system

Abstract

We provide the system. [Solution] A means of building a model for obtaining general health information from a database, analyzing it, and providing health advice, A means for acquiring user biometric and behavioral information and transmitting it to the cloud, A means for analyzing individual user data on the cloud and generating a life program tailored to their health status and lifestyle, A means of providing the generated life program to the user and delivering reminders to support its execution, A means of acquiring user feedback and execution data and adjusting the life program, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Due to modern busy lives, many individuals find it difficult to obtain the information necessary to maintain their health and practice appropriate lifestyle improvement measures. As a result, it is impossible to take health measures suitable for individual health conditions and lifestyles, increasing the risk of health deterioration. The present invention aims to support individual health improvement and realize a healthy lifestyle by timely and efficiently providing health advice suitable for users. 【Means for Solving the Problems】 【0005】 This invention provides a means for acquiring general health information from a database, analyzing it, and constructing a model for providing health advice, as well as means for acquiring a user's biometric and behavioral information and transmitting it to the cloud. Furthermore, it provides means for analyzing the user's individual data on the cloud and generating a life program tailored to their health status and lifestyle. The generated life program is provided to the user, and means are included for delivering reminders to support its implementation. The system also acquires user feedback and implementation data and adjusts the life program accordingly. Through these means, the invention provides a system that supports users in easily implementing health improvement measures. 【0006】 "General health information" refers to information that is useful for maintaining health and preventing disease in common to many people, and is a collection of data obtained from reliable sources. 【0007】 A "database" is a digital information storage system designed to accumulate information and enable efficient management, retrieval, and analysis. 【0008】 "Health advice" refers to specific suggestions and instructions aimed at improving an individual's health condition or preventing illness. 【0009】 "Means of building a model" refers to the process of designing and creating mechanisms and calculation methods for analyzing data and making predictions and suggestions tailored to specific purposes. 【0010】 "Biometric information" refers to data about a user's physical state and function, including, for example, heart rate, steps taken, and sleep duration. 【0011】 "Behavioral information" refers to data about a user's daily activities and lifestyle, including location information and activity patterns. 【0012】 "Cloud" refers to computer resources and data storage provided via the internet, which users can use to store and process data. 【0013】 A "life program" is a specific lifestyle improvement plan tailored to each user's health condition and lifestyle habits, and includes elements such as diet, exercise, and sleep. 【0014】 A "reminder" is a notification function that informs users of important actions or schedules and encourages them to take action. 【0015】 "Feedback" refers to evaluations and opinions provided by users, and is important information for improving and adjusting the system. 【0016】 "Execution data" refers to the actions taken by users based on their life program and the data obtained as a result of those actions. [Brief explanation of the drawing] 【0017】 [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]Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0018】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0019】 First, the language used in the following description will be described. 【0020】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0021】 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. 【0022】 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. 【0023】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0024】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0025】 [First Embodiment] 【0026】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0027】 As shown in Figure 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. 【0028】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0029】 The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0030】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0031】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0032】 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. 【0033】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0034】 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. 【0035】 The 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. 【0036】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0037】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0038】 To implement this invention, it is necessary to build a system based on a cloud computing environment and coordinate processing according to the respective roles of the server, terminal, and user. The following is an explanation of the basic functions of each, along with specific examples. 【0039】 The server first acquires a wide range of health data from reliable health-related information sources on the internet and analyzes it using natural language processing and machine learning techniques. This allows it to build models for providing general health advice and store them in a database. 【0040】 The device collects biometric and behavioral information from the user's daily life through sensors and apps. This information is transmitted in real time to a server in the cloud, where it is individually analyzed based on the user's registered health goals and preferences. 【0041】 On the cloud, the system evaluates the user's current health status based on collected data and generates an optimal "life program" based on this evaluation. This program includes multiple elements such as diet, exercise, sleep, and stress management, and proposes specific and achievable improvement measures for the user. 【0042】 Users receive details about this program via their device and instructions on how to incorporate it into their daily lives. For example, if a user wants to improve their sleep, the server analyzes the user's sleep patterns and suggests an optimal sleep duration. The app on the device also sends reminders at specific times each night to help users go to bed at the recommended time. 【0043】 Meanwhile, the results and feedback from the user's actions are sent back to the server via the device, and the life program is adjusted as needed. For example, if a user is unable to complete the suggested amount of exercise, the server analyzes the reason and proposes a new exercise plan to help them achieve their goals without overexerting themselves. 【0044】 In this way, the system can dynamically adapt to the user's situation and continuously provide better lifestyle improvements. Through this interaction, users can manage their health at their own pace without undue pressure, reducing future health risks and maintaining long-term health. 【0045】 The following describes the processing flow. 【0046】 Step 1: 【0047】 The server crawls trusted health-related databases to collect the latest general health information. This includes online resources such as research papers, news articles, and medical websites. The collected information is analyzed using natural language processing techniques and integrated into a base model for health advice. 【0048】 Step 2: 【0049】 The device acquires everyday biometric information (e.g., heart rate, steps taken, calorie consumption) and behavioral information (e.g., location information, activity history) from the user's smartphone or wearable device. The acquired data is transmitted to the server in real time. 【0050】 Step 3: 【0051】 The server analyzes the received personal data in the cloud to assess the user's current health status and lifestyle. This allows for the identification of health risks tailored to individual needs and goals, and the establishment of a general framework for necessary improvement measures. 【0052】 Step 4: 【0053】 Based on the analysis results, the server generates a "life program" optimized for the user. This program includes specific actions aimed at achieving individual goals, such as dietary guidance, exercise plans, and sleep improvement strategies. 【0054】 Step 5: 【0055】 The device notifies the user of the generated life program and presents specific implementation methods and timelines. This includes alert and reminder functions to support the user in carrying out the plan in their daily life. 【0056】 Step 6: 【0057】 Users execute life programs based on instructions from their devices and record their progress and achievements. User feedback is also sent to the server via the device, and the program's effectiveness and areas for improvement are incorporated as part of the analysis. 【0058】 Step 7: 【0059】 The server evaluates the life program based on user feedback and execution data, and adjusts its content as needed. This ensures continuous optimization to achieve maximum effectiveness. 【0060】 Through these steps, the system provides users with personalized health improvement strategies and supports sustainable healthcare practices. 【0061】 (Example 1) 【0062】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0063】 In recent years, people's awareness of lifestyle-related diseases and health has increased, but it remains difficult to develop and implement appropriate and sustainable health management plans for each individual. While general health information is readily available, applying it to individual lifestyles and health conditions is challenging. Furthermore, there is a lack of mechanisms to support daily health management and to incorporate feedback and adjustments as needed. There is a need to address this challenge and provide users with methods to efficiently manage their own health. 【0064】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0065】 This invention includes a server that includes means for acquiring general health-related information from information sources, analyzing the data using natural language processing and machine learning techniques, and building a model for providing health advice; means for collecting biometric and behavioral data from the user's daily life through sensors and application software and transmitting it using cloud infrastructure technology; and means for analyzing the user's individual data on the cloud and generating a program tailored to their health status and daily habits. This makes it possible to quickly and efficiently provide users with an optimal health management plan that can be adapted to their individual health condition. 【0066】 "General health information" refers to a broad category of public health and medical data obtained from reliable sources. 【0067】 A “source” is a publicly available database or website on the internet or other media for collecting health-related information. 【0068】 "Natural language processing" is a technology that enables computers to understand and analyze human language. 【0069】 "Machine learning technology" is a part of artificial intelligence technology that uses data to train computer models for prediction and classification. 【0070】 A "model" is a set of algorithmic structures and patterns built to provide health advice. 【0071】 A "user" is an individual who utilizes this system and is the entity that receives health information and services. 【0072】 "Biometric data" refers to data that includes physical measurements such as a user's heart rate and body temperature. 【0073】 "Behavioral data" refers to data that shows a user's activity patterns and behavioral history. 【0074】 A "sensor" is a device that detects physical changes and generates data. 【0075】 "Application software" refers to programs developed to provide specific functions to users. 【0076】 "Cloud infrastructure technology" refers to technology that provides data storage and analysis over the internet. 【0077】 "Cloud" refers to a distributed computing network that provides services and data via the internet. 【0078】 "Individualized data" refers to data about the unique health status and behavior associated with a specific user. 【0079】 "Health status" refers to information about the user's physical and mental health. 【0080】 "Daily habits" refer to the patterns of actions and activities that users perform on a daily basis. 【0081】 A "program" is a plan or guideline for lifestyle improvement proposed based on the user's health condition. 【0082】 A "notification function" is a system function that informs users of information based on specified conditions. 【0083】 "Implementation results" refer to data about the activities performed and goals achieved by users. 【0084】 "Feedback" refers to the opinions and reactions from users regarding system proposals and activities. 【0085】 This invention is implemented through a comprehensive system to support health management. The server, terminal, and user interact with each other, and each component functions as follows: 【0086】 The server first obtains general health-related information from reliable sources. Web scraping techniques can be used for data collection. The acquired data is then analyzed using natural language processing and machine learning techniques with Python libraries such as TENSORFLOW® and NLTK. This builds a model for generating health advice. This model is stored in a database and serves as the foundation for the advice provided to users. 【0087】 The device is the primary device for collecting biometric and behavioral data from the user's daily life. Sensors built into smartwatches and fitness trackers record data such as heart rate, steps taken, and calorie consumption. The recorded data is transmitted in real time to a server via cloud infrastructure technology using Bluetooth technology. This allows the system to understand the user's health status and provide necessary information to the server. 【0088】 In this system, the user is the recipient of information, acquiring life programs generated through their terminal and incorporating them into their daily life. For example, if a user wishes to improve their eating habits, they can input a prompt such as "Please suggest an appropriate meal plan" into the generating AI model, and receive optimal meal suggestions from the server. Based on these suggestions, the user can adjust their own meal plan and aim to achieve their goals. 【0089】 This system supports users in continuous health management and enables them to implement specific improvement measures tailored to their own health condition. By employing advanced technology to adapt to individual needs, this invention can provide users with a feasible and sustainable method of health management. 【0090】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0091】 Step 1: 【0092】 The server collects general health-related information from reliable sources on the internet. It uses a list of URLs of these sources as input. Specifically, it extracts information using web scraping techniques and stores it as text data. This output data is the raw material for subsequent processing. 【0093】 Step 2: 【0094】 The server analyzes the collected health-related information using natural language processing and machine learning algorithms. The text data obtained in Step 1 is used as input. Specifically, the NLTK library in Python is used to tokenize the data and extract important keywords. This data is then fed into a machine learning model to train it for generating health advice. The trained model is obtained as output. 【0095】 Step 3: 【0096】 The device collects biometric and behavioral data from the user's daily life. It uses data directly obtained from sensors in smartwatches and fitness trackers (e.g., heart rate, step count) as input. This data is collected by the device using Bluetooth technology and transmitted to the cloud in real time. As output, a user lifestyle data set is constructed. 【0097】 Step 4: 【0098】 The server analyzes user data sent from the terminal and evaluates the individual's health status. The user data obtained in step 3 is used as input. Specifically, this data is fed into the machine learning model built earlier to perform predictions. This evaluates the current health status, and the results are added to the user's profile database. 【0099】 Step 5: 【0100】 The server generates an optimal lifestyle program for the user based on the analysis results. It uses the health assessment data obtained in step 4 as input. Utilizing the generation AI model, it outputs prompts such as, "Please suggest a meal plan tailored to the user's health condition." The output includes a specific meal plan and exercise plan tailored to the user. 【0101】 Step 6: 【0102】 The user receives the generated life program through their device. The input is program information provided by the server. Specifically, the device's health management app displays the program content to the user using its notification function. The output provides the user with specific guidance for daily health management. 【0103】 Step 7: 【0104】 The system collects user activity results and feedback from the device and sends it to the server. Inputs include data on the user's exercise and diet, as well as feedback information. This data is entered via an interface on the device and sent to the server. The output is an updated results and feedback dataset. 【0105】 Step 8: 【0106】 The server adjusts the life program based on the collected feedback information. The feedback data obtained in step 7 is used as input. The machine learning algorithm is applied again to generate new suggestions for improving the program. As output, the adjusted life program is created and resent to the user, enabling more effective health management. 【0107】 (Application Example 1) 【0108】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0109】 In a health management system, it is necessary to build a mechanism that continuously proposes and supports the implementation of specific improvement measures tailored to the individual health condition of each user. To achieve this, a device that closely supports the user's daily life is required. Furthermore, to realize improvements in the user's health, it is crucial to have a function that can dynamically adjust the life program. 【0110】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0111】 In this invention, the server includes means for acquiring general health information from data storage, analyzing it, and constructing a data model for providing health advice; means for acquiring the user's biometric and behavioral data and transmitting it to a virtual space; and means for analyzing the user's individual data in the virtual space and generating improvement programs tailored to their health status and lifestyle. This makes it possible to efficiently provide and support a health improvement program optimized for the user in their daily life. 【0112】 "Health information" refers to data and knowledge related to the user's health status, and this includes medical data and lifestyle information. 【0113】 "Data storage" refers to a technology or device that stores various types of data and makes them accessible as needed. 【0114】 A "data model" is a framework that defines the structure and methods of manipulating data for a specific purpose. 【0115】 "Biometric data" refers to various numerical values ​​or indicators that show an individual's physical condition. Specific examples include heart rate and body temperature. 【0116】 "Behavioral data" refers to data related to an individual's actions and activities in their daily life. 【0117】 A "virtual space" is a computer domain created using digital technology for processing real or virtual data. 【0118】 "Individual data" refers to data associated with a specific user, which enables customized processing for each user. 【0119】 An "improvement program" is a plan that outlines a series of activities and guidelines aimed at improving the health of users. 【0120】 A "notification" is a message or alert that a system uses to communicate information to a user. 【0121】 "Opinions" refer to feedback and evaluations provided by users, and are data used to adjust the system. 【0122】 "Execution data" refers to data that shows the activities actually performed by the user and the results thereof. 【0123】 "Lifestyle machinery and devices" are devices that are closely involved in the user's daily life and support health management. 【0124】 To implement this invention, it is necessary to build a user-facing system to support health management. This system begins with installing various sensors and data collection applications on a terminal to acquire the user's biometric and behavioral data. The terminal transmits this data to a server in the cloud in real time. 【0125】 The server first collects general health information from reliable sources on the internet and stores it in data storage. Next, it uses natural language processing and machine learning techniques to analyze this health data and build a data model to provide health advice. Analytical tools such as Python and R are used for this process. Individual user data is analyzed through a virtual space on the cloud, and improvement programs are generated that are tailored to the user's health status and lifestyle. These improvement programs include various health elements such as diet, exercise, sleep, and stress management. 