System

Abstract

A system is provided.SOLUTION: A system, comprising: means for a user to input health information; means for obtaining activity data of the user from a wearable device; means for analyzing the health information and the activity data of the user to generate a personalized training plan and diet plan; means for providing the generated training plan and diet plan to a terminal of the user; and means for predicting and graphically presenting a change in health condition of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many people want to manage their health at home, but maintaining ongoing motivation is a challenge. Another problem is the overwhelming amount of information, making it difficult to find the optimal method. Furthermore, it can be difficult for users to obtain accurate feedback when self-evaluating, which can prevent effective training and dietary management. To address these challenges, the present invention aims to provide a system that provides users with personalized training and meal plans and helps them maintain their motivation. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes a means for a user to input health information, a means for acquiring the user's activity data from a wearable device, a means for analyzing the user's health information and activity data to generate personalized training and meal plans, a means for providing the generated training and meal plans to the user's device, and a means for predicting changes in the user's health status and presenting them as images. Furthermore, the system also includes a means for generating and providing messages based on behavioral science to the user to maintain motivation, and a means for analyzing the user's exercise form and providing feedback on areas for improvement in real time, enabling more effective health management.

[0006] "User information input means" means a device or interface through which a user inputs their health information and profile data into the system.

[0007] A "wearable device" is a portable electronic device worn by a user to measure and record physical activity and physiological data.

[0008] "Activity data acquisition means" refers to a device or interface for importing user activity data from a wearable device into the system.

[0009] "Health Information" means data related to your health and fitness goals, such as your age, gender, height, weight, and goals.

[0010] "Data Analysis Method" means the algorithms and processes that use a user's health and activity data to generate personalized training and meal plans.

[0011] The "training plan generation means" is a device or interface that creates an optimal training plan for the user based on the analysis results.

[0012] A "meal plan generator" is a device or interface that creates an appropriate meal plan based on a user's health information and goals.

[0013] The "plan providing means" refers to a device or interface for delivering the generated training plan and meal plan to the user's terminal.

[0014] The "health condition change prediction means" is a device or interface that predicts future changes in health condition based on the user's activity data and health information, and presents this as an image.

[0015] A "motivational tool" is a device or process that provides users with messages and reminders based on behavioral science.

[0016] An "exercise form analysis means" is a device or process that captures a user's exercise form using a camera or sensor, analyzes it in real time, and provides feedback on areas for improvement. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention is a system for a user-dedicated AI personal health assistant that provides personal training to help users achieve their goals (e.g., improving health, losing weight, building muscle). This system includes the following main components to enable users to efficiently and sustainably manage their health:

[0039] 1. User registration and data collection features:

[0040] On your device: Users create an account using a dedicated app or web portal, enter their profile information (e.g., name, gender, age, height, weight, health goals), and sync their wearable device (e.g., smartwatch) to the app and set it up to collect activity data.

[0041] Server: Stores the profile information you enter and activity data obtained from the wearable device (e.g., steps, heart rate, sleep patterns) in a database.

[0042] 2. Personal advice generation function:

[0043] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. The analysis algorithm takes into account the user's past activity data and health information.

[0044] Server: Sends the generated training and meal plans to the user's device, and also uses the generative AI model to generate images of the user's "before and after" appearance and presents them to the user.

[0045] 3. Training guidance and meal plan suggestions:

[0046] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[0047] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[0048] 4. Progress management and motivation maintenance features:

[0049] Server: Analyzes the user's training and diet progress data, evaluates their achievement, and adjusts their training menu and meal plan as needed.

[0050] Server: Generates messages and reminders based on behavioral science to help users maintain their motivation.

[0051] Specific examples

[0052] 1. User Registration:

[0053] User device: User A starts the app and enters his name "A Taro", gender "male", age "30", height "170cm", weight "70kg", and goal "diet". The wearable device is synchronized and heart rate and activity data is sent to the app.

[0054] Server: Stores these data in a database and prepares them for analysis.

[0055] 2. Generate personalized advice:

[0056] Server: The analytical algorithm operates based on User A's information stored in the database. It analyzes the past week's activity data and generates a training plan such as "30 minutes of jogging with the goal of burning 500 calories a day." It also generates a "high-protein, low-calorie meal plan" and suggests specific menus. Using the generative AI model, it predicts and creates an image of User A's body shape if he or she achieves his or her goal.

[0057] User's device: Receives and displays the generated training plan, meal plan, and prediction image.

[0058] 3. Training Guidance:

[0059] User device: A virtual coach provides audio and video instruction on the correct squat form. User A performs the exercise in front of the camera, and the device captures and analyzes their form in real time, providing feedback.

[0060] 4. Track your progress and stay motivated:

[0061] Server: Monitors user A's weekly training results and generates a report such as "Last week's average calorie consumption was 450 calories." If necessary, adjust the training plan for the following week.

[0062] On your device: Receive and review progress reports as notifications, as well as encouragement and training reminders from your virtual coach.

[0063] This allows users to effectively and sustainably manage their health. The entire system is designed to seamlessly support the flow from collecting a series of data to providing personalized advice and progress management.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] User's device:

[0067] Users launch the dedicated app and proceed to the account creation page.

[0068] Enter basic information such as your name, email address, and password.

[0069] Configure your wearable device for synchronization and allow data collection from the device.

[0070] Step 2:

[0071] server:

[0072] We receive the basic information you enter and store it in a secure database.

[0073] Set up a scheduled task to periodically sync activity data from your wearable device.

[0074] Activity data includes steps taken, heart rate, calories burned, sleep patterns, and more.

[0075] Step 3:

[0076] User's device:

[0077] Go to your profile settings page.

[0078] Enter detailed health information such as gender, age, height, weight, and goals (e.g., diet, muscle building).

[0079] Step 4:

[0080] server:

[0081] The detailed health information entered is stored in a database, ready to begin the analysis process.

[0082] The data analysis algorithm combines and analyzes the user's health information and activity data from the wearable device.

[0083] Step 5:

[0084] server:

[0085] Analytical algorithms evaluate the user's data and generate a customized training plan based on their health and exercise habits.

[0086] We also create optimal meal plans for users under the supervision of a nutritionist.

[0087] Step 6:

[0088] server:

[0089] The generated training and meal plans are sent to the user's device.

[0090] At the same time, a generative AI model is used to generate images of the user's appearance before and after achieving their goal, which are then sent to the user's device.

[0091] Step 7:

[0092] User's device:

[0093] A virtual coach will explain the details of the training plan and important points to note using audio and video.

[0094] Display a list of recommended meal plans and recipes.

[0095] Step 8:

[0096] User's device:

[0097] It uses cameras and sensors to capture the user's exercise form.

[0098] The captured data is sent to a server in real time for form analysis.

[0099] Step 9:

[0100] server:

[0101] Analyze your exercise form in real time and identify areas for improvement.

[0102] Generate feedback and send it to the user's device.

[0103] Step 10:

[0104] User's device:

[0105] A virtual coach displays analysis results and feedback to users, teaching them correct exercise form.

[0106] Step 11:

[0107] server:

[0108] It regularly analyzes the user's training results and food intake data and updates progress data.

[0109] Adjust your training and nutrition plans accordingly based on your updated progress data.

[0110] Step 12:

[0111] server:

[0112] Generate motivational messages and reminders based on behavioral science.

[0113] Send generated messages and reminders to the user's device.

[0114] Step 13:

[0115] User's device:

[0116] Receive and view progress data, updated plans, and motivational messages as notifications.

[0117] Encouraging messages and training reminders from the virtual coach are also displayed at appropriate times.

[0118] This series of steps provides users with a system that allows them to effectively and sustainably manage their health.

[0119] Example 1

[0120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] Conventional health management systems struggle to optimally utilize users' health and activity data to provide personalized exercise and meal plans suited to each individual user. Furthermore, they lack sufficient means to maintain users' motivation and appropriately manage their progress, making it difficult to maintain sustainable health management. A system that solves these issues and enables users to manage their health effectively and sustainably is needed.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0123] In this invention, the server

[0124] a means for a user to input health information;

[0125] means for acquiring user activity data from the wearable device;

[0126] means for analyzing the user's health information and activity data to generate personalized exercise and meal plans;

[0127] means for providing the generated exercise plan and meal plan to a user's terminal;

[0128] A means for predicting changes in a user's health status using a generative AI model and presenting the predicted changes as a graphic; and

[0129] A means to analyze your progress and adjust your exercise and meal plans as needed;

[0130] Generate behavioral science-based messages and reminders to keep users motivated,

[0131] This allows users to effectively and sustainably manage their health.

[0132] "Means for users to input health information" refers to a function that provides an interface for users to input information about their own health.

[0133] "Means for obtaining user activity data from wearable devices" refers to the ability to collect activity data such as a user's steps, heart rate, and sleep patterns from devices such as smartwatches and fitness trackers.

[0134] "Means for analyzing a user's health information and activity data to generate personalized exercise and meal plans" refers to a function that creates exercise and meal plans optimized for individual users based on the acquired health information and activity data.

[0135] "Means for providing the generated exercise plan and meal plan to the user's device" refers to a function that sends the created exercise plan and meal plan to the user's device, such as a smartphone or tablet, and displays them.

[0136] "Means for predicting changes in a user's health status using a generative AI model and presenting them as images" refers to a function that utilizes artificial intelligence technology to predict the progress and changes in a user's health status and presents them to the user as visual images.

[0137] "Means for analyzing the user's progress data and adjusting the exercise menu and meal plan as necessary" refers to a function that analyzes the user's exercise and diet progress and dynamically adjusts the exercise menu and meal plan according to the results.

[0138] "A means of generating messages and reminders based on behavioral science to maintain user motivation" is a function that uses knowledge from behavioral science to create encouraging messages and reminders that make it easy for users to continue, and notifies them of these.

[0139] This invention is a personal health assistant system that provides individualized training and meal plans to help users achieve their goals, such as improving their health, losing weight, and building muscle. The system includes the following main components to help users manage their health efficiently and sustainably:

[0140] User registration and data collection features

[0141] Users create an account using a dedicated app or web portal, entering profile information such as name, gender, age, height, weight, and goals, using a standard user device such as a smartphone or tablet.

[0142] Users sync their smartwatches or other wearable devices with the app, which automatically collects daily activity data (e.g., steps taken, heart rate, and sleep patterns). Specifically, Apple Watches and Fitbits are used.

[0143] The server stores the entered profile information and activity data obtained from the wearable device in a database.

[0144] Personal advice generator

[0145] The server analyzes the user's health and activity data stored in the database, using data analysis tools such as Python and R.

[0146] The server uses generative AI models, such as machine learning libraries TensorFlow and PyTorch, to generate customized exercise and meal plans for the user.

[0147] The server predicts the changes in the user's body shape after achieving their goal and generates an image using a generative AI model, providing the user with visual feedback.

[0148] The server transmits the generated exercise plan and meal plan to the user's terminal.

[0149] Training guidance and meal plan suggestions

[0150] The user's device will then display the received exercise and meal plans using a dedicated app.

[0151] The user exercises under the guidance of a virtual coach. The virtual coach provides audio and video instructions on exercise form, such as squats and push-ups, and the user's device uses a camera to capture and analyze their exercise form in real time.

[0152] The user's device will display a list of recommended ingredients and recipes, providing them in a way that makes them easy to incorporate into everyday life.

[0153] Progress management and motivation maintenance features

[0154] The server analyzes the user's exercise and diet progress data and adjusts the new plan based on their performance.

[0155] The server generates messages and reminders based on behavioral science, and sends encouraging messages and progress reports to users. For example, a message such as "Keep up the good work this week!" is sent to the user's device.

[0156] Specific examples

[0157] Suppose User A launches the app and enters their name, gender, age, height, weight, and goal. User A sets "weight loss" as their goal and synchronizes their wearable device with the app. The server saves User A's information and uses the generative AI model to generate a customized exercise plan of "30 minutes of jogging with the goal of burning 500 calories a day" and a meal plan of "high-protein, low-calorie meals." The generative AI model is then used to predict how User A's body shape will change if they achieve their goal, and the results are displayed as an image.

[0158] When User A starts exercising, the virtual coach instructs them on the correct form, and the user's device analyzes the exercise in real time via a camera and provides feedback. At the end of the week, the server analyzes the progress and notifies User A of a new plan if necessary.

[0159] This allows the user to effectively and sustainably manage their health.

[0160] An example of a prompt is:

[0161] "Please create a diet plan for User A based on the following information: Age 30, Gender Male, Goal is to lose 5kg in 1 month."

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] Step 1: User Registration

[0164] A user launches the app and creates an account

[0165] Using a dedicated app or web portal, users enter their profile information, such as name, gender, age, height, weight, and goals. The data is sent from the user's device to a server and stored in a database.

[0166] Input: Name, Gender, Age, Height, Weight, Health Goals

[0167] Output: Registration information is saved in the database

[0168] Step 2: Device Sync and Data Collection

[0169] The user syncs the wearable device

[0170] A user syncs a wearable device, such as a smartwatch, with the app, which sends activity data (e.g., steps taken, heart rate, sleep patterns) to the server through the app.

[0171] Input: Sync settings for wearable devices

[0172] Output: Activity data is saved on the server

[0173] Step 3: Data analysis

[0174] The server analyzes the data

[0175] The server analyzes the user's health and activity data stored in the database, using data analysis tools such as Python and R to evaluate the user's current health status and exercise habits.

[0176] Input: Health information and activity data

[0177] Output: User's health status assessment data as analysis results

[0178] Step 4: Create a personal plan

[0179] The server generates a plan using the generative AI model

[0180] Based on the analysis results, the server uses a generative AI model (a machine learning model using TensorFlow or PyTorch) to generate a customized exercise plan and meal plan.

[0181] Input: Health status assessment data

[0182] Output: Customized exercise and meal plans

[0183] Step 5: Generate predicted image

[0184] The server generates a predicted image after the goal is achieved.

[0185] The server uses the generative AI model to generate an image that predicts how the user's body shape will change if they achieve their goal.

[0186] Input: Health assessment data, exercise plan, meal plan

[0187] Output: Predicted image after goal is achieved

[0188] Step 6: Submit your plan

[0189] The server sends the exercise plan and meal plan to the device.

[0190] The server sends the generated exercise plan, meal plan, and predicted image to the user's terminal, which displays them.

[0191] Input: Exercise plan, Meal plan, Predictive image

[0192] Output: The plan and image are displayed on the user's device.

[0193] Step 7: Training Instruction

[0194] The user exercises according to the instructions of the virtual coach

[0195] The user performs exercises under the guidance of a virtual coach, and the user's device uses a camera to capture and analyze their exercise form in real time.

[0196] Input: User's exercise data (camera footage)

[0197] Output: Real-time feedback

[0198] Step 8: View Meal Plan

[0199] The user's device displays the meal plan.

[0200] The user's device displays a list of recommended ingredients and recipes, providing information in a format that is easy for the user to implement in their daily lives.

[0201] Input: Meal plan data

[0202] Output: Display of recommended ingredients and recipe

[0203] Step 9: Progress data analysis

[0204] The server analyzes the progress data and adjusts the plan as needed

[0205] The server analyzes the user's training and dietary progress data and adjusts the exercise menu and meal plan according to the user's level of achievement.

[0206] Input: training data, dietary data

[0207] Output: A new, tailored exercise and meal plan

[0208] Step 10: Stay motivated

[0209] The server generates and notifies a motivation message.

[0210] The server generates messages and reminders based on behavioral science and sends them to the user's device to keep them motivated.

[0211] Input: Progress data

[0212] Output: Cheerful messages and reminder notifications

[0213] (Application example 1)

[0214] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0215] Traditional fitness and health management systems struggle to motivate users and provide insufficient personalized advice. Many systems also lack the ability to provide real-time feedback to users, making it difficult to ensure they are performing exercises with proper form. Furthermore, there are limited ways to efficiently track activity at gyms and other facilities.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0217] In this invention, the server includes a means for a user to input health information, a means for acquiring the user's activity data from the wearable device, and a means for analyzing the user's health information and activity data to generate personalized training and meal plans. This allows the training and meal plans to be provided to the user's dedicated device. Furthermore, a means for predicting changes in the user's health status and presenting them as images allows the user to visualize their goals. Furthermore, a means for a virtual instructor to provide real-time feedback on the user's exercise form during exercise allows the user to maintain proper exercise form. Furthermore, a means for synchronizing activity data upon check-in at a health facility or training gym allows comprehensive tracking of the user's fitness activities.

[0218] "User" refers to an individual who provides health information and activity data in an Invention.

[0219] "Health Information" is profile information entered by the user, such as height, weight, age, gender, and goals.

[0220] "Wearable devices" refers to devices such as smartwatches and fitness trackers that collect user activity data.

[0221] "Activity data" refers to information such as a user's number of steps, heart rate, amount of exercise, and sleep patterns.

[0222] A "personalized training plan" is an exercise menu customized for you based on your health and activity data.

[0223] A "meal plan" is a plan that provides recommended dietary habits and recipes based on a user's health goals.

[0224] "User-dedicated devices" are devices used by users, such as smartphones, tablets, and personal computers.

[0225] A "virtual instructor" is a digital coach that monitors the user's exercise form and provides real-time feedback on areas for improvement.

[0226] "Means for predicting changes in health status and presenting them as images" is a method that uses a generative AI model to provide simulated images of what a user will look like after achieving their goal.

[0227] "Facility check-in" is the process by which a user scans a QR code to enter a health facility or gym.

[0228] "Means for synchronizing activity data" refers to a method of automatically collecting and recording data by linking a user's wearable device with an application when checking in to a facility.

[0229] The system collects and analyzes users' health and activity data to provide personalized training and meal plans, predict changes in the user's health, and provides real-time exercise form feedback from a virtual coach and data synchronization upon facility check-in.

[0230] The server includes the following components:

[0231] 1. A means for users to enter health information:

[0232] The server provides a user interface for inputting health information such as age, gender, height, weight, and health goals through the user terminal, allowing users to easily input their own health information.

[0233] 2. Means for obtaining user activity data from wearable devices:

[0234] The server sets up an API to retrieve activity data from the user's smartwatch or fitness tracker, allowing it to collect data such as the user's steps, heart rate, exercise volume, and sleep patterns in real time.

[0235] 3. Means for analyzing your health and activity data to generate personalized training and meal plans:

[0236] The server runs machine learning algorithms to analyze the collected health and activity data, which allows it to generate optimal training and meal plans for the user.

[0237] 4. Means for providing generated training and meal plans to a user's device:

[0238] The server then sends the generated training and meal plans to the user's smartphone or tablet, allowing the user to view and follow the plans on their own dedicated device.

[0239] 5. A means for a virtual instructor to provide real-time feedback on exercise form as the user exercises:

[0240] The user device uses a camera to capture the user's exercise form, and a virtual instructor evaluates the accuracy of the form in real time and provides necessary feedback, helping the user to continue exercising with correct form.

[0241] 6. A method for predicting changes in the user's health status and presenting them as images:

[0242] The server uses a generative AI model to simulate the changes in the user's body shape when they achieve their goal, generating an image that is then provided to the user to help maintain their motivation.

[0243] 7. Means of syncing activity data when checking into a facility:

[0244] By scanning a QR code when users check in to a facility, the app automatically synchronizes activity data from the wearable device to the app, allowing users to efficiently track their gym activities.

[0245] Examples of concrete examples and prompts

[0246] Examples:

[0247] Consider a scenario where a gym member creates an account, syncs their activity data to an app, and follows a training plan generated based on that data.

[0248] Example prompt sentence:

[0249] Prompt: Gym member A has created an account and synced their activity data. Please generate a training plan and meal plan based on the following profile and activity data:

[0250] Profile information: Age 30, weight 70kg, goal is diet

[0251] Activity data: Approximately 10,000 steps per day, average heart rate 80 bpm

[0252] Dietary data: Average calorie intake: 2,100 calories

[0253] This allows users to effectively manage their health through a dedicated device and continue training while maintaining motivation. The system is designed to seamlessly support the flow from collecting a series of data to providing personalized advice and progress management.

[0254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0255] Step 1:

[0256] The user launches a dedicated app on their smartphone or tablet and enters their health information.

[0257] Specific operation: The user creates an account by entering information such as age, gender, height, weight, and health goals. The entered data is sent from the device to the server.

[0258] Input: Age, Gender, Height, Weight, Health Goals

[0259] Output: User's health profile data

[0260] Step 2:

[0261] The terminal synchronizes with the wearable device and acquires the user's activity data.

[0262] Specific operation: A user uses a smartwatch or fitness tracker and syncs activity data (number of steps, heart rate, exercise volume, sleep data) to a dedicated app. The data is then sent to a server via the device.

