System

Abstract

An object of the system according to the embodiment is to provide effective career consulting in which a client can freely speak.SOLUTION: A system according to an embodiment includes a question answering unit, a problem deepening unit, an assessment unit, a carrier step presentation unit, and a job offer unit. The question answering unit answers the question of the client by using the generated AI. The problem digging unit digs a problem of the client. The assessment unit performs carrier assessment of the client. The carrier step presentation unit presents future carrier steps of the client. A job offer providing part provides the optimum job offer from the job offer information of the market.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 relied on the specialized knowledge and skills of career consultants, making it difficult for clients to speak their true feelings freely.

[0005] The system according to the embodiment aims to provide effective career counseling that allows clients to freely speak their true feelings. [Means for solving the problem]

[0006] The system according to the embodiment includes a question answering unit, a problem digging unit, an assessment unit, a career step presentation unit, and a job offering unit. The question answering unit answers questions from clients using a generation AI. The problem digging unit digs into the client's problems. The assessment unit conducts a career assessment of the client. The career step presentation unit presents the client's future career steps. The job offering unit provides the most suitable job offers from job information on the market. [Effects of the Invention]

[0007] The system according to the embodiment allows clients to freely speak their true feelings and provides effective career counseling. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A career consulting system according to an embodiment of the present invention is a system that uses generative AI to quickly and effectively solve a client's career problems. This allows the client to easily speak their true feelings and receive effective career consulting.

[0029] A career consulting system according to an embodiment includes a question answering unit, a problem digging unit, an assessment unit, a career step presentation unit, and a job offer providing unit. The question answering unit answers questions from a client. For example, if a client asks, "What kind of job is right for me?", the generation AI suggests an appropriate job based on the client's background information and interests. The question answering unit can also analyze the client's past question history to provide personalized answers. For example, it can provide more specific answers based on topics that have been frequently asked in the past. The problem digging unit digs deeper into the client's issues. For example, if a client says, "I'm not satisfied with my current job," the generation AI can explore the cause and provide advice based on career theory. The problem digging unit can also analyze the client's past career data to identify long-term career patterns. For example, it can analyze past work history and skill changes. The assessment unit conducts a career assessment for the client. For example, if a client says, "I want to know my strengths," the generation AI can conduct a career assessment, identify the client's strengths based on the results, and suggest appropriate career steps. The assessment department can also integrate and analyze the client's career assessment results with other data. For example, it can combine performance evaluations and feedback with the career assessment results for analysis. The career step suggestion department suggests the client's future career steps. For example, if a client asks, "What career step should I take next?", the generation AI will suggest an appropriate career step based on the client's current situation and goals. The career step suggestion department can also analyze the client's career goals in detail and propose a long-term career plan. For example, it can suggest specific steps for achieving goals. The job provision department provides the most suitable job from job information in the market. For example, if a client says, "I'm looking for a new job," the generation AI will collect and analyze job information in the market and suggest suitable jobs for the client. The job provision department can also analyze the client's past application history to suggest the most suitable job.For example, similar job offers are suggested based on data on job types and companies to which a client has previously applied. This allows the career consulting system according to the embodiment to quickly and effectively resolve the client's career challenges. For example, the client can receive advice on finding a job that suits them and plan their future career steps. It is also possible to find the most suitable job based on job information in the market.

[0030] The question answering unit can analyze a client's past question history and provide personalized answers. For example, the question answering unit stores questions previously asked by the client in a database and analyzes that history to understand the client's interests. For example, it provides more specific answers based on topics that have been frequently asked in the past. The question answering unit also analyzes a client's past question history and builds a system that generates personalized answers. For example, it provides related information based on occupations and skills in which the client has previously expressed interest. The question answering unit also analyzes a client's question history and develops an algorithm for providing personalized answers. For example, it compares the content of past questions with the content of current questions and generates consistent answers. This makes it possible to provide more individualized answers to clients.

[0031] The question answering unit can provide answers to client questions using visual or interactive content. The question answering unit, for example, builds a system that provides answers to client questions using visual content. For example, it presents explanations of occupations using diagrams or graphs. The question answering unit also provides answers to client questions using interactive content. For example, it performs simulations of occupations that interest the client. The question answering unit also develops a platform for providing answers using visual or interactive content. For example, it visually shows answers to client questions using videos or animations. This makes it possible to provide visual and interactive answers to clients.

[0032] The question answering unit can respond to questions in different languages ​​from clients and provide a service that can be used by clients globally. The question answering unit, for example, builds a multilingual system to respond to questions in different languages. For example, it can respond to questions in multiple languages ​​such as English, French, and Chinese. Furthermore, when a client asks a question in a different language, the question answering unit provides an answer corresponding to that language. For example, when a client asks a question in Spanish, it provides an answer in Spanish. Furthermore, the question answering unit develops a multilingual generation AI to provide a service that can be used by clients globally. For example, it builds a system that translates questions and answers in different languages ​​in real time. This makes it possible to respond to questions in different languages ​​and provide a service that can be used by clients globally.

[0033] The Problem Digging Department can analyze a client's past career data and identify long-term career patterns. For example, the Problem Digging Department collects a client's past career data and performs analysis to identify long-term career patterns. For example, it analyzes changes in past work history and skills. The Problem Digging Department also analyzes a client's career data chronologically and builds a system to identify long-term career patterns. For example, it identifies career turning points and important events. The Problem Digging Department also develops an algorithm to identify long-term career patterns based on a client's past career data. For example, it predicts future career paths based on past data. This makes it possible to identify a client's long-term career patterns and provide more appropriate advice.