【0126】 The device provides the user with the generated improvement program and sends notifications to assist in running the program. Smartphones and smartwatches are used for this purpose. User feedback and execution data are then sent back to a server in the cloud to evaluate the program's effectiveness and dynamically adjust it as needed. 【0127】 As a concrete example, a user might receive notifications about optimized sleep duration based on their activity data from the past week. An example of a prompt using a generative AI model would be a request such as, "If the user only slept for 7 hours, please suggest an optimal lifestyle program based on that data." In this way, the system aims to provide continuous health management and improvement, supporting long-term health maintenance. 【0128】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0129】 Step 1: 【0130】 The device collects the user's biometric and behavioral data through sensors and apps. Inputs include real-time data such as heart rate, steps taken, activity time, and sleep duration. This data is transmitted in real time by the device to a server in the cloud. Outputs are stored in the cloud as the user's continuous health data. 【0131】 Step 2: 【0132】 The server analyzes user biometric and behavioral data collected in the cloud. The input is a collection of health data accumulated over time. The server analyzes this data using natural language processing and machine learning techniques. It performs various analyses using Python and R languages ​​to evaluate the user's health status. The output is a current health assessment report based on the user's health condition. 【0133】 Step 3: 【0134】 Based on the analysis results, the server generates an improvement program tailored to the user's health status and lifestyle. Inputs include a current assessment report and general health information. A generation AI model is used to create an optimized improvement program. The output is a personalized life program for the user, which includes improvement measures for exercise, diet, sleep, and other areas. 【0135】 Step 4: 【0136】 The terminal provides the user with the generated life program and sends notifications to assist in its execution. The input is the life program retrieved from the cloud. Based on this, the terminal notifies the user via voice or display. The output is the specific execution instructions received by the user. 【0137】 Step 5: 【0138】 Users adjust their lifestyle habits based on the life program received from their device. They send feedback and results to the cloud via the device. Inputs are the user's execution data and feedback. Outputs are newly accumulated execution data, which are used to adjust the next improvement program. 【0139】 Step 6: 【0140】 The server analyzes user feedback and execution data to dynamically adjust the improvement program. Inputs are execution data and feedback. The server compares this to the database history and, if necessary, performs re-analysis using a generated AI model. The output is a newly adjusted improvement program. 【0141】 Step 7: 【0142】 As a concrete example, the server starts the analysis with the prompt, "If the user only slept for 7 hours, please suggest an optimal lifestyle program based on that data." The input is past sleep data, and the output is an adjusted program that recommends 8 hours of sleep. 【0143】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0144】 To implement this invention, a system comprising a server utilizing cloud infrastructure, a terminal for understanding the user's status, and an emotion engine is used. The specific functions and implementation methods of each are described below. 【0145】 The server periodically collects general health-related information via the internet and analyzes it using natural language processing technology. Based on this analysis, a base model for health advice to be provided to the user is built. Furthermore, when the user's biometric and behavioral information is transmitted from the terminal, the server analyzes this as individual data on the cloud and generates a "life program" tailored to their health status and lifestyle. In this process, the user's emotional data is also considered via an emotion engine and reflected in the program. 【0146】 The device acquires biometric information in real time from the user's smartphone or wearable device. The device also incorporates an emotion engine that analyzes the user's voice tone and facial expressions to recognize their emotions at that time. The information obtained by the emotion engine is sent to the cloud to evaluate the user's stress level and emotional tendencies. 【0147】 For example, if a user is experiencing stress at work, the emotion engine analyzes changes in the user's voice and facial expressions and recognizes a high stress level. The server receives this information and generates a relaxation program as part of the life program aimed at reducing stress. The device notifies the user of suitable breathing exercises and meditation suggestions and encourages them to incorporate necessary relaxation activities into their schedule. 【0148】 Users incorporate and implement life programs presented through their devices into their daily routines. Progress and feedback are sent from the device to the server, where this information is stored and used to create future programs. Because feedback from the emotion engine is constantly reflected, users can continuously receive more appropriate advice tailored to their emotional state. 【0149】 Thus, the present invention enables users to receive personalized support based on their individual health and emotional states, thereby improving the efficiency of long-term and sustainable health management. 【0150】 The following describes the processing flow. 【0151】 Step 1: 【0152】 The server patrols major health-related databases to retrieve the latest medical information and health guidance. The retrieved information is analyzed using natural language processing technology to create a foundational model for providing health advice. 【0153】 Step 2: 【0154】 The device collects biometric information from the user's smart device, including parameters such as heart rate, sleep patterns, and activity levels. Simultaneously, an emotion engine analyzes the user's voice and facial expressions to generate emotional data. 【0155】 Step 3: 【0156】 The device transmits collected biometric and emotional data to a server in the cloud. This data reflects the user's health and emotional state. 【0157】 Step 4: 【0158】 The server analyzes data received on the cloud and generates a personalized "life program" based on the user's health status and lifestyle. During this process, the program is adjusted based on the user's emotional data, including stress reduction measures and emotional management techniques. 【0159】 Step 5: 【0160】 The device notifies the user of the generated program. It also sends reminders that suggest specific health activities and relaxation times tailored to the user's daily schedule. 【0161】 Step 6: 【0162】 Users follow the instructions on the device and incorporate and execute the life program into their daily lives. Users record their progress using the device and provide feedback for reassessment. 【0163】 Step 7: 【0164】 The server analyzes user feedback and execution data to evaluate the effectiveness of the program's lifecycle. It continuously optimizes the program, taking into account newly discovered trends and areas for improvement. 【0165】 Through this process, the system continues to provide personalized healthcare tailored to the user's individual health and emotional needs. 【0166】 (Example 2) 【0167】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0168】 Traditional health management systems only provide general health advice, and struggle to offer personalized advice tailored to the individual health and emotional states of each user. Furthermore, they lacked systems to effectively incorporate user feedback, making it challenging to improve the efficiency of long-term health management. 【0169】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0170】 This invention includes a server that includes means for acquiring general health-related information from information sources, analyzing it using natural language processing technology, and constructing a model for providing health advice; means for acquiring the user's biometric and behavioral data and transmitting it to data storage; and means for analyzing the user's individual data on the data storage and generating a lifestyle improvement program tailored to the user's health status and lifestyle. This makes it possible to provide personalized advice based on the user's individual health status and emotional state. 【0171】 "Health-related information" refers to general knowledge and data related to an individual's health status, and is collected from official health information sources. 【0172】 "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and utilize human language. 【0173】 "Biometric data" refers to data about an individual user's physiological state, such as heart rate and exercise level. 【0174】 "Behavioral data" refers to data about users' activities and daily behavioral patterns. 【0175】 "Data storage" refers to a storage system, including the cloud, for accumulating and saving acquired data. 【0176】 A "lifestyle improvement program" is a program tailored to an individual's health condition and lifestyle habits, provided with the aim of helping users maintain or improve their health. 【0177】 "Emotion recognition technology" refers to technology that analyzes and identifies a user's emotional state from factors such as voice and facial expressions. 【0178】 A "notification" is a message or alert used to quickly convey information to a user. 【0179】 The system of this invention utilizes advanced analytical techniques using servers, terminals, and generative AI models. The objective of this system is to provide personalized health advice that is tailored to the individual health and emotional state of the user. 【0180】 The server uses network technologies to retrieve general health-related information from various sources. Specifically, it leverages web scraping techniques to extract information from reliable health sources. This information is then analyzed using natural language processing techniques (e.g., using Python's NLTK library) and used to build health advice models for users. 【0181】 The device acquires biometric and behavioral data from the user's daily life. This role is played by smartphones and wearable devices. These devices transmit data to the cloud using Bluetooth technology. Furthermore, the device is equipped with emotion recognition technology, which analyzes voice tone and facial expression data to evaluate the user's emotions. For example, the device collects this data through its microphone and camera functions and uses algorithms to determine the emotional state. 【0182】 Users receive lifestyle improvement programs provided by a server via a smartphone application. These programs include advice tailored to the user's specific health and emotional state. For example, breathing exercises and meditation guidance are suggested through the application to reduce stress. Users can integrate these programs into their daily lives and send feedback on their implementation to the server via their device. 【0183】 As a concrete example, consider a scenario where a user is experiencing stress at work. The device recognizes a high stress level based on the user's voice and facial expressions. In response, the server generates a stress reduction program and suggests relaxation techniques. The user receives and implements these suggestions through the application. 【0184】 An example of a prompt message would be something like, "Suggest a stress reduction program based on the user's current emotional state and health data." 【0185】 This system can improve the efficiency of health management by providing users with appropriate support based on their individual health and emotional states. 【0186】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0187】 Step 1: 【0188】 The server retrieves general health-related information from the internet. The input is data from reliable health sources. The output is the collected, raw health information data. This data is extracted using web scraping techniques and stored as a preparatory step for natural language processing techniques. 【0189】 Step 2: 【0190】 The server uses natural language processing techniques to analyze the collected health information data. The input is the raw data collected in step 1. For data processing, the NLTK library in Python is used to tokenize the data and extract important keywords. The output is the analyzed health data used to build a health advice model. 【0191】 Step 3: 【0192】 The terminal acquires biometric and behavioral data in real time using the user's smart device. Input is sensor information obtained from the smart device (e.g., heart rate, activity level). Data processing involves pre-processing and formatting this data. Output is a set of the user's biometric and activity data, transmitted to the terminal via Bluetooth. 【0193】 Step 4: 【0194】 The device collects user emotional data using emotion recognition technology. Inputs include audio data from the microphone and facial expression data from the camera. To analyze this data, it uses a speech analysis algorithm and OpenCV to determine the user's emotional state. The output is user emotional data, indicating stress levels and emotional tendencies. 【0195】 Step 5: 【0196】 The server integrates biometric, behavioral, and emotional data transmitted from terminals and analyzes it on data storage. The input consists of multiple datasets collected from terminals. As a data computation, a learning algorithm is applied to the integrated data to analyze individual health conditions and lifestyle habits. The output is a lifestyle improvement program optimized for the user. 【0197】 Step 6: 【0198】 Users receive lifestyle improvement programs provided by the server and incorporate them into their daily lives. The input is the suggested lifestyle improvement program sent from the server. Specifically, users check the program on a smartphone app and perform the suggested breathing and meditation techniques. The output is feedback and progress information on the completed program. 【0199】 Step 7: 【0200】 The server receives feedback from the user and incorporates it into the next program creation. The input is the feedback information sent by the user in step 6. As part of data processing, the feedback data is analyzed and the lifestyle improvement program is adjusted. The output is the adjusted new lifestyle improvement program, which will be used in the next provision. 【0201】 (Application Example 2) 【0202】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0203】 In modern society, managing the health of the elderly is a crucial issue, but there is a lack of means to appropriately grasp individual health and emotional states in real time and improve the quality of care. Furthermore, there is a need for a system that automatically suggests care activities tailored to each individual's emotional state. 【0204】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0205】 In this invention, the server includes means for acquiring general health information from a database, analyzing it, and constructing a model for providing health advice; means for acquiring the user's biometric and behavioral information and transmitting it to the cloud; and means for analyzing the user's individual data on the cloud and generating a life program tailored to their health condition and lifestyle. This makes it possible to provide personalized care programs based on the individual's health and emotional state. 【0206】 "Health information" refers to data indicating the user's physical condition and related general guidelines and knowledge. 【0207】 A "database" refers to a system that systematically stores large amounts of information and organizes it so that it can be quickly searched and used as needed. 【0208】 "Model building" refers to the process of analyzing data and creating a framework that enables prediction and classification according to specific purposes. 【0209】 "Biometric information" refers to data that indicates the physiological state of the body, such as the user's heart rate and body temperature. 【0210】 "Behavioral information" refers to data about users' activity patterns and habits. 【0211】 "Cloud" refers to data storage and computing services that can be accessed via the internet. 【0212】 "Individualized data analysis" refers to the process of analyzing information about a specific user in detail to understand their unique state and tendencies. 【0213】 A "life program" refers to an activity plan aimed at maintaining or improving health, which is designed based on the user's health condition and lifestyle. 【0214】 A "reminder" refers to a notification set to prompt a user to take a specific action. 【0215】 "Feedback" refers to evaluations and comments provided by users regarding their actions or the content provided by the system. 【0216】 An "emotion engine" refers to a technology that analyzes a user's voice tone, facial expressions, and other factors to evaluate their emotional state. 【0217】 "Caregiving activities" refer to a series of actions taken to provide specific physical or mental support. 【0218】 A "relaxation program" refers to activities designed to reduce stress and promote mental and physical well-being. 【0219】 This invention is a system that utilizes cloud infrastructure and an emotion engine to provide personalized care programs based on an individual's health and emotional state. The system has the following configuration and operation: 【0220】 First, the server periodically retrieves general health information from a database via the internet and analyzes this data using natural language processing technology. This builds a foundational model for providing health advice. This model is then optimized using a generative AI model to provide users with the most appropriate health advice. 【0221】 The device acquires the user's biometric and behavioral information in real time through wearable devices such as smartphones and smart glasses. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice tone and facial expressions to evaluate their emotional state. The user's emotional data is sent to the cloud and used for data analysis on the server. 【0222】 When a user is feeling anxious, the emotion engine detects this, and the server generates a relaxation program based on that information. For example, if an elderly person shows signs of anxiety in the afternoon, a notification is sent to their device suggesting breathing exercises or light exercise to alleviate tension. At this time, the AI ​​model generates the optimal suggestion using a prompt message that reads, "Based on the emotional state and activity schedule shown by the elderly person in the afternoon, please output advice to suggest the most suitable relaxation program for them." 【0223】 This system will enable personalized care support to improve the quality of care. 【0224】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0225】 Step 1: 【0226】 The server retrieves general health information from a database. It takes health information accessed via an API as input, and outputs health information data in text format. Natural language processing techniques are used to analyze the data and build a foundational model for health advice. Specifically, the Python Natural Language Toolkit (NLTK) is used for analysis, tokenizing the text and extracting key health indicators. 【0227】 Step 2: 【0228】 The device acquires the user's biometric and behavioral information in real time. Inputs include biometric signals from sensors and behavioral data via Bluetooth, while output is a set of these raw data. It collects data such as heart rate, distance traveled, and activity time using a smartphone or smart glasses. Specifically, it collects data from wearable devices, stores it locally, and then sends it to the cloud. 【0229】 Step 3: 【0230】 The server analyzes individual biometric and behavioral data received on the cloud. The input is a user dataset sent from the terminal, and the output is an evaluation of health status and lifestyle habits generated through the analysis. Machine learning algorithms are used for data analysis to generate an optimal life program for the user. Specifically, the data is normalized and fed into a machine learning model for prediction and evaluation. 【0231】 Step 4: 【0232】 The device provides the user with a generated life program. The input is a life program suggestion from the server, and the output is a reminder and recommended activity displayed on the user's smartphone. Specifically, the reminder is displayed in the notification area, and the program details can be viewed within the app. 【0233】 Step 5: 【0234】 The server evaluates the user's emotional state using an emotion engine. Inputs include voice tone and facial expression data from the terminal, and output is the evaluation result of the user's emotional state. Specifically, it uses speech recognition and facial recognition technology to analyze emotions and determine stress levels and emotional tendencies. 