System

Abstract

An object of the system according to the embodiment is to provide necessary information to a information poor, to discover a potential labor force, and to cause the worker to participate in a labor market.SOLUTION: A system according to an embodiment includes an information providing unit and a labor mining unit. The information providing unit provides information required by the information poor. The workforce mining component mines potential workforces to participate in the labor market.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide information to the informationally disadvantaged or discover potential labor force, so there is room for improvement.

[0005] The system according to the embodiment aims to provide necessary information to the informationally disadvantaged, discover potential labor force, and enable them to participate in the labor market. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit and a labor force mining unit. The information providing unit provides information needed by information-poor people. The labor force mining unit mines potential labor and allows them to participate in the labor market. [Effects of the Invention]

[0007] The system according to the embodiment can provide necessary information to the informationally disadvantaged, discover potential labor force, and enable them to participate in the labor market. [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 support system according to the embodiment of the present invention is a system that provides information that information-poor people need and discovers potential labor force. As a result, the support system makes it easier for information-poor people to obtain the information they need, and allows potential labor force to participate in the labor market.

[0029] The support system according to the embodiment includes an information providing unit and a labor resource extraction unit. The information providing unit provides information needed by information-poor individuals. For example, the information providing unit uses a generation AI to generate appropriate answers to user questions. The information providing unit can also use the generation AI to analyze the user's past question history and provide individually customized information. For example, when a user inputs a question such as "Please tell me where the nearest hospital is," the generation AI responds with "The nearest hospital is XX Hospital. The address is XX, XX-cho, XX-shi, XX-no. XX." The generation AI also automatically extracts relevant information based on the user's past question history and provides it to the user. The labor resource extraction unit extracts potential labor resources and invites them into the labor market. For example, the labor resource extraction unit uses the generation AI to analyze the user's skills and experience and, based on the results, suggests appropriate occupations and workplaces. The labor resource extraction unit can also use the generation AI to analyze the user's hobbies and interests and suggest more suitable occupations. For example, if a user inputs the question, "I have programming experience. What kind of job would be suitable for me?", the generative AI will suggest, "Based on your skills, a software developer or data analyst would be suitable." This allows the assistance system according to the embodiment to make it easier for the information-poor to obtain the information they need, thereby allowing potential workers to participate in the labor market. For example, by enabling information-poor people to use digital devices, online information gathering and communication will become easier, promoting their participation in society. Furthermore, by enabling potential workers to find suitable jobs, the labor market will be revitalized.

[0030] The information provision unit can use the generation AI to analyze the past question history of information-poor individuals and provide individually customized information. The information provision unit, for example, uses the generation AI to analyze the past question history of information-poor individuals and provide individually customized information. For example, a user who has asked many health-related questions in the past is given priority in being provided with the latest health information and information on nearby medical institutions. The generation AI also automatically extracts relevant information based on the user's past question history and provides it to the user. For example, a user who has asked many traffic information in the past is provided with the latest traffic conditions and public transportation operation information. The generation AI also analyzes the user's past question history and provides information based on the user's interests and concerns. For example, a user who has asked many questions about tourist destinations in the past is provided with the latest tourist information and event information. This makes it possible to provide individually customized information to information-poor individuals.

[0031] The information provision unit can use the generation AI to evaluate the user's level of understanding of the provided information in real time and provide additional information according to the level of understanding. For example, the information provision unit can use the generation AI to evaluate the extent to which the user understands the provided information in real time and provide additional information according to the level of understanding. For example, if the user does not understand basic information, a more detailed explanation is provided. The generation AI can also pose simple quizzes or questions to evaluate the user's level of understanding and provide additional information based on the answers. For example, if the user is unable to answer accurately, the generation AI can provide another explanation. The generation AI can also monitor the user's level of understanding in real time and adjust the way information is provided according to the level of understanding. For example, it can add explanations using diagrams and illustrations to make it easier for the user to understand. This makes it possible to provide information according to the user's level of understanding.

