system

Abstract

The system according to this embodiment aims to collect and analyze health data and provide advice on improving lifestyle habits. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice on improving lifestyle habits based on the analysis results obtained by the analysis unit. The monitoring unit monitors the user's stress level.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that the collection and analysis of health data were not sufficiently carried out, and specific advice for improving lifestyle habits was not provided.

[0005] The system according to the embodiment aims to collect and analyze health data and provide advice for improving lifestyle habits.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a monitoring unit. The data collection unit collects health data. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides advice on improving lifestyle habits based on the analysis results obtained by the analysis unit. The monitoring unit monitors the user's stress level. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze health data and provide advice on improving lifestyle habits. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is designed for people in their 30s and 40s, a demographic where an increasing number of people are expected to have poor health checkup results. The system utilizes an AI agent in conjunction with IoT devices to autonomously collect and analyze health data in a smart home environment, providing real-time advice on improving lifestyle habits and contributing to increased health awareness and disease prevention. The health management system involves the AI ​​agent working with smart glasses and smart bottles to monitor the user's alcohol consumption in real time. If the user frequently drinks alcohol, the AI ​​agent analyzes the triggers for drinking and suggests alternative beverages. It also works with smart lighting and smart speakers to automatically activate relaxation mode, providing a relaxing environment. Next, the AI ​​agent works with a smart fitness mirror to provide real-time exercise programs as a virtual fitness trainer. It monitors the user's exercise performance and sets exercise challenges incorporating gamification elements. The smart fitness mirror provides real-time feedback to maintain user motivation. Furthermore, the AI ​​agent works with wearable devices (such as smartwatches), smart thermostats, and smart lighting to monitor the user's daily stress levels. If high stress levels are detected, the AI ​​agent automatically adjusts room lighting and temperature, and plays relaxation music through a smart speaker, implementing stress-reducing measures. It also integrates with local smart home networks to group users with similar health risks and connects with regular health checkup data. The AI ​​agent analyzes the diagnostic results and provides personalized health plans. It also collaborates with local clinics and hospitals to suggest fitness events and health seminars based on the diagnostic results. Finally, an AI nutritional diagnostic function is added to the kitchen assistant, which integrates with smart refrigerators and ovens. The AI ​​analyzes the user's dietary data and calculates necessary nutrients and calories in real time. Based on this, it suggests optimal recipes, and smart devices guide the cooking process, providing comprehensive management of the user's health.In this way, the AI ​​agent works in conjunction with IoT devices to autonomously collect and analyze health data in a smart home environment, and provides real-time advice on improving lifestyle habits, thereby contributing to increased health awareness and the promotion of disease prevention. As a result, the health management system can contribute to improving users' health awareness and promoting disease prevention.

[0029] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a provision unit, and a monitoring unit. The data collection unit collects health data. Health data includes, but is not limited to, heart rate, blood pressure, body temperature, and activity level. The data collection unit monitors the user's alcohol consumption in real time, for example, in conjunction with smart glasses or a smart bottle. The data collection unit can also monitor the user's daily stress level in conjunction with a wearable device, a smart thermostat, or smart lighting. For example, the data collection unit measures the amount of alcohol consumed using the sensors in smart glasses and collects the data. The smart bottle measures the amount of alcohol consumed in real time and collects the data. The wearable device measures heart rate and activity level and collects the data. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the collected heart rate data and evaluates the user's health status. The analysis unit can also analyze situations that trigger alcohol consumption and suggest alternative beverages. For example, the analysis unit analyzes triggers for drinking, such as stress and social events, and suggests non-alcoholic or health beverages. The service unit provides lifestyle improvement advice based on the analysis results obtained by the analysis unit. Lifestyle improvement advice includes, but is not limited to, dietary improvements, exercise recommendations, and improved sleep quality. For example, the service unit can automatically activate relaxation mode in conjunction with smart lighting and smart speakers to provide a relaxing environment. The service unit can also automatically adjust room lighting and temperature and play relaxation music from a smart speaker if it detects increased stress levels, thus implementing stress reduction measures. For example, the service unit adjusts the color and brightness of the lighting to provide a relaxing environment. The smart speaker plays relaxation music to reduce stress. The monitoring unit monitors the user's stress level. Stress level measurement uses, but is not limited to, heart rate variability, skin electrical activity, and self-reporting. For example, the monitoring unit measures heart rate variability to assess the stress level.Skin electrical activity is measured to assess stress levels. Stress levels are assessed based on self-reports. This enables the health management system according to the embodiment to collect, analyze, provide advice on health data, and monitor stress levels.

[0030] The data collection unit collects health data. This data includes, but is not limited to, heart rate, blood pressure, body temperature, and activity level. The data collection unit can, for example, work with smart glasses or smart bottles to monitor the user's alcohol consumption in real time. Specifically, smart glasses have built-in sensors that can detect the amount and type of liquid the user has consumed. Smart bottles use built-in sensors to measure alcohol consumption in real time and transmit this data to the data collection unit. Furthermore, the data collection unit can also work with wearable devices, smart thermostats, and smart lighting to monitor the user's stress levels in their daily lives. Wearable devices collect data such as heart rate, activity level, and sleep patterns, and use this data to assess the user's stress level. Smart thermostats monitor indoor temperature and humidity, providing data to assess the user's comfort level. Smart lighting adjusts the color and brightness of the lighting to collect data that improves the user's relaxation level. As a result, the data collection unit can collect a wide range of data from various devices and understand the user's health status and stress level in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. This analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, it analyzes collected heart rate data to assess the user's health status. For example, it can analyze heart rate variability patterns and, if abnormal heart rate fluctuations are detected, can provide early warnings of health risks. The analysis department can also analyze situations that trigger alcohol consumption and suggest alternative beverages. For example, it can analyze triggers such as stress or social events and suggest non-alcoholic or health drinks. Furthermore, the analysis department can assess the user's daily stress level and provide specific advice for stress reduction. For example, it can analyze heart rate variability and skin electrical activity data to assess the user's stress level. This allows the analysis department to quickly and accurately analyze collected data and understand the user's health status and stress level in real time. Additionally, the analysis department can utilize historical data and statistical information to conduct long-term health risk assessments and trend analyses. For example, it can predict fluctuations in specific health risks based on past health data and develop future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The service provider provides lifestyle improvement advice based on the analysis results obtained by the analysis provider. This advice includes, but is not limited to, dietary improvements, exercise recommendations, and improved sleep quality. Specifically, the service provider can automatically activate relaxation mode in conjunction with smart lighting and smart speakers to provide a relaxing environment. For example, if high stress levels are detected, it can automatically adjust room lighting and temperature, and play relaxation music through a smart speaker to reduce stress. The service provider adjusts the color and brightness of the lighting to create a relaxing environment. The smart speaker plays relaxation music to reduce stress. The service provider can also provide dietary improvement advice tailored to the user's health condition. For example, based on the user's health data, it can suggest a nutritionally balanced meal plan to support dietary improvements. Furthermore, the service provider can recommend exercise and provide specific advice for improving sleep quality. For example, based on the user's activity data, it can suggest an appropriate exercise plan to support the habit of exercising. It can also provide advice to improve sleep quality based on sleep data, thereby comprehensively improving the user's health. This allows the service provider to offer specific advice tailored to the user's health condition and support improvements in their lifestyle.

[0033] The monitoring unit monitors the user's stress level. Stress level measurement may include, but is not limited to, heart rate variability, skin electrical activity, and self-reporting. Specifically, the monitoring unit measures heart rate variability to assess stress levels. Heart rate variability is an important indicator for assessing a user's stress level by analyzing the pattern of heart rate fluctuations. It also measures skin electrical activity to assess stress levels. Skin electrical activity is an indicator for assessing a user's stress level by measuring changes in the electrical resistance of the skin. Furthermore, stress levels can also be assessed based on self-reporting. Based on the stress levels self-reported by the user, the monitoring unit assesses the user's subjective stress level. This allows the monitoring unit to comprehensively evaluate and monitor the user's stress level in real time. Furthermore, based on the collected data, the monitoring unit can continuously track fluctuations in stress levels and support long-term stress management. For example, based on past stress level data, it can predict fluctuations in stress levels under specific situations or conditions and formulate future countermeasures. The monitoring unit can also issue early warnings if abnormal stress levels are detected. This allows the monitoring unit to monitor users' stress levels in real time, enabling long-term stress management and anomaly detection, thereby improving the overall reliability and safety of the system.

