System

Abstract

An object of a system according to an embodiment is to digitize characteristics and values of users and efficiently share the characteristics and values with companies.SOLUTION: A system includes a characteristic grasping part, a digitizing part, and a sharing part. The characteristic grasping unit grasps a characteristic, an intention, and a value of the user. The digitizing unit digitizes and visualizes the value of the user as a business person based on the characteristics, the orientation, and the value of the user grasped by the characteristic grasping unit. The sharing unit shares the value of the user quantified by the quantifying unit with the company under the agreement of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide an efficient process for sharing user characteristics and values ​​with companies, and there is room for improvement.

[0005] The system according to the embodiment aims to quantify the characteristics and value of users and share them efficiently with companies. [Means for solving the problem]

[0006] The system according to the embodiment includes a characteristic grasping unit, a quantification unit, and a sharing unit. The characteristic grasping unit grasps the characteristics, inclinations, and value of a user. The quantification unit quantifies and visualizes the value of the user as a business person based on the characteristics, inclinations, and value grasped by the characteristic grasping unit. The sharing unit shares the user's value quantified by the quantification unit with a company with the user's consent. [Effects of the Invention]

[0007] The system according to the embodiment can quantify the characteristics and value of users and share them efficiently with companies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) The ability measurement system according to an embodiment of the present invention is a system that grasps a user's characteristics, inclinations, and values, and then quantifies and visualizes the user's value as a business person based on the characteristics, inclinations, and values. As a result, the ability measurement system quantifies and visualizes the user's value as a business person based on the user's characteristics, inclinations, and values, making it possible to share this information with companies.

[0029] The ability measurement system according to the embodiment includes a characteristic understanding unit, a quantification unit, and a sharing unit. The characteristic understanding unit understands the user's characteristics, inclinations, and values. For example, the characteristic understanding unit analyzes the results of questionnaires and tests answered by the user to evaluate the user's personality, skills, interests, and so on. The characteristic understanding unit also uses a generation AI to understand the user's characteristics, inclinations, and values ​​in detail. For example, the generation AI performs analysis based on prompts containing instructions from the user about what the user wants the generation AI to do. The quantification unit quantifies and visualizes the user's value as a businessperson based on the user's characteristics, inclinations, and values ​​understood by the characteristic understanding unit. For example, the quantification unit numerically represents the user's leadership ability, communication ability, problem-solving ability, and so on, and visually displays them as graphs or charts. This makes it easier for the user to understand their own strengths and weaknesses. The sharing unit shares the user's value quantified by the quantification unit with a company with the user's consent. For example, the system can compare the user's skill set with the skill set desired by the company to determine compatibility. This allows companies to objectively evaluate the abilities of users when they are looking for employment or changing jobs. As a result, the ability measurement system according to the embodiment quantifies and visualizes the value of a user as a business person based on the user's characteristics, inclinations, and values, making it possible to share this information with companies.

[0030] The characteristic understanding unit analyzes the content of a user's SNS posts or emails, and can understand the characteristics, inclinations, and values ​​from their daily actions and comments. For example, the characteristic understanding unit analyzes the user's SNS posts and extracts the characteristics and inclinations from the content of the posts. For example, the user's interests are identified based on frequently posted keywords and hashtags. The characteristic understanding unit also analyzes the content of the user's emails and evaluates the characteristics and inclinations from the tone and content of the emails. For example, the content of business emails can be used to understand communication style and leadership tendencies. The characteristic understanding unit also integrates the results of SNS and email analysis to comprehensively evaluate the characteristics, inclinations, and values ​​from the user's daily actions and comments. For example, a user who makes many positive comments can be determined to have positive characteristics. In this way, by analyzing the content of a user's SNS posts and emails, the characteristics, inclinations, and values ​​can be understood from their daily actions and comments.

[0031] The characteristic understanding unit can analyze a user's past work history and resume to evaluate changes in work experience and skills. The characteristic understanding unit, for example, analyzes a user's work history and evaluates changes in work experience and skills. For example, it understands career growth based on changes in job content and job titles. The characteristic understanding unit also analyzes the contents of the resume to evaluate the user's skill set based on educational background and qualification information. For example, it determines the depth of specialized knowledge based on acquired qualifications and training programs attended. The characteristic understanding unit also integrates data from the work history and resume to comprehensively evaluate the user's career path and changes in skills. For example, it determines that a user with experience in different industries has diverse skills. In this way, it is possible to evaluate changes in work experience and skills by analyzing a user's past work history and resume.

[0032] The characteristic understanding unit collects information about the user's hobbies and lifestyle, and can use it to understand the user's characteristics, inclinations, and values. The characteristic understanding unit, for example, collects information about the user's hobbies and evaluates the user's characteristics and inclinations. For example, a user who likes outdoor activities is determined to have an active characteristic. The characteristic understanding unit also collects information about the user's lifestyle and evaluates the user's characteristics and inclinations. For example, a user who is highly health-conscious is determined to have high self-management skills. The characteristic understanding unit also integrates data about the user's hobbies and lifestyle, and comprehensively evaluates the user's characteristics, inclinations, and values. For example, a user with diverse hobbies is determined to have diverse values. In this way, collecting information about the user's hobbies and lifestyle can be used to understand the user's characteristics, inclinations, and values.

