system

Abstract

We provide the system. [Solution] A means of inputting an individual's biometric data and generating a body composition profile based on this data, A means of customizing and providing a training plan based on the aforementioned profile, A method for generating a nutrition plan that takes into account dietary preferences and restrictions, A means of recording user progress and sending feedback to maintain motivation, A means of proposing fashion and activities based on the user's lifestyle, A system that includes this.

Description

【Technical Field】 , 【0005】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 in 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】 Conventional person-to-person personal training services are often expensive and have a high threshold for users who feel anxious about person-to-person interaction. In addition, it is difficult to provide a customized service based on individual fitness goals and lifestyles, resulting in difficulties in continuing training. Furthermore, there is also a problem that the means for maintaining users' motivation and improving the overall lifestyle are limited. 【Means for Solving the Problems】 【0005】 This invention provides a means for inputting an individual's biometric data and generating a body composition profile from it. Based on this profile, it provides a means for providing a customized training plan aligned with the user's health goals. It also supports the user's overall health management by generating and providing a nutrition plan that takes into account their dietary preferences and restrictions. Furthermore, it provides a means for improving the overall quality of life by sending feedback to maintain motivation using the user's feedback and progress data, and by providing fashion and activity suggestions based on their lifestyle. 【0006】 "Personal biometric data" refers to information about an individual's physical characteristics and lifestyle, such as height, weight, age, gender, and activity level. 【0007】 A "body composition profile" is information about the composition and structure of the body calculated based on an individual's biological data, and is used to assess health status and create exercise plans. 【0008】 A "training plan" is an exercise program designed to meet the user's physical fitness level and health goals. 【0009】 "Customization" refers to adjusting the content to suit the user's specific needs and preferences. 【0010】 A "nutrition plan" is a set of dietary guidance and menu suggestions designed to maintain and improve health, based on dietary preferences and nutritional balance. 【0011】 "Feedback to maintain motivation" refers to information that provides encouragement and advice to help users continue to work towards their set goals. 【0012】 "Lifestyle-based fashion and activity suggestions" refers to providing information that recommends clothing and activities to participate in according to the user's lifestyle and preferences. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0014】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 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 a single 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. 【0017】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0019】 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). 【0020】 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." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 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. 【0024】 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). 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 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". 【0034】 This invention is a system that uses a server, terminal, and user interface to support personalized health management for individual users. The system begins with the input of an individual's biometric data and, based on a body composition profile generated by the server, provides the user with a personalized training and nutrition plan. 【0035】 First, the user inputs basic biometric data into the device using a smartphone or PC. The device sends this data to a server, which then creates a body composition profile based on it. This profile forms the basis for training and nutrition plans tailored to each user's health goals. 【0036】 Next, the server automatically generates a training plan using an AI algorithm based on individual profile information. This plan is customized to take into account the user's preferences and available resources (e.g., availability of training equipment). The generated plan is provided to the user via a terminal. The terminal displays specific training content and visual guides (e.g., training videos). 【0037】 Furthermore, the server generates a nutrition plan to support a healthy diet based on the user's dietary preferences and nutritional restrictions. This plan is provided to the user as daily meal suggestions, and recipes and ingredient lists are also provided from the terminal as needed. 【0038】 The results of users' actual training and meal plan implementation are recorded on their devices as daily progress. The server analyzes this progress data to evaluate the user's progress toward their goals. In addition, it continuously sends feedback and encouraging messages to users to maintain their motivation. 【0039】 Regarding lifestyle improvements, the server provides appropriate fashion suggestions and recommended activities (e.g., local fitness events) based on the user's interests and activity history. This information can be easily accessed and viewed through the user interface. 【0040】 As described above, the present invention provides users with health management and lifestyle support tailored to their individual needs, thereby achieving continuous and effective health improvement. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The user accesses the application using a smartphone or PC. They enter the necessary information for initial registration (name, email address, password) and create an account. 【0044】 Step 2: 【0045】 Users enter their height, weight, age, gender, and health goals (e.g., weight loss, muscle gain) within the app. This data forms the basis for creating their health profile. 【0046】 Step 3: 【0047】 The device transmits the entered biometric data to the server. The data is encrypted using a secure communication protocol. 【0048】 Step 4: 【0049】 The server generates a body composition profile based on the biometric data it receives. The generated profile is stored in a database and used to create customized training and nutrition plans for each user. 【0050】 Step 5: 【0051】 The server considers the user's body composition profile and health goals, and uses an AI algorithm to generate a customized training plan. This training plan includes details such as exercise duration, frequency, and intensity. 【0052】 Step 6: 【0053】 The device displays a training plan provided by the server to the user. The plan includes specific exercise descriptions and, if necessary, video links. 【0054】 Step 7: 【0055】 Users enter their dietary preferences and restrictions (e.g., vegetarian, allergy information) into the app. This generates a customized nutrition plan. 【0056】 Step 8: 【0057】 The server uses a nutrition algorithm based on the user's dietary information to create a healthy meal plan. This plan takes into account daily calorie intake and nutrient balance. 【0058】 Step 9: 【0059】 The device displays a nutrition plan to the user. The plan includes recommended meal menus and recipes, as well as a shopping list if necessary. 【0060】 Step 10: 【0061】 Users report their progress by entering their daily training and meal records into the app. 【0062】 Step 11: 【0063】 The server analyzes user progress data and uses statistical models to evaluate goal achievement. It then generates feedback and motivational messages based on the level of achievement. 【0064】 Step 12: 【0065】 The device displays feedback and motivational messages provided by the server to the user. These messages include praise and advice for future goals. 【0066】 Step 13: 【0067】 The server analyzes the user's lifestyle data and generates fashion suggestions and information about events they should attend based on their interests and activity history. 【0068】 Step 14: 【0069】 The device notifies the user with lifestyle suggestions, including outfit suggestions and event announcements based on their interests. 【0070】 (Example 1) 【0071】 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." 【0072】 Providing effective and efficient health management and lifestyle support to individual users is challenging. In particular, there is a need for a system that automatically generates precise training and nutrition plans based on individual biometric information and encourages daily lifestyle improvements in accordance with these plans. Furthermore, maintaining users' continuous motivation, evaluating their progress in lifestyle development and achievement of health goals, and providing personalized feedback are also crucial challenges. 【0073】 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. 【0074】 In this invention, the server includes means for receiving biometric information and generating a body composition model based on it, means for individualizing and providing an exercise plan using the generated AI model, and means for generating a nutrition plan that takes into account dietary preferences and restrictions. This makes it possible to provide personalized health management and lifestyle suggestions to individual users. 【0075】 "Biometric information" refers to basic data about the user's body, including height, weight, age, and gender. 【0076】 A "body composition model" is a detailed profile of a person's body structure, generated based on their biological information. 【0077】 An "exercise plan" is a specific exercise plan that is tailored to the user's health goals and body composition model, and should be followed accordingly. 【0078】 A "nutrition plan" is a plan that proposes a nutritionally balanced meal plan to the user, taking into account their dietary preferences and restrictions. 【0079】 A "generative AI model" is a system that uses artificial intelligence technology to automatically generate useful information and suggestions from data. 【0080】 "Lifestyle suggestions" refer to recommendations for specific activities or behaviors based on the user's interests and past data. 【0081】 "Personalized health management" refers to a health management approach that is customized according to each individual's characteristics and needs. 【0082】 This invention is a system that provides users with personalized health management and lifestyle improvements by coordinating users, terminals, and servers. First, the user uses a terminal such as a smartphone or PC to input their biometric information. This biometric information includes data such as height, weight, age, and gender. An application on the terminal transmits the entered biometric information to the server. 【0083】 The server uses a generative AI model to generate a body composition model of the user based on the transmitted biometric information. This body composition model is then used as the basis for subsequent exercise and nutritional planning. As a specific example, the server calculates health indicators such as the user's BMI and uses the results to form the body composition model. 【0084】 Next, the server utilizes a generative AI model to automatically generate an exercise plan based on the user's health goals and available exercise equipment. For example, by inputting a prompt such as "Create a training plan using dumbbells for weight loss" into the generative AI model, an appropriate exercise plan will be created. Furthermore, the server considers the user's dietary preferences and restrictions to generate a personalized nutrition plan. This nutrition plan includes suggestions for specific meal menus and nutritional balance. 【0085】 The generated exercise and nutrition plans are provided to the user via a device. The device displays training videos and detailed nutrition information, allowing the user to implement daily activities based on this information. 【0086】 The server also records and continuously analyzes the user's exercise and dietary progress. This analysis is provided to the user via their device as feedback to evaluate their progress towards their goals and maintain their motivation. Furthermore, the server suggests lifestyle changes based on the user's interests and past data, recommending appropriate activities and events. 【0087】 This system allows users to maximize the benefits of personalized health management and improve their daily lives. 【0088】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0089】 Step 1: 【0090】 Users launch the application using their smartphone or PC and enter their biometric information. This information includes height, weight, age, and gender. The entered data is sent from the device to the server, allowing the server to receive the user's basic health data. 【0091】 Step 2: 【0092】 The server uses a generative AI model to generate a body composition model of the user based on the received biometric information. Specifically, it calculates health indicators such as BMI and estimates body composition, including body size and basal metabolic rate, based on these. The output result is the constructed body composition model. 【0093】 Step 3: 【0094】 The server generates an exercise plan using a generative AI model based on a body composition model and the user's health goals. The server considers the user's preferences and available exercise equipment, and inputs prompts into the generative AI model. Prompts such as "Create a basic training program" are used. The output is a customized exercise plan. 【0095】 Step 4: 【0096】 The server generates a personalized nutrition plan based on the user's dietary preferences and restrictions. Utilizing a generation AI model, it suggests a balanced menu while taking into account the food restrictions specified by the user. The output is a nutrition plan tailored to the user. 【0097】 Step 5: 【0098】 The device displays the exercise and nutrition plans received from the server to the user. The screen on the device shows specific training content and visual guides (videos and diagrams) to be performed. The user can then carry out their daily activities according to these guidelines. 【0099】 Step 6: 【0100】 Users actually follow their exercise and nutrition plans and record the results on their device. The device sends the user's activity data to a server, accumulating progress. This allows the user's achievement status to be tracked. 【0101】 Step 7: 【0102】 The server analyzes user progress data and evaluates goal achievement. Based on the generated analysis results, it creates feedback and encouraging messages and sends them to the user via the device. This further supports user motivation. 【0103】 (Application Example 1) 【0104】 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." 【0105】 In modern society, individual health management is becoming increasingly diverse, and there is a growing demand for customized exercise plans and nutritional guidance tailored to each individual's lifestyle and health condition. However, achieving this requires effective data analysis and personalized information provision. Furthermore, efficiently providing users with information on local community events is also a crucial challenge. 【0106】 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. 【0107】 In this invention, the server includes means for inputting personal health data and generating physical composition characteristics based on this data, means for adjusting and providing an exercise plan based on the characteristics, and means for generating nutritional guidance that takes into account dietary preferences and restrictions. This makes it possible to provide each user with personalized health management and information on events in their local community. 【0108】 "Health-related data" refers to information about the user's physical condition and lifestyle. 【0109】 "Body composition characteristics" are indicators that show an individual's body type and physical condition, and form the basis of health management. 【0110】 An "exercise plan" refers to the training schedule and content created based on the user's health condition and goals. 【0111】 "Nutritional guidance" refers to providing meal suggestions and plans that take into account an individual's dietary preferences and restrictions. 【0112】 "Evaluation to maintain motivation" refers to methods of providing feedback based on the user's progress to continuously motivate them. 【0113】 "Health promotion events within cities" refer to information about fitness events and health-related activities held in the community, with the aim of promoting the health of residents. 【0114】 The system that realizes this invention consists of a server, a terminal, and a user. The user inputs health-related data into the terminal using a smartphone or PC. The terminal sends the input data to the server, which generates physical composition characteristics. The server has an AI engine installed that automatically generates an exercise plan and nutritional guidance based on the input data. This AI engine uses deep learning frameworks such as TENSORFLOW® and PyTorch. 【0115】 The generated plans and guidance are customized to the user's preferences and available resources, and delivered to the user via their device. The device is equipped with a React Native application that displays visual instructions and audio guidance. Furthermore, Firebase Cloud Messaging is used to send push notifications to the user with information on motivational assessments and local health promotion events. 【0116】 For example, the server can analyze user data and send a notification such as, "We recommend you participate in a health walk event held in a city park this weekend." The following prompt can also be used for the generative AI model: "Please input the user's current health data and generate a recommended fitness plan. Also, please list and provide information on health-related events currently taking place in the area." 【0117】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0118】 Step 1: 【0119】 Users input biometric data into the device using their smartphone or PC. This input data includes height, weight, age, gender, exercise habits, and dietary preferences. The device then transmits this data to the server. 【0120】 Step 2: 【0121】 The server generates body composition characteristics based on biometric data received from the terminal. Specifically, it passes the received data to an AI engine to calculate characteristics such as body fat percentage, muscle mass, and basal metabolic rate. This process uses deep learning frameworks such as TensorFlow and PyTorch. 