【0235】 Step 6: 【0236】 The server generates a relaxation program based on the evaluated emotional state. The input is the evaluation results from the emotion engine, and the output is a list of care and relaxation activities to suggest to the user. Specifically, it uses a prompt message such as, "Based on the emotional state and activity schedule shown by the elderly person in the afternoon, output advice to suggest the most suitable relaxation program for them," to create the program using a generative AI model. 【0237】 Step 7: 【0238】 Users incorporate the provided program into their daily lives. The input is the program content provided by the device, and the output is the user's execution status and feedback. Specifically, the user performs the notified activity and inputs the results and impressions into the app. 【0239】 Step 8: 【0240】 The server acquires user feedback and execution data to adjust the life program. Its input is user feedback data, and its output is the adjusted life program. Specifically, it analyzes the feedback and incorporates it into the next program creation, resulting in more personalized suggestions. 【0241】 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. 【0242】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0243】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0244】 [Second Embodiment] 【0245】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0246】 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. 【0247】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0248】 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. 【0249】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0250】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0251】 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. 【0252】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0253】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0254】 The 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. 【0255】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0256】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0257】 To implement this invention, it is necessary to build a system based on a cloud computing environment and coordinate processing according to the respective roles of the server, terminal, and user. The following is an explanation of the basic functions of each, along with specific examples. 【0258】 The server first acquires a wide range of health data from reliable health-related information sources on the internet and analyzes it using natural language processing and machine learning techniques. This allows it to build models for providing general health advice and store them in a database. 【0259】 The device collects biometric and behavioral information from the user's daily life through sensors and apps. This information is transmitted in real time to a server in the cloud, where it is individually analyzed based on the user's registered health goals and preferences. 【0260】 On the cloud, the system evaluates the user's current health status based on collected data and generates an optimal "life program" based on this evaluation. This program includes multiple elements such as diet, exercise, sleep, and stress management, and proposes specific and achievable improvement measures for the user. 【0261】 Users receive details about this program via their device and instructions on how to incorporate it into their daily lives. For example, if a user wants to improve their sleep, the server analyzes the user's sleep patterns and suggests an optimal sleep duration. The app on the device also sends reminders at specific times each night to help users go to bed at the recommended time. 【0262】 Meanwhile, the results and feedback from the user's actions are sent back to the server via the device, and the life program is adjusted as needed. For example, if a user is unable to complete the suggested amount of exercise, the server analyzes the reason and proposes a new exercise plan to help them achieve their goals without overexerting themselves. 【0263】 In this way, the system can dynamically adapt to the user's situation and continuously provide better lifestyle improvements. Through this interaction, users can manage their health at their own pace without undue pressure, reducing future health risks and maintaining long-term health. 【0264】 The following describes the processing flow. 【0265】 Step 1: 【0266】 The server crawls trusted health-related databases to collect the latest general health information. This includes online resources such as research papers, news articles, and medical websites. The collected information is analyzed using natural language processing techniques and integrated into a base model for health advice. 【0267】 Step 2: 【0268】 The device acquires everyday biometric information (e.g., heart rate, steps taken, calorie consumption) and behavioral information (e.g., location information, activity history) from the user's smartphone or wearable device. The acquired data is transmitted to the server in real time. 【0269】 Step 3: 【0270】 The server analyzes the received personal data in the cloud to assess the user's current health status and lifestyle. This allows for the identification of health risks tailored to individual needs and goals, and the establishment of a general framework for necessary improvement measures. 【0271】 Step 4: 【0272】 Based on the analysis results, the server generates a "life program" optimized for the user. This program includes specific actions to achieve individual goals, such as dietary guidance, exercise plans, and sleep improvement strategies. 【0273】 Step 5: 【0274】 The device notifies the user of the generated life program and presents specific implementation methods and timelines. This includes alert and reminder functions to support the user in carrying out the plan in their daily life. 【0275】 Step 6: 【0276】 Users execute life programs based on instructions from their devices and record their progress and achievements. User feedback is also sent to the server via the device, and the program's effectiveness and areas for improvement are incorporated as part of the analysis. 【0277】 Step 7: 【0278】 The server evaluates the life program based on user feedback and execution data, and adjusts its content as needed. This ensures continuous optimization to achieve maximum effectiveness. 【0279】 Through these steps, the system provides users with personalized health improvement strategies and supports sustainable healthcare practices. 【0280】 (Example 1) 【0281】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0282】 In recent years, people's awareness of lifestyle-related diseases and health has increased, but it remains difficult to develop and implement appropriate and sustainable health management plans for each individual. While general health information is readily available, applying it to individual lifestyles and health conditions is challenging. Furthermore, there is a lack of mechanisms to support daily health management and to incorporate feedback and adjustments as needed. There is a need to address this challenge and provide users with methods to efficiently manage their own health. 【0283】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0284】 In this invention, the server includes means for obtaining general health-related information from an information source, analyzing the data using natural language processing and machine learning technologies, and constructing a model for providing health advice; means for collecting biometric data and behavioral data in the user's daily life through sensors and application software, and transmitting them using cloud infrastructure technology; and means for analyzing the user's individual data on the cloud and generating a program according to the health status and daily habits. Thereby, it is possible to quickly and efficiently provide the user with an optimal health management plan and adapt to the individual health status. 【0285】 "General health-related information" is a general term for data related to public health and medicine obtained from reliable information sources. 【0286】 "Information source" refers to publicly available databases or sites on the Internet or other media for collecting health-related information. 【0287】 "Natural language processing" is a technology for a computer to understand and analyze human language. 【0288】 "Machine learning technology" is a part of artificial intelligence technology that trains a computer model using data for prediction and classification. 【0289】 "Model" refers to a set of structures and patterns of algorithms constructed to provide health advice. 【0290】 "User" refers to an individual who uses this system and is the subject of receiving health information and services. 【0291】 "Biometric data" refers to data that means physical measurement values such as the user's heart rate and body temperature. 【0292】 "Behavioral data" refers to data indicating the user's activity patterns and movement history. 【0293】 A "sensor" is a device that detects physical changes and generates data. 【0294】 "Application software" refers to programs developed to provide specific functions to users. 【0295】 "Cloud infrastructure technology" refers to technology that provides data storage and analysis over the internet. 【0296】 "Cloud" refers to a distributed computing network that provides services and data via the internet. 【0297】 "Individualized data" refers to data about the unique health status and behavior associated with a specific user. 【0298】 "Health status" refers to information about the user's physical and mental health. 【0299】 "Daily habits" refer to the patterns of actions and activities that users perform on a daily basis. 【0300】 A "program" is a plan or guideline for lifestyle improvement proposed based on the user's health condition. 【0301】 A "notification function" is a system function that informs users of information based on specified conditions. 【0302】 "Implementation results" refer to data about the activities performed and goals achieved by users. 【0303】 "Feedback" refers to the opinions and reactions from users regarding system proposals and activities. 【0304】 This invention is implemented through a comprehensive system to support health management. The server, terminal, and user interact with each other, and each component functions as follows: 【0305】 The server first obtains general health-related information from reliable information sources. Web scraping technology can be used for data collection. The acquired data is analyzed using natural language processing and machine learning techniques with Python libraries such as TensorFlow and NLTK. This builds a model for generating health advice. This model functions as a basis for the advice provided to users by being stored in a database. 【0306】 The terminal is the main device for collecting biometric and behavioral data in the user's daily life. Sensors installed in smartwatches and fitness trackers record data such as heart rate, steps taken, and calorie consumption. The recorded data is transmitted in real-time to the server through cloud-based technology using Bluetooth technology. This enables the function of grasping the user's health status and supplying necessary information to the server. 【0307】 The user is the subject who receives information in this system, obtains the life program generated through the terminal, and incorporates it into daily life. For example, when the user wishes to improve their diet, by inputting a prompt sentence "Please propose an appropriate meal plan" to the generated AI model, they receive an optimal meal proposal from the server. Based on this proposal, the user can adjust their own meal plan and aim to achieve the goal. 【0308】 With this system, users are supported in continuous health management and can execute specific improvement measures according to their own health status. This invention can provide a method of feasible and sustainable health management for users by using advanced technologies adapted to individual needs. 【0309】 The flow of specific processing in Example 1 will be described using FIG. 11. 【0310】 Step 1: 【0311】 The server collects general health-related information from reliable sources on the internet. It uses a list of URLs of these sources as input. Specifically, it extracts information using web scraping techniques and stores it as text data. This output data is the raw material for subsequent processing. 【0312】 Step 2: 【0313】 The server analyzes the collected health-related information using natural language processing and machine learning algorithms. The text data obtained in Step 1 is used as input. Specifically, the NLTK library in Python is used to tokenize the data and extract important keywords. This data is then fed into a machine learning model to train it for generating health advice. The trained model is obtained as output. 【0314】 Step 3: 【0315】 The device collects biometric and behavioral data from the user's daily life. It uses data directly obtained from sensors in smartwatches and fitness trackers (e.g., heart rate, step count) as input. This data is collected by the device using Bluetooth technology and transmitted to the cloud in real time. As output, a user lifestyle data set is constructed. 【0316】 Step 4: 【0317】 The server analyzes user data sent from the terminal and evaluates the individual's health status. The user data obtained in step 3 is used as input. Specifically, this data is fed into the machine learning model built earlier to perform predictions. This evaluates the current health status, and the results are added to the user's profile database. 【0318】 Step 5: 【0319】 The server generates an optimal lifestyle program for the user based on the analysis results. It uses the health assessment data obtained in step 4 as input. Utilizing the generation AI model, it outputs prompts such as, "Please suggest a meal plan tailored to the user's health condition." The output includes a specific meal plan and exercise plan tailored to the user. 【0320】 Step 6: 【0321】 The user receives the generated life program through their device. The input is program information provided by the server. Specifically, the device's health management app displays the program content to the user using its notification function. The output provides the user with specific guidance for daily health management. 【0322】 Step 7: 【0323】 The system collects user activity results and feedback from the device and sends it to the server. Inputs include data on the user's exercise and diet, as well as feedback information. This data is entered via an interface on the device and sent to the server. The output is an updated results and feedback dataset. 【0324】 Step 8: 【0325】 The server adjusts the life program based on the collected feedback information. The feedback data obtained in step 7 is used as input. The machine learning algorithm is applied again to generate new suggestions for improving the program. As output, the adjusted life program is created and resent to the user, enabling more effective health management. 【0326】 (Application Example 1) 【0327】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0328】 In a health management system, it is necessary to build a mechanism that continuously proposes and supports the implementation of specific improvement measures tailored to the individual health condition of each user. To achieve this, a device that closely supports the user's daily life is required. Furthermore, to realize improvements in the user's health, it is crucial to have a function that can dynamically adjust the life program. 【0329】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0330】 In this invention, the server includes means for acquiring general health information from data storage, analyzing it, and constructing a data model for providing health advice; means for acquiring the user's biometric and behavioral data and transmitting it to a virtual space; and means for analyzing the user's individual data in the virtual space and generating improvement programs tailored to their health status and lifestyle. This makes it possible to efficiently provide and support a health improvement program optimized for the user in their daily life. 【0331】 "Health information" refers to data and knowledge related to the user's health status, and this includes medical data and lifestyle information. 【0332】 "Data storage" refers to a technology or device that stores various types of data and makes them accessible as needed. 【0333】 A "data model" is a framework that defines the structure and methods of manipulating data for a specific purpose. 【0334】 "Biometric data" refers to various numerical values ​​or indicators that show an individual's physical condition. Specific examples include heart rate and body temperature. 【0335】 "Behavioral data" refers to data related to an individual's actions and activities in their daily life. 【0336】 A "virtual space" is a computer domain created using digital technology for processing real or virtual data. 【0337】 "Individual data" refers to data associated with a specific user, which enables customized processing for each user. 【0338】 An "improvement program" is a plan that outlines a series of activities and guidelines aimed at improving the health of users. 【0339】 A "notification" is a message or alert that a system uses to communicate information to a user. 【0340】 "Opinions" refer to feedback and evaluations provided by users, and are data used to adjust the system. 【0341】 "Execution data" refers to data that shows the activities actually performed by the user and the results thereof. 【0342】 "Lifestyle machinery and devices" are devices that are closely involved in the user's daily life and support health management. 【0343】 To implement this invention, it is necessary to build a user-facing system to support health management. This system begins with installing various sensors and data collection applications on a terminal to acquire the user's biometric and behavioral data. The terminal transmits this data to a server in the cloud in real time. 【0344】 The server first collects general health information from reliable sources on the internet and stores it in data storage. Next, it uses natural language processing and machine learning techniques to analyze this health data and build a data model to provide health advice. Analytical tools such as Python and R are used for this process. Individual user data is analyzed through a virtual space on the cloud, and improvement programs are generated that are tailored to the user's health status and lifestyle. These improvement programs include various health elements such as diet, exercise, sleep, and stress management. 【0345】 The device provides the user with the generated improvement program and sends notifications to assist in running the program. Smartphones and smartwatches are used for this purpose. User feedback and execution data are then sent back to a server in the cloud to evaluate the program's effectiveness and dynamically adjust it as needed. 【0346】 As a concrete example, a user might receive notifications about optimized sleep duration based on their activity data from the past week. An example of a prompt using a generative AI model would be a request such as, "If the user only slept for 7 hours, please suggest an optimal lifestyle program based on that data." In this way, the system aims to provide continuous health management and improvement, supporting long-term health maintenance. 【0347】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0348】 Step 1: 【0349】 The device collects the user's biometric and behavioral data through sensors and apps. Inputs include real-time data such as heart rate, steps taken, activity time, and sleep duration. This data is transmitted in real time by the device to a server in the cloud. Outputs are stored in the cloud as the user's continuous health data. 【0350】 Step 2: 【0351】 The server analyzes user biometric and behavioral data collected in the cloud. The input is a collection of health data accumulated over time. The server analyzes this data using natural language processing and machine learning techniques. It performs various analyses using Python and R languages ​​to evaluate the user's health status. The output is a current health assessment report based on the user's health condition. 【0352】 Step 3: 【0353】 Based on the analysis results, the server generates an improvement program tailored to the user's health status and lifestyle. Inputs include a current assessment report and general health information. A generation AI model is used to create an optimized improvement program. The output is a personalized life program for the user, which includes improvement measures for exercise, diet, sleep, and other areas. 【0354】 Step 4: 【0355】 The terminal provides the user with the generated life program and sends notifications to assist in its execution. The input is the life program retrieved from the cloud. Based on this, the terminal notifies the user via voice or display. The output is the specific execution instructions received by the user. 【0356】 Step 5: 【0357】 Users adjust their lifestyle habits based on the life program received from their device. They send feedback and results to the cloud via the device. Inputs are the user's execution data and feedback. Outputs are newly accumulated execution data, which are used to adjust the next improvement program. 【0358】 Step 6: 【0359】 The server analyzes user feedback and execution data to dynamically adjust the improvement program. Inputs are execution data and feedback. The server compares this to the database history and, if necessary, performs re-analysis using a generated AI model. The output is a newly adjusted improvement program. 