[0034] The Issue Deepening Department can analyze issues specific to a client's industry and apply career theories specialized for that industry. For example, the Issue Deepening Department analyzes issues specific to the client's industry and applies career theories that address those issues. For example, it analyzes trends and issues in the IT industry. The Issue Deepening Department also builds a data collection system for analyzing issues specific to a client's industry. For example, it collects and analyzes industry news and reports. The Issue Deepening Department also analyzes industry-specific issues and develops algorithms for applying career theories that address those issues. For example, it provides career advice tailored to the characteristics of the industry. This makes it possible to apply career theories that address the issues specific to the client's industry.

[0035] The Problem Digging Department can combine different career theories to provide the client with the optimal approach. For example, the Problem Digging Department builds a system that combines different career theories to provide the client with the optimal approach. For example, it combines career anchor theory and self-efficacy theory. The Problem Digging Department also develops an algorithm for combining different career theories depending on the client's issues. For example, it selects the optimal career theory depending on the client's situation. The Problem Digging Department also builds a database for combining different career theories to provide the client with the optimal approach. For example, it registers the characteristics and scope of application of each career theory in the database. This makes it possible to combine different career theories to provide the client with the optimal approach.

[0036] The Problem Digging Department can compare a client's career challenges with the success stories of other clients and propose best practices. For example, the Problem Digging Department builds a system that compares a client's career challenges with the success stories of other clients and proposes best practices. For example, it uses successful cases in the same industry as a reference. The Problem Digging Department also registers the success stories of other clients in a database and proposes best practices according to the client's career challenges. For example, it provides detailed information about the success stories. The Problem Digging Department also develops an algorithm for comparing a client's career challenges with the success stories of other clients. For example, it analyzes commonalities and patterns in the success stories. This makes it possible to compare a client's career challenges with other success stories and propose best practices.

[0037] The assessment department can integrate and analyze the client's career assessment results with other data. For example, the assessment department builds a system that integrates the client's career assessment results with other data and performs comprehensive analysis. For example, it combines performance evaluations and feedback with the career assessment results for analysis. The assessment department also builds a database for integrating the client's career assessment results with other data. For example, it centrally manages performance evaluations and feedback and links them with the career assessment results. The assessment department also develops an algorithm for integrating and analyzing the client's career assessment results with other data. For example, it improves the reliability of the career assessment results based on the performance evaluations and feedback. This allows the client's career assessment results to be integrated with other data for analysis.

[0038] The assessment department can track the client's assessment results over time and monitor career progress. The assessment department, for example, builds a system that tracks the client's career assessment results over time and monitors career progress. For example, it conducts assessments periodically and displays the results over time. The assessment department also builds a database for tracking the client's assessment results over time. For example, it stores past assessment results and visualizes career progress. The assessment department also develops an algorithm for tracking the client's career assessment results over time and monitoring career progress. For example, it analyzes changes in assessment results and evaluates career progress. This makes it possible to monitor the client's career progress over time.

[0039] The assessment department can combine different career assessment tools to provide a comprehensive assessment. For example, the assessment department builds a system that combines different career assessment tools to provide a comprehensive assessment. For example, it combines personality tests and skill assessments. The assessment department also analyzes the client's career assessment results using multiple tools to provide a comprehensive assessment. For example, it integrates and analyzes the results of different assessment tools. The assessment department also develops an algorithm for combining different career assessment tools to provide a comprehensive assessment. For example, it integrates the results of each tool to perform an overall evaluation. This makes it possible to combine different career assessment tools to provide a comprehensive assessment.

[0040] The assessment department can compare the client's assessment results with data from other clients to identify relative strengths and weaknesses. For example, the assessment department builds a system to compare the client's career assessment results with data from other clients to identify relative strengths and weaknesses. For example, comparison with other clients in the same industry. The assessment department also registers data from other clients in a database and compares it with the client's assessment results. For example, comparison with other clients in the same occupation. The assessment department also develops an algorithm to compare the client's career assessment results with data from other clients. For example, it performs analysis to identify relative strengths and weaknesses. This makes it possible to compare the client's assessment results with data from other clients to identify relative strengths and weaknesses.

[0041] The career step presentation unit can analyze the client's career goals in detail and propose a long-term career plan. The career step presentation unit, for example, builds a system that analyzes the client's career goals in detail and proposes a long-term career plan. For example, it proposes specific steps for achieving the goals. The career step presentation unit also builds a data collection system for analyzing the client's career goals in detail. For example, it collects and analyzes information related to the goals. The career step presentation unit also develops an algorithm for analyzing the client's career goals in detail and proposing a long-term career plan. For example, it proposes the optimal career path for achieving the goals. This makes it possible to analyze the client's career goals in detail and propose a long-term career plan.

[0042] The career step presentation unit can analyze a client's skill set, identify gaps in required skills, and propose steps to fill them. The career step presentation unit, for example, analyzes a client's skill set and builds a system to identify gaps in required skills. For example, it compares current skills with skills required to achieve a goal. The career step presentation unit also builds a data collection system to analyze a client's skill set. For example, it collects and analyzes information related to skills. The career step presentation unit also develops an algorithm to analyze a client's skill set, identify gaps in required skills, and propose steps to fill them. For example, it proposes specific steps for skill improvement. This makes it possible to analyze a client's skill set, identify gaps in required skills, and propose steps to fill them.

[0043] The career step presentation unit can simulate different career paths and propose the optimal career step to a client. The career step presentation unit, for example, builds a system that simulates different career paths and proposes the optimal career step to a client. For example, it compares multiple career paths and selects the optimal one. The career step presentation unit also builds a data collection system for simulating the client's career path. For example, it collects and analyzes information related to the career paths. The career step presentation unit also develops an algorithm for simulating different career paths and proposing the optimal career step to a client. For example, it proposes the optimal career path based on the simulation results. In this way, it is possible to simulate different career paths and propose the optimal career step to a client.

