System

Abstract

An object of a system according to an embodiment is to grasp a change in a physical condition or a psychological state of a user in real time and provide individual support.SOLUTION: A system includes a question generation part, a data analysis part, a real-time data provision part, and an individual support provision part. The question generator sends a question to the user every morning. The data analysis unit analyzes answer data of the user to the question transmitted by the question generation unit. The real-time data providing unit provides the data analyzed by the data analysis unit to the user in real time. The individual support providing unit provides individual support to the user based on the data analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to adequately grasp changes in a user's physical and psychological state in real time and provide individual support.

[0005] The system according to the embodiment aims to grasp changes in the user's physical and psychological state in real time and provide individual support. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generation unit, a data analysis unit, a real-time data provision unit, and an individual support provision unit. The question generation unit sends a question to a user every morning. The data analysis unit analyzes the user's response data to the question sent by the question generation unit. The real-time data provision unit provides the user with the data analyzed by the data analysis unit in real time. The individual support provision unit provides the user with individual support based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp changes in the user's physical condition and psychological state in real time and provide individual support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In a health management system according to an embodiment of the present invention, users answer questions posed by an AI every morning, and a generation AI analyzes the answers to provide real-time data and personalized support. This allows the health management system to grasp changes in the user's physical and psychological state and provide personalized support, thereby improving employee health and enhancing the effectiveness of internal communication and care.

[0029] A health management system according to an embodiment includes a question generation unit, a data analysis unit, a real-time data provision unit, and an individual support provision unit. The question generation unit sends questions to a user every morning. For example, the generation AI sends questions such as "Did you sleep well last night?" and "How are you feeling this morning?" to understand the user's physical and psychological state. The generation AI can also customize questions based on the user's past response history to more accurately understand the user's physical and psychological state. The data analysis unit analyzes the user's response data to the questions sent by the question generation unit. For example, the generation AI can understand changes in the user's physical and psychological state based on the user's response data and provide necessary support. The generation AI can also perform more accurate analysis by referring to the user's past health data and medical records. The real-time data provision unit provides the user with the data analyzed by the data analysis unit in real time. For example, the generation AI can provide data such as "Your sleep time this week is less than average" based on the analyzed data and offer advice for improvement. The generation AI can also provide data at an appropriate time, taking into account the user's current activity status and location information. The individual support providing unit provides individual support to the user based on the data analyzed by the data analysis unit. For example, if the user is feeling stressed, the generation AI suggests relaxation methods or counseling. The generation AI can also provide support according to the user's emotional state and provide emotional care. As a result, the health management system according to the embodiment can grasp changes in the user's physical and psychological state and provide individual support, thereby improving the health of employees and enhancing the effectiveness of internal communication and care.

[0030] The question generation unit customizes questions based on the user's past answer history, enabling a more accurate understanding of the user's physical condition and psychological state. The question generation unit, for example, analyzes the user's past answer history and extracts specific patterns and trends. For example, if there was a period in the past when the user was prone to stress, the question generation unit customizes and sends questions related to that period. The question generation unit also generates questions tailored to the user's individual health and psychological state based on the user's answer history. For example, to a user who has often suffered from sleep deprivation in the past, the question generation unit sends detailed questions related to sleep. The question generation unit also utilizes the user's answer history to create questions that focus on specific health issues or psychological states. For example, to a user who has been prone to anxiety in the past, the question generation unit sends questions related to anxiety. In this way, by customizing questions based on the user's past answer history, a more accurate understanding of the user's physical condition and psychological state is possible.

[0031] The question generation unit can optimize the timing of questions to match the user's lifestyle rhythm, thereby improving the response rate. The question generation unit, for example, analyzes the user's lifestyle rhythm and sets the optimal timing for questions. For example, if the user finds it easiest to answer questions during their morning commute, the question generation unit sends the questions during that time period. The question generation unit also identifies the time period when the user is most likely to answer based on the user's past answer times, and sends the questions at that time. For example, if the user often answers questions in the evening, the question generation unit sends the questions in the evening. The question generation unit also dynamically adjusts the time for sending questions to match the user's lifestyle rhythm. For example, if the user wakes up late on weekends, the time for sending questions on weekends is delayed. This optimizes the timing of questions to match the user's lifestyle rhythm, thereby improving the response rate.

[0032] The question generation unit can expand the question format to a multimodal format using not only text but also audio and images, thereby increasing the variety of user answers. For example, the question generation unit introduces audio questions in addition to text questions. For example, it utilizes speech recognition technology so that users can answer by audio. The question generation unit also introduces image questions so that users can answer using images. For example, users can answer questions by uploading an image showing their physical condition. The question generation unit also introduces a multimodal question format so that users can answer using text, audio, or images. For example, it allows users to select the format that is easiest for them to answer. In this way, the variety of user answers is increased by expanding the question format to multimodal.

