimation function can be used to identify the learning material content that the candidate is most interested in. For example, facial expressions and voices during learning can be analyzed to identify the topics of greatest interest. The analysis unit can also clarify the specific content and scope of the learning material content that the candidate is interested in and evaluate specific topics, learning formats, types of learning materials, etc. The analysis unit can also clarify specific evaluation criteria and measurement methods for motivation to learn and evaluate using motivation scores, feedback, etc. This makes it possible to identify the learning material content that the candidate is most interested in and customize it to increase their motivation to learn.

[0099] The analysis unit can use the emotion estimation function to identify the feedback format that evokes the most positive emotions in the candidate and provide appropriate feedback. For example, the emotion estimation function can be used to identify the feedback format that evokes the most positive emotions in the candidate. For example, the content and format of the feedback can be evaluated. The analysis unit can also clarify specific evaluation criteria and measurement methods for positive emotions and evaluate using emotion scores, feedback, etc. The analysis unit can also clarify the specific content and type of feedback format and provide verbal feedback, written feedback, etc. This makes it possible to identify the feedback format that evokes the most positive emotions in the candidate and provide appropriate feedback.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The information gathering department collects information about the candidate's background, current capabilities, and background, for example, by analyzing the candidate's resume, CV, skill sheet, online profile, past project reports, work diary, and social media accounts. Step 2: The analysis unit analyzes the information collected by the information collection unit, such as the candidate's past work experience, skills, educational background, qualifications, interests, specific skill sets and job performance abilities, and emotional responses using an emotion estimation function to assess stress tolerance and motivation. Step 3: The material generation unit generates reskilling materials based on the results of the analysis by the analysis unit. For example, it generates materials according to the candidate's desired field (such as programming languages ​​in the IT field, or digital marketing and data analysis in the marketing field) and provides materials optimized for the candidate's learning style and pace.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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. An information gathering department that collects information on the background, current abilities, and background of job-change candidates; an analysis unit that analyzes the information collected by the information collection unit; a teaching material generation unit that generates teaching materials for reskilling based on the results of the analysis by the analysis unit. A system characterized by:

2. The information collecting unit Collect candidate information such as resumes, CVs, skill sheets, and online profiles 2. The system of claim 1.

3. The analysis unit Analyze candidates' social media activity and online community comments to understand trends in their interests 2. The system of claim 1.

4. The analysis unit Analyze candidates' health and lifestyle data to suggest job aptitude and work style 2. The system of claim 1.

5. The teaching material generation unit Analyzes candidate learning styles and pace to generate individually optimized learning materials 2. The system of claim 1.

6. The teaching material generation unit Using emotion estimation, the system analyzes the stress and motivation fluctuations experienced by candidates during their studies and provides encouragement and advice at the appropriate time.

2. The system of claim 1.

7. The system comprises: Using emotion estimation, the system analyzes the candidate's emotional state while studying and suggests breaks and refreshments at appropriate times.

2. The system of claim 1.

8. The system comprises: Use sentiment estimation to identify which feedback formats generate the most positive sentiment among candidates and provide appropriate feedback 2. The system of claim 1.

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