System

Abstract

An object of a system according to an embodiment is to integrally analyze a plurality of health data and detect a health risk at an early stage.SOLUTION: In accordance with an embodiment, a system comprises an environmental sensor, a wearable device, a smartmat, a diet tracking camera, an audio sensor, and a multi-modal AI. The environment sensor measures room temperature and humidity. The wearable device records heart rate and activity. The SmartMat tracks weight changes and movements. The diet tracking camera visualizes dietary habits. The audio sensor picks up the indication of the mental state. The multi-modal AI can comprehensively analyze the environmental sensor, the wearable device, the smart mat, the diet tracking camera, and the audio sensor to detect health risks at an early stage and send an alert to notify the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately analyze multiple health data sets in an integrated manner to detect health risks early, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively analyze multiple health data sets and detect health risks at an early stage. [Means for solving the problem]

[0006] The system according to the embodiment includes an environmental sensor, a wearable device, a smart mat, a food tracking camera, an audio sensor, and a multimodal AI. The environmental sensor measures room temperature and humidity. The wearable device records heart rate and activity level. The smart mat tracks weight changes and movements. The food tracking camera visualizes dietary habits. The audio sensor picks up signs of mental state. The multimodal AI comprehensively analyzes data from the environmental sensor, wearable device, smart mat, food tracking camera, and audio sensor, enabling early detection of health risks and issuing alerts to notify the user. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively analyze multiple health data sets and detect health risks at an early stage. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The health management system according to the embodiment of the present invention is a system that constantly collects data from multiple sensors installed in the home and analyzes and monitors it using multimodal AI. This allows the health management system to comprehensively grasp the user's health condition and support timely health management.

[0029] A health management system according to an embodiment includes an environmental sensor, a wearable device, a smart mat, a food tracking camera, and an audio sensor. The environmental sensor measures room temperature and humidity. For example, a sensor installed in a living room or bedroom records fluctuations in room temperature and humidity 24 hours a day. The environmental sensor can measure, for example, a temperature range (0 to 40 degrees Celsius) and a humidity range (0 to 100%). The wearable device records heart rate and activity. For example, a smart watch measures a user's heart rate and monitors daily exercise and stress levels. The wearable device can use optical or electrical heart rate measurement methods to record activity in units of steps or calories burned. The smart mat tracks weight changes and movements. For example, a smart mat installed in a bathroom measures a user's weight every morning and records the data. For example, the smart mat can measure weight to within 0.1 kg and track movement types (walking, running). The food tracking camera visualizes eating habits. For example, a camera installed on a dining table can automatically capture a user's meal and analyze the ingredients and calories. A meal tracking camera can analyze, for example, the type of food, calorie intake, and nutrient balance. A voice sensor can pick up signs of mental state. For example, a voice sensor installed in a living room can monitor a user's conversation and detect signs of stress or anxiety. A voice sensor can analyze, for example, tone of voice, speaking speed, and word choice. This allows a health management system to comprehensively grasp a user's health status and support timely health management. For example, data from environmental sensors and wearable devices can be combined to analyze the relationship between a user's living environment and health status. Furthermore, weight management and dietary improvements can be suggested based on data from a smart mat and a meal tracking camera. Furthermore, data from voice sensors can be used to detect mental health risks early and take appropriate measures.

[0030] Environmental sensors can record fluctuations in room temperature and humidity 24 hours a day. For example, they are installed in living rooms or bedrooms and record fluctuations in room temperature and humidity 24 hours a day. For example, environmental sensors measure the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and update the data in real time. This allows you to maintain a comfortable living environment by constantly monitoring fluctuations in room temperature and humidity.

[0031] Wearable devices can measure heart rate and monitor daily exercise and stress levels. For example, a wearable device such as a smartwatch measures a user's heart rate and monitors daily exercise and stress levels. For example, wearable devices can use optical or electrical heart rate measurement methods to record activity in units of steps or calories burned. This allows changes in health status to be detected by monitoring heart rate, exercise, and stress levels.

[0032] A smart mat can measure a user's weight every morning and record the data. For example, the smart mat is installed in a bathroom and measures the user's weight every morning and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. This allows users to track weight changes by measuring their weight every morning and recording the data.

[0033] A meal tracking camera can automatically photograph a user's meals and analyze the ingredients and calories. The meal tracking camera is installed, for example, on a dining table and automatically photographs a user's meals and analyzes the ingredients and calories. For example, the meal tracking camera can use image recognition technology to analyze the types and amounts of ingredients and calculate calories. This allows users to visualize their eating habits by automatically photographing meals and analyzing the ingredients and calories.

[0034] A voice sensor can monitor a user's conversation and detect signs of stress and anxiety. The voice sensor is installed, for example, in a living room, and monitors a user's conversation and detects signs of stress and anxiety. For example, the voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress and anxiety. This makes it possible to monitor a user's mental state by monitoring conversation and detecting signs of stress and anxiety.

[0035] Based on data from environmental sensors, it is possible to automatically adjust the optimal room temperature and humidity settings for each season. Environmental sensors are installed, for example, in the living room or bedroom, and record fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor measures the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and updates the data in real time. A system can be built that automatically adjusts the optimal room temperature and humidity settings for each season based on the data from the environmental sensors. For example, the room temperature can be set higher in winter and the humidity can be adjusted to be lower in summer. This makes it possible to provide a comfortable living environment by automatically adjusting the optimal room temperature and humidity settings for each season.