[0033] The question generation unit can expand the content of questions not only about health but also about work performance and lifestyle habits, thereby collecting comprehensive data. For example, the question generation unit introduces questions about work performance in addition to questions about health. For example, it sends a question such as, "How is your work going today?" The question generation unit also introduces questions about lifestyle habits to collect comprehensive data about the user. For example, it sends a question such as, "Do you have a daily exercise habit?" The question generation unit also combines questions about health, work performance, and lifestyle habits in a balanced manner to collect comprehensive data. For example, it sends questions on different themes each week. This makes it possible to collect comprehensive data by expanding the content of the questions.

[0034] The data analysis unit can perform more accurate analysis by referring to the user's past health data and medical records. The data analysis unit, for example, analyzes the user's past health data and compares it with current response data. For example, it compares past blood pressure data with current response data to understand changes in health status. The data analysis unit also refers to the user's medical records and uses them in analyzing the response data. For example, it evaluates the current health status based on past diagnostic results and prescription drug information. The data analysis unit also integrates the user's past health data and medical records to perform a comprehensive analysis. For example, it combines past health checkup results with current response data for analysis. In this way, by referring to the user's past health data and medical records, more accurate analysis is possible.

[0035] The data analysis unit analyzes the response data in association with the user's living environment and work environment, thereby clarifying the influence of environmental factors. The data analysis unit, for example, collects data on the user's living environment and uses it to analyze the response data. For example, it analyzes changes in health status based on data such as living environment and commuting time. The data analysis unit also collects data on the user's work environment and analyzes it in association with the response data. For example, it evaluates changes in health status based on workplace stress factors and working hours. The data analysis unit also integrates data on the living environment and work environment and uses it to analyze the response data. For example, it analyzes changes in health status taking both the living environment and work environment into consideration. In this way, by analyzing the response data in association with the user's living environment and work environment, it is possible to clarify the influence of environmental factors.

[0036] The data analysis unit can compare the data with other users' data to identify common health trends and problems. The data analysis unit, for example, compares the response data of other users to identify common health trends. For example, it compares data of users in the same age group to identify common health problems. The data analysis unit also analyzes the frequency of occurrence of specific health problems based on the data of other users. For example, it compares data of users engaged in specific occupations to identify trends in occupational diseases. The data analysis unit also compares the data with other users to suggest measures to address common health problems. For example, it provides common advice to users with the same health problem. In this way, by comparing the data of other users, it is possible to identify common health trends and problems.

[0037] The data analysis unit can improve the reliability of the analysis results by referring to external health databases and research data. The data analysis unit, for example, refers to external health databases and uses them in analyzing the response data. For example, the reliability of the analysis results is improved based on national health statistical data. The data analysis unit also refers to research data and uses it in analyzing the response data. For example, the analysis results are supplemented based on the latest medical research data. The data analysis unit also integrates external databases and research data and uses it in analyzing the response data. For example, multiple data sources are combined to improve the accuracy of the analysis results. In this way, the reliability of the analysis results is improved by referring to external health databases and research data.

[0038] The real-time data providing unit can provide data at an appropriate timing by taking into account the user's current activity status and location information. The real-time data providing unit, for example, analyzes the user's current activity status and provides data at the optimal timing. For example, it provides health data while the user is taking a break. The real-time data providing unit also provides data at an appropriate timing based on the user's location information. For example, it suggests relaxation methods when the user is at home. The real-time data providing unit also integrates the activity status and location information and provides data at the optimal timing. For example, it provides stress management advice while the user is commuting. In this way, it is possible to provide data at an appropriate timing by taking into account the user's current activity status and location information.

[0039] The real-time data providing unit can customize the format of data provision according to the user's preferences and provide the data in a format that is visually easy to understand. The real-time data providing unit customizes the format of data provision according to the user's preferences, for example. For example, data is provided in graph or chart format. The real-time data providing unit also provides the data in a format that is visually easy to understand. For example, data is displayed using color-coded graphs or icons. The real-time data providing unit also improves the format of data provision based on user feedback. For example, it allows the user to select the format that is easiest to understand. In this way, by customizing the format of data provision according to the user's preferences, data can be provided in a format that is visually easy to understand.

[0040] The real-time data providing unit can provide a wider variety of data by linking with wearable devices such as smartwatches and fitness trackers. The real-time data providing unit, for example, links with smartwatches and fitness trackers to provide real-time data. For example, it evaluates health status based on heart rate and step count data. The real-time data providing unit also provides real-time health advice based on data collected from the wearable device. For example, if the amount of exercise is low, it provides advice to encourage exercise. The real-time data providing unit also links with wearable devices to monitor the user's health data in real time. For example, it provides advice to improve sleep quality based on sleep data. In this way, by linking with wearable devices, a wider variety of data can be provided.

[0041] The real-time data providing unit can also share data with the user's family and medical professionals to support comprehensive health management. The real-time data providing unit, for example, shares the user's health data with family members to support comprehensive health management. For example, family members can understand the user's health condition and provide support. The real-time data providing unit also shares the user's health data with medical professionals to receive professional advice. For example, a doctor can diagnose and treat the user based on the user's data. The real-time data providing unit also works with family members and medical professionals to provide comprehensive support for the user's health management. For example, regular health checks and counseling can be provided. In this way, comprehensive health management can be supported by sharing data with the user's family members and medical professionals.