[0033] The characteristic understanding unit can analyze the user's health data and incorporate the user's health condition or lifestyle habits into the characteristic evaluation. The characteristic understanding unit can, for example, analyze data from a fitness tracker to evaluate the user's health condition. For example, the level of health consciousness can be determined based on daily exercise volume and sleep data. The characteristic understanding unit can also evaluate the user's lifestyle habits based on the health data. For example, a user who leads a regular lifestyle can be determined to have high self-management skills. The characteristic understanding unit can also integrate the health data with other characteristic data to comprehensively evaluate the user's characteristics, inclinations, and values. For example, a user with good health data can be determined to have high stress tolerance. In this way, the health condition and lifestyle habits can be incorporated into the characteristic evaluation by analyzing the user's health data.

[0034] The quantification unit can quantify businessperson value by industry and occupation type based on the user's characteristic data, making it comparable. The quantification unit, for example, quantifies businessperson value by industry based on the user's characteristic data. For example, in the IT industry, technical skills are emphasized, so the numerical value for technical skills is set high. The quantification unit also quantifies businessperson value by occupation type, making it comparable. For example, communication skills are emphasized in sales jobs, so the numerical value for those skills is set high. The quantification unit also integrates the quantified data by industry and occupation type to comprehensively evaluate the user's businessperson value. For example, a user who receives high evaluations in multiple industries and occupation types is determined to be a versatile businessperson. In this way, businessperson value by industry and occupation type can be quantified and made comparable based on the user's characteristic data.

[0035] The quantification unit can analyze a user's past projects and business results and reflect specific performance in the quantification. The quantification unit, for example, analyzes a user's past project data and quantifies performance. For example, performance is evaluated based on the project success rate and degree of achievement. The quantification unit also analyzes business results and reflects them in specific numbers. For example, performance is evaluated based on the sales target achievement rate and cost reduction effect. The quantification unit also integrates project data and business results to comprehensively quantify the user's business person value. For example, a user with multiple successful projects is determined to have high performance. In this way, by analyzing a user's past projects and business results, specific performance can be reflected in the quantification.

[0036] When quantifying a user's value, the quantification unit can incorporate standards from different cultural spheres and countries to evaluate from an international perspective. The quantification unit, for example, incorporates standards from different cultural spheres to quantify the user's value. For example, cooperation is emphasized in Asia, so the numerical value for that is set high. The quantification unit also takes into account the business culture and values ​​of each country to evaluate from an international perspective. For example, leadership is emphasized in Europe and the United States, so the numerical value for that is set high. The quantification unit also integrates standards from different cultural spheres and countries to comprehensively evaluate the user's businessperson value. For example, a user who receives high ratings in multiple cultural spheres is determined to be an international businessperson. In this way, by incorporating standards from different cultural spheres and countries, the user's value can be evaluated from an international perspective.

[0037] When quantifying a user's value, the quantifying unit can also include soft skills such as teamwork and cooperation in the evaluation. The quantifying unit, for example, quantifies the user's teamwork ability and reflects it in the businessperson value. For example, the unit evaluates teamwork ability based on team evaluations in past projects. The quantifying unit also quantifies cooperation and reflects it in the businessperson value. For example, the unit evaluates cooperation based on feedback from colleagues and superiors. The quantifying unit also integrates soft skill data to comprehensively evaluate the user's businessperson value. For example, a user with high teamwork ability and cooperation is determined to have high soft skills. In this way, by including soft skills such as teamwork and cooperation in the evaluation, the user's value can be comprehensively evaluated.

[0038] The sharing unit can compare the skill sets required by companies in detail with the user's skill sets and update the degree of compatibility in real time. For example, the sharing unit could register the skill sets required by companies in a database and build a system that automatically compares them with the user's skill sets. For example, it could perform skill matching based on company job information. The sharing unit could also update the user's skill sets in real time and keep the degree of compatibility with the skill sets required by companies up to date. For example, it could immediately reflect newly acquired qualifications or skills. The sharing unit could also develop a system that automatically recommends highly compatible users to companies. For example, it could present the most suitable candidates to the company based on the results of skill matching. This would allow the skill sets required by companies to be compared in detail with the user's skill sets and update the degree of compatibility in real time.

[0039] The sharing unit can customize and provide quantified information about a user based on a company's evaluation criteria and the type of person they are looking for. The sharing unit, for example, registers a company's evaluation criteria in a database and builds a system that customizes and provides quantified information about a user. For example, different evaluation criteria are set for each company. The sharing unit also customizes the quantified information about a user based on the type of person a company is looking for. For example, for a company that places importance on leadership, the sharing unit provides an emphasis on leadership scores. The sharing unit also provides customized information to companies and develops a system that recommends candidates who best fit the skill sets and type of person a company is looking for. For example, it generates reports tailored to the needs of the company. This makes it possible to customize and provide quantified information about a user based on a company's evaluation criteria and the type of person they are looking for.