[0044] The career step presentation unit can compare the client's career steps with the success stories of other clients and propose best practices. The career step presentation unit, for example, builds a system that compares the client's career steps with the success stories of other clients and proposes best practices. For example, it uses successful cases in the same industry as a reference. The career step presentation unit also registers the success stories of other clients in a database and proposes best practices according to the client's career steps. For example, it provides detailed information about the success stories. The career step presentation unit also develops an algorithm for comparing the client's career steps with the success stories of other clients. For example, it analyzes commonalities and patterns in the success stories. This makes it possible to compare the client's career steps with other success stories and propose best practices.

[0045] The job offering unit can analyze a client's past application history and suggest the most suitable job. The job offering unit, for example, analyzes a client's past application history and builds a system that suggests the most suitable job. For example, it suggests similar job offers based on data on job types and companies applied for in the past. The job offering unit also stores the client's application history in a database and analyzes that data to suggest the most suitable job. For example, it analyzes patterns in the application history and identifies suitable job offers. The job offering unit also analyzes a client's past application history and develops an algorithm to suggest the most suitable job. For example, it builds a system that matches application history with job information. This makes it possible to analyze a client's past application history and suggest the most suitable job.

[0046] The job provision department can match a client's skill set with the job requirements of the market and identify the most suitable job. For example, the job provision department builds a system that matches a client's skill set with the job requirements of the market. For example, it uses a skill matrix to compare the client's skills with the job requirements. The job provision department also registers the client's skill set in a database and matches it with the job requirements of the market. For example, it calculates the degree of agreement between the skill set and the job requirements and identifies the most suitable job. The job provision department also develops an algorithm for matching a client's skill set with the job requirements of the market. For example, it analyzes the correlation between the skill set and the job requirements and proposes the most suitable job. This makes it possible to match a client's skill set with the job requirements of the market and identify the most suitable job.

[0047] The job providing unit can analyze a client's job search history and make personalized job suggestions. The job providing unit, for example, analyzes a client's job search history and builds a system that makes personalized job suggestions. For example, it suggests suitable jobs based on past search keywords and browsing history. The job providing unit also stores a client's job search history in a database and analyzes that data to make personalized job suggestions. For example, it analyzes search history patterns to identify suitable jobs. The job providing unit also analyzes a client's job search history and develops an algorithm for making personalized job suggestions. For example, it builds a system that matches search history with job information. This makes it possible to analyze a client's job search history and make personalized job suggestions.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The career consulting system may further include a health management unit that monitors the client's health condition. For example, it may analyze the client's stress level and sleep patterns and provide career advice based on the client's health condition. The health management unit may also collect the client's health data and propose a long-term health management plan. For example, it may suggest regular exercise and dietary improvements. Furthermore, the health management unit may integrate and analyze the client's health condition with other data to provide comprehensive career advice. For example, it may analyze the relationship between health condition and work performance.

[0050] The career consulting system can further include a hobby analysis unit that analyzes the hobbies and interests of a client. For example, the system can analyze the client's hobby activities and provide career advice based on the analysis. The hobby analysis unit can also suggest jobs and skills related to the client's hobby. For example, if the client's hobby is photography, the system can suggest related jobs and skills. Furthermore, the hobby analysis unit can integrate and analyze the client's hobby data with other data to provide comprehensive career advice. For example, the system can suggest a career plan that takes into account the balance between hobbies and work.

[0051] The career consulting system may further include a network analysis unit that analyzes the client's network. For example, the network analysis unit may analyze the client's social network to identify connections that will be useful for the client's career. The network analysis unit may also provide career advice that utilizes the client's network. For example, the network analysis unit may suggest career steps that utilize connections in a specific industry. The network analysis unit may also integrate and analyze the client's network data with other data to provide comprehensive career advice. For example, the network analysis unit may analyze the relationship between connections and career success.

[0052] The career consulting system may further include a learning analysis unit that analyzes the client's learning history. For example, the system may analyze the education and training the client has received in the past and provide career advice based on that analysis. The learning analysis unit may also suggest future learning plans based on the client's learning history. For example, the learning analysis unit may suggest courses to improve specific skills. Furthermore, the learning analysis unit may integrate and analyze the client's learning data with other data to provide comprehensive career advice. For example, the learning analysis unit may analyze the relationship between learning history and career success.

[0053] The career consulting system can further include a lifestyle analysis unit that analyzes the client's lifestyle. For example, it can analyze the client's lifestyle habits and values ​​and provide career advice based on the results. The lifestyle analysis unit can also suggest occupations and skills related to the client's lifestyle. For example, it can suggest occupations suitable for remote work to a client who wishes to work remotely. Furthermore, the lifestyle analysis unit can integrate and analyze the client's lifestyle data with other data to provide comprehensive career advice. For example, it can suggest a career plan that takes into account the balance between lifestyle and work.

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

[0055] Step 1: The question answering unit answers the client's question. For example, if the client asks, "What kind of job would suit me?", the generative AI will suggest suitable jobs based on the client's background information and interests. The question answering unit can also analyze the client's past question history to provide personalized answers. For example, it can provide more specific answers based on topics that have been frequently asked in the past. Step 2: The problem-digging section delves deeper into the client's issues. For example, if a client says, "I'm not satisfied with my current job," the generative AI will explore the cause and provide advice based on career theory. The problem-digging section can also analyze the client's past career data and identify long-term career patterns. For example, it can analyze past work history and skill changes. Step 3: The assessment department conducts a career assessment of the client. For example, if a client says, "I want to know my strengths," the generative AI will conduct a career assessment, identify their strengths based on the results, and suggest appropriate career steps. The assessment department can also integrate and analyze the client's career assessment results with other data. For example, it can analyze performance evaluations and feedback in combination with the career assessment results. Step 4: The career step suggestion unit presents the client with future career steps. For example, if a client asks, "What career step should I take next?", the generation AI will suggest an appropriate career step based on the client's current situation and goals. The career step suggestion unit can also perform a detailed analysis of the client's career goals and propose a long-term career plan. For example, it can suggest specific steps to achieve the goal. Step 5: The job provider provides the most suitable job from the job information in the market. For example, if a client says, "I'm looking for a new job," the generation AI collects and analyzes job information in the market and suggests suitable jobs for the client. The job provider can also analyze the client's past application history to suggest the most suitable job. For example, it suggests similar jobs based on data on the types of jobs and companies that the client has applied for in the past.