[0042] The individual support providing unit can refer to the user's past support history and provide more effective support. The individual support providing unit, for example, analyzes the user's past support history and utilizes it in current support. For example, it re-proposes support methods that were effective in the past. The individual support providing unit also provides support according to individual needs based on the user's support history. For example, it proposes stress management methods to a user who needed stress management in the past. The individual support providing unit also refers to the past support history and provides effective support. For example, it proposes a similar method to a user for whom a relaxation method was effective in the past. In this way, more effective support can be provided by referring to the user's past support history.

[0043] The individual support providing unit customizes the support content to suit the user's work environment and lifestyle habits, thereby increasing feasibility. The individual support providing unit customizes the support content, for example, taking into account the user's work environment. For example, for a user who does a lot of desk work, it suggests stretching methods that can be done in the office. The individual support providing unit also adjusts the support content to suit the user's lifestyle habits. For example, for a user who often works night shifts, it suggests nighttime relaxation methods. The individual support providing unit also integrates the work environment and lifestyle habits to provide feasible support content. For example, for a busy user, it suggests relaxation methods that can be done in a short amount of time. In this way, by customizing the support content to suit the user's work environment and lifestyle habits, it is possible to increase feasibility.

[0044] The individual support providing unit can provide comprehensive support in cooperation with online counseling and mental health apps. The individual support providing unit, for example, can provide individual support in cooperation with online counseling. For example, if the user is feeling stressed, it can suggest online counseling. The individual support providing unit can also provide comprehensive support in cooperation with mental health apps. For example, if the user is feeling anxious, it can suggest relaxation methods through the mental health app. The individual support providing unit can also integrate online counseling and mental health apps to provide comprehensive support. For example, the user can receive daily support through the app while receiving counseling. In this way, comprehensive support can be provided by cooperation with online counseling and mental health apps.

[0045] The individual support providing unit can share the support content with the user's family and friends, thereby building a social support network. The individual support providing unit, for example, shares the user's support content with family, thereby building a social support network. For example, family members understand the user's health condition and provide support. The individual support providing unit also shares the user's support content with friends, thereby building a social support network. For example, friends understand the user's health condition and provide support. The individual support providing unit also works with family and friends to provide comprehensive support for the user's health management. For example, regular health checks and counseling are provided. In this way, the support content can be shared with the user's family and friends, thereby building a social support network.

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

[0047] The health management system can further include a nutrition management unit that collects the user's dietary data and provides advice on nutritional balance. For example, when the user inputs the details of their daily meals, the nutrition management unit analyzes the calorie and nutrient intake and suggests a balanced diet. The nutrition management unit can also provide meal plans based on the user's health goals. For example, a low-calorie meal plan can be suggested for a user who is trying to lose weight. Furthermore, the nutrition management unit can also suggest safe ingredients taking into account the user's allergy information. This makes it possible to utilize the user's dietary data for more comprehensive health management.

[0048] The health management system can further include an exercise management unit that collects the user's exercise data and provides an exercise program. For example, when the user inputs the details of their daily exercise, the exercise management unit analyzes the amount of exercise and calories burned and suggests an appropriate exercise program. The exercise management unit can also provide an exercise plan according to the user's fitness level. For example, it can suggest a plan to start with light exercise for beginners and a high-intensity exercise plan for advanced users. Furthermore, the exercise management unit can provide exercise advice according to the user's goals. For example, it can suggest a strength training program to a user who wants to increase their muscle strength. This makes it possible to utilize the user's exercise data for more effective health management.

[0049] The health management system can further include a sleep management unit that collects the user's sleep data and provides advice to improve the quality of their sleep. For example, if the user inputs the amount of time and quality of their sleep each night, the sleep management unit analyzes their sleep patterns and provides advice for improvement. The sleep management unit can also provide a sleep plan that matches the user's lifestyle. For example, for a user who works many night shifts, it can provide advice to support daytime sleep. Furthermore, the sleep management unit can take the user's stress level into consideration and suggest relaxation methods. This makes it possible to utilize the user's sleep data to support higher quality sleep.

