system

Abstract

The system according to the embodiment aims to provide effective career counseling that allows clients to freely speak their true feelings. [Solution] A system according to an embodiment includes a question receiving unit, a theory application unit, an assessment unit, and a job offering unit. The question receiving unit receives questions from clients. The theory application unit analyzes the questions received by the question receiving unit and applies career theory. The assessment unit analyzes the strengths and weaknesses of clients based on the career theory applied by the theory application unit. The job offering unit offers job offers based on the results of the analysis by the assessment unit.

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 receiving unit, a theory application unit, an assessment unit, and a job offering unit. The question receiving unit receives questions from clients. The theory application unit analyzes the questions received by the question receiving unit and applies career theory. The assessment unit analyzes the strengths and weaknesses of clients based on the career theory applied by the theory application unit. The job offering unit offers job offers based on the results of the analysis by the assessment unit. [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 utilizes a generative AI to quickly and effectively resolve a client's career challenges. In this career consulting system, a client inputs career-related questions. The generative AI analyzes the questions, applies appropriate career theories, and provides advice. Furthermore, the generative AI uses a career assessment to analyze the client's strengths and weaknesses and suggest future career steps. Finally, the generative AI selects the most suitable job from market job information. For example, when a client inputs a career-related question, the client can freely input their career concerns and aspirations. For example, a question such as, "I'm not satisfied with my current job, but I don't know what career path is right for me." This information is input into the generative AI. The generative AI then analyzes the input question and applies appropriate career theories to provide advice. The generative AI applies career theories tailored to the client's situation and provides specific advice. For example, if a client is considering changing jobs, the generative AI applies career theories related to job changes and provides advice on the advantages and disadvantages of changing jobs and how to choose a new job. Furthermore, the generative AI uses a career assessment to analyze the client's strengths and weaknesses. Generative AI analyzes a client's past experience, skills, personality, etc. to identify their strengths and weaknesses. For example, if a client has excellent leadership skills, Generative AI will suggest a career path that utilizes those strengths. Finally, Generative AI provides the client with the most suitable job listings from job listings on the market. Generative AI considers the client's wishes, strengths, and weaknesses to search for and provide the most suitable job. For example, if a client wants a career in the IT industry, Generative AI will search for job listings in the IT industry and provide the client with the most suitable job listing. This service allows clients to find a career path that suits them without relying on specialized knowledge or skills. In addition, because Generative AI responds in a conversational format, clients feel free to speak their mind and receive effective career counseling.This allows the career consulting system to quickly and effectively resolve the career issues of clients.

[0029] A career consulting system according to an embodiment includes a question receiving unit, a theory application unit, an assessment unit, and a job offer providing unit. The question receiving unit receives questions about a client's career. The questions about a client's career may be in text, audio, video, or other formats, but are not limited to these examples. The question receiving unit, for example, receives questions input by the client in text format. The question receiving unit can also receive questions input by the client in audio format. The question receiving unit can also receive questions input by the client in video format. For example, the question receiving unit may receive a question input by the client in text format, such as, "I'm not satisfied with my current job, but I don't know what career path is right for me." The question receiving unit can also receive a similar question input by the client in audio format. The question receiving unit can also receive questions input by the client in video format. The theory application unit analyzes the questions received by the question receiving unit and applies an appropriate career theory. Examples of career theories include, but are not limited to, Holland's Career Choice Theory and Super's Career Development Theory. For example, if a client is considering changing jobs, the theory application unit applies career theories related to career changes and provides advice on the advantages and disadvantages of changing jobs and how to choose a new job. Furthermore, if a client is considering a career change, the theory application unit can also apply career theories related to career changes and provide advice on how to make the career change and what to be careful about. Furthermore, if a client is considering career advancement, the theory application unit can also apply career theories related to career advancement and provide advice on how to advance their career and the skills they need. The assessment unit analyzes the client's strengths and weaknesses based on the career theories applied by the theory application unit. Examples of strengths and weaknesses analysis include, but are not limited to, skill evaluation and personality trait evaluation. For example, the assessment unit can perform a detailed analysis of the client's past work experience to identify strengths and weaknesses.The assessment department can also perform a detailed analysis of a client's skill set to identify their strengths and weaknesses. Furthermore, the assessment department can also perform a detailed analysis of a client's personality traits to identify their strengths and weaknesses. For example, if a client has excellent leadership skills, the assessment department can suggest a career path that utilizes those strengths. Furthermore, if a client has excellent communication skills, the assessment department can suggest a career path that utilizes those strengths. Furthermore, if a client has excellent analytical skills, the assessment department can suggest a career path that utilizes those strengths. The job offering department provides optimal job offers based on the results of the analysis by the assessment department. Job offers include, but are not limited to, job type, work location, and salary conditions. For example, if a client desires a career in the IT industry, the job offering department can search for job listings in the IT industry and provide the client with the optimal job offers. Furthermore, if a client desires a career in the medical industry, the job offering department can search for job listings in the medical industry and provide the client with the optimal job offers. Furthermore, if the client desires a career in the education industry, the job providing unit can search for job information in the education industry and provide the client with the most suitable job. This allows the career consulting system according to the embodiment to quickly and effectively solve the client's career challenges.

[0030] The question reception unit can analyze the client's past question history and select a question reception method. For example, the question reception unit can prioritize suggesting question formats that the client has frequently used in the past. The question reception unit can also receive questions during time periods that the client has preferred in the past. Furthermore, the question reception unit can analyze the content of the client's past questions and automatically suggest related questions. For example, the question reception unit prioritizes suggesting question formats that the client has frequently used in the past. To analyze the client's past question history, the question reception unit can collect and analyze data such as question content, question frequency, and question type. For example, the question reception unit can analyze the content of the client's past questions using text analysis technology and automatically suggest related questions. The question reception unit can also analyze the frequency of the client's past questions and prioritize suggesting frequently used question formats. Furthermore, the question reception unit can analyze the type of the client's past questions and automatically suggest related questions. This allows the question reception unit to provide an optimal question reception method based on the client's past question history. Some or all of the above-described processing in the question reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit can input the client's past question history data into the generation AI and have the generation AI select a question receiving method.

[0031] When receiving questions, the question receiving unit can filter questions based on the client's current career situation and areas of interest. For example, the question receiving unit prioritizes receiving questions related to the client's current job content. The question receiving unit can also filter and receive related questions based on the client's areas of interest. Furthermore, the question receiving unit can automatically suggest questions according to the client's career stage. For example, the question receiving unit prioritizes receiving questions related to the client's current job content. The question receiving unit can collect and analyze data such as the client's current job content, career goals, and skill set to evaluate the client's current career situation and areas of interest. For example, the question receiving unit can analyze the client's current job content using text analysis technology and prioritize receiving related questions. The question receiving unit can also analyze the client's areas of interest and filter and receive related questions. Furthermore, the question receiving unit can evaluate the client's career stage and automatically suggest questions according to the career stage. This allows the question receiving unit to receive appropriate questions based on the client's current career situation and areas of interest. Some or all of the above-described processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit may input the client's career status data into the generation AI and have the generation AI perform question filtering.