[0056] (Example 2) A career consulting system according to an embodiment of the present invention is a system that uses generative AI to quickly and effectively solve a client's career problems. This allows the client to easily speak their true feelings and receive effective career consulting.

[0057] A career consulting system according to an embodiment includes a question answering unit, a problem digging unit, an assessment unit, a career step presentation unit, and a job offer providing unit. The question answering unit answers questions from a client. For example, if a client asks, "What kind of job is right for me?", the generation AI suggests an appropriate job based on the client's background information and interests. The question answering unit can also analyze the client's past question history to provide personalized answers. For example, it can provide more specific answers based on topics that have been frequently asked in the past. The problem digging unit digs deeper into the client's issues. For example, if a client says, "I'm not satisfied with my current job," the generation AI can explore the cause and provide advice based on career theory. The problem digging unit can also analyze the client's past career data to identify long-term career patterns. For example, it can analyze past work history and skill changes. The assessment unit conducts a career assessment for the client. For example, if a client says, "I want to know my strengths," the generation AI can conduct a career assessment, identify the client's strengths based on the results, and suggest appropriate career steps. The assessment department can also integrate and analyze the client's career assessment results with other data. For example, it can combine performance evaluations and feedback with the career assessment results for analysis. The career step suggestion department suggests the client's future career steps. For example, if a client asks, "What career step should I take next?", the generation AI will suggest an appropriate career step based on the client's current situation and goals. The career step suggestion department can also analyze the client's career goals in detail and propose a long-term career plan. For example, it can suggest specific steps for achieving goals. The job provision department provides the most suitable job from job information in the market. For example, if a client says, "I'm looking for a new job," the generation AI will collect and analyze job information in the market and suggest suitable jobs for the client. The job provision department can also analyze the client's past application history to suggest the most suitable job.For example, similar job offers are suggested based on data on job types and companies to which a client has previously applied. This allows the career consulting system according to the embodiment to quickly and effectively resolve the client's career challenges. For example, the client can receive advice on finding a job that suits them and plan their future career steps. It is also possible to find the most suitable job based on job information in the market.

[0058] The question answering unit can analyze a client's past question history and provide personalized answers. For example, the question answering unit stores questions previously asked by the client in a database and analyzes that history to understand the client's interests. For example, it provides more specific answers based on topics that have been frequently asked in the past. The question answering unit also analyzes a client's past question history and builds a system that generates personalized answers. For example, it provides related information based on occupations and skills in which the client has previously expressed interest. The question answering unit also analyzes a client's question history and develops an algorithm for providing personalized answers. For example, it compares the content of past questions with the content of current questions and generates consistent answers. This makes it possible to provide more individualized answers to clients.

[0059] The question answering unit can analyze the client's non-verbal communication and generate more appropriate answers. The question answering unit, for example, uses a camera to analyze the client's facial expressions and collects facial expression data in real time. For example, if the client is smiling, it provides a positive answer. The question answering unit also uses a microphone to analyze the client's tone of voice and collects tone of voice data. For example, if the client speaks in a calm voice, it provides a calm answer. The question answering unit also analyzes non-verbal communication data and develops an algorithm to generate more appropriate answers. For example, it estimates the client's emotional state based on facial expressions and tone of voice and provides an answer accordingly. This makes it possible to provide answers that take the client's non-verbal information into consideration.

[0060] The question answering unit uses the emotion estimation function to provide answers that correspond to the emotional state of the client, thereby increasing the client's sense of security. The question answering unit, for example, uses the emotion estimation function to analyze the client's emotional state in real time. For example, if the client is feeling anxious, it provides an answer that gives a sense of security. The question answering unit also develops an algorithm for providing answers that correspond to the client's emotional state. For example, if the client is feeling stressed, it provides advice to help the client relax. The question answering unit also uses the emotion estimation function to build a system that analyzes the client's emotional state and provides answers that correspond to that. For example, if the client is feeling happy, it provides positive feedback. This makes it possible to provide answers that correspond to the client's emotional state, thereby increasing the client's sense of security.

[0061] The question answering unit can provide answers to client questions using visual or interactive content. The question answering unit, for example, builds a system that provides answers to client questions using visual content. For example, it presents explanations of occupations using diagrams or graphs. The question answering unit also provides answers to client questions using interactive content. For example, it performs simulations of occupations that interest the client. The question answering unit also develops a platform for providing answers using visual or interactive content. For example, it visually shows answers to client questions using videos or animations. This makes it possible to provide visual and interactive answers to clients.

[0062] The question answering unit can respond to questions in different languages ​​from clients and provide a service that can be used by clients globally. The question answering unit, for example, builds a multilingual system to respond to questions in different languages. For example, it can respond to questions in multiple languages ​​such as English, French, and Chinese. Furthermore, when a client asks a question in a different language, the question answering unit provides an answer corresponding to that language. For example, when a client asks a question in Spanish, it provides an answer in Spanish. Furthermore, the question answering unit develops a multilingual generation AI to provide a service that can be used by clients globally. For example, it builds a system that translates questions and answers in different languages ​​in real time. This makes it possible to respond to questions in different languages ​​and provide a service that can be used by clients globally.