【0122】 Step 3: 【0123】 The server uses an AI algorithm to create an appropriate exercise plan based on the generated physical characteristics. This plan is adjusted according to the user's health goals and resources (such as available training equipment). Visual content data is also included in the plan to facilitate visual instruction. 【0124】 Step 4: 【0125】 The server simultaneously uses an AI model to generate nutritional guidance, taking into account the user's dietary preferences and restrictions. This includes suggesting meal menus and recipes that consider balanced nutrition. 【0126】 Step 5: 【0127】 The device receives exercise plans and nutritional guidance sent from the server and displays them to the user. The application used is developed with React Native and supports intuitive understanding by providing visual and audio guidance. 【0128】 Step 6: 【0129】 The device records user feedback and activity data, sending it back to the server periodically. This allows the server to monitor progress, adjust plans as needed, and send motivational feedback. 【0130】 Step 7: 【0131】 The server collects information about health promotion events within the local area and makes recommendations based on the user's interests and activity history. This information is sent to the user via push notifications using Firebase Cloud Messaging. 【0132】 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. 【0133】 This invention provides users with a more personalized experience by incorporating an emotion engine into a system that supports personal health management. This system includes multiple components for managing and analyzing server, terminal, and user information. 【0134】 First, users access the system via smartphone or PC and input biometric data and health goals. This input data, including information about their emotional state, is sent to the server. The device is equipped with sensors such as cameras and microphones, which are used to perform voice and facial expression analysis and capture the user's emotions in real time. 【0135】 The server generates a body composition profile based on the received biometric data and creates training and nutrition plans that correspond to health goals. It also uses an emotion engine to extract the user's emotional state from audio and video data and adjusts the plans based on that information. Specifically, if the user is feeling stressed, it will suggest light exercises or yoga that have a relaxing effect. 【0136】 The device presents the user with a personalized plan received from the server. Training and nutrition plans are tailored to the user's preferences and displayed as media, including visual and auditory guidelines. Music recommendation features are also enhanced by an emotion engine, suggesting playlists that match the user's current emotional state. 【0137】 For progress management, users input their daily exercise and dietary results, which are collected by the server. The server also uses an emotion engine to analyze past emotional trends and generate messages to maintain user motivation. These messages offer encouragement and guidance tailored to the user's emotional state. 【0138】 This invention makes it possible to provide users with more optimized health management and lifestyle support, as well as emotional support. Overall, this system comprehensively supports the user's health status and enables guidance that closely addresses individual needs and emotional states. 【0139】 The following describes the processing flow. 【0140】 Step 1: 【0141】 The user logs into the application using a smartphone or PC. After logging in, they enter biometric data and health information such as height, weight, age, gender, and health goals. 【0142】 Step 2: 【0143】 The device uses its camera and microphone to record the user's facial expressions and voice, and feeds this data to the emotion engine. The emotion engine detects the user's emotional state (e.g., happiness, stress, fatigue) in real time and sends this data to the server. 【0144】 Step 3: 【0145】 The server combines biometric and emotional data received to generate a body composition profile. Simultaneously, it uses AI algorithms to create customized training and nutrition plans based on the user's health goals. 【0146】 Step 4: 【0147】 The server uses an emotion engine to analyze the user's emotional state and adjust the training plan accordingly. For example, if the user is feeling stressed, it might suggest relaxation-focused exercises and prepare encouraging messages to boost their motivation. 【0148】 Step 5: 【0149】 The device displays a personalized training and nutrition plan provided by the server to the user. The training plan includes specific exercises to be performed, the number of sets, repetitions, and links to related video tutorials. 【0150】 Step 6: 【0151】 Users record their daily training progress and meal details on their device. Recording emotional changes (e.g., changes in mood before and after exercise) is also recommended. 【0152】 Step 7: 【0153】 The server analyzes progress and sentiment data to assess the user's progress toward their health goals. Based on the user's sentiment, it provides motivational messages and guidance on the next steps. 【0154】 Step 8: 【0155】 The device notifies the user of feedback, motivational messages, and recommended music and relaxation videos provided by the server. This information is tailored to the user's current emotional state. 【0156】 The above outlines the specific processing flow for implementing the present invention's system, which incorporates an emotion engine. 【0157】 (Example 2) 【0158】 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 will be referred to as the "terminal." 【0159】 Traditional health management systems have faced challenges in providing personalized health plans that take into account the emotional state of individual users. Furthermore, the automation of feedback necessary for maintaining motivation is insufficient, making it difficult to continuously support users' health management. Additionally, there is a need for effective visual and auditory guidance in various plans. 【0160】 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. 【0161】 In this invention, the server includes means for inputting an individual's biometric data and extracting a combination of this data and emotional state data; means for generating a body composition profile based on the extracted data; means for customizing a training plan and suggesting relaxation activities based on the profile and emotional state data; means for analyzing the user's emotional state using an emotional engine and recommending music and exercises; means for providing visual and auditory guidance based on the customized training plan; and means for recording the user's progress and sending automatically generated feedback to maintain motivation. This enables personalized health management support tailored to the user's emotional state. 【0162】 "Biometric data" refers to measurable information about the human body, such as heart rate, weight, and activity level. 【0163】 "Emotional state data" refers to information that indicates the user's psychological and emotional state, such as stress levels and relaxation levels. 【0164】 A "body composition profile" refers to a collection of individual data that reflects a user's physical characteristics and health status. 【0165】 An "emotion engine" refers to a software component that analyzes a user's voice and facial expression data to infer their emotional state. 【0166】 A "training plan" refers to a customized set of exercise and workout instructions based on the user's health goals. 【0167】 "Visual and auditory guidance" refers to a form of information transmission that provides instruction to users through videos and audio. 【0168】 "Motivational feedback" refers to encouraging and guiding messages provided to help users maintain their motivation to achieve their goals. 【0169】 "Recommending music and exercise" refers to the process of selecting and suggesting appropriate music and exercise forms based on the user's emotional state and personal preferences. 【0170】 This invention is a personalized system that utilizes an emotion engine to effectively support users' health management. This system consists of three main components: a server, a terminal, and a user. 【0171】 First, users input biometric and emotional state data via a smartphone or PC application. This data input collects physical data such as heart rate and activity level, as well as mental data such as "stress" and "relaxation." The data entered by the user is transmitted to a server via the internet. 【0172】 Next, the device uses sensors such as cameras and microphones to analyze the user's facial expressions and voice. This allows for an accurate estimation of the user's emotional state. The sensor data is pre-processed within the device before being sent to a server for detailed analysis by an emotion engine. 【0173】 The server is the central component for integrating data received from users and generating individual body composition profiles. In this process, an emotion engine is utilized to extract emotional patterns from the obtained data and create a health plan optimized for the user's condition. The training plan can suggest exercise and relaxation activities and provide music recommendations based on the user's emotional state. 【0174】 The device presents the user with customized training and nutrition plans received from the server. Visual and auditory guidance can show specific exercise steps and recommended foods. The emotion engine also provides music playlists tailored to the user's current emotional state, helping to maintain a comfortable training environment. 【0175】 Progress is recorded and managed on a server, with users entering daily activity data into their devices. Based on past data, feedback designed to maintain motivation is automatically generated and sent to the user. 【0176】 For example, if a user enters into the application that "I'm stressed out because work has been so busy this week," the emotion engine can recommend a yoga plan aimed at stress relief and also provide appropriate relaxation music. 【0177】 An example of a prompt from a generative AI model is, "Please suggest a training plan that takes into account the user's current emotional state." This allows the system to provide optimal guidance that takes emotional data into consideration. 【0178】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0179】 Step 1: 【0180】 Users access a smartphone or PC interface and input biometric and emotional state data. This input data includes heart rate, weight, activity level, and stress level. This data is then sent to the server as input. The data is encrypted and securely transmitted over the network to reach the server. 【0181】 Step 2: 【0182】 The server analyzes received biometric and emotional state data to generate individual body composition profiles. Statistical methods are used to extract profile information such as body fat percentage and muscle mass from the input data. Furthermore, data processing is performed to analyze the user's emotional patterns over a week, based on the emotional state data. The resulting output includes a detailed body composition profile and emotional analysis results. 【0183】 Step 3: 【0184】 The device uses its built-in camera and microphone to capture the user's real-time facial expressions and voice data. From this input data, it uses facial recognition algorithms and voice analysis technology to infer the emotional state and sends it to a server. The server then receives the updated emotional data and can generate a more detailed emotional profile. The output is the user's emotional inference result at that moment. 【0185】 Step 4: 【0186】 The server creates individualized training and nutrition plans based on an integrated body composition profile and emotional state. Using a generative AI model, it selects and customizes the most appropriate plan based on the input profile information. The input is profile information and emotional analysis results, and the output is the customized plan. For example, if high stress levels are detected, relaxation-focused exercises will be suggested. 【0187】 Step 5: 【0188】 The terminal presents the user with a customized plan sent from the server. Specifically, the plan is displayed in a multimedia format utilizing both visual and auditory elements. Exercise video guidelines and recommended meal lists are provided as input, and a corresponding plan is output. A music playlist tailored to the user's emotional state is also displayed. 【0189】 Step 6: 【0190】 Users input their daily progress, specifically their training and meal results, into their device. This input data is then sent back to the server and used by the emotion engine to evaluate progress and generate motivational messages. Based on the user's progress data, feedback and plan adjustments are generated. 【0191】 (Application Example 2) 【0192】 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". 【0193】 In personal health management, conventional systems struggle to provide health plans that adequately consider biometric data and emotions, and they lack personalized feedback and motivation tailored to the user's emotional state. Therefore, there is a need to provide both optimized health management and emotional support simultaneously for each user. 【0194】 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. 【0195】 In this invention, the server includes means for acquiring personal state data and generating a physical composition profile based on it, means for analyzing the emotional state using an emotion engine and adjusting the health plan based on it, and means for providing media based on the user's emotions, equipped with a music recommendation function. This makes it possible to simultaneously provide a health plan that closely corresponds to individual needs and emotional states, and emotional support. 【0196】 "Personal status data" refers to data that includes the user's physical and physiological information, which is analyzed and used in health management. 【0197】 A "body composition profile" is a collection of information that indicates a person's body composition and health status, generated based on acquired personal condition data. 【0198】 An "exercise plan" is a plan that provides a customized exercise schedule and content tailored to the user's individual physical composition profile. 【0199】 A "nutrition plan" is a plan for creating a diet that takes into account the user's dietary preferences and restrictions, with the aim of maintaining and promoting health. 【0200】 "Means of recording results" refer to methods for saving users' health-related results and progress for subsequent analysis and feedback. 【0201】 "Responses to maintain motivation" refer to feedback and guidance provided to users with the aim of maintaining and improving their motivation to continue health management activities. 【0202】 "Lifestyle-based suggestions" refer to fashion and activity recommendations that take into account the user's usual lifestyle and behavioral patterns. 【0203】 An "emotion engine" is a technology that analyzes a user's emotional state and enables emotion-based interactions. 【0204】 The "music recommendation function" is a feature that takes into account the user's emotions and preferences to suggest songs and music playlists that are appropriate for the time. 【0205】 "Real-time guidance" is a support function that analyzes the user's emotional state and health condition in real time and provides appropriate audio and visual guidance on the spot. 【0206】 To realize this invention, the user must first use a dedicated terminal to input personal status data and health goals and access the system. The terminal is equipped with input sensors such as a camera and microphone, which allow for real-time capture of the user's facial expressions and voice. Based on the information from these sensors, an emotion engine is activated to analyze the user's emotional state. 【0207】 The server receives data sent from the terminal and generates a user's physical composition profile. Based on this profile, a personalized exercise and nutrition plan is created that is best suited to the user. Furthermore, an emotion engine is used to assess the user's emotional state, and the plan is adjusted based on that information. If relaxation is needed, yoga or light exercises that help reduce stress are suggested. 【0208】 The device presents the user with a personalized plan received from the server. The display uses audio and visual guidance, and a music recommendation feature provides playlists tailored to the user's current emotional state. For progress management, users input their daily activity results, which the server collects and analyzes. The server uses an emotion engine to analyze past emotional trends and generates messages to maintain user motivation. 【0209】 As a concrete example, a scenario could be envisioned where, upon waking up in the morning, a consumer robot suggests, "Good morning. You seem a little stressed today, so how about trying some relaxation yoga?" and then plays relaxing music. This would comprehensively provide not only health management but also emotional support for the user. An example of a prompt to the generative AI model in this system would be, "Generate a health plan that takes into account the current emotional state and provide a playlist suitable for relaxation." 【0210】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0211】 Step 1: 【0212】 Users input their personal status data and health goals through devices such as smartphones and PCs. The device then uses its camera and microphone to capture the user's facial expressions and voice in real time, generating emotional state data. This data is then sent to a server. 【0213】 Step 2: 【0214】 The server receives user state data and emotional state data sent from the terminal and generates a user body composition profile based on this data. It analyzes the input data and creates a profile that takes into account physical characteristics such as weight, height, and age. 【0215】 Step 3: 【0216】 The server creates customized exercise and nutrition plans based on the user's physical composition profile. Simultaneously, it uses an emotional engine to adjust these plans to address the user's emotional state. For example, if the user is feeling stressed, it might suggest light exercise or yoga sessions to promote relaxation. 【0217】 Step 4: 【0218】 The device receives customized exercise and nutrition plans from the server and presents them to the user. The device provides instructions through visual or audio guidance to help the user implement the plan. Furthermore, it uses a music recommendation feature to provide playlists tailored to the user's mood. 【0219】 Step 5: 【0220】 Users input their daily activity results and meal details into a device. The device aggregates this data and sends it back to the server. The server records this performance data and analyzes emotional trends based on past data. 