[0032] When accepting questions, the question acceptance unit can prioritize accepting questions by taking into account the client's geographical location information. For example, if the client is located in a specific region, the question acceptance unit can prioritize accepting career information related to that region. Furthermore, if the client is located overseas, the question acceptance unit can prioritize accepting international career information. Furthermore, if the client is working remotely, the question acceptance unit can prioritize accepting questions related to remote work. For example, if the client is located in a specific region, the question acceptance unit can prioritize accepting career information related to that region. The question acceptance unit can use technologies such as GPS data, IP addresses, and location information services to acquire the client's geographical location information. For example, the question acceptance unit can acquire the client's GPS data and prioritize accepting relevant career information based on that data. The question acceptance unit can also analyze the client's IP address to identify the geographical location information. Furthermore, the question acceptance unit can acquire the client's geographical location information using location information services. This allows the question acceptance unit to prioritize accepting questions that are highly relevant based on the client's geographical location information. Some or all of the above-described processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit may input geographical location information data of the client into the generation AI to determine the priority of the questions.

[0033] The question receiving unit can analyze the client's social media activity when receiving a question and receive related questions. For example, the question receiving unit can analyze the client's social media posts and suggest related questions. The question receiving unit can also filter questions based on the client's areas of interest on social media. Furthermore, the question receiving unit can prioritize questions based on the frequency of the client's social media activity. For example, the question receiving unit can analyze the client's social media posts and suggest related questions. The question receiving unit can collect and analyze data such as the post content, the number of likes, and the number of followers to analyze the client's social media activity. For example, the question receiving unit can analyze the client's social media posts using text analysis technology and suggest related questions. The question receiving unit can also analyze the client's areas of interest on social media and filter related questions. Furthermore, the question receiving unit can analyze the frequency of the client's social media activity and prioritize questions. This allows the question receiving unit to receive related questions based on the client's social media activity. Some or all of the above-described processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit can input the client's social media activity data into the generation AI and have the generation AI perform question filtering.

[0034] When applying a theory, the theory application unit can adjust the level of detail of the theory based on the client's career goals. For example, if the client's career goals are clear, the theory application unit can apply a detailed career theory. Furthermore, if the client's career goals are unclear, the theory application unit can apply a basic career theory. Furthermore, if the client's career goals fluctuate, the theory application unit can apply a flexible career theory. For example, if the client's career goals are clear, the theory application unit can apply a detailed career theory. The theory application unit can collect and analyze data such as short-term goals, long-term goals, and skill development goals to identify the client's career goals. For example, the theory application unit can analyze the client's short-term goals and apply a detailed career theory. Furthermore, the theory application unit can analyze the client's long-term goals and apply a basic career theory. Furthermore, the theory application unit can analyze the client's skill development goals and apply a flexible career theory. This allows the theory application unit to apply a career theory with an appropriate level of detail depending on the client's career goals. Some or all of the above-described processing in the theory application unit may be performed, for example, using AI or without AI. For example, the theory application unit can input the client's career goal data into the generation AI and adjust the level of detail of the theory.

[0035] When applying the theory, the theory application unit can apply a career theory according to the client's industry and occupation. For example, if the client is in the IT industry, the theory application unit can apply a career theory specialized for the IT industry. Furthermore, if the client is in the medical industry, the theory application unit can apply a career theory specialized for the medical industry. Furthermore, if the client is in the education industry, the theory application unit can apply a career theory specialized for the education industry. For example, if the client is in the IT industry, the theory application unit can apply a career theory specialized for the IT industry. The theory application unit can collect and analyze data such as the type of industry and the type of occupation to identify the client's industry and occupation. For example, the theory application unit can analyze the client's type of industry and apply a career theory specialized for the IT industry. Furthermore, the theory application unit can analyze the client's type of occupation and apply a career theory specialized for the medical industry. Furthermore, the theory application unit can comprehensively analyze the client's industry and occupation and apply a career theory specialized for the education industry. This allows the theory application unit to apply an appropriate career theory according to the client's industry and occupation. Some or all of the above-described processing in the theory application unit may be performed using, for example, AI, or may be performed without using AI. For example, the theory application unit may input data on the client's industry and job type into the generation AI and have the generation AI apply the career theory.

[0036] When applying theories, the theory application unit can determine the priority of theories based on the client's career stage. For example, if the client is a new graduate, the theory application unit can prioritize applying a basic career theory. Furthermore, if the client is a mid-career employee, the theory application unit can prioritize applying a specialized career theory. Furthermore, if the client is a manager, the theory application unit can prioritize applying a leadership career theory. For example, if the client is a new graduate, the theory application unit can prioritize applying a basic career theory. The theory application unit can collect and analyze data such as new graduate, mid-career, and senior career data to identify the client's career stage. For example, the theory application unit can analyze the client's new graduate data and prioritize applying a basic career theory. Furthermore, the theory application unit can analyze the client's mid-career data and prioritize applying a specialized career theory. Furthermore, the theory application unit can analyze the client's senior career data and prioritize applying a leadership career theory. This allows the theory application unit to prioritize applying an appropriate career theory according to the client's career stage. Some or all of the above-described processing in the theory application unit may be performed using, for example, AI, or may be performed without using AI. For example, the theory application unit may input the client's career stage data into the generation AI to determine the priority of the theories.

[0037] When applying a theory, the theory application unit can select a theory to apply by referring to the client's past career counseling history. For example, the theory application unit can apply a relevant career theory based on the content of the client's past consultations. The theory application unit can also re-apply a career theory that was effective based on the client's past consultation history. Furthermore, the theory application unit can analyze the client's past consultation history and propose a new career theory. For example, the theory application unit can apply a relevant career theory based on the content of the client's past consultations. To analyze the client's past career counseling history, the theory application unit can collect and analyze data such as the content of the past consultations, the frequency of consultations, and the type of consultation. For example, the theory application unit can analyze the content of the client's past consultations using text analysis technology and apply a relevant career theory. The theory application unit can also analyze the frequency of the client's past consultations and re-apply an effective career theory. Furthermore, the theory application unit can analyze the type of consultations the client has had and propose a new career theory. In this way, the theory application unit can apply an appropriate career theory based on the client's past career counseling history. Some or all of the above-described processing in the theory application unit may be performed using, for example, AI, or may be performed without using AI. For example, the theory application unit may input the client's past career counseling history data into the generation AI and have the generation AI select a career theory.

[0038] During an assessment, the assessment department can analyze a client's past experience and skills to identify strengths and weaknesses. For example, the assessment department can analyze a client's past work experience in detail to identify strengths and weaknesses. The assessment department can also analyze a client's skill set in detail to identify strengths and weaknesses. Furthermore, the assessment department can analyze a client's past project experience in detail to identify strengths and weaknesses. For example, the assessment department can analyze a client's past work experience in detail to identify strengths and weaknesses. The assessment department can collect and analyze data such as work experience, technical skills, and soft skills to evaluate a client's past experience and skills. For example, the assessment department can analyze a client's work experience using text analysis technology to identify strengths and weaknesses. The assessment department can also analyze a client's technical skills to identify strengths and weaknesses. Furthermore, the assessment department can analyze a client's soft skills to identify strengths and weaknesses. In this way, the assessment department can accurately identify strengths and weaknesses by analyzing a client's past experience and skills in detail. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit may input the client's past experience and skill data into the generation AI and have the generation AI identify strengths and weaknesses.

[0039] The assessment unit can apply an assessment method based on the client's personality traits during the assessment. For example, if the client is introverted, the assessment unit can apply a self-reflective assessment method. Furthermore, if the client is extroverted, the assessment unit can apply an interactive assessment method. Furthermore, if the client is analytical, the assessment unit can apply a data-driven assessment method. For example, if the client is introverted, the assessment unit can apply a self-reflective assessment method. The assessment unit can collect and analyze data, such as a personality diagnostic test and behavioral pattern analysis, to evaluate the client's personality traits. For example, the assessment unit can analyze the results of the client's personality diagnostic test and apply a self-reflective assessment method. Furthermore, the assessment unit can analyze the client's behavioral patterns and apply an interactive assessment method. Furthermore, the assessment unit can evaluate the client's analytical traits and apply a data-driven assessment method. This allows the assessment unit to provide an appropriate assessment method according to the client's personality traits. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit may input personality trait data of the client into the generation AI and cause the generation AI to apply the assessment method.