[0032] The information provision unit can use the generation AI to utilize voice input and image recognition to accommodate information-vulnerable people with visual or hearing impairments. The information provision unit, for example, uses the generation AI to utilize voice input to provide information to information-vulnerable people with visual impairments. For example, a user inputs a question by voice, and the generation AI provides a response by voice. The generation AI also uses image recognition technology to provide information to information-vulnerable people with hearing impairments. For example, a user inputs a question using sign language or gestures, and the generation AI provides a response in text. The generation AI also combines voice input and image recognition to provide information to information-vulnerable people with visual or hearing impairments. For example, a user inputs a question by voice, and the generation AI provides a response in the form of an image or text. This makes it possible to accommodate information-vulnerable people with visual or hearing impairments.

[0033] The information provision unit can use the generation AI to provide information that corresponds to different languages ​​and dialects, thereby realizing support for the informationally disadvantaged in multicultural societies. The information provision unit can, for example, use the generation AI to provide information that corresponds to different languages, thereby supporting the informationally disadvantaged in multicultural societies. For example, a user inputs a question in their native language, and the generation AI provides an answer in that language. The generation AI can also provide information that corresponds to dialects, thereby supporting the informationally disadvantaged in each region. For example, a user inputs a question in a dialect, and the generation AI provides an answer in that dialect. The generation AI can also be equipped with a translation function to support different languages ​​and dialects, thereby supporting the informationally disadvantaged in multicultural societies. For example, a user inputs a question in a different language, and the generation AI automatically translates and provides an answer. This can realize support for the informationally disadvantaged in multicultural societies.

[0034] The labor mining unit uses the generation AI to analyze not only the user's skills and experience, but also their hobbies and interests, and can suggest more suitable occupations. For example, the labor mining unit uses the generation AI to analyze the user's skills and experience, as well as their hobbies and interests, and suggest more suitable occupations. For example, for a user whose hobby is programming, the generation AI can suggest occupations such as software developer or data analyst. The generation AI can also analyze the user's hobbies and interests and suggest occupations based on that. For example, for a user whose hobby is cooking, the generation AI can suggest occupations such as chef or food consultant. The generation AI can also comprehensively analyze the user's skills, experience, hobbies, and interests and suggest the most suitable occupation. For example, for a user whose hobby is sports, the generation AI can suggest occupations such as sports trainer or fitness instructor. This makes it possible to suggest occupations based on the user's hobbies and interests.

[0035] The labor mining unit can use the generation AI to collect user feedback on the proposed occupations and improve the accuracy of the suggestions. The labor mining unit, for example, uses the generation AI to collect user feedback on the proposed occupations and improve the accuracy of the suggestions based on the results. For example, it evaluates whether the user is satisfied with the proposed occupation. The generation AI also analyzes the user feedback and improves the algorithm for improving the accuracy of the suggestions. For example, it adjusts the criteria for suggesting occupations based on the user's opinions. The generation AI also collects user feedback in real time and improves the accuracy of the suggestions. For example, it analyzes what the user thinks about the proposed occupations. This allows the accuracy of the occupation suggestions to be improved based on the user's feedback.

[0036] The labor mining unit can use generation AI to make occupational suggestions that accommodate new work styles such as remote work and freelancing. The labor mining unit, for example, uses generation AI to make occupational suggestions that accommodate remote work. For example, it suggests occupations that allow the user to work from home or companies that allow remote work. The generation AI also makes occupational suggestions that allow for freelance work. For example, it suggests occupations that allow the user to work as a freelancer, utilizing their skills. The generation AI also makes occupational suggestions that accommodate new work styles. For example, it suggests occupations that allow the user to work part-time or on a project basis. This makes it possible to make occupational suggestions that accommodate new work styles.

