System

Abstract

An object of a system according to an embodiment is to analyze sleep data and behavior data of a user and provide advice for health improvement.SOLUTION: A system includes a data collection part, a data analysis part, an advice provision part, a plan creation part, and a monitoring part. The data collection unit collects sleep data and behavior data of a user. The data analysis unit analyzes the data collected by the data collection unit. The advice providing unit provides advice for health improvement on the basis of an analysis result obtained by the data analysis unit. The plan creating unit creates a health plan based on the advice provided by the advice providing unit. The monitoring unit continuously monitors the health condition of the user based on the plan made by the plan making 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 not effectively utilized users' sleep data and behavioral data to improve their health, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze sleep data and behavioral data of a user and provide advice for improving health. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, an advice provision unit, a plan creation unit, and a monitoring unit. The data collection unit collects sleep data and behavioral data of a user. The data analysis unit analyzes the data collected by the data collection unit. The advice provision unit provides advice for improving health based on the analysis results obtained by the data analysis unit. The plan creation unit creates a health plan based on the advice provided by the advice provision unit. The monitoring unit continuously monitors the user's health condition based on the plan created by the plan creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the sleep data and behavioral data of the user and provide advice for improving health. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health improvement system according to an embodiment of the present invention automatically collects a user's sleep data and behavioral data, analyzes the data using a generation AI, and provides specific advice and plans for improving the user's health. This allows the health improvement system to effectively improve the user's health condition.

[0029] A health improvement system according to an embodiment includes a data collection unit, a data analysis unit, an advice provision unit, a plan creation unit, and a monitoring unit. The data collection unit collects sleep data and behavioral data of a user. For example, using sensors in a smartwatch or smartphone, it collects data such as the user's sleep time, sleep quality, daytime activity level, heart rate, and number of steps. The data collection unit also monitors the user's living environment data (e.g., room temperature, humidity, and lighting) using sensors and collects this environmental data. The data collection unit also automatically records the user's diet using image recognition technology and collects dietary data. The data analysis unit uses a generation AI to analyze the collected data. For example, the generation AI analyzes the user's sleep patterns and behavioral patterns to gain insights into their health status. The generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also analyze various aspects of the data using a multimodal generation AI. The advice provision unit provides advice for improving health based on the analysis results obtained by the data analysis unit. For example, the generation AI proposes advice for improving sleep quality and a specific plan for increasing daytime activity. The plan creation unit creates a health plan based on the advice provided by the advice provision unit. For example, the health plan includes a daily exercise plan, dietary advice, and stress management methods. The monitoring unit continuously monitors the user's health condition based on the plan created by the plan creation unit. For example, the monitoring unit periodically analyzes the user's sleep data and behavioral data and evaluates the progress of improvement. This allows the health improvement system according to the embodiment to effectively improve the user's health condition. For example, the user can understand their own health condition in real time and receive appropriate advice. The generation AI also creates an optimal plan based on the user's data and provides it to the user.

[0030] The data collection unit can monitor the user's living environment and collect environmental data. For example, to monitor the user's living environment, the data collection unit measures room temperature and humidity using a smart home device and collects data. This allows the data collection unit to analyze the impact of the environment on health. The data collection unit also installs a lighting sensor to measure the brightness of the room and collects data to analyze the impact of changes in lighting on sleep quality. The data collection unit also measures indoor air quality using an air quality sensor and collects data. This data is used to analyze the impact of air quality on health. By collecting data on the user's living environment, the data collection unit can analyze the impact of the environment on health and provide more appropriate health improvement advice.

[0031] The data collection unit can automatically record the user's meal contents using image recognition technology and collect meal data. For example, when the user eats, the data collection unit takes a photo of the meal using a smartphone camera and automatically records the meal contents using image recognition technology. This collects meal data. The data collection unit also takes a photo of the meal using a smartwatch camera and analyzes the meal contents using image recognition technology. The nutrients and calories of the meal are automatically recorded. The data collection unit also takes a photo of the meal using a smart home device camera and analyzes the meal contents using image recognition technology. The type and amount of meal are automatically recorded. This automatically records the user's meal contents, allowing for accurate collection of meal data and improving the accuracy of advice and plans for improving health.

[0032] The data collection unit can collect pet behavioral data and analyze the correlation with the user's health condition. For example, to collect pet behavioral data, the data collection unit monitors activity levels and sleep patterns using a pet wearable device. This analyzes the correlation between the pet's health condition and the user's health condition. The data collection unit also tracks the pet's movements using a smart home device and collects data. The data collection unit analyzes the impact of the pet's activity level on the user's stress level. The data collection unit also installs a pet camera and records the pet's behavior. The recorded data is analyzed to identify the correlation between the pet's behavioral patterns and the user's health condition. This allows the collection of pet behavioral data and the analysis of the correlation with the user's health condition to provide more comprehensive health improvement advice.

[0033] The data analysis unit can incorporate the user's genetic information into the analysis and identify individual health risks. To incorporate the user's genetic information into the analysis, the data analysis unit, for example, collects a DNA sample using a genetic testing kit and inputs the analysis results into the generation AI. This identifies individual health risks. The data analysis unit also references a genetic database to obtain the user's genetic information. This analyzes specific genetic risks. The data analysis unit also uses genetic information provided by a medical institution and inputs it into the generation AI. This identifies individual health risks. In this way, by incorporating the user's genetic information into the analysis, individual health risks can be identified and more accurate health improvement advice can be provided.

[0034] The data analysis unit integrates the user's past medical data into the analysis, thereby obtaining more accurate insights into the user's health condition. For example, to integrate the user's past medical data into the analysis, the data analysis unit acquires data from an electronic medical record system and inputs it into the generation AI. This obtains more accurate insights into the user's health condition. The data analysis unit also uses medical records provided by medical institutions and inputs them into the generation AI. This analyzes changes in the user's health condition. The data analysis unit also acquires data from a health management app and inputs it into the generation AI. This identifies trends in the user's health condition. This allows the user's past medical data to be integrated into the analysis, thereby obtaining more accurate insights into the user's health condition.