[0040] During an assessment, the assessment department can select an assessment method taking into account the client's geographic location information. For example, if the client is in an urban area, the assessment department can apply an assessment method specialized for urban areas. Furthermore, if the client is in a rural area, the assessment department can apply an assessment method specialized for rural areas. Furthermore, if the client is overseas, the assessment department can apply an international assessment method. For example, if the client is in an urban area, the assessment department can apply an assessment method specialized for urban areas. The assessment department can use technologies such as GPS data, IP addresses, and location information services to obtain the client's geographic location information. For example, the assessment department can obtain the client's GPS data and apply an assessment method specialized for urban areas based on that data. The assessment department can also analyze the client's IP address to identify the client's geographic location information. Furthermore, the assessment department can obtain the client's geographic location information using location information services. This allows the assessment department to provide the optimal assessment method based on the client's geographic location information. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit may input the client's geographical location information data into the generation AI and have the generation AI select an assessment method.

[0041] During an assessment, the assessment department can analyze the client's social media activity to identify strengths and weaknesses. For example, the assessment department can analyze the content of the client's social media posts to identify strengths and weaknesses. The assessment department can also analyze the client's areas of interest on social media to identify strengths and weaknesses. Furthermore, the assessment department can analyze the frequency of the client's social media activities to identify strengths and weaknesses. For example, the assessment department can analyze the content of the client's social media posts to identify strengths and weaknesses. To analyze the client's social media activities, the assessment department can collect and analyze data such as the content of posts, the number of likes, and the number of followers. For example, the assessment department can analyze the content of the client's social media posts using text analysis technology to identify strengths and weaknesses. The assessment department can also analyze the client's areas of interest on social media to identify strengths and weaknesses. Furthermore, the assessment department can analyze the frequency of the client's social media activities to identify strengths and weaknesses. This allows the assessment department to identify strengths and weaknesses based on the client's social media activity. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit may input the client's social media activity data into the generation AI and have the generation AI identify strengths and weaknesses.

[0042] When providing job offers, the job providing unit can select job offers by referring to the client's past application history. For example, the job providing unit can suggest related job offers based on job offers to which the client has applied in the past. The job providing unit can also analyze trends in successful applications from the client's past application history and suggest optimal job offers. Furthermore, the job providing unit can suggest related job offers for which the client has not yet applied by referring to the client's past application history. For example, the job providing unit can suggest related job offers based on job offers to which the client has applied in the past. To analyze the client's past application history, the job providing unit can collect and analyze data such as the job types applied for in the past, application timing, and application results. For example, the job providing unit can analyze the job types applied for in the past by the client and suggest related job offers. The job providing unit can also analyze the timing of the client's past applications and suggest optimal job offers based on trends in successful applications. Furthermore, the job providing unit can analyze the results of the client's past application and suggest related job offers for which the client has not yet applied. In this way, the job providing unit can provide optimal job offers based on the client's past application history. Some or all of the above-described processing in the job offer providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the job offer providing unit may input the client's past application history data into the generation AI and have the generation AI select job offers.

[0043] When providing job offers, the job providing unit can filter job information based on the client's desired industry and job type. For example, the job providing unit can prioritize providing job information specialized in the client's desired industry. The job providing unit can also prioritize providing job information specialized in the client's desired job type. Furthermore, the job providing unit can filter and provide related job information based on the client's desired industry and job type. For example, the job providing unit prioritizes providing job information specialized in the client's desired industry. The job providing unit can collect and analyze data such as the type of desired industry and the type of desired job type to identify the client's desired industry and job type. For example, the job providing unit can analyze the type of industry the client desires and filter and provide related job information. The job providing unit can also analyze the type of job type the client desires and filter and provide related job information. Furthermore, the job providing unit can comprehensively analyze the client's desired industry and job type to provide optimal job information. This allows the job providing unit to provide appropriate job information based on the client's desired industry and job type. Some or all of the above-described processing in the job providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the job providing unit may input data on the client's desired industry and job type into the generation AI, and have the generation AI filter the job information.

[0044] The job providing unit can provide optimal job offers by taking into account the client's geographic location information when providing job offers. For example, if the client is located in a specific region, the job providing unit can prioritize providing job information related to that region. Furthermore, if the client is located overseas, the job providing unit can prioritize providing international job information. Furthermore, if the client desires remote work, the job providing unit can prioritize providing job information related to remote work. For example, if the client is located in a specific region, the job providing unit can prioritize providing job information related to that region. The job providing unit can use technologies such as GPS data, IP addresses, and location information services to obtain the client's geographic location information. For example, the job providing unit can obtain the client's GPS data and prioritize providing relevant job information based on that data. The job providing unit can also analyze the client's IP address to identify the client's geographic location information. Furthermore, the job providing unit can obtain the client's geographic location information using location information services. This allows the job providing unit to provide optimal job offers based on the client's geographic location information. Some or all of the above-described processing in the job offer providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the job offer providing unit may input the geographical location information data of the client to the generation AI and cause the generation AI to select a job offer.

[0045] The job providing unit can analyze the social media activity of the client and provide relevant job offers when providing job offers. For example, the job providing unit can analyze the content of the client's social media posts and suggest relevant job offers. The job providing unit can also filter job information based on the client's areas of interest on social media. Furthermore, the job providing unit can prioritize job offers based on the frequency of the client's social media activity. For example, the job providing unit can analyze the content of the client's social media posts and suggest relevant job offers. The job providing unit can collect and analyze data such as the content of the posts, the number of likes, and the number of followers to analyze the client's social media activity. For example, the job providing unit can analyze the content of the client's social media posts using text analysis technology and suggest relevant job offers. The job providing unit can also analyze the client's areas of interest on social media and filter relevant job offers. Furthermore, the job providing unit can analyze the frequency of the client's social media activity and prioritize job offers. In this way, the job providing unit can provide relevant job offers based on the client's social media activity. Some or all of the above-described processing in the job providing unit can be performed, for example, using AI or without AI. For example, the job provision department can input the client's social media activity data into the generation AI and have the generation AI select jobs.

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

[0047] The question receiving unit can analyze the client's past question history and learn question patterns. For example, it can preferentially suggest question formats that the client has frequently used in the past. The question receiving unit can also accept questions during time periods that the client has preferred in the past. Furthermore, the question receiving unit can analyze the content of the client's past questions and automatically suggest related questions. This allows the question receiving unit to provide the optimal question receiving method based on the client's past question history.

[0048] The question receiving unit can perform filtering based on the client's current career situation and areas of interest. For example, questions related to the client's current job content are preferentially received. The question receiving unit can also filter and receive related questions based on the client's areas of interest. Furthermore, the question receiving unit can automatically suggest questions according to the client's career stage. This allows the question receiving unit to receive appropriate questions based on the client's current career situation and areas of interest.

[0049] The question reception unit can prioritize receiving questions by taking into account the geographical location information of the client. For example, if the client is in a specific region, it can prioritize receiving career information related to that region. In addition, if the client is overseas, the question reception unit can also prioritize receiving international career information. Furthermore, if the client is working remotely, the question reception unit can also prioritize receiving questions related to remote work. This allows the question reception unit to prioritize receiving questions that are highly relevant based on the geographical location information of the client.