[0050] The health management system may further include a fluid management unit that collects a user's fluid intake data and supports appropriate hydration. For example, the user can input their daily fluid intake, and the fluid management unit can provide advice on appropriate hydration. The fluid management unit can also suggest fluid intake amounts based on the user's activity level and weather conditions. For example, for a user who exercises a lot, advice emphasizing post-exercise hydration is provided. Furthermore, the fluid management unit can take into account the user's health condition and provide hydration advice for specific health issues. This makes it possible to utilize the user's fluid intake data to support a healthier lifestyle.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The question generator sends questions to the user every morning. For example, the generation AI sends questions such as "Did you sleep well last night?" or "How are you feeling this morning?" to understand the user's physical and psychological state. The generation AI can also customize questions based on the user's past response history, enabling a more accurate understanding of the user's physical and psychological state. Step 2: The data analysis unit analyzes the user's response data to the questions sent by the question generation unit. For example, the generation AI can understand changes in the user's physical and psychological state based on the user's response data and provide necessary support. The generation AI can also refer to the user's past health data and medical records to perform more accurate analysis. Step 3: The real-time data provider provides the data analyzed by the data analyzer to the user in real time. For example, the generator AI can provide data such as "your sleep time this week is less than average" based on the analyzed data and give advice on how to improve. The generator AI can also provide data at the appropriate time, taking into account the user's current activity status and location information. Step 4: The individual support provider provides individual support to the user based on the data analyzed by the data analyzer. For example, if the user is feeling stressed, the generator AI may suggest relaxation methods or counseling. The generator AI may also provide support according to the user's emotional state and provide emotional care.

[0053] (Example 2) In a health management system according to an embodiment of the present invention, users answer questions posed by an AI every morning, and a generation AI analyzes the answers to provide real-time data and personalized support. This allows the health management system to grasp changes in the user's physical and psychological state and provide personalized support, thereby improving employee health and enhancing the effectiveness of internal communication and care.

[0054] A health management system according to an embodiment includes a question generation unit, a data analysis unit, a real-time data provision unit, and an individual support provision unit. The question generation unit sends questions to a user every morning. For example, the generation AI sends questions such as "Did you sleep well last night?" and "How are you feeling this morning?" to understand the user's physical and psychological state. The generation AI can also customize questions based on the user's past response history to more accurately understand the user's physical and psychological state. The data analysis unit analyzes the user's response data to the questions sent by the question generation unit. For example, the generation AI can understand changes in the user's physical and psychological state based on the user's response data and provide necessary support. The generation AI can also perform more accurate analysis by referring to the user's past health data and medical records. The real-time data provision unit provides the user with the data analyzed by the data analysis unit in real time. For example, the generation AI can provide data such as "Your sleep time this week is less than average" based on the analyzed data and offer advice for improvement. The generation AI can also provide data at an appropriate time, taking into account the user's current activity status and location information. The individual support providing unit provides individual support to the user based on the data analyzed by the data analysis unit. For example, if the user is feeling stressed, the generation AI suggests relaxation methods or counseling. The generation AI can also provide support according to the user's emotional state and provide emotional care. As a result, the health management system according to the embodiment can grasp changes in the user's physical and psychological state and provide individual support, thereby improving the health of employees and enhancing the effectiveness of internal communication and care.

[0055] The question generation unit customizes questions based on the user's past answer history, enabling a more accurate understanding of the user's physical condition and psychological state. The question generation unit, for example, analyzes the user's past answer history and extracts specific patterns and trends. For example, if there was a period in the past when the user was prone to stress, the question generation unit customizes and sends questions related to that period. The question generation unit also generates questions tailored to the user's individual health and psychological state based on the user's answer history. For example, to a user who has often suffered from sleep deprivation in the past, the question generation unit sends detailed questions related to sleep. The question generation unit also utilizes the user's answer history to create questions that focus on specific health issues or psychological states. For example, to a user who has been prone to anxiety in the past, the question generation unit sends questions related to anxiety. In this way, by customizing questions based on the user's past answer history, a more accurate understanding of the user's physical condition and psychological state is possible.

[0056] The question generation unit can optimize the timing of questions to match the user's lifestyle rhythm, thereby improving the response rate. The question generation unit, for example, analyzes the user's lifestyle rhythm and sets the optimal timing for questions. For example, if the user finds it easiest to answer questions during their morning commute, the question generation unit sends the questions during that time period. The question generation unit also identifies the time period when the user is most likely to answer based on the user's past answer times, and sends the questions at that time. For example, if the user often answers questions in the evening, the question generation unit sends the questions in the evening. The question generation unit also dynamically adjusts the time for sending questions to match the user's lifestyle rhythm. For example, if the user wakes up late on weekends, the time for sending questions on weekends is delayed. This optimizes the timing of questions to match the user's lifestyle rhythm, thereby improving the response rate.

[0057] The question generation unit uses the emotion estimation function to generate questions according to the user's emotional state, enabling more accurate data collection. The question generation unit, for example, analyzes the user's emotional state in real time and generates questions based on the results. For example, if the user is feeling stressed, it sends questions related to stress. The question generation unit also uses the emotion estimation function to customize questions according to the user's emotional state. For example, if the user is feeling positive, it sends questions that bring out those emotions. The question generation unit also adjusts the content and format of the questions based on the user's emotional state. For example, if the user is tired, it selects a simple question format. This enables more accurate data collection by using the emotion estimation function to generate questions according to the user's emotional state.