[0263] Input: steps, heart rate, exercise, sleep data

[0264] Output: User activity data

[0265] Step 3:

[0266] The server analyzes the user's health and activity data and generates personalized training and meal plans.

[0267] Specific operation: Based on the health information and activity data received by the server, the data is analyzed using machine learning algorithms to generate personalized training and meal plans, which are then sent to the user's device.

[0268] Input: User health profile data, User activity data

[0269] Output: Personalized training plan, meal plan

[0270] Step 4:

[0271] The server uses the generated AI model to predict changes in the user's body shape after achieving their goal and generates an image of it.

[0272] How it works: The server uses the user's health information and a predictive algorithm to simulate what the patient will look like after achieving their goal, generating an image of the results, which is then sent to the user's device.

[0273] Input: User's health profile data, goals

[0274] Output: Predicted image

[0275] Step 5:

[0276] When users check in to the gym, they scan a QR code to sync their activity data.

[0277] What it does: When a user arrives at the gym, they scan a QR code and sync their latest activity data from their wearable device to the app, which then sends the synced data to the server.

[0278] Input: QR code scan, latest activity data

[0279] Output: Gym activity data synchronization

[0280] Step 6:

[0281] The user terminal uses a virtual instructor to provide real-time feedback on the user's exercise form.

[0282] Specific Movements: The smartphone camera captures the user's exercise form and analyzes it in real time. The virtual instructor compares it with the correct form and provides necessary feedback via voice or text.

[0283] Input: Video data of exercise form

[0284] Output: Form improvement feedback

[0285] Step 7:

[0286] The server analyzes the user's progress data and generates messages to keep them motivated.

[0287] Specific operation: The server continuously analyzes the user's training and diet data, and generates and sends motivational messages based on behavioral science, thereby maintaining the user's motivation.

[0288] Input: training data, dietary data

[0289] Output: Motivation message

[0290] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0291] This invention is a system for a personal AI health assistant that provides personalized training to help users achieve their goals. It also incorporates an emotion engine that recognizes the user's emotions, enabling richer interactions. The system includes the following main components to help users manage their health efficiently and sustainably:

[0292] 1. User registration and data collection features:

[0293] User's device: The user creates an account using a dedicated app and enters profile information (name, gender, age, height, weight, goals, etc.), then syncs the wearable device with the app and configures it to collect activity data.

[0294] Server: Stores the profile information you enter and activity data obtained from the wearable device (e.g., steps, heart rate, sleep patterns) in a database.

[0295] 2. Personal advice generation function:

[0296] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. It also uses a generative AI model to generate images of the user's "before and after" appearance and displays them on the user's device.

[0297] 3. Training guidance and meal plan suggestions:

[0298] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[0299] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[0300] 4. Progress management and motivation maintenance features:

[0301] Server: Analyzes the user's training and diet progress data, evaluates their achievement, and adjusts their training menu and meal plan as needed.

[0302] Server: Generates messages and reminders based on behavioral science to help users maintain their motivation.

[0303] 5. Emotion recognition and response capabilities:

[0304] On the user's device: The emotion engine recognizes emotions from the user's voice and facial expressions, and provides appropriate feedback and advice.

[0305] Server: Generates personalized motivational messages and reminders based on the emotional data acquired by the emotion engine.

[0306] Server: Analyzes emotional data and provides appropriate support messages and training advice based on the user's mental state.

[0307] Specific examples

[0308] 1. User Registration:

[0309] User device: User B starts the app and enters her name "B", gender "female", age "25", height "160cm", weight "55kg", and goal "to increase muscle strength". She synchronizes her wearable device and sends heart rate and activity data to the app.

[0310] Server: Stores these data in a database and prepares them for analysis.

[0311] 2. Generate personalized advice:

[0312] Server: Analyzes User B's information and generates a meal plan based on his / her health condition and exercise habits, including a "strength training plan three times a week" and a "high-protein diet." It also predicts and creates an image of how his / her body will change when he / she achieves his / her goal.

[0313] User's device: Receives and displays the generated training plan, meal plan, and predicted image.

[0314] 3. Training Guidance:

[0315] User device: A virtual coach provides audio and video instruction on correct deadlift form. User B performs the exercise in front of the camera, while the device captures and analyzes their form in real time, providing feedback.

[0316] 4. Emotion recognition:

[0317] User's device: The emotion engine analyzes User B's facial expressions and voice to recognize impatience and fatigue during training.

[0318] Server: Based on the recognized emotion data, it generates a supportive message such as, "Take a short break. You're doing a great job."

[0319] User's device: The virtual coach displays a cheering message and calls out to User B.

[0320] 5. Track your progress and stay motivated:

[0321] Server: Periodically analyzes User B's training results and food intake data, generates progress reports, and updates the training plan as needed.

[0322] On your device: Receive and review plan updates and progress reports as notifications, along with timely reminders and encouragement to keep you motivated.

[0323] This allows users to efficiently execute personalized training and meal plans that take into account their emotional state, further maintaining motivation. The entire system seamlessly connects users' health information and emotional state, functioning as an optimal health management solution.

[0324] The processing flow will be explained below.

[0325] Step 1:

[0326] User's device:

[0327] Users launch the dedicated app and proceed to the account creation page.

[0328] Enter basic information such as your name, email address, and password.

[0329] Configure your wearable device for synchronization and allow data collection from the device.

[0330] Step 2:

[0331] server:

[0332] We receive the basic information you enter and store it in a secure database.

[0333] Set up a scheduled task to sync activity data from your wearable device at regular intervals.

[0334] Activity data includes steps taken, heart rate, calories burned, sleep patterns, and more.

[0335] Step 3:

[0336] User's device:

[0337] Go to your profile settings page and enter your details, such as your gender, age, height, weight, and health goals (e.g., weight loss, muscle building).

[0338] Step 4:

[0339] server:

[0340] The entered details are stored in a database and the data analysis algorithm is prepared.

[0341] This will be combined with activity data obtained from wearable devices to begin analyzing the user's health information.

[0342] Step 5:

[0343] server:

[0344] The analytical algorithm evaluates the user's data to analyze their health and exercise habits.

[0345] Based on the results, a customized training and meal plan is generated.

[0346] Step 6:

[0347] server:

[0348] The generated training and meal plans are sent to the user's device.

[0349] Using a generative AI model, images of the user's appearance before and after achieving their goal are generated and sent to the user's device.

[0350] Step 7:

[0351] User's device:

[0352] A virtual coach will explain the details of the training plan and important points to note using audio and video.

[0353] Display a list of recommended meal plans and recipes.

[0354] Step 8:

[0355] User's device:

[0356] It uses cameras and sensors to capture the user's exercise form.

[0357] The captured data is sent to a server in real time for form analysis.

[0358] Step 9:

[0359] server:

[0360] Analyze your exercise form in real time and identify areas for improvement.

[0361] Generate feedback and send it to the user's device.

[0362] Step 10:

[0363] User's device:

[0364] A virtual coach displays analysis results and feedback to users, teaching them correct exercise form.

[0365] Step 11:

[0366] server:

[0367] The emotion engine captures the user's voice and facial expressions to recognize their emotions.

[0368] Generate appropriate feedback and advice based on the perceived emotions.

[0369] Step 12:

[0370] server:

[0371] Generate personalized motivational messages and reminders based on emotional data.

[0372] Send generated messages and reminders to the user's device.

[0373] Step 13:

[0374] User's device:

[0375] The virtual coach displays encouraging messages and advice based on the emotions recognized by the emotion engine.

[0376] Step 14:

[0377] server:

[0378] It regularly analyzes the user's training results and food intake data and updates progress data.

[0379] Adjust your training and nutrition plans accordingly based on your updated progress data.

[0380] Step 15:

[0381] server:

[0382] It generates messages and reminders based on behavioral science to help maintain motivation and sends them to the user's device.

[0383] Step 16:

[0384] User's device:

[0385] Receive and view progress data, updated plans, and motivational messages as notifications.

[0386] Displays encouraging messages and training reminders from a virtual coach at appropriate times.

[0387] Through this series of steps, users are provided with personalized training and meal plans that take their emotional state into account, helping them to effectively and sustainably manage their health.

[0388] Example 2

[0389] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0390] Conventional health management systems have limitations in providing users with personalized advice and training plans, and lack consideration for users' emotions and motivation. Furthermore, they lack real-time exercise form advice and emotion-based feedback, making it difficult for users to maintain their motivation to continue training.

[0391] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input health information, means for acquiring the user's activity data from the wearable device, means for analyzing the user's health information and activity data to generate personalized training plans and meal plans, means for providing the generated training plans and meal plans to the user's terminal, means for predicting changes in the user's health condition and presenting them as images, means for analyzing the user's voice and facial expressions using an emotion engine and sending emotion recognition data to the server, and means for the server to generate cheering messages based on the emotion recognition data and provide them to the user's terminal. This allows the user to efficiently perform personalized training and meal plans that take their emotional state into account, further maintaining their motivation.

[0392] "User" means a person who uses the system to input health information and receive training and meal plans.

[0393] "Health Information" refers to profile information entered by users, such as name, gender, age, height, weight, and goals.

[0394] A "wearable device" is a device, such as a Fitbit or Apple Watch, that collects user activity data and transmits it to the system.

[0395] "Activity data" refers to data about a user's daily physical movements and conditions, such as the number of steps taken, heart rate, and sleep patterns, obtained from wearable devices.

[0396] A "personalized training plan" is an exercise plan that is individually customized for you based on your health and activity data.

[0397] "Meal Plan" means a personalized meal plan based on a User's health and activity data.

[0398] A "terminal" refers to an information processing device such as a smartphone, tablet, or PC that a user uses to access the system.

[0399] An "emotion engine" is a software or hardware component that analyzes a user's voice and facial expressions to recognize emotions.

[0400] "Emotion Recognition Data" means data regarding a user's emotional state that is captured and analyzed by the Emotion Engine.

[0401] "Server" refers to the central processing system that collects, stores, and analyzes users' health and activity data and provides various services.

[0402] "Encouragement messages" are messages of encouragement that are generated by the server based on emotion recognition data and provided to maintain the user's motivation.

[0403] This invention is a system of AI personal health assistants dedicated to users, providing personalized training to help users achieve their goals. It also combines an emotion engine that recognizes the user's emotions to enable richer interactions. The main components of the entire system are as follows:

[0404] 1. User registration and data collection features:

[0405] On the user's device: The user creates an account using a dedicated app and enters profile information (such as name, gender, age, height, weight, and goals), then syncs a wearable device (such as a Fitbit or Apple Watch) to the app and configures it to collect activity data (e.g., steps, heart rate, and sleep patterns).

[0406] Server: Stores the entered profile information and activity data obtained from the wearable device in a database.

[0407] 2. Personal advice generation function:

[0408] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. It also uses a generative AI model (such as GPT-3) to generate images of the user's "before and after" appearance and displays them on the user's device.

[0409] 3. Training guidance and meal plan suggestions:

[0410] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[0411] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[0412] 4. Progress management and motivation maintenance features:

[0413] Server: Analyzes the user's training and diet progress data, evaluates achievement, adjusts training menus and meal plans as needed, and generates messages and reminders based on behavioral science to help users maintain their motivation.

[0414] 5. Emotion recognition and response capabilities:

[0415] User device: An emotion engine (for example, voice recognition software or a facial recognition camera) recognizes emotions from the user's voice and facial expressions. Once emotions are recognized, the device provides appropriate feedback and advice.

[0416] Server: The emotion engine generates personalized encouragement messages and reminders based on the emotional data acquired, providing appropriate training advice according to the user's mental state.

[0417] Specific examples

[0418] 1. User Registration:

[0419] User device: User B starts the app and enters her name "B", gender "female", age "25", height "160cm", weight "55kg", and goal "to increase muscle strength". She synchronizes her wearable device and sends heart rate and activity data to the app.

[0420] Server: Stores these data in a database and prepares them for analysis.

[0421] 2. Generate personalized advice:

[0422] Server: Analyzes User B's information and generates a meal plan based on his / her health condition and exercise habits, including a "strength training plan three times a week" and a "high-protein diet." It also predicts and creates an image of how his / her body will change when he / she achieves his / her goal.

[0423] User's device: Receives and displays the generated training plan, meal plan, and predicted image.

[0424] 3. Training Guidance:

[0425] User device: A virtual coach provides audio and video instructions on the correct deadlift form. User B performs the exercise in front of the camera, and the device captures and analyzes their form in real time, providing feedback.

[0426] 4. Emotion recognition:

[0427] User's device: The emotion engine analyzes User B's facial expressions and voice to recognize impatience and fatigue during training.

[0428] Server: Based on the recognized emotion data, it generates a supportive message such as, "Take a short break. You're doing a great job."

[0429] User's device: The virtual coach displays a cheering message and calls out to User B.

[0430] 5. Track your progress and stay motivated:

[0431] Server: Periodically analyzes User B's training results and food intake data, generates progress reports, and updates the training plan as needed.

[0432] On your device: Receive and review plan updates and progress reports as notifications, along with timely reminders and encouragement to keep you motivated.

[0433] Prompt Sentence Examples

[0434] "If a user is a 25-year-old female, 160cm tall, and weighs 55kg, and has set the goal of increasing muscle strength, please generate the current appearance and the predicted appearance after continuing strength training three times a week for three months."

[0435] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0436] Specific processing steps of the program

[0437] Step 1: User registration and data collection

[0438] The user downloads and launches the app. This is the step where they enter their name, gender, age, height, weight, and goals on the account creation screen and synchronize their wearable device. This synchronization setting sends activity data such as heart rate and number of steps from the wearable device to the app. The device then sends the entered profile information and data acquired from the wearable device to the server. The server receives this data and stores it in a database.

[0439] Input: User profile information (name, gender, age, height, weight, goal), activity data from wearable device.

[0440] Output: Profile information and activity data stored in a database.

[0441] Step 2: Analyze the data and generate personalized advice

[0442] The server retrieves user information and activity data from the database. The server then sends prompts to the generative AI model to generate images of how the user will look before and after achieving their goal. The analysis process analyzes the user's health and exercise habits to generate personalized training and meal plans. The generated plans and predicted images are sent to the device.

[0443] Input: User information stored in the database, prompt text.

[0444] Output: Before and after images based on generative AI models, personalized training and meal plans.

[0445] Step 3: Providing training guidance

[0446] The user starts a training session using the app. The device displays a virtual coach and explains the generated training plan to the user using audio and video. The user performs the training in front of the camera, and the device captures their movements. The captured data is analyzed in real time and feedback is provided. Recommended ingredients and recipes are also displayed.

[0447] Input: Generated training plan, meal plan, and movement data from the camera.

[0448] Output: Real-time analysis of behavior, feedback, recommended ingredients and recipes.

[0449] Step 4: Track progress and adjust your plan

[0450] The server periodically analyzes the user's activity and dietary data to assess the user's progress, generating a personalized progress report and adjusting personalized training and diet plans. The analysis results are then sent to the user's device.

[0451] Input: User activity data, meal data.

[0452] Output: Progress report, tailored training and meal plans.

[0453] Step 5: Recognize emotions and stay motivated

[0454] The user's device activates an emotion engine that analyzes the user's voice and facial expressions in real time. The analyzed emotion data is sent to a server. Based on this data, the server generates encouraging messages and reminders and provides them to the user's device. This helps to maintain the user's motivation.

[0455] Input: User's voice data, facial expression data.

[0456] Output: Emotion recognition data, generated cheer messages and reminders.

[0457] (Application example 2)

[0458] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0459] Conventional health management systems make it difficult for users to receive efficient and personalized training guidance in physical stores. In addition, due to a lack of emotion recognition technology, it is difficult to maintain motivation during training or appropriately respond to fatigue, which prevents a sufficient improvement in the user experience.

[0460] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's health information and activity data to generate personalized training plans and meal plans, means for providing training guidance to the user via a virtual coach, and means for recognizing the user's emotions using an emotion engine and providing feedback accordingly. This makes it possible to provide personalized training guidance and maintain the user's motivation.

[0461] A "means for a user to input health information" is an interface through which a user inputs their own health-related data.

[0462] "Means for obtaining user activity data from a wearable device" refers to a function for collecting activity data from a device worn by the user.

[0463] "Means for analyzing a user's health information and activity data to generate personalized training and meal plans" refers to a function that generates individually optimized training and meal plans based on the user's health information and activity data.

[0464] "Means for providing the generated training plan and meal plan to the user's device" refers to the function by which the server sends the generated plan to the user's device and displays it.

[0465] "Means for predicting changes in the user's health condition and presenting them as images" is a function that predicts changes in the user's health condition, visualizes the results, and presents them to the user.

[0466] "Means for providing training guidance to users via a virtual coach" refers to a function that uses a virtual instructor to guide and instruct users on training methods.

[0467] "Means for recognizing user emotions using an emotion engine and providing feedback accordingly" is a function that analyzes the user's emotions and provides feedback and advice based on the results.

[0468] "Means for generating and providing users with reminders and encouraging messages to maintain motivation" is a function that creates and provides appropriate reminders and encouraging messages to maintain users' motivation.

[0469] This invention provides a user-specific AI personal health assistant system that supports health management and training. The system analyzes the user's health information and emotional state and provides personalized training and meal plans accordingly.

[0470] Hardware and software used

[0471] The following hardware and software are used to realize the system.

[0472] Hardware: Smartphones, smart glasses, wearable devices (e.g., smartwatches)

[0473] software:

[0474] Mobile application (iOS / Android)

[0475] Emotion recognition engine (e.g. Microsoft Azure Emotion API)

[0476] Generative AI models (e.g., OpenAI GPT-3)

[0477] Database (e.g. Firebase)

[0478] Data collection

[0479] Using a mobile app, users enter their profile information (such as name, gender, age, height, weight, and goals), and sync their wearable device to collect activity data such as heart rate, activity level, and sleep patterns. This data is then stored on a server for analysis.

[0480] Generate personalized advice

[0481] The server analyzes the user's stored health and activity data to generate personalized training and meal plans. Using a generative AI model (OpenAI GPT-3), it predicts the specific plan and how the body shape will change when the goal is achieved, and generates images of the plan.

[0482] Training Guidance and Emotion Recognition

[0483] During training, a virtual coach provides real-time guidance to the user via smartphone or smart glasses. An emotion recognition engine (Microsoft Azure Emotion API) recognizes emotions from the user's facial expressions and voice and provides appropriate feedback. Appropriate encouraging messages such as "Take a short break" are provided to the user.

[0484] Maintaining motivation

[0485] The server generates behavioral science-based messages and reminders, sends notifications to users to keep them motivated, and periodically analyzes their progress data to adjust their training and meal plans accordingly.

[0486] Specific examples

[0487] For example, when a user arrives at the gym, they open their smartphone and check the designated training plan. They put on smart glasses and train under the guidance of a virtual coach. If fatigue is detected through emotion recognition, a message will automatically appear encouraging them to take a break. After training, they will also receive a progress report and their next training content will be automatically updated. They can also check their meal plan through the app and try out the recommended recipes.

[0488] Prompt Sentence Examples

[0489] "Enter your health information, including your name, age, gender, and goals. Then generate training and meal plan suggestions."

[0490] This allows users to receive personalized health management and training support, enabling sustainable health management.

[0491] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0492] Step 1:

[0493] The user's device provides an interface for the user to input health information, such as name, gender, age, height, weight, and goals. The input information is temporarily stored by the user's device and then sent to the server.

[0494] Step 2:

[0495] The server stores the health information received from the user's device in a database. At the same time, it also collects activity data (heart rate, number of steps, sleep patterns, etc.) from the user's synchronized wearable device and stores this data in the database. This provides the basic data for analysis.

[0496] Step 3:

[0497] The server analyzes the user's health information and activity data stored in the database. This process uses an AI model (e.g., OpenAI GPT-3) to generate personalized training and meal plans. For example, it might suggest a "strength training plan three times a week based on the user's health information" and a "high-protein meal plan." These generated plans are then stored again on the server.

[0498] Step 4:

[0499] The server sends the generated training plan and meal plan to the user's device, which displays the received plan for the user to review.

[0500] Step 5:

[0501] The user's device activates a virtual coach function that guides the user through the training session. The virtual coach uses a camera to capture the user's exercise form and provides real-time feedback, such as "Your deadlift form is incorrect."

[0502] Step 6:

[0503] The user's device uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize emotions from the user's facial expressions and voice. This data is sent to a server, which automatically generates feedback according to the user's emotional state. For example, if the user expresses "feeling tired," a message such as "take a short break" is generated.

[0504] Step 7:

[0505] The server generates motivational reminders and encouraging messages based on behavioral science. This is also done using a generative AI model. The created reminders and messages are sent to the user's device and displayed to the user. For example, a message such as "You're almost there, keep trying!" may be displayed.