【0360】 Step 7: 【0361】 As a concrete example, the server starts the analysis with the prompt, "If the user only slept for 7 hours, please suggest an optimal lifestyle program based on that data." The input is past sleep data, and the output is an adjusted program that recommends 8 hours of sleep. 【0362】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0363】 To implement this invention, a system comprising a server utilizing cloud infrastructure, a terminal for understanding the user's status, and an emotion engine is used. The specific functions and implementation methods of each are described below. 【0364】 The server periodically collects general health-related information via the internet and analyzes it using natural language processing technology. Based on this analysis, a base model for health advice to be provided to the user is built. Furthermore, when the user's biometric and behavioral information is transmitted from the terminal, the server analyzes this as individual data on the cloud and generates a "life program" tailored to their health status and lifestyle. In this process, the user's emotional data is also considered via an emotion engine and reflected in the program. 【0365】 The device acquires biometric information in real time from the user's smartphone or wearable device. The device also incorporates an emotion engine that analyzes the user's voice tone and facial expressions to recognize their emotions at that time. The information obtained by the emotion engine is sent to the cloud to evaluate the user's stress level and emotional tendencies. 【0366】 For example, if a user is experiencing stress at work, the emotion engine analyzes changes in the user's voice and facial expressions and recognizes a high stress level. The server receives this information and generates a relaxation program as part of the life program aimed at reducing stress. The device notifies the user of suitable breathing exercises and meditation suggestions and encourages them to incorporate necessary relaxation activities into their schedule. 【0367】 Users incorporate and implement life programs presented through their devices into their daily routines. Progress and feedback are sent from the device to the server, where this information is stored and used to create future programs. Because feedback from the emotion engine is constantly reflected, users can continuously receive more appropriate advice tailored to their emotional state. 【0368】 Thus, the present invention enables users to receive personalized support based on their individual health and emotional states, thereby improving the efficiency of long-term and sustainable health management. 【0369】 The following describes the processing flow. 【0370】 Step 1: 【0371】 The server patrols major health-related databases to retrieve the latest medical information and health guidance. The retrieved information is analyzed using natural language processing technology to create a foundational model for providing health advice. 【0372】 Step 2: 【0373】 The device collects biometric information from the user's smart device, including parameters such as heart rate, sleep patterns, and activity levels. Simultaneously, an emotion engine analyzes the user's voice and facial expressions to generate emotional data. 【0374】 Step 3: 【0375】 The device transmits collected biometric and emotional data to a server in the cloud. This data reflects the user's health and emotional state. 【0376】 Step 4: 【0377】 The server analyzes data received on the cloud and generates a personalized "life program" based on the user's health status and lifestyle. During this process, the program is adjusted based on the user's emotional data, including stress reduction measures and emotional management techniques. 【0378】 Step 5: 【0379】 The device notifies the user of the generated program. It also sends reminders that suggest specific health activities and relaxation times tailored to the user's daily schedule. 【0380】 Step 6: 【0381】 Users follow the instructions on the device and incorporate and execute the life program into their daily lives. Users record their progress using the device and provide feedback for reassessment. 【0382】 Step 7: 【0383】 The server analyzes user feedback and execution data to evaluate the effectiveness of the program's lifecycle. It continuously optimizes the program, taking into account newly discovered trends and areas for improvement. 【0384】 Through this process, the system continues to provide personalized healthcare tailored to the user's individual health and emotional needs. 【0385】 (Example 2) 【0386】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0387】 Traditional health management systems only provide general health advice, and struggle to offer personalized advice tailored to the individual health and emotional states of each user. Furthermore, they lacked systems to effectively incorporate user feedback, making it challenging to improve the efficiency of long-term health management. 【0388】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0389】 This invention includes a server that includes means for acquiring general health-related information from information sources, analyzing it using natural language processing technology, and constructing a model for providing health advice; means for acquiring the user's biometric and behavioral data and transmitting it to data storage; and means for analyzing the user's individual data on the data storage and generating a lifestyle improvement program tailored to the user's health status and lifestyle. This makes it possible to provide personalized advice based on the user's individual health status and emotional state. 【0390】 "Health-related information" refers to general knowledge and data related to an individual's health status, and is collected from official health information sources. 【0391】 "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and utilize human language. 【0392】 "Biometric data" refers to data about an individual user's physiological state, such as heart rate and exercise level. 【0393】 "Behavioral data" refers to data about users' activities and daily behavioral patterns. 【0394】 "Data storage" refers to a storage system, including the cloud, for accumulating and saving acquired data. 【0395】 A "lifestyle improvement program" is a program tailored to an individual's health condition and lifestyle habits, provided with the aim of helping users maintain or improve their health. 【0396】 "Emotion recognition technology" refers to technology that analyzes and identifies a user's emotional state from factors such as voice and facial expressions. 【0397】 A "notification" is a message or alert used to quickly convey information to a user. 【0398】 The system of this invention utilizes advanced analytical techniques using servers, terminals, and generative AI models. The objective of this system is to provide personalized health advice that is tailored to the individual health and emotional state of the user. 【0399】 The server uses network technologies to retrieve general health-related information from various sources. Specifically, it leverages web scraping techniques to extract information from reliable health sources. This information is then analyzed using natural language processing techniques (e.g., using Python's NLTK library) and used to build health advice models for users. 【0400】 The device acquires biometric and behavioral data from the user's daily life. This role is played by smartphones and wearable devices. These devices transmit data to the cloud using Bluetooth technology. Furthermore, the device is equipped with emotion recognition technology, which analyzes voice tone and facial expression data to evaluate the user's emotions. For example, the device collects this data through its microphone and camera functions and uses algorithms to determine the emotional state. 【0401】 Users receive lifestyle improvement programs provided by a server via a smartphone application. These programs include advice tailored to the user's specific health and emotional state. For example, breathing exercises and meditation guidance are suggested through the application to reduce stress. Users can integrate these programs into their daily lives and send feedback on their implementation to the server via their device. 【0402】 As a concrete example, consider a scenario where a user is experiencing stress at work. The device recognizes a high stress level based on the user's voice and facial expressions. In response, the server generates a stress reduction program and suggests relaxation techniques. The user receives and implements these suggestions through the application. 【0403】 An example of a prompt message would be something like, "Suggest a stress reduction program based on the user's current emotional state and health data." 【0404】 This system can improve the efficiency of health management by providing users with appropriate support based on their individual health and emotional states. 【0405】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0406】 Step 1: 【0407】 The server retrieves general health-related information from the internet. The input is data from reliable health sources. The output is the collected, raw health information data. This data is extracted using web scraping techniques and stored as preparation for natural language processing techniques. 【0408】 Step 2: 【0409】 The server uses natural language processing techniques to analyze the collected health information data. The input is the raw data collected in step 1. For data processing, the NLTK library in Python is used to tokenize the data and extract important keywords. The output is the analyzed health data used to build a health advice model. 【0410】 Step 3: 【0411】 The terminal acquires biometric and behavioral data in real time using the user's smart device. Input is sensor information obtained from the smart device (e.g., heart rate, activity level). Data processing involves pre-processing and formatting this data. Output is a set of the user's biometric and activity data, transmitted to the terminal via Bluetooth. 【0412】 Step 4: 【0413】 The device collects user emotional data using emotion recognition technology. Inputs include audio data from the microphone and facial expression data from the camera. To analyze this data, it uses a speech analysis algorithm and OpenCV to determine the user's emotional state. The output is user emotional data, indicating stress levels and emotional tendencies. 【0414】 Step 5: 【0415】 The server integrates biometric, behavioral, and emotional data transmitted from terminals and analyzes it on data storage. The input consists of multiple datasets collected from terminals. As a data computation, a learning algorithm is applied to the integrated data to analyze individual health conditions and lifestyle habits. The output is a lifestyle improvement program optimized for the user. 【0416】 Step 6: 【0417】 Users receive lifestyle improvement programs provided by the server and incorporate them into their daily lives. The input is the suggested lifestyle improvement program sent from the server. Specifically, users check the program on a smartphone app and perform the suggested breathing and meditation techniques. The output is feedback and progress information on the completed program. 【0418】 Step 7: 【0419】 The server receives feedback from the user and incorporates it into the next program creation. The input is the feedback information sent by the user in step 6. As part of data processing, the feedback data is analyzed and the lifestyle improvement program is adjusted. The output is the adjusted new lifestyle improvement program, which will be used in the next provision. 【0420】 (Application Example 2) 【0421】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0422】 In modern society, managing the health of the elderly is a crucial issue, but there is a lack of means to appropriately grasp individual health and emotional states in real time and improve the quality of care. Furthermore, there is a need for a system that automatically suggests care activities tailored to each individual's emotional state. 【0423】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0424】 In this invention, the server includes means for acquiring general health information from a database, analyzing it, and constructing a model for providing health advice; means for acquiring the user's biometric and behavioral information and transmitting it to the cloud; and means for analyzing the user's individual data on the cloud and generating a life program tailored to their health condition and lifestyle. This makes it possible to provide personalized care programs based on the individual's health and emotional state. 【0425】 "Health information" refers to data indicating the user's physical condition and related general guidelines and knowledge. 【0426】 A "database" refers to a system that systematically stores large amounts of information and organizes it so that it can be quickly searched and used as needed. 【0427】 "Model building" refers to the process of analyzing data and creating a framework that enables prediction and classification according to specific purposes. 【0428】 "Biometric information" refers to data that indicates the physiological state of the body, such as the user's heart rate and body temperature. 【0429】 "Behavioral information" refers to data about users' activity patterns and habits. 【0430】 "Cloud" refers to data storage and computing services that can be accessed via the internet. 【0431】 "Individualized data analysis" refers to the process of analyzing information about a specific user in detail to understand their unique state and tendencies. 【0432】 A "life program" refers to an activity plan aimed at maintaining or improving health, which is designed based on the user's health condition and lifestyle. 【0433】 A "reminder" refers to a notification set to prompt a user to take a specific action. 【0434】 "Feedback" refers to evaluations and comments provided by users regarding their actions or the content provided by the system. 【0435】 An "emotion engine" refers to a technology that analyzes a user's voice tone, facial expressions, and other factors to evaluate their emotional state. 【0436】 "Caregiving activities" refer to a series of actions taken to provide specific physical or mental support. 【0437】 A "relaxation program" refers to activities designed to reduce stress and promote mental and physical well-being. 【0438】 This invention is a system that utilizes cloud infrastructure and an emotion engine to provide personalized care programs based on an individual's health and emotional state. The system has the following configuration and operation: 【0439】 First, the server periodically retrieves general health information from a database via the internet and analyzes this data using natural language processing technology. This builds a foundational model for providing health advice. This model is then optimized using a generative AI model to provide users with the most appropriate health advice. 【0440】 The device acquires the user's biometric and behavioral information in real time through wearable devices such as smartphones and smart glasses. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice tone and facial expressions to evaluate their emotional state. The user's emotional data is sent to the cloud and used for data analysis on the server. 【0441】 When a user is feeling anxious, the emotion engine detects this, and the server generates a relaxation program based on that information. For example, if an elderly person shows signs of anxiety in the afternoon, a notification is sent to their device suggesting breathing exercises or light exercise to alleviate tension. At this time, the AI ​​model generates the optimal suggestion using a prompt message that reads, "Based on the emotional state and activity schedule shown by the elderly person in the afternoon, please output advice to suggest the most suitable relaxation program for them." 【0442】 This system will enable personalized care support to improve the quality of care. 【0443】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0444】 Step 1: 【0445】 The server retrieves general health information from a database. It takes health information accessed via an API as input, and outputs health information data in text format. Natural language processing techniques are used to analyze the data and build a foundational model for health advice. Specifically, the Python Natural Language Toolkit (NLTK) is used for analysis, tokenizing the text and extracting key health indicators. 【0446】 Step 2: 【0447】 The device acquires the user's biometric and behavioral information in real time. Inputs include biometric signals from sensors and behavioral data via Bluetooth, while output is a set of these raw data. It collects data such as heart rate, distance traveled, and activity time using a smartphone or smart glasses. Specifically, it collects data from wearable devices, stores it locally, and then sends it to the cloud. 【0448】 Step 3: 【0449】 The server analyzes individual biometric and behavioral data received on the cloud. The input is a user dataset sent from the terminal, and the output is an evaluation of health status and lifestyle habits generated through the analysis. Machine learning algorithms are used for data analysis to generate an optimal life program for the user. Specifically, the data is normalized and fed into a machine learning model for prediction and evaluation. 【0450】 Step 4: 【0451】 The device provides the user with a generated life program. The input is a life program suggestion from the server, and the output is a reminder and recommended activity displayed on the user's smartphone. Specifically, the reminder is displayed in the notification area, and the program details can be viewed within the app. 【0452】 Step 5: 【0453】 The server evaluates the user's emotional state using an emotion engine. Inputs include voice tone and facial expression data from the terminal, and output is the evaluation result of the user's emotional state. Specifically, it uses speech recognition and facial recognition technology to analyze emotions and determine stress levels and emotional tendencies. 【0454】 Step 6: 【0455】 The server generates a relaxation program based on the evaluated emotional state. The input is the evaluation results from the emotion engine, and the output is a list of care and relaxation activities to suggest to the user. Specifically, it uses a prompt message such as, "Based on the emotional state and activity schedule shown by the elderly person in the afternoon, output advice to suggest the most suitable relaxation program for them," to create the program using a generative AI model. 【0456】 Step 7: 【0457】 Users incorporate the provided programs into their daily lives. The input is the program content provided by the device, and the output is the user's execution status and feedback. Specifically, the user performs the notified activity and inputs the results and impressions into the app. 【0458】 Step 8: 【0459】 The server acquires user feedback and execution data to adjust the life program. Its input is user feedback data, and its output is the adjusted life program. Specifically, it analyzes the feedback and incorporates it into the next program creation, resulting in more personalized suggestions. 【0460】 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. 【0461】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0462】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0463】 [Third Embodiment] 【0464】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0465】 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. 【0466】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0467】 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. 【0468】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0469】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0470】 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. 【0471】 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. 【0472】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0473】 The 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. 【0474】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0475】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0476】 To implement this invention, it is necessary to build a system based on a cloud computing environment and coordinate processing according to the respective roles of the server, terminal, and user. The following is an explanation of the basic functions of each, along with specific examples. 【0477】 The server first acquires a wide range of health data from reliable health-related information sources on the internet and analyzes it using natural language processing and machine learning techniques. This allows it to build models for providing general health advice and store them in a database. 【0478】 The device collects biometric and behavioral information from the user's daily life through sensors and apps. This information is transmitted in real time to a server in the cloud, where it is individually analyzed based on the user's registered health goals and preferences. 【0479】 On the cloud, the system evaluates the user's current health status based on collected data and generates an optimal "life program" based on this evaluation. This program includes multiple elements such as diet, exercise, sleep, and stress management, and proposes specific and achievable improvement measures for the user. 