[0058] The question generation unit can expand the question format to a multimodal format using not only text but also audio and images, thereby increasing the variety of user answers. For example, the question generation unit introduces audio questions in addition to text questions. For example, it utilizes speech recognition technology so that users can answer by audio. The question generation unit also introduces image questions so that users can answer using images. For example, users can answer questions by uploading an image showing their physical condition. The question generation unit also introduces a multimodal question format so that users can answer using text, audio, or images. For example, it allows users to select the format that is easiest for them to answer. In this way, the variety of user answers is increased by expanding the question format to multimodal.

[0059] The question generation unit can expand the content of questions not only about health but also about work performance and lifestyle habits, thereby collecting comprehensive data. For example, the question generation unit introduces questions about work performance in addition to questions about health. For example, it sends a question such as, "How is your work going today?" The question generation unit also introduces questions about lifestyle habits to collect comprehensive data about the user. For example, it sends a question such as, "Do you have a daily exercise habit?" The question generation unit also combines questions about health, work performance, and lifestyle habits in a balanced manner to collect comprehensive data. For example, it sends questions on different themes each week. This makes it possible to collect comprehensive data by expanding the content of the questions.

[0060] The question generation unit uses the emotion estimation function to suggest a question format that is easy for the user to answer in real time, thereby improving the quality of answers. The question generation unit, for example, uses the emotion estimation function to suggest a question format that corresponds to the user's emotional state. For example, if the user is tired, it suggests a simple multiple-choice question. The question generation unit also analyzes the user's emotional state in real time and selects the optimal question format based on the results. For example, if the user is relaxed, it suggests a detailed descriptive question. The question generation unit also uses the emotion estimation function to dynamically adjust the question format that is easiest for the user to answer. For example, it changes the question format depending on the user's emotional state. In this way, the emotion estimation function is used to suggest a question format that is easy for the user to answer, thereby improving the quality of answers.

[0061] The data analysis unit can perform more accurate analysis by referring to the user's past health data and medical records. The data analysis unit, for example, analyzes the user's past health data and compares it with current response data. For example, it compares past blood pressure data with current response data to understand changes in health status. The data analysis unit also refers to the user's medical records and uses them in analyzing the response data. For example, it evaluates the current health status based on past diagnostic results and prescription drug information. The data analysis unit also integrates the user's past health data and medical records to perform a comprehensive analysis. For example, it combines past health checkup results with current response data for analysis. In this way, by referring to the user's past health data and medical records, more accurate analysis is possible.

[0062] The data analysis unit analyzes the response data in association with the user's living environment and work environment, thereby clarifying the influence of environmental factors. The data analysis unit, for example, collects data on the user's living environment and uses it to analyze the response data. For example, it analyzes changes in health status based on data such as living environment and commuting time. The data analysis unit also collects data on the user's work environment and analyzes it in association with the response data. For example, it evaluates changes in health status based on workplace stress factors and working hours. The data analysis unit also integrates data on the living environment and work environment and uses it to analyze the response data. For example, it analyzes changes in health status taking both the living environment and work environment into consideration. In this way, by analyzing the response data in association with the user's living environment and work environment, it is possible to clarify the influence of environmental factors.

[0063] The data analysis unit can analyze the user's emotional state using the emotion estimation function and provide health advice based on changes in emotion. The data analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The data analysis unit also provides health advice based on the user's emotional state. For example, if the user is feeling stressed, it suggests relaxation methods. The data analysis unit also provides health advice according to changes in emotion based on the emotion estimation data. For example, if the user's emotional state is worsening, it suggests counseling. In this way, by analyzing the user's emotional state using the emotion estimation function and providing health advice based on changes in emotion, the user's health management is improved.

[0064] The data analysis unit can compare the data with other users' data to identify common health trends and problems. The data analysis unit, for example, compares the response data of other users to identify common health trends. For example, it compares data of users in the same age group to identify common health problems. The data analysis unit also analyzes the frequency of occurrence of specific health problems based on the data of other users. For example, it compares data of users engaged in specific occupations to identify trends in occupational diseases. The data analysis unit also compares the data with other users to suggest measures to address common health problems. For example, it provides common advice to users with the same health problem. In this way, by comparing the data of other users, it is possible to identify common health trends and problems.

[0065] The data analysis unit can improve the reliability of the analysis results by referring to external health databases and research data. The data analysis unit, for example, refers to external health databases and uses them in analyzing the response data. For example, the reliability of the analysis results is improved based on national health statistical data. The data analysis unit also refers to research data and uses it in analyzing the response data. For example, the analysis results are supplemented based on the latest medical research data. The data analysis unit also integrates external databases and research data and uses it in analyzing the response data. For example, multiple data sources are combined to improve the accuracy of the analysis results. In this way, the reliability of the analysis results is improved by referring to external health databases and research data.