[0506] Step 8:

[0507] The user's progress data is periodically sent to a server, which analyzes it. Based on this progress data, the training and meal plans are updated as needed to provide the user with the optimal plan. For example, the next training menu will be optimized based on data such as "muscle strength improved by 5% in three weeks."

[0508] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0509] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0510] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0511] [Second embodiment]

[0512] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0513] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0514] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0515] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0516] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0517] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0518] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0519] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0520] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0521] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0522] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0523] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0524] This invention is a system for a user-dedicated AI personal health assistant that provides personal training to help users achieve their goals (e.g., improving health, losing weight, building muscle). This system includes the following main components to enable users to efficiently and sustainably manage their health:

[0525] 1. User registration and data collection features:

[0526] On your device: Users create an account using a dedicated app or web portal, enter their profile information (e.g., name, gender, age, height, weight, health goals), and sync their wearable device (e.g., smartwatch) to the app and set it up to collect activity data.

[0527] Server: Stores the profile information you enter and activity data obtained from the wearable device (e.g., steps, heart rate, sleep patterns) in a database.

[0528] 2. Personal advice generation function:

[0529] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. The analysis algorithm takes into account the user's past activity data and health information.

[0530] Server: Sends the generated training and meal plans to the user's device, and also uses the generative AI model to generate images of the user's "before and after" appearance and presents them to the user.

[0531] 3. Training guidance and meal plan suggestions:

[0532] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[0533] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[0534] 4. Progress management and motivation maintenance features:

[0535] Server: Analyzes the user's training and diet progress data, evaluates their achievement, and adjusts their training menu and meal plan as needed.

[0536] Server: Generates messages and reminders based on behavioral science to help users maintain their motivation.

[0537] Specific examples

[0538] 1. User Registration:

[0539] User device: User A starts the app and enters his name "A Taro", gender "male", age "30", height "170cm", weight "70kg", and goal "diet". The wearable device is synchronized and heart rate and activity data is sent to the app.

[0540] Server: Stores these data in a database and prepares them for analysis.

[0541] 2. Generate personalized advice:

[0542] Server: The analytical algorithm operates based on User A's information stored in the database. It analyzes the past week's activity data and generates a training plan such as "30 minutes of jogging with the goal of burning 500 calories a day." It also generates a "high-protein, low-calorie meal plan" and suggests specific menus. Using the generative AI model, it predicts and creates an image of User A's body shape if he or she achieves his or her goal.

[0543] User's device: Receives and displays the generated training plan, meal plan, and prediction image.

[0544] 3. Training Guidance:

[0545] User device: A virtual coach provides audio and video instruction on the correct squat form. User A performs the exercise in front of the camera, and the device captures and analyzes their form in real time, providing feedback.

[0546] 4. Track your progress and stay motivated:

[0547] Server: Monitors user A's weekly training results and generates a report such as "Last week's average calorie consumption was 450 calories." If necessary, adjust the training plan for the following week.

[0548] On your device: Receive and review progress reports as notifications, as well as encouragement and training reminders from your virtual coach.

[0549] This allows users to effectively and sustainably manage their health. The entire system is designed to seamlessly support the flow from collecting a series of data to providing personalized advice and progress management.

[0550] The processing flow will be explained below.

[0551] Step 1:

[0552] User's device:

[0553] Users launch the dedicated app and proceed to the account creation page.

[0554] Enter basic information such as your name, email address, and password.

[0555] Configure your wearable device for synchronization and allow data collection from the device.

[0556] Step 2:

[0557] server:

[0558] We receive the basic information you enter and store it in a secure database.

[0559] Set up a scheduled task to periodically sync activity data from your wearable device.

[0560] Activity data includes steps taken, heart rate, calories burned, sleep patterns, and more.

[0561] Step 3:

[0562] User's device:

[0563] Go to your profile settings page.

[0564] Enter detailed health information such as gender, age, height, weight, and goals (e.g., diet, muscle building).

[0565] Step 4:

[0566] server:

[0567] The detailed health information entered is stored in a database, ready to begin the analysis process.

[0568] The data analysis algorithm combines and analyzes the user's health information and activity data from the wearable device.

[0569] Step 5:

[0570] server:

[0571] Analytical algorithms evaluate the user's data and generate a customized training plan based on their health and exercise habits.

[0572] We also create optimal meal plans for users under the supervision of a nutritionist.

[0573] Step 6:

[0574] server:

[0575] The generated training and meal plans are sent to the user's device.

[0576] At the same time, a generative AI model is used to generate images of the user's appearance before and after achieving their goal, which are then sent to the user's device.

[0577] Step 7:

[0578] User's device:

[0579] A virtual coach will explain the details of the training plan and important points to note using audio and video.

[0580] Display a list of recommended meal plans and recipes.

[0581] Step 8:

[0582] User's device:

[0583] It uses cameras and sensors to capture the user's exercise form.

[0584] The captured data is sent to a server in real time for form analysis.

[0585] Step 9:

[0586] server:

[0587] Analyze your exercise form in real time and identify areas for improvement.

[0588] Generate feedback and send it to the user's device.

[0589] Step 10:

[0590] User's device:

[0591] A virtual coach displays analysis results and feedback to users, teaching them correct exercise form.

[0592] Step 11:

[0593] server:

[0594] It regularly analyzes the user's training results and food intake data and updates progress data.

[0595] Adjust your training and nutrition plans accordingly based on your updated progress data.

[0596] Step 12:

[0597] server:

[0598] Generate motivational messages and reminders based on behavioral science.

[0599] Send generated messages and reminders to the user's device.

[0600] Step 13:

[0601] User's device:

[0602] Receive and view progress data, updated plans, and motivational messages as notifications.

[0603] Encouraging messages and training reminders from the virtual coach are also displayed at appropriate times.

[0604] This series of steps provides users with a system that allows them to effectively and sustainably manage their health.

[0605] Example 1

[0606] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0607] Conventional health management systems struggle to optimally utilize users' health and activity data to provide personalized exercise and meal plans suited to each individual user. Furthermore, they lack sufficient means to maintain users' motivation and appropriately manage their progress, making it difficult to maintain sustainable health management. A system that solves these issues and enables users to manage their health effectively and sustainably is needed.

[0608] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0609] In this invention, the server

[0610] a means for a user to input health information;

[0611] means for acquiring user activity data from the wearable device;

[0612] means for analyzing the user's health information and activity data to generate personalized exercise and meal plans;

[0613] means for providing the generated exercise plan and meal plan to a user's terminal;

[0614] A means for predicting changes in a user's health status using a generative AI model and presenting the predicted changes as a graphic; and

[0615] A means to analyze your progress and adjust your exercise and meal plans as needed;

[0616] Generate behavioral science-based messages and reminders to keep users motivated,

[0617] This allows users to effectively and sustainably manage their health.

[0618] "Means for users to input health information" refers to a function that provides an interface for users to input information about their own health.

[0619] "Means for obtaining user activity data from wearable devices" refers to the ability to collect activity data such as a user's steps, heart rate, and sleep patterns from devices such as smartwatches and fitness trackers.

[0620] "Means for analyzing a user's health information and activity data to generate personalized exercise and meal plans" refers to a function that creates exercise and meal plans optimized for individual users based on the acquired health information and activity data.

[0621] "Means for providing the generated exercise plan and meal plan to the user's device" refers to a function that sends the created exercise plan and meal plan to the user's device, such as a smartphone or tablet, and displays them.

[0622] "Means for predicting changes in a user's health status using a generative AI model and presenting them as images" refers to a function that utilizes artificial intelligence technology to predict the progress and changes in a user's health status and presents them to the user as visual images.

[0623] "Means for analyzing the user's progress data and adjusting the exercise menu and meal plan as necessary" refers to a function that analyzes the user's exercise and diet progress and dynamically adjusts the exercise menu and meal plan according to the results.

[0624] "A means of generating messages and reminders based on behavioral science to maintain user motivation" is a function that uses knowledge from behavioral science to create encouraging messages and reminders that make it easy for users to continue, and notifies them of these.

[0625] This invention is a personal health assistant system that provides individualized training and meal plans to help users achieve their goals, such as improving their health, losing weight, and building muscle. The system includes the following main components to help users manage their health efficiently and sustainably:

[0626] User registration and data collection features

[0627] Users create an account using a dedicated app or web portal, entering profile information such as name, gender, age, height, weight, and goals, using a standard user device such as a smartphone or tablet.

[0628] Users sync their smartwatches or other wearable devices with the app, which automatically collects daily activity data (e.g., steps taken, heart rate, and sleep patterns). Specifically, Apple Watches and Fitbits are used.

[0629] The server stores the entered profile information and activity data obtained from the wearable device in a database.

[0630] Personal advice generator

[0631] The server analyzes the user's health and activity data stored in the database, using data analysis tools such as Python and R.

[0632] The server uses generative AI models, such as machine learning libraries TensorFlow and PyTorch, to generate customized exercise and meal plans for the user.

[0633] The server predicts the changes in the user's body shape after achieving their goal and generates an image using a generative AI model, providing the user with visual feedback.

[0634] The server transmits the generated exercise plan and meal plan to the user's terminal.

[0635] Training guidance and meal plan suggestions

[0636] The user's device will then display the received exercise and meal plans using a dedicated app.

[0637] The user exercises under the guidance of a virtual coach. The virtual coach provides audio and video instructions on exercise form, such as squats and push-ups, and the user's device uses a camera to capture and analyze their exercise form in real time.

[0638] The user's device will display a list of recommended ingredients and recipes, providing them in a way that makes them easy to incorporate into everyday life.

[0639] Progress management and motivation maintenance features

[0640] The server analyzes the user's exercise and diet progress data and adjusts the new plan based on their performance.

[0641] The server generates messages and reminders based on behavioral science, and sends encouraging messages and progress reports to users. For example, a message such as "Keep up the good work this week!" is sent to the user's device.

[0642] Specific examples

[0643] Suppose User A launches the app and enters their name, gender, age, height, weight, and goal. User A sets "weight loss" as their goal and synchronizes their wearable device with the app. The server saves User A's information and uses the generative AI model to generate a customized exercise plan of "30 minutes of jogging with the goal of burning 500 calories a day" and a meal plan of "high-protein, low-calorie meals." The generative AI model is then used to predict how User A's body shape will change if they achieve their goal, and the results are displayed as an image.

[0644] When User A starts exercising, the virtual coach instructs them on the correct form, and the user's device analyzes the exercise in real time via a camera and provides feedback. At the end of the week, the server analyzes the progress and notifies User A of a new plan if necessary.

[0645] This allows the user to effectively and sustainably manage their health.

[0646] An example of a prompt is:

[0647] "Please create a diet plan for User A based on the following information: Age 30, Gender Male, Goal is to lose 5kg in 1 month."

[0648] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0649] Step 1: User Registration

[0650] A user launches the app and creates an account

[0651] Using a dedicated app or web portal, users enter their profile information, such as name, gender, age, height, weight, and goals. The data is sent from the user's device to a server and stored in a database.

[0652] Input: Name, Gender, Age, Height, Weight, Health Goals

[0653] Output: Registration information is saved in the database

[0654] Step 2: Device Sync and Data Collection

[0655] The user syncs the wearable device

[0656] A user syncs a wearable device, such as a smartwatch, with the app, which sends activity data (e.g., steps taken, heart rate, sleep patterns) to the server through the app.

[0657] Input: Sync settings for wearable devices

[0658] Output: Activity data is saved on the server

[0659] Step 3: Data analysis

[0660] The server analyzes the data

[0661] The server analyzes the user's health and activity data stored in the database, using data analysis tools such as Python and R to evaluate the user's current health status and exercise habits.

[0662] Input: Health information and activity data

[0663] Output: User's health status assessment data as analysis results

[0664] Step 4: Create a personal plan

[0665] The server generates a plan using the generative AI model

[0666] Based on the analysis results, the server uses a generative AI model (a machine learning model using TensorFlow or PyTorch) to generate a customized exercise plan and meal plan.

[0667] Input: Health status assessment data

[0668] Output: Customized exercise and meal plans

[0669] Step 5: Generate predicted image

[0670] The server generates a predicted image after the goal is achieved.

[0671] The server uses the generative AI model to generate an image that predicts how the user's body shape will change if they achieve their goal.

[0672] Input: Health assessment data, exercise plan, meal plan

[0673] Output: Predicted image after goal is achieved

[0674] Step 6: Submit your plan

[0675] The server sends the exercise plan and meal plan to the device.

[0676] The server sends the generated exercise plan, meal plan, and predicted image to the user's terminal, which displays them.

[0677] Input: Exercise plan, Meal plan, Predictive image

[0678] Output: The plan and image are displayed on the user's device.

[0679] Step 7: Training Instruction

[0680] The user exercises according to the instructions of the virtual coach

[0681] The user performs exercises under the guidance of a virtual coach, and the user's device uses a camera to capture and analyze their exercise form in real time.

[0682] Input: User's exercise data (camera footage)

[0683] Output: Real-time feedback

[0684] Step 8: View Meal Plan

[0685] The user's device displays the meal plan.

[0686] The user's device displays a list of recommended ingredients and recipes, providing information in a format that is easy for the user to implement in their daily lives.

[0687] Input: Meal plan data

[0688] Output: Display of recommended ingredients and recipe

[0689] Step 9: Progress data analysis

[0690] The server analyzes the progress data and adjusts the plan as needed

[0691] The server analyzes the user's training and dietary progress data and adjusts the exercise menu and meal plan according to the user's level of achievement.

[0692] Input: training data, dietary data

[0693] Output: A new, tailored exercise and meal plan

[0694] Step 10: Stay motivated

[0695] The server generates and notifies a motivation message.

[0696] The server generates messages and reminders based on behavioral science and sends them to the user's device to keep them motivated.

[0697] Input: Progress data

[0698] Output: Cheerful messages and reminder notifications

[0699] (Application example 1)

[0700] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0701] Traditional fitness and health management systems struggle to motivate users and provide insufficient personalized advice. Many systems also lack the ability to provide real-time feedback to users, making it difficult to ensure they are performing exercises with proper form. Furthermore, there are limited ways to efficiently track activity at gyms and other facilities.

[0702] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0703] In this invention, the server includes a means for a user to input health information, a means for acquiring the user's activity data from the wearable device, and a means for analyzing the user's health information and activity data to generate personalized training and meal plans. This allows the training and meal plans to be provided to the user's dedicated device. Furthermore, a means for predicting changes in the user's health status and presenting them as images allows the user to visualize their goals. Furthermore, a means for a virtual instructor to provide real-time feedback on the user's exercise form during exercise allows the user to maintain proper exercise form. Furthermore, a means for synchronizing activity data upon check-in at a health facility or training gym allows comprehensive tracking of the user's fitness activities.

[0704] "User" refers to an individual who provides health information and activity data in an Invention.

[0705] "Health Information" is profile information entered by the user, such as height, weight, age, gender, and goals.

[0706] "Wearable devices" refers to devices such as smartwatches and fitness trackers that collect user activity data.

[0707] "Activity data" refers to information such as a user's number of steps, heart rate, amount of exercise, and sleep patterns.

[0708] A "personalized training plan" is an exercise menu customized for you based on your health and activity data.

[0709] A "meal plan" is a plan that provides recommended dietary habits and recipes based on a user's health goals.

[0710] "User-dedicated devices" are devices used by users, such as smartphones, tablets, and personal computers.

[0711] A "virtual instructor" is a digital coach that monitors the user's exercise form and provides real-time feedback on areas for improvement.

[0712] "Means for predicting changes in health status and presenting them as images" is a method that uses a generative AI model to provide simulated images of what a user will look like after achieving their goal.

[0713] "Facility check-in" is the process by which a user scans a QR code to enter a health facility or gym.

[0714] "Means for synchronizing activity data" refers to a method of automatically collecting and recording data by linking a user's wearable device with an application when checking in to a facility.

[0715] The system collects and analyzes users' health and activity data to provide personalized training and meal plans, predict changes in the user's health, and provides real-time exercise form feedback from a virtual coach and data synchronization upon facility check-in.

[0716] The server includes the following components:

[0717] 1. A means for users to enter health information:

[0718] The server provides a user interface for inputting health information such as age, gender, height, weight, and health goals through the user terminal, allowing users to easily input their own health information.

[0719] 2. Means for obtaining user activity data from wearable devices:

[0720] The server sets up an API to retrieve activity data from the user's smartwatch or fitness tracker, allowing it to collect data such as the user's steps, heart rate, exercise volume, and sleep patterns in real time.

[0721] 3. Means for analyzing your health and activity data to generate personalized training and meal plans:

[0722] The server runs machine learning algorithms to analyze the collected health and activity data, which allows it to generate optimal training and meal plans for the user.

[0723] 4. Means for providing generated training and meal plans to a user's device:

[0724] The server then sends the generated training and meal plans to the user's smartphone or tablet, allowing the user to view and follow the plans on their own dedicated device.

[0725] 5. A means for a virtual instructor to provide real-time feedback on exercise form as the user exercises:

[0726] The user device uses a camera to capture the user's exercise form, and a virtual instructor evaluates the accuracy of the form in real time and provides necessary feedback, helping the user to continue exercising with correct form.

[0727] 6. A method for predicting changes in the user's health status and presenting them as images:

[0728] The server uses a generative AI model to simulate the changes in the user's body shape when they achieve their goal, generating an image that is then provided to the user to help maintain their motivation.

[0729] 7. Means of syncing activity data when checking into a facility:

[0730] By scanning a QR code when users check in to a facility, the app automatically synchronizes activity data from the wearable device to the app, allowing users to efficiently track their gym activities.

[0731] Examples of concrete examples and prompts

[0732] Examples:

[0733] Consider a scenario where a gym member creates an account, syncs their activity data to an app, and follows a training plan generated based on that data.

[0734] Example prompt sentence:

[0735] Prompt: Gym member A has created an account and synced their activity data. Please generate a training plan and meal plan based on the following profile and activity data:

[0736] Profile information: Age 30, weight 70kg, goal is diet

[0737] Activity data: Approximately 10,000 steps per day, average heart rate 80 bpm

[0738] Dietary data: Average calorie intake: 2,100 calories

[0739] This allows users to effectively manage their health through a dedicated device and continue training while maintaining motivation. The system is designed to seamlessly support the flow from collecting a series of data to providing personalized advice and progress management.

[0740] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0741] Step 1:

[0742] The user launches a dedicated app on their smartphone or tablet and enters their health information.

[0743] Specific operation: The user creates an account by entering information such as age, gender, height, weight, and health goals. The entered data is sent from the device to the server.

[0744] Input: Age, Gender, Height, Weight, Health Goals

[0745] Output: User's health profile data

[0746] Step 2:

[0747] The terminal synchronizes with the wearable device and acquires the user's activity data.

[0748] Specific operation: A user uses a smartwatch or fitness tracker and syncs activity data (number of steps, heart rate, exercise volume, sleep data) to a dedicated app. The data is then sent to a server via the device.

[0749] Input: steps, heart rate, exercise, sleep data

[0750] Output: User activity data

[0751] Step 3:

[0752] The server analyzes the user's health and activity data and generates personalized training and meal plans.

[0753] Specific operation: Based on the health information and activity data received by the server, the data is analyzed using machine learning algorithms to generate personalized training and meal plans, which are then sent to the user's device.

[0754] Input: User health profile data, User activity data

[0755] Output: Personalized training plan, meal plan

[0756] Step 4:

[0757] The server uses the generated AI model to predict changes in the user's body shape after achieving their goal and generates an image of it.

[0758] How it works: The server uses the user's health information and a predictive algorithm to simulate what the patient will look like after achieving their goal, generating an image of the results, which is then sent to the user's device.

[0759] Input: User's health profile data, goals

[0760] Output: Predicted image

[0761] Step 5:

[0762] When users check in to the gym, they scan a QR code to sync their activity data.

[0763] What it does: When a user arrives at the gym, they scan a QR code and sync their latest activity data from their wearable device to the app, which then sends the synced data to the server.

[0764] Input: QR code scan, latest activity data

[0765] Output: Gym activity data synchronization

[0766] Step 6:

[0767] The user terminal uses a virtual instructor to provide real-time feedback on the user's exercise form.

[0768] Specific Movements: The smartphone camera captures the user's exercise form and analyzes it in real time. The virtual instructor compares it with the correct form and provides necessary feedback via voice or text.

[0769] Input: Video data of exercise form

[0770] Output: Form improvement feedback

[0771] Step 7:

[0772] The server analyzes the user's progress data and generates messages to keep them motivated.

[0773] Specific operation: The server continuously analyzes the user's training and diet data, and generates and sends motivational messages based on behavioral science, thereby maintaining the user's motivation.