【0480】 Users receive details about this program via their device and instructions on how to incorporate it into their daily lives. For example, if a user wants to improve their sleep, the server analyzes the user's sleep patterns and suggests an optimal sleep duration. The app on the device also sends reminders at specific times each night to help users go to bed at the recommended time. 【0481】 Meanwhile, the results and feedback from the user's actions are sent back to the server via the device, and the life program is adjusted as needed. For example, if a user is unable to complete the suggested amount of exercise, the server analyzes the reason and proposes a new exercise plan to help them achieve their goals without overexerting themselves. 【0482】 In this way, the system can dynamically adapt to the user's situation and continuously provide better lifestyle improvements. Through this interaction, users can manage their health at their own pace without undue pressure, reducing future health risks and maintaining long-term health. 【0483】 The following describes the processing flow. 【0484】 Step 1: 【0485】 The server crawls trusted health-related databases to collect the latest general health information. This includes online resources such as research papers, news articles, and medical websites. The collected information is analyzed using natural language processing techniques and integrated into a base model for health advice. 【0486】 Step 2: 【0487】 The device acquires everyday biometric information (e.g., heart rate, steps taken, calorie consumption) and behavioral information (e.g., location information, activity history) from the user's smartphone or wearable device. The acquired data is transmitted to the server in real time. 【0488】 Step 3: 【0489】 The server analyzes the received personal data in the cloud to assess the user's current health status and lifestyle. This allows for the identification of health risks tailored to individual needs and goals, and the establishment of a general framework for necessary improvement measures. 【0490】 Step 4: 【0491】 Based on the analysis results, the server generates a "life program" optimized for the user. This program includes specific actions to achieve individual goals, such as dietary guidance, exercise plans, and sleep improvement strategies. 【0492】 Step 5: 【0493】 The device notifies the user of the generated life program and presents specific implementation methods and timelines. This includes alert and reminder functions to support the user in carrying out the plan in their daily life. 【0494】 Step 6: 【0495】 Users execute life programs based on instructions from their devices and record their progress and achievements. User feedback is also sent to the server via the device, and the program's effectiveness and areas for improvement are incorporated as part of the analysis. 【0496】 Step 7: 【0497】 The server evaluates the life program based on user feedback and execution data, and adjusts its content as needed. This ensures continuous optimization to achieve maximum effectiveness. 【0498】 Through these steps, the system provides users with personalized health improvement strategies and supports sustainable healthcare practices. 【0499】 (Example 1) 【0500】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0501】 In recent years, people's awareness of lifestyle-related diseases and health has increased, but it remains difficult to develop and implement appropriate and sustainable health management plans for each individual. While general health information is readily available, applying it to individual lifestyles and health conditions is challenging. Furthermore, there is a lack of mechanisms to support daily health management and to incorporate feedback and adjustments as needed. There is a need to address this challenge and provide users with methods to efficiently manage their own health. 【0502】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0503】 This invention includes a server that includes means for acquiring general health-related information from information sources, analyzing the data using natural language processing and machine learning techniques, and building a model for providing health advice; means for collecting biometric and behavioral data from the user's daily life through sensors and application software and transmitting it using cloud infrastructure technology; and means for analyzing the user's individual data on the cloud and generating a program tailored to their health status and daily habits. This makes it possible to quickly and efficiently provide users with an optimal health management plan that can be adapted to their individual health condition. 【0504】 "General health information" refers to a broad category of public health and medical data obtained from reliable sources. 【0505】 A “source” is a publicly available database or website on the internet or other media for collecting health-related information. 【0506】 "Natural language processing" is a technology that enables computers to understand and analyze human language. 【0507】 "Machine learning technology" is a part of artificial intelligence technology that uses data to train computer models for prediction and classification. 【0508】 A "model" is a set of algorithmic structures and patterns built to provide health advice. 【0509】 A "user" is an individual who utilizes this system and is the entity that receives health information and services. 【0510】 "Biometric data" refers to data that includes physical measurements such as a user's heart rate and body temperature. 【0511】 "Behavioral data" refers to data that shows a user's activity patterns and behavioral history. 【0512】 A "sensor" is a device that detects physical changes and generates data. 【0513】 "Application software" refers to programs developed to provide specific functions to users. 【0514】 "Cloud infrastructure technology" refers to technology that provides data storage and analysis over the internet. 【0515】 "Cloud" refers to a distributed computing network that provides services and data via the internet. 【0516】 "Individualized data" refers to data about the unique health status and behavior associated with a specific user. 【0517】 "Health status" refers to information about the user's physical and mental health. 【0518】 "Daily habits" refer to the patterns of actions and activities that users perform on a daily basis. 【0519】 A "program" is a plan or guideline for lifestyle improvement proposed based on the user's health condition. 【0520】 A "notification function" is a system function that informs users of information based on specified conditions. 【0521】 "Implementation results" refer to data about the activities performed and goals achieved by users. 【0522】 "Feedback" refers to the opinions and reactions from users regarding system proposals and activities. 【0523】 This invention is implemented through a comprehensive system to support health management. The server, terminal, and user interact with each other, and each component functions as follows: 【0524】 The server first obtains general health-related information from reliable sources. Web scraping techniques can be used for data collection. The acquired data is then analyzed using natural language processing and machine learning techniques with Python libraries such as TensorFlow and NLTK. This builds a model for generating health advice. This model is stored in a database and serves as the foundation for the advice provided to users. 【0525】 The device is the primary device for collecting biometric and behavioral data from the user's daily life. Sensors built into smartwatches and fitness trackers record data such as heart rate, steps taken, and calorie consumption. The recorded data is transmitted in real time to a server via cloud infrastructure technology using Bluetooth technology. This allows the system to understand the user's health status and provide necessary information to the server. 【0526】 In this system, the user is the recipient of information, acquiring life programs generated through their terminal and incorporating them into their daily life. For example, if a user wishes to improve their eating habits, they can input a prompt such as "Please suggest an appropriate meal plan" into the generating AI model, and receive optimal meal suggestions from the server. Based on these suggestions, the user can adjust their own meal plan and aim to achieve their goals. 【0527】 This system supports users in continuous health management and enables them to implement specific improvement measures tailored to their own health condition. By employing advanced technology to adapt to individual needs, this invention can provide users with a feasible and sustainable method of health management. 【0528】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0529】 Step 1: 【0530】 The server collects general health-related information from reliable sources on the internet. It uses a list of URLs of these sources as input. Specifically, it extracts information using web scraping techniques and stores it as text data. This output data is the raw material for subsequent processing. 【0531】 Step 2: 【0532】 The server analyzes the collected health-related information using natural language processing and machine learning algorithms. The text data obtained in Step 1 is used as input. Specifically, the NLTK library in Python is used to tokenize the data and extract important keywords. This data is then fed into a machine learning model to train it for generating health advice. The trained model is obtained as output. 【0533】 Step 3: 【0534】 The device collects biometric and behavioral data from the user's daily life. It uses data directly obtained from sensors in smartwatches and fitness trackers (e.g., heart rate, step count) as input. This data is collected by the device using Bluetooth technology and transmitted to the cloud in real time. As output, a user lifestyle data set is constructed. 【0535】 Step 4: 【0536】 The server analyzes user data sent from the terminal and evaluates the individual's health status. The user data obtained in step 3 is used as input. Specifically, this data is fed into the machine learning model built earlier to perform predictions. This evaluates the current health status, and the results are added to the user's profile database. 【0537】 Step 5: 【0538】 The server generates an optimal lifestyle program for the user based on the analysis results. It uses the health assessment data obtained in step 4 as input. Utilizing the generation AI model, it outputs prompts such as, "Please suggest a meal plan tailored to the user's health condition." The output includes a specific meal plan and exercise plan tailored to the user. 【0539】 Step 6: 【0540】 The user receives the generated life program through their device. The input is program information provided by the server. Specifically, the device's health management app displays the program content to the user using its notification function. The output provides the user with specific guidance for daily health management. 【0541】 Step 7: 【0542】 The system collects user activity results and feedback from the device and sends it to the server. Inputs include data on the user's exercise and diet, as well as feedback information. This data is entered via an interface on the device and sent to the server. The output is an updated results and feedback dataset. 【0543】 Step 8: 【0544】 The server adjusts the life program based on the collected feedback information. The feedback data obtained in step 7 is used as input. The machine learning algorithm is applied again to generate new suggestions for improving the program. As output, the adjusted life program is created and resent to the user, enabling more effective health management. 【0545】 (Application Example 1) 【0546】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0547】 In a health management system, it is necessary to build a mechanism that continuously proposes and supports the implementation of specific improvement measures tailored to the individual health condition of each user. To achieve this, a device that closely supports the user's daily life is required. Furthermore, to realize improvements in the user's health, it is crucial to have a function that can dynamically adjust the life program. 【0548】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0549】 In this invention, the server includes means for acquiring general health information from data storage, analyzing it, and constructing a data model for providing health advice; means for acquiring the user's biometric and behavioral data and transmitting it to a virtual space; and means for analyzing the user's individual data in the virtual space and generating improvement programs tailored to their health status and lifestyle. This makes it possible to efficiently provide and support a health improvement program optimized for the user in their daily life. 【0550】 "Health information" refers to data and knowledge related to the user's health status, and this includes medical data and lifestyle information. 【0551】 "Data storage" refers to a technology or device that stores various types of data and makes them accessible as needed. 【0552】 A "data model" is a framework that defines the structure and methods of manipulating data for a specific purpose. 【0553】 "Biometric data" refers to various numerical values ​​or indicators that show an individual's physical condition. Specific examples include heart rate and body temperature. 【0554】 "Behavioral data" refers to data related to an individual's actions and activities in their daily life. 【0555】 A "virtual space" is a computer domain created using digital technology for processing real or virtual data. 【0556】 "Individual data" refers to data associated with a specific user, which enables customized processing for each user. 【0557】 An "improvement program" is a plan that outlines a series of activities and guidelines aimed at improving the health of users. 【0558】 A "notification" is a message or alert that a system uses to communicate information to a user. 【0559】 "Opinions" refer to feedback and evaluations provided by users, and are data used to adjust the system. 【0560】 "Execution data" refers to data that shows the activities actually performed by the user and the results thereof. 【0561】 "Lifestyle machinery and devices" are devices that are closely involved in the user's daily life and support health management. 【0562】 To implement this invention, it is necessary to build a user-facing system to support health management. This system begins with installing various sensors and data collection applications on a terminal to acquire the user's biometric and behavioral data. The terminal transmits this data to a server in the cloud in real time. 【0563】 The server first collects general health information from reliable sources on the internet and stores it in data storage. Next, it uses natural language processing and machine learning techniques to analyze this health data and build a data model to provide health advice. Analytical tools such as Python and R are used for this process. Individual user data is analyzed through a virtual space on the cloud, and improvement programs are generated that are tailored to the user's health status and lifestyle. These improvement programs include various health elements such as diet, exercise, sleep, and stress management. 【0564】 The device provides the user with the generated improvement program and sends notifications to assist in running the program. Smartphones and smartwatches are used for this purpose. User feedback and execution data are then sent back to a server in the cloud to evaluate the program's effectiveness and dynamically adjust it as needed. 【0565】 As a concrete example, a user might receive notifications about optimized sleep duration based on their activity data from the past week. An example of a prompt using a generative AI model would be a request such as, "If the user only slept for 7 hours, please suggest an optimal lifestyle program based on that data." In this way, the system aims to provide continuous health management and improvement, supporting long-term health maintenance. 【0566】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0567】 Step 1: 【0568】 The device collects the user's biometric and behavioral data through sensors and apps. Inputs include real-time data such as heart rate, steps taken, activity time, and sleep duration. This data is transmitted in real time by the device to a server in the cloud. Outputs are stored in the cloud as the user's continuous health data. 【0569】 Step 2: 【0570】 The server analyzes user biometric and behavioral data collected in the cloud. The input is a collection of health data accumulated over time. The server analyzes this data using natural language processing and machine learning techniques. It performs various analyses using Python and R languages ​​to evaluate the user's health status. The output is a current health assessment report based on the user's health condition. 【0571】 Step 3: 【0572】 Based on the analysis results, the server generates an improvement program tailored to the user's health status and lifestyle. Inputs include a current assessment report and general health information. A generation AI model is used to create an optimized improvement program. The output is a personalized life program for the user, which includes improvement measures for exercise, diet, sleep, and other areas. 【0573】 Step 4: 【0574】 The terminal provides the user with the generated life program and sends notifications to assist in its execution. The input is the life program retrieved from the cloud. Based on this, the terminal notifies the user via voice or display. The output is the specific execution instructions received by the user. 【0575】 Step 5: 【0576】 Users adjust their lifestyle habits based on the life program received from their device. They send feedback and results to the cloud via the device. Inputs are the user's execution data and feedback. Outputs are newly accumulated execution data, which are used to adjust the next improvement program. 【0577】 Step 6: 【0578】 The server analyzes user feedback and execution data to dynamically adjust the improvement program. Inputs are execution data and feedback. The server compares this to the database history and, if necessary, performs re-analysis using a generated AI model. The output is a newly adjusted improvement program. 【0579】 Step 7: 【0580】 As a concrete example, the server starts the analysis with the prompt, "If the user only slept for 7 hours, please suggest an optimal lifestyle program based on that data." The input is past sleep data, and the output is an adjusted program that recommends 8 hours of sleep. 【0581】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0582】 To implement this invention, a system comprising a server utilizing cloud infrastructure, a terminal for understanding the user's status, and an emotion engine is used. The specific functions and implementation methods of each are described below. 【0583】 The server periodically collects general health-related information via the internet and analyzes it using natural language processing technology. Based on this analysis, a base model for health advice to be provided to the user is built. Furthermore, when the user's biometric and behavioral information is transmitted from the terminal, the server analyzes this as individual data on the cloud and generates a "life program" tailored to their health status and lifestyle. In this process, the user's emotional data is also considered via an emotion engine and reflected in the program. 【0584】 The device acquires biometric information in real time from the user's smartphone or wearable device. The device also incorporates an emotion engine that analyzes the user's voice tone and facial expressions to recognize their emotions at that time. The information obtained by the emotion engine is sent to the cloud to evaluate the user's stress level and emotional tendencies. 【0585】 For example, if a user is experiencing stress at work, the emotion engine analyzes changes in the user's voice and facial expressions and recognizes a high stress level. The server receives this information and generates a relaxation program as part of the life program aimed at reducing stress. The device notifies the user of suitable breathing exercises and meditation suggestions and encourages them to incorporate necessary relaxation activities into their schedule. 【0586】 Users incorporate and implement life programs presented through their devices into their daily routines. Progress and feedback are sent from the device to the server, where this information is stored and used to create future programs. Because feedback from the emotion engine is constantly reflected, users can continuously receive more appropriate advice tailored to their emotional state. 【0587】 Thus, the present invention enables users to receive personalized support based on their individual health and emotional states, thereby improving the efficiency of long-term and sustainable health management. 【0588】 The following describes the processing flow. 【0589】 Step 1: 【0590】 The server patrols major health-related databases to retrieve the latest medical information and health guidance. The retrieved information is analyzed using natural language processing technology to create a foundational model for providing health advice. 