[0037] The labor mining unit can use the generation AI to suggest occupations across different industries and fields, thereby diversifying the user's career path. The labor mining unit, for example, uses the generation AI to suggest occupations across different industries, thereby diversifying the user's career path. For example, it suggests occupations that allow the user to gain experience in different industries. The generation AI can also suggest occupations in different fields, thereby diversifying the user's career path. For example, it suggests occupations that allow the user to work in both technical and creative fields. The generation AI can also suggest occupations across different industries and fields, thereby diversifying the user's career path. For example, it suggests occupations that allow the user to utilize skills in different industries. This can diversify the user's career path.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The information provision unit can also use the generation AI to monitor the user's health condition and provide information useful for health management. For example, when the user inputs their daily health data, the generation AI analyzes the data and provides appropriate health advice. The generation AI can also send reminders for regular health checks based on the user's health condition. Furthermore, the generation AI can monitor the user's health condition and, if an abnormality is detected, provide information recommending a visit to a medical institution. This allows the user to manage their health condition more effectively.

[0040] The information provision unit can also use the generation AI to analyze the user's learning history and provide an individually customized learning plan. For example, the generation AI can propose an optimal learning plan based on the user's past learning content and progress. The generation AI can also adjust the learning plan according to the user's learning style and pace. Furthermore, the generation AI can analyze the user's learning history and provide advice to maximize the effectiveness of learning. This allows the user to study efficiently.

[0041] The information provision unit can also use the generation AI to analyze the user's purchasing history and make individually customized product suggestions. For example, the generation AI can suggest optimal products based on products the user has purchased in the past or products in which the user is interested. The generation AI can also analyze the user's purchasing history and provide discount information and campaign information for related products. Furthermore, the generation AI can analyze the user's purchasing history and suggest new products that match the user's preferences. This makes it easier for users to find products that suit them.

[0042] The Labor Discovery Department can also use the Generative AI to suggest remote work or freelance work based on the user's skills and experience. For example, it can suggest remote work jobs that the user can work from home. The Generative AI can also analyze the user's skills and experience and suggest freelance work. Furthermore, the Generative AI can suggest project-based work based on the user's skills and experience. This allows users to choose a work style that suits their lifestyle.

[0043] The Workforce Identification Department can also use the Generative AI to suggest jobs in different industries and fields to diversify the user's career path. For example, it can suggest jobs that allow the user to gain experience in a different industry. The Generative AI can also analyze the user's skills and experience to suggest jobs in different fields. Furthermore, the Generative AI can also suggest jobs across different industries and fields to diversify the user's career path. This allows the user to choose a variety of career paths.

[0044] The labor mining department can also use the generation AI to collect user feedback and improve the accuracy of career suggestions. For example, the user can evaluate their satisfaction with the suggested careers. The generation AI can also analyze user feedback and improve the career suggestion algorithm. Furthermore, the generation AI can collect user feedback in real time and improve the accuracy of career suggestions. This allows users to receive more suitable career suggestions.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The information provider provides the information needed by those with limited information. For example, the information provider uses a generation AI to generate appropriate answers to questions from users. The information provider can also use the generation AI to analyze the user's past question history and provide individually customized information. For example, if a user inputs the question, "Please tell me where the nearest hospital is," the generation AI will respond, "The nearest hospital is XX Hospital. The address is XX-cho, XX-shi, XX-no. XX." The generation AI also automatically extracts relevant information based on the user's past question history and provides it to the user. Step 2: The labor extraction department discovers potential labor and recruits them into the labor market. For example, the labor extraction department uses generative AI to analyze the user's skills and experience and, based on that, suggests suitable occupations and workplaces. The labor extraction department can also use generative AI to analyze the user's hobbies and interests and suggest more suitable occupations. For example, if a user inputs the question, "I have programming experience. What kind of job would be suitable for me?", the generative AI would suggest, "Based on your skills, occupations as a software developer or data analyst would be suitable."

[0047] (Example 2) The support system according to the embodiment of the present invention is a system that provides information that information-poor people need and discovers potential labor force. As a result, the support system makes it easier for information-poor people to obtain the information they need, and allows potential labor force to participate in the labor market.

