System

Abstract

An object of a system according to an embodiment is to collect and analyze personal health data and provide appropriate health advice.SOLUTION: A system according to an embodiment includes a health data collection unit, a health data analysis unit, and a health advice providing unit. The health data collection unit collects health data of a user. The health data analysis unit analyzes the health data collected by the health data collection unit. The health advice providing unit provides health advice based on a result of the analysis by the health 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 effectively collect and analyze personal health data and provide appropriate health advice.

[0005] The system according to the embodiment aims to collect and analyze personal health data and provide appropriate health advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data collection unit, a health data analysis unit, and a health advice provision unit. The health data collection unit collects health data of a user. The health data analysis unit analyzes the health data collected by the health data collection unit. The health advice provision unit provides health advice based on the results of the analysis by the health data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect and analyze personal health data and provide appropriate health advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The HealthBot Companion system according to an embodiment of the present invention automatically collects a user's health data, analyzes it using a generation AI, and provides health advice, thereby assisting the user in managing their health and helping them improve their lifestyle.

[0029] The HealthBot Companion system according to the embodiment includes a health data collection unit, a health data analysis unit, and a health advice provision unit. The health data collection unit collects a user's health data, such as weight, blood pressure, heart rate, sleep patterns, and exercise volume. The health data collection unit can also collect data through a wearable device or a smartphone app. For example, a wearable device can monitor a user's heart rate and exercise volume in real time and collect data. The smartphone app can collect health data manually entered by the user. The health data collection unit can also collect data provided by medical institutions, such as blood pressure measurement data and test results provided by medical institutions. The health data analysis unit analyzes the collected health data. For example, the generation AI can analyze the data using statistical analysis or machine learning algorithms. The generation AI can also detect outliers and analyze trends. For example, the generation AI can analyze a user's blood pressure data and detect abnormal values. The generation AI can also analyze trends in the user's exercise volume and assess the risk of insufficient or excessive exercise. The health advice providing unit provides health advice based on the analysis results. For example, the generation AI may suggest a balanced diet and an appropriate exercise plan based on the user's diet and exercise habits. The generation AI may also analyze sleep data and provide advice on how to get good quality sleep. For example, the generation AI may analyze the user's sleep patterns and provide specific advice on how to improve sleep quality. This allows the HealthBot Companion system according to the embodiment to assist the user in managing their health and improve their lifestyle. For example, the user can practice a healthy lifestyle based on the advice provided by the system. The system may also continuously monitor the user's health data and update the advice as needed. This allows the user to constantly monitor their health status and take appropriate measures.

[0030] The health data collection unit can collect health data in real time and immediately issue an alert if an abnormal value is detected. The health data collection unit, for example, builds a system that monitors a user's health data in real time and immediately issues an alert if an abnormal value is detected. For example, an alert is issued if the heart rate is abnormally high. The health data collection unit can also issue an alert if the blood pressure is abnormally low. For example, the generation AI analyzes the user's blood pressure data in real time and issues an alert if an abnormal value is detected. Furthermore, the health data collection unit can also issue an alert if the user's body temperature is abnormally high. For example, the generation AI analyzes the user's body temperature data in real time and issues an alert if an abnormal value is detected. This allows for a rapid response by immediately issuing an alert if an abnormal value is detected.

[0031] The health data collection unit can simultaneously record health data and living environment data and analyze the impact of environmental factors on health. For example, the health data collection unit simultaneously collects a user's health data and living environment data and uses a generation AI to analyze the impact of environmental factors on health. For example, the health data collection unit can evaluate the relationship between temperature and exercise volume. The health data collection unit can also analyze the relationship between humidity and sleep patterns. For example, the generation AI can analyze data on the user's sleep patterns and humidity to evaluate the impact of humidity on sleep. Furthermore, the health data collection unit can analyze the relationship between noise levels and stress. For example, the generation AI can analyze data on the user's stress level and noise level to evaluate the impact of noise on stress. This allows for more effective health management for users by recording living environment data and analyzing the impact of environmental factors on health.