[0040] The sharing department can integrate the quantified information into a company's recruitment process and utilize it during interviews and selection. For example, the sharing department can integrate the quantified information into a company's recruitment process and build a system to utilize it during interviews and selection. For example, it can enable interviewers to check candidates' skill sets in real time. The sharing department can also evaluate candidates' suitability based on the quantified information during the recruitment process. For example, it can customize interview questions based on the results of skill matching. The sharing department can also share the quantified information throughout the recruitment process to maintain consistency in selection. For example, it can develop a system in which all interviewers make evaluations based on the same information. This allows the quantified information to be integrated into a company's recruitment process and utilized during interviews and selection.

[0041] The sharing department can incorporate the quantified information into a company's human resource development programs to support employee growth. The sharing department, for example, builds a system that incorporates the quantified information into a company's human resource development programs to support employee growth. For example, it identifies employee skill gaps and provides appropriate training. The sharing department also creates individual growth plans based on the quantified information in human resource development programs. For example, it evaluates employees' strengths and weaknesses and provides customized training plans. The sharing department also develops a system that monitors employee growth in real time and provides feedback based on the quantified information. For example, it visualizes training progress. This makes it possible to incorporate the quantified information into a company's human resource development programs to support employee growth.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The characteristic understanding unit can collect information about the user's hobbies and lifestyle and use it to understand the characteristics, inclinations, and values. For example, information about the user's hobbies can be collected to evaluate the characteristics and inclinations. A user who likes outdoor activities can be determined to have active characteristics. Information about the user's lifestyle can also be collected to evaluate the characteristics and inclinations. A user who is highly health-conscious can be determined to have high self-management skills. Furthermore, data about hobbies and lifestyle can be integrated to comprehensively evaluate the user's characteristics, inclinations, and values. A user with diverse hobbies can be determined to have diverse values. In this way, collecting information about the user's hobbies and lifestyle can be used to understand the characteristics, inclinations, and values.

[0044] The characteristic understanding unit can analyze the user's health data and incorporate the health condition or lifestyle habits into the characteristic evaluation. For example, data from a fitness tracker can be analyzed to evaluate the user's health condition. The level of health consciousness can be determined based on daily exercise volume and sleep data. The user's lifestyle habits can also be evaluated based on the health data. A user who leads a regular lifestyle can be determined to have high self-management skills. Furthermore, the health data can be integrated with other characteristic data to comprehensively evaluate the user's characteristics, inclinations, and values. A user with good health data can be determined to have high stress tolerance. In this way, by analyzing the user's health data, the health condition and lifestyle habits can be incorporated into the characteristic evaluation.

[0045] The characteristic understanding unit can analyze a user's past work history and resume to evaluate changes in work experience and skills. For example, it can analyze a user's work history and evaluate changes in work experience and skills. Career growth can be understood based on changes in job content and job titles. It can also analyze the contents of the resume to evaluate the user's skill set based on educational background and qualification information. It can determine the depth of specialized knowledge based on acquired qualifications and training programs attended. Furthermore, it can integrate data from the work history and resume to comprehensively evaluate the user's career path and changes in skills. A user with experience in different industries can be determined to have diverse skills. This makes it possible to evaluate changes in work experience and skills by analyzing a user's past work history and resume.

[0046] The characteristic identification unit analyzes the content of a user's social media posts or emails to understand the characteristics, inclinations, and values ​​from their daily actions and comments. For example, it analyzes a user's social media posts and extracts characteristics and inclinations from the content of the posts. It can identify the user's interests based on frequently posted keywords and hashtags. It can also analyze the content of a user's emails to evaluate the characteristics and inclinations from the tone and content of the emails. It can identify communication style and leadership tendencies from the content of business emails. Furthermore, it can integrate the results of SNS and email analysis to comprehensively evaluate the characteristics, inclinations, and values ​​from the user's daily actions and comments. A user who makes many positive comments can be determined to have positive characteristics. In this way, by analyzing the content of a user's social media posts and emails, it is possible to understand the characteristics, inclinations, and values ​​from their daily actions and comments.

[0047] The quantification unit can quantify businessperson value by industry and occupation type based on the user's characteristic data, making it comparable. For example, businessperson value by industry is quantified based on the user's characteristic data. In the IT industry, technical skills are emphasized, so the numerical value for technical skills can be set high. Businessperson value by occupation type is also quantified and made comparable. In sales jobs, communication skills are emphasized, so the numerical value for those skills can be set high. Furthermore, the quantified data by industry and occupation type can be integrated to comprehensively evaluate the user's businessperson value. A user who receives high evaluations in multiple industries and occupations can be determined to be a versatile businessperson. In this way, businessperson value by industry and occupation type can be quantified and made comparable based on the user's characteristic data.

[0048] The quantification unit can analyze a user's past projects and business results, and reflect specific performance in the quantification. For example, it can analyze a user's past project data and quantify performance. Performance can be evaluated based on the project success rate and degree of achievement. It can also analyze business results and reflect them in specific numbers. Performance can be evaluated based on the rate at which sales targets are achieved and the cost reduction effect. Furthermore, it can integrate project data and business results to comprehensively quantify the user's business person value. A user with multiple successful projects can be determined to have high performance. In this way, by analyzing a user's past projects and business results, it is possible to reflect specific performance in the quantification.