[0036] Based on data from environmental sensors, advice can be generated that provides optimal environmental conditions for plant growth. Environmental sensors are installed, for example, in living rooms or bedrooms, and record fluctuations in room temperature and humidity 24 hours a day. For example, environmental sensors measure the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and update the data in real time. A system can be built that generates advice that provides optimal room temperature and humidity for plant growth based on environmental sensor data. For example, it can suggest environmental conditions suitable for a specific plant. This allows plants to maintain their health by providing optimal environmental conditions for growth.

[0037] Based on data from environmental sensors, it is possible to provide information useful for managing the health of pets. Environmental sensors are installed, for example, in the living room or bedroom, and record fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor measures the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and updates the data in real time. A system can be built that provides information useful for managing the health of pets based on the data from the environmental sensors. For example, it can evaluate whether the room temperature and humidity are suitable for pets. This allows the system to provide information useful for managing the health of pets, thereby maintaining their health.

[0038] Advice for optimizing energy consumption can be generated based on data from environmental sensors. Environmental sensors are installed, for example, in the living room or bedroom, and record fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor measures the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and updates the data in real time. A system can be built that generates advice for optimizing energy consumption based on environmental sensor data. For example, it can suggest ways to use air conditioners and heaters efficiently. This can improve energy efficiency by providing advice for optimizing energy consumption.

[0039] An individual exercise program can be automatically generated based on data from a wearable device. A wearable device, for example, a smartwatch, measures a user's heart rate and monitors daily exercise volume and stress level. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity volume in units of steps or calories burned. A system can be built that automatically generates an individual exercise program based on data from a wearable device. For example, an exercise menu can be suggested based on the user's heart rate and activity volume. This allows the automatic generation of an individual exercise program to provide the user with the optimal exercise.

[0040] Based on data from wearable devices, sleep quality can be evaluated and improvement measures can be proposed. A wearable device, for example, a smartwatch, measures a user's heart rate and monitors daily exercise and stress levels. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity levels in units of steps or calories burned. A system for evaluating sleep quality can be built based on data from wearable devices. For example, the depth and cycle of sleep can be analyzed from the heart rate and activity level. This allows the system to evaluate sleep quality and propose improvement measures, thereby improving the user's sleep quality.

[0041] Advice to support stress management in the workplace can be generated based on data from wearable devices. A wearable device, for example, a smartwatch, measures a user's heart rate and monitors daily exercise and stress levels. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity levels in units of steps or calories burned. A system is constructed that generates advice to support stress management in the workplace based on data from wearable devices. For example, stress levels can be evaluated based on heart rate and activity levels. This can reduce workplace stress by providing advice to support stress management in the workplace.

[0042] Based on data from wearable devices, it is possible to add functions to support health management while traveling. For example, a wearable device such as a smartwatch measures the user's heart rate and monitors daily exercise and stress levels. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity levels in units of steps or calories burned. Based on data from wearable devices, a system can be built that adds functions to support health management while traveling. For example, health status can be monitored from heart rate and activity levels. This allows the provision of functions to support health management while traveling, making it possible to maintain health while traveling.

[0043] Based on the data from the smart mat, an individualized diet plan can be automatically generated. The smart mat is installed, for example, in the bathroom, and measures the user's weight every morning and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. A system that automatically generates an individualized diet plan based on the data from the smart mat can be built. For example, it can propose meal and exercise plans based on weight changes and movement data. This allows the user to be provided with the optimal diet by automatically generating an individualized diet plan.

[0044] Advice on improving posture can be provided based on data from a smart mat. A smart mat is installed, for example, in a bathroom, and measures the user's weight every morning and records the data. For example, a smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. A system that provides advice on improving posture can be built based on data from the smart mat. For example, data on standing and walking style can be analyzed to suggest correct posture. This allows the user's posture to be improved by providing advice on improving posture.

[0045] Based on the data from the smart mat, it is possible to add a function to support pet weight management. For example, a smart mat is installed in the bathroom, measures the user's weight every morning, and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. Based on the data from the smart mat, a system can be built that adds a function to support pet weight management. For example, it can monitor changes in a pet's weight and suggest appropriate diet and exercise. This allows the provision of a function to support pet weight management, thereby helping to maintain pet health.

[0046] Based on the data from the smart mat, it is possible to suggest safety measures within the home. A smart mat is installed, for example, in the bathroom, and measures the user's weight every morning and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. A system can be built that suggests safety measures within the home based on the data from the smart mat. For example, it can identify areas with a high risk of falling and suggest measures to prevent falls. By suggesting safety measures within the home, it is possible to strengthen safety measures such as preventing falls.

[0047] An individual nutrition plan can be automatically generated based on data from a meal tracking camera. The meal tracking camera is installed, for example, on a dining table and automatically photographs the user's meal, analyzing the ingredients and calories. For example, the meal tracking camera can use image recognition technology to analyze the types and quantities of ingredients and calculate calories. A system that automatically generates an individual nutrition plan based on data from the meal tracking camera can be built. For example, the system can analyze the meal contents and calories and propose a balanced nutrition plan. By automatically generating an individual nutrition plan, it is possible to provide optimal nutritional management to the user.