[0032] The health data collection unit can simultaneously collect health data of the pet and analyze the correlation between the health status of the pet and the owner. For example, the health data collection unit simultaneously collects health data of the user and the pet and analyzes the correlation between health status using the generation AI. For example, it evaluates the relationship between the amount of exercise the pet receives and the amount of exercise the owner receives. The health data collection unit can also analyze the relationship between the diet of the pet and the diet of the owner. For example, the generation AI analyzes the diet of the pet and the diet of the owner to evaluate the impact of diet on health. Furthermore, the health data collection unit can analyze the relationship between the sleep pattern of the pet and the sleep pattern of the owner. For example, the generation AI analyzes the sleep pattern of the pet and the sleep pattern of the owner to evaluate the impact of sleep on health. This enables more comprehensive health management by analyzing the correlation between the health status of the pet and the owner.

[0033] The health data collection unit can also collect workplace environment data and analyze the impact of the workplace environment on health. For example, the health data collection unit simultaneously collects a user's workplace environment data and health data and uses the generation AI to analyze the impact of the workplace environment on health. For example, the relationship between desk work time and lower back pain can be evaluated. The health data collection unit can also analyze the relationship between workplace temperature and stress level. For example, the generation AI can analyze data on the user's stress level and workplace temperature to evaluate the impact of temperature on stress. Furthermore, the health data collection unit can analyze the relationship between workplace noise levels and concentration. For example, the generation AI can analyze data on the user's concentration and workplace noise levels to evaluate the impact of noise on concentration. This allows for analysis of the impact of the workplace environment on health, thereby improving health management in the workplace.

[0034] The health advice providing unit can analyze genetic information and provide personalized advice based on genetic risk. The health advice providing unit can, for example, analyze a user's genetic information and provide personalized advice based on genetic risk. For example, it can suggest an appropriate diet plan if there is a genetic risk of high blood pressure. The health advice providing unit can also assess the risk of specific diseases based on the user's genetic information and suggest preventive measures. For example, the generation AI can analyze a user's genetic information and suggest an appropriate exercise plan if there is a high risk of diabetes. Furthermore, the health advice providing unit can provide advice tailored to individual health goals based on the user's genetic information. For example, the generation AI can analyze a user's genetic information and provide diet and exercise advice based on individual health goals. This allows the user to manage their health more effectively by providing personalized advice based on genetic risk.

[0035] The health advice providing unit can take into account the health data of family members and provide advice based on the health status of the entire family. For example, the health advice providing unit collects health data of the user's family and provides advice based on the health status of the entire family. For example, it can propose a meal plan for the entire family. The health advice providing unit can also evaluate the health risks of the entire family based on the family's health data and propose preventive measures. For example, the generating AI can analyze the family's health data and propose appropriate preventive measures if there is a high risk of a particular disease. Furthermore, the health advice providing unit can provide advice tailored to the health goals of the entire family. For example, the generating AI can analyze the family's health data and provide dietary and exercise advice based on the health goals of the entire family. This supports health management for the entire family by providing advice based on the health status of the entire family.

[0036] The health advice providing unit can suggest activities based on the user's hobbies and interests, and provide a way to maintain health while having fun. For example, the health advice providing unit can suggest activities for maintaining health while having fun, based on the user's hobbies and interests. For example, it can suggest dance exercises to a user who likes dancing. The health advice providing unit can also suggest healthy recipes based on the user's interests. For example, it can suggest healthy cooking recipes to a user who likes cooking. Furthermore, the health advice providing unit can also suggest specific activities for practicing a healthy lifestyle based on the user's hobbies and interests. For example, it can suggest hiking or camping to a user who likes the outdoors. In this way, by suggesting activities based on the user's hobbies and interests, it is possible to maintain health while having fun.

[0037] The lifestyle improvement unit can analyze a user's lifestyle rhythm and suggest optimal timings for eating and exercising. The lifestyle improvement unit can, for example, analyze a user's lifestyle rhythm and suggest optimal timings for eating and exercising. For example, it can suggest the timing of breakfast or the time of day for exercise. The lifestyle improvement unit can also make personalized suggestions for eating and exercising based on the user's lifestyle rhythm. For example, the generation AI can analyze a user's lifestyle rhythm and suggest eating and exercising that matches individual health goals. Furthermore, the lifestyle improvement unit can monitor a user's lifestyle rhythm in real time and suggest optimal timings for eating and exercising. For example, the generation AI can analyze a user's lifestyle rhythm and suggest optimal timings in real time. This allows the user's health to be managed more effectively by suggesting optimal timings for eating and exercising.