[0034] The data collection unit can monitor a user's alcohol consumption in real time in conjunction with smart glasses and smart bottles. For example, the data collection unit can measure alcohol consumption using the sensors in smart glasses and collect the data. The data collection unit can also measure alcohol consumption in real time using a smart bottle and collect the data. For example, a smart bottle measures alcohol consumption, collects the data, and transmits it to a smartphone. The data collection unit can also integrate the data from the smart glasses and smart bottle to comprehensively monitor the user's alcohol consumption. For example, the data collection unit combines the data from the smart glasses sensors and the smart bottle to understand the user's alcohol consumption in real time. This allows for real-time monitoring of the user's alcohol consumption. For example, smart glasses have built-in sensors to measure alcohol consumption. For example, a smart bottle measures alcohol consumption and collects the data. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input data from smart glasses and smart bottles into a generating AI and have the generating AI perform alcohol consumption monitoring.

[0035] The analysis unit can analyze situations that trigger drinking and suggest alternative beverages. For example, the analysis unit can analyze triggers for drinking, such as stress or social events, and suggest non-alcoholic or health drinks. For example, the analysis unit can analyze a user's stress level and suggest an alternative beverage when stress levels are high. The analysis unit can also analyze data from social events to identify situations that trigger drinking and suggest alternative beverages. For example, the analysis unit can analyze a user's calendar information and suggest an alternative beverage before a social event. The analysis unit can also analyze a user's drinking history to identify patterns that trigger drinking and suggest alternative beverages. For example, the analysis unit can analyze a user's drinking history and suggest an alternative beverage if there is a high amount of drinking on specific days or times. This allows for the reduction of alcohol consumption by analyzing triggers for drinking and suggesting alternative beverages. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can input data on users' stress levels and social events into a generating AI, which can then analyze triggers for drinking and suggest alternative beverages.

[0036] The service provider can automatically activate relaxation mode in conjunction with smart lighting and smart speakers to provide a relaxing environment. For example, the service provider can adjust the color and brightness of smart lighting to provide a relaxing environment. The service provider can also play relaxation music using a smart speaker to provide a relaxing environment. For example, the service provider can change the color of smart lighting to a warm color and adjust the brightness to provide a relaxing environment. The service provider can also play relaxation music using a smart speaker to reduce stress. For example, the service provider can play nature sounds or healing music from a smart speaker to provide a relaxing environment. The service provider can also link smart lighting and smart speakers to automatically activate relaxation mode. For example, the service provider can monitor the user's stress level and activate relaxation mode if it detects that stress has increased. This allows the relaxation mode to be automatically activated and a relaxing environment to be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input commands for smart lighting and smart speakers into the generating AI, and have the generating AI execute the activation of relaxation mode.

[0037] The monitoring unit can monitor the user's daily stress levels in conjunction with wearable devices, smart thermostats, and smart lighting. For example, the monitoring unit can measure heart rate and activity levels using wearable devices and evaluate stress levels. It can also measure room temperature using a smart thermostat and evaluate stress levels. For example, the monitoring unit can detect an increase in stress levels when the room temperature is high. Furthermore, the monitoring unit can measure the brightness and color of lighting using smart lighting and evaluate stress levels. For example, the monitoring unit can detect an increase in stress levels when the lighting is too bright. This allows for monitoring of the user's daily stress levels. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data from wearable devices, smart thermostats, and smart lighting into a generating AI and have the generating AI perform stress level monitoring.

[0038] The service provider can automatically adjust room lighting and temperature, and play relaxation music from a smart speaker, if it detects that stress levels have risen, thereby implementing stress reduction measures. For example, the service provider can adjust the color and brightness of smart lighting to provide a relaxing environment. The service provider can also adjust the room temperature using a smart thermostat to provide a relaxing environment. For example, the service provider can adjust the room temperature to a comfortable level to reduce stress. The service provider can also play relaxation music using a smart speaker to provide a relaxing environment. For example, the service provider can play nature sounds or healing music from a smart speaker to reduce stress. The service provider can also coordinate smart lighting, a smart thermostat, and a smart speaker to automatically implement stress reduction measures. For example, the service provider can monitor the user's stress level and implement stress reduction measures if it detects that stress levels have risen. This allows for the automatic implementation of stress reduction measures when stress levels rise. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the supply unit can input the control of smart lighting, smart thermostats, and smart speakers into the generating AI, and have the generating AI execute stress reduction measures.

[0039] The data collection unit can work with local smart home networks to group users with similar health risks. For example, the data collection unit can collect users' health data through the local smart home network and identify users with similar health risks. The data collection unit can also group users with similar health risks and provide health management plans for each group. For example, the data collection unit can group users with high cardiovascular risk and provide a cardiovascular risk management plan. The data collection unit can also group users with high diabetes risk and provide a diabetes risk management plan. For example, the data collection unit can analyze blood glucose data to identify and group users with high diabetes risk. This allows for effective health management by grouping users with similar health risks. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input health data collected from the local smart home network into a generating AI and have the generating AI perform the grouping of users with similar health risks.

[0040] The analysis department can analyze regular health checkup data and provide individualized health plans. For example, the analysis department collects regular health checkup data and evaluates the user's health status. The analysis department can also create individualized health plans based on the health checkup data and provide them to the service provider. For example, the analysis department analyzes blood test results and physical measurement data to evaluate the user's health status. The analysis department can also identify the user's health risks and create health plans tailored to those risks. For example, the analysis department can create a cardiovascular risk management plan for users with a high cardiovascular risk. The analysis department can also create a diabetes risk management plan for users with a high diabetes risk. For example, the analysis department can analyze blood glucose data and create a health plan for users with a high diabetes risk, including advice on diet and exercise. This allows for the analysis of regular health checkup data and the provision of individualized health plans. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input regular health checkup data into a generating AI and have the generating AI create individualized health plans.

[0041] The service provider can collaborate with local clinics and medical offices to propose fitness events and health seminars based on diagnostic results. For example, the service provider can receive diagnostic results from local clinics and medical offices and evaluate the user's health status. The service provider can also propose fitness events and health seminars based on the diagnostic results. For example, the service provider can propose a fitness event for cardiovascular risk management to a user with a high cardiovascular risk. The service provider can also propose a health seminar for diabetes risk management to a user with a high diabetes risk. For example, the service provider can collaborate with local clinics and medical offices to plan fitness events and health seminars tailored to the user's health status. This allows the service provider to propose fitness events and health seminars based on diagnostic results. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input diagnostic results into a generating AI and have the generating AI execute proposals for fitness events and health seminars.

[0042] The data collection unit can collect user meal data in conjunction with smart refrigerators and smart ovens. For example, the data collection unit can collect food management data using a smart refrigerator and understand the user's meal data. The data collection unit can also collect cooking data using a smart oven and understand the user's meal data. For example, the data collection unit can use the smart refrigerator's sensors to understand the food inventory status and collect the user's meal data. The data collection unit can also collect cooking data from a smart oven and understand the user's meal data. For example, the data collection unit can analyze the smart oven's cooking history and collect the user's meal data. The data collection unit can also integrate data from smart refrigerators and smart ovens to comprehensively understand the user's meal data. For example, the data collection unit can combine food data from a smart refrigerator and cooking data from a smart oven to collect the user's meal data. This allows the data collection of the user's meal data to be performed. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from smart refrigerators and smart ovens into a generating AI and have the generating AI perform the collection of meal data.

[0043] The analysis unit can analyze meal data and calculate necessary nutrients and calories. For example, the analysis unit can analyze collected meal data and evaluate the user's nutritional status. The analysis unit can also calculate necessary nutrients and calories based on meal data and provide them to the supply unit. For example, the analysis unit can analyze meal data and evaluate the user's intake of nutrients such as vitamins, minerals, and proteins. The analysis unit can also calculate calories based on meal data and evaluate the user's calorie intake. For example, the analysis unit can analyze meal data using a calorie chart for ingredients and evaluate the user's calorie intake. The analysis unit can also evaluate nutritional balance based on meal data and calculate necessary nutrients and calories. For example, the analysis unit can analyze meal data, evaluate the user's nutritional balance, and calculate necessary nutrients and calories. This allows the analysis unit to analyze meal data and calculate necessary nutrients and calories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI perform the calculation of necessary nutrients and calories.