【0221】 Step 6: 【0222】 The server utilizes an emotion engine to analyze the user's emotional trends and progress, and generates feedback messages to maintain user motivation. These messages will include encouragement and guidance, tailored to the user's emotional state. 【0223】 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. 【0224】 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. 【0225】 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. 【0226】 [Second Embodiment] 【0227】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0228】 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. 【0229】 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). 【0230】 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. 【0231】 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. 【0232】 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). 【0233】 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. 【0234】 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. 【0235】 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. 【0236】 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. 【0237】 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. 【0238】 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". 【0239】 This invention is a system that uses a server, terminal, and user interface to support personalized health management for individual users. The system begins with the input of an individual's biometric data and, based on a body composition profile generated by the server, provides the user with a personalized training and nutrition plan. 【0240】 First, the user inputs basic biometric data into the device using a smartphone or PC. The device sends this data to a server, which then creates a body composition profile based on it. This profile forms the basis for training and nutrition plans tailored to each user's health goals. 【0241】 Next, the server automatically generates a training plan using an AI algorithm based on individual profile information. This plan is customized to take into account the user's preferences and available resources (e.g., availability of training equipment). The generated plan is provided to the user via a terminal. The terminal displays specific training content and visual guides (e.g., training videos). 【0242】 Furthermore, the server generates a nutrition plan to support a healthy diet based on the user's dietary preferences and nutritional restrictions. This plan is provided to the user as daily meal suggestions, and recipes and ingredient lists are also provided from the terminal as needed. 【0243】 The results of users' actual training and meal plan implementation are recorded on their devices as daily progress. The server analyzes this progress data to evaluate the user's progress toward their goals. In addition, it continuously sends feedback and encouraging messages to users to maintain their motivation. 【0244】 Regarding lifestyle improvements, the server provides appropriate fashion suggestions and recommended activities (e.g., local fitness events) based on the user's interests and activity history. This information can be easily accessed and viewed through the user interface. 【0245】 As described above, the present invention provides users with health management and lifestyle support tailored to their individual needs, thereby achieving continuous and effective health improvement. 【0246】 The following describes the processing flow. 【0247】 Step 1: 【0248】 The user accesses the application using a smartphone or PC. They enter the necessary information for initial registration (name, email address, password) and create an account. 【0249】 Step 2: 【0250】 Users enter their height, weight, age, gender, and health goals (e.g., weight loss, muscle gain) within the app. This data forms the basis for creating their health profile. 【0251】 Step 3: 【0252】 The device transmits the entered biometric data to the server. The data is encrypted using a secure communication protocol. 【0253】 Step 4: 【0254】 The server generates a body composition profile based on the biometric data it receives. The generated profile is stored in a database and used to create customized training and nutrition plans for each user. 【0255】 Step 5: 【0256】 The server considers the user's body composition profile and health goals, and uses an AI algorithm to generate a customized training plan. This training plan includes details such as exercise duration, frequency, and intensity. 【0257】 Step 6: 【0258】 The device displays a training plan provided by the server to the user. The plan includes specific exercise descriptions and, if necessary, video links. 【0259】 Step 7: 【0260】 Users enter their dietary preferences and restrictions (e.g., vegetarian, allergy information) into the app. This generates a customized nutrition plan. 【0261】 Step 8: 【0262】 The server uses a nutrition algorithm based on the user's dietary information to create a healthy meal plan. This plan takes into account daily calorie intake and nutrient balance. 【0263】 Step 9: 【0264】 The device displays a nutrition plan to the user. The plan includes recommended meal menus and recipes, as well as a shopping list if necessary. 【0265】 Step 10: 【0266】 Users report their progress by entering their daily training and meal records into the app. 【0267】 Step 11: 【0268】 The server analyzes user progress data and uses statistical models to evaluate goal achievement. It then generates feedback and motivational messages based on the level of achievement. 【0269】 Step 12: 【0270】 The device displays feedback and motivational messages provided by the server to the user. These messages include praise and advice for future goals. 【0271】 Step 13: 【0272】 The server analyzes the user's lifestyle data and generates fashion suggestions and information about events they should attend based on their interests and activity history. 【0273】 Step 14: 【0274】 The device notifies the user with lifestyle suggestions, including outfit suggestions and event announcements based on their interests. 【0275】 (Example 1) 【0276】 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." 【0277】 Providing effective and efficient health management and lifestyle support to individual users is challenging. In particular, there is a need for a system that automatically generates precise training and nutrition plans based on individual biometric information and encourages daily lifestyle improvements in accordance with these plans. Furthermore, maintaining users' continuous motivation, evaluating their progress in lifestyle development and achievement of health goals, and providing personalized feedback are also crucial challenges. 【0278】 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. 【0279】 In this invention, the server includes means for receiving biometric information and generating a body composition model based on it, means for individualizing and providing an exercise plan using the generated AI model, and means for generating a nutrition plan that takes into account dietary preferences and restrictions. This makes it possible to provide personalized health management and lifestyle suggestions to individual users. 【0280】 "Biometric information" refers to basic data about the user's body, including height, weight, age, and gender. 【0281】 The "body composition model" is a detailed profile regarding the constitution of the body, generated based on an individual's biological information. 【0282】 The "exercise plan" is a specific exercise plan to be executed, individualized according to the user's health goals and body composition model. 【0283】 The "nutrition plan" is a plan that proposes a nutritionally balanced diet provided to the user considering dietary preferences and restrictions. 【0284】 The "generative AI model" is a mechanism that automatically generates useful information and proposals from data by leveraging artificial intelligence technology. 【0285】 The "lifestyle suggestions" are recommendations for specific activities or actions based on the user's interests and past data. 【0286】 "Personalized health management" is an approach to health management customized according to the characteristics and needs of each individual. 【0287】 This invention is a system that coordinates a user, a terminal, and a server to provide personalized health management and life improvement to the user. First, the user uses a terminal such as a smartphone or a PC to input their biological information. This biological information includes data such as height, weight, age, and gender. The application on the terminal transmits the input biological information to the server. 【0288】 Based on the transmitted biological information, the server utilizes the generative AI model to generate the user's body composition model. This body composition model is used as the basic data for subsequent exercise plans and nutrition plans. As a specific example, the server calculates health indicators such as the user's BMI and uses the results to form the body composition model. 【0289】 Next, the server utilizes a generative AI model to automatically generate an exercise plan based on the user's health goals and available exercise equipment. For example, by inputting a prompt such as "Create a training plan using dumbbells for weight loss" into the generative AI model, an appropriate exercise plan will be created. Furthermore, the server considers the user's dietary preferences and restrictions to generate a personalized nutrition plan. This nutrition plan includes suggestions for specific meal menus and nutritional balance. 【0290】 The generated exercise and nutrition plans are provided to the user via a device. The device displays training videos and detailed nutrition information, allowing the user to implement daily activities based on this information. 【0291】 The server also records and continuously analyzes the user's exercise and dietary progress. This analysis is provided to the user via their device as feedback to evaluate their progress towards their goals and maintain their motivation. Furthermore, the server suggests lifestyle changes based on the user's interests and past data, recommending appropriate activities and events. 【0292】 This system allows users to maximize the benefits of personalized health management and improve their daily lives. 【0293】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0294】 Step 1: 【0295】 Users launch the application using their smartphone or PC and enter their biometric information. This information includes height, weight, age, and gender. The entered data is sent from the device to the server, allowing the server to receive the user's basic health data. 【0296】 Step 2: 【0297】 The server uses a generative AI model to generate a body composition model of the user based on the received biometric information. Specifically, it calculates health indicators such as BMI and estimates body composition, including body size and basal metabolic rate, based on these. The output result is the constructed body composition model. 【0298】 Step 3: 【0299】 The server generates an exercise plan using a generative AI model based on a body composition model and the user's health goals. The server considers the user's preferences and available exercise equipment, and inputs prompts into the generative AI model. Prompts such as "Create a basic training program" are used. The output is a customized exercise plan. 【0300】 Step 4: 【0301】 The server generates a personalized nutrition plan based on the user's dietary preferences and restrictions. Utilizing a generation AI model, it suggests a balanced menu while taking into account the food restrictions specified by the user. The output is a nutrition plan tailored to the user. 【0302】 Step 5: 【0303】 The device displays the exercise and nutrition plans received from the server to the user. The screen on the device shows specific training content and visual guides (videos and diagrams) to be performed. The user can then carry out their daily activities according to these guidelines. 【0304】 Step 6: 【0305】 Users actually follow their exercise and nutrition plans and record the results on their device. The device sends the user's activity data to a server, accumulating progress. This allows the user's achievement status to be tracked. 【0306】 Step 7: 【0307】 The server analyzes the user's progress data and evaluates goal achievement. It creates feedback and motivational messages based on the generated analysis results and sends them to the user via the terminal. This further supports the user's motivation. 【0308】 (Application Example 1) 【0309】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0310】 In modern society, individual health management has diversified, and there is a demand to provide customized exercise plans and nutritional guidance according to an individual's lifestyle and health condition. However, to achieve this, effective data analysis and personalized information provision are necessary. Also, efficiently providing event information in the local community to users is an important issue. 【0311】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means. 【0312】 In this invention, the server includes means for inputting data related to an individual's health and generating physical composition characteristics based on this, means for adjusting and providing an exercise plan based on the characteristics, and means for generating nutritional guidance considering preferences and restrictions regarding diet. This enables personalized health management for each user and the provision of event information in the local community. 【0313】 "Data related to health" refers to information regarding the user's physical condition and lifestyle. 【0314】 "Physical composition characteristics" are indicators showing an individual's body type and physical state, and are the basis for health management. 【0315】 "Exercise plan" refers to the training schedule and content created based on the user's health condition and goals. 【0316】 "Nutritional guidance" refers to providing meal suggestions and plans that take into account an individual's dietary preferences and restrictions. 【0317】 "Evaluation to maintain motivation" refers to methods of providing feedback based on the user's progress to continuously motivate them. 【0318】 "Health promotion events within cities" refer to information about fitness events and health-related activities held in the community, with the aim of promoting the health of residents. 【0319】 The system that realizes this invention consists of a server, a terminal, and a user. The user inputs health-related data into the terminal using a smartphone or PC. The terminal sends the input data to the server, which generates physical composition characteristics. An AI engine is installed on the server and automatically generates an exercise plan and nutritional guidance based on the input data. This AI engine uses deep learning frameworks such as TensorFlow and PyTorch. 【0320】 The generated plans and guidance are customized to the user's preferences and available resources, and delivered to the user via their device. The device is equipped with a React Native application that displays visual instructions and audio guidance. Furthermore, Firebase Cloud Messaging is used to send push notifications to the user with information on motivational assessments and local health promotion events. 【0321】 For example, the server can analyze user data and send a notification such as, "We recommend you participate in a health walk event held in a city park this weekend." The following prompt can also be used for the generative AI model: "Please input the user's current health data and generate a recommended fitness plan. Also, please list and provide information on health-related events currently taking place in the area." 【0322】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0323】 Step 1: 【0324】 Users input biometric data into the device using their smartphone or PC. This input data includes height, weight, age, gender, exercise habits, and dietary preferences. The device then transmits this data to the server. 【0325】 Step 2: 【0326】 The server generates body composition characteristics based on biometric data received from the terminal. Specifically, it passes the received data to an AI engine to calculate characteristics such as body fat percentage, muscle mass, and basal metabolic rate. This process uses deep learning frameworks such as TensorFlow and PyTorch. 【0327】 Step 3: 【0328】 The server uses an AI algorithm to create an appropriate exercise plan based on the generated physical characteristics. This plan is adjusted according to the user's health goals and resources (such as available training equipment). Visual content data is also included in the plan to facilitate visual instruction. 【0329】 Step 4: 【0330】 The server simultaneously uses an AI model to generate nutritional guidance, taking into account the user's dietary preferences and restrictions. This includes suggesting meal menus and recipes that consider balanced nutrition. 【0331】 Step 5: 【0332】 The device receives exercise plans and nutritional guidance sent from the server and displays them to the user. The application used is developed with React Native and supports intuitive understanding by providing visual and audio guidance. 【0333】 Step 6: 【0334】 The device records user feedback and activity data, sending it back to the server periodically. This allows the server to monitor progress, adjust plans as needed, and send motivational feedback. 【0335】 Step 7: 【0336】 The server collects information about health promotion events within the local area and makes recommendations based on the user's interests and activity history. This information is sent to the user via push notifications using Firebase Cloud Messaging. 【0337】 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. 【0338】 This invention provides users with a more personalized experience by incorporating an emotion engine into a system that supports personal health management. This system includes multiple components for managing and analyzing server, terminal, and user information. 【0339】 First, users access the system via smartphone or PC and input biometric data and health goals. This input data, including information about their emotional state, is sent to the server. The device is equipped with sensors such as cameras and microphones, which are used to perform voice and facial expression analysis and capture the user's emotions in real time. 【0340】 The server generates a body composition profile based on the received biometric data and creates training and nutrition plans that correspond to health goals. It also uses an emotion engine to extract the user's emotional state from audio and video data and adjusts the plans based on that information. Specifically, if the user is feeling stressed, it will suggest light exercises or yoga that have a relaxing effect. 【0341】 The device presents the user with a personalized plan received from the server. Training and nutrition plans are tailored to the user's preferences and displayed as media, including visual and auditory guidelines. Music recommendation features are also enhanced by an emotion engine, suggesting playlists that match the user's current emotional state. 