[0035] The data analysis unit can analyze the data of all family members and evaluate the health status of the entire family. For example, to analyze the data of all family members, the data analysis unit collects data from each member's smartwatch or smartphone and inputs it into the generation AI. This evaluates the health status of the entire family. The data analysis unit also obtains medical data of all family members and inputs it into the generation AI. This identifies health risks for the entire family. The data analysis unit also collects living environment data of all family members and inputs it into the generation AI. This evaluates the health status of the entire family. By analyzing the data of all family members, the health status of the entire family can be evaluated and more comprehensive health improvement advice can be provided.

[0036] The data analysis unit can analyze the user's workplace environment data and identify the impact of workplace stress on health. To analyze the user's workplace environment data, the data analysis unit, for example, collects environmental data such as the temperature, humidity, and lighting in the workplace and inputs it into the generation AI. This identifies the impact of workplace stress on health. The data analysis unit also monitors activity levels and heart rates at work and inputs them into the generation AI. This identifies the impact of workplace stress on health. The data analysis unit also collects voice data at work and evaluates stress levels using voice analysis technology. This identifies the impact of workplace stress on health. This allows the user's workplace environment data to be analyzed to identify the impact of workplace stress on health and provide more appropriate health improvement advice.

[0037] The advice providing unit can re-suggest the most effective advice based on the user's past behavioral data. For example, the advice providing unit analyzes the user's past behavioral data and collects data from a smartwatch or smartphone to identify the most effective advice, and inputs the data into the generation AI. This allows for re-suggestion of effective advice. The advice providing unit also acquires data from a health management app and inputs it into the generation AI. This allows for provision of effective advice. The advice providing unit also uses data provided by medical institutions and inputs it into the generation AI. This allows for provision of effective advice. This allows for re-suggestion of the most effective advice based on the user's past behavioral data, maximizing the effect of health improvement.

[0038] The advice providing unit can provide advice that takes into account the user's social environment. For example, in order to provide advice that takes into account the user's social environment, the advice providing unit collects support status from family and friends and inputs it into the generation AI. This provides advice that takes social support into account. The advice providing unit also analyzes the user's SNS data and identifies relationships with family and friends. This provides advice that takes social support into account. The advice providing unit also collects data on the user's living environment and inputs it into the generation AI. This provides advice that takes social support into account. This provides advice that takes into account the user's social environment, making it easier for the user to accept the advice and increasing the effectiveness of health improvement.

[0039] The advice providing unit can provide advice that also takes into account the health condition of the user's pet. For example, in order to provide advice that takes into account the health condition of the user's pet, the advice providing unit collects data from a wearable device for the pet and inputs it into the generation AI. This provides advice that takes into account the pet's health condition. The advice providing unit also acquires medical data for the pet and inputs it into the generation AI. This provides advice that takes into account the pet's health condition. The advice providing unit also collects behavioral data for the pet and inputs it into the generation AI. This provides advice that takes into account the pet's health condition. This provides advice that takes into account the health condition of the user's pet, making it easier for the user to accept the advice and increasing the effectiveness of health improvement.

[0040] The advice providing unit can provide health improvement advice suited to the user's work environment. For example, in order to provide health improvement advice suited to the user's work environment, the advice providing unit collects environmental data such as the temperature, humidity, and lighting in the workplace and inputs it into the generation AI. This provides advice suited to the work environment. The advice providing unit also monitors the amount of activity and heart rate at work and inputs it into the generation AI. This provides advice suited to the work environment. The advice providing unit also collects voice data at work and evaluates stress levels using voice analysis technology. This provides advice suited to the work environment. This provides health improvement advice suited to the user's work environment, making it easier for the user to accept the advice and increasing the effectiveness of health improvement.

[0041] The plan creation unit can re-propose the most effective plan based on the user's past health data. For example, the plan creation unit analyzes the user's past health data and collects data from a smartwatch or smartphone to identify the most effective plan, and inputs this data into the generation AI. This allows for the re-proposition of an effective plan. The plan creation unit also obtains data from a health management app and inputs it into the generation AI. This allows for the provision of an effective plan. The plan creation unit also uses data provided by medical institutions and inputs it into the generation AI. This allows for the provision of an effective plan. This maximizes the effectiveness of health improvement by re-proposing the most effective plan based on the user's past health data.

[0042] The plan creation unit can create a health plan that takes into account the user's social environment. For example, in order to create a health plan that takes into account the user's social environment, the plan creation unit collects support information from family and friends and inputs it into the generation AI. This allows for the creation of a plan that takes social support into account. The plan creation unit also analyzes the user's SNS data and identifies relationships with family and friends. This allows for the creation of a plan that takes social support into account. The plan creation unit also collects data on the user's living environment and inputs it into the generation AI. This allows for the creation of a plan that takes social support into account. This allows for the creation of a health plan that takes into account the user's social environment, making it easier for the user to implement the plan and increasing the effectiveness of health improvement.

[0043] The plan creation unit can create a health plan that also takes into account the health condition of the user's pet. For example, to create a health plan that takes into account the health condition of the user's pet, the plan creation unit collects data from a wearable device for the pet and inputs it into the generation AI. This creates a plan that takes into account the pet's health condition. The plan creation unit also acquires medical data for the pet and inputs it into the generation AI. This creates a plan that takes into account the pet's health condition. The plan creation unit also collects behavioral data for the pet and inputs it into the generation AI. This creates a plan that takes into account the pet's health condition. This creates a health plan that also takes into account the health condition of the user's pet, making it easier for the user to implement the plan and increasing the effectiveness of health improvement.