[0048] Based on data from a meal tracking camera, it is possible to add a function to support food management within the home. A meal tracking camera is installed, for example, on a dining table and automatically photographs the user's meal, analyzing the ingredients and calories. For example, a meal tracking camera can use image recognition technology to analyze the type and amount of ingredients and calculate calories. A system to support food management within the home can be built based on data from the meal tracking camera. For example, it can monitor food inventory and suggest necessary ingredients. By providing a function to support food management within the home, it is possible to reduce food waste and achieve efficient food management.

[0049] Based on the data from the meal tracking camera, it is possible to provide recipe suggestions and cooking advice. The meal tracking camera is installed, for example, on a dining table and automatically photographs the user's meal, analyzing the ingredients and calories. For example, the meal tracking camera can use image recognition technology to analyze the types and quantities of ingredients and calculate calories. Based on the data from the meal tracking camera, a system can be built that provides recipe suggestions and cooking advice. For example, recipes based on the types and quantities of ingredients can be suggested. This allows the user's eating habits to be improved by providing recipe suggestions and cooking advice.

[0050] An individualized mental health plan can be automatically generated based on data from a voice sensor. The voice sensor is installed, for example, in a living room to monitor the user's conversation and detect signs of stress or anxiety. For example, the voice sensor can analyze the tone of voice, speaking speed, and word choice to detect signs of stress or anxiety. A system that automatically generates an individualized mental health plan based on data from the voice sensor can be built. For example, the system can evaluate stress levels from the tone of voice and speaking style and propose an appropriate mental health plan. By automatically generating an individualized mental health plan, it is possible to provide optimal mental health management to the user.

[0051] Based on data from a voice sensor, it is possible to identify the cause of stress and suggest countermeasures. A voice sensor is installed, for example, in a living room to monitor a user's conversation and detect signs of stress or anxiety. For example, a voice sensor can analyze the tone of voice, speaking speed, and word choice to detect signs of stress or anxiety. A system can be built that identifies the cause of stress and suggests countermeasures based on data from the voice sensor. For example, the cause of stress can be analyzed from the tone of voice and speaking style. This allows the cause of stress to be identified and countermeasures to be suggested, thereby reducing the user's stress.

[0052] Advice to support mental health management in the workplace can be generated based on data from a voice sensor. A voice sensor is installed, for example, in a living room to monitor a user's conversation and detect signs of stress or anxiety. For example, a voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress or anxiety. A system can be built that generates advice to support mental health management in the workplace based on data from the voice sensor. For example, stress levels can be evaluated from the tone of voice and speaking style. This can reduce stress in the workplace by providing advice to support mental health management in the workplace.

[0053] Based on data from voice sensors, it is possible to add functions that support the improvement of communication within the home. A voice sensor is installed, for example, in the living room to monitor the user's conversation and detect signs of stress or anxiety. For example, a voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress or anxiety. Based on data from voice sensors, a system can be built that supports the improvement of communication within the home. For example, the quality of communication can be evaluated based on the tone of voice and speaking style. This can improve the quality of communication within the home by providing functions that support the improvement of communication within the home.

[0054] Based on data from voice sensors, it is possible to add functions that support the improvement of communication within the home. A voice sensor is installed, for example, in the living room to monitor the user's conversation and detect signs of stress or anxiety. For example, a voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress or anxiety. Based on data from voice sensors, a system can be built that supports the improvement of communication within the home. For example, the quality of communication can be evaluated based on the tone of voice and speaking style. This can improve the quality of communication within the home by providing functions that support the improvement of communication within the home.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The health management system can also be equipped with a sleep sensor that monitors the user's sleep patterns. For example, a sensor installed in the bed can analyze the depth and cycle of the user's sleep and evaluate the quality of sleep. This allows the system to understand the user's sleep patterns and propose appropriate measures to improve their sleep. Furthermore, based on the data from the sleep sensor, the system can provide advice on optimizing the user's sleep environment. For example, a comfortable sleeping environment can be created by adjusting the temperature and humidity in the bedroom.

[0057] The health management system can also be equipped with a skin sensor that monitors the user's skin condition. For example, a sensor worn on the arm can measure the amount of moisture and oil in the user's skin and evaluate the health of the skin. This allows the system to understand the user's skin condition and provide appropriate skin care advice. Furthermore, based on the data from the skin sensor, it can also suggest cosmetics and skin care products suitable for the user's skin. For example, if the user has dry skin, it can recommend products with high moisturizing effects.

[0058] The health management system can also be equipped with a function to evaluate allergy risk based on the user's dietary content. For example, it can analyze data from a food tracking camera to determine whether the ingredients consumed by the user contain allergens. This allows the system to evaluate the allergy risk based on the user's dietary content and provide appropriate advice. It can also propose a meal plan to reduce allergy risk. For example, it can suggest alternative ingredients that do not contain allergens.

[0059] Health management systems can also be equipped with a function to monitor a user's water intake. For example, a wearable device can record the user's water intake and provide alerts to encourage proper hydration. This allows the system to understand the user's water intake and support appropriate hydration. The system can also evaluate the user's health condition based on the water intake data and suggest the amount of water needed. For example, it can recommend hydration after exercise or in a hot environment.