[0063] The question answering unit uses the emotion estimation function to reframe questions based on the client's emotions, thereby drawing out deeper insights. The question answering unit, for example, uses the emotion estimation function to analyze the client's emotional state and reframe questions based on those emotions. For example, if the client is feeling anxious, the question answering unit asks questions that will alleviate the anxiety. The question answering unit also develops an algorithm for reframing questions based on the client's emotions. For example, if the client is excited, the question answering unit asks questions that make use of that excitement. The question answering unit also uses the emotion estimation function to reframe questions based on the client's emotions, thereby building a system that draws out deeper insights. For example, if the client is feeling sad, the question answering unit asks questions that are more in tune with that emotion. This allows the question to be reframed based on the client's emotions and draws out deeper insights.

[0064] The Problem Digging Department can analyze a client's past career data and identify long-term career patterns. For example, the Problem Digging Department collects a client's past career data and performs analysis to identify long-term career patterns. For example, it analyzes changes in past work history and skills. The Problem Digging Department also analyzes a client's career data chronologically and builds a system to identify long-term career patterns. For example, it identifies career turning points and important events. The Problem Digging Department also develops an algorithm to identify long-term career patterns based on a client's past career data. For example, it predicts future career paths based on past data. This makes it possible to identify a client's long-term career patterns and provide more appropriate advice.

[0065] The Issue Deepening Department can analyze issues specific to a client's industry and apply career theories specialized for that industry. For example, the Issue Deepening Department analyzes issues specific to the client's industry and applies career theories that address those issues. For example, it analyzes trends and issues in the IT industry. The Issue Deepening Department also builds a data collection system for analyzing issues specific to a client's industry. For example, it collects and analyzes industry news and reports. The Issue Deepening Department also analyzes industry-specific issues and develops algorithms for applying career theories that address those issues. For example, it provides career advice tailored to the characteristics of the industry. This makes it possible to apply career theories that address the issues specific to the client's industry.

[0066] The problem dive unit can use the emotion estimation function to identify the client's emotional barriers and propose a career theory for overcoming them. The problem dive unit, for example, uses the emotion estimation function to identify the client's emotional barriers. For example, if the client is feeling anxious or stressed, the problem dive unit identifies those emotions. The problem dive unit also builds a system that proposes a career theory for overcoming the client's emotional barriers. For example, it applies a career theory that corresponds to the emotional barriers. The problem dive unit also uses the emotion estimation function to develop an algorithm that identifies the client's emotional barriers and proposes a career theory for overcoming them. For example, it proposes specific steps for overcoming the emotional barriers. This makes it possible to identify the client's emotional barriers and propose a career theory for overcoming them.

[0067] The Problem Digging Department can combine different career theories to provide the client with the optimal approach. For example, the Problem Digging Department builds a system that combines different career theories to provide the client with the optimal approach. For example, it combines career anchor theory and self-efficacy theory. The Problem Digging Department also develops an algorithm for combining different career theories depending on the client's issues. For example, it selects the optimal career theory depending on the client's situation. The Problem Digging Department also builds a database for combining different career theories to provide the client with the optimal approach. For example, it registers the characteristics and scope of application of each career theory in the database. This makes it possible to combine different career theories to provide the client with the optimal approach.

[0068] The Problem Digging Department can compare a client's career challenges with the success stories of other clients and propose best practices. For example, the Problem Digging Department builds a system that compares a client's career challenges with the success stories of other clients and proposes best practices. For example, it uses successful cases in the same industry as a reference. The Problem Digging Department also registers the success stories of other clients in a database and proposes best practices according to the client's career challenges. For example, it provides detailed information about the success stories. The Problem Digging Department also develops an algorithm for comparing a client's career challenges with the success stories of other clients. For example, it analyzes commonalities and patterns in the success stories. This makes it possible to compare a client's career challenges with other success stories and propose best practices.

[0069] The problem digging unit uses the emotion estimation function to apply a career theory based on the client's emotions, allowing the provision of more effective advice. The problem digging unit, for example, uses the emotion estimation function to build a system that applies a career theory based on the client's emotions. For example, if the client is feeling anxious, a career theory that corresponds to that emotion is applied. The problem digging unit also develops an algorithm for applying a career theory based on the client's emotions. For example, it selects the optimal career theory according to the client's emotional state. The problem digging unit also uses the emotion estimation function to build a system that applies a career theory based on the client's emotions and provides more effective advice. For example, it provides specific advice according to the client's emotional state. This allows the application of a career theory based on the client's emotions and the provision of more effective advice.

[0070] The assessment department can integrate and analyze the client's career assessment results with other data. For example, the assessment department builds a system that integrates the client's career assessment results with other data and performs comprehensive analysis. For example, it combines performance evaluations and feedback with the career assessment results for analysis. The assessment department also builds a database for integrating the client's career assessment results with other data. For example, it centrally manages performance evaluations and feedback and links them with the career assessment results. The assessment department also develops an algorithm for integrating and analyzing the client's career assessment results with other data. For example, it improves the reliability of the career assessment results based on the performance evaluations and feedback. This allows the client's career assessment results to be integrated with other data for analysis.

[0071] The assessment department can track the client's assessment results over time and monitor career progress. The assessment department, for example, builds a system that tracks the client's career assessment results over time and monitors career progress. For example, it conducts assessments periodically and displays the results over time. The assessment department also builds a database for tracking the client's assessment results over time. For example, it stores past assessment results and visualizes career progress. The assessment department also develops an algorithm for tracking the client's career assessment results over time and monitoring career progress. For example, it analyzes changes in assessment results and evaluates career progress. This makes it possible to monitor the client's career progress over time.