[0044] The service provider can suggest an optimal recipe, and a smart device can guide the cooking process. For example, the service provider can suggest an optimal recipe based on nutrients and calories calculated by the analysis unit. The service provider can also guide the cooking process using a smart device. For example, the service provider can suggest a recipe that takes into account the user's nutritional balance and guide the cooking process using a smart device. The service provider can also suggest a recipe that takes into account the user's calorie intake and guide the cooking process using a smart device. For example, the service provider can suggest a low-calorie recipe for a user who needs to restrict calories and guide the cooking process using a smart device. The service provider can also suggest a recipe that supplements a specific nutrient for a user who is deficient in that nutrient and guide the cooking process using a smart device. For example, the service provider can suggest a recipe that is rich in vitamin C for a user who is deficient in vitamin C and guide the cooking process using a smart device. This allows the service provider to suggest an optimal recipe and guide the cooking process. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the supply unit can input the results of nutrient and calorie calculations into a generating AI, which can then generate the AI ​​to suggest the optimal recipe and provide cooking instructions.

[0045] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can select the collection method that yields the most accurate data from the user's past data. The data collection unit can also customize the collection method based on the user's past data to improve accuracy. For example, the data collection unit can analyze the user's past data and change the collection method to maintain data consistency. The data collection unit can also dynamically adjust the collection method based on the user's past data. For example, the data collection unit can analyze the user's past data and select the optimal collection method. This allows the data collection unit to analyze the user's past health data and select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal collection method.

[0046] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit will prioritize collecting dietary data. Similarly, if the user is interested in exercise, the data collection unit can prioritize collecting exercise data. For example, if the user is interested in stress management, the data collection unit will prioritize collecting stress level data. Furthermore, the data collection unit can dynamically adjust the collected data based on the user's current lifestyle and areas of interest. For example, the data collection unit can analyze the user's lifestyle and areas of interest to collect the most relevant data. This allows for filtering of health data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0047] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of oxygen concentration data. Similarly, if the user is in an urban area, the data collection unit can prioritize the collection of air quality data. For example, if the user is near the sea, the data collection unit will prioritize the collection of humidity data. Furthermore, the data collection unit can dynamically adjust the collected data based on the user's geographical location. For example, the data collection unit can analyze the user's geographical location and collect the most relevant data. This allows for the priority collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI collect highly relevant data.

[0048] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user posts about exercise on social media, the data collection unit will prioritize collecting exercise data. Similarly, if the user posts about food on social media, the data collection unit can prioritize collecting dietary data. For example, if the user posts about stress on social media, the data collection unit will prioritize collecting stress level data. Furthermore, the data collection unit can dynamically adjust the collected data based on the user's social media activity. For example, the data collection unit analyzes the user's social media activity and collects the most relevant data. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0049] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. The analysis unit can also perform a concise analysis on general health data. For example, the analysis unit will perform a detailed analysis on data of high interest to the user. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the health data. For example, the analysis unit will evaluate the importance of the health data and determine the optimal level of detail of the analysis. This allows the level of detail of the analysis to be adjusted based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0050] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an algorithm that evaluates exercise performance to exercise data. It can also apply an algorithm that evaluates nutritional balance to diet data. For example, the analysis unit can apply an algorithm that evaluates stress management to stress level data. Furthermore, the analysis unit can dynamically adjust the analysis algorithm depending on the category of health data. For example, the analysis unit can evaluate the category of health data and determine the optimal analysis algorithm. This allows the optimal analysis algorithm to be applied according to the category of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of health data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0051] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can also analyze current data while referring to past data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the timing of health data collection. For example, the analysis unit may evaluate the timing of health data collection and determine the optimal analysis priority. This allows the analysis priority to be determined based on the timing of health data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the timing of health data collection into a generating AI and have the generating AI determine the analysis priority.

[0052] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. The analysis unit can also evaluate the relevance of health data and determine the optimal order of analysis. For example, the analysis unit can evaluate the correlations and common factors of the data and determine the optimal order of analysis. This allows the order of analysis to be adjusted based on the relevance of health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0053] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice for important health data. It can also provide concise advice for general health data. For example, the service provider can provide detailed advice for data of high interest to the user. Furthermore, the service provider can dynamically adjust the level of detail of advice based on the importance of the health data. For example, the service provider can evaluate the importance of the health data and determine the optimal level of detail for the advice. This allows the service provider to adjust the level of detail of advice based on the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0054] The service provider can apply different advice algorithms depending on the category of health data when providing advice. For example, for exercise data, the service provider can apply an algorithm that suggests an exercise program. For diet data, the service provider can also apply an algorithm that suggests nutritional balance. For example, for stress level data, the service provider can apply an algorithm that suggests stress management. Furthermore, the service provider can dynamically adjust the advice algorithm depending on the category of health data. For example, the service provider can evaluate the category of health data and determine the optimal advice algorithm. This allows the service provider to apply the most appropriate advice algorithm according to the category of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of health data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0055] The service provider can determine the priority of advice based on the timing of health data collection when providing advice. For example, the service provider can provide advice based on recently collected data. Alternatively, the service provider can provide advice based on current data while referring to past data. For example, the service provider can provide advice based on data collected during a specific period. Furthermore, the service provider can dynamically adjust the priority of advice based on the timing of health data collection. For example, the service provider can evaluate the timing of health data collection and determine the optimal priority of advice. This allows the service provider to determine the priority of advice based on the timing of health data collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the timing of health data collection into a generating AI and have the generating AI determine the priority of advice.

[0056] The service provider can adjust the order of advice based on the relevance of health data when providing advice. For example, the service provider can provide advice based on highly relevant data. It can also provide advice later for less relevant data. For example, the service provider can dynamically adjust the order of advice based on the relevance of the data. The service provider can also evaluate the relevance of health data and determine the optimal order of advice. For example, the service provider can evaluate the correlation and common factors of the data and determine the optimal order of advice. This allows the order of advice to be adjusted based on the relevance of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0057] The monitoring unit can improve the accuracy of monitoring based on the interrelationships of health data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by considering the interrelationships between exercise data and heart rate data. The monitoring unit can also improve the accuracy of monitoring by considering the interrelationships between diet data and blood glucose data. For example, the monitoring unit can improve the accuracy of monitoring by considering the interrelationships between stress level data and sleep data. Furthermore, the monitoring unit can dynamically adjust the accuracy of monitoring based on the interrelationships of health data. For example, the monitoring unit evaluates the interrelationships of health data and determines the optimal monitoring accuracy. This allows for improved monitoring accuracy based on the interrelationships of health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the interrelationships of health data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0058] The monitoring unit can perform monitoring based on the attribute information of the health data submitter during monitoring. For example, the monitoring unit can adjust the monitoring criteria considering the user's age. The monitoring unit can also adjust the monitoring criteria considering the user's gender. For example, the monitoring unit can adjust the monitoring criteria considering the user's lifestyle. Furthermore, the monitoring unit can dynamically adjust the monitoring criteria based on the attribute information of the health data submitter. For example, the monitoring unit can evaluate the attribute information of the health data submitter and determine the optimal monitoring criteria. This enables monitoring based on the attribute information of the health data submitter. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the attribute information of the health data submitter into a generating AI and have the generating AI perform the adjustment of the monitoring criteria.

[0059] The monitoring unit can perform monitoring based on the geographical distribution of health data during monitoring. For example, if the user is at high altitude, the monitoring unit will prioritize monitoring oxygen concentration data. Similarly, if the user is in an urban area, the monitoring unit can prioritize monitoring air quality data. For example, if the user is near the sea, the monitoring unit will prioritize monitoring humidity data. Furthermore, the monitoring unit can dynamically adjust monitoring criteria based on the geographical distribution of health data. For example, the monitoring unit can evaluate the geographical distribution of health data and determine the optimal monitoring criteria. This enables monitoring based on the geographical distribution of health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical distribution of health data into a generating AI and have the generating AI adjust the monitoring criteria.