[0774] Input: training data, dietary data

[0775] Output: Motivation message

[0776] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0777] This invention is a system for a personal AI health assistant that provides personalized training to help users achieve their goals. It also incorporates an emotion engine that recognizes the user's emotions, enabling richer interactions. The system includes the following main components to help users manage their health efficiently and sustainably:

[0778] 1. User registration and data collection features:

[0779] User's device: The user creates an account using a dedicated app and enters profile information (name, gender, age, height, weight, goals, etc.), then syncs the wearable device with the app and configures it to collect activity data.

[0780] Server: Stores the profile information you enter and activity data obtained from the wearable device (e.g., steps, heart rate, sleep patterns) in a database.

[0781] 2. Personal advice generation function:

[0782] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. It also uses a generative AI model to generate images of the user's "before and after" appearance and displays them on the user's device.

[0783] 3. Training guidance and meal plan suggestions:

[0784] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[0785] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[0786] 4. Progress management and motivation maintenance features:

[0787] Server: Analyzes the user's training and diet progress data, evaluates their achievement, and adjusts their training menu and meal plan as needed.

[0788] Server: Generates messages and reminders based on behavioral science to help users maintain their motivation.

[0789] 5. Emotion recognition and response capabilities:

[0790] On the user's device: The emotion engine recognizes emotions from the user's voice and facial expressions, and provides appropriate feedback and advice.

[0791] Server: Generates personalized motivational messages and reminders based on the emotional data acquired by the emotion engine.

[0792] Server: Analyzes emotional data and provides appropriate support messages and training advice based on the user's mental state.

[0793] Specific examples

[0794] 1. User Registration:

[0795] User device: User B starts the app and enters her name "B", gender "female", age "25", height "160cm", weight "55kg", and goal "to increase muscle strength". She synchronizes her wearable device and sends heart rate and activity data to the app.

[0796] Server: Stores these data in a database and prepares them for analysis.

[0797] 2. Generate personalized advice:

[0798] Server: Analyzes User B's information and generates a meal plan based on his / her health condition and exercise habits, including a "strength training plan three times a week" and a "high-protein diet." It also predicts and creates an image of how his / her body will change when he / she achieves his / her goal.

[0799] User's device: Receives and displays the generated training plan, meal plan, and predicted image.

[0800] 3. Training Guidance:

[0801] User device: A virtual coach provides audio and video instruction on correct deadlift form. User B performs the exercise in front of the camera, while the device captures and analyzes their form in real time, providing feedback.

[0802] 4. Emotion recognition:

[0803] User's device: The emotion engine analyzes User B's facial expressions and voice to recognize impatience and fatigue during training.

[0804] Server: Based on the recognized emotion data, it generates a supportive message such as, "Take a short break. You're doing a great job."

[0805] User's device: The virtual coach displays a cheering message and calls out to User B.

[0806] 5. Track your progress and stay motivated:

[0807] Server: Periodically analyzes User B's training results and food intake data, generates progress reports, and updates the training plan as needed.

[0808] On your device: Receive and review plan updates and progress reports as notifications, along with timely reminders and encouragement to keep you motivated.

[0809] This allows users to efficiently execute personalized training and meal plans that take into account their emotional state, further maintaining motivation. The entire system seamlessly connects users' health information and emotional state, functioning as an optimal health management solution.

[0810] The processing flow will be explained below.

[0811] Step 1:

[0812] User's device:

[0813] Users launch the dedicated app and proceed to the account creation page.

[0814] Enter basic information such as your name, email address, and password.

[0815] Configure your wearable device for synchronization and allow data collection from the device.

[0816] Step 2:

[0817] server:

[0818] We receive the basic information you enter and store it in a secure database.

[0819] Set up a scheduled task to sync activity data from your wearable device at regular intervals.

[0820] Activity data includes steps taken, heart rate, calories burned, sleep patterns, and more.

[0821] Step 3:

[0822] User's device:

[0823] Go to your profile settings page and enter your details, such as your gender, age, height, weight, and health goals (e.g., weight loss, muscle building).

[0824] Step 4:

[0825] server:

[0826] The entered details are stored in a database and the data analysis algorithm is prepared.

[0827] This will be combined with activity data obtained from wearable devices to begin analyzing the user's health information.

[0828] Step 5:

[0829] server:

[0830] The analytical algorithm evaluates the user's data to analyze their health and exercise habits.

[0831] Based on the results, a customized training and meal plan is generated.

[0832] Step 6:

[0833] server:

[0834] The generated training and meal plans are sent to the user's device.

[0835] Using a generative AI model, images of the user's appearance before and after achieving their goal are generated and sent to the user's device.

[0836] Step 7:

[0837] User's device:

[0838] A virtual coach will explain the details of the training plan and important points to note using audio and video.

[0839] Display a list of recommended meal plans and recipes.

[0840] Step 8:

[0841] User's device:

[0842] It uses cameras and sensors to capture the user's exercise form.

[0843] The captured data is sent to a server in real time for form analysis.

[0844] Step 9:

[0845] server:

[0846] Analyze your exercise form in real time and identify areas for improvement.

[0847] Generate feedback and send it to the user's device.

[0848] Step 10:

[0849] User's device:

[0850] A virtual coach displays analysis results and feedback to users, teaching them correct exercise form.

[0851] Step 11:

[0852] server:

[0853] The emotion engine captures the user's voice and facial expressions to recognize their emotions.

[0854] Generate appropriate feedback and advice based on the perceived emotions.

[0855] Step 12:

[0856] server:

[0857] Generate personalized motivational messages and reminders based on emotional data.

[0858] Send generated messages and reminders to the user's device.

[0859] Step 13:

[0860] User's device:

[0861] The virtual coach displays encouraging messages and advice based on the emotions recognized by the emotion engine.

[0862] Step 14:

[0863] server:

[0864] It regularly analyzes the user's training results and food intake data and updates progress data.

[0865] Adjust your training and nutrition plans accordingly based on your updated progress data.

[0866] Step 15:

[0867] server:

[0868] It generates messages and reminders based on behavioral science to help maintain motivation and sends them to the user's device.

[0869] Step 16:

[0870] User's device:

[0871] Receive and view progress data, updated plans, and motivational messages as notifications.

[0872] Displays encouraging messages and training reminders from a virtual coach at appropriate times.

[0873] Through this series of steps, users are provided with personalized training and meal plans that take their emotional state into account, helping them to effectively and sustainably manage their health.

[0874] Example 2

[0875] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0876] Conventional health management systems have limitations in providing users with personalized advice and training plans, and lack consideration for users' emotions and motivation. Furthermore, they lack real-time exercise form advice and emotion-based feedback, making it difficult for users to maintain their motivation to continue training.

[0877] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input health information, means for acquiring the user's activity data from the wearable device, means for analyzing the user's health information and activity data to generate personalized training plans and meal plans, means for providing the generated training plans and meal plans to the user's terminal, means for predicting changes in the user's health condition and presenting them as images, means for analyzing the user's voice and facial expressions using an emotion engine and sending emotion recognition data to the server, and means for the server to generate cheering messages based on the emotion recognition data and provide them to the user's terminal. This allows the user to efficiently perform personalized training and meal plans that take their emotional state into account, further maintaining their motivation.

[0878] "User" means a person who uses the system to input health information and receive training and meal plans.

[0879] "Health Information" refers to profile information entered by users, such as name, gender, age, height, weight, and goals.

[0880] A "wearable device" is a device, such as a Fitbit or Apple Watch, that collects user activity data and transmits it to the system.

[0881] "Activity data" refers to data about a user's daily physical movements and conditions, such as the number of steps taken, heart rate, and sleep patterns, obtained from wearable devices.

[0882] A "personalized training plan" is an exercise plan that is individually customized for you based on your health and activity data.

[0883] "Meal Plan" means a personalized meal plan based on a User's health and activity data.

[0884] A "terminal" refers to an information processing device such as a smartphone, tablet, or PC that a user uses to access the system.

[0885] An "emotion engine" is a software or hardware component that analyzes a user's voice and facial expressions to recognize emotions.

[0886] "Emotion Recognition Data" means data regarding a user's emotional state that is captured and analyzed by the Emotion Engine.

[0887] "Server" refers to the central processing system that collects, stores, and analyzes users' health and activity data and provides various services.

[0888] "Encouragement messages" are messages of encouragement that are generated by the server based on emotion recognition data and provided to maintain the user's motivation.

[0889] This invention is a system of AI personal health assistants dedicated to users, providing personalized training to help users achieve their goals. It also combines an emotion engine that recognizes the user's emotions to enable richer interactions. The main components of the entire system are as follows:

[0890] 1. User registration and data collection features:

[0891] On the user's device: The user creates an account using a dedicated app and enters profile information (such as name, gender, age, height, weight, and goals), then syncs a wearable device (such as a Fitbit or Apple Watch) to the app and configures it to collect activity data (e.g., steps, heart rate, and sleep patterns).

[0892] Server: Stores the entered profile information and activity data obtained from the wearable device in a database.

[0893] 2. Personal advice generation function:

[0894] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. It also uses a generative AI model (such as GPT-3) to generate images of the user's "before and after" appearance and displays them on the user's device.

[0895] 3. Training guidance and meal plan suggestions:

[0896] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[0897] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[0898] 4. Progress management and motivation maintenance features:

[0899] Server: Analyzes the user's training and diet progress data, evaluates achievement, adjusts training menus and meal plans as needed, and generates messages and reminders based on behavioral science to help users maintain their motivation.

[0900] 5. Emotion recognition and response capabilities:

[0901] User device: An emotion engine (for example, voice recognition software or a facial recognition camera) recognizes emotions from the user's voice and facial expressions. Once emotions are recognized, the device provides appropriate feedback and advice.

[0902] Server: The emotion engine generates personalized encouragement messages and reminders based on the emotional data acquired, providing appropriate training advice according to the user's mental state.

[0903] Specific examples

[0904] 1. User Registration:

[0905] User device: User B starts the app and enters her name "B", gender "female", age "25", height "160cm", weight "55kg", and goal "to increase muscle strength". She synchronizes her wearable device and sends heart rate and activity data to the app.

[0906] Server: Stores these data in a database and prepares them for analysis.

[0907] 2. Generate personalized advice:

[0908] Server: Analyzes User B's information and generates a meal plan based on his / her health condition and exercise habits, including a "strength training plan three times a week" and a "high-protein diet." It also predicts and creates an image of how his / her body will change when he / she achieves his / her goal.

[0909] User's device: Receives and displays the generated training plan, meal plan, and predicted image.

[0910] 3. Training Guidance:

[0911] User device: A virtual coach provides audio and video instructions on the correct deadlift form. User B performs the exercise in front of the camera, and the device captures and analyzes their form in real time, providing feedback.

[0912] 4. Emotion recognition:

[0913] User's device: The emotion engine analyzes User B's facial expressions and voice to recognize impatience and fatigue during training.

[0914] Server: Based on the recognized emotion data, it generates a supportive message such as, "Take a short break. You're doing a great job."

[0915] User's device: The virtual coach displays a cheering message and calls out to User B.

[0916] 5. Track your progress and stay motivated:

[0917] Server: Periodically analyzes User B's training results and food intake data, generates progress reports, and updates the training plan as needed.

[0918] On your device: Receive and review plan updates and progress reports as notifications, along with timely reminders and encouragement to keep you motivated.

[0919] Prompt Sentence Examples

[0920] "If a user is a 25-year-old female, 160cm tall, and weighs 55kg, and has set the goal of increasing muscle strength, please generate the current appearance and the predicted appearance after continuing strength training three times a week for three months."

[0921] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0922] Specific processing steps of the program

[0923] Step 1: User registration and data collection

[0924] The user downloads and launches the app. This is the step where they enter their name, gender, age, height, weight, and goals on the account creation screen and synchronize their wearable device. This synchronization setting sends activity data such as heart rate and number of steps from the wearable device to the app. The device then sends the entered profile information and data acquired from the wearable device to the server. The server receives this data and stores it in a database.

[0925] Input: User profile information (name, gender, age, height, weight, goal), activity data from wearable device.

[0926] Output: Profile information and activity data stored in a database.

[0927] Step 2: Analyze the data and generate personalized advice

[0928] The server retrieves user information and activity data from the database. The server then sends prompts to the generative AI model to generate images of how the user will look before and after achieving their goal. The analysis process analyzes the user's health and exercise habits to generate personalized training and meal plans. The generated plans and predicted images are sent to the device.

[0929] Input: User information stored in the database, prompt text.

[0930] Output: Before and after images based on generative AI models, personalized training and meal plans.

[0931] Step 3: Providing training guidance

[0932] The user starts a training session using the app. The device displays a virtual coach and explains the generated training plan to the user using audio and video. The user performs the training in front of the camera, and the device captures their movements. The captured data is analyzed in real time and feedback is provided. Recommended ingredients and recipes are also displayed.

[0933] Input: Generated training plan, meal plan, and movement data from the camera.

[0934] Output: Real-time analysis of behavior, feedback, recommended ingredients and recipes.

[0935] Step 4: Track progress and adjust your plan

[0936] The server periodically analyzes the user's activity and dietary data to assess the user's progress, generating a personalized progress report and adjusting personalized training and diet plans. The analysis results are then sent to the user's device.

[0937] Input: User activity data, meal data.

[0938] Output: Progress report, tailored training and meal plans.

[0939] Step 5: Recognize emotions and stay motivated

[0940] The user's device activates an emotion engine that analyzes the user's voice and facial expressions in real time. The analyzed emotion data is sent to a server. Based on this data, the server generates encouraging messages and reminders and provides them to the user's device. This helps to maintain the user's motivation.

[0941] Input: User's voice data, facial expression data.

[0942] Output: Emotion recognition data, generated cheer messages and reminders.

[0943] (Application example 2)

[0944] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0945] Conventional health management systems make it difficult for users to receive efficient and personalized training guidance in physical stores. In addition, due to a lack of emotion recognition technology, it is difficult to maintain motivation during training or appropriately respond to fatigue, which prevents a sufficient improvement in the user experience.

[0946] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's health information and activity data to generate personalized training plans and meal plans, means for providing training guidance to the user via a virtual coach, and means for recognizing the user's emotions using an emotion engine and providing feedback accordingly. This makes it possible to provide personalized training guidance and maintain the user's motivation.

[0947] A "means for a user to input health information" is an interface through which a user inputs their own health-related data.

[0948] "Means for obtaining user activity data from a wearable device" refers to a function for collecting activity data from a device worn by the user.

[0949] "Means for analyzing a user's health information and activity data to generate personalized training and meal plans" refers to a function that generates individually optimized training and meal plans based on the user's health information and activity data.

[0950] "Means for providing the generated training plan and meal plan to the user's device" refers to the function by which the server sends the generated plan to the user's device and displays it.

[0951] "Means for predicting changes in the user's health condition and presenting them as images" is a function that predicts changes in the user's health condition, visualizes the results, and presents them to the user.

[0952] "Means for providing training guidance to users via a virtual coach" refers to a function that uses a virtual instructor to guide and instruct users on training methods.

[0953] "Means for recognizing user emotions using an emotion engine and providing feedback accordingly" is a function that analyzes the user's emotions and provides feedback and advice based on the results.

[0954] "Means for generating and providing users with reminders and encouraging messages to maintain motivation" is a function that creates and provides appropriate reminders and encouraging messages to maintain users' motivation.

[0955] This invention provides a user-specific AI personal health assistant system that supports health management and training. The system analyzes the user's health information and emotional state and provides personalized training and meal plans accordingly.

[0956] Hardware and software used

[0957] The following hardware and software are used to realize the system.

[0958] Hardware: Smartphones, smart glasses, wearable devices (e.g., smartwatches)

[0959] software:

[0960] Mobile application (iOS / Android)

[0961] Emotion recognition engine (e.g. Microsoft Azure Emotion API)

[0962] Generative AI models (e.g., OpenAI GPT-3)

[0963] Database (e.g. Firebase)

[0964] Data collection

[0965] Using a mobile app, users enter their profile information (such as name, gender, age, height, weight, and goals), and sync their wearable device to collect activity data such as heart rate, activity level, and sleep patterns. This data is then stored on a server for analysis.

[0966] Generate personalized advice

[0967] The server analyzes the user's stored health and activity data to generate personalized training and meal plans. Using a generative AI model (OpenAI GPT-3), it predicts the specific plan and how the body shape will change when the goal is achieved, and generates images of the plan.

[0968] Training Guidance and Emotion Recognition

[0969] During training, a virtual coach provides real-time guidance to the user via smartphone or smart glasses. An emotion recognition engine (Microsoft Azure Emotion API) recognizes emotions from the user's facial expressions and voice and provides appropriate feedback. Appropriate encouraging messages such as "Take a short break" are provided to the user.

[0970] Maintaining motivation

[0971] The server generates behavioral science-based messages and reminders, sends notifications to users to keep them motivated, and periodically analyzes their progress data to adjust their training and meal plans accordingly.

[0972] Specific examples

[0973] For example, when a user arrives at the gym, they open their smartphone and check the designated training plan. They put on smart glasses and train under the guidance of a virtual coach. If fatigue is detected through emotion recognition, a message will automatically appear encouraging them to take a break. After training, they will also receive a progress report and their next training content will be automatically updated. They can also check their meal plan through the app and try out the recommended recipes.

[0974] Prompt Sentence Examples

[0975] "Enter your health information, including your name, age, gender, and goals. Then generate training and meal plan suggestions."

[0976] This allows users to receive personalized health management and training support, enabling sustainable health management.

[0977] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0978] Step 1:

[0979] The user's device provides an interface for the user to input health information, such as name, gender, age, height, weight, and goals. The input information is temporarily stored by the user's device and then sent to the server.

[0980] Step 2:

[0981] The server stores the health information received from the user's device in a database. At the same time, it also collects activity data (heart rate, number of steps, sleep patterns, etc.) from the user's synchronized wearable device and stores this data in the database. This provides the basic data for analysis.

[0982] Step 3:

[0983] The server analyzes the user's health information and activity data stored in the database. This process uses an AI model (e.g., OpenAI GPT-3) to generate personalized training and meal plans. For example, it might suggest a "strength training plan three times a week based on the user's health information" and a "high-protein meal plan." These generated plans are then stored again on the server.

[0984] Step 4:

[0985] The server sends the generated training plan and meal plan to the user's device, which displays the received plan for the user to review.

[0986] Step 5:

[0987] The user's device activates a virtual coach function that guides the user through the training session. The virtual coach uses a camera to capture the user's exercise form and provides real-time feedback, such as "Your deadlift form is incorrect."

[0988] Step 6:

[0989] The user's device uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize emotions from the user's facial expressions and voice. This data is sent to a server, which automatically generates feedback according to the user's emotional state. For example, if the user expresses "feeling tired," a message such as "take a short break" is generated.

[0990] Step 7:

[0991] The server generates motivational reminders and encouraging messages based on behavioral science. This is also done using a generative AI model. The created reminders and messages are sent to the user's device and displayed to the user. For example, a message such as "You're almost there, keep trying!" may be displayed.

[0992] Step 8:

[0993] The user's progress data is periodically sent to a server, which analyzes it. Based on this progress data, the training and meal plans are updated as needed to provide the user with the optimal plan. For example, the next training menu will be optimized based on data such as "muscle strength improved by 5% in three weeks."

[0994] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0995] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0996] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0997] [Third embodiment]

[0998] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0999] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1000] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1001] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1002] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1003] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1004] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1005] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1006] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1007] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1008] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1009] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1010] This invention is a system for a user-dedicated AI personal health assistant that provides personal training to help users achieve their goals (e.g., improving health, losing weight, building muscle). This system includes the following main components to enable users to efficiently and sustainably manage their health:

[1011] 1. User registration and data collection features:

[1012] On your device: Users create an account using a dedicated app or web portal, enter their profile information (e.g., name, gender, age, height, weight, health goals), and sync their wearable device (e.g., smartwatch) to the app and set it up to collect activity data.

[1013] Server: Stores the profile information you enter and activity data obtained from the wearable device (e.g., steps, heart rate, sleep patterns) in a database.

[1014] 2. Personal advice generation function:

[1015] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. The analysis algorithm takes into account the user's past activity data and health information.

[1016] Server: Sends the generated training and meal plans to the user's device, and also uses the generative AI model to generate images of the user's "before and after" appearance and presents them to the user.

[1017] 3. Training guidance and meal plan suggestions:

[1018] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[1019] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[1020] 4. Progress management and motivation maintenance features:

[1021] Server: Analyzes the user's training and diet progress data, evaluates their achievement, and adjusts their training menu and meal plan as needed.

[1022] Server: Generates messages and reminders based on behavioral science to help users maintain their motivation.

[1023] Specific examples

[1024] 1. User Registration:

[1025] User device: User A starts the app and enters his name "A Taro", gender "male", age "30", height "170cm", weight "70kg", and goal "diet". The wearable device is synchronized and heart rate and activity data is sent to the app.

