System

Abstract

An object of a system according to an embodiment is to efficiently perform progress management of an interview or life guidance based on information of a medical interview sheet.SOLUTION: A system includes an interview part, a life guidance part, and a progress management part. The interview unit performs an interview based on the information input to the interview sheet. The life guidance unit provides real-time life guidance based on the interview result obtained by the interview unit. The progress management part manages the progress situation of the life guidance performed by the life guidance part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not efficiently manage the progress of interviews and lifestyle guidance based on information from medical questionnaires, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently manage the progress of interviews and lifestyle guidance based on information from a medical questionnaire. [Means for solving the problem]

[0006] The system according to the embodiment includes an interview unit, a life guidance unit, and a progress management unit. The interview unit conducts an interview based on information entered in a medical questionnaire. The life guidance unit provides life guidance in real time based on the interview results obtained by the interview unit. The progress management unit manages the progress of the life guidance conducted by the life guidance unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the progress of interviews and lifestyle guidance based on the information from the medical questionnaire. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In a health support system according to an embodiment of the present invention, a generation AI conducts an interview based on information entered in a medical questionnaire, provides lifestyle advice in real time based on the interview results, and manages progress using an app or appliance. This allows the health support system to grasp the user's health condition and help them develop appropriate lifestyle habits.

[0029] A health support system according to an embodiment includes a medical questionnaire input unit, an interview unit, a lifestyle guidance unit, and a progress management unit. The medical questionnaire input unit allows a user to input information about their health condition and lifestyle habits. For example, the user inputs information such as dietary habits, exercise habits, sleep time, and stress level. The interview unit allows a generation AI to conduct an interview with the user based on the information input into the medical questionnaire. For example, the generation AI may ask questions about the user's health condition and lifestyle habits to collect detailed information. The generation AI conducts the interview using a text generation AI (e.g., LLM). The lifestyle guidance unit provides lifestyle guidance in real time based on the results of the interview. For example, the generation AI may suggest dietary improvements, exercise recommendations, and stress management methods. The generation AI may provide specific advice based on the user's health condition and lifestyle habits. The progress management unit manages the progress of the lifestyle guidance using an app or appliance. For example, the user can check their daily lifestyle record and progress through the app or appliance. This enables the health support system to support the user in living a healthy lifestyle.

[0030] The questionnaire input unit provides real-time feedback on the information entered by the user, improving the accuracy of the input. For example, when a user enters information into the questionnaire, the generation AI analyzes the input in real time and points out typos and inaccurate information. For example, if a user enters "vegetables" when entering their dietary information, the generation AI will ask for the specific type and amount. The generation AI also evaluates the user's input in real time and asks additional questions as necessary. For example, if a user enters "twice a week" when entering their exercise habits, the generation AI will ask for the specific type and time of exercise. The generation AI also provides instant feedback based on the information entered by the user, improving the accuracy of the input. For example, if a user enters "6 hours" when entering their sleep time, the generation AI will provide advice on the ideal amount of sleep. This improves the accuracy of the user's input.

[0031] The medical questionnaire input unit can automatically import the user's past health data and lifestyle habit data to complement the input on the medical questionnaire. The medical questionnaire input unit, for example, builds a system that automatically imports the user's past health data and complements the input on the medical questionnaire. For example, it references past medical records and data from health apps to automatically complement the input content. It also automatically imports lifestyle habit data to complement the input on the medical questionnaire. For example, it automatically inputs exercise habits and sleep patterns based on data from a smartwatch or fitness tracker. It also develops a system that complements the input on the medical questionnaire based on the user's past health data and lifestyle habit data. For example, it references past meal records to automatically suggest current meal contents. This reduces the input work for the user.

[0032] The medical questionnaire input unit can use voice input or image recognition technology to make it easier for users to input information. The medical questionnaire input unit, for example, uses voice input technology to allow users to respond by voice when inputting information into the medical questionnaire. For example, the user responds by voice to the question, "Please tell us about your recent meals." Furthermore, image recognition technology is used to automatically extract information when a user inputs information into the medical questionnaire simply by uploading an image. For example, when a photo of a meal is uploaded, the ingredients and amounts are automatically recognized. Furthermore, a combination of voice input and image recognition technology allows users to input information into the medical questionnaire more easily. For example, a user can upload a video of their exercise, and the type and duration of the exercise can be automatically analyzed. This simplifies the user's input process.

[0033] The medical questionnaire input unit may add a function that allows sharing with family or friends, and may enrich the input content by receiving feedback from others. The medical questionnaire input unit may add, for example, a function that allows sharing of the medical questionnaire information with family and friends, and may enrich the input content by receiving feedback from others. For example, a family member may add a comment about the user's diet. Furthermore, using the sharing function, the user may share the medical questionnaire information with friends, and enrich the input content by receiving feedback. For example, a friend may provide advice about exercise habits. Furthermore, a system may be constructed that adds a function to share the medical questionnaire information, and enriches the input content by receiving feedback from others. For example, a family member or friend may give their opinion on the user's sleep patterns. This enriches the input content, and more accurate information may be obtained.

[0034] The interview unit can automatically summarize the contents of the interview so that the user can check it later. For example, the interview unit has the generation AI analyze the contents of the conversation with the user in real time during the interview and automatically generate a summary. For example, it summarizes important points and advice so that the user can check it later. In addition, a system is built that automatically summarizes the contents of the interview so that the user can check it later. For example, a summary is sent by email after the interview ends. In addition, the generation AI automatically summarizes the contents of the interview so that the user can check it later. For example, the interview content is saved in text format so that the user can check it in an app. This allows the user to check the contents of the interview later.

