System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving basic information sent by a user to perform an initial evaluation; means for optimizing a walking program based on a sent goal; means for analyzing a photographed walking form of the user to provide improvements; means for analyzing an inputted meal record to provide nutritional guidance; and means for analyzing inputted emotional data to provide psychological support.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] Walking is a very effective form of exercise for maintaining health, but maximizing its effectiveness requires appropriate programming, form, nutritional guidance, and psychological support. However, for users who lack the confidence to individually adjust these aspects or who find it difficult to receive appropriate advice on a continuous basis, it can be difficult to continue walking and sustain the benefits. Therefore, a system that comprehensively supports all elements and allows users to continue a healthy walking lifestyle without hesitation is needed. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving basic information about a user's walking and making an initial assessment, a means for optimizing a walking program based on the user's goals, a means for analyzing a user's photographed walking form and suggesting areas for improvement, a means for analyzing input food records and providing nutritional advice, and a means for analyzing input emotional data and providing psychological support. This system allows users to receive comprehensive support in all aspects of walking, maximizing health benefits.

[0006] "Basic information" is information necessary for an initial assessment, such as the user's age, gender, health condition, walking experience, etc.

[0007] "Initial assessment" is a process of assessing the user's current health condition and fitness for walking based on basic information entered by the user.

[0008] A "walking program" is a training plan that includes walking schedules, distances, speeds, and rest timings, created based on a user's individual goals.

[0009] "Form" refers to the user's posture and movements while walking, and maintaining proper form can reduce the risk of injury and maximize the effectiveness of exercise.

[0010] A "food record" is information that records the contents and amounts of food that a user ingests in a day.

[0011] "Nutrition guidance" is the process of analyzing a user's food records and providing advice on nutritional balance and specific dietary improvements based on health goals.

[0012] "Emotion data" is information that records the user's psychological state, such as emotions, worries, and stress, regarding walking.

[0013] "Psychological support" is the process of providing advice based on the user's emotional data to help maintain motivation for walking and manage stress.

[0014] "Analysis" refers to analyzing input information (forms, food records, emotional data, etc.) using AI models and algorithms to extract areas for improvement and advice. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] System Overview

[0037] This invention is a system that utilizes generative AI models to maximize the health benefits of walking. The system performs an initial assessment based on the user's basic information and provides an individually customized walking program based on that assessment. It also analyzes the user's form, food records, and emotional data to provide appropriate support.

[0038] Program processing and specific examples

[0039] User registration and initial evaluation

[0040] 1. Enter your user information:

[0041] Device: The user enters their basic information (age, gender, health condition, walking experience, etc.) into the application.

[0042] Server: Receives this information and stores it in a database.

[0043] 2. Conducting an Initial Assessment:

[0044] Server: Conducts an initial assessment based on the user's basic information and generates reference data appropriate for age, gender, and health condition.

[0045] Optimizing your walking program

[0046] 1. Receive goal setting:

[0047] Device: The user sets a walking goal (e.g., dieting).

[0048] Server: Receives the goals and generates a personalized training program based on the user's initial assessment and goals.

[0049] 2. Generate customized programs:

[0050] Server: The AI ​​model creates a training schedule (number of times per week, walking time per session, walking speed, rest timing, etc.) needed to achieve the goal.

[0051] Form Improvements

[0052] 1. Form recording instructions:

[0053] Device: Instruct the user to capture their posture and movements while walking.

[0054] User: Follow the instructions to record a video while walking.

[0055] 2. Video Analysis:

[0056] Device: Upload the captured video to the server.

[0057] Server: Analyzes video to identify areas for improvement in posture and form.

[0058] 3. Providing Feedback:

[0059] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[0060] Terminal: Display feedback to the user.

[0061] Nutritional guidance

[0062] 1. Enter your food record:

[0063] Terminal: The user inputs the details of their daily meals.

[0064] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[0065] 2. Analysis of dietary data:

[0066] Terminal: Sends the entered meal data to the server.

[0067] Server: Analyzes the received data using a nutritional management AI model and provides nutritional guidance in line with goals.

[0068] 3. Providing nutritional advice:

[0069] Server: Based on the analysis results, it provides the user with nutritional advice appropriate for their diet (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[0070] Terminal: Notifies the user of nutritional advice.

[0071] Psychological support

[0072] 1. Emotional input and goal setting:

[0073] Device: The user enters their feelings and worries about walking in diary format.

[0074] User: Write down your thoughts and stress about walking.

[0075] 2. Emotional Data Analysis:

[0076] Terminal: Sends the input emotion data to the server.

[0077] Server: The sentiment analysis model analyzes the data and generates psychological support suggestions for the user.

[0078] 3. Providing psychological support:

[0079] Server: Provides users with effective stress management and motivational strategies (e.g., "practice deep breathing before walking" or "exercises that encourage positive thinking").

[0080] Terminal: Displays psychological support advice to the user.

[0081] Specific examples

[0082] Let's say a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. After that, if the user sets a goal of dieting, the server generates a walking program of 30 minutes, 3 times a week.

[0083] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides specific advice such as "straighten your back" or "correct the way you swing your arms."

[0084] Furthermore, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad."

[0085] Finally, when the user enters their feelings about walking in diary format, the server analyzes this and provides psychological support such as "deep breathing techniques" and "positive thinking techniques."

[0086] As described above, the system of the present invention can comprehensively support the user's walking and maximize the health benefits.

[0087] The processing flow will be explained below.

[0088] User registration and initial evaluation

[0089] Step 1:

[0090] Device: The user installs the application and enters basic information (age, gender, health status, walking experience, etc.).

[0091] Step 2:

[0092] Terminal: Sends the entered information to the server.

[0093] Server: Stores the received basic information in a database.

[0094] Step 3:

[0095] Server: Based on the user's basic information, an initial evaluation is performed and reference data appropriate for the user is generated.

[0096] Optimizing your walking program

[0097] Step 1:

[0098] Device: The user sets walking goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[0099] Step 2:

[0100] Terminal: Sends goal settings to the server.

[0101] Server: Based on goals and initial assessments, an AI model is used to generate a personalized training program.

[0102] Step 3:

[0103] Server: Sends the generated training program (number of times per week, walking time per session, walking speed, timing of rest, etc.) to the user.

[0104] Terminal: displays the training program to the user.

[0105] Form Improvements

[0106] Step 1:

[0107] Device: Instruct the user to capture their posture and movements while walking.

[0108] Step 2:

[0109] User: Take a video while walking and save it on the device.

[0110] Step 3:

[0111] Device: Upload the captured video to the server.

[0112] Step 4:

[0113] Server: Analyzes the video and identifies areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[0114] Step 5:

[0115] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[0116] Terminal: Display feedback to the user.

[0117] Nutritional guidance

[0118] Step 1:

[0119] Terminal: Provides a screen where users can input their daily dietary information.

[0120] Step 2:

[0121] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[0122] Step 3:

[0123] Terminal: Sends the entered meal data to the server.

[0124] Step 4:

[0125] Server: Analyzes the received dietary data using a nutritional management AI model and generates nutritional guidance that matches the user's goals.

[0126] Step 5:

[0127] Server: Based on the analysis results, it provides specific nutritional advice to users (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[0128] Terminal: Notifies the user of nutritional advice.

[0129] Psychological support

[0130] Step 1:

[0131] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[0132] Step 2:

[0133] User: Enter your thoughts and stress about walking.

[0134] Step 3:

[0135] Terminal: Sends the input emotion data to the server.

[0136] Step 4:

[0137] Server: Analyzes the emotional data and generates customized psychological support suggestions for the user.

[0138] Step 5:

[0139] Server: Based on the analysis results, the server provides users with effective methods for managing stress and maintaining motivation (e.g., "practice deep breathing before walking" or "exercises that encourage positive thinking").

[0140] Terminal: Displays psychological support advice to the user.

[0141] In this way, the server, terminal, and user work together at each processing step to provide support to maximize the effect of the user's walking.

[0142] Example 1

[0143] 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."

[0144] In modern society, while personalized health management and exercise guidance are important, providing such guidance requires significant resources. Furthermore, users often find it difficult to manage themselves, making it difficult to consistently receive effective exercise and dietary guidance and psychological support. Therefore, there is a demand for a system that can comprehensively support users' health status and provide individually optimized exercise programs, nutritional guidance, and psychological support.

[0145] 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.

[0146] In this invention, the server includes means for receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing an exercise program based on the transmitted goals, means for analyzing photographed user movements and providing improvements, means for analyzing input dietary records and providing nutritional guidance, and means for analyzing input emotion data and providing psychological support. This makes it possible to comprehensively support the user's health condition and consistently provide individually optimized exercise programs, nutritional guidance, and psychological support.

[0147] "Basic information sent by the user" refers to basic data such as age, sex, health condition, and exercise experience that the user provides to the system.

[0148] "Initial evaluation" is a process of comprehensively evaluating the user's health condition and fitness for exercise based on basic information.

[0149] "Goals" are specific objectives or hopes that users wish to achieve through the system.

[0150] An "exercise program" refers to an exercise schedule and content customized to a user's individual goals.

[0151] "Movement" refers to the posture and movements of the user when exercising.

[0152] "Analysis" is the process by which the server processes the data sent by the user and extracts meaningful information.

[0153] "Points for improvement" are specific points that should be corrected in order for the user to exercise more effectively.

[0154] "Diet record" refers to the data that a user enters into the system about their daily diet.

[0155] "Nutrition guidance" is specific advice provided to users on how to lead a healthy diet based on their food records.

[0156] "Emotion data" is information about a user's own emotions and mental state that is input into the system by the user.

[0157] "Psychological support" refers to advice and support provided to users to help them maintain their mental health.

[0158] "Health status" refers to the overall state of a user's physical and mental health.

[0159] "Exercise experience" refers to information such as the history of exercise the user has done up to now, the frequency of exercise, and the details of exercise.

[0160] A "training schedule" refers to a schedule or plan for a user to exercise systematically.

[0161] "Posture" refers to the position and shape of the body that a user assumes during exercise or daily life.

[0162] MODE FOR CARRYING OUT THE INVENTION

[0163] The present invention is a system for automating health management and exercise guidance for users. The system utilizes a server, a terminal, and a generative AI model to provide users with individually optimized exercise programs, form improvement, nutritional guidance, and psychological support.

[0164] Hardware and Software Configuration

[0165] Server: A high-performance server is used to run AI libraries such as Python, TensorFlow, Keras, OpenPose, and BERT. This server manages the database, performs initial evaluation, generates training programs, analyzes videos, analyzes food data, and analyzes emotion data.

[0166] Terminal: A device used by a user, such as a smartphone or tablet. The terminal provides an interface for the user to input information and sends the input data to a server.

[0167] User: Refers to an individual who uses the system and inputs the information necessary to manage their own health and receive exercise guidance.

[0168] System Operation Overview

[0169] 1. User registration and initial evaluation

[0170] The user uses the application to input basic information (age, gender, health status, exercise experience, etc.). The device sends this information to the server, which then performs an initial evaluation based on the received information and stores it in a database.

[0171] 2. Optimize your walking program

[0172] The user sets exercise goals (e.g., dieting) through the terminal. The server receives these goals and generates an individual training program based on the initial assessment data and the goals. The generated program is stored in a database and provided to the user.

[0173] 3. Improving your form

[0174] Users take videos of themselves walking with their smartphones and upload them to the server. The server analyzes the videos and identifies areas for improvement in posture and form. This is done using a posture analysis model using OpenPose. Based on the analysis results, specific improvement advice is generated and provided to the user.

[0175] 4. Nutritional guidance

[0176] Users enter their daily food records into the application. The device sends this data to the server. The server uses a nutrition management AI model to analyze the food data and provide nutritional guidance tailored to the user's goals. The analysis results are then sent to the device and presented to the user.

[0177] 5. Psychological support

[0178] Users enter their feelings and concerns about walking into the application in diary format. The device then sends this data to a server. The server then uses the natural language processing library BERT to analyze the emotional data and generate psychological support suggestions, providing users with methods for managing stress and maintaining motivation.

[0179] Specific examples

[0180] For example, suppose a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. Next, if the user sets "diet" as a goal, the server generates a walking program for 30 minutes, three times a week.

[0181] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides advice such as "straighten your back" or "correct the way you swing your arms." Additionally, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to your breakfast" or "add nuts to your lunch salad."

[0182] Furthermore, when users enter their feelings about walking in diary format, the server analyzes the data and provides psychological support such as "deep breathing techniques" and "positive thinking techniques."

[0183] The system aims to provide comprehensive support for users' walking and maximize the health benefits.

[0184] Prompt Sentence Examples

[0185] "When walking, be mindful of the following: keep your back straight and adjust your arm swing."

[0186] "Add more protein to your breakfast, such as eggs or yogurt."

[0187] "Practice deep breathing exercises before walking. This will help you relax and improve your workout."

[0188] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0189] Step 1:

[0190] The user opens the application on a device such as a smartphone or tablet and enters basic information such as age, gender, health status, and exercise experience. This information is then obtained.

[0191] Input: Basic information such as the user's age, gender, health status, and exercise experience

[0192] Output: Basic information data

[0193] Specific operation: The user enters information into the form and presses the "Complete" button to submit. The device temporarily saves the information and sends it to the server.

[0194] Step 2:

[0195] The terminal sends the basic information entered by the user to the server, which then stores the information in a database and performs an initial evaluation for each user.

[0196] Input: Basic information data

[0197] Output: Initial evaluation data

[0198] What happens: The server creates a new user record in the database and runs an initial evaluation script using Python. The evaluation results are saved in the database.

[0199] Step 3:

[0200] The user sets a specific walking goal (e.g., dieting) within the application. The set goal is sent as input data from the device to the server.

[0201] Input: Walking goal

[0202] Output: Target data

[0203] Specific operation: The user inputs a goal, and the device sends it to the server, which then stores the goal data in a database.

[0204] Step 4:

[0205] The server generates an individual training program using an AI model (using Keras) based on the received goal data and initial evaluation data, which is then stored in a database and provided to the user.

[0206] Input: Target data, initial evaluation data

[0207] Output: Individual training program

[0208] Specific operation: The server dynamically generates training programs using the Keras library and stores them in a database.

[0209] Step 5:

[0210] The user records their walking form with their smartphone and uploads the video to a server via the application.

[0211] Input: Video of walking

[0212] Output: Recorded video data

[0213] Specific operation: The user takes a video using the device's camera and sends it to the server using the upload function. This operation can be easily performed within the application.

[0214] Step 6:

[0215] The server receives the uploaded video and analyzes it using an AI model (a posture analysis model using OpenPose). As a result of the analysis, it identifies areas for improvement in the user's posture and form.

[0216] Input: Recorded video data

[0217] Output:Form Improvements

[0218] Specific behavior: The server uses the OpenPose library to analyze the video, analyze the data obtained, and identify problems with posture and behavior. This information is stored in a database.

[0219] Step 7:

[0220] Based on the analysis results, the server generates specific advice for the user on how to improve their form, and sends the advice to the terminal.

[0221] Input: Form Improvements

[0222] Output: Improvement advice

[0223] Specific operation: The server generates appropriate advice for the user based on the identified improvements. The generated advice is notified to the device and displayed to the user.

[0224] Step 8:

[0225] The user enters their daily food record into the application and sends the data to the server.

[0226] Input: Food log

[0227] Output: Food record data

[0228] Specific operation: When the user enters the meal details into the application form and presses the submit button, the data is sent from the terminal to the server.

[0229] Step 9:

[0230] The server analyzes the received dietary record data using a nutritional management AI model to generate data for providing nutritional guidance in line with goals. The analysis results are stored in a database.

[0231] Input: Food record data

[0232] Output: Nutritional guidance data

[0233] Specific operation: The server receives the food record data, analyzes it using a nutrition management AI model, and calculates nutritional balance and areas for improvement. This data is then stored in a database.

[0234] Step 10:

[0235] Based on the analysis results, the server generates nutritional guidance appropriate for the user and transmits the guidance content to the user's terminal.

[0236] Input: Nutritional guidance data

[0237] Output: Nutritional advice

[0238] Specific operation: The server generates nutritional advice appropriate for the user based on the analysis results obtained from the nutrition management AI model. This advice is sent to the user's device and displayed within the application.

[0239] Step 11:

[0240] The user enters their feelings and worries about walking into the application in diary format, and the data is sent to the server.

[0241] Input: Emotion data

[0242] Output: Emotion recording data

[0243] Specific operation: When the user enters their feelings or concerns into the application and presses the send button, the data is sent from the device to the server.

[0244] Step 12:

[0245] The server analyzes the received emotional data using the natural language processing library BERT and generates psychological support suggestions, including methods for managing stress and maintaining motivation.

[0246] Input: Emotion recording data

[0247] Output: Psychological support suggestions

[0248] How it works: The server uses the BERT library to analyze the sentiment data and extract patterns to understand the user's mental state. These data are then stored in a database.

[0249] Step 13:

[0250] Based on the analysis results, the server generates advice for providing psychological support to the user and sends it to the user's terminal.

[0251] Input: Psychological support suggestions

[0252] Output: Psychological support advice

[0253] Specific operation: Based on the acquired psychological support suggestions, the server generates appropriate advice for the user (e.g., "deep breathing" or "positive thinking") and sends it to the device. The device notifies and displays this to the user.

[0254] In this way, the system can comprehensively support the user's health and consistently provide individually optimized exercise programs, nutritional guidance, and psychological support.

[0255] (Application example 1)

[0256] 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."

[0257] Health management is becoming increasingly important in modern society, and many people are seeking a lifestyle that incorporates walking. However, there is no system that can comprehensively design and implement effective walking programs, improve one's form, maintain nutritional balance, and provide psychological support. Furthermore, linking these systems with food delivery services to easily provide individually optimized meal plans is also a challenge. Given this background, there is a need for a system that can provide users with comprehensive health support.

[0258] 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.