[0044] The plan creation unit can create a health plan suited to the user's work environment. For example, to create a health plan suited to the user's work environment, the plan creation unit collects environmental data such as the temperature, humidity, and lighting in the workplace and inputs it into the generation AI. This creates a plan suited to the work environment. The plan creation unit also monitors the amount of activity and heart rate at work and inputs it into the generation AI. This creates a plan suited to the work environment. The plan creation unit also collects voice data at work and evaluates stress levels using voice analysis technology. This creates a plan suited to the work environment. This creates a health plan suited to the user's work environment, making it easier for the user to follow the plan and increasing the effectiveness of health improvement.

[0045] The monitoring unit can continuously monitor the user's living environment data and provide feedback according to changes in the environment. For example, to continuously monitor the user's living environment data, the monitoring unit measures room temperature and humidity using a smart home device and provides feedback according to changes in the environment. The monitoring unit also installs a lighting sensor to measure the brightness of the room and provides feedback according to changes in lighting. The monitoring unit also measures indoor air quality using an air quality sensor and provides feedback according to changes in the environment. In this way, the user's living environment data is continuously monitored and feedback according to changes in the environment is provided, thereby providing more appropriate health improvement advice.

[0046] The monitoring unit can continuously monitor the user's dietary data and provide feedback regarding dietary improvements. For example, to continuously monitor the user's dietary data, the monitoring unit takes photos of meals using a smartphone camera and analyzes the dietary content using image recognition technology. This provides feedback regarding dietary improvements. The monitoring unit also takes photos of meals using a smartwatch camera and analyzes the dietary content using image recognition technology. This provides feedback regarding dietary improvements. The monitoring unit also takes photos of meals using a smart home device camera and analyzes the dietary content using image recognition technology. This provides feedback regarding dietary improvements. By continuously monitoring the user's dietary data and providing feedback regarding dietary improvements, the monitoring unit can provide more appropriate health improvement advice.

[0047] The monitoring unit can also monitor the health condition of the user's pet and provide feedback according to the pet's health condition. For example, to monitor the health condition of the user's pet, the monitoring unit measures activity levels and sleep patterns using a pet wearable device and provides feedback according to the pet's health condition. The monitoring unit also tracks the pet's movements using a smart home device and provides feedback according to the pet's health condition. The monitoring unit also installs a pet camera and records the pet's behavior. The monitoring unit analyzes the recorded data and provides feedback according to the pet's health condition. In this way, the health condition of the user's pet can also be monitored and feedback according to the pet's health condition can be provided, thereby providing more comprehensive health improvement advice.

[0048] The monitoring unit can monitor the user's workplace environment data and provide feedback according to workplace stress. For example, to monitor the user's workplace environment data, the monitoring unit collects environmental data such as the temperature, humidity, and lighting of the workplace and provides feedback according to workplace stress. The monitoring unit also monitors activity levels and heart rate at the workplace and provides feedback according to workplace stress. The monitoring unit also collects voice data at the workplace and evaluates stress levels using voice analysis technology. This provides feedback according to workplace stress. In this way, by monitoring the user's workplace environment data and providing feedback according to workplace stress, more appropriate health improvement advice can be provided.

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

[0050] The health improvement system can further include an activity suggestion unit based on the user's hobbies and interests. For example, if the user likes music, the activity suggestion unit can suggest relaxing music. If the user likes outdoor activities, the activity suggestion unit can suggest nearby hiking trails and parks. If the user likes reading, the activity suggestion unit can suggest books that are useful for reducing stress. In this way, suggesting activities based on the user's hobbies and interests can increase the user's motivation to improve their health.

[0051] The data collection unit not only collects data on the user's living environment, but also data on the user's lifestyle habits. For example, it collects data on the user's meal times, meal contents, exercise time, and sleep time. It can also collect data on the user's work schedule and commuting time. It can also collect data on the user's hobbies and leisure activities. This allows the system to grasp the user's overall lifestyle habits and provide more comprehensive health improvement advice.

[0052] The data collection unit not only records the user's dietary content using image recognition technology, but can also be equipped with a function to evaluate the nutritional balance of the meal. For example, it can analyze photos of the meal and automatically calculate the nutrients and calories of each ingredient. If the meal is unbalanced in nutritional content, it can suggest a balanced meal. Furthermore, it can evaluate the user's long-term nutritional balance based on the user's diet history and provide advice on supplementing necessary nutrients. This allows for a more detailed understanding of the user's dietary content and provides specific advice for improving health.

[0053] The data collection unit can not only collect pet behavior data, but also have the function of evaluating the pet's health condition. For example, it can analyze the pet's activity level and sleep patterns to detect abnormalities in health. It can also monitor the pet's diet and weight fluctuations to evaluate health risks. Furthermore, it can provide appropriate exercise and dietary advice based on the pet's health condition. This allows for a comprehensive evaluation of the pet's health condition and provides the user with specific advice on pet health management.

[0054] The data analysis unit not only incorporates the user's genetic information into the analysis, but can also incorporate the user's family history data into the analysis. For example, it can collect family medical history and genetic risks and input them into the generation AI. This makes it possible to identify health risks based on family history. It can also collect data on family lifestyles and health conditions and incorporate them into the analysis. Furthermore, it can provide preventive health management advice based on family history data. This allows for a comprehensive health risk assessment that takes the user's family history into account and provides more accurate health improvement advice.