[0035] The interview unit provides interview content in multiple languages, making it possible to accommodate users from different cultural backgrounds. The interview unit, for example, builds a system that provides interview content in multiple languages, making it possible to accommodate users from different cultural backgrounds. For example, interviews are conducted in languages ​​such as English, Spanish, and Chinese. In addition, a generation AI translates the interview content in real time, making it possible to accommodate users from different cultural backgrounds. For example, a user receives questions in Japanese and answers in English. In addition, a system is developed that provides interview content in multiple languages, making it possible to accommodate users from different cultural backgrounds. For example, an interview is conducted in the language selected by the user. This makes it possible to accommodate users from different cultural backgrounds.

[0036] The interview unit can refer to the user's health data in real time during the interview and provide more specific advice. For example, the interview unit will build a system in which the generation AI refers to the user's health data in real time during the interview and provides specific advice. For example, it may suggest dietary improvements based on the user's blood pressure data. The generation AI will also refer to the user's health data in real time during the interview and provide specific advice. For example, it may recommend exercise based on the user's exercise data. A system will also be developed in which the generation AI will refer to the user's health data in real time during the interview and provide more specific advice. For example, it may provide advice on improving sleep based on the user's sleep data. This will allow more specific advice to be provided to the user.

[0037] The lifestyle guidance department can analyze the user's lifestyle data in real time and provide instant feedback. For example, the lifestyle guidance department will build a system in which a generation AI collects a user's lifestyle data in real time and provides instant feedback. For example, it will analyze dietary content and exercise volume in real time and immediately suggest areas for improvement. It will also analyze a user's lifestyle data in real time and provide instant feedback. For example, it will provide advice on improving sleep quality based on sleep data. It will also develop a system in which a generation AI analyzes a user's lifestyle data in real time and provide instant feedback. For example, it will monitor stress levels in real time and make suggestions for stress reduction. This will allow instant feedback to be provided to the user.

[0038] The lifestyle guidance department can compare the user's past data, evaluate their progress, and suggest the next step. For example, the lifestyle guidance department will build a system in which the generation AI compares the user's past data with their current data and evaluate their progress. For example, it will compare past exercise data with their current data and evaluate the effectiveness of the exercise. It will also compare the user's past data with their current data, evaluate their progress, and suggest the next step. For example, it will compare past dietary data with their current data and suggest areas for improving their diet. It will also develop a system in which the generation AI compares the user's past data with their current data, evaluate their progress, and suggest the next step. For example, it will compare past sleep data with their current data and give advice on improving the quality of their sleep. This will enable it to evaluate the user's progress and suggest the next step.

[0039] The lifestyle guidance unit can link the user's lifestyle guidance content with other health management apps to achieve comprehensive health management. For example, the lifestyle guidance unit constructs a system in which a generation AI links the user's lifestyle guidance content with other health management apps to achieve comprehensive health management. For example, it shares data with a diet management app and an exercise management app. It also links with other health management apps to manage the user's lifestyle guidance content in an integrated manner. For example, it links with a sleep management app to provide lifestyle guidance based on sleep data. It also develops a system in which the generation AI links the user's lifestyle guidance content with other health management apps to achieve comprehensive health management. For example, it links with a stress management app to provide lifestyle guidance based on stress data. This enables comprehensive health management to be achieved.

[0040] The lifestyle guidance unit can share the user's lifestyle advice with family or friends so that they can receive support. The lifestyle guidance unit, for example, builds a system in which the generation AI shares the user's lifestyle advice with family and friends so that they can receive support. For example, family members check the user's diet and provide advice. The lifestyle advice is also shared with family and friends so that they can receive support. For example, friends check the user's exercise habits and send encouraging messages. The generation AI also develops a system in which the generation AI shares the user's lifestyle advice with family and friends so that they can receive support. For example, family members check the user's sleep patterns and provide advice. This allows the user to receive support from family and friends.

[0041] The progress management unit can automatically analyze the user's progress data and provide rewards or incentives according to the level of achievement. The progress management unit, for example, builds a system in which an app automatically analyzes the user's progress data and provides rewards or incentives according to the level of achievement. For example, points are awarded when a goal is achieved. The progress management unit also automatically analyzes the user's progress data and provides rewards or incentives according to the level of achievement. For example, a badge is earned when an exercise goal is achieved. The progress management unit also develops a system in which an app automatically analyzes the user's progress data and provides rewards or incentives according to the level of achievement. For example, a coupon is provided when a diet goal is achieved. In this way, rewards and incentives are provided according to the user's level of achievement.

[0042] The progress management unit can visualize the user's progress data to enable intuitive understanding. The progress management unit, for example, builds a system that enables an app to visualize the user's progress data to enable intuitive understanding. For example, the progress status is displayed using graphs and charts. The progress management unit also visualizes the user's progress data to enable intuitive understanding. For example, the exercise completion status is displayed in calendar format. The progress management unit also develops a system that enables an app to visualize the user's progress data to enable intuitive understanding. For example, the progress status is displayed using icons and colors. This allows the user to intuitively understand the progress data.