[0259] In this invention, the server includes means for receiving basic information sent by the user and making an initial evaluation, means for optimizing the walking program based on the sent goals, means for analyzing a photograph of the user's walking form and suggesting areas for improvement, means for analyzing the input meal record and providing nutritional advice, means for analyzing the input emotion data and providing psychological support, means for calculating calories burned based on the collected walking data and proposing an appropriate meal plan, and means for proposing a food delivery plan to provide a meal plan linked to the user's walking data. This allows for comprehensive support not only for the user's walking life but also for their daily meals and psychological health, enabling comprehensive health support.

[0260] "User information" refers to basic information provided by the user, such as age, gender, health status, walking experience, etc.

[0261] A "walking program" is a mobility training schedule that is individually customized based on the user's basic information and goals.

[0262] "Walking form" refers to the user's posture and movements while walking.

[0263] "Dietary records" are data on the contents and amounts of food consumed by a user each day.

[0264] "Emotion data" is information about emotions and worries about walking input by the user.

[0265] "Calories burned" refers to the amount of energy consumed through physical activity and is calculated based on walking data.

[0266] A "meal plan" is an appropriate meal plan and its composition that is suggested based on the user's walking data and nutritional analysis.

[0267] A "food delivery plan" is a meal delivery plan provided based on the user's optimal meal plan.

[0268] "Walking data" refers to information such as the number of steps taken and walking speed, and is collected through smart devices.

[0269] The "means for receiving basic information and making an initial evaluation" is a method in which the system receives basic information provided by the user and evaluates the initial state of the user based on that information.

[0270] The "means for optimizing a walking program based on goals" is a method for designing and generating an individually optimized walking program based on goals set by the user.

[0271] The "means of analyzing a user's filmed walking form and suggesting areas for improvement" is a method of analyzing a walking video filmed by the user and suggesting areas for improvement to the user's form based on the results of the analysis.

[0272] The "means for analyzing the input dietary record and providing nutritional guidance" is a method for analyzing the dietary details input by the user and providing nutritional guidance based on the analysis results.

[0273] The "means for analyzing input emotion data and providing psychological support" is a method for analyzing emotion data input by a user and providing psychological support based on the results.

[0274] "Means for calculating calories burned based on collected walking data and proposing an appropriate meal plan" refers to a method for calculating energy consumption using data collected during walking and proposing a meal plan suited to the user.

[0275] "Means for proposing food delivery plans to provide meal plans linked to the user's walking data" refers to a method for determining the optimal meal plan based on collected walking data and proposing a food delivery service based on that plan.

[0276] This system receives basic information sent by the user and optimizes walking programs based on that information. The system analyzes the user's walking data, food records, and emotional data to provide comprehensive health support. It also has a function to suggest optimal meal plans in conjunction with food delivery plans.

[0277] System configuration

[0278] This system uses the following hardware and software:

[0279] Programming language: Python

[0280] Generative AI model: GPT-4

[0281] Database: MySQL

[0282] Server: AWS EC2

[0283] App development platform: React Native (mobile app)

[0284] Processing flow

[0285] User information registration and initial evaluation

[0286] The user enters basic information into the terminal and sends it to the server. The server stores the received basic information in a MySQL database and performs an initial evaluation. Based on this initial evaluation, a walking program suitable for the user is generated.

[0287] Creating a walking program

[0288] The server uses a generative AI model to generate a customized walking program based on the user's basic information and goals, and inputs prompts such as the following into the generative AI model:

[0289] User age: 40

[0290] Gender: Female

[0291] Walking experience: Beginner

[0292] Goal: Lose weight

[0293] Based on this information, we will suggest the best walking program for you.

[0294] Improve your walking form

[0295] Users record their walking form and upload it to a server from their device. The server analyzes the video and identifies areas for improvement. This information is then analyzed using a generative AI model to provide appropriate advice to the user.

[0296] Food records and nutritional advice

[0297] The user inputs their daily dietary information into the device and sends it to the server. The server analyzes the received dietary data and generates nutritional advice. For example, the following prompt sentence is input into the generative AI model:

[0298] Breakfast: Toast, eggs, coffee

[0299] Lunch: Sandwich, salad

[0300] Dinner: Grilled fish, vegetable soup

[0301] Based on this food record, provide appropriate nutritional advice.

[0302] Emotional data and psychological support

[0303] The user inputs their feelings and concerns about walking into the device and sends them to the server, which analyzes the input emotional data and provides appropriate psychological support.

[0304] Food delivery plan proposal

[0305] The system calculates the calorie expenditure based on the user's walking data and generates a meal plan based on this. The server then connects the generated meal plan to a food delivery service and suggests the most suitable meal for the user. In this case, the following prompt is also input into the generative AI model:

[0306] Today's steps: 8000

[0307] Walking speed: 5km / h

[0308] This information will help you provide optimal nutritional advice and meal plans.

[0309] Specific examples

[0310] For example, if a 40-year-old woman uses this system to manage her daily walking and diet, the process would be as follows: The user enters initial information and walks according to the generated walking program. She records a video of herself walking to receive advice on improving her form, and then enters her daily food record to receive nutritional guidance. She follows the meal plan suggested based on the calories burned and orders appropriate meals from the food delivery plan. This series of processes allows the user to maintain a healthy lifestyle.

[0311] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0312] Step 1:

[0313] The user enters basic information (age, gender, health condition, walking experience, etc.) into the device. The entered basic information is sent from the device to the server. The basic information received by the server is stored in a MySQL database. Here, the data format is sent as JSON and inserted into the database using SQL statements.

[0314] Step 2:

[0315] The server performs an initial evaluation of the user based on the stored basic information. The results of the initial evaluation are generated using a generative AI model (GPT-4). The input data is user information, and an appropriate evaluation result is generated based on this. The server inputs the following prompt sentence into the generative AI model and obtains the evaluation result.

[0316] User age: 40

[0317] Gender: Female

[0318] Walking experience: Beginner

[0319] Use this information to make your initial assessment.

[0320] As a result, an initial evaluation report is generated for the user.

[0321] Step 3:

[0322] The user sets walking goals on the device. Possible goals include "dieting" or "improving physical fitness." The user's set goals are sent from the device to the server. The server generates an individually customized walking program based on the initial assessment and goals. This process also uses a generative AI model, and the following prompt is input:

[0323] User age: 40

[0324] Gender: Female

[0325] Walking experience: Beginner

[0326] Goal: Lose weight

[0327] Based on this information, we will suggest the best walking program for you.

[0328] As a result, a walking program is generated that specifies the number of times per week, the duration of each walk, walking speed, and rest timing.

[0329] Step 4:

[0330] Users record their walking form with their device's camera. The video is then uploaded from the device to a server. The server then analyzes the video using a generative AI model to identify areas for improvement in posture and form. The following prompts are used:

[0331] Analyze the video while walking.

[0332] Based on the analysis results, the server generates specific feedback for improving form, such as straightening your back or correcting the way you swing your arms, and sends this to the device.

[0333] Step 5:

[0334] The user enters the details of their daily meals into the device and sends this meal record to the server. The server then analyzes the received meal record using an AI model for nutritional management and generates nutritional advice appropriate for the user. For example, the following prompt sentences are used:

[0335] Breakfast: Toast, eggs, coffee

[0336] Lunch: Sandwich, salad

[0337] Dinner: Grilled fish, vegetable soup

[0338] Based on this food record, provide appropriate nutritional advice.

[0339] The analysis results in specific advice, such as adding more protein to your breakfast or adding nuts to your lunch salad, which is displayed on the device.

[0340] Step 6:

[0341] The user enters their feelings and worries about walking in diary format into the device and sends it to the server. The server analyzes the received emotional data and suggests appropriate psychological support. The following prompts are used:

[0342] The user has entered their thoughts and concerns. Based on this information, please suggest psychological support.

[0343] As a result, specific psychological support advice, such as deep breathing techniques and positive thinking techniques, is generated and displayed on the device.

[0344] Step 7:

[0345] The server calculates the calories burned based on walking data collected from the user's steps and walking speed. Based on the calculated calories, it proposes an appropriate meal plan. Furthermore, it uses a generative AI model to generate a specific meal plan using prompt sentences such as the following:

[0346] Today's steps: 8000

[0347] Walking speed: 5km / h

[0348] This information will help you provide optimal nutritional advice and meal plans.

[0349] Based on this suggestion, a food delivery plan is created and presented to the user, who can then order appropriate meals from the suggested meal plan.

[0350] 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.

[0351] System Overview

[0352] This invention is a system that utilizes a generative AI model to maximize the health benefits of walking. The system performs an initial assessment based on the user's basic information and provides an individually customized walking program based on that assessment. It also analyzes the user's form, food records, and emotional data to provide appropriate support. The system also incorporates an emotion engine that recognizes the user's emotions.

[0353] Program processing and specific examples

[0354] User registration and initial evaluation

[0355] 1. Enter your user information

[0356] Device: The user enters their basic information (age, gender, health condition, walking experience, etc.) into the application.

[0357] Server: Receives this information and stores it in a database.

[0358] 2. Conducting an initial evaluation

[0359] Server: Performs an initial evaluation based on the user's basic information and generates reference data suitable for the user.

[0360] Optimizing your walking program

[0361] 1. Receiving goal setting

[0362] Device: The user sets walking goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[0363] Server: Receives the goals and generates a personalized training program based on the user's initial assessment and goals.

[0364] 2. Creating a customized program

[0365] Server: The AI ​​model creates a training schedule (number of times per week, walking time per session, walking speed, rest timing, etc.) needed to achieve the goal.

[0366] 3. Notice to Users

[0367] Server: Sends the generated training program to the user.

[0368] Terminal: displays the training program to the user.

[0369] Form Improvements

[0370] 1. Form recording instructions

[0371] Device: Instruct the user to capture their posture and movements while walking.

[0372] 2. Recording and uploading videos

[0373] User: Take a video while walking and save it on the device.

[0374] Device: Upload the captured video to the server.

[0375] 3. Video analysis and feedback

[0376] Server: Analyzes the video and identifies areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[0377] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[0378] Terminal: Display feedback to the user.

[0379] Nutritional guidance

[0380] 1. Enter your food record

[0381] Terminal: Provides a screen where the user can input their daily dietary information.

[0382] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[0383] 2. Submitting and analyzing dietary data

[0384] Terminal: Sends the entered meal data to the server.

[0385] Server: Analyzes the received dietary data using a nutritional management AI model and provides nutritional guidance in line with goals.

[0386] 3. Providing nutritional guidance

[0387] Server: Based on the analysis results, it provides the user with nutritional advice appropriate for their diet (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[0388] Terminal: Notifies the user of nutritional advice.

[0389] Psychological support and emotional engine

[0390] 1. Emotion Input and Recognition

[0391] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[0392] User: Enter your thoughts and stress about walking.

[0393] 2. Transmission and analysis of emotional data

[0394] Terminal: Sends the input emotion data to the server.

[0395] Server: The emotion engine analyzes the emotion data and evaluates the emotional state.

[0396] 3. Providing psychological support

[0397] Server: Based on the analysis results, it generates customized psychological support suggestions for the user (e.g., "practice deep breathing before walking" or "exercises that promote positive thinking").

[0398] Terminal: Displays psychological support advice to the user.

[0399] Specific examples

[0400] Let's say a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. After that, if the user sets a goal of dieting, the server generates a walking program of 30 minutes, 3 times a week.

[0401] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides specific advice such as "straighten your back" or "correct the way you swing your arms."

[0402] Furthermore, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad."

[0403] When users enter their feelings about walking in diary format, the server's emotion engine analyzes them and provides psychological support such as "deep breathing techniques" and "positive thinking methods."

[0404] By utilizing the emotion engine, more personalized support can be provided by providing care appropriate to the user's individual emotional state. This system can comprehensively support the user's walking and maximize health benefits.

[0405] The processing flow will be explained below.

[0406] User registration and initial evaluation

[0407] Step 1:

[0408] Device: The user installs the application and enters basic information (age, gender, health status, walking experience, etc.).

[0409] Step 2:

[0410] Terminal: Sends the entered information to the server.

[0411] Step 3:

[0412] Server: Stores the received basic information in a database.

[0413] Step 4:

[0414] Server: Performs an initial evaluation based on the user's basic information and generates reference data suitable for the user.

[0415] Optimizing your walking program

[0416] Step 1:

[0417] Terminal: The user inputs the walking goal (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[0418] Step 2:

[0419] Terminal: Sends goal settings to the server.

[0420] Step 3:

[0421] Server: Uses AI models to generate personalized training programs based on goals and initial assessment results.

[0422] Step 4:

[0423] Server: Sends the generated training program to the user.

[0424] Step 5:

[0425] Terminal: displays the training program to the user.

[0426] Form Improvements

[0427] Step 1:

[0428] Device: Instruct the user to capture their posture and movements while walking.

[0429] Step 2:

[0430] User: Take a video while walking and save it on the device.

[0431] Step 3:

[0432] Device: Upload the captured video to the server.

[0433] Step 4:

[0434] Server: Analyzes the video and detects areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[0435] Step 5:

[0436] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[0437] Step 6:

[0438] Terminal: Display feedback to the user.

[0439] Nutritional guidance

[0440] Step 1:

[0441] Terminal: Provides a screen where users can input their daily dietary information.

[0442] Step 2:

[0443] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[0444] Step 3:

[0445] Terminal: Sends the entered meal data to the server.

[0446] Step 4:

[0447] Server: Analyzes the received dietary data using a nutritional management AI model and generates nutritional guidance that matches the user's goals.

[0448] Step 5:

[0449] Server: Based on the analysis results, it provides specific nutritional advice to users (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[0450] Step 6:

[0451] Terminal: Notifies the user of nutritional advice.

[0452] Psychological support and emotional engine

[0453] Step 1:

[0454] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[0455] Step 2:

[0456] User: Enter your thoughts and stress about walking.

[0457] Step 3:

[0458] Terminal: Sends the input emotion data to the server.

[0459] Step 4:

[0460] Server: The emotion engine analyzes the emotion data and evaluates the emotional state.

[0461] Step 5:

[0462] Server: Based on the analysis results, it generates customized psychological support suggestions for the user (e.g., "practice deep breathing before walking" or "exercises that promote positive thinking").

[0463] Step 6:

[0464] Terminal: Displays psychological support advice to the user.

[0465] In this way, the server, terminal, and user work together at each processing step to provide comprehensive support to maximize the effectiveness of the user's walking.

[0466] Example 2

[0467] 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."

[0468] Conventional health management systems lack a personalized approach, making it difficult to respond appropriately to individual users' situations and goals. Furthermore, there were no systems capable of providing advanced support, such as improving walking form, analyzing food records, or providing psychological support based on emotional data. This made it difficult to maximize the health benefits for users.

[0469] 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 receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing the exercise program based on the transmitted goals, means for analyzing the user's photographed exercise form and providing improvements, means for analyzing the input dietary record and providing nutritional advice, means for analyzing the input emotional data and providing psychological support, means for instructing the user to record their exercise form, and means for notifying the user of the generated program. This enables a personalized approach according to the user's individual situation and goals, and makes it possible to maximize the user's health benefits through improvements in walking form, analysis of the dietary record, and advanced psychological support based on the emotional data.

[0470] "Basic information" refers to initial data about an individual user, such as the user's age, gender, health condition, and exercise experience.

[0471] An "initial assessment" is an assessment based on basic information about the user, and this assessment confirms the user's health condition and exercise experience.

[0472] An "exercise program" is a customized exercise plan based on the goals set by the user, specifically including the number of walking sessions, duration, and speed.

[0473] "Exercise form" refers to the posture and movements of the user when exercising, and by analyzing this, areas for improvement can be identified.

[0474] "Nutrition guidance" refers to advice based on data entered by the user about their daily diet, and indicates nutritional intake methods that are suitable for the user's goals.

[0475] "Emotion data" refers to data entered in diary format by the user about emotions and worries felt during exercise or daily life.

[0476] "Psychological support" refers to support provided based on an assessment of the user's emotional state through analysis of emotional data, and includes, for example, stress relief and promotion of positive thinking.

[0477] The "recording instruction means" is a system function that instructs the user to record their exercise form.

[0478] "Notification means" refers to the system's function of informing users of information such as exercise programs generated by the server, analysis results, nutritional guidance, and psychological support.

[0479] Overall system overview

[0480] This invention is a system for managing a user's health by analyzing the user's exercise program, exercise form, diet record, and emotional data, and providing personalized advice. The system consists of three entities: a server, a terminal, and the user.

[0481] Hardware and software used

[0482] Hardware

[0483] Terminals: Smartphones and personal computers are mainly used.

[0484] Server: Cloud or dedicated servers are used.

[0485] Camera: Smartphones and webcams are used.

[0486] software

[0487] Database: A relational database management system such as MySQL or PostgreSQL.

[0488] Statistical analysis model: R was used.

[0489] AI model: Built using TensorFlow and PyTorch.

[0490] Video analysis: Using OpenCV.

[0491] Natural Language Processing (NLP): Uses SpaCy, BERT, etc.

[0492] User information and initial evaluation

[0493] The user inputs basic information (e.g., age, gender, health status, exercise experience, etc.) via the terminal. This information is sent to the server, which stores it in a database.

[0494] The server performs an initial assessment using a statistical analysis model implemented in R based on the stored basic information, and generates reference data according to the user's health condition and exercise experience. This reference data will serve as the basis for a customized program that will be generated later.

[0495] Generating a customized exercise program

[0496] The user sets exercise goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.) using the device. These goals are sent to the server, which compares them with the initial assessment data and generates an individualized exercise program using an AI model built in Python (using TensorFlow and PyTorch). For example, a program for walking three times a week for 30 minutes each time is generated.

[0497] The generated exercise program is sent to the terminal by the server, and the user can check it and put it into practice.

[0498] Recording and analyzing exercise form

[0499] While the exercise program is running, the user is prompted to record their walking form. The user can record the video of their exercise using a smartphone or webcam and save it on their device.

[0500] The device uploads the saved video to a server. The server uses OpenCV to analyze the video and uses an AI model to identify areas for improvement in posture and form. Based on the analysis results, specific advice such as "straighten your back" or "correct your arm swing" is generated and sent to the device.