[0055] The data analysis unit not only integrates the user's past medical data into the analysis, but can also integrate the user's past fitness data into the analysis. For example, past exercise history and training data can be collected and input into the generation AI. This allows for evaluation of changes and effects of exercise habits. It can also suggest optimal exercise plans based on past fitness data. Furthermore, past fitness data can be combined with medical data to identify health trends and provide preventative health management advice. This allows for a comprehensive health assessment that takes into account the user's past fitness data, enabling more accurate health improvement advice to be provided.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data collection unit collects the user's sleep data and behavioral data. For example, it uses sensors in a smartwatch or smartphone to collect data such as the user's sleep duration, sleep quality, daytime activity, heart rate, and number of steps. The data collection unit also monitors the user's living environment data (e.g., room temperature, humidity, and lighting) using sensors and collects this environmental data. Furthermore, the data collection unit automatically records the user's diet using image recognition technology and collects dietary data. Step 2: In the data analysis section, the generation AI analyzes the collected data. For example, the generation AI analyzes the user's sleep patterns and behavioral patterns to gain insights into their health status. The generation AI analyzes the data using text generation AI (e.g., LLM). The generation AI can also use multimodal generation AI to analyze various aspects of the data. Step 3: The advice provider provides advice for improving health based on the analysis results obtained by the data analyzer. For example, the generator AI may suggest advice for improving sleep quality or a specific plan for increasing daytime activity. Step 4: The plan creation unit creates a health plan based on the advice provided by the advice provision unit, for example, providing a health plan including a daily exercise plan, dietary advice, stress management methods, etc. Step 5: The monitoring unit continuously monitors the user's health condition based on the plan created by the plan creation unit. For example, the monitoring unit periodically analyzes the user's sleep data and behavioral data and evaluates the progress of improvement.

[0058] (Example 2) A health improvement system according to an embodiment of the present invention automatically collects a user's sleep data and behavioral data, analyzes the data using a generation AI, and provides specific advice and plans for improving the user's health. This allows the health improvement system to effectively improve the user's health condition.

[0059] A health improvement system according to an embodiment includes a data collection unit, a data analysis unit, an advice provision unit, a plan creation unit, and a monitoring unit. The data collection unit collects sleep data and behavioral data of a user. For example, using sensors in a smartwatch or smartphone, it collects data such as the user's sleep time, sleep quality, daytime activity level, heart rate, and number of steps. The data collection unit also monitors the user's living environment data (e.g., room temperature, humidity, and lighting) using sensors and collects this environmental data. The data collection unit also automatically records the user's diet using image recognition technology and collects dietary data. The data analysis unit uses a generation AI to analyze the collected data. For example, the generation AI analyzes the user's sleep patterns and behavioral patterns to gain insights into their health status. The generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also analyze various aspects of the data using a multimodal generation AI. The advice provision unit provides advice for improving health based on the analysis results obtained by the data analysis unit. For example, the generation AI proposes advice for improving sleep quality and a specific plan for increasing daytime activity. The plan creation unit creates a health plan based on the advice provided by the advice provision unit. For example, the health plan includes a daily exercise plan, dietary advice, and stress management methods. The monitoring unit continuously monitors the user's health condition based on the plan created by the plan creation unit. For example, the monitoring unit periodically analyzes the user's sleep data and behavioral data and evaluates the progress of improvement. This allows the health improvement system according to the embodiment to effectively improve the user's health condition. For example, the user can understand their own health condition in real time and receive appropriate advice. The generation AI also creates an optimal plan based on the user's data and provides it to the user.

[0060] The data collection unit can estimate the user's emotional state in real time and adjust the frequency and timing of data collection according to emotional fluctuations. For example, the data collection unit monitors heart rate and electrodermal activity using sensors in a smartwatch or smartphone to estimate the user's emotional state in real time. When emotional fluctuations are large, the data collection unit increases the frequency of data collection. The data collection unit also uses voice analysis technology to analyze the user's tone of voice and speaking style to estimate the user's emotional state. Collecting data when emotions are stable provides more accurate data. The data collection unit also uses facial recognition technology to analyze facial expressions to estimate the user's emotional state. Collecting data when emotional fluctuations are small minimizes the influence of emotions. In this way, adjusting the frequency and timing of data collection according to the user's emotional state allows for more accurate data to be collected and improves the accuracy of advice and plans for improving health.

[0061] The data collection unit can monitor the user's living environment and collect environmental data. For example, to monitor the user's living environment, the data collection unit measures room temperature and humidity using a smart home device and collects data. This allows the data collection unit to analyze the impact of the environment on health. The data collection unit also installs a lighting sensor to measure the brightness of the room and collects data to analyze the impact of changes in lighting on sleep quality. The data collection unit also measures indoor air quality using an air quality sensor and collects data. This data is used to analyze the impact of air quality on health. By collecting data on the user's living environment, the data collection unit can analyze the impact of the environment on health and provide more appropriate health improvement advice.

[0062] The data collection unit can automatically record the user's meal contents using image recognition technology and collect meal data. For example, when the user eats, the data collection unit takes a photo of the meal using a smartphone camera and automatically records the meal contents using image recognition technology. This collects meal data. The data collection unit also takes a photo of the meal using a smartwatch camera and analyzes the meal contents using image recognition technology. The nutrients and calories of the meal are automatically recorded. The data collection unit also takes a photo of the meal using a smart home device camera and analyzes the meal contents using image recognition technology. The type and amount of meal are automatically recorded. This automatically records the user's meal contents, allowing for accurate collection of meal data and improving the accuracy of advice and plans for improving health.

[0063] The data collection unit can collect pet behavioral data and analyze the correlation with the user's health condition. For example, to collect pet behavioral data, the data collection unit monitors activity levels and sleep patterns using a pet wearable device. This analyzes the correlation between the pet's health condition and the user's health condition. The data collection unit also tracks the pet's movements using a smart home device and collects data. The data collection unit analyzes the impact of the pet's activity level on the user's stress level. The data collection unit also installs a pet camera and records the pet's behavior. The recorded data is analyzed to identify the correlation between the pet's behavioral patterns and the user's health condition. This allows the collection of pet behavioral data and the analysis of the correlation with the user's health condition to provide more comprehensive health improvement advice.