[1026] Server: Stores these data in a database and prepares them for analysis.

[1027] 2. Generate personalized advice:

[1028] Server: The analytical algorithm operates based on User A's information stored in the database. It analyzes the past week's activity data and generates a training plan such as "30 minutes of jogging with the goal of burning 500 calories a day." It also generates a "high-protein, low-calorie meal plan" and suggests specific menus. Using the generative AI model, it predicts and creates an image of User A's body shape if he or she achieves his or her goal.

[1029] User's device: Receives and displays the generated training plan, meal plan, and prediction image.

[1030] 3. Training Guidance:

[1031] User device: A virtual coach provides audio and video instruction on the correct squat form. User A performs the exercise in front of the camera, and the device captures and analyzes their form in real time, providing feedback.

[1032] 4. Track your progress and stay motivated:

[1033] Server: Monitors user A's weekly training results and generates a report such as "Last week's average calorie consumption was 450 calories." If necessary, adjust the training plan for the following week.

[1034] On your device: Receive and review progress reports as notifications, as well as encouragement and training reminders from your virtual coach.

[1035] This allows users to effectively and sustainably manage their health. The entire system is designed to seamlessly support the flow from collecting a series of data to providing personalized advice and progress management.

[1036] The processing flow will be explained below.

[1037] Step 1:

[1038] User's device:

[1039] Users launch the dedicated app and proceed to the account creation page.

[1040] Enter basic information such as your name, email address, and password.

[1041] Configure your wearable device for synchronization and allow data collection from the device.

[1042] Step 2:

[1043] server:

[1044] We receive the basic information you enter and store it in a secure database.

[1045] Set up a scheduled task to periodically sync activity data from your wearable device.

[1046] Activity data includes steps taken, heart rate, calories burned, sleep patterns, and more.

[1047] Step 3:

[1048] User's device:

[1049] Go to your profile settings page.

[1050] Enter detailed health information such as gender, age, height, weight, and goals (e.g., diet, muscle building).

[1051] Step 4:

[1052] server:

[1053] The detailed health information entered is stored in a database, ready to begin the analysis process.

[1054] The data analysis algorithm combines and analyzes the user's health information and activity data from the wearable device.

[1055] Step 5:

[1056] server:

[1057] Analytical algorithms evaluate the user's data and generate a customized training plan based on their health and exercise habits.

[1058] We also create optimal meal plans for users under the supervision of a nutritionist.

[1059] Step 6:

[1060] server:

[1061] The generated training and meal plans are sent to the user's device.

[1062] At the same time, a generative AI model is used to generate images of the user's appearance before and after achieving their goal, which are then sent to the user's device.

[1063] Step 7:

[1064] User's device:

[1065] A virtual coach will explain the details of the training plan and important points to note using audio and video.

[1066] Display a list of recommended meal plans and recipes.

[1067] Step 8:

[1068] User's device:

[1069] It uses cameras and sensors to capture the user's exercise form.

[1070] The captured data is sent to a server in real time for form analysis.

[1071] Step 9:

[1072] server:

[1073] Analyze your exercise form in real time and identify areas for improvement.

[1074] Generate feedback and send it to the user's device.

[1075] Step 10:

[1076] User's device:

[1077] A virtual coach displays analysis results and feedback to users, teaching them correct exercise form.

[1078] Step 11:

[1079] server:

[1080] It regularly analyzes the user's training results and food intake data and updates progress data.

[1081] Adjust your training and nutrition plans accordingly based on your updated progress data.

[1082] Step 12:

[1083] server:

[1084] Generate motivational messages and reminders based on behavioral science.

[1085] Send generated messages and reminders to the user's device.

[1086] Step 13:

[1087] User's device:

[1088] Receive and view progress data, updated plans, and motivational messages as notifications.

[1089] Encouraging messages and training reminders from the virtual coach are also displayed at appropriate times.

[1090] This series of steps provides users with a system that allows them to effectively and sustainably manage their health.

[1091] Example 1

[1092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1093] Conventional health management systems struggle to optimally utilize users' health and activity data to provide personalized exercise and meal plans suited to each individual user. Furthermore, they lack sufficient means to maintain users' motivation and appropriately manage their progress, making it difficult to maintain sustainable health management. A system that solves these issues and enables users to manage their health effectively and sustainably is needed.

[1094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1095] In this invention, the server

[1096] a means for a user to input health information;

[1097] means for acquiring user activity data from the wearable device;

[1098] means for analyzing the user's health information and activity data to generate personalized exercise and meal plans;

[1099] means for providing the generated exercise plan and meal plan to a user's terminal;

[1100] A means for predicting changes in a user's health status using a generative AI model and presenting the predicted changes as a graphic; and

[1101] A means to analyze your progress and adjust your exercise and meal plans as needed;

[1102] Generate behavioral science-based messages and reminders to keep users motivated,

[1103] This allows users to effectively and sustainably manage their health.

[1104] "Means for users to input health information" refers to a function that provides an interface for users to input information about their own health.

[1105] "Means for obtaining user activity data from wearable devices" refers to the ability to collect activity data such as a user's steps, heart rate, and sleep patterns from devices such as smartwatches and fitness trackers.

[1106] "Means for analyzing a user's health information and activity data to generate personalized exercise and meal plans" refers to a function that creates exercise and meal plans optimized for individual users based on the acquired health information and activity data.

[1107] "Means for providing the generated exercise plan and meal plan to the user's device" refers to a function that sends the created exercise plan and meal plan to the user's device, such as a smartphone or tablet, and displays them.

[1108] "Means for predicting changes in a user's health status using a generative AI model and presenting them as images" refers to a function that utilizes artificial intelligence technology to predict the progress and changes in a user's health status and presents them to the user as visual images.

[1109] "Means for analyzing the user's progress data and adjusting the exercise menu and meal plan as necessary" refers to a function that analyzes the user's exercise and diet progress and dynamically adjusts the exercise menu and meal plan according to the results.

[1110] "A means of generating messages and reminders based on behavioral science to maintain user motivation" is a function that uses knowledge from behavioral science to create encouraging messages and reminders that make it easy for users to continue, and notifies them of these.

[1111] This invention is a personal health assistant system that provides individualized training and meal plans to help users achieve their goals, such as improving their health, losing weight, and building muscle. The system includes the following main components to help users manage their health efficiently and sustainably:

[1112] User registration and data collection features

[1113] Users create an account using a dedicated app or web portal, entering profile information such as name, gender, age, height, weight, and goals, using a standard user device such as a smartphone or tablet.

[1114] Users sync their smartwatches or other wearable devices with the app, which automatically collects daily activity data (e.g., steps taken, heart rate, and sleep patterns). Specifically, Apple Watches and Fitbits are used.

[1115] The server stores the entered profile information and activity data obtained from the wearable device in a database.

[1116] Personal advice generator

[1117] The server analyzes the user's health and activity data stored in the database, using data analysis tools such as Python and R.

[1118] The server uses generative AI models, such as machine learning libraries TensorFlow and PyTorch, to generate customized exercise and meal plans for the user.

[1119] The server predicts the changes in the user's body shape after achieving their goal and generates an image using a generative AI model, providing the user with visual feedback.

[1120] The server transmits the generated exercise plan and meal plan to the user's terminal.

[1121] Training guidance and meal plan suggestions

[1122] The user's device will then display the received exercise and meal plans using a dedicated app.

[1123] The user exercises under the guidance of a virtual coach. The virtual coach provides audio and video instructions on exercise form, such as squats and push-ups, and the user's device uses a camera to capture and analyze their exercise form in real time.

[1124] The user's device will display a list of recommended ingredients and recipes, providing them in a way that makes them easy to incorporate into everyday life.

[1125] Progress management and motivation maintenance features

[1126] The server analyzes the user's exercise and diet progress data and adjusts the new plan based on their performance.

[1127] The server generates messages and reminders based on behavioral science, and sends encouraging messages and progress reports to users. For example, a message such as "Keep up the good work this week!" is sent to the user's device.

[1128] Specific examples

[1129] Suppose User A launches the app and enters their name, gender, age, height, weight, and goal. User A sets "weight loss" as their goal and synchronizes their wearable device with the app. The server saves User A's information and uses the generative AI model to generate a customized exercise plan of "30 minutes of jogging with the goal of burning 500 calories a day" and a meal plan of "high-protein, low-calorie meals." The generative AI model is then used to predict how User A's body shape will change if they achieve their goal, and the results are displayed as an image.

[1130] When User A starts exercising, the virtual coach instructs them on the correct form, and the user's device analyzes the exercise in real time via a camera and provides feedback. At the end of the week, the server analyzes the progress and notifies User A of a new plan if necessary.

[1131] This allows the user to effectively and sustainably manage their health.

[1132] An example of a prompt is:

[1133] "Please create a diet plan for User A based on the following information: Age 30, Gender Male, Goal is to lose 5kg in 1 month."

[1134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1135] Step 1: User Registration

[1136] A user launches the app and creates an account

[1137] Using a dedicated app or web portal, users enter their profile information, such as name, gender, age, height, weight, and goals. The data is sent from the user's device to a server and stored in a database.

[1138] Input: Name, Gender, Age, Height, Weight, Health Goals

[1139] Output: Registration information is saved in the database

[1140] Step 2: Device Sync and Data Collection

[1141] The user syncs the wearable device

[1142] A user syncs a wearable device, such as a smartwatch, with the app, which sends activity data (e.g., steps taken, heart rate, sleep patterns) to the server through the app.

[1143] Input: Sync settings for wearable devices

[1144] Output: Activity data is saved on the server

[1145] Step 3: Data analysis

[1146] The server analyzes the data

[1147] The server analyzes the user's health and activity data stored in the database, using data analysis tools such as Python and R to evaluate the user's current health status and exercise habits.

[1148] Input: Health information and activity data

[1149] Output: User's health status assessment data as analysis results

[1150] Step 4: Create a personal plan

[1151] The server generates a plan using the generative AI model

[1152] Based on the analysis results, the server uses a generative AI model (a machine learning model using TensorFlow or PyTorch) to generate a customized exercise plan and meal plan.

[1153] Input: Health status assessment data

[1154] Output: Customized exercise and meal plans

[1155] Step 5: Generate predicted image

[1156] The server generates a predicted image after the goal is achieved.

[1157] The server uses the generative AI model to generate an image that predicts how the user's body shape will change if they achieve their goal.

[1158] Input: Health assessment data, exercise plan, meal plan

[1159] Output: Predicted image after goal is achieved

[1160] Step 6: Submit your plan

[1161] The server sends the exercise plan and meal plan to the device.

[1162] The server sends the generated exercise plan, meal plan, and predicted image to the user's terminal, which displays them.

[1163] Input: Exercise plan, Meal plan, Predictive image

[1164] Output: The plan and image are displayed on the user's device.

[1165] Step 7: Training Instruction

[1166] The user exercises according to the instructions of the virtual coach

[1167] The user performs exercises under the guidance of a virtual coach, and the user's device uses a camera to capture and analyze their exercise form in real time.

[1168] Input: User's exercise data (camera footage)

[1169] Output: Real-time feedback

[1170] Step 8: View Meal Plan

[1171] The user's device displays the meal plan.

[1172] The user's device displays a list of recommended ingredients and recipes, providing information in a format that is easy for the user to implement in their daily lives.

[1173] Input: Meal plan data

[1174] Output: Display of recommended ingredients and recipe

[1175] Step 9: Progress data analysis

[1176] The server analyzes the progress data and adjusts the plan as needed

[1177] The server analyzes the user's training and dietary progress data and adjusts the exercise menu and meal plan according to the user's level of achievement.

[1178] Input: training data, dietary data

[1179] Output: A new, tailored exercise and meal plan

[1180] Step 10: Stay motivated

[1181] The server generates and notifies a motivation message.

[1182] The server generates messages and reminders based on behavioral science and sends them to the user's device to keep them motivated.

[1183] Input: Progress data

[1184] Output: Cheerful messages and reminder notifications

[1185] (Application example 1)

[1186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1187] Traditional fitness and health management systems struggle to motivate users and provide insufficient personalized advice. Many systems also lack the ability to provide real-time feedback to users, making it difficult to ensure they are performing exercises with proper form. Furthermore, there are limited ways to efficiently track activity at gyms and other facilities.

[1188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1189] In this invention, the server includes a means for a user to input health information, a means for acquiring the user's activity data from the wearable device, and a means for analyzing the user's health information and activity data to generate personalized training and meal plans. This allows the training and meal plans to be provided to the user's dedicated device. Furthermore, a means for predicting changes in the user's health status and presenting them as images allows the user to visualize their goals. Furthermore, a means for a virtual instructor to provide real-time feedback on the user's exercise form during exercise allows the user to maintain proper exercise form. Furthermore, a means for synchronizing activity data upon check-in at a health facility or training gym allows comprehensive tracking of the user's fitness activities.

[1190] "User" refers to an individual who provides health information and activity data in an Invention.

[1191] "Health Information" is profile information entered by the user, such as height, weight, age, gender, and goals.

[1192] "Wearable devices" refers to devices such as smartwatches and fitness trackers that collect user activity data.

[1193] "Activity data" refers to information such as a user's number of steps, heart rate, amount of exercise, and sleep patterns.

[1194] A "personalized training plan" is an exercise menu customized for you based on your health and activity data.

[1195] A "meal plan" is a plan that provides recommended dietary habits and recipes based on a user's health goals.

[1196] "User-dedicated devices" are devices used by users, such as smartphones, tablets, and personal computers.

[1197] A "virtual instructor" is a digital coach that monitors the user's exercise form and provides real-time feedback on areas for improvement.

[1198] "Means for predicting changes in health status and presenting them as images" is a method that uses a generative AI model to provide simulated images of what a user will look like after achieving their goal.

[1199] "Facility check-in" is the process by which a user scans a QR code to enter a health facility or gym.

[1200] "Means for synchronizing activity data" refers to a method of automatically collecting and recording data by linking a user's wearable device with an application when checking in to a facility.

[1201] The system collects and analyzes users' health and activity data to provide personalized training and meal plans, predict changes in the user's health, and provides real-time exercise form feedback from a virtual coach and data synchronization upon facility check-in.

[1202] The server includes the following components:

[1203] 1. A means for users to enter health information:

[1204] The server provides a user interface for inputting health information such as age, gender, height, weight, and health goals through the user terminal, allowing users to easily input their own health information.

[1205] 2. Means for obtaining user activity data from wearable devices:

[1206] The server sets up an API to retrieve activity data from the user's smartwatch or fitness tracker, allowing it to collect data such as the user's steps, heart rate, exercise volume, and sleep patterns in real time.

[1207] 3. Means for analyzing your health and activity data to generate personalized training and meal plans:

[1208] The server runs machine learning algorithms to analyze the collected health and activity data, which allows it to generate optimal training and meal plans for the user.

[1209] 4. Means for providing generated training and meal plans to a user's device:

[1210] The server then sends the generated training and meal plans to the user's smartphone or tablet, allowing the user to view and follow the plans on their own dedicated device.

[1211] 5. A means for a virtual instructor to provide real-time feedback on exercise form as the user exercises:

[1212] The user device uses a camera to capture the user's exercise form, and a virtual instructor evaluates the accuracy of the form in real time and provides necessary feedback, helping the user to continue exercising with correct form.

[1213] 6. A method for predicting changes in the user's health status and presenting them as images:

[1214] The server uses a generative AI model to simulate the changes in the user's body shape when they achieve their goal, generating an image that is then provided to the user to help maintain their motivation.

[1215] 7. Means of syncing activity data when checking into a facility:

[1216] By scanning a QR code when users check in to a facility, the app automatically synchronizes activity data from the wearable device to the app, allowing users to efficiently track their gym activities.

[1217] Examples of concrete examples and prompts

[1218] Examples:

[1219] Consider a scenario where a gym member creates an account, syncs their activity data to an app, and follows a training plan generated based on that data.

[1220] Example prompt sentence:

[1221] Prompt: Gym member A has created an account and synced their activity data. Please generate a training plan and meal plan based on the following profile and activity data:

[1222] Profile information: Age 30, weight 70kg, goal is diet

[1223] Activity data: Approximately 10,000 steps per day, average heart rate 80 bpm

[1224] Dietary data: Average calorie intake: 2,100 calories

[1225] This allows users to effectively manage their health through a dedicated device and continue training while maintaining motivation. The system is designed to seamlessly support the flow from collecting a series of data to providing personalized advice and progress management.

[1226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1227] Step 1:

[1228] The user launches a dedicated app on their smartphone or tablet and enters their health information.

[1229] Specific operation: The user creates an account by entering information such as age, gender, height, weight, and health goals. The entered data is sent from the device to the server.

[1230] Input: Age, Gender, Height, Weight, Health Goals

[1231] Output: User's health profile data

[1232] Step 2:

[1233] The terminal synchronizes with the wearable device and acquires the user's activity data.

[1234] Specific operation: A user uses a smartwatch or fitness tracker and syncs activity data (number of steps, heart rate, exercise volume, sleep data) to a dedicated app. The data is then sent to a server via the device.

[1235] Input: steps, heart rate, exercise, sleep data

[1236] Output: User activity data

[1237] Step 3:

[1238] The server analyzes the user's health and activity data and generates personalized training and meal plans.

[1239] Specific operation: Based on the health information and activity data received by the server, the data is analyzed using machine learning algorithms to generate personalized training and meal plans, which are then sent to the user's device.

[1240] Input: User health profile data, User activity data

[1241] Output: Personalized training plan, meal plan

[1242] Step 4:

[1243] The server uses the generated AI model to predict changes in the user's body shape after achieving their goal and generates an image of it.

[1244] How it works: The server uses the user's health information and a predictive algorithm to simulate what the patient will look like after achieving their goal, generating an image of the results, which is then sent to the user's device.

[1245] Input: User's health profile data, goals

[1246] Output: Predicted image

[1247] Step 5:

[1248] When users check in to the gym, they scan a QR code to sync their activity data.

[1249] What it does: When a user arrives at the gym, they scan a QR code and sync their latest activity data from their wearable device to the app, which then sends the synced data to the server.

[1250] Input: QR code scan, latest activity data

[1251] Output: Gym activity data synchronization

[1252] Step 6:

[1253] The user terminal uses a virtual instructor to provide real-time feedback on the user's exercise form.

[1254] Specific Movements: The smartphone camera captures the user's exercise form and analyzes it in real time. The virtual instructor compares it with the correct form and provides necessary feedback via voice or text.

[1255] Input: Video data of exercise form

[1256] Output: Form improvement feedback

[1257] Step 7:

[1258] The server analyzes the user's progress data and generates messages to keep them motivated.

[1259] Specific operation: The server continuously analyzes the user's training and diet data, and generates and sends motivational messages based on behavioral science, thereby maintaining the user's motivation.

[1260] Input: training data, dietary data

[1261] Output: Motivation message

[1262] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1263] This invention is a system for a personal AI health assistant that provides personalized training to help users achieve their goals. It also incorporates an emotion engine that recognizes the user's emotions, enabling richer interactions. The system includes the following main components to help users manage their health efficiently and sustainably:

[1264] 1. User registration and data collection features:

[1265] User's device: The user creates an account using a dedicated app and enters profile information (name, gender, age, height, weight, goals, etc.), then syncs the wearable device with the app and configures it to collect activity data.

[1266] Server: Stores the profile information you enter and activity data obtained from the wearable device (e.g., steps, heart rate, sleep patterns) in a database.

[1267] 2. Personal advice generation function:

[1268] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. It also uses a generative AI model to generate images of the user's "before and after" appearance and displays them on the user's device.

[1269] 3. Training guidance and meal plan suggestions:

[1270] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[1271] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[1272] 4. Progress management and motivation maintenance features:

[1273] Server: Analyzes the user's training and diet progress data, evaluates their achievement, and adjusts their training menu and meal plan as needed.

[1274] Server: Generates messages and reminders based on behavioral science to help users maintain their motivation.

[1275] 5. Emotion recognition and response capabilities:

[1276] On the user's device: The emotion engine recognizes emotions from the user's voice and facial expressions, and provides appropriate feedback and advice.

[1277] Server: Generates personalized motivational messages and reminders based on the emotional data acquired by the emotion engine.

[1278] Server: Analyzes emotional data and provides appropriate support messages and training advice based on the user's mental state.

[1279] Specific examples

[1280] 1. User Registration:

[1281] User device: User B starts the app and enters her name "B", gender "female", age "25", height "160cm", weight "55kg", and goal "to increase muscle strength". She synchronizes her wearable device and sends heart rate and activity data to the app.

[1282] Server: Stores these data in a database and prepares them for analysis.