[0501] Food record entry and nutritional advice

[0502] The terminal provides a screen for the user to input daily meal plans, such as bread and eggs for breakfast, salad and chicken for lunch, and fish and rice for dinner.

[0503] The device sends the input data to a server, where the server's nutritional management AI model (using SpaCy and other deep learning technologies) analyzes the data. Based on this, nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad" is generated and sent to the device.

[0504] Emotional data input and psychological support

[0505] The user uses the emotion input screen of the device to enter their feelings and worries about walking in diary format. For example, they might enter something like, "I felt really good after walking today."

[0506] The device sends the input emotional data to the server, where it is analyzed by the server's emotion engine (using NLP models such as BERT). Based on the analysis results, psychological support suggestions such as "practice deep breathing before walking" and "exercises to promote positive thinking" are generated and notified to the device as mentioned above.

[0507] Example prompts for generative AI models

[0508] Here are some example prompts to input to the generative AI model:

[0509] "What kind of training schedule would be best to create a walking program suitable for a 40-year-old woman who is new to walking?"

[0510] "Please provide users with advice on balanced nutrition based on their dietary data."

[0511] "How can I analyze video of my walking to identify areas for improvement?"

[0512] The above is a detailed description of an embodiment of the present invention, which allows the user to maximize their health benefits.

[0513] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0514] Step 1:

[0515] Enter basic information

[0516] Terminal: Displays a screen for the user to enter basic information (e.g., age, gender, health status, exercise experience, etc.). As a specific example of input, the user enters "Age: 40 years old," "Gender: Female," and "Exercise experience: Beginner."

[0517] User: Enter basic information and press the send button.

[0518] Terminal: Sends the entered basic information to the server.

[0519] Input: User basic information

[0520] Output: Basic information about the user sent to the server

[0521] Step 2:

[0522] Conducting an initial evaluation

[0523] Server: Receives basic information sent by the user and stores it in a database.

[0524] Server: Analyzes basic information using a statistical analysis model implemented in R and performs an initial evaluation. Here, baseline data is generated based on the user's age, health status, etc.

[0525] Input: User basic information

[0526] Output: Baseline data for initial evaluation

[0527] Step 3:

[0528] Receiving goal setting

[0529] Device: Displays a screen for setting exercise goals. The user sets "diet" as an example.

[0530] User: Enter a goal and press the submit button.

[0531] Terminal: Sends the entered goal to the server.

[0532] Input: User-defined exercise goals

[0533] Output: Movement goal sent to the server

[0534] Step 4:

[0535] Generate customized programs

[0536] Server: Based on the baseline data from the initial evaluation and the user's exercise goals, a generative AI model built in Python (using TensorFlow and PyTorch) is used to generate an individual exercise program. Specifically, a walking program of 30 minutes, three times a week, is generated.

[0537] Server: Sends the generated exercise program to the terminal.

[0538] Input: Initial evaluation data, user's exercise goals

[0539] Output: Customized exercise program

[0540] Step 5:

[0541] Exercise program notification

[0542] Terminal: Receives the customized exercise program sent from the server and notifies the user.

[0543] User: Check notifications and view exercise program details.

[0544] Input: Customized exercise program

[0545] Output: An exercise program that can be viewed by the user

[0546] Step 6:

[0547] Form recording instructions

[0548] Device: Notifies the user at the set time to record a video of their exercise form.

[0549] User: Uses the camera function on a smartphone to take a photo of their exercise form.

[0550] Input: Recording timing instructions

[0551] Output: Video of exercise form taken by the user

[0552] Step 7:

[0553] Video recording and uploading

[0554] User: Record a video of your exercise form and save it on your device.

[0555] Terminal: Presents the user with the option to upload, and when they press the upload button, the video is sent to the server.

[0556] Input: Video of exercise form taken by the user

[0557] Output: Video of exercise form uploaded to the server

[0558] Step 8:

[0559] Video analysis and feedback

[0560] Server: Receives the uploaded video and analyzes it using OpenCV. Using a generative AI model, it identifies areas for improvement in posture and form (e.g., "straighten your back" or "correct your arm swing").

[0561] Server: Based on the analysis results, specific feedback for the user is generated and sent to the device.

[0562] Terminal: Notifies the user of the generated feedback.

[0563] Input: Video of exercise form

[0564] Output: Specific form feedback

[0565] Step 9:

[0566] Entering a food record

[0567] Terminal: Provides a screen for the user to input meal details, such as bread and eggs for breakfast, salad and chicken for lunch, and fish and rice for dinner.

[0568] User: Enter the meal details and press the send button.

[0569] Terminal: Sends the entered meal details to the server.

[0570] Input: User's diet

[0571] Output: Meal details sent to the server

[0572] Step 10:

[0573] Analysis of dietary data

[0574] Server: Receives meal data and analyzes it using a nutrition management AI model (using SpaCy and other deep learning technologies). As a result of the analysis, it generates specific nutritional advice such as "add more protein to breakfast" or "add nuts to lunch."

[0575] Server: Sends the analysis results to the device.

[0576] Terminal: Notifies the user of the generated nutritional advice.

[0577] Input: Meal data

[0578] Output: Nutritional guidance

[0579] Step 11:

[0580] Entering emotion data

[0581] Device: Provides an emotion input screen and allows the user to input their emotions and worries. For example, they can input "I felt great after walking today."

[0582] User: Enter emotion data and press the send button.

[0583] Terminal: Sends the input emotion data to the server.

[0584] Input: User emotion data

[0585] Output: Emotion data sent to the server

[0586] Step 12:

[0587] Emotional data analysis and psychological support

[0588] Server: Receives emotional data and analyzes it using an emotion engine (using NLP models such as BERT). Based on the analysis results, it generates psychological support suggestions such as "practice deep breathing before walking" and "exercises to promote positive thinking."

[0589] Server: Sends the generated psychological support proposals to the device.

[0590] Terminal: Notifies the user and displays psychological support information.

[0591] Input: Emotion data

[0592] Output: Psychological support suggestions

[0593] The above is the specific processing flow of the system program. This enables a personalized approach according to the individual situation and goals of the user, and can lead to greater health benefits.

[0594] (Application example 2)

[0595] 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."

[0596] Robot operators working in factories can accumulate fatigue due to long hours of work, which can reduce productivity. They also face a higher risk of health problems due to poor walking form and inappropriate dietary management. Furthermore, psychological stress can significantly reduce overall work efficiency. To comprehensively solve these issues and improve work efficiency, a system that provides appropriate walking programs and health management support is needed.

[0597] 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 receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing a walking program based on the transmitted goals, and means for analyzing a photograph of the user's walking form and providing improvements. This enables the provision of a customized walking program based on the user's individual information. Furthermore, the means for analyzing input dietary records to provide nutritional guidance and the means for analyzing input emotional data to provide psychological support enable comprehensive health management and psychological support. Furthermore, the means for providing a customized walking program to improve the work efficiency of a factory worker and the means for recording and analyzing the user's posture and movements during walking to improve the user's form and providing specific feedback for improvement enable comprehensive support for the user's work efficiency and health.

[0598] "Basic information" refers to personal data provided by the user, such as age, gender, health status, walking experience, etc.

[0599] "Initial evaluation" is a process of comprehensively evaluating the user's health condition, walking experience, etc. based on the user's basic information.

[0600] A "walking program" is a program that provides instructions for a walking schedule and method that is optimized according to the user's goals.

[0601] "Walking form" refers to the user's posture and movements while walking.

[0602] "Nutrition guidance" involves analyzing the dietary records entered by the user and providing advice on appropriate nutritional intake from the perspective of health management.

[0603] "Emotion data" is information about emotions and stress input by the user.

[0604] "Psychological support" provides users with advice on mental care and stress relief based on analyzed emotional data.

[0605] "Improving work efficiency" refers to improving the efficiency of work performed by users in factories and other places.

[0606] A "customized walking program" is a walking schedule or method specifically designed to suit the characteristics and health condition of an individual user.

[0607] "Form improvement feedback" involves recording the user's walking posture and movements, analyzing them, and providing specific areas for improvement.

[0608] The present invention is a system that provides walking programs and comprehensive health support for robot operators working in factories. The system performs an initial assessment based on the user's basic information and then provides an individually customized walking program based on that assessment. The system also analyzes the user's form, dietary records, and emotional data to provide appropriate support. The specific steps for implementing this system are described below.

[0609] First, the user uses their smartphone to enter their basic information (age, gender, occupation, working hours, health condition, walking experience, etc.) into the application. This information is sent to the server and stored in a database. The server then performs an initial evaluation based on this information and generates a walking program and health management criteria that are suitable for the user. A generative AI model supports this evaluation and provides an optimized program.

[0610] Next, when the user starts walking, the smartphone device prompts them to record their posture and movements while walking. The user follows the instructions, records their walking posture, and uploads the video to a server. The server then uses an AI model to analyze the video and identify areas for improvement in posture and form. The analysis results are provided to the user as specific improvement feedback (e.g., "Relax your shoulders" or "Move your arms more widely").

[0611] Users also input their daily dietary information into their smartphone. The input data is sent to a server, where an AI model analyzes the dietary data. Based on the analysis results, appropriate nutritional advice is provided from a health management perspective (e.g., "Eat more vegetables at lunch" or "Eat more protein").

[0612] Furthermore, users enter their emotional state regarding walking and work in diary format on their smartphone. This emotional data is sent to the server and analyzed by the emotion engine. Based on the analysis results, psychological support suggestions for the user are generated (e.g., "relaxation techniques" and "positive thinking exercises").

[0613] The hardware used includes smartphones (iPhone, Android devices), and the software used includes Amazon Web Services (AWS), generative AI models (such as OpenAI's GPT series), and IBM Watson's built-in emotion analysis tools.

[0614] Specific examples

[0615] The user enters the following information into the application:

[0616] Age: 35

[0617] Gender: Male

[0618] Occupation: Robot operator

[0619] Walking goal: Reduce fatigue

[0620] An example of the prompt statement generated is:

[0621] "35 years old, male, robot operator. I'm a beginner at walking and I'm trying to reduce fatigue."

[0622] "Generate a customized program of walking times and duration per week to help users manage their health."

[0623] Through these procedures and analysis, users are provided with a personalized walking program, feedback on their form, nutritional guidance, and psychological support, all of which contribute to the overall improvement of their work efficiency and health.

[0624] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0625] Step 1:

[0626] Users use their smartphone to enter their basic information (age, gender, occupation, working hours, health condition, walking experience, etc.) into the application.

[0627] The basic information entered is sent from the terminal to the server and stored in a database.

[0628] Step 2:

[0629] The server will make an initial assessment based on the basic information sent.

[0630] Using a generative AI model, data such as the user's health condition and walking experience is analyzed to generate an optimal walking program and health management standards.

[0631] The output of this initial assessment is an optimized health care standard and a basic training program.

[0632] Step 3:

[0633] When a user starts walking using a smartphone device, the device will instruct it to record their posture and movements while walking.

[0634] The user saves the recorded video on the device and uploads it to the server.

[0635] Step 4:

[0636] The server receives the uploaded video and analyzes it using an AI model.

[0637] As a result of analyzing walking form, areas for improvement in posture and movement are identified.

[0638] The results of this analysis are provided to the user as specific feedback for improvement (e.g., "relax your shoulders" or "make your arm movements larger").

[0639] Step 5:

[0640] Users simply enter their daily dietary information into their smartphone.

[0641] The input data is sent from the device to a server, which then uses an AI model to analyze the food data.

[0642] As a result of the analysis, appropriate nutritional advice is provided from the perspective of health management (e.g., "eat more vegetables at lunch" or "eat more protein").

[0643] Step 6:

[0644] Users enter their emotional state regarding walking and work in diary format on their smartphone.

[0645] Emotion data is sent from the device to a server, which then analyzes it using an emotion engine.

[0646] As a result of the analysis, psychological support suggestions for the user are generated (e.g., "relaxation techniques," "positive thinking exercises," etc.).

[0647] Step 7:

[0648] The generated walking program, health management guidelines, form improvement feedback, nutritional advice, and psychological support suggestions are sent to the user's smartphone and provided to them.

[0649] By receiving this comprehensive support, users can improve their work efficiency and health.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] [Second embodiment]

[0654] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0655] 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.

[0656] 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).

[0657] 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.

[0658] 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.

[0659] 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).

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] 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.

[0665] 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."

[0666] System Overview

[0667] This invention is a system that utilizes generative AI models to maximize the health benefits of walking. The system performs an initial assessment based on the user's basic information and provides an individually customized walking program based on that assessment. It also analyzes the user's form, food records, and emotional data to provide appropriate support.

[0668] Program processing and specific examples

[0669] User registration and initial evaluation

[0670] 1. Enter your user information:

[0671] Device: The user enters their basic information (age, gender, health condition, walking experience, etc.) into the application.

[0672] Server: Receives this information and stores it in a database.

[0673] 2. Conducting an Initial Assessment:

[0674] Server: Conducts an initial assessment based on the user's basic information and generates reference data appropriate for age, gender, and health condition.

[0675] Optimizing your walking program

[0676] 1. Receive goal setting:

[0677] Device: The user sets a walking goal (e.g., dieting).

[0678] Server: Receives the goals and generates a personalized training program based on the user's initial assessment and goals.

[0679] 2. Generate customized programs:

[0680] Server: The AI ​​model creates a training schedule (number of times per week, walking time per session, walking speed, rest timing, etc.) needed to achieve the goal.

[0681] Form Improvements

[0682] 1. Form recording instructions:

[0683] Device: Instruct the user to capture their posture and movements while walking.

[0684] User: Follow the instructions to record a video while walking.

[0685] 2. Video Analysis:

[0686] Device: Upload the captured video to the server.

[0687] Server: Analyzes video to identify areas for improvement in posture and form.

[0688] 3. Providing Feedback:

[0689] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[0690] Terminal: Display feedback to the user.

[0691] Nutritional guidance

[0692] 1. Enter your food record:

[0693] Terminal: The user inputs the details of their daily meals.

[0694] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[0695] 2. Analysis of dietary data:

[0696] Terminal: Sends the entered meal data to the server.

[0697] Server: Analyzes the received data using a nutritional management AI model and provides nutritional guidance in line with goals.

[0698] 3. Providing nutritional advice:

[0699] Server: Based on the analysis results, it provides the user with nutritional advice appropriate for their diet (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[0700] Terminal: Notifies the user of nutritional advice.

[0701] Psychological support

[0702] 1. Emotional input and goal setting:

[0703] Device: The user enters their feelings and worries about walking in diary format.

[0704] User: Write down your thoughts and stress about walking.

[0705] 2. Emotional Data Analysis:

[0706] Terminal: Sends the input emotion data to the server.

[0707] Server: The sentiment analysis model analyzes the data and generates psychological support suggestions for the user.

[0708] 3. Providing psychological support:

[0709] Server: Provides users with effective stress management and motivational strategies (e.g., "practice deep breathing before walking" or "exercises that encourage positive thinking").

[0710] Terminal: Displays psychological support advice to the user.

[0711] Specific examples

[0712] Let's say a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. After that, if the user sets a goal of dieting, the server generates a walking program of 30 minutes, 3 times a week.

[0713] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides specific advice such as "straighten your back" or "correct the way you swing your arms."

[0714] Furthermore, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad."

[0715] Finally, when the user enters their feelings about walking in diary format, the server analyzes this and provides psychological support such as "deep breathing techniques" and "positive thinking techniques."

[0716] As described above, the system of the present invention can comprehensively support the user's walking and maximize the health benefits.

[0717] The processing flow will be explained below.

[0718] User registration and initial evaluation

[0719] Step 1:

[0720] Device: The user installs the application and enters basic information (age, gender, health status, walking experience, etc.).

[0721] Step 2:

[0722] Terminal: Sends the entered information to the server.

[0723] Server: Stores the received basic information in a database.

[0724] Step 3:

[0725] Server: Based on the user's basic information, an initial evaluation is performed and reference data appropriate for the user is generated.

[0726] Optimizing your walking program

[0727] Step 1:

[0728] Device: The user sets walking goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[0729] Step 2:

[0730] Terminal: Sends goal settings to the server.

[0731] Server: Based on goals and initial assessments, an AI model is used to generate a personalized training program.

[0732] Step 3:

[0733] Server: Sends the generated training program (number of times per week, walking time per session, walking speed, timing of rest, etc.) to the user.

[0734] Terminal: displays the training program to the user.

[0735] Form Improvements

[0736] Step 1:

[0737] Device: Instruct the user to capture their posture and movements while walking.

[0738] Step 2:

[0739] User: Take a video while walking and save it on the device.

[0740] Step 3:

[0741] Device: Upload the captured video to the server.

[0742] Step 4:

[0743] Server: Analyzes the video and identifies areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[0744] Step 5:

[0745] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[0746] Terminal: Display feedback to the user.

[0747] Nutritional guidance

[0748] Step 1:

[0749] Terminal: Provides a screen where users can input their daily dietary information.

[0750] Step 2:

[0751] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[0752] Step 3:

[0753] Terminal: Sends the entered meal data to the server.

[0754] Step 4:

[0755] Server: Analyzes the received dietary data using a nutritional management AI model and generates nutritional guidance that matches the user's goals.

[0756] Step 5:

[0757] Server: Based on the analysis results, it provides specific nutritional advice to users (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[0758] Terminal: Notifies the user of nutritional advice.

[0759] Psychological support

[0760] Step 1:

[0761] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[0762] Step 2:

[0763] User: Enter your thoughts and stress about walking.

[0764] Step 3:

[0765] Terminal: Sends the input emotion data to the server.

[0766] Step 4:

[0767] Server: Analyzes the emotional data and generates customized psychological support suggestions for the user.

[0768] Step 5:

[0769] Server: Based on the analysis results, the server provides users with effective methods for managing stress and maintaining motivation (e.g., "practice deep breathing before walking" or "exercises that encourage positive thinking").

[0770] Terminal: Displays psychological support advice to the user.

[0771] In this way, the server, terminal, and user work together at each processing step to provide support to maximize the effect of the user's walking.

[0772] Example 1

[0773] 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."