[0064] The data collection unit can collect voice data of the user and analyze the stress level and emotional state. For example, to collect the user's voice data, the data collection unit records everyday conversations using a microphone on a smartphone and analyzes the stress level and emotional state using voice analysis technology. The data collection unit also records voice using a microphone on a smartwatch and identifies the emotional state using voice analysis technology. This allows for the stress level to be evaluated. The data collection unit also records voice using a microphone on a smart home device and analyzes the emotional state using voice analysis technology. This allows for the user's stress level to be monitored. By collecting the user's voice data and analyzing the stress level and emotional state, more accurate health improvement advice can be provided.

[0065] The data collection unit uses the emotion estimation function to collect data when the user is relaxed, thereby obtaining more accurate data. For example, the data collection unit uses the emotion estimation function to monitor the heart rate and electrodermal activity using sensors in the smartwatch to collect data when the user is relaxed. Data is collected when a relaxed state is detected. The data collection unit also uses voice analysis technology to analyze the user's tone of voice and speaking style. Data is collected when a relaxed state is detected. The data collection unit also uses face recognition technology to analyze facial expressions. Data is collected when a relaxed state is detected. In this way, by collecting data when the user is relaxed, the influence of emotions is minimized and more accurate data is collected.

[0066] The data analysis unit can analyze the user's emotional data and identify the impact of emotional fluctuations on the health condition. To analyze the user's emotional data, the data analysis unit, for example, monitors the heart rate and electrodermal activity using a sensor in the smartwatch to identify the impact of emotional fluctuations on the health condition. The data analysis unit also uses voice analysis technology to analyze the user's tone of voice and speaking style to identify the impact of emotional fluctuations on the health condition. The data analysis unit also uses facial recognition technology to analyze facial expressions to identify the impact of emotional fluctuations on the health condition. In this way, the data analysis unit analyzes the user's emotional data and identifies the impact of emotional fluctuations on the health condition, thereby providing more appropriate health improvement advice.

[0067] The data analysis unit can incorporate the user's genetic information into the analysis and identify individual health risks. To incorporate the user's genetic information into the analysis, the data analysis unit, for example, collects a DNA sample using a genetic testing kit and inputs the analysis results into the generation AI. This identifies individual health risks. The data analysis unit also references a genetic database to obtain the user's genetic information. This analyzes specific genetic risks. The data analysis unit also uses genetic information provided by a medical institution and inputs it into the generation AI. This identifies individual health risks. In this way, by incorporating the user's genetic information into the analysis, individual health risks can be identified and more accurate health improvement advice can be provided.

[0068] The data analysis unit integrates the user's past medical data into the analysis, thereby obtaining more accurate insights into the user's health condition. For example, to integrate the user's past medical data into the analysis, the data analysis unit acquires data from an electronic medical record system and inputs it into the generation AI. This obtains more accurate insights into the user's health condition. The data analysis unit also uses medical records provided by medical institutions and inputs them into the generation AI. This analyzes changes in the user's health condition. The data analysis unit also acquires data from a health management app and inputs it into the generation AI. This identifies trends in the user's health condition. This allows the user's past medical data to be integrated into the analysis, thereby obtaining more accurate insights into the user's health condition.

[0069] The data analysis unit can analyze the data of all family members and evaluate the health status of the entire family. For example, to analyze the data of all family members, the data analysis unit collects data from each member's smartwatch or smartphone and inputs it into the generation AI. This evaluates the health status of the entire family. The data analysis unit also obtains medical data of all family members and inputs it into the generation AI. This identifies health risks for the entire family. The data analysis unit also collects living environment data of all family members and inputs it into the generation AI. This evaluates the health status of the entire family. By analyzing the data of all family members, the health status of the entire family can be evaluated and more comprehensive health improvement advice can be provided.

[0070] The data analysis unit can analyze the user's workplace environment data and identify the impact of workplace stress on health. To analyze the user's workplace environment data, the data analysis unit, for example, collects environmental data such as the temperature, humidity, and lighting in the workplace and inputs it into the generation AI. This identifies the impact of workplace stress on health. The data analysis unit also monitors activity levels and heart rates at work and inputs them into the generation AI. This identifies the impact of workplace stress on health. The data analysis unit also collects voice data at work and evaluates stress levels using voice analysis technology. This identifies the impact of workplace stress on health. This allows the user's workplace environment data to be analyzed to identify the impact of workplace stress on health and provide more appropriate health improvement advice.

[0071] The data analysis unit can use the emotion estimation function to predict health risks based on the user's emotional state. For example, the data analysis unit uses the emotion estimation function to monitor heart rate and electrodermal activity using sensors in the smartwatch to predict health risks based on the user's emotional state. The data analysis unit analyzes the impact of emotional fluctuations on health risks. The data analysis unit also uses voice analysis technology to analyze the user's tone of voice and speaking style. The data analysis unit identifies the impact of emotional fluctuations on health risks. The data analysis unit also uses facial recognition technology to analyze facial expressions. The data analysis unit identifies the impact of emotional fluctuations on health risks. As a result, the emotion estimation function can predict health risks based on the user's emotional state, thereby providing more appropriate health improvement advice.

[0072] The advice providing unit can provide advice that takes into account the user's emotional state and propose it in a form that is emotionally easy to accept. For example, in order to provide advice that takes into account the user's emotional state, the advice providing unit analyzes the user's emotions in real time using an emotion estimation function and provides advice that elicits positive emotions. The advice providing unit also uses voice analysis technology to analyze the user's tone of voice and speaking style and provides advice in a form that is emotionally easy to accept. The advice providing unit also uses face recognition technology to analyze facial expressions and provides advice in a form that is emotionally easy to accept. In this way, providing advice that takes into account the user's emotional state makes it easier for the user to accept the advice and increases the effectiveness of health improvement.