[0066] The data analysis unit can use the emotion estimation function to analyze the association between the user's emotional state and health state and suggest health management based on the emotion. The data analysis unit, for example, uses the emotion estimation function to analyze the association between the user's emotional state and health state. For example, it compares the emotion score with health data to identify the association. The data analysis unit also suggests health management based on the association between the emotional state and health state. For example, it suggests relaxation methods to a user whose emotional state is deteriorating. The data analysis unit also suggests health management based on the emotion estimation data. For example, it provides advice on maintaining positive emotions to a user whose emotional state is good. In this way, by using the emotion estimation function to analyze the association between the user's emotional state and health state and suggesting health management based on the emotion, the user's health management is improved.

[0067] The real-time data providing unit can provide data at an appropriate timing by taking into account the user's current activity status and location information. The real-time data providing unit, for example, analyzes the user's current activity status and provides data at the optimal timing. For example, it provides health data while the user is taking a break. The real-time data providing unit also provides data at an appropriate timing based on the user's location information. For example, it suggests relaxation methods when the user is at home. The real-time data providing unit also integrates the activity status and location information and provides data at the optimal timing. For example, it provides stress management advice while the user is commuting. In this way, it is possible to provide data at an appropriate timing by taking into account the user's current activity status and location information.

[0068] The real-time data providing unit can customize the format of data provision according to the user's preferences and provide the data in a format that is visually easy to understand. The real-time data providing unit customizes the format of data provision according to the user's preferences, for example. For example, data is provided in graph or chart format. The real-time data providing unit also provides the data in a format that is visually easy to understand. For example, data is displayed using color-coded graphs or icons. The real-time data providing unit also improves the format of data provision based on user feedback. For example, it allows the user to select the format that is easiest to understand. In this way, by customizing the format of data provision according to the user's preferences, data can be provided in a format that is visually easy to understand.

[0069] The real-time data providing unit uses the emotion estimation function to provide data according to the user's emotional state, thereby eliciting positive emotions. The real-time data providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide data based on the results. For example, if the user is feeling stressed, the real-time data providing unit suggests relaxation methods. The real-time data providing unit also provides data according to the user's emotional state to elicit positive emotions. For example, if the user is feeling positive emotions, the real-time data providing unit provides advice on how to maintain those emotions. The real-time data providing unit also provides data according to the user's emotional state based on the emotion estimation data. For example, if the user is tired, the real-time data providing unit provides data for relaxation. In this way, by using the emotion estimation function to provide data according to the user's emotional state, positive emotions can be elicited.

[0070] The real-time data providing unit can provide a wider variety of data by linking with wearable devices such as smartwatches and fitness trackers. The real-time data providing unit, for example, links with smartwatches and fitness trackers to provide real-time data. For example, it evaluates health status based on heart rate and step count data. The real-time data providing unit also provides real-time health advice based on data collected from the wearable device. For example, if the amount of exercise is low, it provides advice to encourage exercise. The real-time data providing unit also links with wearable devices to monitor the user's health data in real time. For example, it provides advice to improve sleep quality based on sleep data. In this way, by linking with wearable devices, a wider variety of data can be provided.

[0071] The real-time data providing unit can also share data with the user's family and medical professionals to support comprehensive health management. The real-time data providing unit, for example, shares the user's health data with family members to support comprehensive health management. For example, family members can understand the user's health condition and provide support. The real-time data providing unit also shares the user's health data with medical professionals to receive professional advice. For example, a doctor can diagnose and treat the user based on the user's data. The real-time data providing unit also works with family members and medical professionals to provide comprehensive support for the user's health management. For example, regular health checks and counseling can be provided. In this way, comprehensive health management can be supported by sharing data with the user's family members and medical professionals.

[0072] The real-time data providing unit uses the emotion estimation function to optimize the timing of data provision based on the user's emotional state, thereby increasing data acceptance. The real-time data providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and optimize the timing of data provision based on the results. For example, data is provided when the user is relaxed. The real-time data providing unit also adjusts the timing of data provision based on the user's emotional state. For example, relaxation methods are suggested when the user is feeling stressed. The real-time data providing unit also provides data at the optimal timing according to the user's emotional state based on the emotion estimation data. For example, health advice is provided when the user is feeling positive. In this way, data acceptance can be increased by optimizing the timing of data provision based on the user's emotional state using the emotion estimation function.

[0073] The individual support providing unit can refer to the user's past support history and provide more effective support. The individual support providing unit, for example, analyzes the user's past support history and utilizes it in current support. For example, it re-proposes support methods that were effective in the past. The individual support providing unit also provides support according to individual needs based on the user's support history. For example, it proposes stress management methods to a user who needed stress management in the past. The individual support providing unit also refers to the past support history and provides effective support. For example, it proposes a similar method to a user for whom a relaxation method was effective in the past. In this way, more effective support can be provided by referring to the user's past support history.

[0074] The individual support providing unit customizes the support content to suit the user's work environment and lifestyle habits, thereby increasing feasibility. The individual support providing unit customizes the support content, for example, taking into account the user's work environment. For example, for a user who does a lot of desk work, it suggests stretching methods that can be done in the office. The individual support providing unit also adjusts the support content to suit the user's lifestyle habits. For example, for a user who often works night shifts, it suggests nighttime relaxation methods. The individual support providing unit also integrates the work environment and lifestyle habits to provide feasible support content. For example, for a busy user, it suggests relaxation methods that can be done in a short amount of time. In this way, by customizing the support content to suit the user's work environment and lifestyle habits, it is possible to increase feasibility.