[0038] The lifestyle improvement unit can analyze social networks and form communities for sharing healthy lifestyles. The lifestyle improvement unit, for example, analyzes a user's social network and forms a community for sharing healthy lifestyles. For example, it connects users who have the same health goals. The lifestyle improvement unit can also suggest specific activities for practicing a healthy lifestyle based on the user's social network. For example, the generation AI analyzes the user's social network and suggests healthy activities. Furthermore, the lifestyle improvement unit can form communities through online forums and offline events based on the user's social network. For example, the generation AI analyzes the user's social network and suggests events for sharing healthy lifestyles. In this way, a community for sharing healthy lifestyles is formed, thereby supporting the user's health management.

[0039] The lifestyle improvement unit can also collect activity data at work or school and suggest health maintenance measures at work or school. For example, the lifestyle improvement unit collects activity data at the user's workplace or school and suggests health maintenance measures at work or school. For example, it suggests stretching exercises to do in between desk work. The lifestyle improvement unit can also suggest personalized health maintenance measures based on the user's activity data at work or school. For example, the generation AI can analyze the user's activity data at work or school and suggest health maintenance measures tailored to individual health goals. Furthermore, the lifestyle improvement unit can monitor the user's activity data at work or school in real time and suggest optimal health maintenance measures. For example, the generation AI can analyze the user's activity data at work or school and suggest optimal health maintenance measures in real time. In this way, the system can support the user's health management by suggesting health maintenance measures at work or school.

[0040] The lifestyle improvement unit can also collect travel and leisure activity data and suggest health maintenance measures at the travel destination. For example, the lifestyle improvement unit collects the user's travel and leisure activity data and suggests health maintenance measures at the travel destination. For example, it suggests meal plans and exercise plans for the trip. The lifestyle improvement unit can also suggest personalized health maintenance measures based on the user's travel and leisure activity data. For example, the generation AI analyzes the user's travel and leisure activity data and suggests health maintenance measures tailored to individual health goals. Furthermore, the lifestyle improvement unit can monitor the user's travel and leisure activity data in real time and suggest optimal health maintenance measures. For example, the generation AI analyzes the user's travel and leisure activity data and suggests optimal health maintenance measures in real time. In this way, the system supports the user's health management by suggesting health maintenance measures at the travel destination.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The HealthBot Companion system can also include a hobby analysis unit that provides health advice based on the user's hobbies and interests. For example, if the user likes music, it can suggest relaxing music. If the user enjoys outdoor activities, it can suggest suitable hiking trails and campsites. Furthermore, if the user enjoys cooking, it can suggest healthy recipes. This makes health management more enjoyable by providing advice based on the user's hobbies and interests.

[0043] The HealthBot Companion system can also be equipped with a lifestyle analysis unit that analyzes the user's lifestyle and suggests optimal meal and exercise timings. For example, it can analyze the user's sleep patterns and suggest the optimal timing for breakfast. It can also analyze the user's activity level and suggest optimal exercise times. It can also suggest efficient break times taking into account the user's work or school schedule. This allows for more effective health management by providing advice based on the user's lifestyle.

[0044] The HealthBot Companion system can also include a social network analysis module that analyzes the user's social network and creates a community for sharing a healthy lifestyle. For example, it can connect users with similar health goals. It can also create communities through online forums and offline events. It can also suggest healthy activities based on the user's social network. This helps support the user's health management by creating a community for sharing a healthy lifestyle.

[0045] The HealthBot Companion system can also be equipped with a workplace / school analysis unit that collects activity data from users at work or school and suggests health maintenance strategies for the workplace or school. For example, it can suggest stretching exercises to do in between desk work. It can also suggest personalized health maintenance strategies based on activity data from the workplace or school. It can also monitor activity data in real time and suggest optimal health maintenance strategies. This allows it to support users' health management by suggesting health maintenance strategies for the workplace or school.

[0046] The HealthBot Companion system can also be equipped with a travel analysis unit that collects data on the user's travel and leisure activities and suggests health maintenance measures at the travel destination. For example, it can suggest meal plans and exercise plans for the trip. It can also suggest personalized health maintenance measures based on the travel and leisure activity data. It can also monitor travel and leisure activity data in real time and suggest optimal health maintenance measures. This can support the user's health management by suggesting health maintenance measures at the travel destination.