[0073] The advice providing unit can re-suggest the most effective advice based on the user's past behavioral data. For example, the advice providing unit analyzes the user's past behavioral data and collects data from a smartwatch or smartphone to identify the most effective advice, and inputs the data into the generation AI. This allows for re-suggestion of effective advice. The advice providing unit also acquires data from a health management app and inputs it into the generation AI. This allows for provision of effective advice. The advice providing unit also uses data provided by medical institutions and inputs it into the generation AI. This allows for provision of effective advice. This allows for re-suggestion of the most effective advice based on the user's past behavioral data, maximizing the effect of health improvement.

[0074] The advice providing unit can provide advice that takes into account the user's social environment. For example, in order to provide advice that takes into account the user's social environment, the advice providing unit collects support status from family and friends and inputs it into the generation AI. This provides advice that takes social support into account. The advice providing unit also analyzes the user's SNS data and identifies relationships with family and friends. This provides advice that takes social support into account. The advice providing unit also collects data on the user's living environment and inputs it into the generation AI. This provides advice that takes social support into account. This provides advice that takes into account the user's social environment, making it easier for the user to accept the advice and increasing the effectiveness of health improvement.

[0075] The advice providing unit can provide advice that also takes into account the health condition of the user's pet. For example, in order to provide advice that takes into account the health condition of the user's pet, the advice providing unit collects data from a wearable device for the pet and inputs it into the generation AI. This provides advice that takes into account the pet's health condition. The advice providing unit also acquires medical data for the pet and inputs it into the generation AI. This provides advice that takes into account the pet's health condition. The advice providing unit also collects behavioral data for the pet and inputs it into the generation AI. This provides advice that takes into account the pet's health condition. This provides advice that takes into account the health condition of the user's pet, making it easier for the user to accept the advice and increasing the effectiveness of health improvement.

[0076] The advice providing unit can provide health improvement advice suited to the user's work environment. For example, in order to provide health improvement advice suited to the user's work environment, the advice providing unit collects environmental data such as the temperature, humidity, and lighting in the workplace and inputs it into the generation AI. This provides advice suited to the work environment. The advice providing unit also monitors the amount of activity and heart rate at work and inputs it into the generation AI. This provides advice suited to the work environment. The advice providing unit also collects voice data at work and evaluates stress levels using voice analysis technology. This provides advice suited to the work environment. This provides health improvement advice suited to the user's work environment, making it easier for the user to accept the advice and increasing the effectiveness of health improvement.

[0077] The advice providing unit can use the emotion estimation function to provide advice when the user is most relaxed. For example, the advice providing unit uses the emotion estimation function to monitor the heart rate and electrodermal activity using sensors in the smartwatch in order to provide advice when the user is most relaxed. The advice providing unit provides advice when a relaxed state is detected. The advice providing unit also uses voice analysis technology to analyze the user's tone of voice and speaking style. The advice providing unit provides advice when a relaxed state is detected. The advice providing unit also uses face recognition technology to analyze facial expressions. The advice providing unit provides advice when a relaxed state is detected. In this way, by providing advice when the user is most relaxed, the user is more likely to accept the advice and the effect of improving their health is enhanced.

[0078] The plan creation unit can create a health plan that takes the user's emotional state into consideration and propose it in an emotionally easy-to-accept format. For example, to create a health plan that takes the user's emotional state into consideration, the plan creation unit uses an emotion estimation function to analyze the user's emotions in real time and create a plan that elicits positive emotions. The plan creation unit also uses voice analysis technology to analyze the user's tone of voice and speaking style and creates a plan that is emotionally easy to accept. The plan creation unit also uses facial recognition technology to analyze facial expressions and create a plan that is emotionally easy to accept. In this way, by creating a health plan that takes the user's emotional state into consideration and proposing it in an emotionally easy-to-accept format, the user can easily follow the plan and improve their health.

[0079] The plan creation unit can re-propose the most effective plan based on the user's past health data. For example, the plan creation unit analyzes the user's past health data and collects data from a smartwatch or smartphone to identify the most effective plan, and inputs this data into the generation AI. This allows for the re-proposition of an effective plan. The plan creation unit also obtains data from a health management app and inputs it into the generation AI. This allows for the provision of an effective plan. The plan creation unit also uses data provided by medical institutions and inputs it into the generation AI. This allows for the provision of an effective plan. This maximizes the effectiveness of health improvement by re-proposing the most effective plan based on the user's past health data.

[0080] The plan creation unit can create a health plan that takes into account the user's social environment. For example, in order to create a health plan that takes into account the user's social environment, the plan creation unit collects support information from family and friends and inputs it into the generation AI. This allows for the creation of a plan that takes social support into account. The plan creation unit also analyzes the user's SNS data and identifies relationships with family and friends. This allows for the creation of a plan that takes social support into account. The plan creation unit also collects data on the user's living environment and inputs it into the generation AI. This allows for the creation of a plan that takes social support into account. This allows for the creation of a health plan that takes into account the user's social environment, making it easier for the user to implement the plan and increasing the effectiveness of health improvement.

[0081] The plan creation unit can create a health plan that also takes into account the health condition of the user's pet. For example, to create a health plan that takes into account the health condition of the user's pet, the plan creation unit collects data from a wearable device for the pet and inputs it into the generation AI. This creates a plan that takes into account the pet's health condition. The plan creation unit also acquires medical data for the pet and inputs it into the generation AI. This creates a plan that takes into account the pet's health condition. The plan creation unit also collects behavioral data for the pet and inputs it into the generation AI. This creates a plan that takes into account the pet's health condition. This creates a health plan that also takes into account the health condition of the user's pet, making it easier for the user to implement the plan and increasing the effectiveness of health improvement.

[0082] The plan creation unit can create a health plan suited to the user's work environment. For example, to create a health plan suited to the user's work environment, the plan creation unit collects environmental data such as the temperature, humidity, and lighting in the workplace and inputs it into the generation AI. This creates a plan suited to the work environment. The plan creation unit also monitors the amount of activity and heart rate at work and inputs it into the generation AI. This creates a plan suited to the work environment. The plan creation unit also collects voice data at work and evaluates stress levels using voice analysis technology. This creates a plan suited to the work environment. This creates a health plan suited to the user's work environment, making it easier for the user to follow the plan and increasing the effectiveness of health improvement.