[0049] The shared section can customize and provide quantified information about users based on a company's evaluation criteria and the type of person they are looking for. For example, a system can be built in which a company's evaluation criteria are registered in a database and quantified information about users is customized and provided. Different evaluation criteria can be set for each company. Furthermore, quantified information about users can be customized based on the type of person the company is looking for. For companies that place importance on leadership, leadership scores can be emphasized. Furthermore, a system can be developed that provides customized information to companies and recommends candidates who best match the skill sets and personality profiles the company is looking for. Reports tailored to the needs of the company can be generated. This makes it possible to customize and provide quantified information about users based on the company's evaluation criteria and the type of person they are looking for.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The characteristic understanding unit understands the user's characteristics, inclinations, and values. For example, the characteristic understanding unit analyzes the results of questionnaires and tests answered by the user and evaluates the user's personality, skills, interests, etc. The characteristic understanding unit also uses the generation AI to understand the user's characteristics, inclinations, and values ​​in detail. For example, the generation AI performs analysis based on prompts that include instructions on what the user wants the generation AI to do. Step 2: The quantification unit quantifies and visualizes the user's value as a businessperson based on the characteristics, aspirations, and values ​​identified by the characteristics identification unit. For example, the quantification unit may express the user's leadership ability, communication ability, problem-solving ability, etc., in numerical terms and visually display them as graphs or charts. This makes it easier for the user to understand their own strengths and weaknesses. Step 3: The sharing unit shares the user's value, quantified by the quantification unit, with the user's consent. For example, it can compare the user's skill set with the skill set required by the company and determine the degree of compatibility. This allows companies to objectively evaluate the user's abilities when looking for employment or changing jobs.

[0052] (Example 2) The ability measurement system according to an embodiment of the present invention is a system that grasps a user's characteristics, inclinations, and values, and then quantifies and visualizes the user's value as a business person based on the characteristics, inclinations, and values. As a result, the ability measurement system quantifies and visualizes the user's value as a business person based on the user's characteristics, inclinations, and values, making it possible to share this information with companies.

[0053] The ability measurement system according to the embodiment includes a characteristic understanding unit, a quantification unit, and a sharing unit. The characteristic understanding unit understands the user's characteristics, inclinations, and values. For example, the characteristic understanding unit analyzes the results of questionnaires and tests answered by the user to evaluate the user's personality, skills, interests, and so on. The characteristic understanding unit also uses a generation AI to understand the user's characteristics, inclinations, and values ​​in detail. For example, the generation AI performs analysis based on prompts containing instructions from the user about what the user wants the generation AI to do. The quantification unit quantifies and visualizes the user's value as a businessperson based on the user's characteristics, inclinations, and values ​​understood by the characteristic understanding unit. For example, the quantification unit numerically represents the user's leadership ability, communication ability, problem-solving ability, and so on, and visually displays them as graphs or charts. This makes it easier for the user to understand their own strengths and weaknesses. The sharing unit shares the user's value quantified by the quantification unit with a company with the user's consent. For example, the system can compare the user's skill set with the skill set desired by the company to determine compatibility. This allows companies to objectively evaluate the abilities of users when they are looking for employment or changing jobs. As a result, the ability measurement system according to the embodiment quantifies and visualizes the value of a user as a business person based on the user's characteristics, inclinations, and values, making it possible to share this information with companies.

[0054] The characteristic understanding unit analyzes the content of a user's SNS posts or emails, and can understand the characteristics, inclinations, and values ​​from their daily actions and comments. For example, the characteristic understanding unit analyzes the user's SNS posts and extracts the characteristics and inclinations from the content of the posts. For example, the user's interests are identified based on frequently posted keywords and hashtags. The characteristic understanding unit also analyzes the content of the user's emails and evaluates the characteristics and inclinations from the tone and content of the emails. For example, the content of business emails can be used to understand communication style and leadership tendencies. The characteristic understanding unit also integrates the results of SNS and email analysis to comprehensively evaluate the characteristics, inclinations, and values ​​from the user's daily actions and comments. For example, a user who makes many positive comments can be determined to have positive characteristics. In this way, by analyzing the content of a user's SNS posts and emails, the characteristics, inclinations, and values ​​can be understood from their daily actions and comments.

[0055] The characteristic understanding unit can analyze a user's past work history and resume to evaluate changes in work experience and skills. The characteristic understanding unit, for example, analyzes a user's work history and evaluates changes in work experience and skills. For example, it understands career growth based on changes in job content and job titles. The characteristic understanding unit also analyzes the contents of the resume to evaluate the user's skill set based on educational background and qualification information. For example, it determines the depth of specialized knowledge based on acquired qualifications and training programs attended. The characteristic understanding unit also integrates data from the work history and resume to comprehensively evaluate the user's career path and changes in skills. For example, it determines that a user with experience in different industries has diverse skills. In this way, it is possible to evaluate changes in work experience and skills by analyzing a user's past work history and resume.