[0047] The HealthBot Companion system can also include a genetic information analysis unit that analyzes a user's genetic information and provides personalized advice based on their genetic risk. For example, it can suggest an appropriate diet plan for a person with a genetic risk of high blood pressure. It can also assess the risk of certain diseases based on the user's genetic information and suggest preventative measures. Furthermore, it can provide advice tailored to individual health goals based on the user's genetic information. This allows for more effective health management by providing personalized advice based on genetic risk.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The health data collection unit collects the user's health data. For example, it collects data such as weight, blood pressure, heart rate, sleep patterns, and amount of exercise. The health data collection unit can also collect data through a wearable device or a smartphone app. For example, a wearable device can monitor the user's heart rate and amount of exercise in real time and collect data. A smartphone app can collect health data manually entered by the user. Furthermore, the health data collection unit can also collect data provided by medical institutions. For example, it can collect blood pressure measurement data and test results provided by medical institutions. Step 2: The health data analysis unit analyzes the collected health data. For example, the generating AI analyzes the data using statistical analysis and machine learning algorithms. The generating AI can also detect outliers and analyze trends. For example, the generating AI can analyze the user's blood pressure data and detect abnormal values. The generating AI can also analyze trends in the user's exercise volume and assess the risk of insufficient or excessive exercise. Step 3: The health advice provider provides health advice based on the analysis results. For example, the generation AI suggests a balanced diet and appropriate exercise plan based on the user's diet and exercise habits. The generation AI can also analyze sleep data and provide advice on how to get good quality sleep. For example, the generation AI can analyze the user's sleep patterns and provide specific advice on how to improve the quality of their sleep.

[0050] (Example 2) The HealthBot Companion system according to an embodiment of the present invention automatically collects a user's health data, analyzes it using a generation AI, and provides health advice, thereby assisting the user in managing their health and helping them improve their lifestyle.

[0051] The HealthBot Companion system according to the embodiment includes a health data collection unit, a health data analysis unit, and a health advice provision unit. The health data collection unit collects a user's health data, such as weight, blood pressure, heart rate, sleep patterns, and exercise volume. The health data collection unit can also collect data through a wearable device or a smartphone app. For example, a wearable device can monitor a user's heart rate and exercise volume in real time and collect data. The smartphone app can collect health data manually entered by the user. The health data collection unit can also collect data provided by medical institutions, such as blood pressure measurement data and test results provided by medical institutions. The health data analysis unit analyzes the collected health data. For example, the generation AI can analyze the data using statistical analysis or machine learning algorithms. The generation AI can also detect outliers and analyze trends. For example, the generation AI can analyze a user's blood pressure data and detect abnormal values. The generation AI can also analyze trends in the user's exercise volume and assess the risk of insufficient or excessive exercise. The health advice providing unit provides health advice based on the analysis results. For example, the generation AI may suggest a balanced diet and an appropriate exercise plan based on the user's diet and exercise habits. The generation AI may also analyze sleep data and provide advice on how to get good quality sleep. For example, the generation AI may analyze the user's sleep patterns and provide specific advice on how to improve sleep quality. This allows the HealthBot Companion system according to the embodiment to assist the user in managing their health and improve their lifestyle. For example, the user can practice a healthy lifestyle based on the advice provided by the system. The system may also continuously monitor the user's health data and update the advice as needed. This allows the user to constantly monitor their health status and take appropriate measures.

[0052] The health data analysis unit can estimate the user's emotional state and analyze the correlation between emotional fluctuations and health data. For example, the health data analysis unit simultaneously collects the user's health data and emotional state and uses the generation AI to analyze the correlation between emotional fluctuations and health data. For example, it compares the user's daily exercise volume with emotional fluctuations to evaluate the impact of exercise on emotions. The health data analysis unit can also analyze the user's diet and emotional fluctuations to evaluate the impact of diet on emotions. For example, the generation AI can analyze the user's diet and emotional fluctuations to evaluate the impact of specific meals on emotions. Furthermore, the health data analysis unit can analyze the user's sleep patterns and emotional fluctuations to evaluate the impact of sleep on emotions. For example, the generation AI can analyze the user's sleep patterns and emotional fluctuations to evaluate the impact of good quality sleep on emotions. This allows for a deeper understanding of the user's health status by analyzing the correlation between emotional fluctuations and health data.