[0043] The progress management unit can store the user's progress data in the cloud and make it accessible from multiple devices. For example, the progress management unit builds a system that allows an app to store the user's progress data in the cloud and access it from multiple devices. For example, the progress data can be checked from a smartphone or tablet. The progress management unit also stores the user's progress data in the cloud and makes it accessible from multiple devices. For example, the progress data can be checked from a PC or a smartwatch. The progress management unit also develops a system that allows the app to store the user's progress data in the cloud and access it from multiple devices. For example, the progress data can be checked from a smart TV or fitness tracker. This allows the user to access the progress data from multiple devices.

[0044] The progress management unit can link the user's progress data with other health management apps to achieve comprehensive health management. For example, the progress management unit builds a system in which an app links the user's progress data with other health management apps to achieve comprehensive health management. For example, it shares data with a diet management app and an exercise management app. It also links with other health management apps to manage the user's progress data in an integrated manner. For example, it links with a sleep management app to manage progress based on sleep data. It also develops a system in which an app links the user's progress data with other health management apps to achieve comprehensive health management. For example, it links with a stress management app to manage progress based on stress data. This allows comprehensive health management to be achieved.

[0045] The lifestyle guidance unit can set individual health goals based on the user's progress data and update advice according to the degree of achievement. The lifestyle guidance unit, for example, builds a system in which a generation AI sets individual health goals based on the user's progress data. For example, based on the user's exercise data, it sets a goal of exercising three times a week. Also, based on the user's progress data, it sets individual health goals and updates advice according to the degree of achievement. For example, based on dietary data, it sets a goal of increasing vegetable intake and updates advice according to the degree of achievement. Also, a system is developed in which a generation AI sets individual health goals based on the user's progress data and updates advice according to the degree of achievement. For example, based on sleep data, it sets a goal of seven hours of sleep each night and updates advice according to the degree of achievement. In this way, advice according to the user's health goals is provided.

[0046] The lifestyle guidance unit can analyze the user's lifestyle data over the long term and propose a sustainable health management plan. For example, the lifestyle guidance unit will build a system in which a generation AI collects the user's lifestyle data over the long term and proposes a sustainable health management plan. For example, a long-term exercise plan will be proposed based on exercise data from the past year. The lifestyle guidance unit will also analyze the user's lifestyle data over the long term and propose a sustainable health management plan. For example, a balanced meal plan will be proposed based on past dietary data. The system will also be developed in which a generation AI will analyze the user's lifestyle data over the long term and propose a sustainable health management plan. For example, a plan for maintaining high-quality sleep will be proposed based on past sleep data. This will result in a sustainable health management plan being proposed.

[0047] The lifestyle guidance department can share the user's health data with other medical institutions to realize comprehensive health management. For example, the lifestyle guidance department will build a system in which the generating AI shares the user's health data with other medical institutions to realize comprehensive health management. For example, medical records and test results will be shared with medical institutions. The user's health data will also be shared with other medical institutions to realize comprehensive health management. For example, data will be shared with hospitals and clinics to receive advice from doctors. The generating AI will also develop a system in which the user's health data will be shared with other medical institutions to realize comprehensive health management. For example, data will be shared with specialists to receive professional advice. This will realize comprehensive health management.

[0048] The lifestyle guidance department can share the user's health data with family or friends and allow them to receive support. For example, the lifestyle guidance department builds a system in which the generation AI can share the user's health data with family and friends and allow them to receive support. For example, family members can check the user's diet and provide advice. The health data can also be shared with family and friends and allow them to receive support. For example, friends can check the user's exercise habits and send encouraging messages. The generation AI can also develop a system in which the generation AI can share the user's health data with family and friends and allow them to receive support. For example, family members can check the user's sleep patterns and provide advice. This allows the user to receive support from family and friends.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] Health support systems can also anonymize users' health data and share it with research institutions to gain new insights into health. For example, anonymizing a user's dietary and exercise data and providing it to research institutions can advance new health research. Furthermore, anonymizing and sharing a user's sleep and stress level data could lead to the development of more effective health management methods. Furthermore, anonymizing and sharing a user's health data can be expected to improve the accuracy of health support for individual users.

[0051] The health support system can also provide lifestyle advice based on the season and weather, using the user's health data. For example, it can recommend taking vitamin D in winter and emphasize the importance of staying hydrated in summer. It can also suggest indoor exercises on rainy days and encourage outdoor activities on sunny days. Furthermore, by providing recipes using seasonal ingredients, users can lead a healthy diet that is in line with the seasons.

[0052] Health support systems can also predict individual health risks and suggest preventative measures based on a user's health data. For example, they can predict the risk of diabetes or high blood pressure based on the user's family history and lifestyle data and suggest early preventative measures. They can also predict the risk of heart disease based on the user's exercise and diet data and provide advice on appropriate exercise and diet. They can also predict mental health risks based on the user's stress level and sleep data and suggest stress management and relaxation methods.

[0053] The health support system can also provide personalized health news and information based on the user's health data. For example, it can provide the latest research results and news on health related to the user's interests. By providing health information based on the user's health condition and lifestyle habits, the user can acquire knowledge to lead a healthier life. Furthermore, it can also provide information on individual health-related events and seminars based on the user's health data.