[1283] 2. Generate personalized advice:

[1284] Server: Analyzes User B's information and generates a meal plan based on his / her health condition and exercise habits, including a "strength training plan three times a week" and a "high-protein diet." It also predicts and creates an image of how his / her body will change when he / she achieves his / her goal.

[1285] User's device: Receives and displays the generated training plan, meal plan, and predicted image.

[1286] 3. Training Guidance:

[1287] User device: A virtual coach provides audio and video instruction on correct deadlift form. User B performs the exercise in front of the camera, while the device captures and analyzes their form in real time, providing feedback.

[1288] 4. Emotion recognition:

[1289] User's device: The emotion engine analyzes User B's facial expressions and voice to recognize impatience and fatigue during training.

[1290] Server: Based on the recognized emotion data, it generates a supportive message such as, "Take a short break. You're doing a great job."

[1291] User's device: The virtual coach displays a cheering message and calls out to User B.

[1292] 5. Track your progress and stay motivated:

[1293] Server: Periodically analyzes User B's training results and food intake data, generates progress reports, and updates the training plan as needed.

[1294] On your device: Receive and review plan updates and progress reports as notifications, along with timely reminders and encouragement to keep you motivated.

[1295] This allows users to efficiently execute personalized training and meal plans that take into account their emotional state, further maintaining motivation. The entire system seamlessly connects users' health information and emotional state, functioning as an optimal health management solution.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] User's device:

[1299] Users launch the dedicated app and proceed to the account creation page.

[1300] Enter basic information such as your name, email address, and password.

[1301] Configure your wearable device for synchronization and allow data collection from the device.

[1302] Step 2:

[1303] server:

[1304] We receive the basic information you enter and store it in a secure database.

[1305] Set up a scheduled task to sync activity data from your wearable device at regular intervals.

[1306] Activity data includes steps taken, heart rate, calories burned, sleep patterns, and more.

[1307] Step 3:

[1308] User's device:

[1309] Go to your profile settings page and enter your details, such as your gender, age, height, weight, and health goals (e.g., weight loss, muscle building).

[1310] Step 4:

[1311] server:

[1312] The entered details are stored in a database and the data analysis algorithm is prepared.

[1313] This will be combined with activity data obtained from wearable devices to begin analyzing the user's health information.

[1314] Step 5:

[1315] server:

[1316] The analytical algorithm evaluates the user's data to analyze their health and exercise habits.

[1317] Based on the results, a customized training and meal plan is generated.

[1318] Step 6:

[1319] server:

[1320] The generated training and meal plans are sent to the user's device.

[1321] Using a generative AI model, images of the user's appearance before and after achieving their goal are generated and sent to the user's device.

[1322] Step 7:

[1323] User's device:

[1324] A virtual coach will explain the details of the training plan and important points to note using audio and video.

[1325] Display a list of recommended meal plans and recipes.

[1326] Step 8:

[1327] User's device:

[1328] It uses cameras and sensors to capture the user's exercise form.

[1329] The captured data is sent to a server in real time for form analysis.

[1330] Step 9:

[1331] server:

[1332] Analyze your exercise form in real time and identify areas for improvement.

[1333] Generate feedback and send it to the user's device.

[1334] Step 10:

[1335] User's device:

[1336] A virtual coach displays analysis results and feedback to users, teaching them correct exercise form.

[1337] Step 11:

[1338] server:

[1339] The emotion engine captures the user's voice and facial expressions to recognize their emotions.

[1340] Generate appropriate feedback and advice based on the perceived emotions.

[1341] Step 12:

[1342] server:

[1343] Generate personalized motivational messages and reminders based on emotional data.

[1344] Send generated messages and reminders to the user's device.

[1345] Step 13:

[1346] User's device:

[1347] The virtual coach displays encouraging messages and advice based on the emotions recognized by the emotion engine.

[1348] Step 14:

[1349] server:

[1350] It regularly analyzes the user's training results and food intake data and updates progress data.

[1351] Adjust your training and nutrition plans accordingly based on your updated progress data.

[1352] Step 15:

[1353] server:

[1354] It generates messages and reminders based on behavioral science to help maintain motivation and sends them to the user's device.

[1355] Step 16:

[1356] User's device:

[1357] Receive and view progress data, updated plans, and motivational messages as notifications.

[1358] Displays encouraging messages and training reminders from a virtual coach at appropriate times.

[1359] Through this series of steps, users are provided with personalized training and meal plans that take their emotional state into account, helping them to effectively and sustainably manage their health.

[1360] Example 2

[1361] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1362] Conventional health management systems have limitations in providing users with personalized advice and training plans, and lack consideration for users' emotions and motivation. Furthermore, they lack real-time exercise form advice and emotion-based feedback, making it difficult for users to maintain their motivation to continue training.

[1363] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input health information, means for acquiring the user's activity data from the wearable device, means for analyzing the user's health information and activity data to generate personalized training plans and meal plans, means for providing the generated training plans and meal plans to the user's terminal, means for predicting changes in the user's health condition and presenting them as images, means for analyzing the user's voice and facial expressions using an emotion engine and sending emotion recognition data to the server, and means for the server to generate cheering messages based on the emotion recognition data and provide them to the user's terminal. This allows the user to efficiently perform personalized training and meal plans that take their emotional state into account, further maintaining their motivation.

[1364] "User" means a person who uses the system to input health information and receive training and meal plans.

[1365] "Health Information" refers to profile information entered by users, such as name, gender, age, height, weight, and goals.

[1366] A "wearable device" is a device, such as a Fitbit or Apple Watch, that collects user activity data and transmits it to the system.

[1367] "Activity data" refers to data about a user's daily physical movements and conditions, such as the number of steps taken, heart rate, and sleep patterns, obtained from wearable devices.

[1368] A "personalized training plan" is an exercise plan that is individually customized for you based on your health and activity data.

[1369] "Meal Plan" means a personalized meal plan based on a User's health and activity data.

[1370] A "terminal" refers to an information processing device such as a smartphone, tablet, or PC that a user uses to access the system.

[1371] An "emotion engine" is a software or hardware component that analyzes a user's voice and facial expressions to recognize emotions.

[1372] "Emotion Recognition Data" means data regarding a user's emotional state that is captured and analyzed by the Emotion Engine.

[1373] "Server" refers to the central processing system that collects, stores, and analyzes users' health and activity data and provides various services.

[1374] "Encouragement messages" are messages of encouragement that are generated by the server based on emotion recognition data and provided to maintain the user's motivation.

[1375] This invention is a system of AI personal health assistants dedicated to users, providing personalized training to help users achieve their goals. It also combines an emotion engine that recognizes the user's emotions to enable richer interactions. The main components of the entire system are as follows:

[1376] 1. User registration and data collection features:

[1377] On the user's device: The user creates an account using a dedicated app and enters profile information (such as name, gender, age, height, weight, and goals), then syncs a wearable device (such as a Fitbit or Apple Watch) to the app and configures it to collect activity data (e.g., steps, heart rate, and sleep patterns).

[1378] Server: Stores the entered profile information and activity data obtained from the wearable device in a database.

[1379] 2. Personal advice generation function:

[1380] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. It also uses a generative AI model (such as GPT-3) to generate images of the user's "before and after" appearance and displays them on the user's device.

[1381] 3. Training guidance and meal plan suggestions:

[1382] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[1383] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[1384] 4. Progress management and motivation maintenance features:

[1385] Server: Analyzes the user's training and diet progress data, evaluates achievement, adjusts training menus and meal plans as needed, and generates messages and reminders based on behavioral science to help users maintain their motivation.

[1386] 5. Emotion recognition and response capabilities:

[1387] User device: An emotion engine (for example, voice recognition software or a facial recognition camera) recognizes emotions from the user's voice and facial expressions. Once emotions are recognized, the device provides appropriate feedback and advice.

[1388] Server: The emotion engine generates personalized encouragement messages and reminders based on the emotional data acquired, providing appropriate training advice according to the user's mental state.

[1389] Specific examples

[1390] 1. User Registration:

[1391] User device: User B starts the app and enters her name "B", gender "female", age "25", height "160cm", weight "55kg", and goal "to increase muscle strength". She synchronizes her wearable device and sends heart rate and activity data to the app.

[1392] Server: Stores these data in a database and prepares them for analysis.

[1393] 2. Generate personalized advice:

[1394] Server: Analyzes User B's information and generates a meal plan based on his / her health condition and exercise habits, including a "strength training plan three times a week" and a "high-protein diet." It also predicts and creates an image of how his / her body will change when he / she achieves his / her goal.

[1395] User's device: Receives and displays the generated training plan, meal plan, and predicted image.

[1396] 3. Training Guidance:

[1397] User device: A virtual coach provides audio and video instructions on the correct deadlift form. User B performs the exercise in front of the camera, and the device captures and analyzes their form in real time, providing feedback.

[1398] 4. Emotion recognition:

[1399] User's device: The emotion engine analyzes User B's facial expressions and voice to recognize impatience and fatigue during training.

[1400] Server: Based on the recognized emotion data, it generates a supportive message such as, "Take a short break. You're doing a great job."

[1401] User's device: The virtual coach displays a cheering message and calls out to User B.

[1402] 5. Track your progress and stay motivated:

[1403] Server: Periodically analyzes User B's training results and food intake data, generates progress reports, and updates the training plan as needed.

[1404] On your device: Receive and review plan updates and progress reports as notifications, along with timely reminders and encouragement to keep you motivated.

[1405] Prompt Sentence Examples

[1406] "If a user is a 25-year-old female, 160cm tall, and weighs 55kg, and has set the goal of increasing muscle strength, please generate the current appearance and the predicted appearance after continuing strength training three times a week for three months."

[1407] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1408] Specific processing steps of the program

[1409] Step 1: User registration and data collection

[1410] The user downloads and launches the app. This is the step where they enter their name, gender, age, height, weight, and goals on the account creation screen and synchronize their wearable device. This synchronization setting sends activity data such as heart rate and number of steps from the wearable device to the app. The device then sends the entered profile information and data acquired from the wearable device to the server. The server receives this data and stores it in a database.

[1411] Input: User profile information (name, gender, age, height, weight, goal), activity data from wearable device.

[1412] Output: Profile information and activity data stored in a database.

[1413] Step 2: Analyze the data and generate personalized advice

[1414] The server retrieves user information and activity data from the database. The server then sends prompts to the generative AI model to generate images of how the user will look before and after achieving their goal. The analysis process analyzes the user's health and exercise habits to generate personalized training and meal plans. The generated plans and predicted images are sent to the device.

[1415] Input: User information stored in the database, prompt text.

[1416] Output: Before and after images based on generative AI models, personalized training and meal plans.

[1417] Step 3: Providing training guidance

[1418] The user starts a training session using the app. The device displays a virtual coach and explains the generated training plan to the user using audio and video. The user performs the training in front of the camera, and the device captures their movements. The captured data is analyzed in real time and feedback is provided. Recommended ingredients and recipes are also displayed.

[1419] Input: Generated training plan, meal plan, and movement data from the camera.

[1420] Output: Real-time analysis of behavior, feedback, recommended ingredients and recipes.

[1421] Step 4: Track progress and adjust your plan

[1422] The server periodically analyzes the user's activity and dietary data to assess the user's progress, generating a personalized progress report and adjusting personalized training and diet plans. The analysis results are then sent to the user's device.

[1423] Input: User activity data, meal data.

[1424] Output: Progress report, tailored training and meal plans.

[1425] Step 5: Recognize emotions and stay motivated

[1426] The user's device activates an emotion engine that analyzes the user's voice and facial expressions in real time. The analyzed emotion data is sent to a server. Based on this data, the server generates encouraging messages and reminders and provides them to the user's device. This helps to maintain the user's motivation.

[1427] Input: User's voice data, facial expression data.

[1428] Output: Emotion recognition data, generated cheer messages and reminders.

[1429] (Application example 2)

[1430] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1431] Conventional health management systems make it difficult for users to receive efficient and personalized training guidance in physical stores. In addition, due to a lack of emotion recognition technology, it is difficult to maintain motivation during training or appropriately respond to fatigue, which prevents a sufficient improvement in the user experience.

[1432] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's health information and activity data to generate personalized training plans and meal plans, means for providing training guidance to the user via a virtual coach, and means for recognizing the user's emotions using an emotion engine and providing feedback accordingly. This makes it possible to provide personalized training guidance and maintain the user's motivation.

[1433] A "means for a user to input health information" is an interface through which a user inputs their own health-related data.

[1434] "Means for obtaining user activity data from a wearable device" refers to a function for collecting activity data from a device worn by the user.

[1435] "Means for analyzing a user's health information and activity data to generate personalized training and meal plans" refers to a function that generates individually optimized training and meal plans based on the user's health information and activity data.

[1436] "Means for providing the generated training plan and meal plan to the user's device" refers to the function by which the server sends the generated plan to the user's device and displays it.

[1437] "Means for predicting changes in the user's health condition and presenting them as images" is a function that predicts changes in the user's health condition, visualizes the results, and presents them to the user.

[1438] "Means for providing training guidance to users via a virtual coach" refers to a function that uses a virtual instructor to guide and instruct users on training methods.

[1439] "Means for recognizing user emotions using an emotion engine and providing feedback accordingly" is a function that analyzes the user's emotions and provides feedback and advice based on the results.

[1440] "Means for generating and providing users with reminders and encouraging messages to maintain motivation" is a function that creates and provides appropriate reminders and encouraging messages to maintain users' motivation.

[1441] This invention provides a user-specific AI personal health assistant system that supports health management and training. The system analyzes the user's health information and emotional state and provides personalized training and meal plans accordingly.

[1442] Hardware and software used

[1443] The following hardware and software are used to realize the system.

[1444] Hardware: Smartphones, smart glasses, wearable devices (e.g., smartwatches)

[1445] software:

[1446] Mobile application (iOS / Android)

[1447] Emotion recognition engine (e.g. Microsoft Azure Emotion API)

[1448] Generative AI models (e.g., OpenAI GPT-3)

[1449] Database (e.g. Firebase)

[1450] Data collection

[1451] Using a mobile app, users enter their profile information (such as name, gender, age, height, weight, and goals), and sync their wearable device to collect activity data such as heart rate, activity level, and sleep patterns. This data is then stored on a server for analysis.

[1452] Generate personalized advice

[1453] The server analyzes the user's stored health and activity data to generate personalized training and meal plans. Using a generative AI model (OpenAI GPT-3), it predicts the specific plan and how the body shape will change when the goal is achieved, and generates images of the plan.

[1454] Training Guidance and Emotion Recognition

[1455] During training, a virtual coach provides real-time guidance to the user via smartphone or smart glasses. An emotion recognition engine (Microsoft Azure Emotion API) recognizes emotions from the user's facial expressions and voice and provides appropriate feedback. Appropriate encouraging messages such as "Take a short break" are provided to the user.

[1456] Maintaining motivation

[1457] The server generates behavioral science-based messages and reminders, sends notifications to users to keep them motivated, and periodically analyzes their progress data to adjust their training and meal plans accordingly.

[1458] Specific examples

[1459] For example, when a user arrives at the gym, they open their smartphone and check the designated training plan. They put on smart glasses and train under the guidance of a virtual coach. If fatigue is detected through emotion recognition, a message will automatically appear encouraging them to take a break. After training, they will also receive a progress report and their next training content will be automatically updated. They can also check their meal plan through the app and try out the recommended recipes.

[1460] Prompt Sentence Examples

[1461] "Enter your health information, including your name, age, gender, and goals. Then generate training and meal plan suggestions."

[1462] This allows users to receive personalized health management and training support, enabling sustainable health management.

[1463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1464] Step 1:

[1465] The user's device provides an interface for the user to input health information, such as name, gender, age, height, weight, and goals. The input information is temporarily stored by the user's device and then sent to the server.

[1466] Step 2:

[1467] The server stores the health information received from the user's device in a database. At the same time, it also collects activity data (heart rate, number of steps, sleep patterns, etc.) from the user's synchronized wearable device and stores this data in the database. This provides the basic data for analysis.

[1468] Step 3:

[1469] The server analyzes the user's health information and activity data stored in the database. This process uses an AI model (e.g., OpenAI GPT-3) to generate personalized training and meal plans. For example, it might suggest a "strength training plan three times a week based on the user's health information" and a "high-protein meal plan." These generated plans are then stored again on the server.

[1470] Step 4:

[1471] The server sends the generated training plan and meal plan to the user's device, which displays the received plan for the user to review.

[1472] Step 5:

[1473] The user's device activates a virtual coach function that guides the user through the training session. The virtual coach uses a camera to capture the user's exercise form and provides real-time feedback, such as "Your deadlift form is incorrect."

[1474] Step 6:

[1475] The user's device uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize emotions from the user's facial expressions and voice. This data is sent to a server, which automatically generates feedback according to the user's emotional state. For example, if the user expresses "feeling tired," a message such as "take a short break" is generated.

[1476] Step 7:

[1477] The server generates motivational reminders and encouraging messages based on behavioral science. This is also done using a generative AI model. The created reminders and messages are sent to the user's device and displayed to the user. For example, a message such as "You're almost there, keep trying!" may be displayed.

[1478] Step 8:

[1479] The user's progress data is periodically sent to a server, which analyzes it. Based on this progress data, the training and meal plans are updated as needed to provide the user with the optimal plan. For example, the next training menu will be optimized based on data such as "muscle strength improved by 5% in three weeks."

[1480] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1481] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1482] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1483] [Fourth embodiment]

[1484] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1485] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1486] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1487] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1488] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1489] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1490] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1491] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1492] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1493] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1494] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1495] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1496] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1497] This invention is a system for a user-dedicated AI personal health assistant that provides personal training to help users achieve their goals (e.g., improving health, losing weight, building muscle). This system includes the following main components to enable users to efficiently and sustainably manage their health:

[1498] 1. User registration and data collection features:

[1499] On your device: Users create an account using a dedicated app or web portal, enter their profile information (e.g., name, gender, age, height, weight, health goals), and sync their wearable device (e.g., smartwatch) to the app and set it up to collect activity data.

[1500] Server: Stores the profile information you enter and activity data obtained from the wearable device (e.g., steps, heart rate, sleep patterns) in a database.

[1501] 2. Personal advice generation function:

[1502] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. The analysis algorithm takes into account the user's past activity data and health information.

[1503] Server: Sends the generated training and meal plans to the user's device, and also uses the generative AI model to generate images of the user's "before and after" appearance and presents them to the user.

[1504] 3. Training guidance and meal plan suggestions:

[1505] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[1506] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[1507] 4. Progress management and motivation maintenance features:

[1508] Server: Analyzes the user's training and diet progress data, evaluates their achievement, and adjusts their training menu and meal plan as needed.

[1509] Server: Generates messages and reminders based on behavioral science to help users maintain their motivation.

[1510] Specific examples

[1511] 1. User Registration:

[1512] User device: User A starts the app and enters his name "A Taro", gender "male", age "30", height "170cm", weight "70kg", and goal "diet". The wearable device is synchronized and heart rate and activity data is sent to the app.

[1513] Server: Stores these data in a database and prepares them for analysis.

[1514] 2. Generate personalized advice:

[1515] Server: The analytical algorithm operates based on User A's information stored in the database. It analyzes the past week's activity data and generates a training plan such as "30 minutes of jogging with the goal of burning 500 calories a day." It also generates a "high-protein, low-calorie meal plan" and suggests specific menus. Using the generative AI model, it predicts and creates an image of User A's body shape if he or she achieves his or her goal.

[1516] User's device: Receives and displays the generated training plan, meal plan, and prediction image.

[1517] 3. Training Guidance:

[1518] User device: A virtual coach provides audio and video instruction on the correct squat form. User A performs the exercise in front of the camera, and the device captures and analyzes their form in real time, providing feedback.

[1519] 4. Track your progress and stay motivated:

[1520] Server: Monitors user A's weekly training results and generates a report such as "Last week's average calorie consumption was 450 calories." If necessary, adjust the training plan for the following week.

[1521] On your device: Receive and review progress reports as notifications, as well as encouragement and training reminders from your virtual coach.

[1522] This allows users to effectively and sustainably manage their health. The entire system is designed to seamlessly support the flow from collecting a series of data to providing personalized advice and progress management.

[1523] The processing flow will be explained below.

[1524] Step 1:

[1525] User's device:

[1526] Users launch the dedicated app and proceed to the account creation page.

[1527] Enter basic information such as your name, email address, and password.

[1528] Configure your wearable device for synchronization and allow data collection from the device.

[1529] Step 2:

[1530] server:

[1531] We receive the basic information you enter and store it in a secure database.

[1532] Set up a scheduled task to periodically sync activity data from your wearable device.

[1533] Activity data includes steps taken, heart rate, calories burned, sleep patterns, and more.

[1534] Step 3:

[1535] User's device:

[1536] Go to your profile settings page.