[0060] The health management system can also be equipped with a posture sensor that monitors the user's posture. For example, a sensor installed in a chair can analyze the user's sitting position and posture and provide advice on maintaining correct posture. This allows the system to understand the user's posture and propose appropriate posture improvement measures. Based on the posture sensor data, the system can also suggest chair and desk heights that are suitable for the user's posture. For example, it can recommend postures that are suitable for long periods of desk work.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The environmental sensor measures the room temperature and humidity. For example, a sensor installed in the living room or bedroom records fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor can measure the temperature range (0 to 40 degrees Celsius) and the humidity range (0% to 100%). Step 2: The wearable device records heart rate and activity. For example, a smartwatch measures the user's heart rate and monitors daily exercise and stress levels. The wearable device can use optical or electrical heart rate measurement methods, for example, and record activity in units of steps or calories burned. Step 3: The smart mat tracks weight changes and movement. For example, a smart mat installed in the bathroom can measure the user's weight every morning and record the data. For example, the smart mat can measure weight to within 0.1 kg and track movement type (walking, running). Step 4: The food tracking camera visualizes the user's eating habits. For example, a camera installed on the dining table automatically captures the user's meals and analyzes the ingredients and calories. The food tracking camera can analyze, for example, the type of food, calorie intake, and nutrient balance. Step 5: A voice sensor picks up signs of mental state. For example, a voice sensor placed in a living room could monitor a user's speech and detect signs of stress or anxiety. For example, the voice sensor could analyze tone of voice, speaking rate, and word choice. Step 6: Multimodal AI comprehensively analyzes data from environmental sensors, wearable devices, smart mats, food tracking cameras, and audio sensors to detect health risks early. This allows for alerts and notifications to be sent to users. For example, data from environmental sensors and wearable devices can be combined to analyze the relationship between a user's living environment and health status. Furthermore, weight management and dietary improvements can be suggested based on data from smart mats and food tracking cameras. Furthermore, audio sensor data can be used to detect mental health risks early and take appropriate measures.

[0063] (Example 2) The health management system according to the embodiment of the present invention is a system that constantly collects data from multiple sensors installed in the home and analyzes and monitors it using multimodal AI. This allows the health management system to comprehensively grasp the user's health condition and support timely health management.

[0064] A health management system according to an embodiment includes an environmental sensor, a wearable device, a smart mat, a food tracking camera, and an audio sensor. The environmental sensor measures room temperature and humidity. For example, a sensor installed in a living room or bedroom records fluctuations in room temperature and humidity 24 hours a day. The environmental sensor can measure, for example, a temperature range (0 to 40 degrees Celsius) and a humidity range (0 to 100%). The wearable device records heart rate and activity. For example, a smart watch measures a user's heart rate and monitors daily exercise and stress levels. The wearable device can use optical or electrical heart rate measurement methods to record activity in units of steps or calories burned. The smart mat tracks weight changes and movements. For example, a smart mat installed in a bathroom measures a user's weight every morning and records the data. For example, the smart mat can measure weight to within 0.1 kg and track movement types (walking, running). The food tracking camera visualizes eating habits. For example, a camera installed on a dining table can automatically capture a user's meal and analyze the ingredients and calories. A meal tracking camera can analyze, for example, the type of food, calorie intake, and nutrient balance. A voice sensor can pick up signs of mental state. For example, a voice sensor installed in a living room can monitor a user's conversation and detect signs of stress or anxiety. A voice sensor can analyze, for example, tone of voice, speaking speed, and word choice. This allows a health management system to comprehensively grasp a user's health status and support timely health management. For example, data from environmental sensors and wearable devices can be combined to analyze the relationship between a user's living environment and health status. Furthermore, weight management and dietary improvements can be suggested based on data from a smart mat and a meal tracking camera. Furthermore, data from voice sensors can be used to detect mental health risks early and take appropriate measures.

[0065] Environmental sensors can record fluctuations in room temperature and humidity 24 hours a day. For example, they are installed in living rooms or bedrooms and record fluctuations in room temperature and humidity 24 hours a day. For example, environmental sensors measure the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and update the data in real time. This allows you to maintain a comfortable living environment by constantly monitoring fluctuations in room temperature and humidity.

[0066] Wearable devices can measure heart rate and monitor daily exercise and stress levels. For example, a wearable device such as a smartwatch measures a user's heart rate and monitors daily exercise and stress levels. For example, wearable devices can use optical or electrical heart rate measurement methods to record activity in units of steps or calories burned. This allows changes in health status to be detected by monitoring heart rate, exercise, and stress levels.

[0067] A smart mat can measure a user's weight every morning and record the data. For example, the smart mat is installed in a bathroom and measures the user's weight every morning and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. This allows users to track weight changes by measuring their weight every morning and recording the data.

[0068] A meal tracking camera can automatically photograph a user's meals and analyze the ingredients and calories. The meal tracking camera is installed, for example, on a dining table and automatically photographs a user's meals and analyzes the ingredients and calories. For example, the meal tracking camera can use image recognition technology to analyze the types and amounts of ingredients and calculate calories. This allows users to visualize their eating habits by automatically photographing meals and analyzing the ingredients and calories.