[0054] The health support system also uses users' health data to form communities for achieving health goals, allowing users to support each other. For example, users with the same goals can interact online and encourage each other. Furthermore, by sharing health information and advice, users can manage their health more effectively. Furthermore, users can compete with each other to increase motivation.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: In the interview section, the generation AI conducts an interview with the user based on the information entered in the questionnaire. For example, the generation AI asks questions about the user's health condition and lifestyle habits to collect detailed information. The generation AI conducts the interview using a text generation AI (e.g., LLM). Step 2: The Lifestyle Guidance Department uses the generated AI to provide real-time lifestyle advice based on the interview results. For example, it suggests dietary improvements, recommended exercise, and stress management methods. The generated AI provides specific advice based on the user's health condition and lifestyle habits. Step 3: The progress management unit manages the progress of lifestyle guidance through an app or appliance. For example, users can record their daily lifestyle habits and check their progress through the app or appliance.

[0057] (Example 2) In a health support system according to an embodiment of the present invention, a generation AI conducts an interview based on information entered in a medical questionnaire, provides lifestyle advice in real time based on the interview results, and manages progress using an app or appliance. This allows the health support system to grasp the user's health condition and help them develop appropriate lifestyle habits.

[0058] A health support system according to an embodiment includes a medical questionnaire input unit, an interview unit, a lifestyle guidance unit, and a progress management unit. The medical questionnaire input unit allows a user to input information about their health condition and lifestyle habits. For example, the user inputs information such as dietary habits, exercise habits, sleep time, and stress level. The interview unit allows a generation AI to conduct an interview with the user based on the information input into the medical questionnaire. For example, the generation AI may ask questions about the user's health condition and lifestyle habits to collect detailed information. The generation AI conducts the interview using a text generation AI (e.g., LLM). The lifestyle guidance unit provides lifestyle guidance in real time based on the results of the interview. For example, the generation AI may suggest dietary improvements, exercise recommendations, and stress management methods. The generation AI may provide specific advice based on the user's health condition and lifestyle habits. The progress management unit manages the progress of the lifestyle guidance using an app or appliance. For example, the user can check their daily lifestyle record and progress through the app or appliance. This enables the health support system to support the user in living a healthy lifestyle.

[0059] The questionnaire input unit provides real-time feedback on the information entered by the user, improving the accuracy of the input. For example, when a user enters information into the questionnaire, the generation AI analyzes the input in real time and points out typos and inaccurate information. For example, if a user enters "vegetables" when entering their dietary information, the generation AI will ask for the specific type and amount. The generation AI also evaluates the user's input in real time and asks additional questions as necessary. For example, if a user enters "twice a week" when entering their exercise habits, the generation AI will ask for the specific type and time of exercise. The generation AI also provides instant feedback based on the information entered by the user, improving the accuracy of the input. For example, if a user enters "6 hours" when entering their sleep time, the generation AI will provide advice on the ideal amount of sleep. This improves the accuracy of the user's input.

[0060] The medical questionnaire input unit can automatically import the user's past health data and lifestyle habit data to complement the input on the medical questionnaire. The medical questionnaire input unit, for example, builds a system that automatically imports the user's past health data and complements the input on the medical questionnaire. For example, it references past medical records and data from health apps to automatically complement the input content. It also automatically imports lifestyle habit data to complement the input on the medical questionnaire. For example, it automatically inputs exercise habits and sleep patterns based on data from a smartwatch or fitness tracker. It also develops a system that complements the input on the medical questionnaire based on the user's past health data and lifestyle habit data. For example, it references past meal records to automatically suggest current meal contents. This reduces the input work for the user.

[0061] The questionnaire input unit can use the emotion estimation function to analyze the user's emotions when entering data and instantly provide advice to reduce stress or anxiety. The questionnaire input unit, for example, uses the emotion estimation function to analyze the user's emotions when entering data on the questionnaire in real time and provide advice to reduce stress or anxiety. For example, if the user feels nervous when entering data, the system can suggest breathing techniques to help them relax. A system can also be constructed that analyzes the user's emotions when entering data and provides specific advice to reduce stress and anxiety. For example, a positive message or words of encouragement can be displayed depending on the input content. The emotion estimation function can also be used to analyze the user's emotions when entering data and instantly provide advice to reduce stress or anxiety. For example, if the user feels anxious when entering data, music to help them relax can be played. This reduces the user's stress and anxiety.

[0062] The medical questionnaire input unit can use voice input or image recognition technology to make it easier for users to input information. The medical questionnaire input unit, for example, uses voice input technology to allow users to respond by voice when inputting information into the medical questionnaire. For example, the user responds by voice to the question, "Please tell us about your recent meals." Furthermore, image recognition technology is used to automatically extract information when a user inputs information into the medical questionnaire simply by uploading an image. For example, when a photo of a meal is uploaded, the ingredients and amounts are automatically recognized. Furthermore, a combination of voice input and image recognition technology allows users to input information into the medical questionnaire more easily. For example, a user can upload a video of their exercise, and the type and duration of the exercise can be automatically analyzed. This simplifies the user's input process.

[0063] The medical questionnaire input unit may add a function that allows sharing with family or friends, and may enrich the input content by receiving feedback from others. The medical questionnaire input unit may add, for example, a function that allows sharing of the medical questionnaire information with family and friends, and may enrich the input content by receiving feedback from others. For example, a family member may add a comment about the user's diet. Furthermore, using the sharing function, the user may share the medical questionnaire information with friends, and enrich the input content by receiving feedback. For example, a friend may provide advice about exercise habits. Furthermore, a system may be constructed that adds a function to share the medical questionnaire information, and enriches the input content by receiving feedback from others. For example, a family member or friend may give their opinion on the user's sleep patterns. This enriches the input content, and more accurate information may be obtained.