[0075] The individual support providing unit can provide support according to the emotional state of the user using the emotion estimation function, thereby providing emotional care. The individual support providing unit, for example, uses the emotion estimation function to analyze the emotional state of the user in real time and provide support based on the results. For example, if the user is feeling stressed, it suggests a relaxation method. The individual support providing unit also provides support according to the emotional state of the user and provides emotional care. For example, if the user is feeling anxious, it suggests counseling. The individual support providing unit also provides support according to the emotional state of the user based on the emotion estimation data. For example, if the user is feeling positive, it provides advice on how to maintain that emotion. In this way, emotional care can be provided by using the emotion estimation function to provide support according to the emotional state of the user.

[0076] The individual support providing unit can provide comprehensive support in cooperation with online counseling and mental health apps. The individual support providing unit, for example, can provide individual support in cooperation with online counseling. For example, if the user is feeling stressed, it can suggest online counseling. The individual support providing unit can also provide comprehensive support in cooperation with mental health apps. For example, if the user is feeling anxious, it can suggest relaxation methods through the mental health app. The individual support providing unit can also integrate online counseling and mental health apps to provide comprehensive support. For example, the user can receive daily support through the app while receiving counseling. In this way, comprehensive support can be provided by cooperation with online counseling and mental health apps.

[0077] The individual support providing unit can share the support content with the user's family and friends, thereby building a social support network. The individual support providing unit, for example, shares the user's support content with family, thereby building a social support network. For example, family members understand the user's health condition and provide support. The individual support providing unit also shares the user's support content with friends, thereby building a social support network. For example, friends understand the user's health condition and provide support. The individual support providing unit also works with family and friends to provide comprehensive support for the user's health management. For example, regular health checks and counseling are provided. In this way, the support content can be shared with the user's family and friends, thereby building a social support network.

[0078] The individual support providing unit can use the emotion estimation function to determine support priorities based on the user's emotional state and provide optimal support. The individual support providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and determine support priorities based on the results. For example, if the user is feeling stressed, stress management support is prioritized. The individual support providing unit also adjusts support priorities based on the user's emotional state. For example, if the user is feeling anxious, counseling is prioritized. The individual support providing unit also provides optimal support according to the user's emotional state based on the emotion estimation data. For example, if the user is feeling positive emotions, advice on maintaining those emotions is provided. In this way, optimal support can be provided by using the emotion estimation function to determine support priorities based on the user's emotional state.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The health management system can further include a nutrition management unit that collects the user's dietary data and provides advice on nutritional balance. For example, when the user inputs the details of their daily meals, the nutrition management unit analyzes the calorie and nutrient intake and suggests a balanced diet. The nutrition management unit can also provide meal plans based on the user's health goals. For example, a low-calorie meal plan can be suggested for a user who is trying to lose weight. Furthermore, the nutrition management unit can also suggest safe ingredients taking into account the user's allergy information. This makes it possible to utilize the user's dietary data for more comprehensive health management.

[0081] The health management system can further include an exercise management unit that collects the user's exercise data and provides an exercise program. For example, when the user inputs the details of their daily exercise, the exercise management unit analyzes the amount of exercise and calories burned and suggests an appropriate exercise program. The exercise management unit can also provide an exercise plan according to the user's fitness level. For example, it can suggest a plan to start with light exercise for beginners and a high-intensity exercise plan for advanced users. Furthermore, the exercise management unit can provide exercise advice according to the user's goals. For example, it can suggest a strength training program to a user who wants to increase their muscle strength. This makes it possible to utilize the user's exercise data for more effective health management.

[0082] The health management system can further include a sleep management unit that collects the user's sleep data and provides advice to improve the quality of their sleep. For example, if the user inputs the amount of time and quality of their sleep each night, the sleep management unit analyzes their sleep patterns and provides advice for improvement. The sleep management unit can also provide a sleep plan that matches the user's lifestyle. For example, for a user who works many night shifts, it can provide advice to support daytime sleep. Furthermore, the sleep management unit can take the user's stress level into consideration and suggest relaxation methods. This makes it possible to utilize the user's sleep data to support higher quality sleep.

[0083] The health management system may further include a fluid management unit that collects a user's fluid intake data and supports appropriate hydration. For example, the user can input their daily fluid intake, and the fluid management unit can provide advice on appropriate hydration. The fluid management unit can also suggest fluid intake amounts based on the user's activity level and weather conditions. For example, for a user who exercises a lot, advice emphasizing post-exercise hydration is provided. Furthermore, the fluid management unit can take into account the user's health condition and provide hydration advice for specific health issues. This makes it possible to utilize the user's fluid intake data to support a healthier lifestyle.