[0774] In modern society, while personalized health management and exercise guidance are important, providing such guidance requires significant resources. Furthermore, users often find it difficult to manage themselves, making it difficult to consistently receive effective exercise and dietary guidance and psychological support. Therefore, there is a demand for a system that can comprehensively support users' health status and provide individually optimized exercise programs, nutritional guidance, and psychological support.

[0775] 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.

[0776] In this invention, the server includes means for receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing an exercise program based on the transmitted goals, means for analyzing photographed user movements and providing improvements, means for analyzing input dietary records and providing nutritional guidance, and means for analyzing input emotion data and providing psychological support. This makes it possible to comprehensively support the user's health condition and consistently provide individually optimized exercise programs, nutritional guidance, and psychological support.

[0777] "Basic information sent by the user" refers to basic data such as age, sex, health condition, and exercise experience that the user provides to the system.

[0778] "Initial evaluation" is a process of comprehensively evaluating the user's health condition and fitness for exercise based on basic information.

[0779] "Goals" are specific objectives or hopes that users wish to achieve through the system.

[0780] An "exercise program" refers to an exercise schedule and content customized to a user's individual goals.

[0781] "Movement" refers to the posture and movements of the user when exercising.

[0782] "Analysis" is the process by which the server processes the data sent by the user and extracts meaningful information.

[0783] "Points for improvement" are specific points that should be corrected in order for the user to exercise more effectively.

[0784] "Diet record" refers to the data that a user enters into the system about their daily diet.

[0785] "Nutrition guidance" is specific advice provided to users on how to lead a healthy diet based on their food records.

[0786] "Emotion data" is information about a user's own emotions and mental state that is input into the system by the user.

[0787] "Psychological support" refers to advice and support provided to users to help them maintain their mental health.

[0788] "Health status" refers to the overall state of a user's physical and mental health.

[0789] "Exercise experience" refers to information such as the history of exercise the user has done up to now, the frequency of exercise, and the details of exercise.

[0790] A "training schedule" refers to a schedule or plan for a user to exercise systematically.

[0791] "Posture" refers to the position and shape of the body that a user assumes during exercise or daily life.

[0792] MODE FOR CARRYING OUT THE INVENTION

[0793] The present invention is a system for automating health management and exercise guidance for users. The system utilizes a server, a terminal, and a generative AI model to provide users with individually optimized exercise programs, form improvement, nutritional guidance, and psychological support.

[0794] Hardware and Software Configuration

[0795] Server: A high-performance server is used to run AI libraries such as Python, TensorFlow, Keras, OpenPose, and BERT. This server manages the database, performs initial evaluation, generates training programs, analyzes videos, analyzes food data, and analyzes emotion data.

[0796] Terminal: A device used by a user, such as a smartphone or tablet. The terminal provides an interface for the user to input information and sends the input data to a server.

[0797] User: Refers to an individual who uses the system and inputs the information necessary to manage their own health and receive exercise guidance.

[0798] System Operation Overview

[0799] 1. User registration and initial evaluation

[0800] The user uses the application to input basic information (age, gender, health status, exercise experience, etc.). The device sends this information to the server, which then performs an initial evaluation based on the received information and stores it in a database.

[0801] 2. Optimize your walking program

[0802] The user sets exercise goals (e.g., dieting) through the terminal. The server receives these goals and generates an individual training program based on the initial assessment data and the goals. The generated program is stored in a database and provided to the user.

[0803] 3. Improving your form

[0804] Users take videos of themselves walking with their smartphones and upload them to the server. The server analyzes the videos and identifies areas for improvement in posture and form. This is done using a posture analysis model using OpenPose. Based on the analysis results, specific improvement advice is generated and provided to the user.

[0805] 4. Nutritional guidance

[0806] Users enter their daily food records into the application. The device sends this data to the server. The server uses a nutrition management AI model to analyze the food data and provide nutritional guidance tailored to the user's goals. The analysis results are then sent to the device and presented to the user.

[0807] 5. Psychological support

[0808] Users enter their feelings and concerns about walking into the application in diary format. The device then sends this data to a server. The server then uses the natural language processing library BERT to analyze the emotional data and generate psychological support suggestions, providing users with methods for managing stress and maintaining motivation.

[0809] Specific examples

[0810] For example, suppose a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. Next, if the user sets "diet" as a goal, the server generates a walking program for 30 minutes, three times a week.

[0811] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides advice such as "straighten your back" or "correct the way you swing your arms." Additionally, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to your breakfast" or "add nuts to your lunch salad."

[0812] Furthermore, when users enter their feelings about walking in diary format, the server analyzes the data and provides psychological support such as "deep breathing techniques" and "positive thinking techniques."

[0813] The system aims to provide comprehensive support for users' walking and maximize the health benefits.

[0814] Prompt Sentence Examples

[0815] "When walking, be mindful of the following: keep your back straight and adjust your arm swing."

[0816] "Add more protein to your breakfast, such as eggs or yogurt."

[0817] "Practice deep breathing exercises before walking. This will help you relax and improve your workout."

[0818] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0819] Step 1:

[0820] The user opens the application on a device such as a smartphone or tablet and enters basic information such as age, gender, health status, and exercise experience. This information is then obtained.

[0821] Input: Basic information such as the user's age, gender, health status, and exercise experience

[0822] Output: Basic information data

[0823] Specific operation: The user enters information into the form and presses the "Complete" button to submit. The device temporarily saves the information and sends it to the server.

[0824] Step 2:

[0825] The terminal sends the basic information entered by the user to the server, which then stores the information in a database and performs an initial evaluation for each user.

[0826] Input: Basic information data

[0827] Output: Initial evaluation data

[0828] What happens: The server creates a new user record in the database and runs an initial evaluation script using Python. The evaluation results are saved in the database.

[0829] Step 3:

[0830] The user sets a specific walking goal (e.g., dieting) within the application. The set goal is sent as input data from the device to the server.

[0831] Input: Walking goal

[0832] Output: Target data

[0833] Specific operation: The user inputs a goal, and the device sends it to the server, which then stores the goal data in a database.

[0834] Step 4:

[0835] The server generates an individual training program using an AI model (using Keras) based on the received goal data and initial evaluation data, which is then stored in a database and provided to the user.

[0836] Input: Target data, initial evaluation data

[0837] Output: Individual training program

[0838] Specific operation: The server dynamically generates training programs using the Keras library and stores them in a database.

[0839] Step 5:

[0840] The user records their walking form with their smartphone and uploads the video to a server via the application.

[0841] Input: Video of walking

[0842] Output: Recorded video data

[0843] Specific operation: The user takes a video using the device's camera and sends it to the server using the upload function. This operation can be easily performed within the application.

[0844] Step 6:

[0845] The server receives the uploaded video and analyzes it using an AI model (a posture analysis model using OpenPose). As a result of the analysis, it identifies areas for improvement in the user's posture and form.

[0846] Input: Recorded video data

[0847] Output:Form Improvements

[0848] Specific behavior: The server uses the OpenPose library to analyze the video, analyze the data obtained, and identify problems with posture and behavior. This information is stored in a database.

[0849] Step 7:

[0850] Based on the analysis results, the server generates specific advice for the user on how to improve their form, and sends the advice to the terminal.

[0851] Input: Form Improvements

[0852] Output: Improvement advice

[0853] Specific operation: The server generates appropriate advice for the user based on the identified improvements. The generated advice is notified to the device and displayed to the user.

[0854] Step 8:

[0855] The user enters their daily food record into the application and sends the data to the server.

[0856] Input: Food log

[0857] Output: Food record data

[0858] Specific operation: When the user enters the meal details into the application form and presses the submit button, the data is sent from the terminal to the server.

[0859] Step 9:

[0860] The server analyzes the received dietary record data using a nutritional management AI model to generate data for providing nutritional guidance in line with goals. The analysis results are stored in a database.

[0861] Input: Food record data

[0862] Output: Nutritional guidance data

[0863] Specific operation: The server receives the food record data, analyzes it using a nutrition management AI model, and calculates nutritional balance and areas for improvement. This data is then stored in a database.

[0864] Step 10:

[0865] Based on the analysis results, the server generates nutritional guidance appropriate for the user and transmits the guidance content to the user's terminal.

[0866] Input: Nutritional guidance data

[0867] Output: Nutritional advice

[0868] Specific operation: The server generates nutritional advice appropriate for the user based on the analysis results obtained from the nutrition management AI model. This advice is sent to the user's device and displayed within the application.

[0869] Step 11:

[0870] The user enters their feelings and worries about walking into the application in diary format, and the data is sent to the server.

[0871] Input: Emotion data

[0872] Output: Emotion recording data

[0873] Specific operation: When the user enters their feelings or concerns into the application and presses the send button, the data is sent from the device to the server.

[0874] Step 12:

[0875] The server analyzes the received emotional data using the natural language processing library BERT and generates psychological support suggestions, including methods for managing stress and maintaining motivation.

[0876] Input: Emotion recording data

[0877] Output: Psychological support suggestions

[0878] How it works: The server uses the BERT library to analyze the sentiment data and extract patterns to understand the user's mental state. These data are then stored in a database.

[0879] Step 13:

[0880] Based on the analysis results, the server generates advice for providing psychological support to the user and sends it to the user's terminal.

[0881] Input: Psychological support suggestions

[0882] Output: Psychological support advice

[0883] Specific operation: Based on the acquired psychological support suggestions, the server generates appropriate advice for the user (e.g., "deep breathing" or "positive thinking") and sends it to the device. The device notifies and displays this to the user.

[0884] In this way, the system can comprehensively support the user's health and consistently provide individually optimized exercise programs, nutritional guidance, and psychological support.

[0885] (Application example 1)

[0886] 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."

[0887] Health management is becoming increasingly important in modern society, and many people are seeking a lifestyle that incorporates walking. However, there is no system that can comprehensively design and implement effective walking programs, improve one's form, maintain nutritional balance, and provide psychological support. Furthermore, linking these systems with food delivery services to easily provide individually optimized meal plans is also a challenge. Given this background, there is a need for a system that can provide users with comprehensive health support.

[0888] 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.

[0889] In this invention, the server includes means for receiving basic information sent by the user and making an initial evaluation, means for optimizing the walking program based on the sent goals, means for analyzing a photograph of the user's walking form and suggesting areas for improvement, means for analyzing the input meal record and providing nutritional advice, means for analyzing the input emotion data and providing psychological support, means for calculating calories burned based on the collected walking data and proposing an appropriate meal plan, and means for proposing a food delivery plan to provide a meal plan linked to the user's walking data. This allows for comprehensive support not only for the user's walking life but also for their daily meals and psychological health, enabling comprehensive health support.

[0890] "User information" refers to basic information provided by the user, such as age, gender, health status, walking experience, etc.

[0891] A "walking program" is a mobility training schedule that is individually customized based on the user's basic information and goals.

[0892] "Walking form" refers to the user's posture and movements while walking.

[0893] "Dietary records" are data on the contents and amounts of food consumed by a user each day.

[0894] "Emotion data" is information about emotions and worries about walking input by the user.

[0895] "Calories burned" refers to the amount of energy consumed through physical activity and is calculated based on walking data.

[0896] A "meal plan" is an appropriate meal plan and its composition that is suggested based on the user's walking data and nutritional analysis.

[0897] A "food delivery plan" is a meal delivery plan provided based on the user's optimal meal plan.

[0898] "Walking data" refers to information such as the number of steps taken and walking speed, and is collected through smart devices.

[0899] The "means for receiving basic information and making an initial evaluation" is a method in which the system receives basic information provided by the user and evaluates the initial state of the user based on that information.

[0900] The "means for optimizing a walking program based on goals" is a method for designing and generating an individually optimized walking program based on goals set by the user.

[0901] The "means of analyzing a user's filmed walking form and suggesting areas for improvement" is a method of analyzing a walking video filmed by the user and suggesting areas for improvement to the user's form based on the results of the analysis.

[0902] The "means for analyzing the input dietary record and providing nutritional guidance" is a method for analyzing the dietary details input by the user and providing nutritional guidance based on the analysis results.

[0903] The "means for analyzing input emotion data and providing psychological support" is a method for analyzing emotion data input by a user and providing psychological support based on the results.

[0904] "Means for calculating calories burned based on collected walking data and proposing an appropriate meal plan" refers to a method for calculating energy consumption using data collected during walking and proposing a meal plan suited to the user.

[0905] "Means for proposing food delivery plans to provide meal plans linked to the user's walking data" refers to a method for determining the optimal meal plan based on collected walking data and proposing a food delivery service based on that plan.

[0906] This system receives basic information sent by the user and optimizes walking programs based on that information. The system analyzes the user's walking data, food records, and emotional data to provide comprehensive health support. It also has a function to suggest optimal meal plans in conjunction with food delivery plans.

[0907] System configuration

[0908] This system uses the following hardware and software:

[0909] Programming language: Python

[0910] Generative AI model: GPT-4

[0911] Database: MySQL

[0912] Server: AWS EC2

[0913] App development platform: React Native (mobile app)

[0914] Processing flow

[0915] User information registration and initial evaluation

[0916] The user enters basic information into the terminal and sends it to the server. The server stores the received basic information in a MySQL database and performs an initial evaluation. Based on this initial evaluation, a walking program suitable for the user is generated.

[0917] Creating a walking program

[0918] The server uses a generative AI model to generate a customized walking program based on the user's basic information and goals, and inputs prompts such as the following into the generative AI model:

[0919] User age: 40

[0920] Gender: Female

[0921] Walking experience: Beginner

[0922] Goal: Lose weight

[0923] Based on this information, we will suggest the best walking program for you.

[0924] Improve your walking form

[0925] Users record their walking form and upload it to a server from their device. The server analyzes the video and identifies areas for improvement. This information is then analyzed using a generative AI model to provide appropriate advice to the user.

[0926] Food records and nutritional advice

[0927] The user inputs their daily dietary information into the device and sends it to the server. The server analyzes the received dietary data and generates nutritional advice. For example, the following prompt sentence is input into the generative AI model:

[0928] Breakfast: Toast, eggs, coffee

[0929] Lunch: Sandwich, salad

[0930] Dinner: Grilled fish, vegetable soup

[0931] Based on this food record, provide appropriate nutritional advice.

[0932] Emotional data and psychological support

[0933] The user inputs their feelings and concerns about walking into the device and sends them to the server, which analyzes the input emotional data and provides appropriate psychological support.

[0934] Food delivery plan proposal

[0935] The system calculates the calorie expenditure based on the user's walking data and generates a meal plan based on this. The server then connects the generated meal plan to a food delivery service and suggests the most suitable meal for the user. In this case, the following prompt is also input into the generative AI model:

[0936] Today's steps: 8000

[0937] Walking speed: 5km / h

[0938] This information will help you provide optimal nutritional advice and meal plans.

[0939] Specific examples

[0940] For example, if a 40-year-old woman uses this system to manage her daily walking and diet, the process would be as follows: The user enters initial information and walks according to the generated walking program. She records a video of herself walking to receive advice on improving her form, and then enters her daily food record to receive nutritional guidance. She follows the meal plan suggested based on the calories burned and orders appropriate meals from the food delivery plan. This series of processes allows the user to maintain a healthy lifestyle.

[0941] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0942] Step 1:

[0943] The user enters basic information (age, gender, health condition, walking experience, etc.) into the device. The entered basic information is sent from the device to the server. The basic information received by the server is stored in a MySQL database. Here, the data format is sent as JSON and inserted into the database using SQL statements.

[0944] Step 2:

[0945] The server performs an initial evaluation of the user based on the stored basic information. The results of the initial evaluation are generated using a generative AI model (GPT-4). The input data is user information, and an appropriate evaluation result is generated based on this. The server inputs the following prompt sentence into the generative AI model and obtains the evaluation result.

[0946] User age: 40

[0947] Gender: Female

[0948] Walking experience: Beginner

[0949] Use this information to make your initial assessment.

[0950] As a result, an initial evaluation report is generated for the user.

[0951] Step 3:

[0952] The user sets walking goals on the device. Possible goals include "dieting" or "improving physical fitness." The user's set goals are sent from the device to the server. The server generates an individually customized walking program based on the initial assessment and goals. This process also uses a generative AI model, and the following prompt is input:

[0953] User age: 40

[0954] Gender: Female

[0955] Walking experience: Beginner

[0956] Goal: Lose weight

[0957] Based on this information, we will suggest the best walking program for you.

[0958] As a result, a walking program is generated that specifies the number of times per week, the duration of each walk, walking speed, and rest timing.

[0959] Step 4:

[0960] Users record their walking form with their device's camera. The video is then uploaded from the device to a server. The server then analyzes the video using a generative AI model to identify areas for improvement in posture and form. The following prompts are used:

[0961] Analyze the video while walking.

[0962] Based on the analysis results, the server generates specific feedback for improving form, such as straightening your back or correcting the way you swing your arms, and sends this to the device.

[0963] Step 5:

[0964] The user enters the details of their daily meals into the device and sends this meal record to the server. The server then analyzes the received meal record using an AI model for nutritional management and generates nutritional advice appropriate for the user. For example, the following prompt sentences are used:

[0965] Breakfast: Toast, eggs, coffee

[0966] Lunch: Sandwich, salad

[0967] Dinner: Grilled fish, vegetable soup

[0968] Based on this food record, provide appropriate nutritional advice.

[0969] The analysis results in specific advice, such as adding more protein to your breakfast or adding nuts to your lunch salad, which is displayed on the device.

[0970] Step 6:

[0971] The user enters their feelings and worries about walking in diary format into the device and sends it to the server. The server analyzes the received emotional data and suggests appropriate psychological support. The following prompts are used:

[0972] The user has entered their thoughts and concerns. Based on this information, please suggest psychological support.

[0973] As a result, specific psychological support advice, such as deep breathing techniques and positive thinking techniques, is generated and displayed on the device.

[0974] Step 7:

[0975] The server calculates the calories burned based on walking data collected from the user's steps and walking speed. Based on the calculated calories, it proposes an appropriate meal plan. Furthermore, it uses a generative AI model to generate a specific meal plan using prompt sentences such as the following:

[0976] Today's steps: 8000

[0977] Walking speed: 5km / h

[0978] This information will help you provide optimal nutritional advice and meal plans.