[0064] The questionnaire input unit can use the emotion estimation function to monitor the emotions of the user when entering data in real time, and provide an interface for eliciting positive emotions. The questionnaire input unit, for example, uses the emotion estimation function to monitor the emotions of the user when entering data in real time, and provide an interface for eliciting positive emotions. For example, a positive message is displayed during entry. A system is also constructed that monitors the emotions of the user when entering data in real time, and provides a specific interface for eliciting positive emotions. For example, encouraging words or success stories are displayed depending on the input content. The emotion estimation function is also used to monitor the emotions of the user when entering data in real time, and provide an interface for eliciting positive emotions. For example, relaxing music is played during entry. This makes it possible to elicit positive emotions from the user.

[0065] The interview unit can analyze the user's facial expression or tone of voice and dynamically change the questions asked according to their emotional state. For example, during an interview, the generation AI analyzes the user's facial expression in real time and dynamically changes the questions asked according to their emotional state. For example, if the user looks anxious, it asks questions to help them relax. A system is also constructed in which the generation AI analyzes the user's tone of voice in real time and dynamically changes the questions asked according to their emotional state. For example, if the user sounds nervous, it asks questions to help them relax. The generation AI also analyzes the user's facial expression and tone of voice during the interview and dynamically changes the questions asked according to their emotional state. For example, if the user is excited, it asks questions to help them calm down. This makes it possible to ask appropriate questions according to the user's emotional state.

[0066] The interview unit can automatically summarize the contents of the interview so that the user can check it later. For example, the interview unit has the generation AI analyze the contents of the conversation with the user in real time during the interview and automatically generate a summary. For example, it summarizes important points and advice so that the user can check it later. In addition, a system is built that automatically summarizes the contents of the interview so that the user can check it later. For example, a summary is sent by email after the interview ends. In addition, the generation AI automatically summarizes the contents of the interview so that the user can check it later. For example, the interview content is saved in text format so that the user can check it in an app. This allows the user to check the contents of the interview later.

[0067] The interview unit uses the emotion estimation function to generate personalized questions based on the user's emotions, thereby eliciting deeper information. The interview unit, for example, uses the emotion estimation function to build a system that generates personalized questions based on the user's emotions. For example, if the user is relaxed, questions are asked to elicit detailed information. In addition, the generation AI analyzes the user's emotions in real time and generates personalized questions based on the emotions. For example, if the user is excited, questions are asked to calm the user down. In addition, the emotion estimation function is used to generate personalized questions based on the user's emotions to elicit deeper information. For example, if the user is feeling anxious, questions are asked to reassure the user. This makes it possible to elicit deeper information from the user.

[0068] The interview unit provides interview content in multiple languages, making it possible to accommodate users from different cultural backgrounds. The interview unit, for example, builds a system that provides interview content in multiple languages, making it possible to accommodate users from different cultural backgrounds. For example, interviews are conducted in languages ​​such as English, Spanish, and Chinese. In addition, a generation AI translates the interview content in real time, making it possible to accommodate users from different cultural backgrounds. For example, a user receives questions in Japanese and answers in English. In addition, a system is developed that provides interview content in multiple languages, making it possible to accommodate users from different cultural backgrounds. For example, an interview is conducted in the language selected by the user. This makes it possible to accommodate users from different cultural backgrounds.

[0069] The interview unit can refer to the user's health data in real time during the interview and provide more specific advice. For example, the interview unit will build a system in which the generation AI refers to the user's health data in real time during the interview and provides specific advice. For example, it may suggest dietary improvements based on the user's blood pressure data. The generation AI will also refer to the user's health data in real time during the interview and provide specific advice. For example, it may recommend exercise based on the user's exercise data. A system will also be developed in which the generation AI will refer to the user's health data in real time during the interview and provide more specific advice. For example, it may provide advice on improving sleep based on the user's sleep data. This will allow more specific advice to be provided to the user.

[0070] The interview unit can use the emotion estimation function to monitor the user's emotional changes during the interview in real time and make suggestions to help them relax at the appropriate time. The interview unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional changes during the interview in real time and makes suggestions to help them relax. For example, if the user is feeling nervous, it will suggest breathing techniques to help them relax. In addition, the generation AI monitors the user's emotional changes in real time during the interview and makes suggestions to help them relax at the appropriate time. For example, if the user is feeling anxious, it will play music to help them relax. In addition, a system will be developed that uses the emotion estimation function to monitor the user's emotional changes during the interview in real time and make suggestions to help them relax. For example, if the user is feeling stressed, it will provide advice to help them relax. This makes it possible to make appropriate suggestions according to the user's emotional changes.

[0071] The lifestyle guidance department can analyze the user's lifestyle data in real time and provide instant feedback. For example, the lifestyle guidance department will build a system in which a generation AI collects a user's lifestyle data in real time and provides instant feedback. For example, it will analyze dietary content and exercise volume in real time and immediately suggest areas for improvement. It will also analyze a user's lifestyle data in real time and provide instant feedback. For example, it will provide advice on improving sleep quality based on sleep data. It will also develop a system in which a generation AI analyzes a user's lifestyle data in real time and provide instant feedback. For example, it will monitor stress levels in real time and make suggestions for stress reduction. This will allow instant feedback to be provided to the user.