[0050] When applying a theory, the theory application unit can adjust the level of detail of the theory based on the client's career goals. For example, if the client's career goals are clear, a detailed career theory is applied. Alternatively, if the client's career goals are vague, the theory application unit can apply a basic career theory. Furthermore, if the client's career goals fluctuate, the theory application unit can apply a flexible career theory. This allows the theory application unit to apply a career theory with an appropriate level of detail depending on the client's career goals.

[0051] During the assessment, the assessment department can apply an assessment method based on the client's personality traits. For example, if the client is introverted, the assessment department can apply a self-reflective assessment method. If the client is extroverted, the assessment department can also apply an interactive assessment method. Furthermore, if the client is analytical, the assessment department can also apply a data-driven assessment method. This allows the assessment department to provide an appropriate assessment method according to the client's personality traits.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The question reception unit receives career-related questions from clients. Client questions can be entered in text, audio, video, or other formats. For example, a client can enter a question in text format such as, "I'm not satisfied with my current job, but I don't know what career path is right for me." Questions can also be entered in audio or video format. Step 2: The theory application section analyzes the questions received by the question reception section and applies an appropriate career theory. Career theories include Holland's career choice theory and Super's career development theory. For example, if a client is considering changing jobs, a career theory related to changing jobs will be applied, and advice will be given on the advantages and disadvantages of changing jobs, how to choose a new job, etc. Step 3: The assessment section analyzes the client's strengths and weaknesses based on the career theory applied by the theory application section. The analysis includes skill assessment and personality trait assessment. For example, the client's past work experience, skill set, and personality traits are analyzed in detail to identify their strengths and weaknesses. Step 4: The Job Offering Department provides the most suitable job offers based on the results analyzed by the Assessment Department. Job offers include job type, work location, salary conditions, etc. For example, if a client wants a career in the IT industry, the Department will search for job information in the IT industry and provide the most suitable job offers.

[0054] (Example 2) A career consulting system according to an embodiment of the present invention utilizes a generative AI to quickly and effectively resolve a client's career challenges. In this career consulting system, a client inputs career-related questions. The generative AI analyzes the questions, applies appropriate career theories, and provides advice. Furthermore, the generative AI uses a career assessment to analyze the client's strengths and weaknesses and suggest future career steps. Finally, the generative AI selects the most suitable job from market job information. For example, when a client inputs a career-related question, the client can freely input their career concerns and aspirations. For example, a question such as, "I'm not satisfied with my current job, but I don't know what career path is right for me." This information is input into the generative AI. The generative AI then analyzes the input question and applies appropriate career theories to provide advice. The generative AI applies career theories tailored to the client's situation and provides specific advice. For example, if a client is considering changing jobs, the generative AI applies career theories related to job changes and provides advice on the advantages and disadvantages of changing jobs and how to choose a new job. Furthermore, the generative AI uses a career assessment to analyze the client's strengths and weaknesses. Generative AI analyzes a client's past experience, skills, personality, etc. to identify their strengths and weaknesses. For example, if a client has excellent leadership skills, Generative AI will suggest a career path that utilizes those strengths. Finally, Generative AI provides the client with the most suitable job listings from job listings on the market. Generative AI considers the client's wishes, strengths, and weaknesses to search for and provide the most suitable job. For example, if a client wants a career in the IT industry, Generative AI will search for job listings in the IT industry and provide the client with the most suitable job listing. This service allows clients to find a career path that suits them without relying on specialized knowledge or skills. In addition, because Generative AI responds in a conversational format, clients feel free to speak their mind and receive effective career counseling.This allows the career consulting system to quickly and effectively resolve the career issues of clients.

[0055] A career consulting system according to an embodiment includes a question receiving unit, a theory application unit, an assessment unit, and a job offer providing unit. The question receiving unit receives questions about a client's career. The questions about a client's career may be in text, audio, video, or other formats, but are not limited to these examples. The question receiving unit, for example, receives questions input by the client in text format. The question receiving unit can also receive questions input by the client in audio format. The question receiving unit can also receive questions input by the client in video format. For example, the question receiving unit may receive a question input by the client in text format, such as, "I'm not satisfied with my current job, but I don't know what career path is right for me." The question receiving unit can also receive a similar question input by the client in audio format. The question receiving unit can also receive questions input by the client in video format. The theory application unit analyzes the questions received by the question receiving unit and applies an appropriate career theory. Examples of career theories include, but are not limited to, Holland's Career Choice Theory and Super's Career Development Theory. For example, if a client is considering changing jobs, the theory application unit applies career theories related to career changes and provides advice on the advantages and disadvantages of changing jobs and how to choose a new job. Furthermore, if a client is considering a career change, the theory application unit can also apply career theories related to career changes and provide advice on how to make the career change and what to be careful about. Furthermore, if a client is considering career advancement, the theory application unit can also apply career theories related to career advancement and provide advice on how to advance their career and the skills they need. The assessment unit analyzes the client's strengths and weaknesses based on the career theories applied by the theory application unit. Examples of strengths and weaknesses analysis include, but are not limited to, skill evaluation and personality trait evaluation. For example, the assessment unit can perform a detailed analysis of the client's past work experience to identify strengths and weaknesses.The assessment department can also perform a detailed analysis of a client's skill set to identify their strengths and weaknesses. Furthermore, the assessment department can also perform a detailed analysis of a client's personality traits to identify their strengths and weaknesses. For example, if a client has excellent leadership skills, the assessment department can suggest a career path that utilizes those strengths. Furthermore, if a client has excellent communication skills, the assessment department can suggest a career path that utilizes those strengths. Furthermore, if a client has excellent analytical skills, the assessment department can suggest a career path that utilizes those strengths. The job offering department provides optimal job offers based on the results of the analysis by the assessment department. Job offers include, but are not limited to, job type, work location, and salary conditions. For example, if a client desires a career in the IT industry, the job offering department can search for job listings in the IT industry and provide the client with the optimal job offers. Furthermore, if a client desires a career in the medical industry, the job offering department can search for job listings in the medical industry and provide the client with the optimal job offers. Furthermore, if the client desires a career in the education industry, the job providing unit can search for job information in the education industry and provide the client with the most suitable job. This allows the career consulting system according to the embodiment to quickly and effectively solve the client's career challenges.

[0056] The question reception unit can estimate the client's emotions and adjust the timing of receiving questions based on the estimated client's emotions. For example, if the client is feeling stressed, the question reception unit can receive questions during a time when the client can relax. Furthermore, if the client is concentrating, the question reception unit can receive questions immediately and respond quickly. Furthermore, if the client is tired, the question reception unit can adjust the timing of receiving questions so that questions are received after the client has rested. For example, if the client is feeling stressed, the question reception unit can receive questions during a time when the client can relax. The question reception unit can use techniques such as facial expression recognition, voice analysis, and text analysis to estimate the client's emotions. For example, the question reception unit can capture the client's facial expression with a camera and estimate the emotion using facial expression recognition technology. Furthermore, the question reception unit can record the client's voice and estimate the emotion using voice analysis technology. Furthermore, the question reception unit can analyze the client's text input and estimate the emotion using text analysis technology. This allows the question reception unit to receive questions at an appropriate timing according to the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the question reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the question reception unit may input facial expression data of a client to the generation AI and cause the generation AI to estimate emotions.