【0591】 Step 2: 【0592】 The device collects biometric information from the user's smart device, including parameters such as heart rate, sleep patterns, and activity levels. Simultaneously, an emotion engine analyzes the user's voice and facial expressions to generate emotional data. 【0593】 Step 3: 【0594】 The device transmits collected biometric and emotional data to a server in the cloud. This data reflects the user's health and emotional state. 【0595】 Step 4: 【0596】 The server analyzes data received on the cloud and generates a personalized "life program" based on the user's health status and lifestyle. During this process, the program is adjusted based on the user's emotional data, including stress reduction measures and emotional management techniques. 【0597】 Step 5: 【0598】 The device notifies the user of the generated program. It also sends reminders that suggest specific health activities and relaxation times tailored to the user's daily schedule. 【0599】 Step 6: 【0600】 Users follow the instructions on the device and incorporate and execute the life program into their daily lives. Users record their progress using the device and provide feedback for reassessment. 【0601】 Step 7: 【0602】 The server analyzes user feedback and execution data to evaluate the effectiveness of the program's lifecycle. It continuously optimizes the program, taking into account newly discovered trends and areas for improvement. 【0603】 Through this process, the system continues to provide personalized healthcare tailored to the user's individual health and emotional needs. 【0604】 (Example 2) 【0605】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0606】 Traditional health management systems only provide general health advice, and struggle to offer personalized advice tailored to the individual health and emotional states of each user. Furthermore, they lacked systems to effectively incorporate user feedback, making it challenging to improve the efficiency of long-term health management. 【0607】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0608】 This invention includes a server that includes means for acquiring general health-related information from information sources, analyzing it using natural language processing technology, and constructing a model for providing health advice; means for acquiring the user's biometric and behavioral data and transmitting it to data storage; and means for analyzing the user's individual data on the data storage and generating a lifestyle improvement program tailored to the user's health status and lifestyle. This makes it possible to provide personalized advice based on the user's individual health status and emotional state. 【0609】 "Health-related information" refers to general knowledge and data related to an individual's health status, and is collected from official health information sources. 【0610】 "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and utilize human language. 【0611】 "Biometric data" refers to data about an individual user's physiological state, such as heart rate and exercise level. 【0612】 "Behavioral data" refers to data about users' activities and daily behavioral patterns. 【0613】 "Data storage" refers to a storage system, including the cloud, for accumulating and saving acquired data. 【0614】 A "lifestyle improvement program" is a program tailored to an individual's health condition and lifestyle habits, provided with the aim of helping users maintain or improve their health. 【0615】 "Emotion recognition technology" refers to technology that analyzes and identifies a user's emotional state from factors such as voice and facial expressions. 【0616】 A "notification" is a message or alert used to quickly convey information to a user. 【0617】 The system of this invention utilizes advanced analytical techniques using servers, terminals, and generative AI models. The objective of this system is to provide personalized health advice that is tailored to the individual health and emotional state of the user. 【0618】 The server uses network technologies to retrieve general health-related information from various sources. Specifically, it leverages web scraping techniques to extract information from reliable health sources. This information is then analyzed using natural language processing techniques (e.g., using Python's NLTK library) and used to build health advice models for users. 【0619】 The device acquires biometric and behavioral data from the user's daily life. This role is played by smartphones and wearable devices. These devices transmit data to the cloud using Bluetooth technology. Furthermore, the device is equipped with emotion recognition technology, which analyzes voice tone and facial expression data to evaluate the user's emotions. For example, the device collects this data through its microphone and camera functions and uses algorithms to determine the emotional state. 【0620】 Users receive lifestyle improvement programs provided by a server via a smartphone application. These programs include advice tailored to the user's specific health and emotional state. For example, breathing exercises and meditation guidance are suggested through the application to reduce stress. Users can integrate these programs into their daily lives and send feedback on their implementation to the server via their device. 【0621】 As a concrete example, consider a scenario where a user is experiencing stress at work. The device recognizes a high stress level based on the user's voice and facial expressions. In response, the server generates a stress reduction program and suggests relaxation techniques. The user receives and implements these suggestions through the application. 【0622】 An example of a prompt message would be something like, "Suggest a stress reduction program based on the user's current emotional state and health data." 【0623】 This system can improve the efficiency of health management by providing users with appropriate support based on their individual health and emotional states. 【0624】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0625】 Step 1: 【0626】 The server retrieves general health-related information from the internet. The input is data from reliable health sources. The output is the collected, raw health information data. This data is extracted using web scraping techniques and stored as preparation for natural language processing techniques. 【0627】 Step 2: 【0628】 The server uses natural language processing techniques to analyze the collected health information data. The input is the raw data collected in step 1. For data processing, the NLTK library in Python is used to tokenize the data and extract important keywords. The output is the analyzed health data used to build a health advice model. 【0629】 Step 3: 【0630】 The terminal acquires biometric and behavioral data in real time using the user's smart device. Input is sensor information obtained from the smart device (e.g., heart rate, activity level). Data processing involves pre-processing and formatting this data. Output is a set of the user's biometric and activity data, transmitted to the terminal via Bluetooth. 【0631】 Step 4: 【0632】 The device collects user emotional data using emotion recognition technology. Inputs include audio data from the microphone and facial expression data from the camera. To analyze this data, it uses a speech analysis algorithm and OpenCV to determine the user's emotional state. The output is user emotional data, indicating stress levels and emotional tendencies. 【0633】 Step 5: 【0634】 The server integrates biometric, behavioral, and emotional data transmitted from terminals and analyzes it on data storage. The input consists of multiple datasets collected from terminals. As a data computation, a learning algorithm is applied to the integrated data to analyze individual health conditions and lifestyle habits. The output is a lifestyle improvement program optimized for the user. 【0635】 Step 6: 【0636】 Users receive lifestyle improvement programs provided by the server and incorporate them into their daily lives. The input is the suggested lifestyle improvement program sent from the server. Specifically, users check the program on a smartphone app and perform the suggested breathing and meditation techniques. The output is feedback and progress information on the completed program. 【0637】 Step 7: 【0638】 The server receives feedback from the user and incorporates it into the next program creation. The input is the feedback information sent by the user in step 6. As part of data processing, the feedback data is analyzed and the lifestyle improvement program is adjusted. The output is the adjusted new lifestyle improvement program, which will be used in the next provision. 【0639】 (Application Example 2) 【0640】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0641】 In modern society, managing the health of the elderly is a crucial issue, but there is a lack of means to appropriately grasp individual health and emotional states in real time and improve the quality of care. Furthermore, there is a need for a system that automatically suggests care activities tailored to each individual's emotional state. 【0642】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0643】 In this invention, the server includes means for acquiring general health information from a database, analyzing it, and constructing a model for providing health advice; means for acquiring the user's biometric and behavioral information and transmitting it to the cloud; and means for analyzing the user's individual data on the cloud and generating a life program tailored to their health condition and lifestyle. This makes it possible to provide personalized care programs based on the individual's health and emotional state. 【0644】 "Health information" refers to data indicating the user's physical condition and related general guidelines and knowledge. 【0645】 A "database" refers to a system that systematically stores large amounts of information and organizes it so that it can be quickly searched and used as needed. 【0646】 "Model building" refers to the process of analyzing data and creating a framework that enables prediction and classification according to specific purposes. 【0647】 "Biometric information" refers to data that indicates the physiological state of the body, such as the user's heart rate and body temperature. 【0648】 "Behavioral information" refers to data about users' activity patterns and habits. 【0649】 "Cloud" refers to data storage and computing services that can be accessed via the internet. 【0650】 "Individualized data analysis" refers to the process of analyzing information about a specific user in detail to understand their unique state and tendencies. 【0651】 A "life program" refers to an activity plan aimed at maintaining or improving health, which is designed based on the user's health condition and lifestyle. 【0652】 A "reminder" refers to a notification set to prompt a user to take a specific action. 【0653】 "Feedback" refers to evaluations and comments provided by users regarding their actions or the content provided by the system. 【0654】 An "emotion engine" refers to a technology that analyzes a user's voice tone, facial expressions, and other factors to evaluate their emotional state. 【0655】 "Caregiving activities" refer to a series of actions taken to provide specific physical or mental support. 【0656】 A "relaxation program" refers to activities designed to reduce stress and promote mental and physical well-being. 【0657】 This invention is a system that utilizes cloud infrastructure and an emotion engine to provide personalized care programs based on an individual's health and emotional state. The system has the following configuration and operation: 【0658】 First, the server periodically retrieves general health information from a database via the internet and analyzes this data using natural language processing technology. This builds a foundational model for providing health advice. This model is then optimized using a generative AI model to provide users with the most appropriate health advice. 【0659】 The device acquires the user's biometric and behavioral information in real time through wearable devices such as smartphones and smart glasses. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice tone and facial expressions to evaluate their emotional state. The user's emotional data is sent to the cloud and used for data analysis on the server. 【0660】 When a user is feeling anxious, the emotion engine detects this, and the server generates a relaxation program based on that information. For example, if an elderly person shows signs of anxiety in the afternoon, a notification is sent to their device suggesting breathing exercises or light exercise to alleviate tension. At this time, the AI ​​model generates the optimal suggestion using a prompt message that reads, "Based on the emotional state and activity schedule shown by the elderly person in the afternoon, please output advice to suggest the most suitable relaxation program for them." 【0661】 This system will enable personalized care support to improve the quality of care. 【0662】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0663】 Step 1: 【0664】 The server retrieves general health information from a database. It takes health information accessed via an API as input, and outputs health information data in text format. Natural language processing techniques are used to analyze the data and build a foundational model for health advice. Specifically, the Python Natural Language Toolkit (NLTK) is used for analysis, tokenizing the text and extracting key health indicators. 【0665】 Step 2: 【0666】 The device acquires the user's biometric and behavioral information in real time. Inputs include biometric signals from sensors and behavioral data via Bluetooth, while output is a set of these raw data. It collects data such as heart rate, distance traveled, and activity time using a smartphone or smart glasses. Specifically, it collects data from wearable devices, stores it locally, and then sends it to the cloud. 【0667】 Step 3: 【0668】 The server analyzes individual biometric and behavioral data received on the cloud. The input is a user dataset sent from the terminal, and the output is an evaluation of health status and lifestyle habits generated through the analysis. Machine learning algorithms are used for data analysis to generate an optimal life program for the user. Specifically, the data is normalized and fed into a machine learning model for prediction and evaluation. 【0669】 Step 4: 【0670】 The device provides the user with a generated life program. The input is a life program suggestion from the server, and the output is a reminder and recommended activity displayed on the user's smartphone. Specifically, the reminder is displayed in the notification area, and the program details can be viewed within the app. 【0671】 Step 5: 【0672】 The server evaluates the user's emotional state using an emotion engine. Inputs include voice tone and facial expression data from the terminal, and output is the evaluation result of the user's emotional state. Specifically, it uses speech recognition and facial recognition technology to analyze emotions and determine stress levels and emotional tendencies. 【0673】 Step 6: 【0674】 The server generates a relaxation program based on the evaluated emotional state. The input is the evaluation results from the emotion engine, and the output is a list of care and relaxation activities to suggest to the user. Specifically, it uses a prompt message such as, "Based on the emotional state and activity schedule shown by the elderly person in the afternoon, output advice to suggest the most suitable relaxation program for them," to create the program using a generative AI model. 【0675】 Step 7: 【0676】 Users incorporate the provided programs into their daily lives. The input is the program content provided by the device, and the output is the user's execution status and feedback. Specifically, the user performs the notified activity and inputs the results and impressions into the app. 【0677】 Step 8: 【0678】 The server acquires user feedback and execution data to adjust the life program. Its input is user feedback data, and its output is the adjusted life program. Specifically, it analyzes the feedback and incorporates it into the next program creation, resulting in more personalized suggestions. 【0679】 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. 【0680】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0681】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0682】 [Fourth Embodiment] 【0683】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0684】 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. 【0685】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0686】 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. 【0687】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0688】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0689】 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. 【0690】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0691】 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. 【0692】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0693】 The 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. 【0694】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0695】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0696】 To implement this invention, it is necessary to build a system based on a cloud computing environment and coordinate processing according to the respective roles of the server, terminal, and user. The following is an explanation of the basic functions of each, along with specific examples. 【0697】 The server first acquires a wide range of health data from reliable health-related information sources on the internet and analyzes it using natural language processing and machine learning techniques. This allows it to build models for providing general health advice and store them in a database. 【0698】 The device collects biometric and behavioral information from the user's daily life through sensors and apps. This information is transmitted in real time to a server in the cloud, where it is individually analyzed based on the user's registered health goals and preferences. 【0699】 On the cloud, the system evaluates the user's current health status based on collected data and generates an optimal "life program" based on this evaluation. This program includes multiple elements such as diet, exercise, sleep, and stress management, and proposes specific and achievable improvement measures for the user. 【0700】 Users receive details about this program via their device and instructions on how to incorporate it into their daily lives. For example, if a user wants to improve their sleep, the server analyzes the user's sleep patterns and suggests an optimal sleep duration. The app on the device also sends reminders at specific times each night to help users go to bed at the recommended time. 【0701】 Meanwhile, the results and feedback from the user's actions are sent back to the server via the device, and the life program is adjusted as needed. For example, if a user is unable to complete the suggested amount of exercise, the server analyzes the reason and proposes a new exercise plan to help them achieve their goals without overexerting themselves. 【0702】 In this way, the system can dynamically adapt to the user's situation and continuously provide better lifestyle improvements. Through this interaction, users can manage their health at their own pace without undue pressure, reducing future health risks and maintaining long-term health. 【0703】 The following describes the processing flow. 【0704】 Step 1: 【0705】 The server crawls trusted health-related databases to collect the latest general health information. This includes online resources such as research papers, news articles, and medical websites. The collected information is analyzed using natural language processing techniques and integrated into a base model for health advice. 【0706】 Step 2: 【0707】 The device acquires everyday biometric information (e.g., heart rate, steps taken, calorie consumption) and behavioral information (e.g., location information, activity history) from the user's smartphone or wearable device. The acquired data is transmitted to the server in real time. 【0708】 Step 3: 【0709】 The server analyzes the received personal data in the cloud to assess the user's current health status and lifestyle. This allows for the identification of health risks tailored to individual needs and goals, and the establishment of a general framework for necessary improvement measures. 【0710】 Step 4: 【0711】 Based on the analysis results, the server generates a "life program" optimized for the user. This program includes specific actions to achieve individual goals, such as dietary guidance, exercise plans, and sleep improvement strategies. 【0712】 Step 5: 【0713】 The device notifies the user of the generated life program and presents specific implementation methods and timelines. This includes alert and reminder functions to support the user in carrying out the plan in their daily life. 【0714】 Step 6: 【0715】 Users execute life programs based on instructions from their devices and record their progress and achievements. User feedback is also sent to the server via the device, and the program's effectiveness and areas for improvement are incorporated as part of the analysis. 【0716】 Step 7: 【0717】 The server evaluates the life program based on user feedback and execution data, and adjusts its content as needed. This ensures continuous optimization to achieve maximum effectiveness. 