[0048] The support system according to the embodiment includes an information providing unit and a labor resource extraction unit. The information providing unit provides information needed by information-poor individuals. For example, the information providing unit uses a generation AI to generate appropriate answers to user questions. The information providing unit can also use the generation AI to analyze the user's past question history and provide individually customized information. For example, when a user inputs a question such as "Please tell me where the nearest hospital is," the generation AI responds with "The nearest hospital is XX Hospital. The address is XX, XX-cho, XX-shi, XX-no. XX." The generation AI also automatically extracts relevant information based on the user's past question history and provides it to the user. The labor resource extraction unit extracts potential labor resources and invites them into the labor market. For example, the labor resource extraction unit uses the generation AI to analyze the user's skills and experience and, based on the results, suggests appropriate occupations and workplaces. The labor resource extraction unit can also use the generation AI to analyze the user's hobbies and interests and suggest more suitable occupations. For example, if a user inputs the question, "I have programming experience. What kind of job would be suitable for me?", the generative AI will suggest, "Based on your skills, a software developer or data analyst would be suitable." This allows the assistance system according to the embodiment to make it easier for the information-poor to obtain the information they need, thereby allowing potential workers to participate in the labor market. For example, by enabling information-poor people to use digital devices, online information gathering and communication will become easier, promoting their participation in society. Furthermore, by enabling potential workers to find suitable jobs, the labor market will be revitalized.

[0049] The information provision unit can use the generation AI to analyze the past question history of information-poor individuals and provide individually customized information. The information provision unit, for example, uses the generation AI to analyze the past question history of information-poor individuals and provide individually customized information. For example, a user who has asked many health-related questions in the past is given priority in being provided with the latest health information and information on nearby medical institutions. The generation AI also automatically extracts relevant information based on the user's past question history and provides it to the user. For example, a user who has asked many traffic information in the past is provided with the latest traffic conditions and public transportation operation information. The generation AI also analyzes the user's past question history and provides information based on the user's interests and concerns. For example, a user who has asked many questions about tourist destinations in the past is provided with the latest tourist information and event information. This makes it possible to provide individually customized information to information-poor individuals.

[0050] The information provision unit can use the generation AI to evaluate the user's level of understanding of the provided information in real time and provide additional information according to the level of understanding. For example, the information provision unit can use the generation AI to evaluate the extent to which the user understands the provided information in real time and provide additional information according to the level of understanding. For example, if the user does not understand basic information, a more detailed explanation is provided. The generation AI can also pose simple quizzes or questions to evaluate the user's level of understanding and provide additional information based on the answers. For example, if the user is unable to answer accurately, the generation AI can provide another explanation. The generation AI can also monitor the user's level of understanding in real time and adjust the way information is provided according to the level of understanding. For example, it can add explanations using diagrams and illustrations to make it easier for the user to understand. This makes it possible to provide information according to the user's level of understanding.

[0051] The information provision unit can use the generation AI to grasp the user's emotional state and provide information to reduce stress. For example, the information provision unit uses the generation AI to analyze the user's emotional state in real time and provide information to reduce stress. For example, if the user is feeling stressed, the information provision unit suggests relaxation methods and stress relief techniques. The generation AI also grasps the user's emotional state and provides information to elicit positive emotions. For example, if the user is feeling anxious, the information provision unit provides encouraging messages and success stories. The generation AI also analyzes the user's emotional state and provides information according to the emotion. For example, if the user is sad, the information provision unit provides entertainment information or information about hobbies to brighten the mood. This makes it possible to provide information according to the user's emotional state.

[0052] The information provision unit can use the generation AI to utilize voice input and image recognition to accommodate information-vulnerable people with visual or hearing impairments. The information provision unit, for example, uses the generation AI to utilize voice input to provide information to information-vulnerable people with visual impairments. For example, a user inputs a question by voice, and the generation AI provides a response by voice. The generation AI also uses image recognition technology to provide information to information-vulnerable people with hearing impairments. For example, a user inputs a question using sign language or gestures, and the generation AI provides a response in text. The generation AI also combines voice input and image recognition to provide information to information-vulnerable people with visual or hearing impairments. For example, a user inputs a question by voice, and the generation AI provides a response in the form of an image or text. This makes it possible to accommodate information-vulnerable people with visual or hearing impairments.