【0342】 For progress management, users input their daily exercise and dietary results, which are collected by the server. The server also uses an emotion engine to analyze past emotional trends and generate messages to maintain user motivation. These messages offer encouragement and guidance tailored to the user's emotional state. 【0343】 This invention makes it possible to provide users with more optimized health management and lifestyle support, as well as emotional support. Overall, this system comprehensively supports the user's health status and enables guidance that closely addresses individual needs and emotional states. 【0344】 The following describes the processing flow. 【0345】 Step 1: 【0346】 The user logs into the application using a smartphone or PC. After logging in, they enter biometric data and health information such as height, weight, age, gender, and health goals. 【0347】 Step 2: 【0348】 The device uses its camera and microphone to record the user's facial expressions and voice, and feeds this data to the emotion engine. The emotion engine detects the user's emotional state (e.g., happiness, stress, fatigue) in real time and sends this data to the server. 【0349】 Step 3: 【0350】 The server combines biometric and emotional data received to generate a body composition profile. Simultaneously, it uses AI algorithms to create customized training and nutrition plans based on the user's health goals. 【0351】 Step 4: 【0352】 The server uses an emotion engine to analyze the user's emotional state and adjust the training plan accordingly. For example, if the user is feeling stressed, it might suggest relaxation-focused exercises and prepare encouraging messages to boost their motivation. 【0353】 Step 5: 【0354】 The device displays a personalized training and nutrition plan provided by the server to the user. The training plan includes specific exercises to be performed, the number of sets, repetitions, and links to related video tutorials. 【0355】 Step 6: 【0356】 Users record their daily training progress and meal details on their device. Recording emotional changes (e.g., changes in mood before and after exercise) is also recommended. 【0357】 Step 7: 【0358】 The server analyzes progress and sentiment data to assess the user's progress toward their health goals. Based on the user's sentiment, it provides motivational messages and guidance on the next steps. 【0359】 Step 8: 【0360】 The device notifies the user of feedback, motivational messages, and recommended music and relaxation videos provided by the server. This information is tailored to the user's current emotional state. 【0361】 The above outlines the specific processing flow for implementing the present invention's system, which incorporates an emotion engine. 【0362】 (Example 2) 【0363】 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". 【0364】 Traditional health management systems have faced challenges in providing personalized health plans that take into account the emotional state of individual users. Furthermore, the automation of feedback necessary for maintaining motivation is insufficient, making it difficult to continuously support users' health management. Additionally, there is a need for effective visual and auditory guidance in various plans. 【0365】 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. 【0366】 In this invention, the server includes means for inputting an individual's biometric data and extracting a combination of this data and emotional state data; means for generating a body composition profile based on the extracted data; means for customizing a training plan and suggesting relaxation activities based on the profile and emotional state data; means for analyzing the user's emotional state using an emotional engine and recommending music and exercises; means for providing visual and auditory guidance based on the customized training plan; and means for recording the user's progress and sending automatically generated feedback to maintain motivation. This enables personalized health management support tailored to the user's emotional state. 【0367】 "Biometric data" refers to measurable information about the human body, such as heart rate, weight, and activity level. 【0368】 "Emotional state data" refers to information that indicates the user's psychological and emotional state, such as stress levels and relaxation levels. 【0369】 A "body composition profile" refers to a collection of individual data that reflects a user's physical characteristics and health status. 【0370】 An "emotion engine" refers to a software component that analyzes a user's voice and facial expression data to infer their emotional state. 【0371】 A "training plan" refers to a customized set of exercise and workout instructions based on the user's health goals. 【0372】 "Visual and auditory guidance" refers to a form of information transmission that provides instruction to users through videos and audio. 【0373】 "Motivational feedback" refers to encouraging and guiding messages provided to help users maintain their motivation to achieve their goals. 【0374】 "Recommending music and exercise" refers to the process of selecting and suggesting appropriate music and exercise forms based on the user's emotional state and personal preferences. 【0375】 This invention is a personalized system that utilizes an emotion engine to effectively support users' health management. This system consists of three main components: a server, a terminal, and a user. 【0376】 First, users input biometric and emotional state data via a smartphone or PC application. This data input collects physical data such as heart rate and activity level, as well as mental data such as "stress" and "relaxation." The data entered by the user is transmitted to a server via the internet. 【0377】 Next, the device uses sensors such as cameras and microphones to analyze the user's facial expressions and voice. This allows for an accurate estimation of the user's emotional state. The sensor data is pre-processed within the device before being sent to a server for detailed analysis by an emotion engine. 【0378】 The server is the central component for integrating data received from users and generating individual body composition profiles. In this process, an emotion engine is utilized to extract emotional patterns from the obtained data and create a health plan optimized for the user's condition. The training plan can suggest exercise and relaxation activities and provide music recommendations based on the user's emotional state. 【0379】 The device presents the user with customized training and nutrition plans received from the server. Visual and auditory guidance can show specific exercise steps and recommended foods. The emotion engine also provides music playlists tailored to the user's current emotional state, helping to maintain a comfortable training environment. 【0380】 Progress is recorded and managed on a server, with users entering daily activity data into their devices. Based on past data, feedback designed to maintain motivation is automatically generated and sent to the user. 【0381】 For example, if a user enters into the application that "I'm stressed out because work has been so busy this week," the emotion engine can recommend a yoga plan aimed at stress relief and also provide appropriate relaxation music. 【0382】 An example of a prompt from a generative AI model is, "Please suggest a training plan that takes into account the user's current emotional state." This allows the system to provide optimal guidance that takes emotional data into consideration. 【0383】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0384】 Step 1: 【0385】 Users access a smartphone or PC interface and input biometric and emotional state data. This input data includes heart rate, weight, activity level, and stress level. This data is then sent to the server as input. The data is encrypted and securely transmitted over the network to reach the server. 【0386】 Step 2: 【0387】 The server analyzes received biometric and emotional state data to generate individual body composition profiles. Statistical methods are used to extract profile information such as body fat percentage and muscle mass from the input data. Furthermore, data processing is performed to analyze the user's emotional patterns over a week, based on the emotional state data. The resulting output includes a detailed body composition profile and emotional analysis results. 【0388】 Step 3: 【0389】 The device uses its built-in camera and microphone to capture the user's real-time facial expressions and voice data. From this input data, it uses facial recognition algorithms and voice analysis technology to infer the emotional state and sends it to a server. The server then receives the updated emotional data and can generate a more detailed emotional profile. The output is the user's emotional inference result at that moment. 【0390】 Step 4: 【0391】 The server creates individualized training and nutrition plans based on an integrated body composition profile and emotional state. Using a generative AI model, it selects and customizes the most appropriate plan based on the input profile information. The input is profile information and emotional analysis results, and the output is the customized plan. For example, if high stress levels are detected, relaxation-focused exercises will be suggested. 【0392】 Step 5: 【0393】 The terminal presents the user with a customized plan sent from the server. Specifically, the plan is displayed in a multimedia format utilizing both visual and auditory elements. Exercise video guidelines and recommended meal lists are provided as input, and a corresponding plan is output. A music playlist tailored to the user's emotional state is also displayed. 【0394】 Step 6: 【0395】 Users input their daily progress, specifically their training and meal results, into their device. This input data is then sent back to the server and used by the emotion engine to evaluate progress and generate motivational messages. Based on the user's progress data, feedback and plan adjustments are generated. 【0396】 (Application Example 2) 【0397】 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." 【0398】 In personal health management, conventional systems struggle to provide health plans that adequately consider biometric data and emotions, and they lack personalized feedback and motivation tailored to the user's emotional state. Therefore, there is a need to provide both optimized health management and emotional support simultaneously for each user. 【0399】 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. 【0400】 In this invention, the server includes means for acquiring personal state data and generating a physical composition profile based on it, means for analyzing the emotional state using an emotion engine and adjusting the health plan based on it, and means for providing media based on the user's emotions, equipped with a music recommendation function. This makes it possible to simultaneously provide a health plan that closely corresponds to individual needs and emotional states, and emotional support. 【0401】 "Personal status data" refers to data that includes the user's physical and physiological information, which is analyzed and used in health management. 【0402】 A "body composition profile" is a collection of information that indicates a person's body composition and health status, generated based on acquired personal condition data. 【0403】 An "exercise plan" is a plan that provides a customized exercise schedule and content tailored to the user's individual physical composition profile. 【0404】 A "nutrition plan" is a plan for creating a diet that takes into account the user's dietary preferences and restrictions, with the aim of maintaining and promoting health. 【0405】 "Means of recording results" refer to methods for saving users' health-related results and progress for subsequent analysis and feedback. 【0406】 "Responses to maintain motivation" refer to feedback and guidance provided to users with the aim of maintaining and improving their motivation to continue health management activities. 【0407】 "Lifestyle-based suggestions" refer to fashion and activity recommendations that take into account the user's usual lifestyle and behavioral patterns. 【0408】 An "emotion engine" is a technology that analyzes a user's emotional state and enables emotion-based interactions. 【0409】 The "music recommendation function" is a feature that takes into account the user's emotions and preferences to suggest songs and music playlists that are appropriate for the time. 【0410】 "Real-time guidance" is a support function that analyzes the user's emotional state and health condition in real time and provides appropriate audio and visual guidance on the spot. 【0411】 To realize this invention, the user must first use a dedicated terminal to input personal status data and health goals and access the system. The terminal is equipped with input sensors such as a camera and microphone, which allow for real-time capture of the user's facial expressions and voice. Based on the information from these sensors, an emotion engine is activated to analyze the user's emotional state. 【0412】 The server receives data sent from the terminal and generates a user's physical composition profile. Based on this profile, a personalized exercise and nutrition plan is created that is best suited to the user. Furthermore, an emotion engine is used to assess the user's emotional state, and the plan is adjusted based on that information. If relaxation is needed, yoga or light exercises that help reduce stress are suggested. 【0413】 The device presents the user with a personalized plan received from the server. The display uses audio and visual guidance, and a music recommendation feature provides playlists tailored to the user's current emotional state. For progress management, users input their daily activity results, which the server collects and analyzes. The server uses an emotion engine to analyze past emotional trends and generates messages to maintain user motivation. 【0414】 As a concrete example, a scenario could be envisioned where, upon waking up in the morning, a consumer robot suggests, "Good morning. You seem a little stressed today, so how about trying some relaxation yoga?" and then plays relaxing music. This would comprehensively provide not only health management but also emotional support for the user. An example of a prompt to the generative AI model in this system would be, "Generate a health plan that takes into account the current emotional state and provide a playlist suitable for relaxation." 【0415】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0416】 Step 1: 【0417】 Users input their personal status data and health goals through devices such as smartphones and PCs. The device then uses its camera and microphone to capture the user's facial expressions and voice in real time, generating emotional state data. This data is then sent to a server. 【0418】 Step 2: 【0419】 The server receives user state data and emotional state data sent from the terminal and generates a user body composition profile based on this data. It analyzes the input data and creates a profile that takes into account physical characteristics such as weight, height, and age. 【0420】 Step 3: 【0421】 The server creates customized exercise and nutrition plans based on the user's physical composition profile. Simultaneously, it uses an emotional engine to adjust these plans to address the user's emotional state. For example, if the user is feeling stressed, it might suggest light exercise or yoga sessions to promote relaxation. 【0422】 Step 4: 【0423】 The device receives customized exercise and nutrition plans from the server and presents them to the user. The device provides instructions through visual or audio guidance to help the user implement the plan. Furthermore, it uses a music recommendation feature to provide playlists tailored to the user's mood. 【0424】 Step 5: 【0425】 Users input their daily activity results and meal details into a device. The device aggregates this data and sends it back to the server. The server records this performance data and analyzes emotional trends based on past data. 【0426】 Step 6: 【0427】 The server utilizes an emotion engine to analyze the user's emotional trends and progress, and generates feedback messages to maintain user motivation. These messages will include encouragement and guidance, tailored to the user's emotional state. 【0428】 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. 【0429】 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. 【0430】 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. 【0431】 [Third Embodiment] 【0432】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0433】 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. 【0434】 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). 【0435】 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. 【0436】 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. 【0437】 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). 【0438】 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. 【0439】 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. 【0440】 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. 【0441】 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. 【0442】 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. 【0443】 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". 【0444】 This invention is a system that uses a server, terminal, and user interface to support personalized health management for individual users. The system begins with the input of an individual's biometric data and, based on a body composition profile generated by the server, provides the user with a personalized training and nutrition plan. 【0445】 First, the user inputs basic biometric data into the device using a smartphone or PC. The device sends this data to a server, which then creates a body composition profile based on it. This profile forms the basis for training and nutrition plans tailored to each user's health goals. 【0446】 Next, the server automatically generates a training plan using an AI algorithm based on individual profile information. This plan is customized to take into account the user's preferences and available resources (e.g., availability of training equipment). The generated plan is provided to the user via a terminal. The terminal displays specific training content and visual guides (e.g., training videos). 【0447】 Furthermore, the server generates a nutrition plan to support a healthy diet based on the user's dietary preferences and nutritional restrictions. This plan is provided to the user as daily meal suggestions, and recipes and ingredient lists are also provided from the terminal as needed. 