[0072] The assessment unit uses the emotion estimation function to interpret the assessment results based on the client's emotional state, allowing the assessment unit to provide more personalized advice. For example, the assessment unit uses the emotion estimation function to build a system that interprets the assessment results based on the client's emotional state. For example, if the client is feeling anxious, the assessment unit provides advice that corresponds to that emotion. The assessment unit also develops an algorithm for interpreting the assessment results based on the client's emotional state. For example, the assessment unit adjusts the interpretation of the assessment results according to the emotional state. The assessment unit also uses the emotion estimation function to build a system that interprets the assessment results based on the client's emotional state, allowing the assessment unit to provide more personalized advice. For example, specific advice according to the emotional state is provided. This allows the assessment results to be interpreted based on the client's emotional state, allowing the assessment unit to provide more personalized advice.

[0073] The assessment department can combine different career assessment tools to provide a comprehensive assessment. For example, the assessment department builds a system that combines different career assessment tools to provide a comprehensive assessment. For example, it combines personality tests and skill assessments. The assessment department also analyzes the client's career assessment results using multiple tools to provide a comprehensive assessment. For example, it integrates and analyzes the results of different assessment tools. The assessment department also develops an algorithm for combining different career assessment tools to provide a comprehensive assessment. For example, it integrates the results of each tool to perform an overall evaluation. This makes it possible to combine different career assessment tools to provide a comprehensive assessment.

[0074] The assessment department can compare the client's assessment results with data from other clients to identify relative strengths and weaknesses. For example, the assessment department builds a system to compare the client's career assessment results with data from other clients to identify relative strengths and weaknesses. For example, comparison with other clients in the same industry. The assessment department also registers data from other clients in a database and compares it with the client's assessment results. For example, comparison with other clients in the same occupation. The assessment department also develops an algorithm to compare the client's career assessment results with data from other clients. For example, it performs analysis to identify relative strengths and weaknesses. This makes it possible to compare the client's assessment results with data from other clients to identify relative strengths and weaknesses.

[0075] The assessment unit can use the emotion estimation function to provide feedback of the assessment results based on the client's emotions, thereby increasing motivation. The assessment unit, for example, uses the emotion estimation function to build a system that provides feedback of the assessment results based on the client's emotional state. For example, feedback that elicits positive emotions is provided. The assessment unit also develops an algorithm for providing feedback of the assessment results based on the client's emotional state. For example, the content of the feedback is adjusted according to the emotional state. The assessment unit also uses the emotion estimation function to build a system that provides feedback of the assessment results based on the client's emotional state, thereby increasing motivation. For example, specific feedback is provided according to the emotional state. This makes it possible to provide feedback of the assessment results based on the client's emotions, thereby increasing motivation.

[0076] The career step presentation unit can analyze the client's career goals in detail and propose a long-term career plan. The career step presentation unit, for example, builds a system that analyzes the client's career goals in detail and proposes a long-term career plan. For example, it proposes specific steps for achieving the goals. The career step presentation unit also builds a data collection system for analyzing the client's career goals in detail. For example, it collects and analyzes information related to the goals. The career step presentation unit also develops an algorithm for analyzing the client's career goals in detail and proposing a long-term career plan. For example, it proposes the optimal career path for achieving the goals. This makes it possible to analyze the client's career goals in detail and propose a long-term career plan.

[0077] The career step presentation unit can analyze a client's skill set, identify gaps in required skills, and propose steps to fill them. The career step presentation unit, for example, analyzes a client's skill set and builds a system to identify gaps in required skills. For example, it compares current skills with skills required to achieve a goal. The career step presentation unit also builds a data collection system to analyze a client's skill set. For example, it collects and analyzes information related to skills. The career step presentation unit also develops an algorithm to analyze a client's skill set, identify gaps in required skills, and propose steps to fill them. For example, it proposes specific steps for skill improvement. This makes it possible to analyze a client's skill set, identify gaps in required skills, and propose steps to fill them.

[0078] The career step presentation unit uses the emotion estimation function to propose career steps based on the emotional state of the client, thereby providing a more realistic plan. The career step presentation unit, for example, uses the emotion estimation function to build a system that proposes career steps based on the emotional state of the client. For example, if the client is feeling anxious, the career step presentation unit proposes a career step that corresponds to that emotion. The career step presentation unit also develops an algorithm for proposing career steps based on the emotional state of the client. For example, the career step presentation unit adjusts the career step according to the emotional state. The career step presentation unit also uses the emotion estimation function to propose career steps based on the emotional state of the client, thereby building a system that provides a more realistic plan. For example, specific career steps according to the emotional state are proposed. This makes it possible to propose career steps based on the emotional state of the client, thereby providing a more realistic plan.

[0079] The career step presentation unit can simulate different career paths and propose the optimal career step to a client. The career step presentation unit, for example, builds a system that simulates different career paths and proposes the optimal career step to a client. For example, it compares multiple career paths and selects the optimal one. The career step presentation unit also builds a data collection system for simulating the client's career path. For example, it collects and analyzes information related to the career paths. The career step presentation unit also develops an algorithm for simulating different career paths and proposing the optimal career step to a client. For example, it proposes the optimal career path based on the simulation results. In this way, it is possible to simulate different career paths and propose the optimal career step to a client.

[0080] The career step presentation unit can compare the client's career steps with the success stories of other clients and propose best practices. The career step presentation unit, for example, builds a system that compares the client's career steps with the success stories of other clients and proposes best practices. For example, it uses successful cases in the same industry as a reference. The career step presentation unit also registers the success stories of other clients in a database and proposes best practices according to the client's career steps. For example, it provides detailed information about the success stories. The career step presentation unit also develops an algorithm for comparing the client's career steps with the success stories of other clients. For example, it analyzes commonalities and patterns in the success stories. This makes it possible to compare the client's career steps with other success stories and propose best practices.