[0979] Based on this suggestion, a food delivery plan is created and presented to the user, who can then order appropriate meals from the suggested meal plan.

[0980] 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.

[0981] System Overview

[0982] This invention is a system that utilizes a generative AI model to maximize the health benefits of walking. The system performs an initial assessment based on the user's basic information and provides an individually customized walking program based on that assessment. It also analyzes the user's form, food records, and emotional data to provide appropriate support. The system also incorporates an emotion engine that recognizes the user's emotions.

[0983] Program processing and specific examples

[0984] User registration and initial evaluation

[0985] 1. Enter your user information

[0986] Device: The user enters their basic information (age, gender, health condition, walking experience, etc.) into the application.

[0987] Server: Receives this information and stores it in a database.

[0988] 2. Conducting an initial evaluation

[0989] Server: Performs an initial evaluation based on the user's basic information and generates reference data suitable for the user.

[0990] Optimizing your walking program

[0991] 1. Receiving goal setting

[0992] Device: The user sets walking goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[0993] Server: Receives the goals and generates a personalized training program based on the user's initial assessment and goals.

[0994] 2. Creating a customized program

[0995] Server: The AI ​​model creates a training schedule (number of times per week, walking time per session, walking speed, rest timing, etc.) needed to achieve the goal.

[0996] 3. Notice to Users

[0997] Server: Sends the generated training program to the user.

[0998] Terminal: displays the training program to the user.

[0999] Form Improvements

[1000] 1. Form recording instructions

[1001] Device: Instruct the user to capture their posture and movements while walking.

[1002] 2. Recording and uploading videos

[1003] User: Take a video while walking and save it on the device.

[1004] Device: Upload the captured video to the server.

[1005] 3. Video analysis and feedback

[1006] Server: Analyzes the video and identifies areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[1007] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[1008] Terminal: Display feedback to the user.

[1009] Nutritional guidance

[1010] 1. Enter your food record

[1011] Terminal: Provides a screen where the user can input their daily dietary information.

[1012] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[1013] 2. Submitting and analyzing dietary data

[1014] Terminal: Sends the entered meal data to the server.

[1015] Server: Analyzes the received dietary data using a nutritional management AI model and provides nutritional guidance in line with goals.

[1016] 3. Providing nutritional guidance

[1017] Server: Based on the analysis results, it provides the user with nutritional advice appropriate for their diet (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[1018] Terminal: Notifies the user of nutritional advice.

[1019] Psychological support and emotional engine

[1020] 1. Emotion Input and Recognition

[1021] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[1022] User: Enter your thoughts and stress about walking.

[1023] 2. Transmission and analysis of emotional data

[1024] Terminal: Sends the input emotion data to the server.

[1025] Server: The emotion engine analyzes the emotion data and evaluates the emotional state.

[1026] 3. Providing psychological support

[1027] Server: Based on the analysis results, it generates customized psychological support suggestions for the user (e.g., "practice deep breathing before walking" or "exercises that promote positive thinking").

[1028] Terminal: Displays psychological support advice to the user.

[1029] Specific examples

[1030] Let's say a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. After that, if the user sets a goal of dieting, the server generates a walking program of 30 minutes, 3 times a week.

[1031] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides specific advice such as "straighten your back" or "correct the way you swing your arms."

[1032] Furthermore, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad."

[1033] When users enter their feelings about walking in diary format, the server's emotion engine analyzes them and provides psychological support such as "deep breathing techniques" and "positive thinking methods."

[1034] By utilizing the emotion engine, more personalized support can be provided by providing care appropriate to the user's individual emotional state. This system can comprehensively support the user's walking and maximize health benefits.

[1035] The processing flow will be explained below.

[1036] User registration and initial evaluation

[1037] Step 1:

[1038] Device: The user installs the application and enters basic information (age, gender, health status, walking experience, etc.).

[1039] Step 2:

[1040] Terminal: Sends the entered information to the server.

[1041] Step 3:

[1042] Server: Stores the received basic information in a database.

[1043] Step 4:

[1044] Server: Performs an initial evaluation based on the user's basic information and generates reference data suitable for the user.

[1045] Optimizing your walking program

[1046] Step 1:

[1047] Terminal: The user inputs the walking goal (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[1048] Step 2:

[1049] Terminal: Sends goal settings to the server.

[1050] Step 3:

[1051] Server: Uses AI models to generate personalized training programs based on goals and initial assessment results.

[1052] Step 4:

[1053] Server: Sends the generated training program to the user.

[1054] Step 5:

[1055] Terminal: displays the training program to the user.

[1056] Form Improvements

[1057] Step 1:

[1058] Device: Instruct the user to capture their posture and movements while walking.

[1059] Step 2:

[1060] User: Take a video while walking and save it on the device.

[1061] Step 3:

[1062] Device: Upload the captured video to the server.

[1063] Step 4:

[1064] Server: Analyzes the video and detects areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[1065] Step 5:

[1066] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[1067] Step 6:

[1068] Terminal: Display feedback to the user.

[1069] Nutritional guidance

[1070] Step 1:

[1071] Terminal: Provides a screen where users can input their daily dietary information.

[1072] Step 2:

[1073] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[1074] Step 3:

[1075] Terminal: Sends the entered meal data to the server.

[1076] Step 4:

[1077] Server: Analyzes the received dietary data using a nutritional management AI model and generates nutritional guidance that matches the user's goals.

[1078] Step 5:

[1079] Server: Based on the analysis results, it provides specific nutritional advice to users (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[1080] Step 6:

[1081] Terminal: Notifies the user of nutritional advice.

[1082] Psychological support and emotional engine

[1083] Step 1:

[1084] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[1085] Step 2:

[1086] User: Enter your thoughts and stress about walking.

[1087] Step 3:

[1088] Terminal: Sends the input emotion data to the server.

[1089] Step 4:

[1090] Server: The emotion engine analyzes the emotion data and evaluates the emotional state.

[1091] Step 5:

[1092] Server: Based on the analysis results, it generates customized psychological support suggestions for the user (e.g., "practice deep breathing before walking" or "exercises that promote positive thinking").

[1093] Step 6:

[1094] Terminal: Displays psychological support advice to the user.

[1095] In this way, the server, terminal, and user work together at each processing step to provide comprehensive support to maximize the effectiveness of the user's walking.

[1096] Example 2

[1097] 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."

[1098] Conventional health management systems lack a personalized approach, making it difficult to respond appropriately to individual users' situations and goals. Furthermore, there were no systems capable of providing advanced support, such as improving walking form, analyzing food records, or providing psychological support based on emotional data. This made it difficult to maximize the health benefits for users.

[1099] 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 receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing the exercise program based on the transmitted goals, means for analyzing the user's photographed exercise form and providing improvements, means for analyzing the input dietary record and providing nutritional advice, means for analyzing the input emotional data and providing psychological support, means for instructing the user to record their exercise form, and means for notifying the user of the generated program. This enables a personalized approach according to the user's individual situation and goals, and makes it possible to maximize the user's health benefits through improvements in walking form, analysis of the dietary record, and advanced psychological support based on the emotional data.

[1100] "Basic information" refers to initial data about an individual user, such as the user's age, gender, health condition, and exercise experience.

[1101] An "initial assessment" is an assessment based on basic information about the user, and this assessment confirms the user's health condition and exercise experience.

[1102] An "exercise program" is a customized exercise plan based on the goals set by the user, specifically including the number of walking sessions, duration, and speed.

[1103] "Exercise form" refers to the posture and movements of the user when exercising, and by analyzing this, areas for improvement can be identified.

[1104] "Nutrition guidance" refers to advice based on data entered by the user about their daily diet, and indicates nutritional intake methods that are suitable for the user's goals.

[1105] "Emotion data" refers to data entered in diary format by the user about emotions and worries felt during exercise or daily life.

[1106] "Psychological support" refers to support provided based on an assessment of the user's emotional state through analysis of emotional data, and includes, for example, stress relief and promotion of positive thinking.

[1107] The "recording instruction means" is a system function that instructs the user to record their exercise form.

[1108] "Notification means" refers to the system's function of informing users of information such as exercise programs generated by the server, analysis results, nutritional guidance, and psychological support.

[1109] Overall system overview

[1110] This invention is a system for managing a user's health by analyzing the user's exercise program, exercise form, diet record, and emotional data, and providing personalized advice. The system consists of three entities: a server, a terminal, and the user.

[1111] Hardware and software used

[1112] Hardware

[1113] Terminals: Smartphones and personal computers are mainly used.

[1114] Server: Cloud or dedicated servers are used.

[1115] Camera: Smartphones and webcams are used.

[1116] software

[1117] Database: A relational database management system such as MySQL or PostgreSQL.

[1118] Statistical analysis model: R was used.

[1119] AI model: Built using TensorFlow and PyTorch.

[1120] Video analysis: Using OpenCV.

[1121] Natural Language Processing (NLP): Uses SpaCy, BERT, etc.

[1122] User information and initial evaluation

[1123] The user inputs basic information (e.g., age, gender, health status, exercise experience, etc.) via the terminal. This information is sent to the server, which stores it in a database.

[1124] The server performs an initial assessment using a statistical analysis model implemented in R based on the stored basic information, and generates reference data according to the user's health condition and exercise experience. This reference data will serve as the basis for a customized program that will be generated later.

[1125] Generating a customized exercise program

[1126] The user sets exercise goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.) using the device. These goals are sent to the server, which compares them with the initial assessment data and generates an individualized exercise program using an AI model built in Python (using TensorFlow and PyTorch). For example, a program for walking three times a week for 30 minutes each time is generated.

[1127] The generated exercise program is sent to the terminal by the server, and the user can check it and put it into practice.

[1128] Recording and analyzing exercise form

[1129] While the exercise program is running, the user is prompted to record their walking form. The user can record the video of their exercise using a smartphone or webcam and save it on their device.

[1130] The device uploads the saved video to a server. The server uses OpenCV to analyze the video and uses an AI model to identify areas for improvement in posture and form. Based on the analysis results, specific advice such as "straighten your back" or "correct your arm swing" is generated and sent to the device.

[1131] Food record entry and nutritional advice

[1132] The terminal provides a screen for the user to input daily meal plans, such as bread and eggs for breakfast, salad and chicken for lunch, and fish and rice for dinner.

[1133] The device sends the input data to a server, where the server's nutritional management AI model (using SpaCy and other deep learning technologies) analyzes the data. Based on this, nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad" is generated and sent to the device.

[1134] Emotional data input and psychological support

[1135] The user uses the emotion input screen of the device to enter their feelings and worries about walking in diary format. For example, they might enter something like, "I felt really good after walking today."

[1136] The device sends the input emotional data to the server, where it is analyzed by the server's emotion engine (using NLP models such as BERT). Based on the analysis results, psychological support suggestions such as "practice deep breathing before walking" and "exercises to promote positive thinking" are generated and notified to the device as mentioned above.

[1137] Example prompts for generative AI models

[1138] Here are some example prompts to input to the generative AI model:

[1139] "What kind of training schedule would be best to create a walking program suitable for a 40-year-old woman who is new to walking?"

[1140] "Please provide users with advice on balanced nutrition based on their dietary data."

[1141] "How can I analyze video of my walking to identify areas for improvement?"

[1142] The above is a detailed description of an embodiment of the present invention, which allows the user to maximize their health benefits.

[1143] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1144] Step 1:

[1145] Enter basic information

[1146] Terminal: Displays a screen for the user to enter basic information (e.g., age, gender, health status, exercise experience, etc.). As a specific example of input, the user enters "Age: 40 years old," "Gender: Female," and "Exercise experience: Beginner."

[1147] User: Enter basic information and press the send button.

[1148] Terminal: Sends the entered basic information to the server.

[1149] Input: User basic information

[1150] Output: Basic information about the user sent to the server

[1151] Step 2:

[1152] Conducting an initial evaluation

[1153] Server: Receives basic information sent by the user and stores it in a database.

[1154] Server: Analyzes basic information using a statistical analysis model implemented in R and performs an initial evaluation. Here, baseline data is generated based on the user's age, health status, etc.

[1155] Input: User basic information

[1156] Output: Baseline data for initial evaluation

[1157] Step 3:

[1158] Receiving goal setting

[1159] Device: Displays a screen for setting exercise goals. The user sets "diet" as an example.

[1160] User: Enter a goal and press the submit button.

[1161] Terminal: Sends the entered goal to the server.

[1162] Input: User-defined exercise goals

[1163] Output: Movement goal sent to the server

[1164] Step 4:

[1165] Generate customized programs

[1166] Server: Based on the baseline data from the initial evaluation and the user's exercise goals, a generative AI model built in Python (using TensorFlow and PyTorch) is used to generate an individual exercise program. Specifically, a walking program of 30 minutes, three times a week, is generated.

[1167] Server: Sends the generated exercise program to the terminal.

[1168] Input: Initial evaluation data, user's exercise goals

[1169] Output: Customized exercise program

[1170] Step 5:

[1171] Exercise program notification

[1172] Terminal: Receives the customized exercise program sent from the server and notifies the user.

[1173] User: Check notifications and view exercise program details.

[1174] Input: Customized exercise program

[1175] Output: An exercise program that can be viewed by the user

[1176] Step 6:

[1177] Form recording instructions

[1178] Device: Notifies the user at the set time to record a video of their exercise form.

[1179] User: Uses the camera function on a smartphone to take a photo of their exercise form.

[1180] Input: Recording timing instructions

[1181] Output: Video of exercise form taken by the user

[1182] Step 7:

[1183] Video recording and uploading

[1184] User: Record a video of your exercise form and save it on your device.

[1185] Terminal: Presents the user with the option to upload, and when they press the upload button, the video is sent to the server.

[1186] Input: Video of exercise form taken by the user

[1187] Output: Video of exercise form uploaded to the server

[1188] Step 8:

[1189] Video analysis and feedback

[1190] Server: Receives the uploaded video and analyzes it using OpenCV. Using a generative AI model, it identifies areas for improvement in posture and form (e.g., "straighten your back" or "correct your arm swing").

[1191] Server: Based on the analysis results, specific feedback for the user is generated and sent to the device.

[1192] Terminal: Notifies the user of the generated feedback.

[1193] Input: Video of exercise form

[1194] Output: Specific form feedback

[1195] Step 9:

[1196] Entering a food record

[1197] Terminal: Provides a screen for the user to input meal details, such as bread and eggs for breakfast, salad and chicken for lunch, and fish and rice for dinner.

[1198] User: Enter the meal details and press the send button.

[1199] Terminal: Sends the entered meal details to the server.

[1200] Input: User's diet

[1201] Output: Meal details sent to the server

[1202] Step 10:

[1203] Analysis of dietary data

[1204] Server: Receives meal data and analyzes it using a nutrition management AI model (using SpaCy and other deep learning technologies). As a result of the analysis, it generates specific nutritional advice such as "add more protein to breakfast" or "add nuts to lunch."

[1205] Server: Sends the analysis results to the device.

[1206] Terminal: Notifies the user of the generated nutritional advice.

[1207] Input: Meal data

[1208] Output: Nutritional guidance

[1209] Step 11:

[1210] Entering emotion data

[1211] Device: Provides an emotion input screen and allows the user to input their emotions and worries. For example, they can input "I felt great after walking today."

[1212] User: Enter emotion data and press the send button.

[1213] Terminal: Sends the input emotion data to the server.

[1214] Input: User emotion data

[1215] Output: Emotion data sent to the server

[1216] Step 12:

[1217] Emotional data analysis and psychological support

[1218] Server: Receives emotional data and analyzes it using an emotion engine (using NLP models such as BERT). Based on the analysis results, it generates psychological support suggestions such as "practice deep breathing before walking" and "exercises to promote positive thinking."

[1219] Server: Sends the generated psychological support proposals to the device.

[1220] Terminal: Notifies the user and displays psychological support information.

[1221] Input: Emotion data

[1222] Output: Psychological support suggestions

[1223] The above is the specific processing flow of the system program. This enables a personalized approach according to the individual situation and goals of the user, and can lead to greater health benefits.

[1224] (Application example 2)

[1225] 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."

[1226] Robot operators working in factories can accumulate fatigue due to long hours of work, which can reduce productivity. They also face a higher risk of health problems due to poor walking form and inappropriate dietary management. Furthermore, psychological stress can significantly reduce overall work efficiency. To comprehensively solve these issues and improve work efficiency, a system that provides appropriate walking programs and health management support is needed.

[1227] 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 receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing a walking program based on the transmitted goals, and means for analyzing a photograph of the user's walking form and providing improvements. This enables the provision of a customized walking program based on the user's individual information. Furthermore, the means for analyzing input dietary records to provide nutritional guidance and the means for analyzing input emotional data to provide psychological support enable comprehensive health management and psychological support. Furthermore, the means for providing a customized walking program to improve the work efficiency of a factory worker and the means for recording and analyzing the user's posture and movements during walking to improve the user's form and providing specific feedback for improvement enable comprehensive support for the user's work efficiency and health.

[1228] "Basic information" refers to personal data provided by the user, such as age, gender, health status, walking experience, etc.

[1229] "Initial evaluation" is a process of comprehensively evaluating the user's health condition, walking experience, etc. based on the user's basic information.

[1230] A "walking program" is a program that provides instructions for a walking schedule and method that is optimized according to the user's goals.

[1231] "Walking form" refers to the user's posture and movements while walking.

[1232] "Nutrition guidance" involves analyzing the dietary records entered by the user and providing advice on appropriate nutritional intake from the perspective of health management.

[1233] "Emotion data" is information about emotions and stress input by the user.

[1234] "Psychological support" provides users with advice on mental care and stress relief based on analyzed emotional data.

[1235] "Improving work efficiency" refers to improving the efficiency of work performed by users in factories and other places.

[1236] A "customized walking program" is a walking schedule or method specifically designed to suit the characteristics and health condition of an individual user.