[0060] The monitoring unit can improve the accuracy of monitoring based on relevant literature on health data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to the latest research papers. The monitoring unit can also improve the accuracy of monitoring by referring to past research data. For example, the monitoring unit can adjust the monitoring criteria by referring to relevant literature. The monitoring unit can also dynamically adjust the monitoring criteria based on relevant literature on health data. For example, the monitoring unit can evaluate relevant literature on health data and determine the optimal monitoring criteria. This allows the accuracy of monitoring to be improved based on relevant literature on health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature on health data into a generating AI and have the generating AI perform the adjustment of the monitoring criteria.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The health management system can optimize the collection of health data based on the user's geographical location. For example, if the user is at high altitude, the data collection unit can prioritize oxygen concentration data collection; if they are in an urban area, it can prioritize air quality data collection. Similarly, if the user is near the coast, it can prioritize humidity data collection. This enables the collection of appropriate data according to the user's geographical location. The data collection unit inputs geographical location information into a data generation AI, which then performs the collection of highly relevant data.

[0063] The health management system can analyze a user's social media activity and collect relevant health data. For example, if a user posts about exercise on social media, the data collection unit can prioritize collecting exercise data; if they post about food, it can prioritize collecting food data. It can also prioritize collecting stress level data if they post about stress. This enables appropriate data collection based on the user's social media activity. The data collection unit inputs social media activity data into a generating AI, which then performs the collection of relevant data.

[0064] The health management system can analyze a user's past health data and select the optimal data collection method. For example, it can select the most accurate data collection method from the user's past data and customize the collection method to improve accuracy. It can also dynamically adjust the collection method based on the user's past data. This allows the system to analyze the user's past health data and select the optimal data collection method. The data collection unit inputs the user's past health data into a generating AI, which then performs the task of selecting the optimal data collection method.

[0065] The health management system can filter health data collection based on the user's current lifestyle and areas of interest. For example, if a user is on a diet, it can prioritize the collection of dietary data; if interested in exercise, it can prioritize the collection of exercise data. Similarly, if interested in stress management, it can prioritize the collection of stress level data. This enables the collection of appropriate data based on the user's current lifestyle and areas of interest. The data collection unit inputs data on the user's lifestyle and areas of interest into a generating AI, which then performs the filtering.

[0066] The health management system can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is at high altitude, oxygen concentration data can be prioritized; if they are in an urban area, air quality data can be prioritized. Similarly, if the user is near the coast, humidity data can be prioritized. This enables the collection of appropriate data based on the user's geographical location. The data collection unit inputs geographical location information into a generating AI, which then performs the collection of highly relevant data.

[0067] The health management system can adjust the level of detail in advice based on the importance of the health data when providing advice. For example, it can provide detailed advice for important health data and concise advice for general health data. It can also provide detailed advice for data of high user interest. This enables the provision of appropriate advice based on the importance of the health data. The system inputs the importance of the health data into the generating AI and allows the AI ​​to adjust the level of detail of the advice.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects health data. This data includes heart rate, blood pressure, body temperature, and activity level. The data collection unit works in conjunction with smart glasses and smart bottles to monitor the user's alcohol consumption in real time. It can also work with wearable devices, smart thermostats, and smart lighting to monitor the user's stress levels in their daily life. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, it can analyze the collected heart rate data to assess the user's health status. It can also analyze the situations that trigger alcohol consumption and suggest alternative beverages. Step 3: The service provider provides lifestyle improvement advice based on the analysis results obtained by the analysis department. This advice includes dietary improvements, exercise recommendations, and improvements to sleep quality. For example, it can automatically activate relaxation mode in conjunction with smart lighting and smart speakers to provide a relaxing environment. It can also automatically adjust room lighting and temperature and play relaxation music through smart speakers if high stress levels are detected, thus implementing stress reduction measures. Step 4: The monitoring unit monitors the user's stress level. Stress levels are measured using methods such as heart rate variability, skin electrical activity, and self-reporting. For example, heart rate variability is measured to assess stress levels. Skin electrical activity is measured to assess stress levels. Stress levels are assessed based on self-reporting.

[0070] (Example of form 2) The health management system according to an embodiment of the present invention is designed for people in their 30s and 40s, a demographic where an increasing number of people are expected to have poor health checkup results. The system utilizes an AI agent in conjunction with IoT devices to autonomously collect and analyze health data in a smart home environment, providing real-time advice on improving lifestyle habits and contributing to increased health awareness and disease prevention. The health management system involves the AI ​​agent working with smart glasses and smart bottles to monitor the user's alcohol consumption in real time. If the user frequently drinks alcohol, the AI ​​agent analyzes the triggers for drinking and suggests alternative beverages. It also works with smart lighting and smart speakers to automatically activate relaxation mode, providing a relaxing environment. Next, the AI ​​agent works with a smart fitness mirror to provide real-time exercise programs as a virtual fitness trainer. It monitors the user's exercise performance and sets exercise challenges incorporating gamification elements. The smart fitness mirror provides real-time feedback to maintain user motivation. Furthermore, the AI ​​agent works with wearable devices (such as smartwatches), smart thermostats, and smart lighting to monitor the user's daily stress levels. If high stress levels are detected, the AI ​​agent automatically adjusts room lighting and temperature, and plays relaxation music through a smart speaker, implementing stress-reducing measures. It also integrates with local smart home networks to group users with similar health risks and connects with regular health checkup data. The AI ​​agent analyzes the diagnostic results and provides personalized health plans. It also collaborates with local clinics and hospitals to suggest fitness events and health seminars based on the diagnostic results. Finally, an AI nutritional diagnostic function is added to the kitchen assistant, which integrates with smart refrigerators and ovens. The AI ​​analyzes the user's dietary data and calculates necessary nutrients and calories in real time. Based on this, it suggests optimal recipes, and smart devices guide the cooking process, providing comprehensive management of the user's health.In this way, the AI ​​agent works in conjunction with IoT devices to autonomously collect and analyze health data in a smart home environment, and provides real-time advice on improving lifestyle habits, thereby contributing to increased health awareness and the promotion of disease prevention. As a result, the health management system can contribute to improving users' health awareness and promoting disease prevention.

[0071] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a provision unit, and a monitoring unit. The data collection unit collects health data. Health data includes, but is not limited to, heart rate, blood pressure, body temperature, and activity level. The data collection unit monitors the user's alcohol consumption in real time, for example, in conjunction with smart glasses or a smart bottle. The data collection unit can also monitor the user's daily stress level in conjunction with a wearable device, a smart thermostat, or smart lighting. For example, the data collection unit measures the amount of alcohol consumed using the sensors in smart glasses and collects the data. The smart bottle measures the amount of alcohol consumed in real time and collects the data. The wearable device measures heart rate and activity level and collects the data. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the collected heart rate data and evaluates the user's health status. The analysis unit can also analyze situations that trigger alcohol consumption and suggest alternative beverages. For example, the analysis unit analyzes triggers for drinking, such as stress and social events, and suggests non-alcoholic or health beverages. The service unit provides lifestyle improvement advice based on the analysis results obtained by the analysis unit. Lifestyle improvement advice includes, but is not limited to, dietary improvements, exercise recommendations, and improved sleep quality. For example, the service unit can automatically activate relaxation mode in conjunction with smart lighting and smart speakers to provide a relaxing environment. The service unit can also automatically adjust room lighting and temperature and play relaxation music from a smart speaker if it detects increased stress levels, thus implementing stress reduction measures. For example, the service unit adjusts the color and brightness of the lighting to provide a relaxing environment. The smart speaker plays relaxation music to reduce stress. The monitoring unit monitors the user's stress level. Stress level measurement uses, but is not limited to, heart rate variability, skin electrical activity, and self-reporting. For example, the monitoring unit measures heart rate variability to assess the stress level.Skin electrical activity is measured to assess stress levels. Stress levels are assessed based on self-reports. This enables the health management system according to the embodiment to collect, analyze, provide advice on health data, and monitor stress levels.