【0718】 Through these steps, the system provides users with personalized health improvement strategies and supports sustainable healthcare practices. 【0719】 (Example 1) 【0720】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0721】 In recent years, people's awareness of lifestyle-related diseases and health has increased, but it remains difficult to develop and implement appropriate and sustainable health management plans for each individual. While general health information is readily available, applying it to individual lifestyles and health conditions is challenging. Furthermore, there is a lack of mechanisms to support daily health management and to incorporate feedback and adjustments as needed. There is a need to address this challenge and provide users with methods to efficiently manage their own health. 【0722】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0723】 This invention includes a server that includes means for acquiring general health-related information from information sources, analyzing the data using natural language processing and machine learning techniques, and building a model for providing health advice; means for collecting biometric and behavioral data from the user's daily life through sensors and application software and transmitting it using cloud infrastructure technology; and means for analyzing the user's individual data on the cloud and generating a program tailored to their health status and daily habits. This makes it possible to quickly and efficiently provide users with an optimal health management plan that can be adapted to their individual health condition. 【0724】 "General health information" refers to a broad category of public health and medical data obtained from reliable sources. 【0725】 A “source” is a publicly available database or website on the internet or other media for collecting health-related information. 【0726】 "Natural language processing" is a technology that enables computers to understand and analyze human language. 【0727】 "Machine learning technology" is a part of artificial intelligence technology that uses data to train computer models for prediction and classification. 【0728】 A "model" is a set of algorithmic structures and patterns built to provide health advice. 【0729】 A "user" is an individual who utilizes this system and is the entity that receives health information and services. 【0730】 "Biometric data" refers to data that includes physical measurements such as a user's heart rate and body temperature. 【0731】 "Behavioral data" refers to data that shows a user's activity patterns and behavioral history. 【0732】 A "sensor" is a device that detects physical changes and generates data. 【0733】 "Application software" refers to programs developed to provide specific functions to users. 【0734】 "Cloud infrastructure technology" refers to technology that provides data storage and analysis over the internet. 【0735】 "Cloud" refers to a distributed computing network that provides services and data via the internet. 【0736】 "Individualized data" refers to data about the unique health status and behavior associated with a specific user. 【0737】 "Health status" refers to information about the user's physical and mental health. 【0738】 "Daily habits" refer to the patterns of actions and activities that users perform on a daily basis. 【0739】 A "program" is a plan or guideline for lifestyle improvement proposed based on the user's health condition. 【0740】 A "notification function" is a system function that informs users of information based on specified conditions. 【0741】 "Implementation results" refer to data about the activities performed and goals achieved by users. 【0742】 "Feedback" refers to the opinions and reactions from users regarding system proposals and activities. 【0743】 This invention is implemented through a comprehensive system to support health management. The server, terminal, and user interact with each other, and each component functions as follows: 【0744】 The server first obtains general health-related information from reliable sources. Web scraping techniques can be used for data collection. The acquired data is then analyzed using natural language processing and machine learning techniques with Python libraries such as TensorFlow and NLTK. This builds a model for generating health advice. This model is stored in a database and serves as the foundation for the advice provided to users. 【0745】 The device is the primary device for collecting biometric and behavioral data from the user's daily life. Sensors built into smartwatches and fitness trackers record data such as heart rate, steps taken, and calorie consumption. The recorded data is transmitted in real time to a server via cloud infrastructure technology using Bluetooth technology. This allows the system to understand the user's health status and provide necessary information to the server. 【0746】 In this system, the user is the recipient of information, acquiring life programs generated through their terminal and incorporating them into their daily life. For example, if a user wishes to improve their eating habits, they can input a prompt such as "Please suggest an appropriate meal plan" into the generating AI model, and receive optimal meal suggestions from the server. Based on these suggestions, the user can adjust their own meal plan and aim to achieve their goals. 【0747】 This system supports users in continuous health management and enables them to implement specific improvement measures tailored to their own health condition. By employing advanced technology to adapt to individual needs, this invention can provide users with a feasible and sustainable method of health management. 【0748】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0749】 Step 1: 【0750】 The server collects general health-related information from reliable sources on the internet. It uses a list of URLs of these sources as input. Specifically, it extracts information using web scraping techniques and stores it as text data. This output data is the raw material for subsequent processing. 【0751】 Step 2: 【0752】 The server analyzes the collected health-related information using natural language processing and machine learning algorithms. The text data obtained in Step 1 is used as input. Specifically, the NLTK library in Python is used to tokenize the data and extract important keywords. This data is then fed into a machine learning model to train it for generating health advice. The trained model is obtained as output. 【0753】 Step 3: 【0754】 The device collects biometric and behavioral data from the user's daily life. It uses data directly obtained from sensors in smartwatches and fitness trackers (e.g., heart rate, step count) as input. This data is collected by the device using Bluetooth technology and transmitted to the cloud in real time. As output, a user lifestyle data set is constructed. 【0755】 Step 4: 【0756】 The server analyzes user data sent from the terminal and evaluates the individual's health status. The user data obtained in step 3 is used as input. Specifically, this data is fed into the machine learning model built earlier to perform predictions. This evaluates the current health status, and the results are added to the user's profile database. 【0757】 Step 5: 【0758】 The server generates an optimal lifestyle program for the user based on the analysis results. It uses the health assessment data obtained in step 4 as input. Utilizing the generation AI model, it outputs prompts such as, "Please suggest a meal plan tailored to the user's health condition." The output includes a specific meal plan and exercise plan tailored to the user. 【0759】 Step 6: 【0760】 The user receives the generated life program through their device. The input is program information provided by the server. Specifically, the device's health management app displays the program content to the user using its notification function. The output provides the user with specific guidance for daily health management. 【0761】 Step 7: 【0762】 The system collects user activity results and feedback from the device and sends it to the server. Inputs include data on the user's exercise and diet, as well as feedback information. This data is entered via an interface on the device and sent to the server. The output is an updated results and feedback dataset. 【0763】 Step 8: 【0764】 The server adjusts the life program based on the collected feedback information. The feedback data obtained in step 7 is used as input. The machine learning algorithm is applied again to generate new suggestions for improving the program. As output, the adjusted life program is created and resent to the user, enabling more effective health management. 【0765】 (Application Example 1) 【0766】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0767】 In a health management system, it is necessary to build a mechanism that continuously proposes and supports the implementation of specific improvement measures tailored to the individual health condition of each user. To achieve this, a device that closely supports the user's daily life is required. Furthermore, to realize improvements in the user's health, it is crucial to have a function that can dynamically adjust the life program. 【0768】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0769】 In this invention, the server includes means for acquiring general health information from data storage, analyzing it, and constructing a data model for providing health advice; means for acquiring the user's biometric and behavioral data and transmitting it to a virtual space; and means for analyzing the user's individual data in the virtual space and generating improvement programs tailored to their health status and lifestyle. This makes it possible to efficiently provide and support a health improvement program optimized for the user in their daily life. 【0770】 "Health information" refers to data and knowledge related to the user's health status, and this includes medical data and lifestyle information. 【0771】 "Data storage" refers to a technology or device that stores various types of data and makes them accessible as needed. 【0772】 A "data model" is a framework that defines the structure and methods of manipulating data for a specific purpose. 【0773】 "Biometric data" refers to various numerical values ​​or indicators that show an individual's physical condition. Specific examples include heart rate and body temperature. 【0774】 "Behavioral data" refers to data related to an individual's actions and activities in their daily life. 【0775】 A "virtual space" is a computer domain created using digital technology for processing real or virtual data. 【0776】 "Individual data" refers to data associated with a specific user, which enables customized processing for each user. 【0777】 An "improvement program" is a plan that outlines a series of activities and guidelines aimed at improving the health of users. 【0778】 A "notification" is a message or alert that a system uses to communicate information to a user. 【0779】 "Opinions" refer to feedback and evaluations provided by users, and are data used to adjust the system. 【0780】 "Execution data" refers to data that shows the activities actually performed by the user and the results thereof. 【0781】 "Lifestyle machinery and devices" are devices that are closely involved in the user's daily life and support health management. 【0782】 To implement this invention, it is necessary to build a user-facing system to support health management. This system begins with installing various sensors and data collection applications on a terminal to acquire the user's biometric and behavioral data. The terminal transmits this data to a server in the cloud in real time. 【0783】 The server first collects general health information from reliable sources on the internet and stores it in data storage. Next, it uses natural language processing and machine learning techniques to analyze this health data and build a data model to provide health advice. Analytical tools such as Python and R are used for this process. Individual user data is analyzed through a virtual space on the cloud, and improvement programs are generated that are tailored to the user's health status and lifestyle. These improvement programs include various health elements such as diet, exercise, sleep, and stress management. 【0784】 The device provides the user with the generated improvement program and sends notifications to assist in running the program. Smartphones and smartwatches are used for this purpose. User feedback and execution data are then sent back to a server in the cloud to evaluate the program's effectiveness and dynamically adjust it as needed. 【0785】 As a concrete example, a user might receive notifications about optimized sleep duration based on their activity data from the past week. An example of a prompt using a generative AI model would be a request such as, "If the user only slept for 7 hours, please suggest an optimal lifestyle program based on that data." In this way, the system aims to provide continuous health management and improvement, supporting long-term health maintenance. 【0786】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0787】 Step 1: 【0788】 The device collects the user's biometric and behavioral data through sensors and apps. Inputs include real-time data such as heart rate, steps taken, activity time, and sleep duration. This data is transmitted in real time by the device to a server in the cloud. Outputs are stored in the cloud as the user's continuous health data. 【0789】 Step 2: 【0790】 The server analyzes user biometric and behavioral data collected in the cloud. The input is a collection of health data accumulated over time. The server analyzes this data using natural language processing and machine learning techniques. It performs various analyses using Python and R languages ​​to evaluate the user's health status. The output is a current health assessment report based on the user's health condition. 【0791】 Step 3: 【0792】 Based on the analysis results, the server generates an improvement program tailored to the user's health status and lifestyle. Inputs include a current assessment report and general health information. A generation AI model is used to create an optimized improvement program. The output is a personalized life program for the user, which includes improvement measures for exercise, diet, sleep, and other areas. 【0793】 Step 4: 【0794】 The terminal provides the user with the generated life program and sends notifications to assist in its execution. The input is the life program retrieved from the cloud. Based on this, the terminal notifies the user via voice or display. The output is the specific execution instructions received by the user. 【0795】 Step 5: 【0796】 Users adjust their lifestyle habits based on the life program received from their device. They send feedback and results to the cloud via the device. Inputs are the user's execution data and feedback. Outputs are newly accumulated execution data, which are used to adjust the next improvement program. 【0797】 Step 6: 【0798】 The server analyzes user feedback and execution data to dynamically adjust the improvement program. Inputs are execution data and feedback. The server compares this to the database history and, if necessary, performs re-analysis using a generated AI model. The output is a newly adjusted improvement program. 【0799】 Step 7: 【0800】 As a concrete example, the server starts the analysis with the prompt, "If the user only slept for 7 hours, please suggest an optimal lifestyle program based on that data." The input is past sleep data, and the output is an adjusted program that recommends 8 hours of sleep. 【0801】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0802】 To implement this invention, a system comprising a server utilizing cloud infrastructure, a terminal for understanding the user's status, and an emotion engine is used. The specific functions and implementation methods of each are described below. 【0803】 The server periodically collects general health-related information via the internet and analyzes it using natural language processing technology. Based on this analysis, a base model for health advice to be provided to the user is built. Furthermore, when the user's biometric and behavioral information is transmitted from the terminal, the server analyzes this as individual data on the cloud and generates a "life program" tailored to their health status and lifestyle. In this process, the user's emotional data is also considered via an emotion engine and reflected in the program. 【0804】 The device acquires biometric information in real time from the user's smartphone or wearable device. The device also incorporates an emotion engine that analyzes the user's voice tone and facial expressions to recognize their emotions at that time. The information obtained by the emotion engine is sent to the cloud to evaluate the user's stress level and emotional tendencies. 【0805】 For example, if a user is experiencing stress at work, the emotion engine analyzes changes in the user's voice and facial expressions and recognizes a high stress level. The server receives this information and generates a relaxation program as part of the life program aimed at reducing stress. The device notifies the user of suitable breathing exercises and meditation suggestions and encourages them to incorporate necessary relaxation activities into their schedule. 【0806】 Users incorporate and implement life programs presented through their devices into their daily routines. Progress and feedback are sent from the device to the server, where this information is stored and used to create future programs. Because feedback from the emotion engine is constantly reflected, users can continuously receive more appropriate advice tailored to their emotional state. 【0807】 Thus, the present invention enables users to receive personalized support based on their individual health and emotional states, thereby improving the efficiency of long-term and sustainable health management. 【0808】 The following describes the processing flow. 【0809】 Step 1: 【0810】 The server patrols major health-related databases to retrieve the latest medical information and health guidance. The retrieved information is analyzed using natural language processing technology to create a foundational model for providing health advice. 【0811】 Step 2: 【0812】 The device collects biometric information from the user's smart device, including parameters such as heart rate, sleep patterns, and activity levels. Simultaneously, an emotion engine analyzes the user's voice and facial expressions to generate emotional data. 【0813】 Step 3: 【0814】 The device transmits collected biometric and emotional data to a server in the cloud. This data reflects the user's health and emotional state. 【0815】 Step 4: 【0816】 The server analyzes data received on the cloud and generates a personalized "life program" based on the user's health status and lifestyle. During this process, the program is adjusted based on the user's emotional data, including stress reduction measures and emotional management techniques. 【0817】 Step 5: 【0818】 The device notifies the user of the generated program. It also sends reminders that suggest specific health activities and relaxation times tailored to the user's daily schedule. 【0819】 Step 6: 【0820】 Users follow the instructions on the device and incorporate and execute the life program into their daily lives. Users record their progress using the device and provide feedback for reassessment. 【0821】 Step 7: 【0822】 The server analyzes user feedback and execution data to evaluate the effectiveness of the program's lifecycle. It continuously optimizes the program, taking into account newly discovered trends and areas for improvement. 【0823】 Through this process, the system continues to provide personalized healthcare tailored to the user's individual health and emotional needs. 【0824】 (Example 2) 【0825】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0826】 Traditional health management systems only provide general health advice, and struggle to offer personalized advice tailored to the individual health and emotional states of each user. Furthermore, they lacked systems to effectively incorporate user feedback, making it challenging to improve the efficiency of long-term health management. 【0827】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0828】 This invention includes a server that includes means for acquiring general health-related information from information sources, analyzing it using natural language processing technology, and constructing a model for providing health advice; means for acquiring the user's biometric and behavioral data and transmitting it to data storage; and means for analyzing the user's individual data on the data storage and generating a lifestyle improvement program tailored to the user's health status and lifestyle. This makes it possible to provide personalized advice based on the user's individual health status and emotional state. 【0829】 "Health-related information" refers to general knowledge and data related to an individual's health status, and is collected from official health information sources. 【0830】 "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and utilize human language. 【0831】 "Biometric data" refers to data about an individual user's physiological state, such as heart rate and exercise level. 【0832】 "Behavioral data" refers to data about users' activities and daily behavioral patterns. 