[1237] "Form improvement feedback" involves recording the user's walking posture and movements, analyzing them, and providing specific areas for improvement.

[1238] The present invention is a system that provides walking programs and comprehensive health support for robot operators working in factories. The system performs an initial assessment based on the user's basic information and then provides an individually customized walking program based on that assessment. The system also analyzes the user's form, dietary records, and emotional data to provide appropriate support. The specific steps for implementing this system are described below.

[1239] First, the user uses their smartphone to enter their basic information (age, gender, occupation, working hours, health condition, walking experience, etc.) into the application. This information is sent to the server and stored in a database. The server then performs an initial evaluation based on this information and generates a walking program and health management criteria that are suitable for the user. A generative AI model supports this evaluation and provides an optimized program.

[1240] Next, when the user starts walking, the smartphone device prompts them to record their posture and movements while walking. The user follows the instructions, records their walking posture, and uploads the video to a server. The server then uses an AI model to analyze the video and identify areas for improvement in posture and form. The analysis results are provided to the user as specific improvement feedback (e.g., "Relax your shoulders" or "Move your arms more widely").

[1241] Users also input their daily dietary information into their smartphone. The input data is sent to a server, where an AI model analyzes the dietary data. Based on the analysis results, appropriate nutritional advice is provided from a health management perspective (e.g., "Eat more vegetables at lunch" or "Eat more protein").

[1242] Furthermore, users enter their emotional state regarding walking and work in diary format on their smartphone. This emotional data is sent to the server and analyzed by the emotion engine. Based on the analysis results, psychological support suggestions for the user are generated (e.g., "relaxation techniques" and "positive thinking exercises").

[1243] The hardware used includes smartphones (iPhone, Android devices), and the software used includes Amazon Web Services (AWS), generative AI models (such as OpenAI's GPT series), and IBM Watson's built-in emotion analysis tools.

[1244] Specific examples

[1245] The user enters the following information into the application:

[1246] Age: 35

[1247] Gender: Male

[1248] Occupation: Robot operator

[1249] Walking goal: Reduce fatigue

[1250] An example of the prompt statement generated is:

[1251] "35 years old, male, robot operator. I'm a beginner at walking and I'm trying to reduce fatigue."

[1252] "Generate a customized program of walking times and duration per week to help users manage their health."

[1253] Through these procedures and analysis, users are provided with a personalized walking program, feedback on their form, nutritional guidance, and psychological support, all of which contribute to the overall improvement of their work efficiency and health.

[1254] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1255] Step 1:

[1256] Users use their smartphone to enter their basic information (age, gender, occupation, working hours, health condition, walking experience, etc.) into the application.

[1257] The basic information entered is sent from the terminal to the server and stored in a database.

[1258] Step 2:

[1259] The server will make an initial assessment based on the basic information sent.

[1260] Using a generative AI model, data such as the user's health condition and walking experience is analyzed to generate an optimal walking program and health management standards.

[1261] The output of this initial assessment is an optimized health care standard and a basic training program.

[1262] Step 3:

[1263] When a user starts walking using a smartphone device, the device will instruct it to record their posture and movements while walking.

[1264] The user saves the recorded video on the device and uploads it to the server.

[1265] Step 4:

[1266] The server receives the uploaded video and analyzes it using an AI model.

[1267] As a result of analyzing walking form, areas for improvement in posture and movement are identified.

[1268] The results of this analysis are provided to the user as specific feedback for improvement (e.g., "relax your shoulders" or "make your arm movements larger").

[1269] Step 5:

[1270] Users simply enter their daily dietary information into their smartphone.

[1271] The input data is sent from the device to a server, which then uses an AI model to analyze the food data.

[1272] As a result of the analysis, appropriate nutritional advice is provided from the perspective of health management (e.g., "eat more vegetables at lunch" or "eat more protein").

[1273] Step 6:

[1274] Users enter their emotional state regarding walking and work in diary format on their smartphone.

[1275] Emotion data is sent from the device to a server, which then analyzes it using an emotion engine.

[1276] As a result of the analysis, psychological support suggestions for the user are generated (e.g., "relaxation techniques," "positive thinking exercises," etc.).

[1277] Step 7:

[1278] The generated walking program, health management guidelines, form improvement feedback, nutritional advice, and psychological support suggestions are sent to the user's smartphone and provided to them.

[1279] By receiving this comprehensive support, users can improve their work efficiency and health.

[1280] 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.

[1281] 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.

[1282] 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.

[1283] [Third embodiment]

[1284] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1285] 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.

[1286] 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).

[1287] 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.

[1288] 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.

[1289] 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).

[1290] 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.

[1291] 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.

[1292] 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.

[1293] 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.

[1294] 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.

[1295] 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."

[1296] System Overview

[1297] This invention is a system that utilizes generative AI models to maximize the health benefits of walking. The system performs an initial assessment based on the user's basic information and provides an individually customized walking program based on that assessment. It also analyzes the user's form, food records, and emotional data to provide appropriate support.

[1298] Program processing and specific examples

[1299] User registration and initial evaluation

[1300] 1. Enter your user information:

[1301] Device: The user enters their basic information (age, gender, health condition, walking experience, etc.) into the application.

[1302] Server: Receives this information and stores it in a database.

[1303] 2. Conducting an Initial Assessment:

[1304] Server: Conducts an initial assessment based on the user's basic information and generates reference data appropriate for age, gender, and health condition.

[1305] Optimizing your walking program

[1306] 1. Receive goal setting:

[1307] Device: The user sets a walking goal (e.g., dieting).

[1308] Server: Receives the goals and generates a personalized training program based on the user's initial assessment and goals.

[1309] 2. Generate customized programs:

[1310] Server: The AI ​​model creates a training schedule (number of times per week, walking time per session, walking speed, rest timing, etc.) needed to achieve the goal.

[1311] Form Improvements

[1312] 1. Form recording instructions:

[1313] Device: Instruct the user to capture their posture and movements while walking.

[1314] User: Follow the instructions to record a video while walking.

[1315] 2. Video Analysis:

[1316] Device: Upload the captured video to the server.

[1317] Server: Analyzes video to identify areas for improvement in posture and form.

[1318] 3. Providing Feedback:

[1319] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[1320] Terminal: Display feedback to the user.

[1321] Nutritional guidance

[1322] 1. Enter your food record:

[1323] Terminal: The user inputs the details of their daily meals.

[1324] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[1325] 2. Analysis of dietary data:

[1326] Terminal: Sends the entered meal data to the server.

[1327] Server: Analyzes the received data using a nutritional management AI model and provides nutritional guidance in line with goals.

[1328] 3. Providing nutritional advice:

[1329] Server: Based on the analysis results, it provides the user with nutritional advice appropriate for their diet (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[1330] Terminal: Notifies the user of nutritional advice.

[1331] Psychological support

[1332] 1. Emotional input and goal setting:

[1333] Device: The user enters their feelings and worries about walking in diary format.

[1334] User: Write down your thoughts and stress about walking.

[1335] 2. Emotional Data Analysis:

[1336] Terminal: Sends the input emotion data to the server.

[1337] Server: The sentiment analysis model analyzes the data and generates psychological support suggestions for the user.

[1338] 3. Providing psychological support:

[1339] Server: Provides users with effective stress management and motivational strategies (e.g., "practice deep breathing before walking" or "exercises that encourage positive thinking").

[1340] Terminal: Displays psychological support advice to the user.

[1341] Specific examples

[1342] Let's say a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. After that, if the user sets a goal of dieting, the server generates a walking program of 30 minutes, 3 times a week.

[1343] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides specific advice such as "straighten your back" or "correct the way you swing your arms."

[1344] Furthermore, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad."

[1345] Finally, when the user enters their feelings about walking in diary format, the server analyzes this and provides psychological support such as "deep breathing techniques" and "positive thinking techniques."

[1346] As described above, the system of the present invention can comprehensively support the user's walking and maximize the health benefits.

[1347] The processing flow will be explained below.

[1348] User registration and initial evaluation

[1349] Step 1:

[1350] Device: The user installs the application and enters basic information (age, gender, health status, walking experience, etc.).

[1351] Step 2:

[1352] Terminal: Sends the entered information to the server.

[1353] Server: Stores the received basic information in a database.

[1354] Step 3:

[1355] Server: Based on the user's basic information, an initial evaluation is performed and reference data appropriate for the user is generated.

[1356] Optimizing your walking program

[1357] Step 1:

[1358] Device: The user sets walking goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[1359] Step 2:

[1360] Terminal: Sends goal settings to the server.

[1361] Server: Based on goals and initial assessments, an AI model is used to generate a personalized training program.

[1362] Step 3:

[1363] Server: Sends the generated training program (number of times per week, walking time per session, walking speed, timing of rest, etc.) to the user.

[1364] Terminal: displays the training program to the user.

[1365] Form Improvements

[1366] Step 1:

[1367] Device: Instruct the user to capture their posture and movements while walking.

[1368] Step 2:

[1369] User: Take a video while walking and save it on the device.

[1370] Step 3:

[1371] Device: Upload the captured video to the server.

[1372] Step 4:

[1373] Server: Analyzes the video and identifies areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[1374] Step 5:

[1375] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[1376] Terminal: Display feedback to the user.

[1377] Nutritional guidance

[1378] Step 1:

[1379] Terminal: Provides a screen where users can input their daily dietary information.

[1380] Step 2:

[1381] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[1382] Step 3:

[1383] Terminal: Sends the entered meal data to the server.

[1384] Step 4:

[1385] Server: Analyzes the received dietary data using a nutritional management AI model and generates nutritional guidance that matches the user's goals.

[1386] Step 5:

[1387] Server: Based on the analysis results, it provides specific nutritional advice to users (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[1388] Terminal: Notifies the user of nutritional advice.

[1389] Psychological support

[1390] Step 1:

[1391] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[1392] Step 2:

[1393] User: Enter your thoughts and stress about walking.

[1394] Step 3:

[1395] Terminal: Sends the input emotion data to the server.

[1396] Step 4:

[1397] Server: Analyzes the emotional data and generates customized psychological support suggestions for the user.

[1398] Step 5:

[1399] Server: Based on the analysis results, the server provides users with effective methods for managing stress and maintaining motivation (e.g., "practice deep breathing before walking" or "exercises that encourage positive thinking").

[1400] Terminal: Displays psychological support advice to the user.

[1401] In this way, the server, terminal, and user work together at each processing step to provide support to maximize the effect of the user's walking.

[1402] Example 1

[1403] 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."

[1404] In modern society, while personalized health management and exercise guidance are important, providing such guidance requires significant resources. Furthermore, users often find it difficult to manage themselves, making it difficult to consistently receive effective exercise and dietary guidance and psychological support. Therefore, there is a demand for a system that can comprehensively support users' health status and provide individually optimized exercise programs, nutritional guidance, and psychological support.

[1405] 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.

[1406] In this invention, the server includes means for receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing an exercise program based on the transmitted goals, means for analyzing photographed user movements and providing improvements, means for analyzing input dietary records and providing nutritional guidance, and means for analyzing input emotion data and providing psychological support. This makes it possible to comprehensively support the user's health condition and consistently provide individually optimized exercise programs, nutritional guidance, and psychological support.

[1407] "Basic information sent by the user" refers to basic data such as age, sex, health condition, and exercise experience that the user provides to the system.

[1408] "Initial evaluation" is a process of comprehensively evaluating the user's health condition and fitness for exercise based on basic information.

[1409] "Goals" are specific objectives or hopes that users wish to achieve through the system.

[1410] An "exercise program" refers to an exercise schedule and content customized to a user's individual goals.

[1411] "Movement" refers to the posture and movements of the user when exercising.

[1412] "Analysis" is the process by which the server processes the data sent by the user and extracts meaningful information.

[1413] "Points for improvement" are specific points that should be corrected in order for the user to exercise more effectively.

[1414] "Diet record" refers to the data that a user enters into the system about their daily diet.

[1415] "Nutrition guidance" is specific advice provided to users on how to lead a healthy diet based on their food records.

[1416] "Emotion data" is information about a user's own emotions and mental state that is input into the system by the user.

[1417] "Psychological support" refers to advice and support provided to users to help them maintain their mental health.

[1418] "Health status" refers to the overall state of a user's physical and mental health.

[1419] "Exercise experience" refers to information such as the history of exercise the user has done up to now, the frequency of exercise, and the details of exercise.

[1420] A "training schedule" refers to a schedule or plan for a user to exercise systematically.

[1421] "Posture" refers to the position and shape of the body that a user assumes during exercise or daily life.

[1422] MODE FOR CARRYING OUT THE INVENTION

[1423] The present invention is a system for automating health management and exercise guidance for users. The system utilizes a server, a terminal, and a generative AI model to provide users with individually optimized exercise programs, form improvement, nutritional guidance, and psychological support.

[1424] Hardware and Software Configuration

[1425] Server: A high-performance server is used to run AI libraries such as Python, TensorFlow, Keras, OpenPose, and BERT. This server manages the database, performs initial evaluation, generates training programs, analyzes videos, analyzes food data, and analyzes emotion data.

[1426] Terminal: A device used by a user, such as a smartphone or tablet. The terminal provides an interface for the user to input information and sends the input data to a server.

[1427] User: Refers to an individual who uses the system and inputs the information necessary to manage their own health and receive exercise guidance.

[1428] System Operation Overview

[1429] 1. User registration and initial evaluation

[1430] The user uses the application to input basic information (age, gender, health status, exercise experience, etc.). The device sends this information to the server, which then performs an initial evaluation based on the received information and stores it in a database.

[1431] 2. Optimize your walking program

[1432] The user sets exercise goals (e.g., dieting) through the terminal. The server receives these goals and generates an individual training program based on the initial assessment data and the goals. The generated program is stored in a database and provided to the user.

[1433] 3. Improving your form

[1434] Users take videos of themselves walking with their smartphones and upload them to the server. The server analyzes the videos and identifies areas for improvement in posture and form. This is done using a posture analysis model using OpenPose. Based on the analysis results, specific improvement advice is generated and provided to the user.

[1435] 4. Nutritional guidance

[1436] Users enter their daily food records into the application. The device sends this data to the server. The server uses a nutrition management AI model to analyze the food data and provide nutritional guidance tailored to the user's goals. The analysis results are then sent to the device and presented to the user.

[1437] 5. Psychological support

[1438] Users enter their feelings and concerns about walking into the application in diary format. The device then sends this data to a server. The server then uses the natural language processing library BERT to analyze the emotional data and generate psychological support suggestions, providing users with methods for managing stress and maintaining motivation.

[1439] Specific examples

[1440] For example, suppose a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. Next, if the user sets "diet" as a goal, the server generates a walking program for 30 minutes, three times a week.

[1441] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides advice such as "straighten your back" or "correct the way you swing your arms." Additionally, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to your breakfast" or "add nuts to your lunch salad."

[1442] Furthermore, when users enter their feelings about walking in diary format, the server analyzes the data and provides psychological support such as "deep breathing techniques" and "positive thinking techniques."

[1443] The system aims to provide comprehensive support for users' walking and maximize the health benefits.

[1444] Prompt Sentence Examples

[1445] "When walking, be mindful of the following: keep your back straight and adjust your arm swing."

[1446] "Add more protein to your breakfast, such as eggs or yogurt."

[1447] "Practice deep breathing exercises before walking. This will help you relax and improve your workout."

[1448] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1449] Step 1:

[1450] The user opens the application on a device such as a smartphone or tablet and enters basic information such as age, gender, health status, and exercise experience. This information is then obtained.

[1451] Input: Basic information such as the user's age, gender, health status, and exercise experience

[1452] Output: Basic information data

[1453] Specific operation: The user enters information into the form and presses the "Complete" button to submit. The device temporarily saves the information and sends it to the server.

[1454] Step 2:

[1455] The terminal sends the basic information entered by the user to the server, which then stores the information in a database and performs an initial evaluation for each user.

[1456] Input: Basic information data

[1457] Output: Initial evaluation data

[1458] What happens: The server creates a new user record in the database and runs an initial evaluation script using Python. The evaluation results are saved in the database.

[1459] Step 3:

[1460] The user sets a specific walking goal (e.g., dieting) within the application. The set goal is sent as input data from the device to the server.

[1461] Input: Walking goal

[1462] Output: Target data

[1463] Specific operation: The user inputs a goal, and the device sends it to the server, which then stores the goal data in a database.

[1464] Step 4:

[1465] The server generates an individual training program using an AI model (using Keras) based on the received goal data and initial evaluation data, which is then stored in a database and provided to the user.

[1466] Input: Target data, initial evaluation data

[1467] Output: Individual training program

[1468] Specific operation: The server dynamically generates training programs using the Keras library and stores them in a database.

[1469] Step 5:

[1470] The user records their walking form with their smartphone and uploads the video to a server via the application.

[1471] Input: Video of walking

[1472] Output: Recorded video data

[1473] Specific operation: The user takes a video using the device's camera and sends it to the server using the upload function. This operation can be easily performed within the application.

[1474] Step 6:

[1475] The server receives the uploaded video and analyzes it using an AI model (a posture analysis model using OpenPose). As a result of the analysis, it identifies areas for improvement in the user's posture and form.

[1476] Input: Recorded video data

[1477] Output:Form Improvements

[1478] Specific behavior: The server uses the OpenPose library to analyze the video, analyze the data obtained, and identify problems with posture and behavior. This information is stored in a database.

[1479] Step 7:

[1480] Based on the analysis results, the server generates specific advice for the user on how to improve their form, and sends the advice to the terminal.

[1481] Input: Form Improvements

[1482] Output: Improvement advice

[1483] Specific operation: The server generates appropriate advice for the user based on the identified improvements. The generated advice is notified to the device and displayed to the user.

[1484] Step 8:

[1485] The user enters their daily food record into the application and sends the data to the server.

[1486] Input: Food log

[1487] Output: Food record data

[1488] Specific operation: When the user enters the meal details into the application form and presses the submit button, the data is sent from the terminal to the server.

[1489] Step 9:

[1490] The server analyzes the received dietary record data using a nutritional management AI model to generate data for providing nutritional guidance in line with goals. The analysis results are stored in a database.