[0053] The information provision unit can use the generation AI to provide information that corresponds to different languages ​​and dialects, thereby realizing support for the informationally disadvantaged in multicultural societies. The information provision unit can, for example, use the generation AI to provide information that corresponds to different languages, thereby supporting the informationally disadvantaged in multicultural societies. For example, a user inputs a question in their native language, and the generation AI provides an answer in that language. The generation AI can also provide information that corresponds to dialects, thereby supporting the informationally disadvantaged in each region. For example, a user inputs a question in a dialect, and the generation AI provides an answer in that dialect. The generation AI can also be equipped with a translation function to support different languages ​​and dialects, thereby supporting the informationally disadvantaged in multicultural societies. For example, a user inputs a question in a different language, and the generation AI automatically translates and provides an answer. This can realize support for the informationally disadvantaged in multicultural societies.

[0054] The information provision unit can use the generation AI to analyze the emotions the user feels when receiving information and provide information that elicits positive emotions. For example, the information provision unit uses the generation AI to analyze the emotions the user feels when receiving information in real time and provide information to elicit positive emotions. For example, if the user is feeling anxious, it provides information that gives a sense of security. The generation AI also grasps the user's emotional state and provides information to elicit positive emotions. For example, if the user is feeling stressed, it suggests ways to relax or relieve stress. The generation AI also analyzes the user's emotions and provides information to elicit positive emotions. For example, if the user is sad, it provides entertainment information or information about hobbies to brighten the mood. This makes it possible to provide information that corresponds to the user's emotions.

[0055] The labor mining unit uses the generation AI to analyze not only the user's skills and experience, but also their hobbies and interests, and can suggest more suitable occupations. For example, the labor mining unit uses the generation AI to analyze the user's skills and experience, as well as their hobbies and interests, and suggest more suitable occupations. For example, for a user whose hobby is programming, the generation AI can suggest occupations such as software developer or data analyst. The generation AI can also analyze the user's hobbies and interests and suggest occupations based on that. For example, for a user whose hobby is cooking, the generation AI can suggest occupations such as chef or food consultant. The generation AI can also comprehensively analyze the user's skills, experience, hobbies, and interests and suggest the most suitable occupation. For example, for a user whose hobby is sports, the generation AI can suggest occupations such as sports trainer or fitness instructor. This makes it possible to suggest occupations based on the user's hobbies and interests.

[0056] The labor mining unit can use the generation AI to collect user feedback on the proposed occupations and improve the accuracy of the suggestions. The labor mining unit, for example, uses the generation AI to collect user feedback on the proposed occupations and improve the accuracy of the suggestions based on the results. For example, it evaluates whether the user is satisfied with the proposed occupation. The generation AI also analyzes the user feedback and improves the algorithm for improving the accuracy of the suggestions. For example, it adjusts the criteria for suggesting occupations based on the user's opinions. The generation AI also collects user feedback in real time and improves the accuracy of the suggestions. For example, it analyzes what the user thinks about the proposed occupations. This allows the accuracy of the occupation suggestions to be improved based on the user's feedback.

[0057] The labor mining unit can use the generation AI to analyze the feelings the user has toward the proposed occupations and make occupation suggestions that elicit positive emotions. The labor mining unit, for example, uses the generation AI to analyze the feelings the user has toward the proposed occupations in real time and make occupation suggestions that elicit positive emotions. For example, it prioritizes suggestions of occupations that are likely to interest the user. The generation AI also grasps the user's emotional state and makes occupation suggestions that elicit positive emotions. For example, it suggests occupations that the user can enjoy working at. The generation AI also analyzes the user's emotions and makes occupation suggestions that elicit positive emotions. For example, it suggests occupations that the user finds rewarding. This makes it possible to make occupation suggestions that correspond to the user's emotions.