【0833】 "Data storage" refers to a storage system, including the cloud, for accumulating and saving acquired data. 【0834】 A "lifestyle improvement program" is a program tailored to an individual's health condition and lifestyle habits, provided with the aim of helping users maintain or improve their health. 【0835】 "Emotion recognition technology" refers to technology that analyzes and identifies a user's emotional state from factors such as voice and facial expressions. 【0836】 A "notification" is a message or alert used to quickly convey information to a user. 【0837】 The system of this invention utilizes advanced analytical techniques using servers, terminals, and generative AI models. The objective of this system is to provide personalized health advice that is tailored to the individual health and emotional state of the user. 【0838】 The server uses network technologies to retrieve general health-related information from various sources. Specifically, it leverages web scraping techniques to extract information from reliable health sources. This information is then analyzed using natural language processing techniques (e.g., using Python's NLTK library) and used to build health advice models for users. 【0839】 The device acquires biometric and behavioral data from the user's daily life. This role is played by smartphones and wearable devices. These devices transmit data to the cloud using Bluetooth technology. Furthermore, the device is equipped with emotion recognition technology, which analyzes voice tone and facial expression data to evaluate the user's emotions. For example, the device collects this data through its microphone and camera functions and uses algorithms to determine the emotional state. 【0840】 Users receive lifestyle improvement programs provided by a server via a smartphone application. These programs include advice tailored to the user's specific health and emotional state. For example, breathing exercises and meditation guidance are suggested through the application to reduce stress. Users can integrate these programs into their daily lives and send feedback on their implementation to the server via their device. 【0841】 As a concrete example, consider a scenario where a user is experiencing stress at work. The device recognizes a high stress level based on the user's voice and facial expressions. In response, the server generates a stress reduction program and suggests relaxation techniques. The user receives and implements these suggestions through the application. 【0842】 An example of a prompt message would be something like, "Suggest a stress reduction program based on the user's current emotional state and health data." 【0843】 This system can improve the efficiency of health management by providing users with appropriate support based on their individual health and emotional states. 【0844】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0845】 Step 1: 【0846】 The server retrieves general health-related information from the internet. The input is data from reliable health sources. The output is the collected, raw health information data. This data is extracted using web scraping techniques and stored as preparation for natural language processing techniques. 【0847】 Step 2: 【0848】 The server uses natural language processing techniques to analyze the collected health information data. The input is the raw data collected in step 1. For data processing, the NLTK library in Python is used to tokenize the data and extract important keywords. The output is the analyzed health data used to build a health advice model. 【0849】 Step 3: 【0850】 The terminal acquires biometric and behavioral data in real time using the user's smart device. Input is sensor information obtained from the smart device (e.g., heart rate, activity level). Data processing involves pre-processing and formatting this data. Output is a set of the user's biometric and activity data, transmitted to the terminal via Bluetooth. 【0851】 Step 4: 【0852】 The device collects user emotional data using emotion recognition technology. Inputs include audio data from the microphone and facial expression data from the camera. To analyze this data, it uses a speech analysis algorithm and OpenCV to determine the user's emotional state. The output is user emotional data, indicating stress levels and emotional tendencies. 【0853】 Step 5: 【0854】 The server integrates biometric, behavioral, and emotional data transmitted from terminals and analyzes it on data storage. The input consists of multiple datasets collected from terminals. As a data computation, a learning algorithm is applied to the integrated data to analyze individual health conditions and lifestyle habits. The output is a lifestyle improvement program optimized for the user. 【0855】 Step 6: 【0856】 Users receive lifestyle improvement programs provided by the server and incorporate them into their daily lives. The input is the suggested lifestyle improvement program sent from the server. Specifically, users check the program on a smartphone app and perform the suggested breathing and meditation techniques. The output is feedback and progress information on the completed program. 【0857】 Step 7: 【0858】 The server receives feedback from the user and incorporates it into the next program creation. The input is the feedback information sent by the user in step 6. As part of data processing, the feedback data is analyzed and the lifestyle improvement program is adjusted. The output is the adjusted new lifestyle improvement program, which will be used in the next provision. 【0859】 (Application Example 2) 【0860】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0861】 In modern society, managing the health of the elderly is a crucial issue, but there is a lack of means to appropriately grasp individual health and emotional states in real time and improve the quality of care. Furthermore, there is a need for a system that automatically suggests care activities tailored to each individual's emotional state. 【0862】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0863】 In this invention, the server includes means for acquiring general health information from a database, analyzing it, and constructing a model for providing health advice; means for acquiring the user's biometric and behavioral information and transmitting it to the cloud; and means for analyzing the user's individual data on the cloud and generating a life program tailored to their health condition and lifestyle. This makes it possible to provide personalized care programs based on the individual's health and emotional state. 【0864】 "Health information" refers to data indicating the user's physical condition and related general guidelines and knowledge. 【0865】 A "database" refers to a system that systematically stores large amounts of information and organizes it so that it can be quickly searched and used as needed. 【0866】 "Model building" refers to the process of analyzing data and creating a framework that enables prediction and classification according to specific purposes. 【0867】 "Biometric information" refers to data that indicates the physiological state of the body, such as the user's heart rate and body temperature. 【0868】 "Behavioral information" refers to data about users' activity patterns and habits. 【0869】 "Cloud" refers to data storage and computing services that can be accessed via the internet. 【0870】 "Individualized data analysis" refers to the process of analyzing information about a specific user in detail to understand their unique state and tendencies. 【0871】 A "life program" refers to an activity plan aimed at maintaining or improving health, which is designed based on the user's health condition and lifestyle. 【0872】 A "reminder" refers to a notification set to prompt a user to take a specific action. 【0873】 "Feedback" refers to evaluations and comments provided by users regarding their actions or the content provided by the system. 【0874】 An "emotion engine" refers to a technology that analyzes a user's voice tone, facial expressions, and other factors to evaluate their emotional state. 【0875】 "Caregiving activities" refer to a series of actions taken to provide specific physical or mental support. 【0876】 A "relaxation program" refers to activities designed to reduce stress and promote mental and physical well-being. 【0877】 This invention is a system that utilizes cloud infrastructure and an emotion engine to provide personalized care programs based on an individual's health and emotional state. The system has the following configuration and operation: 【0878】 First, the server periodically retrieves general health information from a database via the internet and analyzes this data using natural language processing technology. This builds a foundational model for providing health advice. This model is then optimized using a generative AI model to provide users with the most appropriate health advice. 【0879】 The device acquires the user's biometric and behavioral information in real time through wearable devices such as smartphones and smart glasses. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice tone and facial expressions to evaluate their emotional state. The user's emotional data is sent to the cloud and used for data analysis on the server. 【0880】 When a user is feeling anxious, the emotion engine detects this, and the server generates a relaxation program based on that information. For example, if an elderly person shows signs of anxiety in the afternoon, a notification is sent to their device suggesting breathing exercises or light exercise to alleviate tension. At this time, the AI ​​model generates the optimal suggestion using a prompt message that reads, "Based on the emotional state and activity schedule shown by the elderly person in the afternoon, please output advice to suggest the most suitable relaxation program for them." 【0881】 This system will enable personalized care support to improve the quality of care. 【0882】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0883】 Step 1: 【0884】 The server retrieves general health information from a database. It takes health information accessed via an API as input, and outputs health information data in text format. Natural language processing techniques are used to analyze the data and build a foundational model for health advice. Specifically, the Python Natural Language Toolkit (NLTK) is used for analysis, tokenizing the text and extracting key health indicators. 【0885】 Step 2: 【0886】 The device acquires the user's biometric and behavioral information in real time. Inputs include biometric signals from sensors and behavioral data via Bluetooth, while output is a set of these raw data. It collects data such as heart rate, distance traveled, and activity time using a smartphone or smart glasses. Specifically, it collects data from wearable devices, stores it locally, and then sends it to the cloud. 【0887】 Step 3: 【0888】 The server analyzes individual biometric and behavioral data received on the cloud. The input is a user dataset sent from the terminal, and the output is an evaluation of health status and lifestyle habits generated through the analysis. Machine learning algorithms are used for data analysis to generate an optimal life program for the user. Specifically, the data is normalized and fed into a machine learning model for prediction and evaluation. 【0889】 Step 4: 【0890】 The device provides the user with a generated life program. The input is a life program suggestion from the server, and the output is a reminder and recommended activity displayed on the user's smartphone. Specifically, the reminder is displayed in the notification area, and the program details can be viewed within the app. 【0891】 Step 5: 【0892】 The server evaluates the user's emotional state using an emotion engine. Inputs include voice tone and facial expression data from the terminal, and output is the evaluation result of the user's emotional state. Specifically, it uses speech recognition and facial recognition technology to analyze emotions and determine stress levels and emotional tendencies. 【0893】 Step 6: 【0894】 The server generates a relaxation program based on the evaluated emotional state. The input is the evaluation results from the emotion engine, and the output is a list of care and relaxation activities to suggest to the user. Specifically, it uses a prompt message such as, "Based on the emotional state and activity schedule shown by the elderly person in the afternoon, output advice to suggest the most suitable relaxation program for them," to create the program using a generative AI model. 【0895】 Step 7: 【0896】 Users incorporate the provided programs into their daily lives. The input is the program content provided by the device, and the output is the user's execution status and feedback. Specifically, the user performs the notified activity and inputs the results and impressions into the app. 【0897】 Step 8: 【0898】 The server acquires user feedback and execution data to adjust the life program. Its input is user feedback data, and its output is the adjusted life program. Specifically, it analyzes the feedback and incorporates it into the next program creation, resulting in more personalized suggestions. 【0899】 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. 【0900】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0901】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0902】 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. 【0903】 Figure 9 shows an 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. 【0904】 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. 【0905】 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. 【0906】 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, motorcycles, etc., 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, for example, based 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. 【0907】 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." 【0908】 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. 【0909】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0910】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0911】 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. 【0912】 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. 【0913】 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. 【0914】 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. 【0915】 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. 【0916】 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. 【0917】 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. 【0918】 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 the like 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. 【0919】 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 as being incorporated by reference. 【0920】 The following is further disclosed regarding the embodiments described above. 【0921】 (Claim 1) 【0922】 A means of building a model for obtaining general health information from a database, analyzing it, and providing health advice, 【0923】 A means for acquiring user biometric and behavioral information and transmitting it to the cloud, 【0924】 A means for analyzing individual user data on the cloud and generating a life program tailored to their health status and lifestyle, 【0925】 A means of providing the generated life program to the user and delivering reminders to support its execution, 【0926】 A means of acquiring user feedback and execution data and adjusting the life program, 【0927】 A system that includes this. 【0928】 (Claim 2) 【0929】 The system according to claim 1, further comprising means for visualizing the user's health information and storing it as a history. 【0930】 (Claim 3) 【0931】 The system according to claim 1, further comprising means for using a machine learning algorithm in individual data analysis. 【0932】 "Example 1" 【0933】 (Claim 1) 【0934】 A means of acquiring general health-related information from various sources, analyzing the data using natural language processing and machine learning techniques, and building a model to provide health advice. 【0935】 A means of collecting biometric and behavioral data from users' daily lives through sensors and application software, and transmitting it using cloud infrastructure technology, 【0936】 A means of analyzing individual user data on the cloud and generating programs tailored to their health status and daily habits, 【0937】 A means of providing the generated program to the user and delivering notification functions to support its execution, 【0938】 A means of collecting user results and feedback and adaptively adjusting the program, 【0939】 A system that includes this. 【0940】 (Claim 2) 【0941】 The system according to claim 1, further comprising means for visualizing the user's health-related data and storing it as a retrospective history. 【0942】 (Claim 3) 【0943】 The system according to claim 1, further comprising means for using a learning algorithm in individual data analysis. 【0944】 "Application Example 1" 【0945】 (Claim 1) 【0946】 A means of acquiring general health information from data storage, analyzing it, and building a data model to provide health advice, 【0947】 A means for acquiring a user's biometric and behavioral data and transmitting it to a virtual space, 【0948】 A means for analyzing individual user data in a virtual space and generating improvement programs tailored to the user's health status and lifestyle, 【0949】 A means of providing the generated improvement program to the user and delivering notifications to support its implementation, 【0950】 A means of acquiring user feedback and execution data, and adjusting the improvement program accordingly. 【0951】 A means of providing support through health improvement using a lifestyle device for managing the user's health status, 【0952】 A system that includes this. 【0953】 (Claim 2) 【0954】 The system according to claim 1, further comprising means for visually displaying and storing the user's health information as a record. 【0955】 (Claim 3) 【0956】 The system according to claim 1, further comprising means for using an information processing algorithm in individual data analysis. 【0957】 "Example 2 of combining an emotion engine" 【0958】 (Claim 1) 【0959】 A means for building a model that obtains general health-related information from various sources, analyzes it using natural language processing technology, and provides health advice, 【0960】 A means for acquiring user biometric and behavioral data and transmitting it to data storage, 【0961】 A means for analyzing individual user data on data storage and generating lifestyle improvement programs tailored to health status and lifestyle habits, 【0962】 A means of acquiring user emotional data using emotion recognition technology and reflecting it in a lifestyle improvement program, 【0963】 A means of providing users with a generated lifestyle improvement program and delivering notifications to support its implementation, 【0964】 A means of acquiring user feedback and execution data and adjusting the lifestyle improvement program, 【0965】 A system that includes this. 【0966】 (Claim 2) 【0967】 The system according to claim 1, further comprising means for visualizing and storing as history user health data and emotional data. 【0968】 (Claim 3) 【0969】 The system according to claim 1, further comprising means for using a learning algorithm in individual data analysis. 【0970】 "Application example 2 when combining with an emotional engine" 【0971】 (Claim 1) 【0972】 A means of building a model for obtaining general health information from a database, analyzing it, and providing health advice, 【0973】 A means for acquiring user biometric and behavioral information and transmitting it to the cloud, 【0974】 A means for analyzing individual user data on the cloud and generating a life program tailored to their health status and lifestyle, 【0975】 A means of providing the generated life program to the user and delivering reminders to support its execution, 【0976】 A means of acquiring user feedback and execution data and adjusting the life program, 【0977】 A means for evaluating the user's emotional state using an emotion engine and generating suggestions suitable for caregiving activities, 【0978】 Means of providing relaxation programs to improve the quality of care activities, 【0979】 A system that includes this. 【0980】 (Claim 2) 【0981】 The system according to claim 1, further comprising means for visualizing the user's health information and storing it as a history. 【0982】 (Claim 3) 【0983】 The system according to claim 1, further comprising means for using a machine learning algorithm in individual data analysis. [Explanation of Symbols] 【0984】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of building a model for obtaining general health information from a database, analyzing it, and providing health advice, A means for acquiring user biometric and behavioral information and transmitting it to the cloud, A means for analyzing individual user data on the cloud and generating a life program tailored to their health status and lifestyle, A means of providing the generated life program to the user and delivering reminders to support its execution, A means of acquiring user feedback and execution data and adjusting the life program, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for visualizing the user's health information and storing it as a history. [Claim 3] The system according to claim 1, further comprising means for using a machine learning algorithm in individual data analysis.

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