[0081] The career step presentation unit uses the emotion estimation function to reframe the client's career steps based on the client's emotions and guide them in a more positive direction. The career step presentation unit, for example, uses the emotion estimation function to build a system that reframes the client's career steps based on the client's emotional state. For example, if the client is feeling anxious, the career step presentation unit suggests a career step that corresponds to the client's emotions. The career step presentation unit also develops an algorithm for reframing the client's career steps based on the client's emotional state. For example, the career step presentation unit adjusts the career steps according to the emotional state. The career step presentation unit also uses the emotion estimation function to build a system that reframes the client's career steps based on the client's emotional state and guides the client in a more positive direction. For example, the career step presentation unit suggests specific career steps according to the emotional state. This makes it possible to reframe the client's career steps based on the client's emotions and guide the client in a more positive direction.

[0082] The job offering unit can analyze a client's past application history and suggest the most suitable job. The job offering unit, for example, analyzes a client's past application history and builds a system that suggests the most suitable job. For example, it suggests similar job offers based on data on job types and companies applied for in the past. The job offering unit also stores the client's application history in a database and analyzes that data to suggest the most suitable job. For example, it analyzes patterns in the application history and identifies suitable job offers. The job offering unit also analyzes a client's past application history and develops an algorithm to suggest the most suitable job. For example, it builds a system that matches application history with job information. This makes it possible to analyze a client's past application history and suggest the most suitable job.

[0083] The job provision department can match a client's skill set with the job requirements of the market and identify the most suitable job. For example, the job provision department builds a system that matches a client's skill set with the job requirements of the market. For example, it uses a skill matrix to compare the client's skills with the job requirements. The job provision department also registers the client's skill set in a database and matches it with the job requirements of the market. For example, it calculates the degree of agreement between the skill set and the job requirements and identifies the most suitable job. The job provision department also develops an algorithm for matching a client's skill set with the job requirements of the market. For example, it analyzes the correlation between the skill set and the job requirements and proposes the most suitable job. This makes it possible to match a client's skill set with the job requirements of the market and identify the most suitable job.

[0084] The job providing unit uses the emotion estimation function to make job suggestions based on the emotional state of the client, thereby providing more suitable job offers. The job providing unit, for example, uses the emotion estimation function to build a system that makes job suggestions based on the emotional state of the client. For example, if the client is feeling anxious, the job providing unit proposes job offers that correspond to that emotion. The job providing unit also develops an algorithm for making job suggestions based on the emotional state of the client. For example, it filters job information according to the emotional state. The job providing unit also uses the emotion estimation function to build a system that makes job suggestions based on the emotional state of the client, thereby providing more suitable job offers. For example, it provides specific job information according to the emotional state. This makes it possible to make job suggestions based on the emotional state of the client, thereby providing more suitable job offers.

[0085] The job providing unit can analyze a client's job search history and make personalized job suggestions. The job providing unit, for example, analyzes a client's job search history and builds a system that makes personalized job suggestions. For example, it suggests suitable jobs based on past search keywords and browsing history. The job providing unit also stores a client's job search history in a database and analyzes that data to make personalized job suggestions. For example, it analyzes search history patterns to identify suitable jobs. The job providing unit also analyzes a client's job search history and develops an algorithm for making personalized job suggestions. For example, it builds a system that matches search history with job information. This makes it possible to analyze a client's job search history and make personalized job suggestions.

[0086] The job providing unit uses the emotion estimation function to filter job information based on the client's emotions, thereby providing more suitable job offers. The job providing unit, for example, uses the emotion estimation function to build a system that filters job information based on the client's emotional state. For example, if the client is feeling anxious, the job providing unit suggests job offers that correspond to that emotion. The job providing unit also develops an algorithm for filtering job information based on the client's emotional state. For example, the job information is filtered according to the emotional state. The job providing unit also uses the emotion estimation function to build a system that filters job information based on the client's emotional state, thereby providing more suitable job offers. For example, specific job information according to the emotional state is provided. This makes it possible to filter job information based on the client's emotions and provide more suitable job offers.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The career consulting system may further include a health management unit that monitors the client's health condition. For example, it may analyze the client's stress level and sleep patterns and provide career advice based on the client's health condition. The health management unit may also collect the client's health data and propose a long-term health management plan. For example, it may suggest regular exercise and dietary improvements. Furthermore, the health management unit may integrate and analyze the client's health condition with other data to provide comprehensive career advice. For example, it may analyze the relationship between health condition and work performance.

[0089] The career consulting system can further include a hobby analysis unit that analyzes the hobbies and interests of a client. For example, the system can analyze the client's hobby activities and provide career advice based on the analysis. The hobby analysis unit can also suggest jobs and skills related to the client's hobby. For example, if the client's hobby is photography, the system can suggest related jobs and skills. Furthermore, the hobby analysis unit can integrate and analyze the client's hobby data with other data to provide comprehensive career advice. For example, the system can suggest a career plan that takes into account the balance between hobbies and work.

[0090] The career consulting system may further include a network analysis unit that analyzes the client's network. For example, the network analysis unit may analyze the client's social network to identify connections that will be useful for the client's career. The network analysis unit may also provide career advice that utilizes the client's network. For example, the network analysis unit may suggest career steps that utilize connections in a specific industry. The network analysis unit may also integrate and analyze the client's network data with other data to provide comprehensive career advice. For example, the network analysis unit may analyze the relationship between connections and career success.