[0072] The lifestyle guidance department can compare the user's past data, evaluate their progress, and suggest the next step. For example, the lifestyle guidance department will build a system in which the generation AI compares the user's past data with their current data and evaluate their progress. For example, it will compare past exercise data with their current data and evaluate the effectiveness of the exercise. It will also compare the user's past data with their current data, evaluate their progress, and suggest the next step. For example, it will compare past dietary data with their current data and suggest areas for improving their diet. It will also develop a system in which the generation AI compares the user's past data with their current data, evaluate their progress, and suggest the next step. For example, it will compare past sleep data with their current data and give advice on improving the quality of their sleep. This will enable it to evaluate the user's progress and suggest the next step.

[0073] The life guidance unit can use the emotion estimation function to provide a motivational message according to the user's emotional state. The life guidance unit, for example, uses the emotion estimation function to build a system that provides a motivational message according to the user's emotional state. For example, if the user is feeling down, an encouraging message is displayed. The life guidance unit also analyzes the user's emotional state in real time and provides a motivational message. For example, if the user is tired, advice is given to refresh the user. The life guidance unit also develops a system that uses the emotion estimation function to provide a motivational message according to the user's emotional state. For example, if the user is feeling stressed, a message to relax is displayed. This makes it possible to maintain the user's motivation.

[0074] The lifestyle guidance unit can link the user's lifestyle guidance content with other health management apps to achieve comprehensive health management. For example, the lifestyle guidance unit constructs a system in which a generation AI links the user's lifestyle guidance content with other health management apps to achieve comprehensive health management. For example, it shares data with a diet management app and an exercise management app. It also links with other health management apps to manage the user's lifestyle guidance content in an integrated manner. For example, it links with a sleep management app to provide lifestyle guidance based on sleep data. It also develops a system in which the generation AI links the user's lifestyle guidance content with other health management apps to achieve comprehensive health management. For example, it links with a stress management app to provide lifestyle guidance based on stress data. This enables comprehensive health management to be achieved.

[0075] The lifestyle guidance unit can share the user's lifestyle advice with family or friends so that they can receive support. The lifestyle guidance unit, for example, builds a system in which the generation AI shares the user's lifestyle advice with family and friends so that they can receive support. For example, family members check the user's diet and provide advice. The lifestyle advice is also shared with family and friends so that they can receive support. For example, friends check the user's exercise habits and send encouraging messages. The generation AI also develops a system in which the generation AI shares the user's lifestyle advice with family and friends so that they can receive support. For example, family members check the user's sleep patterns and provide advice. This allows the user to receive support from family and friends.

[0076] The life guidance unit can use the emotion estimation function to provide lifestyle guidance based on the user's emotions and provide an approach to elicit positive emotions. The life guidance unit, for example, uses the emotion estimation function to build a system that provides lifestyle guidance based on the user's emotions. For example, if the user is feeling depressed, advice is provided to elicit positive emotions. The life guidance unit also analyzes the user's emotional state in real time and provides lifestyle guidance to elicit positive emotions. For example, if the user is tired, advice is provided to refresh the user. The system also uses the emotion estimation function to develop a system that provides lifestyle guidance based on the user's emotions and provides an approach to elicit positive emotions. For example, if the user is feeling stressed, advice is provided to relax. This makes it possible to elicit positive emotions in the user.

[0077] The progress management unit can automatically analyze the user's progress data and provide rewards or incentives according to the level of achievement. The progress management unit, for example, builds a system in which an app automatically analyzes the user's progress data and provides rewards or incentives according to the level of achievement. For example, points are awarded when a goal is achieved. The progress management unit also automatically analyzes the user's progress data and provides rewards or incentives according to the level of achievement. For example, a badge is earned when an exercise goal is achieved. The progress management unit also develops a system in which an app automatically analyzes the user's progress data and provides rewards or incentives according to the level of achievement. For example, a coupon is provided when a diet goal is achieved. In this way, rewards and incentives are provided according to the user's level of achievement.

[0078] The progress management unit can visualize the user's progress data to enable intuitive understanding. The progress management unit, for example, builds a system that enables an app to visualize the user's progress data to enable intuitive understanding. For example, the progress status is displayed using graphs and charts. The progress management unit also visualizes the user's progress data to enable intuitive understanding. For example, the exercise completion status is displayed in calendar format. The progress management unit also develops a system that enables an app to visualize the user's progress data to enable intuitive understanding. For example, the progress status is displayed using icons and colors. This allows the user to intuitively understand the progress data.

[0079] The progress management unit uses the emotion estimation function to suggest a progress management method according to the user's emotional state, thereby maintaining motivation. The progress management unit, for example, uses the emotion estimation function to build a system that suggests a progress management method according to the user's emotional state. For example, if the user is feeling down, an encouraging message is displayed. The progress management unit also analyzes the user's emotional state in real time and suggests a progress management method to maintain motivation. For example, if the user is tired, advice is given to refresh the user. The emotion estimation function is also used to develop a system that suggests a progress management method according to the user's emotional state and maintains motivation. For example, if the user is feeling stressed, a message to relax is displayed. This makes it possible to maintain the user's motivation.