[0069] A voice sensor can monitor a user's conversation and detect signs of stress and anxiety. The voice sensor is installed, for example, in a living room, and monitors a user's conversation and detects signs of stress and anxiety. For example, the voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress and anxiety. This makes it possible to monitor a user's mental state by monitoring conversation and detecting signs of stress and anxiety.

[0070] Based on data from environmental sensors, it is possible to automatically adjust the optimal room temperature and humidity settings for each season. Environmental sensors are installed, for example, in the living room or bedroom, and record fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor measures the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and updates the data in real time. A system can be built that automatically adjusts the optimal room temperature and humidity settings for each season based on the data from the environmental sensors. For example, the room temperature can be set higher in winter and the humidity can be adjusted to be lower in summer. This makes it possible to provide a comfortable living environment by automatically adjusting the optimal room temperature and humidity settings for each season.

[0071] Based on data from environmental sensors, advice can be generated that provides optimal environmental conditions for plant growth. Environmental sensors are installed, for example, in living rooms or bedrooms, and record fluctuations in room temperature and humidity 24 hours a day. For example, environmental sensors measure the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and update the data in real time. A system can be built that generates advice that provides optimal room temperature and humidity for plant growth based on environmental sensor data. For example, it can suggest environmental conditions suitable for a specific plant. This allows plants to maintain their health by providing optimal environmental conditions for growth.

[0072] Based on data from the environmental sensor, the room temperature and humidity can be adjusted according to the user's emotional state. The environmental sensor is installed, for example, in the living room or bedroom, and records fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor measures the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and updates the data in real time. Using the emotion estimation function, we will build a system that adjusts the room temperature and humidity according to the user's emotional state. For example, it can provide a relaxing environment when the user is under high stress. This makes it possible to provide a comfortable living environment by adjusting the room temperature and humidity according to the user's emotional state.

[0073] Based on data from environmental sensors, it is possible to provide information useful for managing the health of pets. Environmental sensors are installed, for example, in the living room or bedroom, and record fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor measures the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and updates the data in real time. A system can be built that provides information useful for managing the health of pets based on the data from the environmental sensors. For example, it can evaluate whether the room temperature and humidity are suitable for pets. This allows the system to provide information useful for managing the health of pets, thereby maintaining their health.

[0074] Advice for optimizing energy consumption can be generated based on data from environmental sensors. Environmental sensors are installed, for example, in the living room or bedroom, and record fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor measures the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and updates the data in real time. A system can be built that generates advice for optimizing energy consumption based on environmental sensor data. For example, it can suggest ways to use air conditioners and heaters efficiently. This can improve energy efficiency by providing advice for optimizing energy consumption.

[0075] Based on data from environmental sensors, the system can learn the environmental conditions that users find comfortable and individually optimize them. Environmental sensors are installed, for example, in the living room or bedroom, and record fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor measures the temperature range (0 to 40 degrees Celsius) and humidity range (0% to 100%) and updates the data in real time. Using emotion estimation functionality, a system can be built that learns the environmental conditions that users find comfortable and individually optimizes them. For example, the system can set the optimal room temperature and humidity based on the user's emotional data. This allows the system to learn the environmental conditions that users find comfortable and individually optimize them, providing a comfortable living environment.

[0076] An individual exercise program can be automatically generated based on data from a wearable device. A wearable device, for example, a smartwatch, measures a user's heart rate and monitors daily exercise volume and stress level. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity volume in units of steps or calories burned. A system can be built that automatically generates an individual exercise program based on data from a wearable device. For example, an exercise menu can be suggested based on the user's heart rate and activity volume. This allows the automatic generation of an individual exercise program to provide the user with the optimal exercise.

[0077] Based on data from wearable devices, sleep quality can be evaluated and improvement measures can be proposed. A wearable device, for example, a smartwatch, measures a user's heart rate and monitors daily exercise and stress levels. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity levels in units of steps or calories burned. A system for evaluating sleep quality can be built based on data from wearable devices. For example, the depth and cycle of sleep can be analyzed from the heart rate and activity level. This allows the system to evaluate sleep quality and propose improvement measures, thereby improving the user's sleep quality.

[0078] Based on data from wearable devices, exercise advice can be provided according to the user's emotional state. A wearable device, for example, a smartwatch, measures the user's heart rate and monitors their daily exercise volume and stress level. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity levels in units of steps or calories burned. Using emotion estimation functionality, a system can be built that provides exercise advice according to the user's emotional state. For example, if stress levels are high, the system can suggest relaxing exercises. This allows the user to maintain their health by providing exercise advice according to their emotional state.

[0079] Advice to support stress management in the workplace can be generated based on data from wearable devices. A wearable device, for example, a smartwatch, measures a user's heart rate and monitors daily exercise and stress levels. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity levels in units of steps or calories burned. A system is constructed that generates advice to support stress management in the workplace based on data from wearable devices. For example, stress levels can be evaluated based on heart rate and activity levels. This can reduce workplace stress by providing advice to support stress management in the workplace.

[0080] Based on data from wearable devices, it is possible to add functions to support health management while traveling. For example, a wearable device such as a smartwatch measures the user's heart rate and monitors daily exercise and stress levels. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity levels in units of steps or calories burned. Based on data from wearable devices, a system can be built that adds functions to support health management while traveling. For example, health status can be monitored from heart rate and activity levels. This allows the provision of functions to support health management while traveling, making it possible to maintain health while traveling.