[0084] The health management system can also be equipped with a stress management unit that monitors the user's stress level in real time and provides stress management advice. For example, when the user inputs their daily stress level, the stress management unit analyzes the causes of stress and suggests appropriate measures. The stress management unit can also provide a stress management plan tailored to the user's lifestyle and work environment. For example, for a user who does a lot of desk work, it can suggest relaxation methods that can be done in the office. Furthermore, the stress management unit can take the user's emotional state into consideration and provide emotional care. This allows the user's stress level to be monitored and more effective stress management to be achieved.

[0085] The health management system can further include a refreshment management unit that suggests refreshment methods based on the user's hobbies and interests. For example, the user can input their hobbies and interests, and the refreshment management unit can suggest appropriate refreshment methods. The refreshment management unit can also provide a refreshment plan based on the user's emotional state. For example, if the user is feeling stressed, the refreshment management unit can suggest relaxation methods. Furthermore, the refreshment management unit can also provide refreshment methods based on the user's lifestyle and work environment. This makes it possible to utilize the user's hobbies and interests to provide more effective refreshment.

[0086] The health management system may further include an entertainment providing unit that analyzes the user's emotional state and provides music and videos based on the emotion. For example, if the user is feeling stressed, relaxing music may be suggested. The entertainment providing unit may also provide video content according to the user's emotional state. For example, if the user is feeling positive, fun videos may be suggested to help maintain those emotions. Furthermore, the entertainment providing unit may also provide entertainment content based on the user's hobbies and interests. This makes it possible to analyze the user's emotional state and provide more effective entertainment.

[0087] The health management system can further include a mental health management unit that analyzes the user's emotional state and provides emotionally based mental health support. For example, if the user is feeling anxious, counseling can be suggested. The mental health management unit can also provide a mental health plan tailored to the user's emotional state. For example, if the user is feeling stressed, relaxation methods can be suggested. The mental health management unit can also provide mental health support tailored to the user's lifestyle and work environment. This makes it possible to analyze the user's emotional state and provide more effective mental health support.

[0088] The health management system may further include a feedback providing unit that analyzes the user's emotional state and provides feedback based on the emotion. For example, if the user has positive emotions, feedback that reinforces those emotions may be provided. The feedback providing unit may also provide feedback according to the user's emotional state. For example, if the user is feeling stressed, relaxation methods may be suggested. Furthermore, the feedback providing unit may provide feedback according to the user's daily rhythm or work environment. This makes it possible to analyze the user's emotional state and provide more effective feedback.

[0089] The health management system can further include a communication management unit that analyzes the user's emotional state and provides communication support based on the emotion. For example, if the user is feeling stressed, it can suggest relaxation methods. The communication management unit can also provide a communication plan based on the user's emotional state. For example, if the user is feeling positive, it can suggest a communication method to maintain that emotion. Furthermore, the communication management unit can provide communication support based on the user's lifestyle and work environment. This makes it possible to analyze the user's emotional state and provide more effective communication support.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The question generator sends questions to the user every morning. For example, the generation AI sends questions such as "Did you sleep well last night?" or "How are you feeling this morning?" to understand the user's physical and psychological state. The generation AI can also customize questions based on the user's past response history, enabling a more accurate understanding of the user's physical and psychological state. Step 2: The data analysis unit analyzes the user's response data to the questions sent by the question generation unit. For example, the generation AI can understand changes in the user's physical and psychological state based on the user's response data and provide necessary support. The generation AI can also refer to the user's past health data and medical records to perform more accurate analysis. Step 3: The real-time data provider provides the data analyzed by the data analyzer to the user in real time. For example, the generator AI can provide data such as "your sleep time this week is less than average" based on the analyzed data and give advice on how to improve. The generator AI can also provide data at the appropriate time, taking into account the user's current activity status and location information. Step 4: The individual support provider provides individual support to the user based on the data analyzed by the data analyzer. For example, if the user is feeling stressed, the generator AI may suggest relaxation methods or counseling. The generator AI may also provide support according to the user's emotional state and provide emotional care.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question generator that sends a question to the user every morning; a data analysis unit that analyzes user response data to the question sent by the question generation unit; a real-time data providing unit that provides the data analyzed by the data analysis unit to a user in real time; an individual support providing unit that provides individual support to the user based on the data analyzed by the data analyzing unit; A system characterized by:

2. The question generation unit Customize questions based on the user's past answer history to more accurately grasp their physical and psychological state.

2. The system of claim 1.

3. The question generation unit Optimize the timing of questions to match the user's daily rhythm, improving response rates 2. The system of claim 1.

4. The question generation unit Generate questions according to the user's emotional state to collect more accurate data 2. The system of claim 1.

5. The question generation unit Expanding the question format to a multimodal format using not only text but also voice and images to increase the diversity of the user's answers.

2. The system of claim 1.

6. The question generation unit Expand the questions beyond health to include work performance and lifestyle habits, and collect comprehensive data.

2. The system of claim 1.

Images