[0053] The health data collection unit can collect health data in real time and immediately issue an alert if an abnormal value is detected. The health data collection unit, for example, builds a system that monitors a user's health data in real time and immediately issues an alert if an abnormal value is detected. For example, an alert is issued if the heart rate is abnormally high. The health data collection unit can also issue an alert if the blood pressure is abnormally low. For example, the generation AI analyzes the user's blood pressure data in real time and issues an alert if an abnormal value is detected. Furthermore, the health data collection unit can also issue an alert if the user's body temperature is abnormally high. For example, the generation AI analyzes the user's body temperature data in real time and issues an alert if an abnormal value is detected. This allows for a rapid response by immediately issuing an alert if an abnormal value is detected.

[0054] The health data collection unit can simultaneously record health data and living environment data and analyze the impact of environmental factors on health. For example, the health data collection unit simultaneously collects a user's health data and living environment data and uses a generation AI to analyze the impact of environmental factors on health. For example, the health data collection unit can evaluate the relationship between temperature and exercise volume. The health data collection unit can also analyze the relationship between humidity and sleep patterns. For example, the generation AI can analyze data on the user's sleep patterns and humidity to evaluate the impact of humidity on sleep. Furthermore, the health data collection unit can analyze the relationship between noise levels and stress. For example, the generation AI can analyze data on the user's stress level and noise level to evaluate the impact of noise on stress. This allows for more effective health management for users by recording living environment data and analyzing the impact of environmental factors on health.

[0055] The health data collection unit can simultaneously collect health data of the pet and analyze the correlation between the health status of the pet and the owner. For example, the health data collection unit simultaneously collects health data of the user and the pet and analyzes the correlation between health status using the generation AI. For example, it evaluates the relationship between the amount of exercise the pet receives and the amount of exercise the owner receives. The health data collection unit can also analyze the relationship between the diet of the pet and the diet of the owner. For example, the generation AI analyzes the diet of the pet and the diet of the owner to evaluate the impact of diet on health. Furthermore, the health data collection unit can analyze the relationship between the sleep pattern of the pet and the sleep pattern of the owner. For example, the generation AI analyzes the sleep pattern of the pet and the sleep pattern of the owner to evaluate the impact of sleep on health. This enables more comprehensive health management by analyzing the correlation between the health status of the pet and the owner.

[0056] The health data collection unit can also collect workplace environment data and analyze the impact of the workplace environment on health. For example, the health data collection unit simultaneously collects a user's workplace environment data and health data and uses the generation AI to analyze the impact of the workplace environment on health. For example, the relationship between desk work time and lower back pain can be evaluated. The health data collection unit can also analyze the relationship between workplace temperature and stress level. For example, the generation AI can analyze data on the user's stress level and workplace temperature to evaluate the impact of temperature on stress. Furthermore, the health data collection unit can analyze the relationship between workplace noise levels and concentration. For example, the generation AI can analyze data on the user's concentration and workplace noise levels to evaluate the impact of noise on concentration. This allows for analysis of the impact of the workplace environment on health, thereby improving health management in the workplace.

[0057] The health data collection unit can estimate emotions in real time when a user enters health data and provide feedback that elicits positive emotions. For example, when a user enters health data, the health data collection unit can estimate emotions in real time using an emotion estimation function and provide feedback that elicits positive emotions. For example, an encouraging message can be displayed during entry. The health data collection unit can also capture the user's facial expressions when entering health data with a camera and analyze the emotions using an emotion estimation algorithm. For example, the health data collection unit can calculate an emotion score based on changes in facial expressions and provide positive feedback. Furthermore, the health data collection unit can record the user's voice when entering health data and estimate emotions using voice analysis technology. For example, the health data collection unit can analyze the tone and speed of voice and provide positive feedback. This provides feedback that elicits positive emotions, thereby improving the user's motivation.

[0058] The health advice providing unit can estimate the user's emotional state and provide advice according to that emotion. For example, the health advice providing unit can estimate the user's emotional state using a generation AI and provide health advice according to that emotion. For example, if stress is high, the health advice providing unit can suggest relaxation methods. The health advice providing unit can also monitor the user's emotional state in real time and provide advice according to the emotion. For example, the generation AI can analyze the user's emotional state and provide specific advice to elicit positive emotions. Furthermore, the health advice providing unit can provide personalized health advice based on the user's emotional state. For example, the generation AI can analyze the user's emotional state and provide advice tailored to individual health goals. This allows the user to manage their health more effectively by providing advice according to their emotions.