[0083] The plan creation unit can use the emotion estimation function to suggest a health plan for the user's time of day when they are most relaxed. For example, the plan creation unit uses the emotion estimation function to monitor the user's heart rate and electrodermal activity using a sensor on the smartwatch to suggest a health plan for the user's time of day when they are most relaxed. A plan is suggested when a relaxed state is detected. The plan creation unit also uses voice analysis technology to analyze the user's tone of voice and speaking style. A plan is suggested when a relaxed state is detected. The plan creation unit also uses face recognition technology to analyze facial expressions. A plan is suggested when a relaxed state is detected. In this way, by suggesting a health plan for the user's time of day when they are most relaxed, the user is more likely to accept the plan and the effect of improving their health is enhanced.

[0084] The monitoring unit can monitor the user's emotional state in real time and adjust feedback according to emotional fluctuations. For example, to monitor the user's emotional state in real time, the monitoring unit measures heart rate and electrodermal activity using sensors in the smartwatch and adjusts feedback according to emotional fluctuations. The monitoring unit also uses voice analysis technology to analyze the user's tone of voice and speaking style and adjusts feedback according to emotional fluctuations. The monitoring unit also uses facial recognition technology to analyze facial expressions and adjust feedback according to emotional fluctuations. In this way, the user's emotional state can be monitored in real time and feedback adjusted according to emotional fluctuations, thereby providing more appropriate health improvement advice.

[0085] The monitoring unit can continuously monitor the user's living environment data and provide feedback according to changes in the environment. For example, to continuously monitor the user's living environment data, the monitoring unit measures room temperature and humidity using a smart home device and provides feedback according to changes in the environment. The monitoring unit also installs a lighting sensor to measure the brightness of the room and provides feedback according to changes in lighting. The monitoring unit also measures indoor air quality using an air quality sensor and provides feedback according to changes in the environment. In this way, the user's living environment data is continuously monitored and feedback according to changes in the environment is provided, thereby providing more appropriate health improvement advice.

[0086] The monitoring unit can continuously monitor the user's dietary data and provide feedback regarding dietary improvements. For example, to continuously monitor the user's dietary data, the monitoring unit takes photos of meals using a smartphone camera and analyzes the dietary content using image recognition technology. This provides feedback regarding dietary improvements. The monitoring unit also takes photos of meals using a smartwatch camera and analyzes the dietary content using image recognition technology. This provides feedback regarding dietary improvements. The monitoring unit also takes photos of meals using a smart home device camera and analyzes the dietary content using image recognition technology. This provides feedback regarding dietary improvements. By continuously monitoring the user's dietary data and providing feedback regarding dietary improvements, the monitoring unit can provide more appropriate health improvement advice.

[0087] The monitoring unit can also monitor the health condition of the user's pet and provide feedback according to the pet's health condition. For example, to monitor the health condition of the user's pet, the monitoring unit measures activity levels and sleep patterns using a pet wearable device and provides feedback according to the pet's health condition. The monitoring unit also tracks the pet's movements using a smart home device and provides feedback according to the pet's health condition. The monitoring unit also installs a pet camera and records the pet's behavior. The monitoring unit analyzes the recorded data and provides feedback according to the pet's health condition. In this way, the health condition of the user's pet can also be monitored and feedback according to the pet's health condition can be provided, thereby providing more comprehensive health improvement advice.

[0088] The monitoring unit can monitor the user's workplace environment data and provide feedback according to workplace stress. For example, to monitor the user's workplace environment data, the monitoring unit collects environmental data such as the temperature, humidity, and lighting of the workplace and provides feedback according to workplace stress. The monitoring unit also monitors activity levels and heart rate at the workplace and provides feedback according to workplace stress. The monitoring unit also collects voice data at the workplace and evaluates stress levels using voice analysis technology. This provides feedback according to workplace stress. In this way, by monitoring the user's workplace environment data and providing feedback according to workplace stress, more appropriate health improvement advice can be provided.

[0089] The monitoring unit can use the emotion estimation function to provide feedback to the user when the user is most relaxed. For example, the monitoring unit uses the emotion estimation function to monitor the heart rate and electrodermal activity using sensors in the smartwatch to provide feedback to the user when the user is most relaxed. Feedback is provided when a relaxed state is detected. The monitoring unit also uses voice analysis technology to analyze the user's tone of voice and speaking style. Feedback is provided when a relaxed state is detected. The monitoring unit also uses facial recognition technology to analyze facial expressions. Feedback is provided when a relaxed state is detected. In this way, providing feedback to the user when the user is most relaxed makes the user more receptive to feedback and enhances the effect of improving their health.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The health improvement system can further include an activity suggestion unit based on the user's hobbies and interests. For example, if the user likes music, the activity suggestion unit can suggest relaxing music. If the user likes outdoor activities, the activity suggestion unit can suggest nearby hiking trails and parks. If the user likes reading, the activity suggestion unit can suggest books that are useful for reducing stress. In this way, suggesting activities based on the user's hobbies and interests can increase the user's motivation to improve their health.

[0092] The data collection unit can estimate the user's emotional state and, based on the estimated emotion, suggest activities to help the user relax when they are feeling stressed. For example, if the user is feeling stressed, the data collection unit can suggest activities such as deep breathing or meditation. If the user is feeling anxious, the data collection unit can suggest music that has a relaxing effect. Furthermore, if the user is feeling tired, the data collection unit can suggest light stretching or a short walk. In this way, by suggesting activities according to the user's emotional state, stress reduction and relaxation effects can be enhanced.