[0072] The data collection unit collects health data. This data includes, but is not limited to, heart rate, blood pressure, body temperature, and activity level. The data collection unit can, for example, work with smart glasses or smart bottles to monitor the user's alcohol consumption in real time. Specifically, smart glasses have built-in sensors that can detect the amount and type of liquid the user has consumed. Smart bottles use built-in sensors to measure alcohol consumption in real time and transmit this data to the data collection unit. Furthermore, the data collection unit can also work with wearable devices, smart thermostats, and smart lighting to monitor the user's stress levels in their daily lives. Wearable devices collect data such as heart rate, activity level, and sleep patterns, and use this data to assess the user's stress level. Smart thermostats monitor indoor temperature and humidity, providing data to assess the user's comfort level. Smart lighting adjusts the color and brightness of the lighting to collect data that improves the user's relaxation level. As a result, the data collection unit can collect a wide range of data from various devices and understand the user's health status and stress level in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0073] The analysis department analyzes the data collected by the data collection department. This analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, it analyzes collected heart rate data to assess the user's health status. For example, it can analyze heart rate variability patterns and, if abnormal heart rate fluctuations are detected, can provide early warnings of health risks. The analysis department can also analyze situations that trigger alcohol consumption and suggest alternative beverages. For example, it can analyze triggers such as stress or social events and suggest non-alcoholic or health drinks. Furthermore, the analysis department can assess the user's daily stress level and provide specific advice for stress reduction. For example, it can analyze heart rate variability and skin electrical activity data to assess the user's stress level. This allows the analysis department to quickly and accurately analyze collected data and understand the user's health status and stress level in real time. Additionally, the analysis department can utilize historical data and statistical information to conduct long-term health risk assessments and trend analyses. For example, it can predict fluctuations in specific health risks based on past health data and develop future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0074] The service provider provides lifestyle improvement advice based on the analysis results obtained by the analysis provider. This advice includes, but is not limited to, dietary improvements, exercise recommendations, and improved sleep quality. Specifically, the service provider can automatically activate relaxation mode in conjunction with smart lighting and smart speakers to provide a relaxing environment. For example, if high stress levels are detected, it can automatically adjust room lighting and temperature, and play relaxation music through a smart speaker to reduce stress. The service provider adjusts the color and brightness of the lighting to create a relaxing environment. The smart speaker plays relaxation music to reduce stress. The service provider can also provide dietary improvement advice tailored to the user's health condition. For example, based on the user's health data, it can suggest a nutritionally balanced meal plan to support dietary improvements. Furthermore, the service provider can recommend exercise and provide specific advice for improving sleep quality. For example, based on the user's activity data, it can suggest an appropriate exercise plan to support the habit of exercising. It can also provide advice to improve sleep quality based on sleep data, thereby comprehensively improving the user's health. This allows the service provider to offer specific advice tailored to the user's health condition and support improvements in their lifestyle.

[0075] The monitoring unit monitors the user's stress level. Stress level measurement may include, but is not limited to, heart rate variability, skin electrical activity, and self-reporting. Specifically, the monitoring unit measures heart rate variability to assess stress levels. Heart rate variability is an important indicator for assessing a user's stress level by analyzing the pattern of heart rate fluctuations. It also measures skin electrical activity to assess stress levels. Skin electrical activity is an indicator for assessing a user's stress level by measuring changes in the electrical resistance of the skin. Furthermore, stress levels can also be assessed based on self-reporting. Based on the stress levels self-reported by the user, the monitoring unit assesses the user's subjective stress level. This allows the monitoring unit to comprehensively evaluate and monitor the user's stress level in real time. Furthermore, based on the collected data, the monitoring unit can continuously track fluctuations in stress levels and support long-term stress management. For example, based on past stress level data, it can predict fluctuations in stress levels under specific situations or conditions and formulate future countermeasures. The monitoring unit can also issue early warnings if abnormal stress levels are detected. This allows the monitoring unit to monitor users' stress levels in real time, enabling long-term stress management and anomaly detection, thereby improving the overall reliability and safety of the system.

[0076] The data collection unit can monitor a user's alcohol consumption in real time in conjunction with smart glasses and smart bottles. For example, the data collection unit can measure alcohol consumption using the sensors in smart glasses and collect the data. The data collection unit can also measure alcohol consumption in real time using a smart bottle and collect the data. For example, a smart bottle measures alcohol consumption, collects the data, and transmits it to a smartphone. The data collection unit can also integrate the data from the smart glasses and smart bottle to comprehensively monitor the user's alcohol consumption. For example, the data collection unit combines the data from the smart glasses sensors and the smart bottle to understand the user's alcohol consumption in real time. This allows for real-time monitoring of the user's alcohol consumption. For example, smart glasses have built-in sensors to measure alcohol consumption. For example, a smart bottle measures alcohol consumption and collects the data. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input data from smart glasses and smart bottles into a generating AI and have the generating AI perform alcohol consumption monitoring.

[0077] The analysis unit can analyze situations that trigger drinking and suggest alternative beverages. For example, the analysis unit can analyze triggers for drinking, such as stress or social events, and suggest non-alcoholic or health drinks. For example, the analysis unit can analyze a user's stress level and suggest an alternative beverage when stress levels are high. The analysis unit can also analyze data from social events to identify situations that trigger drinking and suggest alternative beverages. For example, the analysis unit can analyze a user's calendar information and suggest an alternative beverage before a social event. The analysis unit can also analyze a user's drinking history to identify patterns that trigger drinking and suggest alternative beverages. For example, the analysis unit can analyze a user's drinking history and suggest an alternative beverage if there is a high amount of drinking on specific days or times. This allows for the reduction of alcohol consumption by analyzing triggers for drinking and suggesting alternative beverages. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can input data on users' stress levels and social events into a generating AI, which can then analyze triggers for drinking and suggest alternative beverages.

[0078] The service provider can automatically activate relaxation mode in conjunction with smart lighting and smart speakers to provide a relaxing environment. For example, the service provider can adjust the color and brightness of smart lighting to provide a relaxing environment. The service provider can also play relaxation music using a smart speaker to provide a relaxing environment. For example, the service provider can change the color of smart lighting to a warm color and adjust the brightness to provide a relaxing environment. The service provider can also play relaxation music using a smart speaker to reduce stress. For example, the service provider can play nature sounds or healing music from a smart speaker to provide a relaxing environment. The service provider can also link smart lighting and smart speakers to automatically activate relaxation mode. For example, the service provider can monitor the user's stress level and activate relaxation mode if it detects that stress has increased. This allows the relaxation mode to be automatically activated and a relaxing environment to be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input commands for smart lighting and smart speakers into the generating AI, and have the generating AI execute the activation of relaxation mode.

[0079] The monitoring unit can monitor the user's daily stress levels in conjunction with wearable devices, smart thermostats, and smart lighting. For example, the monitoring unit can measure heart rate and activity levels using wearable devices and evaluate stress levels. It can also measure room temperature using a smart thermostat and evaluate stress levels. For example, the monitoring unit can detect an increase in stress levels when the room temperature is high. Furthermore, the monitoring unit can measure the brightness and color of lighting using smart lighting and evaluate stress levels. For example, the monitoring unit can detect an increase in stress levels when the lighting is too bright. This allows for monitoring of the user's daily stress levels. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data from wearable devices, smart thermostats, and smart lighting into a generating AI and have the generating AI perform stress level monitoring.

[0080] The service provider can automatically adjust room lighting and temperature, and play relaxation music from a smart speaker, if it detects that stress levels have risen, thereby implementing stress reduction measures. For example, the service provider can adjust the color and brightness of smart lighting to provide a relaxing environment. The service provider can also adjust the room temperature using a smart thermostat to provide a relaxing environment. For example, the service provider can adjust the room temperature to a comfortable level to reduce stress. The service provider can also play relaxation music using a smart speaker to provide a relaxing environment. For example, the service provider can play nature sounds or healing music from a smart speaker to reduce stress. The service provider can also coordinate smart lighting, a smart thermostat, and a smart speaker to automatically implement stress reduction measures. For example, the service provider can monitor the user's stress level and implement stress reduction measures if it detects that stress levels have risen. This allows for the automatic implementation of stress reduction measures when stress levels rise. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the supply unit can input the control of smart lighting, smart thermostats, and smart speakers into the generating AI, and have the generating AI execute stress reduction measures.