[0058] The labor mining unit can use generation AI to make occupational suggestions that accommodate new work styles such as remote work and freelancing. The labor mining unit, for example, uses generation AI to make occupational suggestions that accommodate remote work. For example, it suggests occupations that allow the user to work from home or companies that allow remote work. The generation AI also makes occupational suggestions that allow for freelance work. For example, it suggests occupations that allow the user to work as a freelancer, utilizing their skills. The generation AI also makes occupational suggestions that accommodate new work styles. For example, it suggests occupations that allow the user to work part-time or on a project basis. This makes it possible to make occupational suggestions that accommodate new work styles.

[0059] The labor mining unit can use the generation AI to suggest occupations across different industries and fields, thereby diversifying the user's career path. The labor mining unit, for example, uses the generation AI to suggest occupations across different industries, thereby diversifying the user's career path. For example, it suggests occupations that allow the user to gain experience in different industries. The generation AI can also suggest occupations in different fields, thereby diversifying the user's career path. For example, it suggests occupations that allow the user to work in both technical and creative fields. The generation AI can also suggest occupations across different industries and fields, thereby diversifying the user's career path. For example, it suggests occupations that allow the user to utilize skills in different industries. This can diversify the user's career path.

[0060] The labor mining unit uses the generation AI to monitor the user's emotions in real time when receiving career suggestions, and can continuously make optimal suggestions. The labor mining unit, for example, uses the generation AI to monitor the user's emotions in real time when receiving career suggestions, and can continuously make optimal suggestions. For example, it prioritizes suggestions of occupations that the user is likely to be interested in. The generation AI also grasps the user's emotional state and makes occupation suggestions that elicit positive emotions. For example, it suggests occupations that the user can enjoy working at. The generation AI also analyzes the user's emotions and makes occupation suggestions that elicit positive emotions. For example, it suggests occupations that the user finds rewarding. This makes it possible to continuously make optimal occupation suggestions based on the user's emotions.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The information provision unit can also use the generation AI to monitor the user's health condition and provide information useful for health management. For example, when the user inputs their daily health data, the generation AI analyzes the data and provides appropriate health advice. The generation AI can also send reminders for regular health checks based on the user's health condition. Furthermore, the generation AI can monitor the user's health condition and, if an abnormality is detected, provide information recommending a visit to a medical institution. This allows the user to manage their health condition more effectively.

[0063] The information provision unit can also use the generation AI to analyze the user's learning history and provide an individually customized learning plan. For example, the generation AI can propose an optimal learning plan based on the user's past learning content and progress. The generation AI can also adjust the learning plan according to the user's learning style and pace. Furthermore, the generation AI can analyze the user's learning history and provide advice to maximize the effectiveness of learning. This allows the user to study efficiently.

[0064] The information provision unit can also use the generation AI to analyze the user's purchasing history and make individually customized product suggestions. For example, the generation AI can suggest optimal products based on products the user has purchased in the past or products in which the user is interested. The generation AI can also analyze the user's purchasing history and provide discount information and campaign information for related products. Furthermore, the generation AI can analyze the user's purchasing history and suggest new products that match the user's preferences. This makes it easier for users to find products that suit them.

[0065] The information provision unit can also use the generation AI to understand the user's emotional state and provide information on relaxation and mental health. For example, if the user is feeling stressed, it can provide relaxation methods and mental health advice. The generation AI can also analyze the user's emotional state and suggest music or meditation guides that will help with relaxation. Furthermore, the generation AI can monitor the user's emotional state and provide information recommending consultation with a specialist if necessary. This allows the user to manage their own mental health more effectively.

[0066] The information provision unit can also use the generation AI to grasp the user's emotional state and provide entertainment information according to the emotion. For example, if the user is tired, it can suggest relaxing movies or music. The generation AI can also analyze the user's emotional state and suggest event information or activities to lift their mood. Furthermore, the generation AI can improve the user's mood by monitoring the user's emotional state and providing entertainment information according to the emotion. This allows the user to enjoy entertainment according to their own emotional state.

[0067] The Labor Discovery Department can also use the Generative AI to suggest remote work or freelance work based on the user's skills and experience. For example, it can suggest remote work jobs that the user can work from home. The Generative AI can also analyze the user's skills and experience and suggest freelance work. Furthermore, the Generative AI can suggest project-based work based on the user's skills and experience. This allows users to choose a work style that suits their lifestyle.