[1537] Enter detailed health information such as gender, age, height, weight, and goals (e.g., diet, muscle building).

[1538] Step 4:

[1539] server:

[1540] The detailed health information entered is stored in a database, ready to begin the analysis process.

[1541] The data analysis algorithm combines and analyzes the user's health information and activity data from the wearable device.

[1542] Step 5:

[1543] server:

[1544] Analytical algorithms evaluate the user's data and generate a customized training plan based on their health and exercise habits.

[1545] We also create optimal meal plans for users under the supervision of a nutritionist.

[1546] Step 6:

[1547] server:

[1548] The generated training and meal plans are sent to the user's device.

[1549] At the same time, a generative AI model is used to generate images of the user's appearance before and after achieving their goal, which are then sent to the user's device.

[1550] Step 7:

[1551] User's device:

[1552] A virtual coach will explain the details of the training plan and important points to note using audio and video.

[1553] Display a list of recommended meal plans and recipes.

[1554] Step 8:

[1555] User's device:

[1556] It uses cameras and sensors to capture the user's exercise form.

[1557] The captured data is sent to a server in real time for form analysis.

[1558] Step 9:

[1559] server:

[1560] Analyze your exercise form in real time and identify areas for improvement.

[1561] Generate feedback and send it to the user's device.

[1562] Step 10:

[1563] User's device:

[1564] A virtual coach displays analysis results and feedback to users, teaching them correct exercise form.

[1565] Step 11:

[1566] server:

[1567] It regularly analyzes the user's training results and food intake data and updates progress data.

[1568] Adjust your training and nutrition plans accordingly based on your updated progress data.

[1569] Step 12:

[1570] server:

[1571] Generate motivational messages and reminders based on behavioral science.

[1572] Send generated messages and reminders to the user's device.

[1573] Step 13:

[1574] User's device:

[1575] Receive and view progress data, updated plans, and motivational messages as notifications.

[1576] Encouraging messages and training reminders from the virtual coach are also displayed at appropriate times.

[1577] This series of steps provides users with a system that allows them to effectively and sustainably manage their health.

[1578] Example 1

[1579] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1580] Conventional health management systems struggle to optimally utilize users' health and activity data to provide personalized exercise and meal plans suited to each individual user. Furthermore, they lack sufficient means to maintain users' motivation and appropriately manage their progress, making it difficult to maintain sustainable health management. A system that solves these issues and enables users to manage their health effectively and sustainably is needed.

[1581] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1582] In this invention, the server

[1583] a means for a user to input health information;

[1584] means for acquiring user activity data from the wearable device;

[1585] means for analyzing the user's health information and activity data to generate personalized exercise and meal plans;

[1586] means for providing the generated exercise plan and meal plan to a user's terminal;

[1587] A means for predicting changes in a user's health status using a generative AI model and presenting the predicted changes as a graphic; and

[1588] A means to analyze your progress and adjust your exercise and meal plans as needed;

[1589] Generate behavioral science-based messages and reminders to keep users motivated,

[1590] This allows users to effectively and sustainably manage their health.

[1591] "Means for users to input health information" refers to a function that provides an interface for users to input information about their own health.

[1592] "Means for obtaining user activity data from wearable devices" refers to the ability to collect activity data such as a user's steps, heart rate, and sleep patterns from devices such as smartwatches and fitness trackers.

[1593] "Means for analyzing a user's health information and activity data to generate personalized exercise and meal plans" refers to a function that creates exercise and meal plans optimized for individual users based on the acquired health information and activity data.

[1594] "Means for providing the generated exercise plan and meal plan to the user's device" refers to a function that sends the created exercise plan and meal plan to the user's device, such as a smartphone or tablet, and displays them.

[1595] "Means for predicting changes in a user's health status using a generative AI model and presenting them as images" refers to a function that utilizes artificial intelligence technology to predict the progress and changes in a user's health status and presents them to the user as visual images.

[1596] "Means for analyzing the user's progress data and adjusting the exercise menu and meal plan as necessary" refers to a function that analyzes the user's exercise and diet progress and dynamically adjusts the exercise menu and meal plan according to the results.

[1597] "A means of generating messages and reminders based on behavioral science to maintain user motivation" is a function that uses knowledge from behavioral science to create encouraging messages and reminders that make it easy for users to continue, and notifies them of these.

[1598] This invention is a personal health assistant system that provides individualized training and meal plans to help users achieve their goals, such as improving their health, losing weight, and building muscle. The system includes the following main components to help users manage their health efficiently and sustainably:

[1599] User registration and data collection features

[1600] Users create an account using a dedicated app or web portal, entering profile information such as name, gender, age, height, weight, and goals, using a standard user device such as a smartphone or tablet.

[1601] Users sync their smartwatches or other wearable devices with the app, which automatically collects daily activity data (e.g., steps taken, heart rate, and sleep patterns). Specifically, Apple Watches and Fitbits are used.

[1602] The server stores the entered profile information and activity data obtained from the wearable device in a database.

[1603] Personal advice generator

[1604] The server analyzes the user's health and activity data stored in the database, using data analysis tools such as Python and R.

[1605] The server uses generative AI models, such as machine learning libraries TensorFlow and PyTorch, to generate customized exercise and meal plans for the user.

[1606] The server predicts the changes in the user's body shape after achieving their goal and generates an image using a generative AI model, providing the user with visual feedback.

[1607] The server transmits the generated exercise plan and meal plan to the user's terminal.

[1608] Training guidance and meal plan suggestions

[1609] The user's device will then display the received exercise and meal plans using a dedicated app.

[1610] The user exercises under the guidance of a virtual coach. The virtual coach provides audio and video instructions on exercise form, such as squats and push-ups, and the user's device uses a camera to capture and analyze their exercise form in real time.

[1611] The user's device will display a list of recommended ingredients and recipes, providing them in a way that makes them easy to incorporate into everyday life.

[1612] Progress management and motivation maintenance features

[1613] The server analyzes the user's exercise and diet progress data and adjusts the new plan based on their performance.

[1614] The server generates messages and reminders based on behavioral science, and sends encouraging messages and progress reports to users. For example, a message such as "Keep up the good work this week!" is sent to the user's device.

[1615] Specific examples

[1616] Suppose User A launches the app and enters their name, gender, age, height, weight, and goal. User A sets "weight loss" as their goal and synchronizes their wearable device with the app. The server saves User A's information and uses the generative AI model to generate a customized exercise plan of "30 minutes of jogging with the goal of burning 500 calories a day" and a meal plan of "high-protein, low-calorie meals." The generative AI model is then used to predict how User A's body shape will change if they achieve their goal, and the results are displayed as an image.

[1617] When User A starts exercising, the virtual coach instructs them on the correct form, and the user's device analyzes the exercise in real time via a camera and provides feedback. At the end of the week, the server analyzes the progress and notifies User A of a new plan if necessary.

[1618] This allows the user to effectively and sustainably manage their health.

[1619] An example of a prompt is:

[1620] "Please create a diet plan for User A based on the following information: Age 30, Gender Male, Goal is to lose 5kg in 1 month."

[1621] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1622] Step 1: User Registration

[1623] A user launches the app and creates an account

[1624] Using a dedicated app or web portal, users enter their profile information, such as name, gender, age, height, weight, and goals. The data is sent from the user's device to a server and stored in a database.

[1625] Input: Name, Gender, Age, Height, Weight, Health Goals

[1626] Output: Registration information is saved in the database

[1627] Step 2: Device Sync and Data Collection

[1628] The user syncs the wearable device

[1629] A user syncs a wearable device, such as a smartwatch, with the app, which sends activity data (e.g., steps taken, heart rate, sleep patterns) to the server through the app.

[1630] Input: Sync settings for wearable devices

[1631] Output: Activity data is saved on the server

[1632] Step 3: Data analysis

[1633] The server analyzes the data

[1634] The server analyzes the user's health and activity data stored in the database, using data analysis tools such as Python and R to evaluate the user's current health status and exercise habits.

[1635] Input: Health information and activity data

[1636] Output: User's health status assessment data as analysis results

[1637] Step 4: Create a personal plan

[1638] The server generates a plan using the generative AI model

[1639] Based on the analysis results, the server uses a generative AI model (a machine learning model using TensorFlow or PyTorch) to generate a customized exercise plan and meal plan.

[1640] Input: Health status assessment data

[1641] Output: Customized exercise and meal plans

[1642] Step 5: Generate predicted image

[1643] The server generates a predicted image after the goal is achieved.

[1644] The server uses the generative AI model to generate an image that predicts how the user's body shape will change if they achieve their goal.

[1645] Input: Health assessment data, exercise plan, meal plan

[1646] Output: Predicted image after goal is achieved

[1647] Step 6: Submit your plan

[1648] The server sends the exercise plan and meal plan to the device.

[1649] The server sends the generated exercise plan, meal plan, and predicted image to the user's terminal, which displays them.

[1650] Input: Exercise plan, Meal plan, Predictive image

[1651] Output: The plan and image are displayed on the user's device.

[1652] Step 7: Training Instruction

[1653] The user exercises according to the instructions of the virtual coach

[1654] The user performs exercises under the guidance of a virtual coach, and the user's device uses a camera to capture and analyze their exercise form in real time.

[1655] Input: User's exercise data (camera footage)

[1656] Output: Real-time feedback

[1657] Step 8: View Meal Plan

[1658] The user's device displays the meal plan.

[1659] The user's device displays a list of recommended ingredients and recipes, providing information in a format that is easy for the user to implement in their daily lives.

[1660] Input: Meal plan data

[1661] Output: Display of recommended ingredients and recipe

[1662] Step 9: Progress data analysis

[1663] The server analyzes the progress data and adjusts the plan as needed

[1664] The server analyzes the user's training and dietary progress data and adjusts the exercise menu and meal plan according to the user's level of achievement.

[1665] Input: training data, dietary data

[1666] Output: A new, tailored exercise and meal plan

[1667] Step 10: Stay motivated

[1668] The server generates and notifies a motivation message.

[1669] The server generates messages and reminders based on behavioral science and sends them to the user's device to keep them motivated.

[1670] Input: Progress data

[1671] Output: Cheerful messages and reminder notifications

[1672] (Application example 1)

[1673] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1674] Traditional fitness and health management systems struggle to motivate users and provide insufficient personalized advice. Many systems also lack the ability to provide real-time feedback to users, making it difficult to ensure they are performing exercises with proper form. Furthermore, there are limited ways to efficiently track activity at gyms and other facilities.

[1675] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1676] In this invention, the server includes a means for a user to input health information, a means for acquiring the user's activity data from the wearable device, and a means for analyzing the user's health information and activity data to generate personalized training and meal plans. This allows the training and meal plans to be provided to the user's dedicated device. Furthermore, a means for predicting changes in the user's health status and presenting them as images allows the user to visualize their goals. Furthermore, a means for a virtual instructor to provide real-time feedback on the user's exercise form during exercise allows the user to maintain proper exercise form. Furthermore, a means for synchronizing activity data upon check-in at a health facility or training gym allows comprehensive tracking of the user's fitness activities.

[1677] "User" refers to an individual who provides health information and activity data in an Invention.

[1678] "Health Information" is profile information entered by the user, such as height, weight, age, gender, and goals.

[1679] "Wearable devices" refers to devices such as smartwatches and fitness trackers that collect user activity data.

[1680] "Activity data" refers to information such as a user's number of steps, heart rate, amount of exercise, and sleep patterns.

[1681] A "personalized training plan" is an exercise menu customized for you based on your health and activity data.

[1682] A "meal plan" is a plan that provides recommended dietary habits and recipes based on a user's health goals.

[1683] "User-dedicated devices" are devices used by users, such as smartphones, tablets, and personal computers.

[1684] A "virtual instructor" is a digital coach that monitors the user's exercise form and provides real-time feedback on areas for improvement.

[1685] "Means for predicting changes in health status and presenting them as images" is a method that uses a generative AI model to provide simulated images of what a user will look like after achieving their goal.

[1686] "Facility check-in" is the process by which a user scans a QR code to enter a health facility or gym.

[1687] "Means for synchronizing activity data" refers to a method of automatically collecting and recording data by linking a user's wearable device with an application when checking in to a facility.

[1688] The system collects and analyzes users' health and activity data to provide personalized training and meal plans, predict changes in the user's health, and provides real-time exercise form feedback from a virtual coach and data synchronization upon facility check-in.

[1689] The server includes the following components:

[1690] 1. A means for users to enter health information:

[1691] The server provides a user interface for inputting health information such as age, gender, height, weight, and health goals through the user terminal, allowing users to easily input their own health information.

[1692] 2. Means for obtaining user activity data from wearable devices:

[1693] The server sets up an API to retrieve activity data from the user's smartwatch or fitness tracker, allowing it to collect data such as the user's steps, heart rate, exercise volume, and sleep patterns in real time.

[1694] 3. Means for analyzing your health and activity data to generate personalized training and meal plans:

[1695] The server runs machine learning algorithms to analyze the collected health and activity data, which allows it to generate optimal training and meal plans for the user.

[1696] 4. Means for providing generated training and meal plans to a user's device:

[1697] The server then sends the generated training and meal plans to the user's smartphone or tablet, allowing the user to view and follow the plans on their own dedicated device.

[1698] 5. A means for a virtual instructor to provide real-time feedback on exercise form as the user exercises:

[1699] The user device uses a camera to capture the user's exercise form, and a virtual instructor evaluates the accuracy of the form in real time and provides necessary feedback, helping the user to continue exercising with correct form.

[1700] 6. A method for predicting changes in the user's health status and presenting them as images:

[1701] The server uses a generative AI model to simulate the changes in the user's body shape when they achieve their goal, generating an image that is then provided to the user to help maintain their motivation.

[1702] 7. Means of syncing activity data when checking into a facility:

[1703] By scanning a QR code when users check in to a facility, the app automatically synchronizes activity data from the wearable device to the app, allowing users to efficiently track their gym activities.

[1704] Examples of concrete examples and prompts

[1705] Examples:

[1706] Consider a scenario where a gym member creates an account, syncs their activity data to an app, and follows a training plan generated based on that data.

[1707] Example prompt sentence:

[1708] Prompt: Gym member A has created an account and synced their activity data. Please generate a training plan and meal plan based on the following profile and activity data:

[1709] Profile information: Age 30, weight 70kg, goal is diet

[1710] Activity data: Approximately 10,000 steps per day, average heart rate 80 bpm

[1711] Dietary data: Average calorie intake: 2,100 calories

[1712] This allows users to effectively manage their health through a dedicated device and continue training while maintaining motivation. The system is designed to seamlessly support the flow from collecting a series of data to providing personalized advice and progress management.

[1713] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1714] Step 1:

[1715] The user launches a dedicated app on their smartphone or tablet and enters their health information.

[1716] Specific operation: The user creates an account by entering information such as age, gender, height, weight, and health goals. The entered data is sent from the device to the server.

[1717] Input: Age, Gender, Height, Weight, Health Goals

[1718] Output: User's health profile data

[1719] Step 2:

[1720] The terminal synchronizes with the wearable device and acquires the user's activity data.

[1721] Specific operation: A user uses a smartwatch or fitness tracker and syncs activity data (number of steps, heart rate, exercise volume, sleep data) to a dedicated app. The data is then sent to a server via the device.

[1722] Input: steps, heart rate, exercise, sleep data

[1723] Output: User activity data

[1724] Step 3:

[1725] The server analyzes the user's health and activity data and generates personalized training and meal plans.

[1726] Specific operation: Based on the health information and activity data received by the server, the data is analyzed using machine learning algorithms to generate personalized training and meal plans, which are then sent to the user's device.

[1727] Input: User health profile data, User activity data

[1728] Output: Personalized training plan, meal plan

[1729] Step 4:

[1730] The server uses the generated AI model to predict changes in the user's body shape after achieving their goal and generates an image of it.

[1731] How it works: The server uses the user's health information and a predictive algorithm to simulate what the patient will look like after achieving their goal, generating an image of the results, which is then sent to the user's device.

[1732] Input: User's health profile data, goals

[1733] Output: Predicted image

[1734] Step 5:

[1735] When users check in to the gym, they scan a QR code to sync their activity data.

[1736] What it does: When a user arrives at the gym, they scan a QR code and sync their latest activity data from their wearable device to the app, which then sends the synced data to the server.

[1737] Input: QR code scan, latest activity data

[1738] Output: Gym activity data synchronization

[1739] Step 6:

[1740] The user terminal uses a virtual instructor to provide real-time feedback on the user's exercise form.

[1741] Specific Movements: The smartphone camera captures the user's exercise form and analyzes it in real time. The virtual instructor compares it with the correct form and provides necessary feedback via voice or text.

[1742] Input: Video data of exercise form

[1743] Output: Form improvement feedback

[1744] Step 7:

[1745] The server analyzes the user's progress data and generates messages to keep them motivated.

[1746] Specific operation: The server continuously analyzes the user's training and diet data, and generates and sends motivational messages based on behavioral science, thereby maintaining the user's motivation.

[1747] Input: training data, dietary data

[1748] Output: Motivation message

[1749] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1750] This invention is a system for a personal AI health assistant that provides personalized training to help users achieve their goals. It also incorporates an emotion engine that recognizes the user's emotions, enabling richer interactions. The system includes the following main components to help users manage their health efficiently and sustainably:

[1751] 1. User registration and data collection features:

[1752] User's device: The user creates an account using a dedicated app and enters profile information (name, gender, age, height, weight, goals, etc.), then syncs the wearable device with the app and configures it to collect activity data.

[1753] Server: Stores the profile information you enter and activity data obtained from the wearable device (e.g., steps, heart rate, sleep patterns) in a database.

[1754] 2. Personal advice generation function:

[1755] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. It also uses a generative AI model to generate images of the user's "before and after" appearance and displays them on the user's device.

[1756] 3. Training guidance and meal plan suggestions:

[1757] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[1758] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[1759] 4. Progress management and motivation maintenance features:

[1760] Server: Analyzes the user's training and diet progress data, evaluates their achievement, and adjusts their training menu and meal plan as needed.

[1761] Server: Generates messages and reminders based on behavioral science to help users maintain their motivation.

[1762] 5. Emotion recognition and response capabilities:

[1763] On the user's device: The emotion engine recognizes emotions from the user's voice and facial expressions, and provides appropriate feedback and advice.

[1764] Server: Generates personalized motivational messages and reminders based on the emotional data acquired by the emotion engine.

[1765] Server: Analyzes emotional data and provides appropriate support messages and training advice based on the user's mental state.

[1766] Specific examples

[1767] 1. User Registration:

[1768] User device: User B starts the app and enters her name "B", gender "female", age "25", height "160cm", weight "55kg", and goal "to increase muscle strength". She synchronizes her wearable device and sends heart rate and activity data to the app.

[1769] Server: Stores these data in a database and prepares them for analysis.

[1770] 2. Generate personalized advice:

[1771] Server: Analyzes User B's information and generates a meal plan based on his / her health condition and exercise habits, including a "strength training plan three times a week" and a "high-protein diet." It also predicts and creates an image of how his / her body will change when he / she achieves his / her goal.

[1772] User's device: Receives and displays the generated training plan, meal plan, and predicted image.

[1773] 3. Training Guidance:

[1774] User device: A virtual coach provides audio and video instruction on correct deadlift form. User B performs the exercise in front of the camera, while the device captures and analyzes their form in real time, providing feedback.

[1775] 4. Emotion recognition:

[1776] User's device: The emotion engine analyzes User B's facial expressions and voice to recognize impatience and fatigue during training.

[1777] Server: Based on the recognized emotion data, it generates a supportive message such as, "Take a short break. You're doing a great job."

[1778] User's device: The virtual coach displays a cheering message and calls out to User B.

[1779] 5. Track your progress and stay motivated:

[1780] Server: Periodically analyzes User B's training results and food intake data, generates progress reports, and updates the training plan as needed.

[1781] On your device: Receive and review plan updates and progress reports as notifications, along with timely reminders and encouragement to keep you motivated.

[1782] This allows users to efficiently execute personalized training and meal plans that take into account their emotional state, further maintaining motivation. The entire system seamlessly connects users' health information and emotional state, functioning as an optimal health management solution.

[1783] The processing flow will be explained below.

[1784] Step 1:

[1785] User's device:

[1786] Users launch the dedicated app and proceed to the account creation page.

[1787] Enter basic information such as your name, email address, and password.

[1788] Configure your wearable device for synchronization and allow data collection from the device.

[1789] Step 2:

[1790] server:

[1791] We receive the basic information you enter and store it in a secure database.

[1792] Set up a scheduled task to sync activity data from your wearable device at regular intervals.

[1793] Activity data includes steps taken, heart rate, calories burned, sleep patterns, and more.