[0081] Based on data from wearable devices, relaxation methods can be suggested according to the user's emotional state. A wearable device, such as a smartwatch, measures the user's heart rate and monitors their daily exercise and stress levels. For example, a wearable device can use optical or electrical heart rate measurement methods to record activity levels in units of steps or calories burned. Using emotion estimation functionality, a system can be built that suggests relaxation methods according to the user's emotional state. For example, methods to help users relax when they are under high stress can be suggested. This allows the user's stress to be reduced by suggesting relaxation methods according to their emotional state.

[0082] Based on the data from the smart mat, an individualized diet plan can be automatically generated. The smart mat is installed, for example, in the bathroom, and measures the user's weight every morning and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. A system that automatically generates an individualized diet plan based on the data from the smart mat can be built. For example, it can propose meal and exercise plans based on weight changes and movement data. This allows the user to be provided with the optimal diet by automatically generating an individualized diet plan.

[0083] Advice on improving posture can be provided based on data from a smart mat. A smart mat is installed, for example, in a bathroom, and measures the user's weight every morning and records the data. For example, a smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. A system that provides advice on improving posture can be built based on data from the smart mat. For example, data on standing and walking style can be analyzed to suggest correct posture. This allows the user's posture to be improved by providing advice on improving posture.

[0084] Based on the data from the smart mat, it is possible to provide weight management advice according to the user's emotional state. For example, a smart mat is installed in a bathroom, measures the user's weight every morning, and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. Using the emotion estimation function, we will build a system that provides weight management advice according to the user's emotional state. For example, it can suggest ways to relax when stress is high. This will support the user's weight management by providing weight management advice according to their emotional state.

[0085] Based on the data from the smart mat, it is possible to add a function to support pet weight management. For example, a smart mat is installed in the bathroom, measures the user's weight every morning, and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. Based on the data from the smart mat, a system can be built that adds a function to support pet weight management. For example, it can monitor changes in a pet's weight and suggest appropriate diet and exercise. This allows the provision of a function to support pet weight management, thereby helping to maintain pet health.

[0086] Based on the data from the smart mat, it is possible to suggest safety measures within the home. A smart mat is installed, for example, in the bathroom, and measures the user's weight every morning and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. A system can be built that suggests safety measures within the home based on the data from the smart mat. For example, it can identify areas with a high risk of falling and suggest measures to prevent falls. By suggesting safety measures within the home, it is possible to strengthen safety measures such as preventing falls.

[0087] Based on the data from the smart mat, an exercise program can be suggested according to the user's emotional state. For example, a smart mat is installed in a bathroom, measures the user's weight every morning, and records the data. For example, the smart mat can measure weight to an accuracy of 0.1 kg and store the data in the cloud or local storage. Using the emotion estimation function, a system can be built that suggests an exercise program according to the user's emotional state. For example, if the user is under high stress, the system can suggest a relaxing exercise. This allows the user to maintain their health by suggesting an exercise program according to their emotional state.

[0088] An individual nutrition plan can be automatically generated based on data from a meal tracking camera. The meal tracking camera is installed, for example, on a dining table and automatically photographs the user's meal, analyzing the ingredients and calories. For example, the meal tracking camera can use image recognition technology to analyze the types and quantities of ingredients and calculate calories. A system that automatically generates an individual nutrition plan based on data from the meal tracking camera can be built. For example, the system can analyze the meal contents and calories and propose a balanced nutrition plan. By automatically generating an individual nutrition plan, it is possible to provide optimal nutritional management to the user.

[0089] Based on data from a meal tracking camera, dietary advice can be provided according to the user's emotional state. The meal tracking camera is installed, for example, on a dining table and automatically photographs the user's meal, analyzing the ingredients and calories. For example, the meal tracking camera can use image recognition technology to analyze the types and amounts of ingredients and calculate calories. Using the emotion estimation function, a system can be built that provides dietary advice according to the user's emotional state. For example, it can suggest meals that will help relax when the user is under high stress. This allows the user to improve their eating habits by providing dietary advice according to their emotional state.

[0090] Based on data from a meal tracking camera, it is possible to add a function to support food management within the home. A meal tracking camera is installed, for example, on a dining table and automatically photographs the user's meal, analyzing the ingredients and calories. For example, a meal tracking camera can use image recognition technology to analyze the type and amount of ingredients and calculate calories. A system to support food management within the home can be built based on data from the meal tracking camera. For example, it can monitor food inventory and suggest necessary ingredients. By providing a function to support food management within the home, it is possible to reduce food waste and achieve efficient food management.

[0091] Based on the data from the meal tracking camera, it is possible to provide recipe suggestions and cooking advice. The meal tracking camera is installed, for example, on a dining table and automatically photographs the user's meal, analyzing the ingredients and calories. For example, the meal tracking camera can use image recognition technology to analyze the types and quantities of ingredients and calculate calories. Based on the data from the meal tracking camera, a system can be built that provides recipe suggestions and cooking advice. For example, recipes based on the types and quantities of ingredients can be suggested. This allows the user's eating habits to be improved by providing recipe suggestions and cooking advice.