[0081] The data collection unit can work with local smart home networks to group users with similar health risks. For example, the data collection unit can collect users' health data through the local smart home network and identify users with similar health risks. The data collection unit can also group users with similar health risks and provide health management plans for each group. For example, the data collection unit can group users with high cardiovascular risk and provide a cardiovascular risk management plan. The data collection unit can also group users with high diabetes risk and provide a diabetes risk management plan. For example, the data collection unit can analyze blood glucose data to identify and group users with high diabetes risk. This allows for effective health management by grouping users with similar health risks. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input health data collected from the local smart home network into a generating AI and have the generating AI perform the grouping of users with similar health risks.

[0082] The analysis department can analyze regular health checkup data and provide individualized health plans. For example, the analysis department collects regular health checkup data and evaluates the user's health status. The analysis department can also create individualized health plans based on the health checkup data and provide them to the service provider. For example, the analysis department analyzes blood test results and physical measurement data to evaluate the user's health status. The analysis department can also identify the user's health risks and create health plans tailored to those risks. For example, the analysis department can create a cardiovascular risk management plan for users with a high cardiovascular risk. The analysis department can also create a diabetes risk management plan for users with a high diabetes risk. For example, the analysis department can analyze blood glucose data and create a health plan for users with a high diabetes risk, including advice on diet and exercise. This allows for the analysis of regular health checkup data and the provision of individualized health plans. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input regular health checkup data into a generating AI and have the generating AI create individualized health plans.

[0083] The service provider can collaborate with local clinics and medical offices to propose fitness events and health seminars based on diagnostic results. For example, the service provider can receive diagnostic results from local clinics and medical offices and evaluate the user's health status. The service provider can also propose fitness events and health seminars based on the diagnostic results. For example, the service provider can propose a fitness event for cardiovascular risk management to a user with a high cardiovascular risk. The service provider can also propose a health seminar for diabetes risk management to a user with a high diabetes risk. For example, the service provider can collaborate with local clinics and medical offices to plan fitness events and health seminars tailored to the user's health status. This allows the service provider to propose fitness events and health seminars based on diagnostic results. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input diagnostic results into a generating AI and have the generating AI execute proposals for fitness events and health seminars.

[0084] The data collection unit can collect user meal data in conjunction with smart refrigerators and smart ovens. For example, the data collection unit can collect food management data using a smart refrigerator and understand the user's meal data. The data collection unit can also collect cooking data using a smart oven and understand the user's meal data. For example, the data collection unit can use the smart refrigerator's sensors to understand the food inventory status and collect the user's meal data. The data collection unit can also collect cooking data from a smart oven and understand the user's meal data. For example, the data collection unit can analyze the smart oven's cooking history and collect the user's meal data. The data collection unit can also integrate data from smart refrigerators and smart ovens to comprehensively understand the user's meal data. For example, the data collection unit can combine food data from a smart refrigerator and cooking data from a smart oven to collect the user's meal data. This allows the data collection of the user's meal data to be performed. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from smart refrigerators and smart ovens into a generating AI and have the generating AI perform the collection of meal data.

[0085] The analysis unit can analyze meal data and calculate necessary nutrients and calories. For example, the analysis unit can analyze collected meal data and evaluate the user's nutritional status. The analysis unit can also calculate necessary nutrients and calories based on meal data and provide them to the supply unit. For example, the analysis unit can analyze meal data and evaluate the user's intake of nutrients such as vitamins, minerals, and proteins. The analysis unit can also calculate calories based on meal data and evaluate the user's calorie intake. For example, the analysis unit can analyze meal data using a calorie chart for ingredients and evaluate the user's calorie intake. The analysis unit can also evaluate nutritional balance based on meal data and calculate necessary nutrients and calories. For example, the analysis unit can analyze meal data, evaluate the user's nutritional balance, and calculate necessary nutrients and calories. This allows the analysis unit to analyze meal data and calculate necessary nutrients and calories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI perform the calculation of necessary nutrients and calories.

[0086] The service provider can suggest an optimal recipe, and a smart device can guide the cooking process. For example, the service provider can suggest an optimal recipe based on nutrients and calories calculated by the analysis unit. The service provider can also guide the cooking process using a smart device. For example, the service provider can suggest a recipe that takes into account the user's nutritional balance and guide the cooking process using a smart device. The service provider can also suggest a recipe that takes into account the user's calorie intake and guide the cooking process using a smart device. For example, the service provider can suggest a low-calorie recipe for a user who needs to restrict calories and guide the cooking process using a smart device. The service provider can also suggest a recipe that supplements a specific nutrient for a user who is deficient in that nutrient and guide the cooking process using a smart device. For example, the service provider can suggest a recipe that is rich in vitamin C for a user who is deficient in vitamin C and guide the cooking process using a smart device. This allows the service provider to suggest an optimal recipe and guide the cooking process. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the supply unit can input the results of nutrient and calorie calculations into a generating AI, which can then generate the AI ​​to suggest the optimal recipe and provide cooking instructions.

[0087] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. Conversely, if the user is relaxed, the data collection unit can advance the collection timing to collect accurate data. For example, if the user is in a hurry, the data collection unit can shorten the collection timing to collect data quickly. The data collection unit can also dynamically adjust the collection timing based on the user's emotions. For example, the data collection unit can analyze the user's emotional data to determine the optimal collection timing. This allows the timing of health data collection to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotional data into a generative AI and have the generative AI adjust the collection timing.

[0088] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can select the collection method that yields the most accurate data from the user's past data. The data collection unit can also customize the collection method based on the user's past data to improve accuracy. For example, the data collection unit can analyze the user's past data and change the collection method to maintain data consistency. The data collection unit can also dynamically adjust the collection method based on the user's past data. For example, the data collection unit can analyze the user's past data and select the optimal collection method. This allows the data collection unit to analyze the user's past health data and select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal collection method.

[0089] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit will prioritize collecting dietary data. Similarly, if the user is interested in exercise, the data collection unit can prioritize collecting exercise data. For example, if the user is interested in stress management, the data collection unit will prioritize collecting stress level data. Furthermore, the data collection unit can dynamically adjust the collected data based on the user's current lifestyle and areas of interest. For example, the data collection unit can analyze the user's lifestyle and areas of interest to collect the most relevant data. This allows for filtering of health data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0090] The data collection unit can estimate the user's emotions and prioritize the health data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress level data. Conversely, if the user is relaxed, the data collection unit can collect overall health data in a balanced manner. For example, if the user is in a hurry, the data collection unit will prioritize collecting only essential data. The data collection unit can also dynamically adjust the priority of collected data based on the user's emotions. For example, the data collection unit can analyze the user's emotional data to determine the optimal priority of collected data. This allows for the prioritization of health data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotional data into a generative AI and have the generative AI determine the priority of collected data.

[0091] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of oxygen concentration data. Similarly, if the user is in an urban area, the data collection unit can prioritize the collection of air quality data. For example, if the user is near the sea, the data collection unit will prioritize the collection of humidity data. Furthermore, the data collection unit can dynamically adjust the collected data based on the user's geographical location. For example, the data collection unit can analyze the user's geographical location and collect the most relevant data. This allows for the priority collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI collect highly relevant data.

[0092] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user posts about exercise on social media, the data collection unit will prioritize collecting exercise data. Similarly, if the user posts about food on social media, the data collection unit can prioritize collecting dietary data. For example, if the user posts about stress on social media, the data collection unit will prioritize collecting stress level data. Furthermore, the data collection unit can dynamically adjust the collected data based on the user's social media activity. For example, the data collection unit analyzes the user's social media activity and collects the most relevant data. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0093] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. Conversely, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. Furthermore, the analysis unit can dynamically adjust the presentation of the analysis based on the user's emotions. For example, the analysis unit can analyze the user's emotional data and determine the optimal presentation of the analysis. This allows the presentation of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotional data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. The analysis unit can also perform a concise analysis on general health data. For example, the analysis unit will perform a detailed analysis on data of high interest to the user. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the health data. For example, the analysis unit will evaluate the importance of the health data and determine the optimal level of detail of the analysis. This allows the level of detail of the analysis to be adjusted based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0095] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an algorithm that evaluates exercise performance to exercise data. It can also apply an algorithm that evaluates nutritional balance to diet data. For example, the analysis unit can apply an algorithm that evaluates stress management to stress level data. Furthermore, the analysis unit can dynamically adjust the analysis algorithm depending on the category of health data. For example, the analysis unit can evaluate the category of health data and determine the optimal analysis algorithm. This allows the optimal analysis algorithm to be applied according to the category of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of health data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. Conversely, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis. The analysis unit can also dynamically adjust the length of the analysis based on the user's emotions. For example, the analysis unit can analyze the user's emotion data to determine the optimal analysis length, thereby adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0097] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can also analyze current data while referring to past data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the timing of health data collection. For example, the analysis unit may evaluate the timing of health data collection and determine the optimal analysis priority. This allows the analysis priority to be determined based on the timing of health data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the timing of health data collection into a generating AI and have the generating AI determine the analysis priority.