【0448】 The results of users' actual training and meal plan implementation are recorded on their devices as daily progress. The server analyzes this progress data to evaluate the user's progress toward their goals. In addition, it continuously sends feedback and encouraging messages to users to maintain their motivation. 【0449】 Regarding lifestyle improvements, the server provides appropriate fashion suggestions and recommended activities (e.g., local fitness events) based on the user's interests and activity history. This information can be easily accessed and viewed through the user interface. 【0450】 As described above, the present invention provides users with health management and lifestyle support tailored to their individual needs, thereby achieving continuous and effective health improvement. 【0451】 The following describes the processing flow. 【0452】 Step 1: 【0453】 The user accesses the application using a smartphone or PC. They enter the necessary information for initial registration (name, email address, password) and create an account. 【0454】 Step 2: 【0455】 Users enter their height, weight, age, gender, and health goals (e.g., weight loss, muscle gain) within the app. This data forms the basis for creating their health profile. 【0456】 Step 3: 【0457】 The device transmits the entered biometric data to the server. The data is encrypted using a secure communication protocol. 【0458】 Step 4: 【0459】 The server generates a body composition profile based on the biometric data it receives. The generated profile is stored in a database and used to create customized training and nutrition plans for each user. 【0460】 Step 5: 【0461】 The server considers the user's body composition profile and health goals, and uses an AI algorithm to generate a customized training plan. This training plan includes details such as exercise duration, frequency, and intensity. 【0462】 Step 6: 【0463】 The device displays a training plan provided by the server to the user. The plan includes specific exercise descriptions and, if necessary, video links. 【0464】 Step 7: 【0465】 Users enter their dietary preferences and restrictions (e.g., vegetarian, allergy information) into the app. This generates a customized nutrition plan. 【0466】 Step 8: 【0467】 The server uses a nutrition algorithm based on the user's dietary information to create a healthy meal plan. This plan takes into account daily calorie intake and nutrient balance. 【0468】 Step 9: 【0469】 The device displays a nutrition plan to the user. The plan includes recommended meal menus and recipes, as well as a shopping list if necessary. 【0470】 Step 10: 【0471】 Users report their progress by entering their daily training and meal records into the app. 【0472】 Step 11: 【0473】 The server analyzes user progress data and uses statistical models to evaluate goal achievement. It then generates feedback and motivational messages based on the level of achievement. 【0474】 Step 12: 【0475】 The device displays feedback and motivational messages provided by the server to the user. These messages include praise and advice for future goals. 【0476】 Step 13: 【0477】 The server analyzes the user's lifestyle data and generates fashion suggestions and information about events they should attend based on their interests and activity history. 【0478】 Step 14: 【0479】 The device notifies the user with lifestyle suggestions, including outfit suggestions and event announcements based on their interests. 【0480】 (Example 1) 【0481】 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." 【0482】 Providing effective and efficient health management and lifestyle support to individual users is challenging. In particular, there is a need for a system that automatically generates precise training and nutrition plans based on individual biometric information and encourages daily lifestyle improvements in accordance with these plans. Furthermore, maintaining users' continuous motivation, evaluating their progress in lifestyle development and achievement of health goals, and providing personalized feedback are also crucial challenges. 【0483】 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. 【0484】 In this invention, the server includes means for receiving biometric information and generating a body composition model based on it, means for individualizing and providing an exercise plan using the generated AI model, and means for generating a nutrition plan that takes into account dietary preferences and restrictions. This makes it possible to provide personalized health management and lifestyle suggestions to individual users. 【0485】 "Biometric information" refers to basic data about the user's body, including height, weight, age, and gender. 【0486】 A "body composition model" is a detailed profile of a person's body structure, generated based on their biological information. 【0487】 An "exercise plan" is a specific exercise plan that is tailored to the user's health goals and body composition model, and should be followed accordingly. 【0488】 A "nutrition plan" is a plan that proposes a nutritionally balanced meal plan to the user, taking into account their dietary preferences and restrictions. 【0489】 A "generative AI model" is a system that uses artificial intelligence technology to automatically generate useful information and suggestions from data. 【0490】 "Lifestyle suggestions" refer to recommendations for specific activities or behaviors based on the user's interests and past data. 【0491】 "Personalized health management" refers to a health management approach that is customized according to each individual's characteristics and needs. 【0492】 This invention is a system that provides users with personalized health management and lifestyle improvements by coordinating users, terminals, and servers. First, the user uses a terminal such as a smartphone or PC to input their biometric information. This biometric information includes data such as height, weight, age, and gender. An application on the terminal transmits the entered biometric information to the server. 【0493】 The server uses a generative AI model to generate a body composition model of the user based on the transmitted biometric information. This body composition model is then used as the basis for subsequent exercise and nutritional planning. As a specific example, the server calculates health indicators such as the user's BMI and uses the results to form the body composition model. 【0494】 Next, the server utilizes a generative AI model to automatically generate an exercise plan based on the user's health goals and available exercise equipment. For example, by inputting a prompt such as "Create a training plan using dumbbells for weight loss" into the generative AI model, an appropriate exercise plan will be created. Furthermore, the server considers the user's dietary preferences and restrictions to generate a personalized nutrition plan. This nutrition plan includes suggestions for specific meal menus and nutritional balance. 【0495】 The generated exercise and nutrition plans are provided to the user via a device. The device displays training videos and detailed nutrition information, allowing the user to implement daily activities based on this information. 【0496】 The server also records and continuously analyzes the user's exercise and dietary progress. This analysis is provided to the user via their device as feedback to evaluate their progress towards their goals and maintain their motivation. Furthermore, the server suggests lifestyle changes based on the user's interests and past data, recommending appropriate activities and events. 【0497】 This system allows users to maximize the benefits of personalized health management and improve their daily lives. 【0498】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0499】 Step 1: 【0500】 Users launch the application using their smartphone or PC and enter their biometric information. This information includes height, weight, age, and gender. The entered data is sent from the device to the server, allowing the server to receive the user's basic health data. 【0501】 Step 2: 【0502】 The server uses a generative AI model to generate a body composition model of the user based on the received biometric information. Specifically, it calculates health indicators such as BMI and estimates body composition, including body size and basal metabolic rate, based on these. The output result is the constructed body composition model. 【0503】 Step 3: 【0504】 The server generates an exercise plan using a generative AI model based on a body composition model and the user's health goals. The server considers the user's preferences and available exercise equipment, and inputs prompts into the generative AI model. Prompts such as "Create a basic training program" are used. The output is a customized exercise plan. 【0505】 Step 4: 【0506】 The server generates a personalized nutrition plan based on the user's dietary preferences and restrictions. Utilizing a generation AI model, it suggests a balanced menu while taking into account the food restrictions specified by the user. The output is a nutrition plan tailored to the user. 【0507】 Step 5: 【0508】 The device displays the exercise and nutrition plans received from the server to the user. The screen on the device shows specific training content and visual guides (videos and diagrams) to be performed. The user can then carry out their daily activities according to these guidelines. 【0509】 Step 6: 【0510】 Users actually follow their exercise and nutrition plans and record the results on their device. The device sends the user's activity data to a server, accumulating progress. This allows the user's achievement status to be tracked. 【0511】 Step 7: 【0512】 The server analyzes user progress data and evaluates goal achievement. Based on the generated analysis results, it creates feedback and encouraging messages and sends them to the user via the device. This further supports user motivation. 【0513】 (Application Example 1) 【0514】 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." 【0515】 In modern society, individual health management is becoming increasingly diverse, and there is a growing demand for customized exercise plans and nutritional guidance tailored to each individual's lifestyle and health condition. However, achieving this requires effective data analysis and personalized information provision. Furthermore, efficiently providing users with information on local community events is also a crucial challenge. 【0516】 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. 【0517】 In this invention, the server includes means for inputting personal health data and generating physical composition characteristics based on this data, means for adjusting and providing an exercise plan based on the characteristics, and means for generating nutritional guidance that takes into account dietary preferences and restrictions. This makes it possible to provide each user with personalized health management and information on events in their local community. 【0518】 "Health-related data" refers to information about the user's physical condition and lifestyle. 【0519】 "Body composition characteristics" are indicators that show an individual's body type and physical condition, and form the basis of health management. 【0520】 An "exercise plan" refers to the training schedule and content created based on the user's health condition and goals. 【0521】 "Nutritional guidance" refers to providing meal suggestions and plans that take into account an individual's dietary preferences and restrictions. 【0522】 "Evaluation to maintain motivation" refers to methods of providing feedback based on the user's progress to continuously motivate them. 【0523】 "Health promotion events within cities" refer to information about fitness events and health-related activities held in the community, with the aim of promoting the health of residents. 【0524】 The system that realizes this invention consists of a server, a terminal, and a user. The user inputs health-related data into the terminal using a smartphone or PC. The terminal sends the input data to the server, which generates physical composition characteristics. An AI engine is installed on the server and automatically generates an exercise plan and nutritional guidance based on the input data. This AI engine uses deep learning frameworks such as TensorFlow and PyTorch. 【0525】 The generated plans and guidance are customized to the user's preferences and available resources, and delivered to the user via their device. The device is equipped with a React Native application that displays visual instructions and audio guidance. Furthermore, Firebase Cloud Messaging is used to send push notifications to the user with information on motivational assessments and local health promotion events. 【0526】 For example, the server can analyze user data and send a notification such as, "We recommend you participate in a health walk event held in a city park this weekend." The following prompt can also be used for the generative AI model: "Please input the user's current health data and generate a recommended fitness plan. Also, please list and provide information on health-related events currently taking place in the area." 【0527】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0528】 Step 1: 【0529】 Users input biometric data into the device using their smartphone or PC. This input data includes height, weight, age, gender, exercise habits, and dietary preferences. The device then transmits this data to the server. 【0530】 Step 2: 【0531】 The server generates body composition characteristics based on biometric data received from the terminal. Specifically, it passes the received data to an AI engine to calculate characteristics such as body fat percentage, muscle mass, and basal metabolic rate. This process uses deep learning frameworks such as TensorFlow and PyTorch. 【0532】 Step 3: 【0533】 The server uses an AI algorithm to create an appropriate exercise plan based on the generated physical characteristics. This plan is adjusted according to the user's health goals and resources (such as available training equipment). Visual content data is also included in the plan to facilitate visual instruction. 【0534】 Step 4: 【0535】 The server simultaneously uses an AI model to generate nutritional guidance, taking into account the user's dietary preferences and restrictions. This includes suggesting meal menus and recipes that consider balanced nutrition. 【0536】 Step 5: 【0537】 The device receives exercise plans and nutritional guidance sent from the server and displays them to the user. The application used is developed with React Native and supports intuitive understanding by providing visual and audio guidance. 【0538】 Step 6: 【0539】 The device records user feedback and activity data, sending it back to the server periodically. This allows the server to monitor progress, adjust plans as needed, and send motivational feedback. 【0540】 Step 7: 【0541】 The server collects information about health promotion events within the local area and makes recommendations based on the user's interests and activity history. This information is sent to the user via push notifications using Firebase Cloud Messaging. 【0542】 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. 【0543】 This invention provides users with a more personalized experience by incorporating an emotion engine into a system that supports personal health management. This system includes multiple components for managing and analyzing server, terminal, and user information. 【0544】 First, users access the system via smartphone or PC and input biometric data and health goals. This input data, including information about their emotional state, is sent to the server. The device is equipped with sensors such as cameras and microphones, which are used to perform voice and facial expression analysis and capture the user's emotions in real time. 【0545】 The server generates a body composition profile based on the received biometric data and creates training and nutrition plans that correspond to health goals. It also uses an emotion engine to extract the user's emotional state from audio and video data and adjusts the plans based on that information. Specifically, if the user is feeling stressed, it will suggest light exercises or yoga that have a relaxing effect. 【0546】 The device presents the user with a personalized plan received from the server. Training and nutrition plans are tailored to the user's preferences and displayed as media, including visual and auditory guidelines. Music recommendation features are also enhanced by an emotion engine, suggesting playlists that match the user's current emotional state. 【0547】 For progress management, users input their daily exercise and dietary results, which are collected by the server. The server also uses an emotion engine to analyze past emotional trends and generate messages to maintain user motivation. These messages offer encouragement and guidance tailored to the user's emotional state. 【0548】 This invention makes it possible to provide users with more optimized health management and lifestyle support, as well as emotional support. Overall, this system comprehensively supports the user's health status and enables guidance that closely addresses individual needs and emotional states. 【0549】 The following describes the processing flow. 【0550】 Step 1: 【0551】 The user logs into the application using a smartphone or PC. After logging in, they enter biometric data and health information such as height, weight, age, gender, and health goals. 【0552】 Step 2: 【0553】 The device uses its camera and microphone to record the user's facial expressions and voice, and feeds this data to the emotion engine. The emotion engine detects the user's emotional state (e.g., happiness, stress, fatigue) in real time and sends this data to the server. 【0554】 Step 3: 【0555】 The server combines biometric and emotional data received to generate a body composition profile. Simultaneously, it uses AI algorithms to create customized training and nutrition plans based on the user's health goals. 【0556】 Step 4: 【0557】 The server uses an emotion engine to analyze the user's emotional state and adjust the training plan accordingly. For example, if the user is feeling stressed, it might suggest relaxation-focused exercises and prepare encouraging messages to boost their motivation. 【0558】 Step 5: 【0559】 The device displays a personalized training and nutrition plan provided by the server to the user. The training plan includes specific exercises to be performed, the number of sets, repetitions, and links to related video tutorials. 【0560】 Step 6: 【0561】 Users record their daily training progress and meal details on their device. Recording emotional changes (e.g., changes in mood before and after exercise) is also recommended. 【0562】 Step 7: 【0563】 The server analyzes progress and sentiment data to assess the user's progress toward their health goals. Based on the user's sentiment, it provides motivational messages and guidance on the next steps. 