[0092] Based on data from a meal tracking camera, a meal plan can be proposed that matches the user's emotional state. The meal tracking camera is installed, for example, on a dining table and automatically photographs the user's meal, analyzing the ingredients and calories. For example, the meal tracking camera can use image recognition technology to analyze the types and amounts of ingredients and calculate calories. Using the emotion estimation function, a system can be built that proposes meal plans that match the user's emotional state. For example, it can suggest meals that will help them relax when they are under a lot of stress. This allows the user's eating habits to be improved by proposing meal plans that match their emotional state.

[0093] An individualized mental health plan can be automatically generated based on data from a voice sensor. The voice sensor is installed, for example, in a living room to monitor the user's conversation and detect signs of stress or anxiety. For example, the voice sensor can analyze the tone of voice, speaking speed, and word choice to detect signs of stress or anxiety. A system that automatically generates an individualized mental health plan based on data from the voice sensor can be built. For example, the system can evaluate stress levels from the tone of voice and speaking style and propose an appropriate mental health plan. By automatically generating an individualized mental health plan, it is possible to provide optimal mental health management to the user.

[0094] Based on data from a voice sensor, it is possible to identify the cause of stress and suggest countermeasures. A voice sensor is installed, for example, in a living room to monitor a user's conversation and detect signs of stress or anxiety. For example, a voice sensor can analyze the tone of voice, speaking speed, and word choice to detect signs of stress or anxiety. A system can be built that identifies the cause of stress and suggests countermeasures based on data from the voice sensor. For example, the cause of stress can be analyzed from the tone of voice and speaking style. This allows the cause of stress to be identified and countermeasures to be suggested, thereby reducing the user's stress.

[0095] Based on data from a voice sensor, it is possible to provide mental health advice according to the user's emotional state. A voice sensor is installed, for example, in a living room to monitor the user's conversation and detect signs of stress or anxiety. For example, a voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress or anxiety. Using the emotion estimation function, a system can be built that provides mental health advice according to the user's emotional state. For example, it can suggest ways to relax when stress is high. This makes it possible to improve the user's mental health by providing mental health advice according to the user's emotional state.

[0096] Advice to support mental health management in the workplace can be generated based on data from a voice sensor. A voice sensor is installed, for example, in a living room to monitor a user's conversation and detect signs of stress or anxiety. For example, a voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress or anxiety. A system can be built that generates advice to support mental health management in the workplace based on data from the voice sensor. For example, stress levels can be evaluated from the tone of voice and speaking style. This can reduce stress in the workplace by providing advice to support mental health management in the workplace.

[0097] Based on data from voice sensors, it is possible to add functions that support the improvement of communication within the home. A voice sensor is installed, for example, in the living room to monitor the user's conversation and detect signs of stress or anxiety. For example, a voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress or anxiety. Based on data from voice sensors, a system can be built that supports the improvement of communication within the home. For example, the quality of communication can be evaluated based on the tone of voice and speaking style. This can improve the quality of communication within the home by providing functions that support the improvement of communication within the home.

[0098] Based on data from the voice sensor, it is possible to suggest relaxation methods according to the user's emotional state. The voice sensor is installed, for example, in a living room to monitor the user's conversation and detect signs of stress or anxiety. For example, the voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress or anxiety. Using the emotion estimation function, a system can be built that suggests relaxation methods according to the user's emotional state. For example, it can suggest relaxation methods when the user is under high stress. This makes it possible to reduce the user's stress by suggesting relaxation methods according to the user's emotional state.

[0099] Based on data from voice sensors, it is possible to add functions that support the improvement of communication within the home. A voice sensor is installed, for example, in the living room to monitor the user's conversation and detect signs of stress or anxiety. For example, a voice sensor can analyze the tone of voice, speaking rate, and word choice to detect signs of stress or anxiety. Based on data from voice sensors, a system can be built that supports the improvement of communication within the home. For example, the quality of communication can be evaluated based on the tone of voice and speaking style. This can improve the quality of communication within the home by providing functions that support the improvement of communication within the home.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The health management system can also be equipped with a sleep sensor that monitors the user's sleep patterns. For example, a sensor installed in the bed can analyze the depth and cycle of the user's sleep and evaluate the quality of sleep. This allows the system to understand the user's sleep patterns and propose appropriate measures to improve their sleep. Furthermore, based on the data from the sleep sensor, the system can provide advice on optimizing the user's sleep environment. For example, a comfortable sleeping environment can be created by adjusting the temperature and humidity in the bedroom.

[0102] The health management system can also have a function to provide music according to the user's emotional state. For example, using the emotion estimation function, if the user is feeling stressed, relaxing music can be played. By providing music according to the user's emotional state, stress can be reduced and a relaxed state can be maintained. In addition, it can also generate an optimal music playlist for each user based on the user's emotional data. For example, music can be suggested according to the user's preferences and emotional state.

[0103] The health management system can also be equipped with a skin sensor that monitors the user's skin condition. For example, a sensor worn on the arm can measure the amount of moisture and oil in the user's skin and evaluate the health of the skin. This allows the system to understand the user's skin condition and provide appropriate skin care advice. Furthermore, based on the data from the skin sensor, it can also suggest cosmetics and skin care products suitable for the user's skin. For example, if the user has dry skin, it can recommend products with high moisturizing effects.