[0059] The health advice providing unit can analyze genetic information and provide personalized advice based on genetic risk. The health advice providing unit can, for example, analyze a user's genetic information and provide personalized advice based on genetic risk. For example, it can suggest an appropriate diet plan if there is a genetic risk of high blood pressure. The health advice providing unit can also assess the risk of specific diseases based on the user's genetic information and suggest preventive measures. For example, the generation AI can analyze a user's genetic information and suggest an appropriate exercise plan if there is a high risk of diabetes. Furthermore, the health advice providing unit can provide advice tailored to individual health goals based on the user's genetic information. For example, the generation AI can analyze a user's genetic information and provide diet and exercise advice based on individual health goals. This allows the user to manage their health more effectively by providing personalized advice based on genetic risk.

[0060] The health advice providing unit can take into account the health data of family members and provide advice based on the health status of the entire family. For example, the health advice providing unit collects health data of the user's family and provides advice based on the health status of the entire family. For example, it can propose a meal plan for the entire family. The health advice providing unit can also evaluate the health risks of the entire family based on the family's health data and propose preventive measures. For example, the generating AI can analyze the family's health data and propose appropriate preventive measures if there is a high risk of a particular disease. Furthermore, the health advice providing unit can provide advice tailored to the health goals of the entire family. For example, the generating AI can analyze the family's health data and provide dietary and exercise advice based on the health goals of the entire family. This supports health management for the entire family by providing advice based on the health status of the entire family.

[0061] The health advice providing unit can suggest activities based on the user's hobbies and interests, and provide a way to maintain health while having fun. For example, the health advice providing unit can suggest activities for maintaining health while having fun, based on the user's hobbies and interests. For example, it can suggest dance exercises to a user who likes dancing. The health advice providing unit can also suggest healthy recipes based on the user's interests. For example, it can suggest healthy cooking recipes to a user who likes cooking. Furthermore, the health advice providing unit can also suggest specific activities for practicing a healthy lifestyle based on the user's hobbies and interests. For example, it can suggest hiking or camping to a user who likes the outdoors. In this way, by suggesting activities based on the user's hobbies and interests, it is possible to maintain health while having fun.

[0062] The health advice providing unit can use the emotion estimation function to estimate the emotion the user is feeling when receiving advice in real time and provide advice that elicits positive emotions. For example, the health advice providing unit can use the emotion estimation function to estimate the emotion the user is feeling when receiving advice in real time and provide advice that elicits positive emotions. For example, it can display an encouraging message. The health advice providing unit can also capture the user's facial expression when receiving advice with a camera and analyze the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression and provide positive advice. Furthermore, the health advice providing unit can record the user's voice when receiving advice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of voice and provide positive advice. In this way, advice that elicits positive emotions can be provided, thereby improving the user's motivation.

[0063] The lifestyle improvement unit can estimate the user's emotional state and suggest lifestyle improvement measures according to the emotion. For example, the lifestyle improvement unit can estimate the user's emotional state using a generation AI and suggest lifestyle improvement measures according to the emotion. For example, if stress is high, it can suggest relaxation methods. The lifestyle improvement unit can also monitor the user's emotional state in real time and suggest lifestyle improvement measures according to the emotion. For example, the generation AI can analyze the user's emotional state and suggest specific improvement measures to bring out positive emotions. Furthermore, the lifestyle improvement unit can suggest personalized lifestyle improvement measures based on the user's emotional state. For example, the generation AI can analyze the user's emotional state and suggest improvement measures tailored to individual health goals. In this way, by suggesting lifestyle improvement measures according to the emotion, the user's quality of life is improved.

[0064] The lifestyle improvement unit can analyze a user's lifestyle rhythm and suggest optimal timings for eating and exercising. The lifestyle improvement unit can, for example, analyze a user's lifestyle rhythm and suggest optimal timings for eating and exercising. For example, it can suggest the timing of breakfast or the time of day for exercise. The lifestyle improvement unit can also make personalized suggestions for eating and exercising based on the user's lifestyle rhythm. For example, the generation AI can analyze a user's lifestyle rhythm and suggest eating and exercising that matches individual health goals. Furthermore, the lifestyle improvement unit can monitor a user's lifestyle rhythm in real time and suggest optimal timings for eating and exercising. For example, the generation AI can analyze a user's lifestyle rhythm and suggest optimal timings in real time. This allows the user's health to be managed more effectively by suggesting optimal timings for eating and exercising.