[0080] The progress management unit can store the user's progress data in the cloud and make it accessible from multiple devices. For example, the progress management unit builds a system that allows an app to store the user's progress data in the cloud and access it from multiple devices. For example, the progress data can be checked from a smartphone or tablet. The progress management unit also stores the user's progress data in the cloud and makes it accessible from multiple devices. For example, the progress data can be checked from a PC or a smartwatch. The progress management unit also develops a system that allows the app to store the user's progress data in the cloud and access it from multiple devices. For example, the progress data can be checked from a smart TV or fitness tracker. This allows the user to access the progress data from multiple devices.

[0081] The progress management unit can link the user's progress data with other health management apps to achieve comprehensive health management. For example, the progress management unit builds a system in which an app links the user's progress data with other health management apps to achieve comprehensive health management. For example, it shares data with a diet management app and an exercise management app. It also links with other health management apps to manage the user's progress data in an integrated manner. For example, it links with a sleep management app to manage progress based on sleep data. It also develops a system in which an app links the user's progress data with other health management apps to achieve comprehensive health management. For example, it links with a stress management app to manage progress based on stress data. This allows comprehensive health management to be achieved.

[0082] The progress management unit uses the emotion estimation function to provide a progress management method based on the user's emotions, and can provide an approach to elicit positive emotions. The progress management unit, for example, uses the emotion estimation function to build a system that provides a progress management method based on the user's emotions. For example, if the user is feeling depressed, advice is provided to elicit positive emotions. The progress management unit also analyzes the user's emotional state in real time and provides a progress management method to elicit positive emotions. For example, if the user is tired, advice is given to refresh the user. The emotion estimation function is also used to develop a system that provides a progress management method based on the user's emotions and provides an approach to elicit positive emotions. For example, if the user is feeling stressed, advice is given to relax. This makes it possible to elicit positive emotions from the user.

[0083] The lifestyle guidance unit can set individual health goals based on the user's progress data and update advice according to the degree of achievement. The lifestyle guidance unit, for example, builds a system in which a generation AI sets individual health goals based on the user's progress data. For example, based on the user's exercise data, it sets a goal of exercising three times a week. Also, based on the user's progress data, it sets individual health goals and updates advice according to the degree of achievement. For example, based on dietary data, it sets a goal of increasing vegetable intake and updates advice according to the degree of achievement. Also, a system is developed in which a generation AI sets individual health goals based on the user's progress data and updates advice according to the degree of achievement. For example, based on sleep data, it sets a goal of seven hours of sleep each night and updates advice according to the degree of achievement. In this way, advice according to the user's health goals is provided.

[0084] The lifestyle guidance unit can analyze the user's lifestyle data over the long term and propose a sustainable health management plan. For example, the lifestyle guidance unit will build a system in which a generation AI collects the user's lifestyle data over the long term and proposes a sustainable health management plan. For example, a long-term exercise plan will be proposed based on exercise data from the past year. The lifestyle guidance unit will also analyze the user's lifestyle data over the long term and propose a sustainable health management plan. For example, a balanced meal plan will be proposed based on past dietary data. The system will also be developed in which a generation AI will analyze the user's lifestyle data over the long term and propose a sustainable health management plan. For example, a plan for maintaining high-quality sleep will be proposed based on past sleep data. This will result in a sustainable health management plan being proposed.

[0085] The life guidance unit can use the emotion estimation function to provide health support according to the user's emotional state and provide an approach to elicit positive emotions. The life guidance unit, for example, uses the emotion estimation function to build a system that provides health support according to the user's emotional state. For example, if the user is feeling depressed, advice is provided to elicit positive emotions. The life guidance unit also analyzes the user's emotional state in real time and provides health support to elicit positive emotions. For example, if the user is tired, advice is provided to refresh the user. The life guidance unit also uses the emotion estimation function to develop a system that provides health support according to the user's emotional state and provides an approach to elicit positive emotions. For example, if the user is feeling stressed, advice is provided to relax. This makes it possible to elicit positive emotions in the user.

[0086] The lifestyle guidance department can share the user's health data with other medical institutions to realize comprehensive health management. For example, the lifestyle guidance department will build a system in which the generating AI shares the user's health data with other medical institutions to realize comprehensive health management. For example, medical records and test results will be shared with medical institutions. The user's health data will also be shared with other medical institutions to realize comprehensive health management. For example, data will be shared with hospitals and clinics to receive advice from doctors. The generating AI will also develop a system in which the user's health data will be shared with other medical institutions to realize comprehensive health management. For example, data will be shared with specialists to receive professional advice. This will realize comprehensive health management.

[0087] The lifestyle guidance department can share the user's health data with family or friends and allow them to receive support. For example, the lifestyle guidance department builds a system in which the generation AI can share the user's health data with family and friends and allow them to receive support. For example, family members can check the user's diet and provide advice. The health data can also be shared with family and friends and allow them to receive support. For example, friends can check the user's exercise habits and send encouraging messages. The generation AI can also develop a system in which the generation AI can share the user's health data with family and friends and allow them to receive support. For example, family members can check the user's sleep patterns and provide advice. This allows the user to receive support from family and friends.