[0057] The question reception unit can analyze the client's past question history and select a question reception method. For example, the question reception unit can prioritize suggesting question formats that the client has frequently used in the past. The question reception unit can also receive questions during time periods that the client has preferred in the past. Furthermore, the question reception unit can analyze the content of the client's past questions and automatically suggest related questions. For example, the question reception unit prioritizes suggesting question formats that the client has frequently used in the past. To analyze the client's past question history, the question reception unit can collect and analyze data such as question content, question frequency, and question type. For example, the question reception unit can analyze the content of the client's past questions using text analysis technology and automatically suggest related questions. The question reception unit can also analyze the frequency of the client's past questions and prioritize suggesting frequently used question formats. Furthermore, the question reception unit can analyze the type of the client's past questions and automatically suggest related questions. This allows the question reception unit to provide an optimal question reception method based on the client's past question history. Some or all of the above-described processing in the question reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit can input the client's past question history data into the generation AI and have the generation AI select a question receiving method.

[0058] When receiving questions, the question receiving unit can filter questions based on the client's current career situation and areas of interest. For example, the question receiving unit prioritizes receiving questions related to the client's current job content. The question receiving unit can also filter and receive related questions based on the client's areas of interest. Furthermore, the question receiving unit can automatically suggest questions according to the client's career stage. For example, the question receiving unit prioritizes receiving questions related to the client's current job content. The question receiving unit can collect and analyze data such as the client's current job content, career goals, and skill set to evaluate the client's current career situation and areas of interest. For example, the question receiving unit can analyze the client's current job content using text analysis technology and prioritize receiving related questions. The question receiving unit can also analyze the client's areas of interest and filter and receive related questions. Furthermore, the question receiving unit can evaluate the client's career stage and automatically suggest questions according to the career stage. This allows the question receiving unit to receive appropriate questions based on the client's current career situation and areas of interest. Some or all of the above-described processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit may input the client's career status data into the generation AI and have the generation AI perform question filtering.

[0059] The question reception unit can estimate the client's emotions and determine the priority of questions to be received based on the estimated client's emotions. For example, if the client is feeling anxious, the question reception unit can prioritize urgent questions. Furthermore, if the client is relaxed, the question reception unit can prioritize detailed questions. Furthermore, if the client is in a hurry, the question reception unit can prioritize brief questions. For example, if the client is feeling anxious, the question reception unit can prioritize urgent questions. The question reception unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the client's emotions. For example, the question reception unit can capture the client's facial expression with a camera and estimate the emotion using facial expression recognition technology. The question reception unit can also record the client's voice and estimate the emotion using voice analysis technology. Furthermore, the question reception unit can analyze the client's text input and estimate the emotion using text analysis technology. This allows the question reception unit to determine the priority of questions according to the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the question reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the question reception unit may input facial expression data of a client to the generation AI and cause the generation AI to estimate emotions.

[0060] When accepting questions, the question acceptance unit can prioritize accepting questions by taking into account the client's geographical location information. For example, if the client is located in a specific region, the question acceptance unit can prioritize accepting career information related to that region. Furthermore, if the client is located overseas, the question acceptance unit can prioritize accepting international career information. Furthermore, if the client is working remotely, the question acceptance unit can prioritize accepting questions related to remote work. For example, if the client is located in a specific region, the question acceptance unit can prioritize accepting career information related to that region. The question acceptance unit can use technologies such as GPS data, IP addresses, and location information services to acquire the client's geographical location information. For example, the question acceptance unit can acquire the client's GPS data and prioritize accepting relevant career information based on that data. The question acceptance unit can also analyze the client's IP address to identify the geographical location information. Furthermore, the question acceptance unit can acquire the client's geographical location information using location information services. This allows the question acceptance unit to prioritize accepting questions that are highly relevant based on the client's geographical location information. Some or all of the above-described processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit may input geographical location information data of the client into the generation AI to determine the priority of the questions.

[0061] The question receiving unit can analyze the client's social media activity when receiving a question and receive related questions. For example, the question receiving unit can analyze the client's social media posts and suggest related questions. The question receiving unit can also filter questions based on the client's areas of interest on social media. Furthermore, the question receiving unit can prioritize questions based on the frequency of the client's social media activity. For example, the question receiving unit can analyze the client's social media posts and suggest related questions. The question receiving unit can collect and analyze data such as the post content, the number of likes, and the number of followers to analyze the client's social media activity. For example, the question receiving unit can analyze the client's social media posts using text analysis technology and suggest related questions. The question receiving unit can also analyze the client's areas of interest on social media and filter related questions. Furthermore, the question receiving unit can analyze the frequency of the client's social media activity and prioritize questions. This allows the question receiving unit to receive related questions based on the client's social media activity. Some or all of the above-described processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit can input the client's social media activity data into the generation AI and have the generation AI perform question filtering.

[0062] The theory application unit can estimate the client's emotions and adjust the selection of the career theory to be applied based on the estimated client's emotions. For example, if the client feels anxious, the theory application unit can apply a career theory that provides a sense of security. Furthermore, if the client feels confident, the theory application unit can apply a career theory that broadens the client's options. For example, if the client feels anxious, the theory application unit can apply a career theory that provides a sense of security. The theory application unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the client's emotions. For example, the theory application unit can capture the client's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The theory application unit can also record the client's voice and estimate the emotions using voice analysis technology. Furthermore, the theory application unit can analyze the client's text input and estimate the emotions using text analysis technology. This allows the theory application unit to apply an appropriate career theory according to the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the theory application unit may be performed using, or without, an AI. For example, the theory application unit may input facial expression data of a client into the generation AI and cause the generation AI to estimate emotions.

[0063] When applying a theory, the theory application unit can adjust the level of detail of the theory based on the client's career goals. For example, if the client's career goals are clear, the theory application unit can apply a detailed career theory. Furthermore, if the client's career goals are unclear, the theory application unit can apply a basic career theory. Furthermore, if the client's career goals fluctuate, the theory application unit can apply a flexible career theory. For example, if the client's career goals are clear, the theory application unit can apply a detailed career theory. The theory application unit can collect and analyze data such as short-term goals, long-term goals, and skill development goals to identify the client's career goals. For example, the theory application unit can analyze the client's short-term goals and apply a detailed career theory. Furthermore, the theory application unit can analyze the client's long-term goals and apply a basic career theory. Furthermore, the theory application unit can analyze the client's skill development goals and apply a flexible career theory. This allows the theory application unit to apply a career theory with an appropriate level of detail depending on the client's career goals. Some or all of the above-described processing in the theory application unit may be performed, for example, using AI or without AI. For example, the theory application unit can input the client's career goal data into the generation AI and adjust the level of detail of the theory.

[0064] When applying the theory, the theory application unit can apply a career theory according to the client's industry and occupation. For example, if the client is in the IT industry, the theory application unit can apply a career theory specialized for the IT industry. Furthermore, if the client is in the medical industry, the theory application unit can apply a career theory specialized for the medical industry. Furthermore, if the client is in the education industry, the theory application unit can apply a career theory specialized for the education industry. For example, if the client is in the IT industry, the theory application unit can apply a career theory specialized for the IT industry. The theory application unit can collect and analyze data such as the type of industry and the type of occupation to identify the client's industry and occupation. For example, the theory application unit can analyze the client's type of industry and apply a career theory specialized for the IT industry. Furthermore, the theory application unit can analyze the client's type of occupation and apply a career theory specialized for the medical industry. Furthermore, the theory application unit can comprehensively analyze the client's industry and occupation and apply a career theory specialized for the education industry. This allows the theory application unit to apply an appropriate career theory according to the client's industry and occupation. Some or all of the above-described processing in the theory application unit may be performed using, for example, AI, or may be performed without using AI. For example, the theory application unit may input data on the client's industry and job type into the generation AI and have the generation AI apply the career theory.