[0056] The characteristic understanding unit uses the emotion estimation function to analyze the emotions of a user when answering a questionnaire or test, and reflects the emotional fluctuations in the characteristic evaluation. The characteristic understanding unit, for example, analyzes the facial expression of a user when answering a questionnaire and reflects the emotional fluctuations in the characteristic evaluation. For example, a user who smiles a lot is determined to have a positive characteristic. The characteristic understanding unit also analyzes the tone of voice when a user answers a test and reflects the emotional fluctuations in the characteristic evaluation. For example, a user who answers in a calm tone is determined to have a calm characteristic. The characteristic understanding unit also uses the emotion estimation function to collect emotional data when answering a questionnaire or test, and reflects the data in the characteristic evaluation. For example, a user who calmly answers even stressful questions is determined to have high stress tolerance. In this way, by using the emotion estimation function, the emotional fluctuations of a user can be reflected in the characteristic evaluation.

[0057] The characteristic understanding unit collects information about the user's hobbies and lifestyle, and can use it to understand the user's characteristics, inclinations, and values. The characteristic understanding unit, for example, collects information about the user's hobbies and evaluates the user's characteristics and inclinations. For example, a user who likes outdoor activities is determined to have an active characteristic. The characteristic understanding unit also collects information about the user's lifestyle and evaluates the user's characteristics and inclinations. For example, a user who is highly health-conscious is determined to have high self-management skills. The characteristic understanding unit also integrates data about the user's hobbies and lifestyle, and comprehensively evaluates the user's characteristics, inclinations, and values. For example, a user with diverse hobbies is determined to have diverse values. In this way, collecting information about the user's hobbies and lifestyle can be used to understand the user's characteristics, inclinations, and values.

[0058] The characteristic understanding unit can analyze the user's health data and incorporate the user's health condition or lifestyle habits into the characteristic evaluation. The characteristic understanding unit can, for example, analyze data from a fitness tracker to evaluate the user's health condition. For example, the level of health consciousness can be determined based on daily exercise volume and sleep data. The characteristic understanding unit can also evaluate the user's lifestyle habits based on the health data. For example, a user who leads a regular lifestyle can be determined to have high self-management skills. The characteristic understanding unit can also integrate the health data with other characteristic data to comprehensively evaluate the user's characteristics, inclinations, and values. For example, a user with good health data can be determined to have high stress tolerance. In this way, the health condition and lifestyle habits can be incorporated into the characteristic evaluation by analyzing the user's health data.

[0059] The characteristic understanding unit can use the emotion estimation function to analyze the user's behavior in apps and websites that the user uses daily and reflect emotional fluctuations in the characteristic evaluation. The characteristic understanding unit, for example, analyzes the user's behavior in apps that the user uses daily and reflects emotional fluctuations in the characteristic evaluation. For example, stress tolerance and concentration are evaluated from behavior in game apps. The characteristic understanding unit also analyzes the user's behavior on websites that the user visits daily and reflects emotional fluctuations in the characteristic evaluation. For example, interest and emotional fluctuations are evaluated from reactions on news sites. The characteristic understanding unit also uses the emotion estimation function to collect behavioral data on apps and websites and reflects the data in the characteristic evaluation. For example, sociability and emotional fluctuations are evaluated from reactions on social media. In this way, by using the emotion estimation function, emotional fluctuations from the user's daily behavior can be reflected in the characteristic evaluation.

[0060] The quantification unit can quantify businessperson value by industry and occupation type based on the user's characteristic data, making it comparable. The quantification unit, for example, quantifies businessperson value by industry based on the user's characteristic data. For example, in the IT industry, technical skills are emphasized, so the numerical value for technical skills is set high. The quantification unit also quantifies businessperson value by occupation type, making it comparable. For example, communication skills are emphasized in sales jobs, so the numerical value for those skills is set high. The quantification unit also integrates the quantified data by industry and occupation type to comprehensively evaluate the user's businessperson value. For example, a user who receives high evaluations in multiple industries and occupation types is determined to be a versatile businessperson. In this way, businessperson value by industry and occupation type can be quantified and made comparable based on the user's characteristic data.

[0061] The quantification unit can analyze a user's past projects and business results and reflect specific performance in the quantification. The quantification unit, for example, analyzes a user's past project data and quantifies performance. For example, performance is evaluated based on the project success rate and degree of achievement. The quantification unit also analyzes business results and reflects them in specific numbers. For example, performance is evaluated based on the sales target achievement rate and cost reduction effect. The quantification unit also integrates project data and business results to comprehensively quantify the user's business person value. For example, a user with multiple successful projects is determined to have high performance. In this way, by analyzing a user's past projects and business results, specific performance can be reflected in the quantification.

[0062] The quantification unit uses the emotion estimation function to quantify the stress and satisfaction experienced by the user in the past, and reflect this in the businessperson value. The quantification unit, for example, uses the emotion estimation function to quantify the stress experienced by the user in the past. For example, stress tolerance is evaluated based on emotion data from a stressful project. The quantification unit also quantifies the user's satisfaction and reflects this in the businessperson value. For example, job satisfaction is evaluated based on emotion data from a highly satisfying project. The quantification unit also integrates stress and satisfaction data to comprehensively quantify the user's businessperson value. For example, a user with high stress tolerance and high satisfaction is determined to have high businessperson value. In this way, by using the emotion estimation function, the stress and satisfaction experienced by the user in the past can be quantified and reflected in the businessperson value.