【0564】 Step 8: 【0565】 The device notifies the user of feedback, motivational messages, and recommended music and relaxation videos provided by the server. This information is tailored to the user's current emotional state. 【0566】 The above outlines the specific processing flow for implementing the present invention's system, which incorporates an emotion engine. 【0567】 (Example 2) 【0568】 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." 【0569】 Traditional health management systems have faced challenges in providing personalized health plans that take into account the emotional state of individual users. Furthermore, the automation of feedback necessary for maintaining motivation is insufficient, making it difficult to continuously support users' health management. Additionally, there is a need for effective visual and auditory guidance in various plans. 【0570】 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. 【0571】 In this invention, the server includes means for inputting an individual's biometric data and extracting a combination of this data and emotional state data; means for generating a body composition profile based on the extracted data; means for customizing a training plan and suggesting relaxation activities based on the profile and emotional state data; means for analyzing the user's emotional state using an emotional engine and recommending music and exercises; means for providing visual and auditory guidance based on the customized training plan; and means for recording the user's progress and sending automatically generated feedback to maintain motivation. This enables personalized health management support tailored to the user's emotional state. 【0572】 "Biometric data" refers to measurable information about the human body, such as heart rate, weight, and activity level. 【0573】 "Emotional state data" refers to information that indicates the user's psychological and emotional state, such as stress levels and relaxation levels. 【0574】 A "body composition profile" refers to a collection of individual data that reflects a user's physical characteristics and health status. 【0575】 An "emotion engine" refers to a software component that analyzes a user's voice and facial expression data to infer their emotional state. 【0576】 A "training plan" refers to a customized set of exercise and workout instructions based on the user's health goals. 【0577】 "Visual and auditory guidance" refers to a form of information transmission that provides instruction to users through videos and audio. 【0578】 "Motivational feedback" refers to encouraging and guiding messages provided to help users maintain their motivation to achieve their goals. 【0579】 "Recommending music and exercise" refers to the process of selecting and suggesting appropriate music and exercise forms based on the user's emotional state and personal preferences. 【0580】 This invention is a personalized system that utilizes an emotion engine to effectively support users' health management. This system consists of three main components: a server, a terminal, and a user. 【0581】 First, users input biometric and emotional state data via a smartphone or PC application. This data input collects physical data such as heart rate and activity level, as well as mental data such as "stress" and "relaxation." The data entered by the user is transmitted to a server via the internet. 【0582】 Next, the device uses sensors such as cameras and microphones to analyze the user's facial expressions and voice. This allows for an accurate estimation of the user's emotional state. The sensor data is pre-processed within the device before being sent to a server for detailed analysis by an emotion engine. 【0583】 The server is the central component for integrating data received from users and generating individual body composition profiles. In this process, an emotion engine is utilized to extract emotional patterns from the obtained data and create a health plan optimized for the user's condition. The training plan can suggest exercise and relaxation activities and provide music recommendations based on the user's emotional state. 【0584】 The device presents the user with customized training and nutrition plans received from the server. Visual and auditory guidance can show specific exercise steps and recommended foods. The emotion engine also provides music playlists tailored to the user's current emotional state, helping to maintain a comfortable training environment. 【0585】 Progress is recorded and managed on a server, with users entering daily activity data into their devices. Based on past data, feedback designed to maintain motivation is automatically generated and sent to the user. 【0586】 For example, if a user enters into the application that "I'm stressed out because work has been so busy this week," the emotion engine can recommend a yoga plan aimed at stress relief and also provide appropriate relaxation music. 【0587】 An example of a prompt from a generative AI model is, "Please suggest a training plan that takes into account the user's current emotional state." This allows the system to provide optimal guidance that takes emotional data into consideration. 【0588】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0589】 Step 1: 【0590】 Users access a smartphone or PC interface and input biometric and emotional state data. This input data includes heart rate, weight, activity level, and stress level. This data is then sent to the server as input. The data is encrypted and securely transmitted over the network to reach the server. 【0591】 Step 2: 【0592】 The server analyzes received biometric and emotional state data to generate individual body composition profiles. Statistical methods are used to extract profile information such as body fat percentage and muscle mass from the input data. Furthermore, data processing is performed to analyze the user's emotional patterns over a week, based on the emotional state data. The resulting output includes a detailed body composition profile and emotional analysis results. 【0593】 Step 3: 【0594】 The device uses its built-in camera and microphone to capture the user's real-time facial expressions and voice data. From this input data, it uses facial recognition algorithms and voice analysis technology to infer the emotional state and sends it to a server. The server then receives the updated emotional data and can generate a more detailed emotional profile. The output is the user's emotional inference result at that moment. 【0595】 Step 4: 【0596】 The server creates individualized training and nutrition plans based on an integrated body composition profile and emotional state. Using a generative AI model, it selects and customizes the most appropriate plan based on the input profile information. The input is profile information and emotional analysis results, and the output is the customized plan. For example, if high stress levels are detected, relaxation-focused exercises will be suggested. 【0597】 Step 5: 【0598】 The terminal presents the user with a customized plan sent from the server. Specifically, the plan is displayed in a multimedia format utilizing both visual and auditory elements. Exercise video guidelines and recommended meal lists are provided as input, and a corresponding plan is output. A music playlist tailored to the user's emotional state is also displayed. 【0599】 Step 6: 【0600】 Users input their daily progress, specifically their training and meal results, into their device. This input data is then sent back to the server and used by the emotion engine to evaluate progress and generate motivational messages. Based on the user's progress data, feedback and plan adjustments are generated. 【0601】 (Application Example 2) 【0602】 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." 【0603】 In personal health management, conventional systems struggle to provide health plans that adequately consider biometric data and emotions, and they lack personalized feedback and motivation tailored to the user's emotional state. Therefore, there is a need to provide both optimized health management and emotional support simultaneously for each user. 【0604】 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. 【0605】 In this invention, the server includes means for acquiring personal state data and generating a physical composition profile based on it, means for analyzing the emotional state using an emotion engine and adjusting the health plan based on it, and means for providing media based on the user's emotions, equipped with a music recommendation function. This makes it possible to simultaneously provide a health plan that closely corresponds to individual needs and emotional states, and emotional support. 【0606】 "Personal status data" refers to data that includes the user's physical and physiological information, which is analyzed and used in health management. 【0607】 A "body composition profile" is a collection of information that indicates a person's body composition and health status, generated based on acquired personal condition data. 【0608】 An "exercise plan" is a plan that provides a customized exercise schedule and content tailored to the user's individual physical composition profile. 【0609】 A "nutrition plan" is a plan for creating a diet that takes into account the user's dietary preferences and restrictions, with the aim of maintaining and promoting health. 【0610】 "Means of recording results" refer to methods for saving users' health-related results and progress for subsequent analysis and feedback. 【0611】 "Responses to maintain motivation" refer to feedback and guidance provided to users with the aim of maintaining and improving their motivation to continue health management activities. 【0612】 "Lifestyle-based suggestions" refer to fashion and activity recommendations that take into account the user's usual lifestyle and behavioral patterns. 【0613】 An "emotion engine" is a technology that analyzes a user's emotional state and enables emotion-based interactions. 【0614】 The "music recommendation function" is a feature that takes into account the user's emotions and preferences to suggest songs and music playlists that are appropriate for the time. 【0615】 "Real-time guidance" is a support function that analyzes the user's emotional state and health condition in real time and provides appropriate audio and visual guidance on the spot. 【0616】 To realize this invention, the user must first use a dedicated terminal to input personal status data and health goals and access the system. The terminal is equipped with input sensors such as a camera and microphone, which allow for real-time capture of the user's facial expressions and voice. Based on the information from these sensors, an emotion engine is activated to analyze the user's emotional state. 【0617】 The server receives data sent from the terminal and generates a user's physical composition profile. Based on this profile, a personalized exercise and nutrition plan is created that is best suited to the user. Furthermore, an emotion engine is used to assess the user's emotional state, and the plan is adjusted based on that information. If relaxation is needed, yoga or light exercises that help reduce stress are suggested. 【0618】 The device presents the user with a personalized plan received from the server. The display uses audio and visual guidance, and a music recommendation feature provides playlists tailored to the user's current emotional state. For progress management, users input their daily activity results, which the server collects and analyzes. The server uses an emotion engine to analyze past emotional trends and generates messages to maintain user motivation. 【0619】 As a concrete example, a scenario could be envisioned where, upon waking up in the morning, a consumer robot suggests, "Good morning. You seem a little stressed today, so how about trying some relaxation yoga?" and then plays relaxing music. This would comprehensively provide not only health management but also emotional support for the user. An example of a prompt to the generative AI model in this system would be, "Generate a health plan that takes into account the current emotional state and provide a playlist suitable for relaxation." 【0620】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0621】 Step 1: 【0622】 Users input their personal status data and health goals through devices such as smartphones and PCs. The device then uses its camera and microphone to capture the user's facial expressions and voice in real time, generating emotional state data. This data is then sent to a server. 【0623】 Step 2: 【0624】 The server receives user state data and emotional state data sent from the terminal and generates a user body composition profile based on this data. It analyzes the input data and creates a profile that takes into account physical characteristics such as weight, height, and age. 【0625】 Step 3: 【0626】 The server creates customized exercise and nutrition plans based on the user's physical composition profile. Simultaneously, it uses an emotional engine to adjust these plans to address the user's emotional state. For example, if the user is feeling stressed, it might suggest light exercise or yoga sessions to promote relaxation. 【0627】 Step 4: 【0628】 The device receives customized exercise and nutrition plans from the server and presents them to the user. The device provides instructions through visual or audio guidance to help the user implement the plan. Furthermore, it uses a music recommendation feature to provide playlists tailored to the user's mood. 【0629】 Step 5: 【0630】 Users input their daily activity results and meal details into a device. The device aggregates this data and sends it back to the server. The server records this performance data and analyzes emotional trends based on past data. 【0631】 Step 6: 【0632】 The server utilizes an emotion engine to analyze the user's emotional trends and progress, and generates feedback messages to maintain user motivation. These messages will include encouragement and guidance, tailored to the user's emotional state. 【0633】 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. 【0634】 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. 【0635】 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. 【0636】 [Fourth Embodiment] 【0637】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0638】 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. 【0639】 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). 【0640】 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. 【0641】 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. 【0642】 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). 【0643】 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. 【0644】 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. 【0645】 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. 【0646】 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. 【0647】 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. 【0648】 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. 【0649】 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". 【0650】 This invention is a system that uses a server, terminal, and user interface to support personalized health management for individual users. The system begins with the input of an individual's biometric data and, based on a body composition profile generated by the server, provides the user with a personalized training and nutrition plan. 【0651】 First, the user inputs basic biometric data into the device using a smartphone or PC. The device sends this data to a server, which then creates a body composition profile based on it. This profile forms the basis for training and nutrition plans tailored to each user's health goals. 【0652】 Next, the server automatically generates a training plan using an AI algorithm based on individual profile information. This plan is customized to take into account the user's preferences and available resources (e.g., availability of training equipment). The generated plan is provided to the user via a terminal. The terminal displays specific training content and visual guides (e.g., training videos). 【0653】 Furthermore, the server generates a nutrition plan to support a healthy diet based on the user's dietary preferences and nutritional restrictions. This plan is provided to the user as daily meal suggestions, and recipes and ingredient lists are also provided from the terminal as needed. 【0654】 The results of users' actual training and meal plan implementation are recorded on their devices as daily progress. The server analyzes this progress data to evaluate the user's progress toward their goals. In addition, it continuously sends feedback and encouraging messages to users to maintain their motivation. 【0655】 Regarding lifestyle improvements, the server provides appropriate fashion suggestions and recommended activities (e.g., local fitness events) based on the user's interests and activity history. This information can be easily accessed and viewed through the user interface. 【0656】 As described above, the present invention provides users with health management and lifestyle support tailored to their individual needs, thereby achieving continuous and effective health improvement. 【0657】 The following describes the processing flow. 【0658】 Step 1: 【0659】 The user accesses the application using a smartphone or PC. They enter the necessary information for initial registration (name, email address, password) and create an account. 【0660】 Step 2: 【0661】 Users enter their height, weight, age, gender, and health goals (e.g., weight loss, muscle gain) within the app. This data forms the basis for creating their health profile. 【0662】 Step 3: 【0663】 The device transmits the entered biometric data to the server. The data is encrypted using a secure communication protocol. 【0664】 Step 4: 【0665】 The server generates a body composition profile based on the biometric data it receives. The generated profile is stored in a database and used to create customized training and nutrition plans for each user. 【0666】 Step 5: 【0667】 The server considers the user's body composition profile and health goals, and uses an AI algorithm to generate a customized training plan. This training plan includes details such as exercise duration, frequency, and intensity. 【0668】 Step 6: 【0669】 The device displays a training plan provided by the server to the user. The plan includes specific exercise descriptions and, if necessary, video links. 【0670】 Step 7: 【0671】 Users enter their dietary preferences and restrictions (e.g., vegetarian, allergy information) into the app. This generates a customized nutrition plan. 【0672】 Step 8: 【0673】 The server uses a nutrition algorithm based on the user's dietary information to create a healthy meal plan. This plan takes into account daily calorie intake and nutrient balance. 【0674】 Step 9: 【0675】 The device displays a nutrition plan to the user. The plan includes recommended meal menus and recipes, as well as a shopping list if necessary. 