[0065] The theory application unit can estimate the client's emotions and adjust the order of theory application based on the estimated client's emotions. For example, if the client is anxious, the theory application unit can apply the most important career theory first. Furthermore, if the client is relaxed, the theory application unit can apply career theories in an orderly manner. Furthermore, if the client is confused, the theory application unit can apply career theories in order, starting with the simplest. For example, if the client is anxious, the theory application unit can apply the most important career theory first. The theory application unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the client's emotions. For example, the theory application unit can capture the client's facial expressions with a camera and estimate the emotions using facial expression recognition technology. Furthermore, the theory application unit can record the client's voice and estimate the emotions using voice analysis technology. Furthermore, the theory application unit can analyze the client's text input and estimate the emotions using text analysis technology. This allows the theory application unit to apply career theories in an appropriate order according to the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the theory application unit may be performed using, or without, an AI. For example, the theory application unit may input facial expression data of a client into the generation AI and cause the generation AI to estimate emotions.

[0066] When applying theories, the theory application unit can determine the priority of theories based on the client's career stage. For example, if the client is a new graduate, the theory application unit can prioritize applying a basic career theory. Furthermore, if the client is a mid-career employee, the theory application unit can prioritize applying a specialized career theory. Furthermore, if the client is a manager, the theory application unit can prioritize applying a leadership career theory. For example, if the client is a new graduate, the theory application unit can prioritize applying a basic career theory. The theory application unit can collect and analyze data such as new graduate, mid-career, and senior career data to identify the client's career stage. For example, the theory application unit can analyze the client's new graduate data and prioritize applying a basic career theory. Furthermore, the theory application unit can analyze the client's mid-career data and prioritize applying a specialized career theory. Furthermore, the theory application unit can analyze the client's senior career data and prioritize applying a leadership career theory. This allows the theory application unit to prioritize applying an appropriate career theory according to the client's career stage. Some or all of the above-described processing in the theory application unit may be performed using, for example, AI, or may be performed without using AI. For example, the theory application unit may input the client's career stage data into the generation AI to determine the priority of the theories.

[0067] When applying a theory, the theory application unit can select a theory to apply by referring to the client's past career counseling history. For example, the theory application unit can apply a relevant career theory based on the content of the client's past counseling sessions. The theory application unit can also re-apply a career theory that was effective based on the client's past counseling session history. Furthermore, the theory application unit can analyze the client's past counseling session history and propose a new career theory. For example, the theory application unit can apply a relevant career theory based on the content of the client's past counseling sessions. To analyze the client's past career counseling session history, the theory application unit can collect and analyze data such as the content of the past counseling sessions, the frequency of the counseling sessions, and the type of counseling sessions. For example, the theory application unit can analyze the content of the client's past counseling sessions using text analysis technology and apply a relevant career theory. The theory application unit can also analyze the frequency of the client's past counseling sessions and re-apply an effective career theory. Furthermore, the theory application unit can analyze the type of counseling sessions and propose a new career theory. In this way, the theory application unit can apply an appropriate career theory based on the client's past career counseling session history. Some or all of the above-described processing in the theory application unit may be performed using, for example, AI, or may be performed without using AI. For example, the theory application unit may input the client's past career counseling history data into the generation AI and have the generation AI select a career theory.

[0068] The assessment unit can estimate the client's emotions and adjust the assessment method based on the estimated client's emotions. For example, if the client is nervous, the assessment unit can provide a relaxing assessment method. Also, if the client is confident, the assessment unit can provide a challenging assessment method. Furthermore, if the client is unsure, the assessment unit can provide an assessment method that broadens the client's options. For example, if the client is nervous, the assessment unit can provide a relaxing assessment method. The assessment unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the client's emotions. For example, the assessment unit can capture the client's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The assessment unit can also record the client's voice and estimate the emotions using voice analysis technology. Furthermore, the assessment unit can analyze the client's text input and estimate the emotions using text analysis technology. This allows the assessment unit to provide an appropriate assessment method depending on the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the assessment unit may be performed using, or without, an AI. For example, the assessment unit may input facial expression data of the client into the generation AI and have the generation AI perform emotion estimation.

[0069] During an assessment, the assessment department can analyze a client's past experience and skills to identify strengths and weaknesses. For example, the assessment department can analyze a client's past work experience in detail to identify strengths and weaknesses. The assessment department can also analyze a client's skill set in detail to identify strengths and weaknesses. Furthermore, the assessment department can analyze a client's past project experience in detail to identify strengths and weaknesses. For example, the assessment department can analyze a client's past work experience in detail to identify strengths and weaknesses. The assessment department can collect and analyze data such as work experience, technical skills, and soft skills to evaluate a client's past experience and skills. For example, the assessment department can analyze a client's work experience using text analysis technology to identify strengths and weaknesses. The assessment department can also analyze a client's technical skills to identify strengths and weaknesses. Furthermore, the assessment department can analyze a client's soft skills to identify strengths and weaknesses. In this way, the assessment department can accurately identify strengths and weaknesses by analyzing a client's past experience and skills in detail. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit may input the client's past experience and skill data into the generation AI and have the generation AI identify strengths and weaknesses.

[0070] The assessment unit can apply an assessment method based on the client's personality traits during the assessment. For example, if the client is introverted, the assessment unit can apply a self-reflective assessment method. Furthermore, if the client is extroverted, the assessment unit can apply an interactive assessment method. Furthermore, if the client is analytical, the assessment unit can apply a data-driven assessment method. For example, if the client is introverted, the assessment unit can apply a self-reflective assessment method. The assessment unit can collect and analyze data, such as a personality diagnostic test and behavioral pattern analysis, to evaluate the client's personality traits. For example, the assessment unit can analyze the results of the client's personality diagnostic test and apply a self-reflective assessment method. Furthermore, the assessment unit can analyze the client's behavioral patterns and apply an interactive assessment method. Furthermore, the assessment unit can evaluate the client's analytical traits and apply a data-driven assessment method. This allows the assessment unit to provide an appropriate assessment method according to the client's personality traits. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit may input personality trait data of the client into the generation AI and cause the generation AI to apply the assessment method.

[0071] The assessment unit can estimate the client's emotions and prioritize assessments based on the estimated client's emotions. For example, if the client is feeling anxious, the assessment unit can prioritize assessments that provide a sense of security. Furthermore, if the client is feeling confident, the assessment unit can prioritize assessments that broaden the client's options. For example, if the client is feeling anxious, the assessment unit can prioritize assessments that provide a sense of security. The assessment unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the client's emotions. For example, the assessment unit can capture the client's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The assessment unit can also record the client's voice and estimate the emotions using voice analysis technology. Furthermore, the assessment unit can analyze the client's text input and estimate the emotions using text analysis technology. This allows the assessment unit to determine the priority of assessments according to the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit can input the client's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0072] During an assessment, the assessment department can select an assessment method taking into account the client's geographic location information. For example, if the client is in an urban area, the assessment department can apply an assessment method specialized for urban areas. Furthermore, if the client is in a rural area, the assessment department can apply an assessment method specialized for rural areas. Furthermore, if the client is overseas, the assessment department can apply an international assessment method. For example, if the client is in an urban area, the assessment department can apply an assessment method specialized for urban areas. The assessment department can use technologies such as GPS data, IP addresses, and location information services to obtain the client's geographic location information. For example, the assessment department can obtain the client's GPS data and apply an assessment method specialized for urban areas based on that data. The assessment department can also analyze the client's IP address to identify the client's geographic location information. Furthermore, the assessment department can obtain the client's geographic location information using location information services. This allows the assessment department to provide the optimal assessment method based on the client's geographic location information. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit may input the client's geographical location information data into the generation AI and have the generation AI select an assessment method.