[0065] The lifestyle improvement unit can analyze social networks and form communities for sharing healthy lifestyles. The lifestyle improvement unit, for example, analyzes a user's social network and forms a community for sharing healthy lifestyles. For example, it connects users who have the same health goals. The lifestyle improvement unit can also suggest specific activities for practicing a healthy lifestyle based on the user's social network. For example, the generation AI analyzes the user's social network and suggests healthy activities. Furthermore, the lifestyle improvement unit can form communities through online forums and offline events based on the user's social network. For example, the generation AI analyzes the user's social network and suggests events for sharing healthy lifestyles. In this way, a community for sharing healthy lifestyles is formed, thereby supporting the user's health management.

[0066] The lifestyle improvement unit can also collect activity data at work or school and suggest health maintenance measures at work or school. For example, the lifestyle improvement unit collects activity data at the user's workplace or school and suggests health maintenance measures at work or school. For example, it suggests stretching exercises to do in between desk work. The lifestyle improvement unit can also suggest personalized health maintenance measures based on the user's activity data at work or school. For example, the generation AI can analyze the user's activity data at work or school and suggest health maintenance measures tailored to individual health goals. Furthermore, the lifestyle improvement unit can monitor the user's activity data at work or school in real time and suggest optimal health maintenance measures. For example, the generation AI can analyze the user's activity data at work or school and suggest optimal health maintenance measures in real time. In this way, the system can support the user's health management by suggesting health maintenance measures at work or school.

[0067] The lifestyle improvement unit can also collect travel and leisure activity data and suggest health maintenance measures at the travel destination. For example, the lifestyle improvement unit collects the user's travel and leisure activity data and suggests health maintenance measures at the travel destination. For example, it suggests meal plans and exercise plans for the trip. The lifestyle improvement unit can also suggest personalized health maintenance measures based on the user's travel and leisure activity data. For example, the generation AI analyzes the user's travel and leisure activity data and suggests health maintenance measures tailored to individual health goals. Furthermore, the lifestyle improvement unit can monitor the user's travel and leisure activity data in real time and suggest optimal health maintenance measures. For example, the generation AI analyzes the user's travel and leisure activity data and suggests optimal health maintenance measures in real time. In this way, the system supports the user's health management by suggesting health maintenance measures at the travel destination.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The HealthBot Companion system can also include a hobby analysis unit that provides health advice based on the user's hobbies and interests. For example, if the user likes music, it can suggest relaxing music. If the user enjoys outdoor activities, it can suggest suitable hiking trails and campsites. Furthermore, if the user enjoys cooking, it can suggest healthy recipes. This makes health management more enjoyable by providing advice based on the user's hobbies and interests.

[0070] The HealthBot Companion system can also be equipped with a relaxation suggestion unit that estimates the user's emotional state and suggests relaxation methods according to the emotion. For example, if the user is feeling stressed, it can suggest deep breathing or meditation. If the user is feeling anxious, it can suggest relaxing music or aromatherapy. Furthermore, if the user is feeling fatigued, it can suggest appropriate resting methods or light stretching. This allows the system to support the user's physical and mental health by providing relaxation methods according to the user's emotional state.

[0071] The HealthBot Companion system can also be equipped with a lifestyle analysis unit that analyzes the user's lifestyle and suggests optimal meal and exercise timings. For example, it can analyze the user's sleep patterns and suggest the optimal timing for breakfast. It can also analyze the user's activity level and suggest optimal exercise times. It can also suggest efficient break times taking into account the user's work or school schedule. This allows for more effective health management by providing advice based on the user's lifestyle.

[0072] The HealthBot Companion system can also include a social network analysis module that analyzes the user's social network and creates a community for sharing a healthy lifestyle. For example, it can connect users with similar health goals. It can also create communities through online forums and offline events. It can also suggest healthy activities based on the user's social network. This helps support the user's health management by creating a community for sharing a healthy lifestyle.

[0073] The HealthBot Companion system can also be equipped with a workplace / school analysis unit that collects activity data from users at work or school and suggests health maintenance strategies for the workplace or school. For example, it can suggest stretching exercises to do in between desk work. It can also suggest personalized health maintenance strategies based on activity data from the workplace or school. It can also monitor activity data in real time and suggest optimal health maintenance strategies. This allows it to support users' health management by suggesting health maintenance strategies for the workplace or school.