[0091] The career consulting system may further include a learning analysis unit that analyzes the client's learning history. For example, the system may analyze the education and training the client has received in the past and provide career advice based on that analysis. The learning analysis unit may also suggest future learning plans based on the client's learning history. For example, the learning analysis unit may suggest courses to improve specific skills. Furthermore, the learning analysis unit may integrate and analyze the client's learning data with other data to provide comprehensive career advice. For example, the learning analysis unit may analyze the relationship between learning history and career success.

[0092] The career consulting system can further include a lifestyle analysis unit that analyzes the client's lifestyle. For example, it can analyze the client's lifestyle habits and values ​​and provide career advice based on the results. The lifestyle analysis unit can also suggest occupations and skills related to the client's lifestyle. For example, it can suggest occupations suitable for remote work to a client who wishes to work remotely. Furthermore, the lifestyle analysis unit can integrate and analyze the client's lifestyle data with other data to provide comprehensive career advice. For example, it can suggest a career plan that takes into account the balance between lifestyle and work.

[0093] The career consulting system can further use a client emotion estimation function to provide career advice based on the client's emotions. For example, if the client is feeling anxious, career advice corresponding to that emotion can be provided. The emotion estimation function can also be used to analyze the client's emotional state in real time and suggest career steps based on that. For example, if the client is feeling stressed, career steps to reduce stress can be suggested. The emotion estimation function can also be used to integrate and analyze the client's emotional data with other data to provide comprehensive career advice. For example, the relationship between emotional state and career success can be analyzed.

[0094] The career consulting system can further use a client emotion estimation function to assist in setting career goals based on the client's emotions. For example, if the client is excited, career goals can be set that take advantage of that emotion. The emotion estimation function can also be used to analyze the client's emotional state and suggest career goals based on that. For example, if the client is calm, career goals can be set based on calm judgment. The emotion estimation function can also be used to integrate and analyze the client's emotional data with other data to set comprehensive career goals. For example, the relationship between emotional state and achievement of career goals can be analyzed.

[0095] The career consulting system can further use a client emotion estimation function to provide feedback based on the client's emotions. For example, if the client is feeling happy, feedback that reinforces that emotion can be provided. The emotion estimation function can also be used to analyze the client's emotional state and provide feedback based on that. For example, if the client is feeling anxious, feedback that alleviates that anxiety can be provided. The emotion estimation function can also be used to integrate and analyze the client's emotional data with other data to provide comprehensive feedback. For example, the relationship between the emotional state and the effect of feedback can be analyzed.

[0096] The career consulting system can further use a client emotion estimation function to reframe career steps based on the client's emotions. For example, if the client is feeling anxious, the system can suggest career steps that correspond to those emotions. The emotion estimation function can also be used to analyze the client's emotional state and reframe career steps based on that. For example, if the client is excited, the system can suggest career steps that make use of that excitement. The emotion estimation function can also be used to integrate and analyze the client's emotional data with other data to reframe career steps comprehensively. For example, the relationship between emotional state and career step success can be analyzed.

[0097] The career consulting system can also use a client emotion estimation function to perform a career assessment based on the client's emotions. For example, if the client is feeling anxious, a career assessment can be performed that corresponds to that emotion. The emotion estimation function can also be used to analyze the client's emotional state and perform a career assessment based on that. For example, if the client is feeling happy, a career assessment can be performed that makes use of that emotion. Furthermore, the emotion estimation function can be used to integrate and analyze the client's emotional data with other data to perform a comprehensive career assessment. For example, the relationship between the emotional state and the results of the career assessment can be analyzed.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The question answering unit answers the client's question. For example, if the client asks, "What kind of job would suit me?", the generative AI will suggest suitable jobs based on the client's background information and interests. The question answering unit can also analyze the client's past question history to provide personalized answers. For example, it can provide more specific answers based on topics that have been frequently asked in the past. Step 2: The problem-digging section delves deeper into the client's issues. For example, if a client says, "I'm not satisfied with my current job," the generative AI will explore the cause and provide advice based on career theory. The problem-digging section can also analyze the client's past career data and identify long-term career patterns. For example, it can analyze past work history and skill changes. Step 3: The assessment department conducts a career assessment of the client. For example, if a client says, "I want to know my strengths," the generative AI will conduct a career assessment, identify their strengths based on the results, and suggest appropriate career steps. The assessment department can also integrate and analyze the client's career assessment results with other data. For example, it can analyze performance evaluations and feedback in combination with the career assessment results. Step 4: The career step suggestion unit presents the client with future career steps. For example, if a client asks, "What career step should I take next?", the generation AI will suggest an appropriate career step based on the client's current situation and goals. The career step suggestion unit can also perform a detailed analysis of the client's career goals and propose a long-term career plan. For example, it can suggest specific steps to achieve the goal. Step 5: The job provider provides the most suitable job from the job information in the market. For example, if a client says, "I'm looking for a new job," the generation AI collects and analyzes job information in the market and suggests suitable jobs for the client. The job provider can also analyze the client's past application history to suggest the most suitable job. For example, it suggests similar jobs based on data on the types of jobs and companies that the client has applied for in the past.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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. Using generative AI, a question answering section that answers questions from clients; a problem deepening unit that deepens the client's problem; an assessment unit that performs a career assessment of the client; a career step presentation unit that presents future career steps to the client; A job offering section that offers the most suitable job from job information in the market. A system characterized by:

2. The question and answer section Analyze the client's past question history and provide personalized answers 2. The system of claim 1.

3. The question and answer section Analyzing the client's non-verbal communication to generate more appropriate responses 2. The system of claim 1.

4. The question and answer section Providing answers that correspond to the client's emotional state, increasing the client's sense of security 2. The system of claim 1.

5. The question and answer section Providing answers to the client's questions using visual and interactive content 2. The system of claim 1.

Images