[0073] During an assessment, the assessment department can analyze the client's social media activity to identify strengths and weaknesses. For example, the assessment department can analyze the content of the client's social media posts to identify strengths and weaknesses. The assessment department can also analyze the client's areas of interest on social media to identify strengths and weaknesses. Furthermore, the assessment department can analyze the frequency of the client's social media activities to identify strengths and weaknesses. For example, the assessment department can analyze the content of the client's social media posts to identify strengths and weaknesses. To analyze the client's social media activities, the assessment department can collect and analyze data such as the content of posts, the number of likes, and the number of followers. For example, the assessment department can analyze the content of the client's social media posts using text analysis technology to identify strengths and weaknesses. The assessment department can also analyze the client's areas of interest on social media to identify strengths and weaknesses. Furthermore, the assessment department can analyze the frequency of the client's social media activities to identify strengths and weaknesses. This allows the assessment department to identify strengths and weaknesses based on the client's social media activity. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit may input the client's social media activity data into the generation AI and have the generation AI identify strengths and weaknesses.

[0074] The job providing unit can estimate the client's emotions and determine the priority of job offers to provide based on the estimated client's emotions. For example, if the client is feeling anxious, the job providing unit can prioritize job offers that provide a sense of security. Furthermore, if the client is feeling confident, the job providing unit can prioritize job offers that broaden the client's options. For example, if the client is feeling anxious, the job providing unit can prioritize job offers that provide a sense of security. The job providing unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the client's emotions. For example, the job providing unit can capture the client's facial expression with a camera and estimate the emotion using facial expression recognition technology. The job providing unit can also record the client's voice and estimate the emotion using voice analysis technology. Furthermore, the job providing unit can analyze the client's text input and estimate the emotion using text analysis technology. This allows the job providing unit to determine the priority of job offers to provide based on the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the job posting unit may be performed using AI, for example, or may be performed without using AI. For example, the job posting unit may input facial expression data of a client into the generation AI and cause the generation AI to estimate emotions.

[0075] When providing job offers, the job providing unit can select job offers by referring to the client's past application history. For example, the job providing unit can suggest related job offers based on job offers to which the client has applied in the past. The job providing unit can also analyze trends in successful applications from the client's past application history and suggest optimal job offers. Furthermore, the job providing unit can suggest related job offers for which the client has not yet applied by referring to the client's past application history. For example, the job providing unit can suggest related job offers based on job offers to which the client has applied in the past. To analyze the client's past application history, the job providing unit can collect and analyze data such as the job types applied for in the past, application timing, and application results. For example, the job providing unit can analyze the job types applied for in the past by the client and suggest related job offers. The job providing unit can also analyze the timing of the client's past applications and suggest optimal job offers based on trends in successful applications. Furthermore, the job providing unit can analyze the results of the client's past application and suggest related job offers for which the client has not yet applied. In this way, the job providing unit can provide optimal job offers based on the client's past application history. Some or all of the above-described processing in the job offer providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the job offer providing unit may input the client's past application history data into the generation AI and have the generation AI select job offers.

[0076] When providing job offers, the job providing unit can filter job information based on the client's desired industry and job type. For example, the job providing unit can prioritize providing job information specialized in the client's desired industry. The job providing unit can also prioritize providing job information specialized in the client's desired job type. Furthermore, the job providing unit can filter and provide related job information based on the client's desired industry and job type. For example, the job providing unit prioritizes providing job information specialized in the client's desired industry. The job providing unit can collect and analyze data such as the type of desired industry and the type of desired job type to identify the client's desired industry and job type. For example, the job providing unit can analyze the type of industry the client desires and filter and provide related job information. The job providing unit can also analyze the type of job type the client desires and filter and provide related job information. Furthermore, the job providing unit can comprehensively analyze the client's desired industry and job type to provide optimal job information. This allows the job providing unit to provide appropriate job information based on the client's desired industry and job type. Some or all of the above-described processing in the job providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the job providing unit may input data on the client's desired industry and job type into the generation AI, and have the generation AI filter the job information.

[0077] The job providing unit can estimate the client's emotions and adjust the display method of job information based on the estimated client's emotions. For example, if the client is nervous, the job providing unit can provide a simple, highly visible display method. Furthermore, if the client is relaxed, the job providing unit can provide a display method including detailed information. Furthermore, if the client is in a hurry, the job providing unit can provide a display method that focuses on the main points. For example, if the client is nervous, the job providing unit can provide a simple, highly visible display method. The job providing unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the client's emotions. For example, the job providing unit can capture the client's facial expression with a camera and estimate the emotion using facial expression recognition technology. Furthermore, the job providing unit can record the client's voice and estimate the emotion using voice analysis technology. Furthermore, the job providing unit can analyze the client's text input and estimate the emotion using text analysis technology. This allows the job providing unit to provide job information in an appropriate display method depending on the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the job posting unit may be performed using AI, for example, or may be performed without using AI. For example, the job posting unit may input facial expression data of a client into the generation AI and cause the generation AI to estimate emotions.

[0078] The job providing unit can provide optimal job offers by taking into account the client's geographic location information when providing job offers. For example, if the client is located in a specific region, the job providing unit can prioritize providing job information related to that region. Furthermore, if the client is located overseas, the job providing unit can prioritize providing international job information. Furthermore, if the client desires remote work, the job providing unit can prioritize providing job information related to remote work. For example, if the client is located in a specific region, the job providing unit can prioritize providing job information related to that region. The job providing unit can use technologies such as GPS data, IP addresses, and location information services to obtain the client's geographic location information. For example, the job providing unit can obtain the client's GPS data and prioritize providing relevant job information based on that data. The job providing unit can also analyze the client's IP address to identify the client's geographic location information. Furthermore, the job providing unit can obtain the client's geographic location information using location information services. This allows the job providing unit to provide optimal job offers based on the client's geographic location information. Some or all of the above-described processing in the job offer providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the job offer providing unit may input the geographical location information data of the client to the generation AI and cause the generation AI to select a job offer.