[0074] The HealthBot Companion system can also be equipped with a lifestyle improvement module that estimates the user's emotional state and suggests lifestyle improvements based on that emotion. For example, if stress levels are high, it can suggest relaxation techniques. It can also monitor the user's emotional state in real time and suggest lifestyle improvements based on the emotion. It can also suggest personalized lifestyle improvements based on the user's emotional state. This can improve the user's quality of life by suggesting lifestyle improvements based on their emotions.

[0075] The HealthBot Companion system can also be equipped with a travel analysis unit that collects data on the user's travel and leisure activities and suggests health maintenance measures at the travel destination. For example, it can suggest meal plans and exercise plans for the trip. It can also suggest personalized health maintenance measures based on the travel and leisure activity data. It can also monitor travel and leisure activity data in real time and suggest optimal health maintenance measures. This can support the user's health management by suggesting health maintenance measures at the travel destination.

[0076] The HealthBot Companion system can also include an activity suggestion unit that estimates the user's emotional state and suggests activities based on that emotion. For example, if the user is feeling stressed, it can suggest activities that have a relaxing effect. Alternatively, if the user is feeling positive, it can suggest energetic activities. Furthermore, it can suggest personalized activities based on the user's emotional state. This allows for more effective health management by suggesting activities based on emotions.

[0077] The HealthBot Companion system can also include a genetic information analysis unit that analyzes a user's genetic information and provides personalized advice based on their genetic risk. For example, it can suggest an appropriate diet plan for a person with a genetic risk of high blood pressure. It can also assess the risk of certain diseases based on the user's genetic information and suggest preventative measures. Furthermore, it can provide advice tailored to individual health goals based on the user's genetic information. This allows for more effective health management by providing personalized advice based on genetic risk.

[0078] The HealthBot Companion system can also include a meal suggestion unit that estimates the user's emotional state and proposes a meal plan based on that emotion. For example, if the user is feeling stressed, it can suggest a meal that will help them relax. If the user is feeling positive, it can suggest an energizing meal. Furthermore, it can propose a personalized meal plan based on the user's emotional state. This allows for more effective health management for the user by suggesting a meal plan based on their emotions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The health data collection unit collects the user's health data. For example, it collects data such as weight, blood pressure, heart rate, sleep patterns, and amount of exercise. The health data collection unit can also collect data through a wearable device or a smartphone app. For example, a wearable device can monitor the user's heart rate and amount of exercise in real time and collect data. A smartphone app can collect health data manually entered by the user. Furthermore, the health data collection unit can also collect data provided by medical institutions. For example, it can collect blood pressure measurement data and test results provided by medical institutions. Step 2: The health data analysis unit analyzes the collected health data. For example, the generating AI analyzes the data using statistical analysis and machine learning algorithms. The generating AI can also detect outliers and analyze trends. For example, the generating AI can analyze the user's blood pressure data and detect abnormal values. The generating AI can also analyze trends in the user's exercise volume and assess the risk of insufficient or excessive exercise. Step 3: The health advice provider provides health advice based on the analysis results. For example, the generation AI suggests a balanced diet and appropriate exercise plan based on the user's diet and exercise habits. The generation AI can also analyze sleep data and provide advice on how to get good quality sleep. For example, the generation AI can analyze the user's sleep patterns and provide specific advice on how to improve the quality of their sleep.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 health data collection unit that collects health data of a user; a health data analysis unit that analyzes the health data collected by the health data collection unit; a health advice providing unit that provides health advice based on the results of the analysis by the health data analysis unit. A system characterized by:

2. The health data collection unit: Collecting the health data in real time and issuing immediate alerts if abnormal values ​​are detected 2. The system of claim 1.

3. The health data collection unit: The health data and living environment data are recorded simultaneously to analyze the impact of environmental factors on health.

2. The system of claim 1.

4. The health data collection unit: Workplace environment data will also be collected to analyze the impact of the work environment on health.

2. The system of claim 1.

5. The health advice providing unit: Estimate your emotional state and provide advice based on your emotions 2. The system of claim 1.

6. The health advice providing unit: The user's emotions when receiving advice are estimated in real time, and the advice is provided to elicit positive emotions.

2. The system of claim 1.

7. The Lifestyle Improvement Department Estimates emotional state and suggests lifestyle improvements based on emotions 2. The system of claim 1.

8. The Lifestyle Improvement Department The system estimates the user's emotions in real time when implementing lifestyle improvements, and provides support to elicit positive emotions.

2. The system of claim 1.

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