[1491] Input: Food record data

[1492] Output: Nutritional guidance data

[1493] Specific operation: The server receives the food record data, analyzes it using a nutrition management AI model, and calculates nutritional balance and areas for improvement. This data is then stored in a database.

[1494] Step 10:

[1495] Based on the analysis results, the server generates nutritional guidance appropriate for the user and transmits the guidance content to the user's terminal.

[1496] Input: Nutritional guidance data

[1497] Output: Nutritional advice

[1498] Specific operation: The server generates nutritional advice appropriate for the user based on the analysis results obtained from the nutrition management AI model. This advice is sent to the user's device and displayed within the application.

[1499] Step 11:

[1500] The user enters their feelings and worries about walking into the application in diary format, and the data is sent to the server.

[1501] Input: Emotion data

[1502] Output: Emotion recording data

[1503] Specific operation: When the user enters their feelings or concerns into the application and presses the send button, the data is sent from the device to the server.

[1504] Step 12:

[1505] The server analyzes the received emotional data using the natural language processing library BERT and generates psychological support suggestions, including methods for managing stress and maintaining motivation.

[1506] Input: Emotion recording data

[1507] Output: Psychological support suggestions

[1508] How it works: The server uses the BERT library to analyze the sentiment data and extract patterns to understand the user's mental state. These data are then stored in a database.

[1509] Step 13:

[1510] Based on the analysis results, the server generates advice for providing psychological support to the user and sends it to the user's terminal.

[1511] Input: Psychological support suggestions

[1512] Output: Psychological support advice

[1513] Specific operation: Based on the acquired psychological support suggestions, the server generates appropriate advice for the user (e.g., "deep breathing" or "positive thinking") and sends it to the device. The device notifies and displays this to the user.

[1514] In this way, the system can comprehensively support the user's health and consistently provide individually optimized exercise programs, nutritional guidance, and psychological support.

[1515] (Application example 1)

[1516] 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."

[1517] Health management is becoming increasingly important in modern society, and many people are seeking a lifestyle that incorporates walking. However, there is no system that can comprehensively design and implement effective walking programs, improve one's form, maintain nutritional balance, and provide psychological support. Furthermore, linking these systems with food delivery services to easily provide individually optimized meal plans is also a challenge. Given this background, there is a need for a system that can provide users with comprehensive health support.

[1518] 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.

[1519] In this invention, the server includes means for receiving basic information sent by the user and making an initial evaluation, means for optimizing the walking program based on the sent goals, means for analyzing a photograph of the user's walking form and suggesting areas for improvement, means for analyzing the input meal record and providing nutritional advice, means for analyzing the input emotion data and providing psychological support, means for calculating calories burned based on the collected walking data and proposing an appropriate meal plan, and means for proposing a food delivery plan to provide a meal plan linked to the user's walking data. This allows for comprehensive support not only for the user's walking life but also for their daily meals and psychological health, enabling comprehensive health support.

[1520] "User information" refers to basic information provided by the user, such as age, gender, health status, walking experience, etc.

[1521] A "walking program" is a mobility training schedule that is individually customized based on the user's basic information and goals.

[1522] "Walking form" refers to the user's posture and movements while walking.

[1523] "Dietary records" are data on the contents and amounts of food consumed by a user each day.

[1524] "Emotion data" is information about emotions and worries about walking input by the user.

[1525] "Calories burned" refers to the amount of energy consumed through physical activity and is calculated based on walking data.

[1526] A "meal plan" is an appropriate meal plan and its composition that is suggested based on the user's walking data and nutritional analysis.

[1527] A "food delivery plan" is a meal delivery plan provided based on the user's optimal meal plan.

[1528] "Walking data" refers to information such as the number of steps taken and walking speed, and is collected through smart devices.

[1529] The "means for receiving basic information and making an initial evaluation" is a method in which the system receives basic information provided by the user and evaluates the initial state of the user based on that information.

[1530] The "means for optimizing a walking program based on goals" is a method for designing and generating an individually optimized walking program based on goals set by the user.

[1531] The "means of analyzing a user's filmed walking form and suggesting areas for improvement" is a method of analyzing a walking video filmed by the user and suggesting areas for improvement to the user's form based on the results of the analysis.

[1532] The "means for analyzing the input dietary record and providing nutritional guidance" is a method for analyzing the dietary details input by the user and providing nutritional guidance based on the analysis results.

[1533] The "means for analyzing input emotion data and providing psychological support" is a method for analyzing emotion data input by a user and providing psychological support based on the results.

[1534] "Means for calculating calories burned based on collected walking data and proposing an appropriate meal plan" refers to a method for calculating energy consumption using data collected during walking and proposing a meal plan suited to the user.

[1535] "Means for proposing food delivery plans to provide meal plans linked to the user's walking data" refers to a method for determining the optimal meal plan based on collected walking data and proposing a food delivery service based on that plan.

[1536] This system receives basic information sent by the user and optimizes walking programs based on that information. The system analyzes the user's walking data, food records, and emotional data to provide comprehensive health support. It also has a function to suggest optimal meal plans in conjunction with food delivery plans.

[1537] System configuration

[1538] This system uses the following hardware and software:

[1539] Programming language: Python

[1540] Generative AI model: GPT-4

[1541] Database: MySQL

[1542] Server: AWS EC2

[1543] App development platform: React Native (mobile app)

[1544] Processing flow

[1545] User information registration and initial evaluation

[1546] The user enters basic information into the terminal and sends it to the server. The server stores the received basic information in a MySQL database and performs an initial evaluation. Based on this initial evaluation, a walking program suitable for the user is generated.

[1547] Creating a walking program

[1548] The server uses a generative AI model to generate a customized walking program based on the user's basic information and goals, and inputs prompts such as the following into the generative AI model:

[1549] User age: 40

[1550] Gender: Female

[1551] Walking experience: Beginner

[1552] Goal: Lose weight

[1553] Based on this information, we will suggest the best walking program for you.

[1554] Improve your walking form

[1555] Users record their walking form and upload it to a server from their device. The server analyzes the video and identifies areas for improvement. This information is then analyzed using a generative AI model to provide appropriate advice to the user.

[1556] Food records and nutritional advice

[1557] The user inputs their daily dietary information into the device and sends it to the server. The server analyzes the received dietary data and generates nutritional advice. For example, the following prompt sentence is input into the generative AI model:

[1558] Breakfast: Toast, eggs, coffee

[1559] Lunch: Sandwich, salad

[1560] Dinner: Grilled fish, vegetable soup

[1561] Based on this food record, provide appropriate nutritional advice.

[1562] Emotional data and psychological support

[1563] The user inputs their feelings and concerns about walking into the device and sends them to the server, which analyzes the input emotional data and provides appropriate psychological support.

[1564] Food delivery plan proposal

[1565] The system calculates the calorie expenditure based on the user's walking data and generates a meal plan based on this. The server then connects the generated meal plan to a food delivery service and suggests the most suitable meal for the user. In this case, the following prompt is also input into the generative AI model:

[1566] Today's steps: 8000

[1567] Walking speed: 5km / h

[1568] This information will help you provide optimal nutritional advice and meal plans.

[1569] Specific examples

[1570] For example, if a 40-year-old woman uses this system to manage her daily walking and diet, the process would be as follows: The user enters initial information and walks according to the generated walking program. She records a video of herself walking to receive advice on improving her form, and then enters her daily food record to receive nutritional guidance. She follows the meal plan suggested based on the calories burned and orders appropriate meals from the food delivery plan. This series of processes allows the user to maintain a healthy lifestyle.

[1571] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1572] Step 1:

[1573] The user enters basic information (age, gender, health condition, walking experience, etc.) into the device. The entered basic information is sent from the device to the server. The basic information received by the server is stored in a MySQL database. Here, the data format is sent as JSON and inserted into the database using SQL statements.

[1574] Step 2:

[1575] The server performs an initial evaluation of the user based on the stored basic information. The results of the initial evaluation are generated using a generative AI model (GPT-4). The input data is user information, and an appropriate evaluation result is generated based on this. The server inputs the following prompt sentence into the generative AI model and obtains the evaluation result.

[1576] User age: 40

[1577] Gender: Female

[1578] Walking experience: Beginner

[1579] Use this information to make your initial assessment.

[1580] As a result, an initial evaluation report is generated for the user.

[1581] Step 3:

[1582] The user sets walking goals on the device. Possible goals include "dieting" or "improving physical fitness." The user's set goals are sent from the device to the server. The server generates an individually customized walking program based on the initial assessment and goals. This process also uses a generative AI model, and the following prompt is input:

[1583] User age: 40

[1584] Gender: Female

[1585] Walking experience: Beginner

[1586] Goal: Lose weight

[1587] Based on this information, we will suggest the best walking program for you.

[1588] As a result, a walking program is generated that specifies the number of times per week, the duration of each walk, walking speed, and rest timing.

[1589] Step 4:

[1590] Users record their walking form with their device's camera. The video is then uploaded from the device to a server. The server then analyzes the video using a generative AI model to identify areas for improvement in posture and form. The following prompts are used:

[1591] Analyze the video while walking.

[1592] Based on the analysis results, the server generates specific feedback for improving form, such as straightening your back or correcting the way you swing your arms, and sends this to the device.

[1593] Step 5:

[1594] The user enters the details of their daily meals into the device and sends this meal record to the server. The server then analyzes the received meal record using an AI model for nutritional management and generates nutritional advice appropriate for the user. For example, the following prompt sentences are used:

[1595] Breakfast: Toast, eggs, coffee

[1596] Lunch: Sandwich, salad

[1597] Dinner: Grilled fish, vegetable soup

[1598] Based on this food record, provide appropriate nutritional advice.

[1599] The analysis results in specific advice, such as adding more protein to your breakfast or adding nuts to your lunch salad, which is displayed on the device.

[1600] Step 6:

[1601] The user enters their feelings and worries about walking in diary format into the device and sends it to the server. The server analyzes the received emotional data and suggests appropriate psychological support. The following prompts are used:

[1602] The user has entered their thoughts and concerns. Based on this information, please suggest psychological support.

[1603] As a result, specific psychological support advice, such as deep breathing techniques and positive thinking techniques, is generated and displayed on the device.

[1604] Step 7:

[1605] The server calculates the calories burned based on walking data collected from the user's steps and walking speed. Based on the calculated calories, it proposes an appropriate meal plan. Furthermore, it uses a generative AI model to generate a specific meal plan using prompt sentences such as the following:

[1606] Today's steps: 8000

[1607] Walking speed: 5km / h

[1608] This information will help you provide optimal nutritional advice and meal plans.

[1609] Based on this suggestion, a food delivery plan is created and presented to the user, who can then order appropriate meals from the suggested meal plan.

[1610] 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.

[1611] System Overview

[1612] This invention is a system that utilizes a generative AI model to maximize the health benefits of walking. The system performs an initial assessment based on the user's basic information and provides an individually customized walking program based on that assessment. It also analyzes the user's form, food records, and emotional data to provide appropriate support. The system also incorporates an emotion engine that recognizes the user's emotions.

[1613] Program processing and specific examples

[1614] User registration and initial evaluation

[1615] 1. Enter your user information

[1616] Device: The user enters their basic information (age, gender, health condition, walking experience, etc.) into the application.

[1617] Server: Receives this information and stores it in a database.

[1618] 2. Conducting an initial evaluation

[1619] Server: Performs an initial evaluation based on the user's basic information and generates reference data suitable for the user.

[1620] Optimizing your walking program

[1621] 1. Receiving goal setting

[1622] Device: The user sets walking goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[1623] Server: Receives the goals and generates a personalized training program based on the user's initial assessment and goals.

[1624] 2. Creating a customized program

[1625] Server: The AI ​​model creates a training schedule (number of times per week, walking time per session, walking speed, rest timing, etc.) needed to achieve the goal.

[1626] 3. Notice to Users

[1627] Server: Sends the generated training program to the user.

[1628] Terminal: displays the training program to the user.

[1629] Form Improvements

[1630] 1. Form recording instructions

[1631] Device: Instruct the user to capture their posture and movements while walking.

[1632] 2. Recording and uploading videos

[1633] User: Take a video while walking and save it on the device.

[1634] Device: Upload the captured video to the server.

[1635] 3. Video analysis and feedback

[1636] Server: Analyzes the video and identifies areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[1637] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[1638] Terminal: Display feedback to the user.

[1639] Nutritional guidance

[1640] 1. Enter your food record

[1641] Terminal: Provides a screen where the user can input their daily dietary information.

[1642] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[1643] 2. Submitting and analyzing dietary data

[1644] Terminal: Sends the entered meal data to the server.

[1645] Server: Analyzes the received dietary data using a nutritional management AI model and provides nutritional guidance in line with goals.

[1646] 3. Providing nutritional guidance

[1647] Server: Based on the analysis results, it provides the user with nutritional advice appropriate for their diet (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[1648] Terminal: Notifies the user of nutritional advice.

[1649] Psychological support and emotional engine

[1650] 1. Emotion Input and Recognition

[1651] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[1652] User: Enter your thoughts and stress about walking.

[1653] 2. Transmission and analysis of emotional data

[1654] Terminal: Sends the input emotion data to the server.

[1655] Server: The emotion engine analyzes the emotion data and evaluates the emotional state.

[1656] 3. Providing psychological support

[1657] Server: Based on the analysis results, it generates customized psychological support suggestions for the user (e.g., "practice deep breathing before walking" or "exercises that promote positive thinking").

[1658] Terminal: Displays psychological support advice to the user.

[1659] Specific examples

[1660] Let's say a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. After that, if the user sets a goal of dieting, the server generates a walking program of 30 minutes, 3 times a week.

[1661] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides specific advice such as "straighten your back" or "correct the way you swing your arms."

[1662] Furthermore, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad."

[1663] When users enter their feelings about walking in diary format, the server's emotion engine analyzes them and provides psychological support such as "deep breathing techniques" and "positive thinking methods."

[1664] By utilizing the emotion engine, more personalized support can be provided by providing care appropriate to the user's individual emotional state. This system can comprehensively support the user's walking and maximize health benefits.

[1665] The processing flow will be explained below.

[1666] User registration and initial evaluation

[1667] Step 1:

[1668] Device: The user installs the application and enters basic information (age, gender, health status, walking experience, etc.).

[1669] Step 2:

[1670] Terminal: Sends the entered information to the server.

[1671] Step 3:

[1672] Server: Stores the received basic information in a database.

[1673] Step 4:

[1674] Server: Performs an initial evaluation based on the user's basic information and generates reference data suitable for the user.

[1675] Optimizing your walking program

[1676] Step 1:

[1677] Terminal: The user inputs the walking goal (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[1678] Step 2:

[1679] Terminal: Sends goal settings to the server.

[1680] Step 3:

[1681] Server: Uses AI models to generate personalized training programs based on goals and initial assessment results.

[1682] Step 4:

[1683] Server: Sends the generated training program to the user.

[1684] Step 5:

[1685] Terminal: displays the training program to the user.

[1686] Form Improvements

[1687] Step 1:

[1688] Device: Instruct the user to capture their posture and movements while walking.

[1689] Step 2:

[1690] User: Take a video while walking and save it on the device.

[1691] Step 3:

[1692] Device: Upload the captured video to the server.

[1693] Step 4:

[1694] Server: Analyzes the video and detects areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[1695] Step 5:

[1696] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[1697] Step 6:

[1698] Terminal: Display feedback to the user.

[1699] Nutritional guidance

[1700] Step 1:

[1701] Terminal: Provides a screen where users can input their daily dietary information.

[1702] Step 2:

[1703] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[1704] Step 3:

[1705] Terminal: Sends the entered meal data to the server.

[1706] Step 4:

[1707] Server: Analyzes the received dietary data using a nutritional management AI model and generates nutritional guidance that matches the user's goals.

[1708] Step 5:

[1709] Server: Based on the analysis results, it provides specific nutritional advice to users (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[1710] Step 6:

[1711] Terminal: Notifies the user of nutritional advice.

[1712] Psychological support and emotional engine

[1713] Step 1:

[1714] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[1715] Step 2:

[1716] User: Enter your thoughts and stress about walking.

[1717] Step 3:

[1718] Terminal: Sends the input emotion data to the server.

[1719] Step 4:

[1720] Server: The emotion engine analyzes the emotion data and evaluates the emotional state.

[1721] Step 5:

[1722] Server: Based on the analysis results, it generates customized psychological support suggestions for the user (e.g., "practice deep breathing before walking" or "exercises that promote positive thinking").

[1723] Step 6:

[1724] Terminal: Displays psychological support advice to the user.

[1725] In this way, the server, terminal, and user work together at each processing step to provide comprehensive support to maximize the effectiveness of the user's walking.

[1726] Example 2

[1727] 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."

[1728] Conventional health management systems lack a personalized approach, making it difficult to respond appropriately to individual users' situations and goals. Furthermore, there were no systems capable of providing advanced support, such as improving walking form, analyzing food records, or providing psychological support based on emotional data. This made it difficult to maximize the health benefits for users.

[1729] 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 receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing the exercise program based on the transmitted goals, means for analyzing the user's photographed exercise form and providing improvements, means for analyzing the input dietary record and providing nutritional advice, means for analyzing the input emotional data and providing psychological support, means for instructing the user to record their exercise form, and means for notifying the user of the generated program. This enables a personalized approach according to the user's individual situation and goals, and makes it possible to maximize the user's health benefits through improvements in walking form, analysis of the dietary record, and advanced psychological support based on the emotional data.

[1730] "Basic information" refers to initial data about an individual user, such as the user's age, gender, health condition, and exercise experience.

[1731] An "initial assessment" is an assessment based on basic information about the user, and this assessment confirms the user's health condition and exercise experience.

[1732] An "exercise program" is a customized exercise plan based on the goals set by the user, specifically including the number of walking sessions, duration, and speed.

[1733] "Exercise form" refers to the posture and movements of the user when exercising, and by analyzing this, areas for improvement can be identified.

[1734] "Nutrition guidance" refers to advice based on data entered by the user about their daily diet, and indicates nutritional intake methods that are suitable for the user's goals.