[0098] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. The analysis unit can also evaluate the relevance of health data and determine the optimal order of analysis. For example, the analysis unit can evaluate the correlations and common factors of the data and determine the optimal order of analysis. This allows the order of analysis to be adjusted based on the relevance of health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0099] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and visually easy-to-understand advice. If the user is relaxed, the service provider can also provide detailed advice. For example, if the user is in a hurry, the service provider can provide concise advice that gets straight to the point. Furthermore, the service provider can dynamically adjust the way advice is presented based on the user's emotions. For example, the service provider can analyze the user's emotion data to determine the optimal way to present the advice. This allows the service provider to adjust the way advice is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way advice is presented.

[0100] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice for important health data. It can also provide concise advice for general health data. For example, the service provider can provide detailed advice for data of high interest to the user. Furthermore, the service provider can dynamically adjust the level of detail of advice based on the importance of the health data. For example, the service provider can evaluate the importance of the health data and determine the optimal level of detail for the advice. This allows the service provider to adjust the level of detail of advice based on the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0101] The service provider can apply different advice algorithms depending on the category of health data when providing advice. For example, for exercise data, the service provider can apply an algorithm that suggests an exercise program. For diet data, the service provider can also apply an algorithm that suggests nutritional balance. For example, for stress level data, the service provider can apply an algorithm that suggests stress management. Furthermore, the service provider can dynamically adjust the advice algorithm depending on the category of health data. For example, the service provider can evaluate the category of health data and determine the optimal advice algorithm. This allows the service provider to apply the most appropriate advice algorithm according to the category of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of health data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0102] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider will provide short, concise advice. Conversely, if the user is relaxed, the service provider can provide detailed advice. For example, if the user is excited, the service provider will provide visually stimulating advice. Furthermore, the service provider can dynamically adjust the length of the advice based on the user's emotions. For example, the service provider can analyze the user's emotion data to determine the optimal advice length, thereby adjusting the advice length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0103] The service provider can determine the priority of advice based on the timing of health data collection when providing advice. For example, the service provider can provide advice based on recently collected data. Alternatively, the service provider can provide advice based on current data while referring to past data. For example, the service provider can provide advice based on data collected during a specific period. Furthermore, the service provider can dynamically adjust the priority of advice based on the timing of health data collection. For example, the service provider can evaluate the timing of health data collection and determine the optimal priority of advice. This allows the service provider to determine the priority of advice based on the timing of health data collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the timing of health data collection into a generating AI and have the generating AI determine the priority of advice.

[0104] The service provider can adjust the order of advice based on the relevance of health data when providing advice. For example, the service provider can provide advice based on highly relevant data. It can also provide advice later for less relevant data. For example, the service provider can dynamically adjust the order of advice based on the relevance of the data. The service provider can also evaluate the relevance of health data and determine the optimal order of advice. For example, the service provider can evaluate the correlation and common factors of the data and determine the optimal order of advice. This allows the order of advice to be adjusted based on the relevance of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0105] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the user is stressed, the monitoring unit can tighten the stress level monitoring criteria. Conversely, if the user is relaxed, the monitoring unit can also loosen the monitoring criteria. For example, if the user is in a hurry, the monitoring unit can simplify the monitoring criteria. The monitoring unit can also dynamically adjust the monitoring criteria based on the user's emotions. For example, the monitoring unit can analyze the user's emotion data and determine the optimal monitoring criteria. This allows the monitoring criteria to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the monitoring criteria.

[0106] The monitoring unit can improve the accuracy of monitoring based on the interrelationships of health data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by considering the interrelationships between exercise data and heart rate data. The monitoring unit can also improve the accuracy of monitoring by considering the interrelationships between diet data and blood glucose data. For example, the monitoring unit can improve the accuracy of monitoring by considering the interrelationships between stress level data and sleep data. Furthermore, the monitoring unit can dynamically adjust the accuracy of monitoring based on the interrelationships of health data. For example, the monitoring unit evaluates the interrelationships of health data and determines the optimal monitoring accuracy. This allows for improved monitoring accuracy based on the interrelationships of health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the interrelationships of health data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0107] The monitoring unit can perform monitoring based on the attribute information of the health data submitter during monitoring. For example, the monitoring unit can adjust the monitoring criteria considering the user's age. The monitoring unit can also adjust the monitoring criteria considering the user's gender. For example, the monitoring unit can adjust the monitoring criteria considering the user's lifestyle. Furthermore, the monitoring unit can dynamically adjust the monitoring criteria based on the attribute information of the health data submitter. For example, the monitoring unit can evaluate the attribute information of the health data submitter and determine the optimal monitoring criteria. This enables monitoring based on the attribute information of the health data submitter. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the attribute information of the health data submitter into a generating AI and have the generating AI perform the adjustment of the monitoring criteria.

[0108] The monitoring unit can estimate the user's emotions and adjust the order in which monitoring results are displayed based on the estimated emotions. For example, if the user is stressed, the monitoring unit may prioritize displaying important results. Conversely, if the user is relaxed, the monitoring unit may display overall results in a balanced manner. For example, if the user is in a hurry, the monitoring unit may prioritize displaying results that highlight key points. Furthermore, the monitoring unit can dynamically adjust the display order of monitoring results based on the user's emotions. For example, the monitoring unit can analyze the user's emotion data and determine the optimal display order of monitoring results. This allows the display order of monitoring results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display order of monitoring results.

[0109] The monitoring unit can perform monitoring based on the geographical distribution of health data during monitoring. For example, if the user is at high altitude, the monitoring unit will prioritize monitoring oxygen concentration data. Similarly, if the user is in an urban area, the monitoring unit can prioritize monitoring air quality data. For example, if the user is near the sea, the monitoring unit will prioritize monitoring humidity data. Furthermore, the monitoring unit can dynamically adjust monitoring criteria based on the geographical distribution of health data. For example, the monitoring unit can evaluate the geographical distribution of health data and determine the optimal monitoring criteria. This enables monitoring based on the geographical distribution of health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical distribution of health data into a generating AI and have the generating AI adjust the monitoring criteria.

[0110] The monitoring unit can improve the accuracy of monitoring based on relevant literature on health data during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to the latest research papers. The monitoring unit can also improve the accuracy of monitoring by referring to past research data. For example, the monitoring unit can adjust the monitoring criteria by referring to relevant literature. The monitoring unit can also dynamically adjust the monitoring criteria based on relevant literature on health data. For example, the monitoring unit can evaluate relevant literature on health data and determine the optimal monitoring criteria. This allows the accuracy of monitoring to be improved based on relevant literature on health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature on health data into a generating AI and have the generating AI perform the adjustment of the monitoring criteria.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] A health management system can estimate a user's emotions and adjust how health data is collected based on those estimates. For example, if a user is stressed, the data collection unit can prioritize monitoring stress levels; if relaxed, it can collect overall health data in a balanced manner. If a user is in a hurry, the unit can quickly collect only the essential data. This enables flexible data collection tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. For instance, the data collection unit can input user emotion data into the generative AI, which can then adjust the data collection method.

[0113] The health management system can optimize the collection of health data based on the user's geographical location. For example, if the user is at high altitude, the data collection unit can prioritize oxygen concentration data collection; if they are in an urban area, it can prioritize air quality data collection. Similarly, if the user is near the coast, it can prioritize humidity data collection. This enables the collection of appropriate data according to the user's geographical location. The data collection unit inputs geographical location information into a data generation AI, which then performs the collection of highly relevant data.