【0676】 Step 10: 【0677】 Users report their progress by entering their daily training and meal records into the app. 【0678】 Step 11: 【0679】 The server analyzes user progress data and uses statistical models to evaluate goal achievement. It then generates feedback and motivational messages based on the level of achievement. 【0680】 Step 12: 【0681】 The device displays feedback and motivational messages provided by the server to the user. These messages include praise and advice for future goals. 【0682】 Step 13: 【0683】 The server analyzes the user's lifestyle data and generates fashion suggestions and information about events they should attend based on their interests and activity history. 【0684】 Step 14: 【0685】 The device notifies the user with lifestyle suggestions, including outfit suggestions and event announcements based on their interests. 【0686】 (Example 1) 【0687】 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". 【0688】 Providing effective and efficient health management and lifestyle support to individual users is challenging. In particular, there is a need for a system that automatically generates precise training and nutrition plans based on individual biometric information and encourages daily lifestyle improvements in accordance with these plans. Furthermore, maintaining users' continuous motivation, evaluating their progress in lifestyle development and achievement of health goals, and providing personalized feedback are also crucial challenges. 【0689】 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. 【0690】 In this invention, the server includes means for receiving biometric information and generating a body composition model based on it, means for individualizing and providing an exercise plan using the generated AI model, and means for generating a nutrition plan that takes into account dietary preferences and restrictions. This makes it possible to provide personalized health management and lifestyle suggestions to individual users. 【0691】 "Biometric information" refers to basic data about the user's body, including height, weight, age, and gender. 【0692】 A "body composition model" is a detailed profile of a person's body structure, generated based on their biological information. 【0693】 An "exercise plan" is a specific exercise plan that is tailored to the user's health goals and body composition model, and should be followed accordingly. 【0694】 A "nutrition plan" is a plan that proposes a nutritionally balanced meal plan to the user, taking into account their dietary preferences and restrictions. 【0695】 A "generative AI model" is a system that uses artificial intelligence technology to automatically generate useful information and suggestions from data. 【0696】 "Lifestyle suggestions" refer to recommendations for specific activities or behaviors based on the user's interests and past data. 【0697】 "Personalized health management" refers to a health management approach that is customized according to each individual's characteristics and needs. 【0698】 This invention is a system that provides users with personalized health management and lifestyle improvements by coordinating users, terminals, and servers. First, the user uses a terminal such as a smartphone or PC to input their biometric information. This biometric information includes data such as height, weight, age, and gender. An application on the terminal transmits the entered biometric information to the server. 【0699】 The server uses a generative AI model to generate a body composition model of the user based on the transmitted biometric information. This body composition model is then used as the basis for subsequent exercise and nutritional planning. As a specific example, the server calculates health indicators such as the user's BMI and uses the results to form the body composition model. 【0700】 Next, the server utilizes a generative AI model to automatically generate an exercise plan based on the user's health goals and available exercise equipment. For example, by inputting a prompt such as "Create a training plan using dumbbells for weight loss" into the generative AI model, an appropriate exercise plan will be created. Furthermore, the server considers the user's dietary preferences and restrictions to generate a personalized nutrition plan. This nutrition plan includes suggestions for specific meal menus and nutritional balance. 【0701】 The generated exercise and nutrition plans are provided to the user via a device. The device displays training videos and detailed nutrition information, allowing the user to implement daily activities based on this information. 【0702】 The server also records and continuously analyzes the user's exercise and dietary progress. This analysis is provided to the user via their device as feedback to evaluate their progress towards their goals and maintain their motivation. Furthermore, the server suggests lifestyle changes based on the user's interests and past data, recommending appropriate activities and events. 【0703】 This system allows users to maximize the benefits of personalized health management and improve their daily lives. 【0704】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0705】 Step 1: 【0706】 Users launch the application using their smartphone or PC and enter their biometric information. This information includes height, weight, age, and gender. The entered data is sent from the device to the server, allowing the server to receive the user's basic health data. 【0707】 Step 2: 【0708】 The server uses a generative AI model to generate a body composition model of the user based on the received biometric information. Specifically, it calculates health indicators such as BMI and estimates body composition, including body size and basal metabolic rate, based on these. The output result is the constructed body composition model. 【0709】 Step 3: 【0710】 The server generates an exercise plan using a generative AI model based on a body composition model and the user's health goals. The server considers the user's preferences and available exercise equipment, and inputs prompts into the generative AI model. Prompts such as "Create a basic training program" are used. The output is a customized exercise plan. 【0711】 Step 4: 【0712】 The server generates a personalized nutrition plan based on the user's dietary preferences and restrictions. Utilizing a generation AI model, it suggests a balanced menu while taking into account the food restrictions specified by the user. The output is a nutrition plan tailored to the user. 【0713】 Step 5: 【0714】 The device displays the exercise and nutrition plans received from the server to the user. The screen on the device shows specific training content and visual guides (videos and diagrams) to be performed. The user can then carry out their daily activities according to these guidelines. 【0715】 Step 6: 【0716】 Users actually follow their exercise and nutrition plans and record the results on their device. The device sends the user's activity data to a server, accumulating progress. This allows the user's achievement status to be tracked. 【0717】 Step 7: 【0718】 The server analyzes user progress data and evaluates goal achievement. Based on the generated analysis results, it creates feedback and encouraging messages and sends them to the user via the device. This further supports user motivation. 【0719】 (Application Example 1) 【0720】 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". 【0721】 In modern society, individual health management is becoming increasingly diverse, and there is a growing demand for customized exercise plans and nutritional guidance tailored to each individual's lifestyle and health condition. However, achieving this requires effective data analysis and personalized information provision. Furthermore, efficiently providing users with information on local community events is also a crucial challenge. 【0722】 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. 【0723】 In this invention, the server includes means for inputting personal health data and generating physical composition characteristics based on this data, means for adjusting and providing an exercise plan based on the characteristics, and means for generating nutritional guidance that takes into account dietary preferences and restrictions. This makes it possible to provide each user with personalized health management and information on events in their local community. 【0724】 "Health-related data" refers to information about the user's physical condition and lifestyle. 【0725】 "Body composition characteristics" are indicators that show an individual's body type and physical condition, and form the basis of health management. 【0726】 An "exercise plan" refers to the training schedule and content created based on the user's health condition and goals. 【0727】 "Nutritional guidance" refers to providing meal suggestions and plans that take into account an individual's dietary preferences and restrictions. 【0728】 "Evaluation to maintain motivation" refers to methods of providing feedback based on the user's progress to continuously motivate them. 【0729】 "Health promotion events within cities" refer to information about fitness events and health-related activities held in the community, with the aim of promoting the health of residents. 【0730】 The system that realizes this invention consists of a server, a terminal, and a user. The user inputs health-related data into the terminal using a smartphone or PC. The terminal sends the input data to the server, which generates physical composition characteristics. An AI engine is installed on the server and automatically generates an exercise plan and nutritional guidance based on the input data. This AI engine uses deep learning frameworks such as TensorFlow and PyTorch. 【0731】 The generated plans and guidance are customized to the user's preferences and available resources, and delivered to the user via their device. The device is equipped with a React Native application that displays visual instructions and audio guidance. Furthermore, Firebase Cloud Messaging is used to send push notifications to the user with information on motivational assessments and local health promotion events. 【0732】 For example, the server can analyze user data and send a notification such as, "We recommend you participate in a health walk event held in a city park this weekend." The following prompt can also be used for the generative AI model: "Please input the user's current health data and generate a recommended fitness plan. Also, please list and provide information on health-related events currently taking place in the area." 【0733】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0734】 Step 1: 【0735】 Users input biometric data into the device using their smartphone or PC. This input data includes height, weight, age, gender, exercise habits, and dietary preferences. The device then transmits this data to the server. 【0736】 Step 2: 【0737】 The server generates body composition characteristics based on biometric data received from the terminal. Specifically, it passes the received data to an AI engine to calculate characteristics such as body fat percentage, muscle mass, and basal metabolic rate. This process uses deep learning frameworks such as TensorFlow and PyTorch. 【0738】 Step 3: 【0739】 The server uses an AI algorithm to create an appropriate exercise plan based on the generated physical characteristics. This plan is adjusted according to the user's health goals and resources (such as available training equipment). Visual content data is also included in the plan to facilitate visual instruction. 【0740】 Step 4: 【0741】 The server simultaneously uses an AI model to generate nutritional guidance, taking into account the user's dietary preferences and restrictions. This includes suggesting meal menus and recipes that consider balanced nutrition. 【0742】 Step 5: 【0743】 The device receives exercise plans and nutritional guidance sent from the server and displays them to the user. The application used is developed with React Native and supports intuitive understanding by providing visual and audio guidance. 【0744】 Step 6: 【0745】 The device records user feedback and activity data, sending it back to the server periodically. This allows the server to monitor progress, adjust plans as needed, and send motivational feedback. 【0746】 Step 7: 【0747】 The server collects information about health promotion events within the local area and makes recommendations based on the user's interests and activity history. This information is sent to the user via push notifications using Firebase Cloud Messaging. 【0748】 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. 【0749】 This invention provides users with a more personalized experience by incorporating an emotion engine into a system that supports personal health management. This system includes multiple components for managing and analyzing server, terminal, and user information. 【0750】 First, users access the system via smartphone or PC and input biometric data and health goals. This input data, including information about their emotional state, is sent to the server. The device is equipped with sensors such as cameras and microphones, which are used to perform voice and facial expression analysis and capture the user's emotions in real time. 【0751】 The server generates a body composition profile based on the received biometric data and creates training and nutrition plans that correspond to health goals. It also uses an emotion engine to extract the user's emotional state from audio and video data and adjusts the plans based on that information. Specifically, if the user is feeling stressed, it will suggest light exercises or yoga that have a relaxing effect. 【0752】 The device presents the user with a personalized plan received from the server. Training and nutrition plans are tailored to the user's preferences and displayed as media, including visual and auditory guidelines. Music recommendation features are also enhanced by an emotion engine, suggesting playlists that match the user's current emotional state. 【0753】 For progress management, users input their daily exercise and dietary results, which are collected by the server. The server also uses an emotion engine to analyze past emotional trends and generate messages to maintain user motivation. These messages offer encouragement and guidance tailored to the user's emotional state. 【0754】 This invention makes it possible to provide users with more optimized health management and lifestyle support, as well as emotional support. Overall, this system comprehensively supports the user's health status and enables guidance that closely addresses individual needs and emotional states. 【0755】 The following describes the processing flow. 【0756】 Step 1: 【0757】 The user logs into the application using a smartphone or PC. After logging in, they enter biometric data and health information such as height, weight, age, gender, and health goals. 【0758】 Step 2: 【0759】 The device uses its camera and microphone to record the user's facial expressions and voice, and feeds this data to the emotion engine. The emotion engine detects the user's emotional state (e.g., happiness, stress, fatigue) in real time and sends this data to the server. 【0760】 Step 3: 【0761】 The server combines biometric and emotional data received to generate a body composition profile. Simultaneously, it uses AI algorithms to create customized training and nutrition plans based on the user's health goals. 【0762】 Step 4: 【0763】 The server uses an emotion engine to analyze the user's emotional state and adjust the training plan accordingly. For example, if the user is feeling stressed, it might suggest relaxation-focused exercises and prepare encouraging messages to boost their motivation. 【0764】 Step 5: 【0765】 The device displays a personalized training and nutrition plan provided by the server to the user. The training plan includes specific exercises to be performed, the number of sets, repetitions, and links to related video tutorials. 【0766】 Step 6: 【0767】 Users record their daily training progress and meal details on their device. Recording emotional changes (e.g., changes in mood before and after exercise) is also recommended. 【0768】 Step 7: 【0769】 The server analyzes progress and sentiment data to assess the user's progress toward their health goals. Based on the user's sentiment, it provides motivational messages and guidance on the next steps. 【0770】 Step 8: 【0771】 The device notifies the user of feedback, motivational messages, and recommended music and relaxation videos provided by the server. This information is tailored to the user's current emotional state. 【0772】 The above outlines the specific processing flow for implementing the present invention's system, which incorporates an emotion engine. 【0773】 (Example 2) 【0774】 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". 【0775】 Traditional health management systems have faced challenges in providing personalized health plans that take into account the emotional state of individual users. Furthermore, the automation of feedback necessary for maintaining motivation is insufficient, making it difficult to continuously support users' health management. Additionally, there is a need for effective visual and auditory guidance in various plans. 【0776】 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. 【0777】 In this invention, the server includes means for inputting an individual's biometric data and extracting a combination of this data and emotional state data; means for generating a body composition profile based on the extracted data; means for customizing a training plan and suggesting relaxation activities based on the profile and emotional state data; means for analyzing the user's emotional state using an emotional engine and recommending music and exercises; means for providing visual and auditory guidance based on the customized training plan; and means for recording the user's progress and sending automatically generated feedback to maintain motivation. This enables personalized health management support tailored to the user's emotional state. 【0778】 "Biometric data" refers to measurable information about the human body, such as heart rate, weight, and activity level. 【0779】 "Emotional state data" refers to information that indicates the user's psychological and emotional state, such as stress levels and relaxation levels. 【0780】 A "body composition profile" refers to a collection of individual data that reflects a user's physical characteristics and health status. 【0781】 An "emotion engine" refers to a software component that analyzes a user's voice and facial expression data to infer their emotional state. 【0782】 A "training plan" refers to a customized set of exercise and workout instructions based on the user's health goals. 【0783】 "Visual and auditory guidance" refers to a form of information transmission that provides instruction to users through videos and audio. 