[0079] The job providing unit can analyze the social media activity of the client and provide relevant job offers when providing job offers. For example, the job providing unit can analyze the content of the client's social media posts and suggest relevant job offers. The job providing unit can also filter job information based on the client's areas of interest on social media. Furthermore, the job providing unit can prioritize job offers based on the frequency of the client's social media activity. For example, the job providing unit can analyze the content of the client's social media posts and suggest relevant job offers. The job providing unit can collect and analyze data such as the content of the posts, the number of likes, and the number of followers to analyze the client's social media activity. For example, the job providing unit can analyze the content of the client's social media posts using text analysis technology and suggest relevant job offers. The job providing unit can also analyze the client's areas of interest on social media and filter relevant job offers. Furthermore, the job providing unit can analyze the frequency of the client's social media activity and prioritize job offers. In this way, the job providing unit can provide relevant job offers based on the client's social media activity. Some or all of the above-described processing in the job providing unit can be performed, for example, using AI or without AI. For example, the job provision department can input the client's social media activity data into the generation AI and have the generation AI select jobs. === Hard Collateral 1-1 === Each of the multiple elements, including the question receiving unit, theory application unit, assessment unit, and job offer providing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the question receiving unit is realized by the reception device 38 of the smart device 14 and receives questions about a client's career. The theory application unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the questions and applies an appropriate career theory. The assessment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the client's strengths and weaknesses. The job offer providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides optimal job offers. === Hard Collateral 1-2 === Each of the multiple elements, including the question receiving unit, theory application unit, assessment unit, and job offer unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the question receiving unit is realized by the microphone 238 of the smart glasses 214 and receives questions about a client's career. The theory application unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the questions and applies an appropriate career theory. The assessment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the client's strengths and weaknesses. The job offer unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides optimal job offers. === Hard Collateral 1-3 === Each of the multiple elements including the question receiving unit, theory application unit, assessment unit, and job offer providing unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the question receiving unit is realized by the microphone 238 of the headset terminal 314 and receives questions about the client's career. The theory application unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the questions and applies an appropriate career theory. The assessment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the client's strengths and weaknesses. The job offer providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides optimal job offers. === Hard Collateral 1-4 === Each of the multiple elements including the question receiving unit, theory application unit, assessment unit, and job offer providing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the question receiving unit is realized by the microphone 238 of the robot 414 and receives questions about the client's career. The theory application unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the questions and applies an appropriate career theory. The assessment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the client's strengths and weaknesses. The job offer providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides optimal job offers.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The question receiving unit can analyze the client's past question history and learn question patterns. For example, it can preferentially suggest question formats that the client has frequently used in the past. The question receiving unit can also accept questions during time periods that the client has preferred in the past. Furthermore, the question receiving unit can analyze the content of the client's past questions and automatically suggest related questions. This allows the question receiving unit to provide the optimal question receiving method based on the client's past question history.

[0082] The question receiving unit can estimate the client's emotions and adjust the timing of receiving questions based on the estimated client's emotions. For example, if the client is feeling stressed, the question receiving unit can receive questions during a time when the client is able to relax. Also, if the client is concentrating, the question receiving unit can receive questions immediately and respond quickly. Furthermore, if the client is tired, the question receiving unit can adjust the timing to receive questions after the client has rested. This allows the question receiving unit to receive questions at an appropriate timing according to the client's emotions.

[0083] The question receiving unit can perform filtering based on the client's current career situation and areas of interest. For example, questions related to the client's current job content are preferentially received. The question receiving unit can also filter and receive related questions based on the client's areas of interest. Furthermore, the question receiving unit can automatically suggest questions according to the client's career stage. This allows the question receiving unit to receive appropriate questions based on the client's current career situation and areas of interest.

[0084] The question reception unit can prioritize receiving questions by taking into account the geographical location information of the client. For example, if the client is in a specific region, it can prioritize receiving career information related to that region. In addition, if the client is overseas, the question reception unit can also prioritize receiving international career information. Furthermore, if the client is working remotely, the question reception unit can also prioritize receiving questions related to remote work. This allows the question reception unit to prioritize receiving questions that are highly relevant based on the geographical location information of the client.

[0085] The question receiving unit can estimate the client's emotions and determine the priority of questions to be received based on the estimated client's emotions. For example, if the client is feeling anxious, it can prioritize urgent questions. Also, if the client is relaxed, the question receiving unit can prioritize detailed questions. Furthermore, if the client is in a hurry, the question receiving unit can prioritize brief questions. In this way, the question receiving unit can prioritize questions according to the client's emotions.

[0086] The theory application unit can estimate the client's emotions and adjust the selection of the career theory to be applied based on the estimated client's emotions. For example, if the client feels anxious, the theory application unit can apply a career theory that provides a sense of security. If the client feels confident, the theory application unit can also apply a challenging career theory. Furthermore, if the client is unsure, the theory application unit can apply a career theory that broadens the client's options. In this way, the theory application unit can apply an appropriate career theory according to the client's emotions.

[0087] When applying a theory, the theory application unit can adjust the level of detail of the theory based on the client's career goals. For example, if the client's career goals are clear, a detailed career theory is applied. Alternatively, if the client's career goals are vague, the theory application unit can apply a basic career theory. Furthermore, if the client's career goals fluctuate, the theory application unit can apply a flexible career theory. This allows the theory application unit to apply a career theory with an appropriate level of detail depending on the client's career goals.

[0088] The assessment unit can estimate the client's emotions and adjust the assessment method based on the estimated client's emotions. For example, if the client is nervous, the assessment unit can provide a relaxing assessment method. If the client is confident, the assessment unit can also provide a challenging assessment method. Furthermore, if the client is unsure, the assessment unit can also provide an assessment method that broadens the client's options. In this way, the assessment unit can provide an appropriate assessment method according to the client's emotions.

[0089] During the assessment, the assessment department can apply an assessment method based on the client's personality traits. For example, if the client is introverted, the assessment department can apply a self-reflective assessment method. If the client is extroverted, the assessment department can also apply an interactive assessment method. Furthermore, if the client is analytical, the assessment department can also apply a data-driven assessment method. This allows the assessment department to provide an appropriate assessment method according to the client's personality traits.

[0090] The job providing unit can estimate the client's emotions and determine the priority of job offers to be provided based on the estimated client's emotions. For example, if the client is feeling anxious, job offers that give a sense of security can be provided preferentially. Also, if the client is feeling confident, the job providing unit can provide challenging job offers preferentially. Furthermore, if the client is unsure, the job providing unit can provide job offers that broaden the client's options preferentially. In this way, the job providing unit can determine the priority of job offers to be provided according to the client's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The question reception unit receives career-related questions from clients. Client questions can be entered in text, audio, video, or other formats. For example, a client can enter a question in text format such as, "I'm not satisfied with my current job, but I don't know what career path is right for me." Questions can also be entered in audio or video format. Step 2: The theory application section analyzes the questions received by the question reception section and applies an appropriate career theory. Career theories include Holland's career choice theory and Super's career development theory. For example, if a client is considering changing jobs, a career theory related to changing jobs will be applied, and advice will be given on the advantages and disadvantages of changing jobs, how to choose a new job, etc. Step 3: The assessment section analyzes the client's strengths and weaknesses based on the career theory applied by the theory application section. The analysis includes skill assessment and personality trait assessment. For example, the client's past work experience, skill set, and personality traits are analyzed in detail to identify their strengths and weaknesses. Step 4: The Job Offering Department provides the most suitable job offers based on the results analyzed by the Assessment Department. Job offers include job type, work location, salary conditions, etc. For example, if a client wants a career in the IT industry, the Department will search for job information in the IT industry and provide the most suitable job offers.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question reception unit that receives questions from clients; a theory application unit that analyzes the question received by the question receiving unit and applies a career theory; an assessment unit that analyzes the strengths and weaknesses of the client based on the career theory applied by the theory application unit; a job offer providing unit that provides job offers based on the results of the analysis by the assessment unit. A system characterized by:

2. The question receiving unit Estimate the client's emotions and adjust the timing of accepting questions based on the estimated client emotions 2. The system of claim 1.

3. The question receiving unit Analyze the client's past question history and determine how to accept questions 2. The system of claim 1.

4. The question receiving unit Filtering questions based on the client's current career status and areas of interest 2. The system of claim 1.

5. The question receiving unit Estimate the client's emotions and prioritize the questions to be accepted based on the estimated client's emotions.

2. The system of claim 1.

6. The question receiving unit When accepting questions, prioritize them based on the client's geographic location.

2. The system of claim 1.

7. The question receiving unit When receiving inquiries, analyze the client's social media activity and receive relevant questions.

2. The system of claim 1.

8. The theory application unit Estimate the client's feelings and adjust the career theory to be applied based on the client's estimated feelings.

2. The system of claim 1.

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