[0104] The health management system can also be equipped with a function to adjust lighting according to the user's emotional state. For example, using the emotion estimation function, warm-colored lighting can be provided when the user wants to relax. This allows for adjusting lighting according to the user's emotional state, thereby providing a comfortable living environment. It can also suggest optimal lighting settings for each individual user based on the user's emotional data. For example, the color and brightness of lighting can be adjusted according to the user's preferences and emotional state.

[0105] The health management system can also be equipped with a function to evaluate allergy risk based on the user's dietary content. For example, it can analyze data from a food tracking camera to determine whether the ingredients consumed by the user contain allergens. This allows the system to evaluate the allergy risk based on the user's dietary content and provide appropriate advice. It can also propose a meal plan to reduce allergy risk. For example, it can suggest alternative ingredients that do not contain allergens.

[0106] The health management system can also have a function to suggest an exercise program according to the user's emotional state. For example, using the emotion estimation function, if the user is feeling stressed, it can suggest a relaxing exercise. This allows the user's health to be maintained by suggesting an exercise program according to the user's emotional state. It can also generate an optimal exercise menu for each user based on the user's emotional data. For example, it can suggest exercises according to the user's preferences and emotional state.

[0107] Health management systems can also be equipped with a function to monitor a user's water intake. For example, a wearable device can record the user's water intake and provide alerts to encourage proper hydration. This allows the system to understand the user's water intake and support appropriate hydration. The system can also evaluate the user's health condition based on the water intake data and suggest the amount of water needed. For example, it can recommend hydration after exercise or in a hot environment.

[0108] The health management system can also be equipped with a function to suggest relaxation methods according to the user's emotional state. For example, by using the emotion estimation function, if the user is feeling stressed, a relaxation method can be suggested. This allows the user's stress to be reduced by suggesting a relaxation method according to the user's emotional state. It can also suggest an optimal relaxation method for each individual user based on the user's emotional data. For example, it can suggest methods such as meditation or deep breathing.

[0109] The health management system can also be equipped with a posture sensor that monitors the user's posture. For example, a sensor installed in a chair can analyze the user's sitting position and posture and provide advice on maintaining correct posture. This allows the system to understand the user's posture and propose appropriate posture improvement measures. Based on the posture sensor data, the system can also suggest chair and desk heights that are suitable for the user's posture. For example, it can recommend postures that are suitable for long periods of desk work.

[0110] The health management system can further include a function to propose a meal plan according to the user's emotional state. For example, using the emotion estimation function, if the user is feeling stressed, a meal that will help them relax can be proposed. This allows the system to improve the user's eating habits by proposing a meal plan according to the user's emotional state. It can also generate an optimal meal menu for each user based on the user's emotional data. For example, it can propose meals according to the user's preferences and emotional state.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The environmental sensor measures the room temperature and humidity. For example, a sensor installed in the living room or bedroom records fluctuations in room temperature and humidity 24 hours a day. For example, the environmental sensor can measure the temperature range (0 to 40 degrees Celsius) and the humidity range (0% to 100%). Step 2: The wearable device records heart rate and activity. For example, a smartwatch measures the user's heart rate and monitors daily exercise and stress levels. The wearable device can use optical or electrical heart rate measurement methods, for example, and record activity in units of steps or calories burned. Step 3: The smart mat tracks weight changes and movement. For example, a smart mat installed in the bathroom can measure the user's weight every morning and record the data. For example, the smart mat can measure weight to within 0.1 kg and track movement type (walking, running). Step 4: The food tracking camera visualizes the user's eating habits. For example, a camera installed on the dining table automatically captures the user's meals and analyzes the ingredients and calories. The food tracking camera can analyze, for example, the type of food, calorie intake, and nutrient balance. Step 5: A voice sensor picks up signs of mental state. For example, a voice sensor placed in a living room could monitor a user's speech and detect signs of stress or anxiety. For example, the voice sensor could analyze tone of voice, speaking rate, and word choice. Step 6: Multimodal AI comprehensively analyzes data from environmental sensors, wearable devices, smart mats, food tracking cameras, and audio sensors to detect health risks early. This allows for alerts and notifications to be sent to users. For example, data from environmental sensors and wearable devices can be combined to analyze the relationship between a user's living environment and health status. Furthermore, weight management and dietary improvements can be suggested based on data from smart mats and food tracking cameras. Furthermore, audio sensor data can be used to detect mental health risks early and take appropriate measures.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0157] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] 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]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an environmental sensor for measuring room temperature and humidity; A wearable device that records heart rate and activity; a smart mat that tracks weight changes and movement; A food tracking camera that visualizes your eating habits, an audio sensor that picks up signs of mental state; Equipped with A multimodal AI is provided that comprehensively analyzes data from the environmental sensor, the wearable device, the smart mat, the food tracking camera, and the audio sensor, It enables early detection of health risks and alerts users. A system characterized by:

2. The food tracking camera includes: Automatically photograph the user's meals and analyze ingredients and calories 2. The system of claim 1.

3. Based on the data from the wearable device, Automatically generate individual exercise programs 2. The system of claim 1.

4. Based on the data of the smart mat, Automatically generate a personalized diet plan 2. The system of claim 1.

5. Based on the data of the audio sensor, Providing mental health advice according to the user's emotional state 2. The system of claim 1.

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