【0784】 "Motivational feedback" refers to encouraging and guiding messages provided to help users maintain their motivation to achieve their goals. 【0785】 "Recommending music and exercise" refers to the process of selecting and suggesting appropriate music and exercise forms based on the user's emotional state and personal preferences. 【0786】 This invention is a personalized system that utilizes an emotion engine to effectively support users' health management. This system consists of three main components: a server, a terminal, and a user. 【0787】 First, users input biometric and emotional state data via a smartphone or PC application. This data input collects physical data such as heart rate and activity level, as well as mental data such as "stress" and "relaxation." The data entered by the user is transmitted to a server via the internet. 【0788】 Next, the device uses sensors such as cameras and microphones to analyze the user's facial expressions and voice. This allows for an accurate estimation of the user's emotional state. The sensor data is pre-processed within the device before being sent to a server for detailed analysis by an emotion engine. 【0789】 The server is the central component for integrating data received from users and generating individual body composition profiles. In this process, an emotion engine is utilized to extract emotional patterns from the obtained data and create a health plan optimized for the user's condition. The training plan can suggest exercise and relaxation activities and provide music recommendations based on the user's emotional state. 【0790】 The device presents the user with customized training and nutrition plans received from the server. Visual and auditory guidance can show specific exercise steps and recommended foods. The emotion engine also provides music playlists tailored to the user's current emotional state, helping to maintain a comfortable training environment. 【0791】 Progress is recorded and managed on a server, with users entering daily activity data into their devices. Based on past data, feedback designed to maintain motivation is automatically generated and sent to the user. 【0792】 For example, if a user enters into the application that "I'm stressed out because work has been so busy this week," the emotion engine can recommend a yoga plan aimed at stress relief and also provide appropriate relaxation music. 【0793】 An example of a prompt from a generative AI model is, "Please suggest a training plan that takes into account the user's current emotional state." This allows the system to provide optimal guidance that takes emotional data into consideration. 【0794】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0795】 Step 1: 【0796】 Users access a smartphone or PC interface and input biometric and emotional state data. This input data includes heart rate, weight, activity level, and stress level. This data is then sent to the server as input. The data is encrypted and securely transmitted over the network to reach the server. 【0797】 Step 2: 【0798】 The server analyzes received biometric and emotional state data to generate individual body composition profiles. Statistical methods are used to extract profile information such as body fat percentage and muscle mass from the input data. Furthermore, data processing is performed to analyze the user's emotional patterns over a week, based on the emotional state data. The resulting output includes a detailed body composition profile and emotional analysis results. 【0799】 Step 3: 【0800】 The device uses its built-in camera and microphone to capture the user's real-time facial expressions and voice data. From this input data, it uses facial recognition algorithms and voice analysis technology to infer the emotional state and sends it to a server. The server then receives the updated emotional data and can generate a more detailed emotional profile. The output is the user's emotional inference result at that moment. 【0801】 Step 4: 【0802】 The server creates individualized training and nutrition plans based on an integrated body composition profile and emotional state. Using a generative AI model, it selects and customizes the most appropriate plan based on the input profile information. The input is profile information and emotional analysis results, and the output is the customized plan. For example, if high stress levels are detected, relaxation-focused exercises will be suggested. 【0803】 Step 5: 【0804】 The terminal presents the user with a customized plan sent from the server. Specifically, the plan is displayed in a multimedia format utilizing both visual and auditory elements. Exercise video guidelines and recommended meal lists are provided as input, and a corresponding plan is output. A music playlist tailored to the user's emotional state is also displayed. 【0805】 Step 6: 【0806】 Users input their daily progress, specifically their training and meal results, into their device. This input data is then sent back to the server and used by the emotion engine to evaluate progress and generate motivational messages. Based on the user's progress data, feedback and plan adjustments are generated. 【0807】 (Application Example 2) 【0808】 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". 【0809】 In personal health management, conventional systems struggle to provide health plans that adequately consider biometric data and emotions, and they lack personalized feedback and motivation tailored to the user's emotional state. Therefore, there is a need to provide both optimized health management and emotional support simultaneously for each user. 【0810】 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. 【0811】 In this invention, the server includes means for acquiring personal state data and generating a physical composition profile based on it, means for analyzing the emotional state using an emotion engine and adjusting the health plan based on it, and means for providing media based on the user's emotions, equipped with a music recommendation function. This makes it possible to simultaneously provide a health plan that closely corresponds to individual needs and emotional states, and emotional support. 【0812】 "Personal status data" refers to data that includes the user's physical and physiological information, which is analyzed and used in health management. 【0813】 A "body composition profile" is a collection of information that indicates a person's body composition and health status, generated based on acquired personal condition data. 【0814】 An "exercise plan" is a plan that provides a customized exercise schedule and content tailored to the user's individual physical composition profile. 【0815】 A "nutrition plan" is a plan for creating a diet that takes into account the user's dietary preferences and restrictions, with the aim of maintaining and promoting health. 【0816】 "Means of recording results" refer to methods for saving users' health-related results and progress for subsequent analysis and feedback. 【0817】 "Responses to maintain motivation" refer to feedback and guidance provided to users with the aim of maintaining and improving their motivation to continue health management activities. 【0818】 "Lifestyle-based suggestions" refer to fashion and activity recommendations that take into account the user's usual lifestyle and behavioral patterns. 【0819】 An "emotion engine" is a technology that analyzes a user's emotional state and enables emotion-based interactions. 【0820】 The "music recommendation function" is a feature that takes into account the user's emotions and preferences to suggest songs and music playlists that are appropriate for the time. 【0821】 "Real-time guidance" is a support function that analyzes the user's emotional state and health condition in real time and provides appropriate audio and visual guidance on the spot. 【0822】 To realize this invention, the user must first use a dedicated terminal to input personal status data and health goals and access the system. The terminal is equipped with input sensors such as a camera and microphone, which allow for real-time capture of the user's facial expressions and voice. Based on the information from these sensors, an emotion engine is activated to analyze the user's emotional state. 【0823】 The server receives data sent from the terminal and generates a user's physical composition profile. Based on this profile, a personalized exercise and nutrition plan is created that is best suited to the user. Furthermore, an emotion engine is used to assess the user's emotional state, and the plan is adjusted based on that information. If relaxation is needed, yoga or light exercises that help reduce stress are suggested. 【0824】 The device presents the user with a personalized plan received from the server. The display uses audio and visual guidance, and a music recommendation feature provides playlists tailored to the user's current emotional state. For progress management, users input their daily activity results, which the server collects and analyzes. The server uses an emotion engine to analyze past emotional trends and generates messages to maintain user motivation. 【0825】 As a concrete example, a scenario could be envisioned where, upon waking up in the morning, a consumer robot suggests, "Good morning. You seem a little stressed today, so how about trying some relaxation yoga?" and then plays relaxing music. This would comprehensively provide not only health management but also emotional support for the user. An example of a prompt to the generative AI model in this system would be, "Generate a health plan that takes into account the current emotional state and provide a playlist suitable for relaxation." 【0826】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0827】 Step 1: 【0828】 Users input their personal status data and health goals through devices such as smartphones and PCs. The device then uses its camera and microphone to capture the user's facial expressions and voice in real time, generating emotional state data. This data is then sent to a server. 【0829】 Step 2: 【0830】 The server receives user state data and emotional state data sent from the terminal and generates a user body composition profile based on this data. It analyzes the input data and creates a profile that takes into account physical characteristics such as weight, height, and age. 【0831】 Step 3: 【0832】 The server creates customized exercise and nutrition plans based on the user's physical composition profile. Simultaneously, it uses an emotional engine to adjust these plans to address the user's emotional state. For example, if the user is feeling stressed, it might suggest light exercise or yoga sessions to promote relaxation. 【0833】 Step 4: 【0834】 The device receives customized exercise and nutrition plans from the server and presents them to the user. The device provides instructions through visual or audio guidance to help the user implement the plan. Furthermore, it uses a music recommendation feature to provide playlists tailored to the user's mood. 【0835】 Step 5: 【0836】 Users input their daily activity results and meal details into a device. The device aggregates this data and sends it back to the server. The server records this performance data and analyzes emotional trends based on past data. 【0837】 Step 6: 【0838】 The server utilizes an emotion engine to analyze the user's emotional trends and progress, and generates feedback messages to maintain user motivation. These messages will include encouragement and guidance, tailored to the user's emotional state. 【0839】 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. 【0840】 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. 【0841】 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. 【0842】 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. 【0843】 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. 【0844】 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. 【0845】 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. 【0846】 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. 【0847】 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." 【0848】 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. 【0849】 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. 【0850】 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. 【0851】 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. 【0852】 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. 【0853】 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. 【0854】 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. 【0855】 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. 【0856】 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. 【0857】 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. 【0858】 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. 【0859】 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. 【0860】 The following is further disclosed regarding the embodiments described above. 【0861】 (Claim 1) 【0862】 A means of inputting an individual's biometric data and generating a body composition profile based on this data, 【0863】 A means of customizing and providing a training plan based on the aforementioned profile, 【0864】 A method for generating a nutrition plan that takes into account dietary preferences and restrictions, 【0865】 A means of recording user progress and sending feedback to maintain motivation, 【0866】 A means of proposing fashion and activities based on the user's lifestyle, 【0867】 A system that includes this. 【0868】 (Claim 2) 【0869】 The system according to claim 1, which sets career goals and provides occupation-related information based on these goals. 【0870】 (Claim 3) 【0871】 The system according to claim 1, which provides guidance using audio and visual means in the provision of a training plan. 【0872】 "Example 1" 【0873】 (Claim 1) 【0874】 A means for receiving biological information and generating a body composition model based on it, 【0875】 A means for individualizing and providing an exercise plan based on the aforementioned model, 【0876】 A means of generating a nutrition plan that takes into account dietary preferences and restrictions, 【0877】 Means for saving the user's progress and sending responses to maintain motivation, 【0878】 A means of providing clothing and activity suggestions based on the user's lifestyle, 【0879】 A means of creating a plan using a generative AI model, 【0880】 A means of providing specific exercise routines and meal suggestions via a terminal, 【0881】 A system that includes this. 【0882】 (Claim 2) 【0883】 The system according to claim 1, which sets occupational goals and provides work-related information based on these goals. 【0884】 (Claim 3) 【0885】 The system according to claim 1, which provides guidance using sound and visual means in providing an exercise plan. 【0886】 "Application Example 1" 【0887】 (Claim 1) 【0888】 A means of inputting personal health data and generating physical characteristics based on this data, 【0889】 Means for adjusting and providing a motion plan based on the aforementioned characteristics, 【0890】 A means of generating nutritional guidance that takes into account dietary preferences and restrictions, 【0891】 A means of recording the user's progress and sending evaluations to maintain motivation, 【0892】 A means of proposing clothing and activities based on the user's lifestyle, 【0893】 A means of providing information on health promotion events within the city, 【0894】 A system that includes this. 【0895】 (Claim 2) 【0896】 The system according to claim 1, which sets occupational goals and provides labor-related information based on these goals. 【0897】 (Claim 3) 【0898】 The system according to claim 1, which provides instructions using voice and image means in providing an exercise plan. 【0899】 "Example 2 of combining an emotion engine" 【0900】 (Claim 1) 【0901】 A method for inputting an individual's biometric data and combining it with emotional state data to extract information, 【0902】 A means for generating a body composition profile based on the extracted data, 【0903】 A means for customizing a training plan and suggesting relaxation activities based on the aforementioned profile and emotional state data, 【0904】 A method that uses an emotion engine to analyze the user's emotional state and recommend music and exercise, 【0905】 Means for providing visual and auditory guidance based on the customized training plan, 【0906】 A means of recording user progress and sending automatically generated feedback to maintain motivation, 【0907】 A system that includes this. 【0908】 (Claim 2) 【0909】 The system according to claim 1, which sets career goals and provides occupation-related information based on these goals. 【0910】 (Claim 3) 【0911】 The system according to claim 1, which provides guidance using audio and visual means in the provision of the training plan, and utilizes an emotion engine to support progress and motivation. 【0912】 "Application example 2 when combining with an emotional engine" 【0913】 (Claim 1) 【0914】 A means of acquiring personal state data and generating a body composition profile based on this data, 【0915】 A means of specializing and providing an exercise plan based on the aforementioned profile, 【0916】 A means of generating a nutrition plan that takes into account dietary preferences and restrictions, 【0917】 A means for recording user performance and sending responses to maintain motivation, 【0918】 A means of proposing fashion and activities based on the user's lifestyle, 【0919】 A means of analyzing emotional states using an emotion engine and adjusting health plans based on that analysis, 【0920】 A means of providing real-time, emotion-responsive audio and visual instruction, 【0921】 A means of providing media based on the user's emotions, equipped with a music recommendation function, 【0922】 A system that includes this. 【0923】 (Claim 2) 【0924】 The system according to claim 1, which sets occupational goals and provides occupational information based on these goals. 【0925】 (Claim 3) 【0926】 The system according to claim 1, which provides guidance using audio and visual means in the provision of a plan and provides motivation according to the emotional state. [Explanation of symbols] 【0927】 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 inputting an individual's biometric data and generating a body composition profile based on this data, A means of customizing and providing a training plan based on the aforementioned profile, A method for generating a nutrition plan that takes into account dietary preferences and restrictions, A means of recording user progress and sending feedback to maintain motivation, A means of proposing fashion and activities based on the user's lifestyle, A system that includes this. [Claim 2] The system according to claim 1, which sets career goals and provides occupation-related information based on these goals. [Claim 3] The system according to claim 1, which provides guidance using audio and visual means in the provision of a training plan.

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