[0068] The Workforce Identification Department can also use the Generative AI to suggest jobs in different industries and fields to diversify the user's career path. For example, it can suggest jobs that allow the user to gain experience in a different industry. The Generative AI can also analyze the user's skills and experience to suggest jobs in different fields. Furthermore, the Generative AI can also suggest jobs across different industries and fields to diversify the user's career path. This allows the user to choose a variety of career paths.

[0069] The Labor Discovery Department can use the generation AI to understand the user's emotional state and suggest occupations that will elicit positive emotions. For example, it can suggest occupations that the user will enjoy working at. The generation AI can also analyze the user's emotional state and suggest occupations that the user will find rewarding. Furthermore, the generation AI can increase user satisfaction by monitoring the user's emotional state and suggesting occupations that will elicit positive emotions. This allows the user to choose an occupation that suits their emotional state.

[0070] The labor mining department can also use the generation AI to collect user feedback and improve the accuracy of career suggestions. For example, the user can evaluate their satisfaction with the suggested careers. The generation AI can also analyze user feedback and improve the career suggestion algorithm. Furthermore, the generation AI can collect user feedback in real time and improve the accuracy of career suggestions. This allows users to receive more suitable career suggestions.

[0071] The labor mining department can use the generation AI to understand the user's emotional state and suggest occupations that will elicit positive emotions. For example, it can suggest occupations that the user is likely to be interested in. The generation AI can also analyze the user's emotional state and suggest occupations that the user will enjoy working in. Furthermore, the generation AI can increase user satisfaction by monitoring the user's emotional state and suggesting occupations that will elicit positive emotions. This allows the user to choose an occupation that suits their own emotional state.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The information provider provides the information needed by those with limited information. For example, the information provider uses a generation AI to generate appropriate answers to questions from users. The information provider can also use the generation AI to analyze the user's past question history and provide individually customized information. For example, if a user inputs the question, "Please tell me where the nearest hospital is," the generation AI will respond, "The nearest hospital is XX Hospital. The address is XX-cho, XX-shi, XX-no. XX." The generation AI also automatically extracts relevant information based on the user's past question history and provides it to the user. Step 2: The labor extraction department discovers potential labor and recruits them into the labor market. For example, the labor extraction department uses generative AI to analyze the user's skills and experience and, based on that, suggests suitable occupations and workplaces. The labor extraction department can also use generative AI to analyze the user's hobbies and interests and suggest more suitable occupations. For example, if a user inputs the question, "I have programming experience. What kind of job would be suitable for me?", the generative AI would suggest, "Based on your skills, occupations as a software developer or data analyst would be suitable."

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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, in order to avoid confusion and to 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.

[0140] 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]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information provision department that provides information needed by information-poor people; A labor force extraction department that discovers potential labor forces and allows them to participate in the labor market. A system characterized by:

2. The information providing unit Using generative AI, the past question history of the information-poor person is analyzed and individually customized information is provided.

2. The system of claim 1.

3. The information providing unit Using generation AI, the system evaluates the user's level of understanding of the information provided in real time and provides additional information according to the level of understanding.

2. The system of claim 1.

4. The information providing unit Using generative AI to understand the user's emotional state and provide information to reduce stress 2. The system of claim 1.

5. The information providing unit Using generative AI, voice input and image recognition will be utilized to accommodate those with visual or hearing impairments who are information-challenged.

2. The system of claim 1.

6. The information providing unit Using generative AI to provide information in different languages ​​and dialects, we will support the informationally disadvantaged in multicultural societies.

2. The system of claim 1.

7. The information providing unit Using generative AI, we analyze the emotions users feel when receiving information and provide information that elicits those positive emotions.

2. The system of claim 1.

8. The labor resource development department Using generative AI, the system analyzes not only a user's skills and experience, but also their hobbies and interests to suggest more suitable occupations.

2. The system of claim 1.

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