[0063] When quantifying a user's value, the quantification unit can incorporate standards from different cultural spheres and countries to evaluate from an international perspective. The quantification unit, for example, incorporates standards from different cultural spheres to quantify the user's value. For example, cooperation is emphasized in Asia, so the numerical value for that is set high. The quantification unit also takes into account the business culture and values ​​of each country to evaluate from an international perspective. For example, leadership is emphasized in Europe and the United States, so the numerical value for that is set high. The quantification unit also integrates standards from different cultural spheres and countries to comprehensively evaluate the user's businessperson value. For example, a user who receives high ratings in multiple cultural spheres is determined to be an international businessperson. In this way, by incorporating standards from different cultural spheres and countries, the user's value can be evaluated from an international perspective.

[0064] When quantifying a user's value, the quantifying unit can also include soft skills such as teamwork and cooperation in the evaluation. The quantifying unit, for example, quantifies the user's teamwork ability and reflects it in the businessperson value. For example, the unit evaluates teamwork ability based on team evaluations in past projects. The quantifying unit also quantifies cooperation and reflects it in the businessperson value. For example, the unit evaluates cooperation based on feedback from colleagues and superiors. The quantifying unit also integrates soft skill data to comprehensively evaluate the user's businessperson value. For example, a user with high teamwork ability and cooperation is determined to have high soft skills. In this way, by including soft skills such as teamwork and cooperation in the evaluation, the user's value can be comprehensively evaluated.

[0065] The quantifying unit can use the emotion estimation function to quantify the emotional fluctuations in teams and projects that the user has participated in in the past, and reflect these in the user's businessperson value. The quantifying unit, for example, uses the emotion estimation function to quantify the emotional fluctuations in teams that the user has participated in in the past. For example, the quantifying unit evaluates teamwork ability based on emotional data within the team. The quantifying unit also quantifies the emotional fluctuations in the project and reflects this in the user's businessperson value. For example, the quantifying unit evaluates stress tolerance and satisfaction based on emotional data during the progress of the project. The quantifying unit also integrates the emotional data from the team or project to comprehensively evaluate the user's businessperson value. For example, a user with little emotional fluctuation is determined to have stable performance. In this way, the emotion estimation function can quantify the emotional fluctuations in teams and projects that the user has participated in in the past, and reflect these in the user's businessperson value.

[0066] The sharing unit can compare the skill sets required by companies in detail with the user's skill sets and update the degree of compatibility in real time. For example, the sharing unit could register the skill sets required by companies in a database and build a system that automatically compares them with the user's skill sets. For example, it could perform skill matching based on company job information. The sharing unit could also update the user's skill sets in real time and keep the degree of compatibility with the skill sets required by companies up to date. For example, it could immediately reflect newly acquired qualifications or skills. The sharing unit could also develop a system that automatically recommends highly compatible users to companies. For example, it could present the most suitable candidates to the company based on the results of skill matching. This would allow the skill sets required by companies to be compared in detail with the user's skill sets and update the degree of compatibility in real time.

[0067] The sharing unit can customize and provide quantified information about a user based on a company's evaluation criteria and the type of person they are looking for. The sharing unit, for example, registers a company's evaluation criteria in a database and builds a system that customizes and provides quantified information about a user. For example, different evaluation criteria are set for each company. The sharing unit also customizes the quantified information about a user based on the type of person a company is looking for. For example, for a company that places importance on leadership, the sharing unit provides an emphasis on leadership scores. The sharing unit also provides customized information to companies and develops a system that recommends candidates who best fit the skill sets and type of person a company is looking for. For example, it generates reports tailored to the needs of the company. This makes it possible to customize and provide quantified information about a user based on a company's evaluation criteria and the type of person they are looking for.

[0068] The sharing unit can use the emotion estimation function to analyze a user's emotional response when a company views quantified information and provide feedback. The sharing unit, for example, uses the emotion estimation function to build a system that analyzes a user's emotional response when a company views quantified information. For example, it analyzes the facial expressions and voice of company personnel. The sharing unit also identifies areas for improvement in the user's quantified information based on the company's emotional response data and provides feedback. For example, it highlights points that the company has shown interest in. The sharing unit also develops a system that customizes and provides information that the company desires based on the emotion estimation data. For example, it adjusts the content of a report according to the company's emotional response. In this way, the emotion estimation function can be used to analyze a user's emotional response when a company views quantified information and provide feedback.

[0069] The sharing department can integrate the quantified information into a company's recruitment process and utilize it during interviews and selection. For example, the sharing department can integrate the quantified information into a company's recruitment process and build a system to utilize it during interviews and selection. For example, it can enable interviewers to check candidates' skill sets in real time. The sharing department can also evaluate candidates' suitability based on the quantified information during the recruitment process. For example, it can customize interview questions based on the results of skill matching. The sharing department can also share the quantified information throughout the recruitment process to maintain consistency in selection. For example, it can develop a system in which all interviewers make evaluations based on the same information. This allows the quantified information to be integrated into a company's recruitment process and utilized during interviews and selection.