[0114] The health management system can analyze a user's social media activity and collect relevant health data. For example, if a user posts about exercise on social media, the data collection unit can prioritize collecting exercise data; if they post about food, it can prioritize collecting food data. It can also prioritize collecting stress level data if they post about stress. This enables appropriate data collection based on the user's social media activity. The data collection unit inputs social media activity data into a generating AI, which then performs the collection of relevant data.

[0115] A health management system can estimate a user's emotions and prioritize health data based on those emotions. For example, if a user is stressed, the data collection unit will prioritize collecting stress level data; if the user is relaxed, it can collect overall health data in a balanced manner. Furthermore, if the user is in a hurry, only essential data can be prioritized. This enables flexible data collection tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. For instance, the data collection unit can input user emotion data into the generative AI, which can then determine the priority of the data to be collected.

[0116] The health management system can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, the analysis unit can provide simple and visually easy-to-understand results, while if they are relaxed, it can provide detailed results. Furthermore, if the user is in a hurry, it can provide concise and to-the-point results. This enables the provision of flexible analysis results tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. For instance, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0117] The health management system can analyze a user's past health data and select the optimal data collection method. For example, it can select the most accurate data collection method from the user's past data and customize the collection method to improve accuracy. It can also dynamically adjust the collection method based on the user's past data. This allows the system to analyze the user's past health data and select the optimal data collection method. The data collection unit inputs the user's past health data into a generating AI, which then performs the task of selecting the optimal data collection method.

[0118] The health management system can filter health data collection based on the user's current lifestyle and areas of interest. For example, if a user is on a diet, it can prioritize the collection of dietary data; if interested in exercise, it can prioritize the collection of exercise data. Similarly, if interested in stress management, it can prioritize the collection of stress level data. This enables the collection of appropriate data based on the user's current lifestyle and areas of interest. The data collection unit inputs data on the user's lifestyle and areas of interest into a generating AI, which then performs the filtering.

[0119] The health management system can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is at high altitude, oxygen concentration data can be prioritized; if they are in an urban area, air quality data can be prioritized. Similarly, if the user is near the coast, humidity data can be prioritized. This enables the collection of appropriate data based on the user's geographical location. The data collection unit inputs geographical location information into a generating AI, which then performs the collection of highly relevant data.

[0120] The health management system can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is stressed, the system can provide simple, visually easy-to-understand advice; if the user is relaxed, it can provide detailed advice. If the user is in a hurry, it can provide concise, to-the-point advice. This enables the provision of flexible advice tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. For example, the system can input user emotion data into the generative AI and have the AI ​​adjust the way advice is presented.

[0121] The health management system can adjust the level of detail in advice based on the importance of the health data when providing advice. For example, it can provide detailed advice for important health data and concise advice for general health data. It can also provide detailed advice for data of high user interest. This enables the provision of appropriate advice based on the importance of the health data. The system inputs the importance of the health data into the generating AI and allows the AI ​​to adjust the level of detail of the advice.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects health data. This data includes heart rate, blood pressure, body temperature, and activity level. The data collection unit works in conjunction with smart glasses and smart bottles to monitor the user's alcohol consumption in real time. It can also work with wearable devices, smart thermostats, and smart lighting to monitor the user's stress levels in their daily life. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, it can analyze the collected heart rate data to assess the user's health status. It can also analyze the situations that trigger alcohol consumption and suggest alternative beverages. Step 3: The service provider provides lifestyle improvement advice based on the analysis results obtained by the analysis department. This advice includes dietary improvements, exercise recommendations, and improvements to sleep quality. For example, it can automatically activate relaxation mode in conjunction with smart lighting and smart speakers to provide a relaxing environment. It can also automatically adjust room lighting and temperature and play relaxation music through smart speakers if high stress levels are detected, thus implementing stress reduction measures. Step 4: The monitoring unit monitors the user's stress level. Stress levels are measured using methods such as heart rate variability, skin electrical activity, and self-reporting. For example, heart rate variability is measured to assess stress levels. Skin electrical activity is measured to assess stress levels. Stress levels are assessed based on self-reporting.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, supply unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit measures the amount of alcohol consumed using sensors in smart glasses or a smart bottle and collects data. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and evaluates the user's health status. The supply unit automatically activates a relaxation mode in cooperation with smart lighting or a smart speaker to provide a relaxing environment. The monitoring unit monitors the user's daily stress level in cooperation with a wearable device, a smart thermostat, and smart lighting. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, supply unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit measures the amount of alcohol consumed using sensors in the smart glasses 214 or a smart bottle and collects data. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and evaluates the user's health status. The supply unit automatically activates a relaxation mode in cooperation with smart lighting and smart speakers to provide a relaxing environment. The monitoring unit monitors the user's daily stress level in cooperation with wearable devices, smart thermostats, and smart lighting. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, supply unit, and monitoring unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit measures the amount of alcohol consumed using the headset terminal 314 or a sensor on a smart bottle and collects data. The analysis unit analyzes the collected data by the identification processing unit 290 of the data processing unit 12 and evaluates the user's health status. The supply unit automatically activates a relaxation mode in cooperation with smart lighting and smart speakers to provide a relaxing environment. The monitoring unit monitors the user's daily stress level in cooperation with wearable devices, smart thermostats, and smart lighting. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, supply unit, and monitoring unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit measures the amount of alcohol consumed using sensors on the robot 414 or a smart bottle and collects data. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and evaluates the user's health status. The supply unit automatically activates a relaxation mode in cooperation with smart lighting and smart speakers to provide a relaxing environment. The monitoring unit monitors the user's daily stress level in cooperation with wearable devices, smart thermostats, and smart lighting. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A data collection unit that collects health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides advice on improving lifestyle habits based on the analysis results obtained by the aforementioned analysis unit, It includes a monitoring unit that monitors the user's stress level. A system characterized by the following features. (Note 2) The aforementioned collection unit is It works in conjunction with smart glasses and smart bottles to monitor the user's alcohol consumption in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the situations that trigger drinking and suggest alternative beverages. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It automatically activates relaxation mode in conjunction with smart lighting and smart speakers, providing a relaxing environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, It works in conjunction with wearable devices, smart thermostats, and smart lighting to monitor the user's stress levels in their daily life. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, If high stress levels are detected, the system will automatically adjust the room lighting and temperature, and play relaxation music through a smart speaker, among other stress-reducing measures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It connects with local smart home networks to group users with similar health risks. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is We analyze regular health checkup data and provide personalized health plans. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, We collaborate with local clinics and medical offices to propose fitness events and health seminars based on diagnostic results. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It collects user meal data by linking with smart refrigerators and smart ovens. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is Analyze your meal data and calculate the necessary nutrients and calories. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, It suggests the optimal recipe, and a smart device guides you through the cooking process. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The monitoring unit, The system estimates user sentiment and adjusts monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The monitoring unit, During monitoring, improve the accuracy of monitoring based on the interrelationships of health data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The monitoring unit, During monitoring, the monitoring is performed based on the attribute information of the person submitting the health data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The monitoring unit, It estimates the user's emotions and adjusts the order in which monitoring results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The monitoring unit, During monitoring, monitoring is performed based on the geographical distribution of health data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The monitoring unit, During monitoring, improve the accuracy of monitoring based on relevant literature on health data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides advice on improving lifestyle habits based on the analysis results obtained by the aforementioned analysis unit, It includes a monitoring unit that monitors the user's stress level. A system characterized by the following features.

2. The aforementioned collection unit is It works in conjunction with smart glasses and smart bottles to monitor the user's alcohol consumption in real time. The system according to feature 1.

3. The aforementioned analysis unit is Analyze the situations that trigger drinking and suggest alternative beverages. The system according to feature 1.

4. The aforementioned supply unit is, It automatically activates relaxation mode in conjunction with smart lighting and smart speakers, providing a relaxing environment. The system according to feature 1.

5. The monitoring unit, It works in conjunction with wearable devices, smart thermostats, and smart lighting to monitor the user's stress levels in their daily life. The system according to feature 1.

6. The aforementioned supply unit is, If high stress levels are detected, the system will automatically adjust the room lighting and temperature, and play relaxation music through a smart speaker, among other stress-reducing measures. The system according to feature 1.

7. The aforementioned collection unit is It connects with local smart home networks to group users with similar health risks. The system according to feature 1.

8. The aforementioned analysis unit is We analyze regular health checkup data and provide personalized health plans. The system according to feature 1.

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