[0093] The data collection unit not only collects data on the user's living environment, but also data on the user's lifestyle habits. For example, it collects data on the user's meal times, meal contents, exercise time, and sleep time. It can also collect data on the user's work schedule and commuting time. It can also collect data on the user's hobbies and leisure activities. This allows the system to grasp the user's overall lifestyle habits and provide more comprehensive health improvement advice.

[0094] The data collection unit not only records the user's dietary content using image recognition technology, but can also be equipped with a function to evaluate the nutritional balance of the meal. For example, it can analyze photos of the meal and automatically calculate the nutrients and calories of each ingredient. If the meal is unbalanced in nutritional content, it can suggest a balanced meal. Furthermore, it can evaluate the user's long-term nutritional balance based on the user's diet history and provide advice on supplementing necessary nutrients. This allows for a more detailed understanding of the user's dietary content and provides specific advice for improving health.

[0095] The data collection unit can not only collect pet behavior data, but also have the function of evaluating the pet's health condition. For example, it can analyze the pet's activity level and sleep patterns to detect abnormalities in health. It can also monitor the pet's diet and weight fluctuations to evaluate health risks. Furthermore, it can provide appropriate exercise and dietary advice based on the pet's health condition. This allows for a comprehensive evaluation of the pet's health condition and provides the user with specific advice on pet health management.

[0096] The data collection unit can collect the user's voice data and analyze their stress level and emotional state, as well as their communication patterns. For example, it can analyze the frequency and content of their conversations to assess the strength of their social connections. It can also analyze the tone and speaking style of their conversations to identify emotional fluctuations. Furthermore, it can provide advice on strengthening social support based on the user's communication patterns. This can strengthen the user's social connections, reduce stress, and stabilize their emotions.

[0097] The data collection unit uses its emotion estimation function to collect data not only when the user is relaxed, but also when the user is concentrating. For example, when the user is concentrating on work or study, the data collection unit can monitor the user's heart rate and electrodermal activity to detect the state of concentration. Data can also be collected when the user is concentrating on a hobby or creative activity. Furthermore, based on the data on the user's state of concentration, the system can identify the time periods when the user can work most efficiently and suggest schedule optimization. This allows the system to understand the user's state of concentration and support efficient time management.

[0098] The data analysis unit analyzes the user's emotional data and can identify not only the impact of emotional fluctuations on health status, but also the impact of emotional fluctuations on performance. For example, the data analysis unit can analyze the user's emotional data and data on work performance and learning progress to evaluate the impact of emotional fluctuations on performance. It can also analyze the impact of emotional fluctuations on athletic and hobby performance. Furthermore, it can provide advice to stabilize emotions based on the impact of emotional fluctuations on performance. This makes it possible to comprehensively evaluate the user's emotional state and provide specific advice to improve performance.

[0099] The data analysis unit not only incorporates the user's genetic information into the analysis, but can also incorporate the user's family history data into the analysis. For example, it can collect family medical history and genetic risks and input them into the generation AI. This makes it possible to identify health risks based on family history. It can also collect data on family lifestyles and health conditions and incorporate them into the analysis. Furthermore, it can provide preventive health management advice based on family history data. This allows for a comprehensive health risk assessment that takes the user's family history into account and provides more accurate health improvement advice.

[0100] The data analysis unit not only integrates the user's past medical data into the analysis, but can also integrate the user's past fitness data into the analysis. For example, past exercise history and training data can be collected and input into the generation AI. This allows for evaluation of changes and effects of exercise habits. It can also suggest optimal exercise plans based on past fitness data. Furthermore, past fitness data can be combined with medical data to identify health trends and provide preventative health management advice. This allows for a comprehensive health assessment that takes into account the user's past fitness data, enabling more accurate health improvement advice to be provided.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The data collection unit collects the user's sleep data and behavioral data. For example, it uses sensors in a smartwatch or smartphone to collect data such as the user's sleep duration, sleep quality, daytime activity, heart rate, and number of steps. The data collection unit also monitors the user's living environment data (e.g., room temperature, humidity, and lighting) using sensors and collects this environmental data. Furthermore, the data collection unit automatically records the user's diet using image recognition technology and collects dietary data. Step 2: In the data analysis section, the generation AI analyzes the collected data. For example, the generation AI analyzes the user's sleep patterns and behavioral patterns to gain insights into their health status. The generation AI analyzes the data using text generation AI (e.g., LLM). The generation AI can also use multimodal generation AI to analyze various aspects of the data. Step 3: The advice provider provides advice for improving health based on the analysis results obtained by the data analyzer. For example, the generator AI may suggest advice for improving sleep quality or a specific plan for increasing daytime activity. Step 4: The plan creation unit creates a health plan based on the advice provided by the advice provision unit, for example, providing a health plan including a daily exercise plan, dietary advice, stress management methods, etc. Step 5: The monitoring unit continuously monitors the user's health condition based on the plan created by the plan creation unit. For example, the monitoring unit periodically analyzes the user's sleep data and behavioral data and evaluates the progress of improvement.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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]

[0170] 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 data collection unit that collects sleep data and behavioral data of a user; a data analysis unit that analyzes the data collected by the data collection unit; an advice providing unit that provides advice for improving health based on the analysis results obtained by the data analysis unit; a plan creation unit that creates a health plan based on the advice provided by the advice providing unit; a monitoring unit that continuously monitors the health condition of the user based on the plan created by the plan creation unit. A system characterized by:

2. The data collection unit The emotional state of the user is estimated in real time, and the frequency and timing of data collection are adjusted according to the emotional fluctuations.

2. The system of claim 1.

3. The data collection unit Monitoring the user's living environment and collecting environmental data 2. The system of claim 1.

4. The data collection unit The user's meal contents are automatically recorded using image recognition technology, and meal data is collected.

2. The system of claim 1.

5. The data collection unit Collecting pet behavior data and analyzing the correlation with the user's health condition 2. The system of claim 1.

6. The data collection unit Collecting the user's voice data and analyzing their stress level and emotional state 2. The system of claim 1.

Images