[0070] The sharing department can incorporate the quantified information into a company's human resource development programs to support employee growth. The sharing department, for example, builds a system that incorporates the quantified information into a company's human resource development programs to support employee growth. For example, it identifies employee skill gaps and provides appropriate training. The sharing department also creates individual growth plans based on the quantified information in human resource development programs. For example, it evaluates employees' strengths and weaknesses and provides customized training plans. The sharing department also develops a system that monitors employee growth in real time and provides feedback based on the quantified information. For example, it visualizes training progress. This makes it possible to incorporate the quantified information into a company's human resource development programs to support employee growth.

[0071] The sharing unit can use the emotion estimation function to analyze a user's emotional response when a company views quantified information and provide feedback. The sharing unit, for example, uses the emotion estimation function to build a system that analyzes a user's emotional response when a company views quantified information. For example, it analyzes the facial expressions and voice of company personnel. The sharing unit also identifies areas for improvement in the user's quantified information based on the company's emotional response data and provides feedback. For example, it highlights points that the company has shown interest in. The sharing unit also develops a system that customizes and provides information that the company desires based on the emotion estimation data. For example, it adjusts the content of a report according to the company's emotional response. In this way, the emotion estimation function can be used to analyze a user's emotional response when a company views quantified information and provide feedback.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The characteristic understanding unit can collect information about the user's hobbies and lifestyle and use it to understand the characteristics, inclinations, and values. For example, information about the user's hobbies can be collected to evaluate the characteristics and inclinations. A user who likes outdoor activities can be determined to have active characteristics. Information about the user's lifestyle can also be collected to evaluate the characteristics and inclinations. A user who is highly health-conscious can be determined to have high self-management skills. Furthermore, data about hobbies and lifestyle can be integrated to comprehensively evaluate the user's characteristics, inclinations, and values. A user with diverse hobbies can be determined to have diverse values. In this way, collecting information about the user's hobbies and lifestyle can be used to understand the characteristics, inclinations, and values.

[0074] The characteristic understanding unit can analyze the user's health data and incorporate the health condition or lifestyle habits into the characteristic evaluation. For example, data from a fitness tracker can be analyzed to evaluate the user's health condition. The level of health consciousness can be determined based on daily exercise volume and sleep data. The user's lifestyle habits can also be evaluated based on the health data. A user who leads a regular lifestyle can be determined to have high self-management skills. Furthermore, the health data can be integrated with other characteristic data to comprehensively evaluate the user's characteristics, inclinations, and values. A user with good health data can be determined to have high stress tolerance. In this way, by analyzing the user's health data, the health condition and lifestyle habits can be incorporated into the characteristic evaluation.

[0075] The characteristic understanding unit can use the emotion estimation function to analyze the emotions of a user when answering a questionnaire or test, and reflect the emotional fluctuations in the characteristic evaluation. For example, the emotion estimation function can analyze the user's facial expressions when answering a questionnaire, and reflect the emotional fluctuations in the characteristic evaluation. A user who smiles a lot can be determined to have positive characteristics. The emotion estimation function can also analyze the tone of voice when the user answers a test, and reflect the emotional fluctuations in the characteristic evaluation. A user who answers in a calm tone can be determined to have a calm characteristic. Furthermore, the emotion estimation function can be used to collect emotional data when answering a questionnaire or test, and reflect the data in the characteristic evaluation. A user who calmly answers even stressful questions can be determined to have high stress tolerance. In this way, the emotion estimation function can be used to reflect the user's emotional fluctuations in the characteristic evaluation.

[0076] The characteristic understanding unit can analyze a user's past work history and resume to evaluate changes in work experience and skills. For example, it can analyze a user's work history and evaluate changes in work experience and skills. Career growth can be understood based on changes in job content and job titles. It can also analyze the contents of the resume to evaluate the user's skill set based on educational background and qualification information. It can determine the depth of specialized knowledge based on acquired qualifications and training programs attended. Furthermore, it can integrate data from the work history and resume to comprehensively evaluate the user's career path and changes in skills. A user with experience in different industries can be determined to have diverse skills. This makes it possible to evaluate changes in work experience and skills by analyzing a user's past work history and resume.

[0077] The characteristic identification unit analyzes the content of a user's social media posts or emails to understand the characteristics, inclinations, and values ​​from their daily actions and comments. For example, it analyzes a user's social media posts and extracts characteristics and inclinations from the content of the posts. It can identify the user's interests based on frequently posted keywords and hashtags. It can also analyze the content of a user's emails to evaluate the characteristics and inclinations from the tone and content of the emails. It can identify communication style and leadership tendencies from the content of business emails. Furthermore, it can integrate the results of SNS and email analysis to comprehensively evaluate the characteristics, inclinations, and values ​​from the user's daily actions and comments. A user who makes many positive comments can be determined to have positive characteristics. In this way, by analyzing the content of a user's social media posts and emails, it is possible to understand the characteristics, inclinations, and values ​​from their daily actions and comments.