[0088] The life guidance unit can use the emotion estimation function to provide health support based on the user's emotions and provide an approach to elicit positive emotions. The life guidance unit, for example, uses the emotion estimation function to build a system that provides health support based on the user's emotions. For example, if the user is feeling depressed, advice is provided to elicit positive emotions. The life guidance unit also analyzes the user's emotional state in real time and provides health support to elicit positive emotions. For example, if the user is tired, advice is provided to refresh the user. The life guidance unit also uses the emotion estimation function to develop a system that provides health support based on the user's emotions and provides an approach to elicit positive emotions. For example, if the user is feeling stressed, advice is provided to relax. This makes it possible to elicit positive emotions in the user.

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

[0090] Health support systems can also anonymize users' health data and share it with research institutions to gain new insights into health. For example, anonymizing a user's dietary and exercise data and providing it to research institutions can advance new health research. Furthermore, anonymizing and sharing a user's sleep and stress level data could lead to the development of more effective health management methods. Furthermore, anonymizing and sharing a user's health data can be expected to improve the accuracy of health support for individual users.

[0091] The health support system can also provide lifestyle advice based on the season and weather, using the user's health data. For example, it can recommend taking vitamin D in winter and emphasize the importance of staying hydrated in summer. It can also suggest indoor exercises on rainy days and encourage outdoor activities on sunny days. Furthermore, by providing recipes using seasonal ingredients, users can lead a healthy diet that is in line with the seasons.

[0092] Health support systems can also predict individual health risks and suggest preventative measures based on a user's health data. For example, they can predict the risk of diabetes or high blood pressure based on the user's family history and lifestyle data and suggest early preventative measures. They can also predict the risk of heart disease based on the user's exercise and diet data and provide advice on appropriate exercise and diet. They can also predict mental health risks based on the user's stress level and sleep data and suggest stress management and relaxation methods.

[0093] The health support system can also provide personalized health news and information based on the user's health data. For example, it can provide the latest research results and news on health related to the user's interests. By providing health information based on the user's health condition and lifestyle habits, the user can acquire knowledge to lead a healthier life. Furthermore, it can also provide information on individual health-related events and seminars based on the user's health data.

[0094] The health support system also uses users' health data to form communities for achieving health goals, allowing users to support each other. For example, users with the same goals can interact online and encourage each other. Furthermore, by sharing health information and advice, users can manage their health more effectively. Furthermore, users can compete with each other to increase motivation.

[0095] The health support system can also estimate the user's emotions and suggest relaxation methods based on the emotions. For example, if the user is feeling stressed, it can suggest yoga or meditation to help them relax. If the user is feeling anxious, it can suggest music or aromatherapy to help them relax. Furthermore, by suggesting relaxation methods according to the user's emotional state, it can reduce the user's stress and anxiety.

[0096] The health support system can also estimate the user's emotions and suggest exercise programs based on those emotions. For example, if the user is feeling down, it can suggest light exercise or stretching to lift their spirits. If the user is tired, it can suggest walking or yoga to refresh themselves. Furthermore, by suggesting an exercise program based on the user's emotional state, it can improve the user's mood and support a healthy lifestyle.

[0097] The health support system can also estimate the user's emotions and suggest meal plans based on the user's emotions. For example, if the user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. If the user is tired, it can suggest meal plans to replenish energy. Furthermore, by suggesting meal plans based on the user's emotional state, it can support the user's health and maintain emotional balance.

[0098] The health support system can also estimate the user's emotions and provide advice on improving sleep based on the user's emotions. For example, if the user feels anxious, the system can suggest breathing techniques or meditation to help them relax. If the user feels stressed, the system can suggest music or aromatherapy to help them relax. Furthermore, by providing advice on improving sleep based on the user's emotional state, the system can improve the user's quality of sleep.

[0099] The health support system can also estimate the user's emotions and provide emotionally-based mental health support. For example, if the user is feeling depressed, it can provide positive messages and encouraging words. If the user is feeling anxious, it can suggest relaxation advice and relaxation methods. Furthermore, by providing mental health support according to the user's emotional state, it can support the user's mental health.

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

[0101] Step 1: In the interview section, the generation AI conducts an interview with the user based on the information entered in the questionnaire. For example, the generation AI asks questions about the user's health condition and lifestyle habits to collect detailed information. The generation AI conducts the interview using a text generation AI (e.g., LLM). Step 2: The Lifestyle Guidance Department uses the generated AI to provide real-time lifestyle advice based on the interview results. For example, it suggests dietary improvements, recommended exercise, and stress management methods. The generated AI provides specific advice based on the user's health condition and lifestyle habits. Step 3: The progress management unit manages the progress of lifestyle guidance through an app or appliance. For example, users can record their daily lifestyle habits and check their progress through the app or appliance.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0136] 7, 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An interview department that conducts interviews based on the information entered in the medical questionnaire; a life guidance department that provides life guidance in real time based on the interview results obtained by the interview department; a progress management unit that manages the progress of the lifestyle guidance performed by the lifestyle guidance unit. A system characterized by:

2. The interview section Analyzing the user's facial expression or tone of voice and dynamically changing questions according to their emotional state 2. The system of claim 1.

3. The life guidance department: Providing motivational messages according to the user's emotional state 2. The system of claim 1.

4. The progress management unit Propose progress management methods according to the user's emotional state to maintain motivation 2. The system of claim 1.

5. The interview section Generate personalized questions based on user sentiment to unlock deeper insights 2. The system of claim 1.

6. The life guidance department: To provide lifestyle guidance based on the user's emotions and an approach to elicit positive emotions 2. The system of claim 1.

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