[1794] Step 3:

[1795] User's device:

[1796] Go to your profile settings page and enter your details, such as your gender, age, height, weight, and health goals (e.g., weight loss, muscle building).

[1797] Step 4:

[1798] server:

[1799] The entered details are stored in a database and the data analysis algorithm is prepared.

[1800] This will be combined with activity data obtained from wearable devices to begin analyzing the user's health information.

[1801] Step 5:

[1802] server:

[1803] The analytical algorithm evaluates the user's data to analyze their health and exercise habits.

[1804] Based on the results, a customized training and meal plan is generated.

[1805] Step 6:

[1806] server:

[1807] The generated training and meal plans are sent to the user's device.

[1808] Using a generative AI model, images of the user's appearance before and after achieving their goal are generated and sent to the user's device.

[1809] Step 7:

[1810] User's device:

[1811] A virtual coach will explain the details of the training plan and important points to note using audio and video.

[1812] Display a list of recommended meal plans and recipes.

[1813] Step 8:

[1814] User's device:

[1815] It uses cameras and sensors to capture the user's exercise form.

[1816] The captured data is sent to a server in real time for form analysis.

[1817] Step 9:

[1818] server:

[1819] Analyze your exercise form in real time and identify areas for improvement.

[1820] Generate feedback and send it to the user's device.

[1821] Step 10:

[1822] User's device:

[1823] A virtual coach displays analysis results and feedback to users, teaching them correct exercise form.

[1824] Step 11:

[1825] server:

[1826] The emotion engine captures the user's voice and facial expressions to recognize their emotions.

[1827] Generate appropriate feedback and advice based on the perceived emotions.

[1828] Step 12:

[1829] server:

[1830] Generate personalized motivational messages and reminders based on emotional data.

[1831] Send generated messages and reminders to the user's device.

[1832] Step 13:

[1833] User's device:

[1834] The virtual coach displays encouraging messages and advice based on the emotions recognized by the emotion engine.

[1835] Step 14:

[1836] server:

[1837] It regularly analyzes the user's training results and food intake data and updates progress data.

[1838] Adjust your training and nutrition plans accordingly based on your updated progress data.

[1839] Step 15:

[1840] server:

[1841] It generates messages and reminders based on behavioral science to help maintain motivation and sends them to the user's device.

[1842] Step 16:

[1843] User's device:

[1844] Receive and view progress data, updated plans, and motivational messages as notifications.

[1845] Displays encouraging messages and training reminders from a virtual coach at appropriate times.

[1846] Through this series of steps, users are provided with personalized training and meal plans that take their emotional state into account, helping them to effectively and sustainably manage their health.

[1847] Example 2

[1848] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1849] Conventional health management systems have limitations in providing users with personalized advice and training plans, and lack consideration for users' emotions and motivation. Furthermore, they lack real-time exercise form advice and emotion-based feedback, making it difficult for users to maintain their motivation to continue training.

[1850] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input health information, means for acquiring the user's activity data from the wearable device, means for analyzing the user's health information and activity data to generate personalized training plans and meal plans, means for providing the generated training plans and meal plans to the user's terminal, means for predicting changes in the user's health condition and presenting them as images, means for analyzing the user's voice and facial expressions using an emotion engine and sending emotion recognition data to the server, and means for the server to generate cheering messages based on the emotion recognition data and provide them to the user's terminal. This allows the user to efficiently perform personalized training and meal plans that take their emotional state into account, further maintaining their motivation.

[1851] "User" means a person who uses the system to input health information and receive training and meal plans.

[1852] "Health Information" refers to profile information entered by users, such as name, gender, age, height, weight, and goals.

[1853] A "wearable device" is a device, such as a Fitbit or Apple Watch, that collects user activity data and transmits it to the system.

[1854] "Activity data" refers to data about a user's daily physical movements and conditions, such as the number of steps taken, heart rate, and sleep patterns, obtained from wearable devices.

[1855] A "personalized training plan" is an exercise plan that is individually customized for you based on your health and activity data.

[1856] "Meal Plan" means a personalized meal plan based on a User's health and activity data.

[1857] A "terminal" refers to an information processing device such as a smartphone, tablet, or PC that a user uses to access the system.

[1858] An "emotion engine" is a software or hardware component that analyzes a user's voice and facial expressions to recognize emotions.

[1859] "Emotion Recognition Data" means data regarding a user's emotional state that is captured and analyzed by the Emotion Engine.

[1860] "Server" refers to the central processing system that collects, stores, and analyzes users' health and activity data and provides various services.

[1861] "Encouragement messages" are messages of encouragement that are generated by the server based on emotion recognition data and provided to maintain the user's motivation.

[1862] This invention is a system of AI personal health assistants dedicated to users, providing personalized training to help users achieve their goals. It also combines an emotion engine that recognizes the user's emotions to enable richer interactions. The main components of the entire system are as follows:

[1863] 1. User registration and data collection features:

[1864] On the user's device: The user creates an account using a dedicated app and enters profile information (such as name, gender, age, height, weight, and goals), then syncs a wearable device (such as a Fitbit or Apple Watch) to the app and configures it to collect activity data (e.g., steps, heart rate, and sleep patterns).

[1865] Server: Stores the entered profile information and activity data obtained from the wearable device in a database.

[1866] 2. Personal advice generation function:

[1867] Server: Analyzes the stored data and evaluates the user's health and exercise habits. Based on this, it generates customized training and meal plans. It also uses a generative AI model (such as GPT-3) to generate images of the user's "before and after" appearance and displays them on the user's device.

[1868] 3. Training guidance and meal plan suggestions:

[1869] User device: The received training and meal plans are provided to the user through guidance from a virtual coach, who monitors the correctness of exercise form via camera and provides real-time feedback.

[1870] On the user's device: Recommended ingredients and recipes are displayed as a list, presented in a way that is easy for the user to follow in their daily lives.

[1871] 4. Progress management and motivation maintenance features:

[1872] Server: Analyzes the user's training and diet progress data, evaluates achievement, adjusts training menus and meal plans as needed, and generates messages and reminders based on behavioral science to help users maintain their motivation.

[1873] 5. Emotion recognition and response capabilities:

[1874] User device: An emotion engine (for example, voice recognition software or a facial recognition camera) recognizes emotions from the user's voice and facial expressions. Once emotions are recognized, the device provides appropriate feedback and advice.

[1875] Server: The emotion engine generates personalized encouragement messages and reminders based on the emotional data acquired, providing appropriate training advice according to the user's mental state.

[1876] Specific examples

[1877] 1. User Registration:

[1878] User device: User B starts the app and enters her name "B", gender "female", age "25", height "160cm", weight "55kg", and goal "to increase muscle strength". She synchronizes her wearable device and sends heart rate and activity data to the app.

[1879] Server: Stores these data in a database and prepares them for analysis.

[1880] 2. Generate personalized advice:

[1881] Server: Analyzes User B's information and generates a meal plan based on his / her health condition and exercise habits, including a "strength training plan three times a week" and a "high-protein diet." It also predicts and creates an image of how his / her body will change when he / she achieves his / her goal.

[1882] User's device: Receives and displays the generated training plan, meal plan, and predicted image.

[1883] 3. Training Guidance:

[1884] User device: A virtual coach provides audio and video instructions on the correct deadlift form. User B performs the exercise in front of the camera, and the device captures and analyzes their form in real time, providing feedback.

[1885] 4. Emotion recognition:

[1886] User's device: The emotion engine analyzes User B's facial expressions and voice to recognize impatience and fatigue during training.

[1887] Server: Based on the recognized emotion data, it generates a supportive message such as, "Take a short break. You're doing a great job."

[1888] User's device: The virtual coach displays a cheering message and calls out to User B.

[1889] 5. Track your progress and stay motivated:

[1890] Server: Periodically analyzes User B's training results and food intake data, generates progress reports, and updates the training plan as needed.

[1891] On your device: Receive and review plan updates and progress reports as notifications, along with timely reminders and encouragement to keep you motivated.

[1892] Prompt Sentence Examples

[1893] "If a user is a 25-year-old female, 160cm tall, and weighs 55kg, and has set the goal of increasing muscle strength, please generate the current appearance and the predicted appearance after continuing strength training three times a week for three months."

[1894] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1895] Specific processing steps of the program

[1896] Step 1: User registration and data collection

[1897] The user downloads and launches the app. This is the step where they enter their name, gender, age, height, weight, and goals on the account creation screen and synchronize their wearable device. This synchronization setting sends activity data such as heart rate and number of steps from the wearable device to the app. The device then sends the entered profile information and data acquired from the wearable device to the server. The server receives this data and stores it in a database.

[1898] Input: User profile information (name, gender, age, height, weight, goal), activity data from wearable device.

[1899] Output: Profile information and activity data stored in a database.

[1900] Step 2: Analyze the data and generate personalized advice

[1901] The server retrieves user information and activity data from the database. The server then sends prompts to the generative AI model to generate images of how the user will look before and after achieving their goal. The analysis process analyzes the user's health and exercise habits to generate personalized training and meal plans. The generated plans and predicted images are sent to the device.

[1902] Input: User information stored in the database, prompt text.

[1903] Output: Before and after images based on generative AI models, personalized training and meal plans.

[1904] Step 3: Providing training guidance

[1905] The user starts a training session using the app. The device displays a virtual coach and explains the generated training plan to the user using audio and video. The user performs the training in front of the camera, and the device captures their movements. The captured data is analyzed in real time and feedback is provided. Recommended ingredients and recipes are also displayed.

[1906] Input: Generated training plan, meal plan, and movement data from the camera.

[1907] Output: Real-time analysis of behavior, feedback, recommended ingredients and recipes.

[1908] Step 4: Track progress and adjust your plan

[1909] The server periodically analyzes the user's activity and dietary data to assess the user's progress, generating a personalized progress report and adjusting personalized training and diet plans. The analysis results are then sent to the user's device.

[1910] Input: User activity data, meal data.

[1911] Output: Progress report, tailored training and meal plans.

[1912] Step 5: Recognize emotions and stay motivated

[1913] The user's device activates an emotion engine that analyzes the user's voice and facial expressions in real time. The analyzed emotion data is sent to a server. Based on this data, the server generates encouraging messages and reminders and provides them to the user's device. This helps to maintain the user's motivation.

[1914] Input: User's voice data, facial expression data.

[1915] Output: Emotion recognition data, generated cheer messages and reminders.

[1916] (Application example 2)

[1917] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1918] Conventional health management systems make it difficult for users to receive efficient and personalized training guidance in physical stores. In addition, due to a lack of emotion recognition technology, it is difficult to maintain motivation during training or appropriately respond to fatigue, which prevents a sufficient improvement in the user experience.

[1919] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's health information and activity data to generate personalized training plans and meal plans, means for providing training guidance to the user via a virtual coach, and means for recognizing the user's emotions using an emotion engine and providing feedback accordingly. This makes it possible to provide personalized training guidance and maintain the user's motivation.

[1920] A "means for a user to input health information" is an interface through which a user inputs their own health-related data.

[1921] "Means for obtaining user activity data from a wearable device" refers to a function for collecting activity data from a device worn by the user.

[1922] "Means for analyzing a user's health information and activity data to generate personalized training and meal plans" refers to a function that generates individually optimized training and meal plans based on the user's health information and activity data.

[1923] "Means for providing the generated training plan and meal plan to the user's device" refers to the function by which the server sends the generated plan to the user's device and displays it.

[1924] "Means for predicting changes in the user's health condition and presenting them as images" is a function that predicts changes in the user's health condition, visualizes the results, and presents them to the user.

[1925] "Means for providing training guidance to users via a virtual coach" refers to a function that uses a virtual instructor to guide and instruct users on training methods.

[1926] "Means for recognizing user emotions using an emotion engine and providing feedback accordingly" is a function that analyzes the user's emotions and provides feedback and advice based on the results.

[1927] "Means for generating and providing users with reminders and encouraging messages to maintain motivation" is a function that creates and provides appropriate reminders and encouraging messages to maintain users' motivation.

[1928] This invention provides a user-specific AI personal health assistant system that supports health management and training. The system analyzes the user's health information and emotional state and provides personalized training and meal plans accordingly.

[1929] Hardware and software used

[1930] The following hardware and software are used to realize the system.

[1931] Hardware: Smartphones, smart glasses, wearable devices (e.g., smartwatches)

[1932] software:

[1933] Mobile application (iOS / Android)

[1934] Emotion recognition engine (e.g. Microsoft Azure Emotion API)

[1935] Generative AI models (e.g., OpenAI GPT-3)

[1936] Database (e.g. Firebase)

[1937] Data collection

[1938] Using a mobile app, users enter their profile information (such as name, gender, age, height, weight, and goals), and sync their wearable device to collect activity data such as heart rate, activity level, and sleep patterns. This data is then stored on a server for analysis.

[1939] Generate personalized advice

[1940] The server analyzes the user's stored health and activity data to generate personalized training and meal plans. Using a generative AI model (OpenAI GPT-3), it predicts the specific plan and how the body shape will change when the goal is achieved, and generates images of the plan.

[1941] Training Guidance and Emotion Recognition

[1942] During training, a virtual coach provides real-time guidance to the user via smartphone or smart glasses. An emotion recognition engine (Microsoft Azure Emotion API) recognizes emotions from the user's facial expressions and voice and provides appropriate feedback. Appropriate encouraging messages such as "Take a short break" are provided to the user.

[1943] Maintaining motivation

[1944] The server generates behavioral science-based messages and reminders, sends notifications to users to keep them motivated, and periodically analyzes their progress data to adjust their training and meal plans accordingly.

[1945] Specific examples

[1946] For example, when a user arrives at the gym, they open their smartphone and check the designated training plan. They put on smart glasses and train under the guidance of a virtual coach. If fatigue is detected through emotion recognition, a message will automatically appear encouraging them to take a break. After training, they will also receive a progress report and their next training content will be automatically updated. They can also check their meal plan through the app and try out the recommended recipes.

[1947] Prompt Sentence Examples

[1948] "Enter your health information, including your name, age, gender, and goals. Then generate training and meal plan suggestions."

[1949] This allows users to receive personalized health management and training support, enabling sustainable health management.

[1950] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1951] Step 1:

[1952] The user's device provides an interface for the user to input health information, such as name, gender, age, height, weight, and goals. The input information is temporarily stored by the user's device and then sent to the server.

[1953] Step 2:

[1954] The server stores the health information received from the user's device in a database. At the same time, it also collects activity data (heart rate, number of steps, sleep patterns, etc.) from the user's synchronized wearable device and stores this data in the database. This provides the basic data for analysis.

[1955] Step 3:

[1956] The server analyzes the user's health information and activity data stored in the database. This process uses an AI model (e.g., OpenAI GPT-3) to generate personalized training and meal plans. For example, it might suggest a "strength training plan three times a week based on the user's health information" and a "high-protein meal plan." These generated plans are then stored again on the server.

[1957] Step 4:

[1958] The server sends the generated training plan and meal plan to the user's device, which displays the received plan for the user to review.

[1959] Step 5:

[1960] The user's device activates a virtual coach function that guides the user through the training session. The virtual coach uses a camera to capture the user's exercise form and provides real-time feedback, such as "Your deadlift form is incorrect."

[1961] Step 6:

[1962] The user's device uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize emotions from the user's facial expressions and voice. This data is sent to a server, which automatically generates feedback according to the user's emotional state. For example, if the user expresses "feeling tired," a message such as "take a short break" is generated.

[1963] Step 7:

[1964] The server generates motivational reminders and encouraging messages based on behavioral science. This is also done using a generative AI model. The created reminders and messages are sent to the user's device and displayed to the user. For example, a message such as "You're almost there, keep trying!" may be displayed.

[1965] Step 8:

[1966] The user's progress data is periodically sent to a server, which analyzes it. Based on this progress data, the training and meal plans are updated as needed to provide the user with the optimal plan. For example, the next training menu will be optimized based on data such as "muscle strength improved by 5% in three weeks."

[1967] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1968] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1969] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1970] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1971] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1972] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1973] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1974] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1975] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1976] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1977] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1978] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1979] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1980] 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.

[1981] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1982] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1983] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1984] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1985] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1986] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1987] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1988] The following is further disclosed regarding the above embodiment.

[1989] (Claim 1)

[1990] a means for a user to input health information;

[1991] means for acquiring user activity data from the wearable device;

[1992] means for analyzing the user's health information and activity data to generate personalized training and meal plans;

[1993] means for providing the generated training plan and meal plan to a user's device;

[1994] A means for predicting changes in the user's health status and presenting the results as an image;

[1995] A system including:

[1996] (Claim 2)

[1997] 10. The system according to claim 1, further comprising means for generating and providing a behavioral science-based message to the user to maintain motivation.

[1998] (Claim 3)

[1999] 10. The system of claim 1, further comprising means for analyzing a user's exercise form and providing real-time feedback on improvements.

[2000] "Example 1"

[2001] (Claim 1)

[2002] a means for a user to input health information;

[2003] means for acquiring user activity data from the wearable device;

[2004] means for analyzing the user's health information and activity data to generate personalized exercise and meal plans;

[2005] means for providing the generated exercise plan and meal plan to a user's terminal;

[2006] A means for predicting changes in a user's health status using a generative AI model and presenting the predicted changes as a graphic; and

[2007] A means to analyze your progress and adjust your exercise and meal plans as needed;

[2008] Generate behavioral science-based messages and reminders to keep users motivated,

[2009] A system including:

[2010] (Claim 2)

[2011] 10. The system of claim 1, further comprising means for generating and providing behavioral science-based messages to the user.

[2012] (Claim 3)

[2013] 10. The system of claim 1, further comprising means for analyzing a user's exercise form and providing real-time feedback on improvements.

[2014] "Application Example 1"

[2015] (Claim 1)

[2016] a means for a user to input health information;

[2017] means for acquiring user activity data from the wearable device;

[2018] means for analyzing the user's health information and activity data to generate personalized training and meal plans;

[2019] means for providing the generated training and meal plans to a user-specific device;

[2020] A means for predicting changes in the user's health status and presenting the results as an image;

[2021] a means for the virtual instructor to provide real-time feedback on exercise form as the user exercises;

[2022] A system including:

[2023] (Claim 2)

[2024] 10. The system according to claim 1, further comprising means for generating and providing a behavioral science-based message to the user to maintain motivation.

[2025] (Claim 3)

[2026] 10. The system of claim 1, further comprising means for synchronizing activity data upon facility check-in for use at a health facility or training gym.

[2027] "Example 2: Combining Emotion Engines"

[2028] (Claim 1)

[2029] a means for a user to input health information;

[2030] means for acquiring user activity data from the wearable device;

[2031] means for analyzing the user's health information and activity data to generate personalized training and meal plans;

[2032] means for providing the generated training plan and meal plan to a user's device;

[2033] A means for predicting changes in the user's health status and presenting the results as an image;

[2034] means for analyzing the user's voice and facial expression using an emotion engine and transmitting emotion recognition data to a server;

[2035] A means for the server to generate a cheering message based on the emotion recognition data and provide it to the user's device;

[2036] A system including:

[2037] (Claim 2)

[2038] 10. The system according to claim 1, further comprising means for generating and providing a behavioral science-based message to the user to maintain motivation.

[2039] (Claim 3)

[2040] 10. The system of claim 1, further comprising means for analyzing a user's exercise form and providing real-time feedback on improvements.

[2041] "Application example 2 when combining emotion engines"

[2042] (Claim 1)

[2043] a means for a user to input health information;

[2044] means for acquiring user activity data from the wearable device;

[2045] means for analyzing the user's health information and activity data to generate personalized training and meal plans;

[2046] means for providing the generated training plan and meal plan to a user's device;

[2047] A means for predicting changes in the user's health status and presenting the results as an image;

[2048] means for providing training instruction to the user via a virtual coach;

[2049] a means for recognizing user emotions through an emotion engine and providing feedback accordingly;

[2050] A means to generate and provide users with motivational reminders and encouraging messages;

[2051] A system including:

[2052] (Claim 2)

[2053] 10. The system of claim 1, further comprising means for generating and providing behavioral science-based messages to the user.

[2054] (Claim 3)

[2055] 10. The system of claim 1, further comprising means for analyzing a user's exercise form and providing real-time feedback on improvements. [Explanation of symbols]

[2056] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input health information; means for acquiring user activity data from the wearable device; means for analyzing the user's health information and activity data to generate personalized training and meal plans; means for providing the generated training plan and meal plan to a user's device; A means for predicting changes in the user's health status and presenting the results as an image; A system including:

2. 10. The system according to claim 1, further comprising means for generating and providing to the user a message based on behavioral science to maintain motivation.

3. 10. The system of claim 1, further comprising means for analyzing a user's exercise form and providing real-time feedback on improvements.

Images