[0078] The quantification unit can quantify businessperson value by industry and occupation type based on the user's characteristic data, making it comparable. For example, businessperson value by industry is quantified based on the user's characteristic data. In the IT industry, technical skills are emphasized, so the numerical value for technical skills can be set high. Businessperson value by occupation type is also quantified and made comparable. In sales jobs, communication skills are emphasized, so the numerical value for those skills can be set high. Furthermore, the quantified data by industry and occupation type can be integrated to comprehensively evaluate the user's businessperson value. A user who receives high evaluations in multiple industries and occupations can be determined to be a versatile businessperson. In this way, businessperson value by industry and occupation type can be quantified and made comparable based on the user's characteristic data.

[0079] The quantification unit can use the emotion estimation function to quantify the stress and satisfaction that the user has experienced in the past and reflect this in their businessperson value. For example, the emotion estimation function can be used to quantify the stress that the user has experienced in the past. Stress tolerance can be evaluated based on emotional data from stressful projects. The user's satisfaction can also be quantified and reflected in their businessperson value. Job satisfaction can be evaluated based on emotional data from projects with high satisfaction. Furthermore, stress and satisfaction data can be integrated to comprehensively quantify the user's businessperson value. Users with high stress tolerance and high satisfaction can be determined to have high businessperson value. As a result, the emotion estimation function can be used to quantify the stress and satisfaction that the user has experienced in the past and reflect this in their businessperson value.

[0080] The quantification unit can analyze a user's past projects and business results, and reflect specific performance in the quantification. For example, it can analyze a user's past project data and quantify performance. Performance can be evaluated based on the project success rate and degree of achievement. It can also analyze business results and reflect them in specific numbers. Performance can be evaluated based on the rate at which sales targets are achieved and the cost reduction effect. Furthermore, it can integrate project data and business results to comprehensively quantify the user's business person value. A user with multiple successful projects can be determined to have high performance. In this way, by analyzing a user's past projects and business results, it is possible to reflect specific performance in the quantification.

[0081] The quantification unit can use the emotion estimation function to quantify the emotional fluctuations in teams and projects that the user has participated in in the past, and reflect this in the businessperson value. For example, the emotion estimation function can be used to quantify the emotional fluctuations in teams that the user has participated in in the past. Teamwork ability can be evaluated based on emotional data within the team. Emotional fluctuations in the project can also be quantified and reflected in the businessperson value. Stress tolerance and satisfaction can be evaluated based on emotional data during the progress of the project. Furthermore, emotional data from the team or project can be integrated to comprehensively evaluate the user's businessperson value. Users with little emotional fluctuation can be determined to have stable performance. As a result, the emotion estimation function can be used to quantify the emotional fluctuations in teams and projects that the user has participated in in the past, and reflect this in the businessperson value.

[0082] The shared section can customize and provide quantified information about users based on a company's evaluation criteria and the type of person they are looking for. For example, a system can be built in which a company's evaluation criteria are registered in a database and quantified information about users is customized and provided. Different evaluation criteria can be set for each company. Furthermore, quantified information about users can be customized based on the type of person the company is looking for. For companies that place importance on leadership, leadership scores can be emphasized. Furthermore, a system can be developed that provides customized information to companies and recommends candidates who best match the skill sets and personality profiles the company is looking for. Reports tailored to the needs of the company can be generated. This makes it possible to customize and provide quantified information about users based on the company's evaluation criteria and the type of person they are looking for.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The characteristic understanding unit understands the user's characteristics, inclinations, and values. For example, the characteristic understanding unit analyzes the results of questionnaires and tests answered by the user and evaluates the user's personality, skills, interests, etc. The characteristic understanding unit also uses the generation AI to understand the user's characteristics, inclinations, and values ​​in detail. For example, the generation AI performs analysis based on prompts that include instructions on what the user wants the generation AI to do. Step 2: The quantification unit quantifies and visualizes the user's value as a businessperson based on the characteristics, aspirations, and values ​​identified by the characteristics identification unit. For example, the quantification unit may express the user's leadership ability, communication ability, problem-solving ability, etc., in numerical terms and visually display them as graphs or charts. This makes it easier for the user to understand their own strengths and weaknesses. Step 3: The sharing unit shares the user's value, quantified by the quantification unit, with the user's consent. For example, it can compare the user's skill set with the skill set required by the company and determine the degree of compatibility. This allows companies to objectively evaluate the user's abilities when looking for employment or changing jobs.

[0085] 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.

[0086] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 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.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 characteristic grasping unit for grasping the characteristics, inclinations, and values ​​of a user; a quantification unit that quantifies and visualizes the value of the user as a business person based on the characteristics, inclinations, and values ​​of the user grasped by the characteristic grasping unit; a sharing unit that shares the value of the user quantified by the quantification unit with a company with the consent of the user. A system characterized by:

2. The characteristic grasping unit Analyzing the user's SNS posts or email content to understand the characteristics, inclinations, and values ​​from daily actions or comments 2. The system of claim 1.

3. The characteristic grasping unit Analyze the user's past work history and resume to evaluate the transition of work experience and skills 2. The system of claim 1.

4. The characteristic grasping unit Analyzing the emotions of the user when answering a questionnaire or test, and reflecting the fluctuations in the emotions in the characteristic evaluation.

2. The system of claim 1.

5. The characteristic grasping unit Collecting information about the user's hobbies and lifestyle and utilizing it to understand the user's characteristics, preferences, and values 2. The system of claim 1.

Images