[1735] "Emotion data" refers to data entered in diary format by the user about emotions and worries felt during exercise or daily life.

[1736] "Psychological support" refers to support provided based on an assessment of the user's emotional state through analysis of emotional data, and includes, for example, stress relief and promotion of positive thinking.

[1737] The "recording instruction means" is a system function that instructs the user to record their exercise form.

[1738] "Notification means" refers to the system's function of informing users of information such as exercise programs generated by the server, analysis results, nutritional guidance, and psychological support.

[1739] Overall system overview

[1740] This invention is a system for managing a user's health by analyzing the user's exercise program, exercise form, diet record, and emotional data, and providing personalized advice. The system consists of three entities: a server, a terminal, and the user.

[1741] Hardware and software used

[1742] Hardware

[1743] Terminals: Smartphones and personal computers are mainly used.

[1744] Server: Cloud or dedicated servers are used.

[1745] Camera: Smartphones and webcams are used.

[1746] software

[1747] Database: A relational database management system such as MySQL or PostgreSQL.

[1748] Statistical analysis model: R was used.

[1749] AI model: Built using TensorFlow and PyTorch.

[1750] Video analysis: Using OpenCV.

[1751] Natural Language Processing (NLP): Uses SpaCy, BERT, etc.

[1752] User information and initial evaluation

[1753] The user inputs basic information (e.g., age, gender, health status, exercise experience, etc.) via the terminal. This information is sent to the server, which stores it in a database.

[1754] The server performs an initial assessment using a statistical analysis model implemented in R based on the stored basic information, and generates reference data according to the user's health condition and exercise experience. This reference data will serve as the basis for a customized program that will be generated later.

[1755] Generating a customized exercise program

[1756] The user sets exercise goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.) using the device. These goals are sent to the server, which compares them with the initial assessment data and generates an individualized exercise program using an AI model built in Python (using TensorFlow and PyTorch). For example, a program for walking three times a week for 30 minutes each time is generated.

[1757] The generated exercise program is sent to the terminal by the server, and the user can check it and put it into practice.

[1758] Recording and analyzing exercise form

[1759] While the exercise program is running, the user is prompted to record their walking form. The user can record the video of their exercise using a smartphone or webcam and save it on their device.

[1760] The device uploads the saved video to a server. The server uses OpenCV to analyze the video and uses an AI model to identify areas for improvement in posture and form. Based on the analysis results, specific advice such as "straighten your back" or "correct your arm swing" is generated and sent to the device.

[1761] Food record entry and nutritional advice

[1762] The terminal provides a screen for the user to input daily meal plans, such as bread and eggs for breakfast, salad and chicken for lunch, and fish and rice for dinner.

[1763] The device sends the input data to a server, where the server's nutritional management AI model (using SpaCy and other deep learning technologies) analyzes the data. Based on this, nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad" is generated and sent to the device.

[1764] Emotional data input and psychological support

[1765] The user uses the emotion input screen of the device to enter their feelings and worries about walking in diary format. For example, they might enter something like, "I felt really good after walking today."

[1766] The device sends the input emotional data to the server, where it is analyzed by the server's emotion engine (using NLP models such as BERT). Based on the analysis results, psychological support suggestions such as "practice deep breathing before walking" and "exercises to promote positive thinking" are generated and notified to the device as mentioned above.

[1767] Example prompts for generative AI models

[1768] Here are some example prompts to input to the generative AI model:

[1769] "What kind of training schedule would be best to create a walking program suitable for a 40-year-old woman who is new to walking?"

[1770] "Please provide users with advice on balanced nutrition based on their dietary data."

[1771] "How can I analyze video of my walking to identify areas for improvement?"

[1772] The above is a detailed description of an embodiment of the present invention, which allows the user to maximize their health benefits.

[1773] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1774] Step 1:

[1775] Enter basic information

[1776] Terminal: Displays a screen for the user to enter basic information (e.g., age, gender, health status, exercise experience, etc.). As a specific example of input, the user enters "Age: 40 years old," "Gender: Female," and "Exercise experience: Beginner."

[1777] User: Enter basic information and press the send button.

[1778] Terminal: Sends the entered basic information to the server.

[1779] Input: User basic information

[1780] Output: Basic information about the user sent to the server

[1781] Step 2:

[1782] Conducting an initial evaluation

[1783] Server: Receives basic information sent by the user and stores it in a database.

[1784] Server: Analyzes basic information using a statistical analysis model implemented in R and performs an initial evaluation. Here, baseline data is generated based on the user's age, health status, etc.

[1785] Input: User basic information

[1786] Output: Baseline data for initial evaluation

[1787] Step 3:

[1788] Receiving goal setting

[1789] Device: Displays a screen for setting exercise goals. The user sets "diet" as an example.

[1790] User: Enter a goal and press the submit button.

[1791] Terminal: Sends the entered goal to the server.

[1792] Input: User-defined exercise goals

[1793] Output: Movement goal sent to the server

[1794] Step 4:

[1795] Generate customized programs

[1796] Server: Based on the baseline data from the initial evaluation and the user's exercise goals, a generative AI model built in Python (using TensorFlow and PyTorch) is used to generate an individual exercise program. Specifically, a walking program of 30 minutes, three times a week, is generated.

[1797] Server: Sends the generated exercise program to the terminal.

[1798] Input: Initial evaluation data, user's exercise goals

[1799] Output: Customized exercise program

[1800] Step 5:

[1801] Exercise program notification

[1802] Terminal: Receives the customized exercise program sent from the server and notifies the user.

[1803] User: Check notifications and view exercise program details.

[1804] Input: Customized exercise program

[1805] Output: An exercise program that can be viewed by the user

[1806] Step 6:

[1807] Form recording instructions

[1808] Device: Notifies the user at the set time to record a video of their exercise form.

[1809] User: Uses the camera function on a smartphone to take a photo of their exercise form.

[1810] Input: Recording timing instructions

[1811] Output: Video of exercise form taken by the user

[1812] Step 7:

[1813] Video recording and uploading

[1814] User: Record a video of your exercise form and save it on your device.

[1815] Terminal: Presents the user with the option to upload, and when they press the upload button, the video is sent to the server.

[1816] Input: Video of exercise form taken by the user

[1817] Output: Video of exercise form uploaded to the server

[1818] Step 8:

[1819] Video analysis and feedback

[1820] Server: Receives the uploaded video and analyzes it using OpenCV. Using a generative AI model, it identifies areas for improvement in posture and form (e.g., "straighten your back" or "correct your arm swing").

[1821] Server: Based on the analysis results, specific feedback for the user is generated and sent to the device.

[1822] Terminal: Notifies the user of the generated feedback.

[1823] Input: Video of exercise form

[1824] Output: Specific form feedback

[1825] Step 9:

[1826] Entering a food record

[1827] Terminal: Provides a screen for the user to input meal details, such as bread and eggs for breakfast, salad and chicken for lunch, and fish and rice for dinner.

[1828] User: Enter the meal details and press the send button.

[1829] Terminal: Sends the entered meal details to the server.

[1830] Input: User's diet

[1831] Output: Meal details sent to the server

[1832] Step 10:

[1833] Analysis of dietary data

[1834] Server: Receives meal data and analyzes it using a nutrition management AI model (using SpaCy and other deep learning technologies). As a result of the analysis, it generates specific nutritional advice such as "add more protein to breakfast" or "add nuts to lunch."

[1835] Server: Sends the analysis results to the device.

[1836] Terminal: Notifies the user of the generated nutritional advice.

[1837] Input: Meal data

[1838] Output: Nutritional guidance

[1839] Step 11:

[1840] Entering emotion data

[1841] Device: Provides an emotion input screen and allows the user to input their emotions and worries. For example, they can input "I felt great after walking today."

[1842] User: Enter emotion data and press the send button.

[1843] Terminal: Sends the input emotion data to the server.

[1844] Input: User emotion data

[1845] Output: Emotion data sent to the server

[1846] Step 12:

[1847] Emotional data analysis and psychological support

[1848] Server: Receives emotional data and analyzes it using an emotion engine (using NLP models such as BERT). Based on the analysis results, it generates psychological support suggestions such as "practice deep breathing before walking" and "exercises to promote positive thinking."

[1849] Server: Sends the generated psychological support proposals to the device.

[1850] Terminal: Notifies the user and displays psychological support information.

[1851] Input: Emotion data

[1852] Output: Psychological support suggestions

[1853] The above is the specific processing flow of the system program. This enables a personalized approach according to the individual situation and goals of the user, and can lead to greater health benefits.

[1854] (Application example 2)

[1855] 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."

[1856] Robot operators working in factories can accumulate fatigue due to long hours of work, which can reduce productivity. They also face a higher risk of health problems due to poor walking form and inappropriate dietary management. Furthermore, psychological stress can significantly reduce overall work efficiency. To comprehensively solve these issues and improve work efficiency, a system that provides appropriate walking programs and health management support is needed.

[1857] 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 receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing a walking program based on the transmitted goals, and means for analyzing a photograph of the user's walking form and providing improvements. This enables the provision of a customized walking program based on the user's individual information. Furthermore, the means for analyzing input dietary records to provide nutritional guidance and the means for analyzing input emotional data to provide psychological support enable comprehensive health management and psychological support. Furthermore, the means for providing a customized walking program to improve the work efficiency of a factory worker and the means for recording and analyzing the user's posture and movements during walking to improve the user's form and providing specific feedback for improvement enable comprehensive support for the user's work efficiency and health.

[1858] "Basic information" refers to personal data provided by the user, such as age, gender, health status, walking experience, etc.

[1859] "Initial evaluation" is a process of comprehensively evaluating the user's health condition, walking experience, etc. based on the user's basic information.

[1860] A "walking program" is a program that provides instructions for a walking schedule and method that is optimized according to the user's goals.

[1861] "Walking form" refers to the user's posture and movements while walking.

[1862] "Nutrition guidance" involves analyzing the dietary records entered by the user and providing advice on appropriate nutritional intake from the perspective of health management.

[1863] "Emotion data" is information about emotions and stress input by the user.

[1864] "Psychological support" provides users with advice on mental care and stress relief based on analyzed emotional data.

[1865] "Improving work efficiency" refers to improving the efficiency of work performed by users in factories and other places.

[1866] A "customized walking program" is a walking schedule or method specifically designed to suit the characteristics and health condition of an individual user.

[1867] "Form improvement feedback" involves recording the user's walking posture and movements, analyzing them, and providing specific areas for improvement.

[1868] The present invention is a system that provides walking programs and comprehensive health support for robot operators working in factories. The system performs an initial assessment based on the user's basic information and then provides an individually customized walking program based on that assessment. The system also analyzes the user's form, dietary records, and emotional data to provide appropriate support. The specific steps for implementing this system are described below.

[1869] First, the user uses their smartphone to enter their basic information (age, gender, occupation, working hours, health condition, walking experience, etc.) into the application. This information is sent to the server and stored in a database. The server then performs an initial evaluation based on this information and generates a walking program and health management criteria that are suitable for the user. A generative AI model supports this evaluation and provides an optimized program.

[1870] Next, when the user starts walking, the smartphone device prompts them to record their posture and movements while walking. The user follows the instructions, records their walking posture, and uploads the video to a server. The server then uses an AI model to analyze the video and identify areas for improvement in posture and form. The analysis results are provided to the user as specific improvement feedback (e.g., "Relax your shoulders" or "Move your arms more widely").

[1871] Users also input their daily dietary information into their smartphone. The input data is sent to a server, where an AI model analyzes the dietary data. Based on the analysis results, appropriate nutritional advice is provided from a health management perspective (e.g., "Eat more vegetables at lunch" or "Eat more protein").

[1872] Furthermore, users enter their emotional state regarding walking and work in diary format on their smartphone. This emotional data is sent to the server and analyzed by the emotion engine. Based on the analysis results, psychological support suggestions for the user are generated (e.g., "relaxation techniques" and "positive thinking exercises").

[1873] The hardware used includes smartphones (iPhone, Android devices), and the software used includes Amazon Web Services (AWS), generative AI models (such as OpenAI's GPT series), and IBM Watson's built-in emotion analysis tools.

[1874] Specific examples

[1875] The user enters the following information into the application:

[1876] Age: 35

[1877] Gender: Male

[1878] Occupation: Robot operator

[1879] Walking goal: Reduce fatigue

[1880] An example of the prompt statement generated is:

[1881] "35 years old, male, robot operator. I'm a beginner at walking and I'm trying to reduce fatigue."

[1882] "Generate a customized program of walking times and duration per week to help users manage their health."

[1883] Through these procedures and analysis, users are provided with a personalized walking program, feedback on their form, nutritional guidance, and psychological support, all of which contribute to the overall improvement of their work efficiency and health.

[1884] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1885] Step 1:

[1886] Users use their smartphone to enter their basic information (age, gender, occupation, working hours, health condition, walking experience, etc.) into the application.

[1887] The basic information entered is sent from the terminal to the server and stored in a database.

[1888] Step 2:

[1889] The server will make an initial assessment based on the basic information sent.

[1890] Using a generative AI model, data such as the user's health condition and walking experience is analyzed to generate an optimal walking program and health management standards.

[1891] The output of this initial assessment is an optimized health care standard and a basic training program.

[1892] Step 3:

[1893] When a user starts walking using a smartphone device, the device will instruct it to record their posture and movements while walking.

[1894] The user saves the recorded video on the device and uploads it to the server.

[1895] Step 4:

[1896] The server receives the uploaded video and analyzes it using an AI model.

[1897] As a result of analyzing walking form, areas for improvement in posture and movement are identified.

[1898] The results of this analysis are provided to the user as specific feedback for improvement (e.g., "relax your shoulders" or "make your arm movements larger").

[1899] Step 5:

[1900] Users simply enter their daily dietary information into their smartphone.

[1901] The input data is sent from the device to a server, which then uses an AI model to analyze the food data.

[1902] As a result of the analysis, appropriate nutritional advice is provided from the perspective of health management (e.g., "eat more vegetables at lunch" or "eat more protein").

[1903] Step 6:

[1904] Users enter their emotional state regarding walking and work in diary format on their smartphone.

[1905] Emotion data is sent from the device to a server, which then analyzes it using an emotion engine.

[1906] As a result of the analysis, psychological support suggestions for the user are generated (e.g., "relaxation techniques," "positive thinking exercises," etc.).

[1907] Step 7:

[1908] The generated walking program, health management guidelines, form improvement feedback, nutritional advice, and psychological support suggestions are sent to the user's smartphone and provided to them.

[1909] By receiving this comprehensive support, users can improve their work efficiency and health.

[1910] 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.

[1911] 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.

[1912] 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.

[1913] [Fourth embodiment]

[1914] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1915] 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.

[1916] 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).

[1917] 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.

[1918] 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.

[1919] 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).

[1920] 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.

[1921] 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.

[1922] 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.

[1923] 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.

[1924] 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.

[1925] 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.

[1926] 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."

[1927] System Overview

[1928] This invention is a system that utilizes generative AI models to maximize the health benefits of walking. The system performs an initial assessment based on the user's basic information and provides an individually customized walking program based on that assessment. It also analyzes the user's form, food records, and emotional data to provide appropriate support.

[1929] Program processing and specific examples

[1930] User registration and initial evaluation

[1931] 1. Enter your user information:

[1932] Device: The user enters their basic information (age, gender, health condition, walking experience, etc.) into the application.

[1933] Server: Receives this information and stores it in a database.

[1934] 2. Conducting an Initial Assessment:

[1935] Server: Conducts an initial assessment based on the user's basic information and generates reference data appropriate for age, gender, and health condition.

[1936] Optimizing your walking program

[1937] 1. Receive goal setting:

[1938] Device: The user sets a walking goal (e.g., dieting).

[1939] Server: Receives the goals and generates a personalized training program based on the user's initial assessment and goals.

[1940] 2. Generate customized programs:

[1941] Server: The AI ​​model creates a training schedule (number of times per week, walking time per session, walking speed, rest timing, etc.) needed to achieve the goal.

[1942] Form Improvements

[1943] 1. Form recording instructions:

[1944] Device: Instruct the user to capture their posture and movements while walking.

[1945] User: Follow the instructions to record a video while walking.

[1946] 2. Video Analysis:

[1947] Device: Upload the captured video to the server.

[1948] Server: Analyzes video to identify areas for improvement in posture and form.

[1949] 3. Providing Feedback:

[1950] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[1951] Terminal: Display feedback to the user.

[1952] Nutritional guidance

[1953] 1. Enter your food record:

[1954] Terminal: The user inputs the details of their daily meals.

[1955] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[1956] 2. Analysis of dietary data:

[1957] Terminal: Sends the entered meal data to the server.

[1958] Server: Analyzes the received data using a nutritional management AI model and provides nutritional guidance in line with goals.

[1959] 3. Providing nutritional advice:

[1960] Server: Based on the analysis results, it provides the user with nutritional advice appropriate for their diet (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[1961] Terminal: Notifies the user of nutritional advice.

[1962] Psychological support

[1963] 1. Emotional input and goal setting:

[1964] Device: The user enters their feelings and worries about walking in diary format.

[1965] User: Write down your thoughts and stress about walking.

[1966] 2. Emotional Data Analysis:

[1967] Terminal: Sends the input emotion data to the server.

[1968] Server: The sentiment analysis model analyzes the data and generates psychological support suggestions for the user.

[1969] 3. Providing psychological support:

[1970] Server: Provides users with effective stress management and motivational strategies (e.g., "practice deep breathing before walking" or "exercises that encourage positive thinking").

[1971] Terminal: Displays psychological support advice to the user.

[1972] Specific examples

[1973] Let's say a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. After that, if the user sets a goal of dieting, the server generates a walking program of 30 minutes, 3 times a week.

[1974] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides specific advice such as "straighten your back" or "correct the way you swing your arms."

[1975] Furthermore, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad."

[1976] Finally, when the user enters their feelings about walking in diary format, the server analyzes this and provides psychological support such as "deep breathing techniques" and "positive thinking techniques."

[1977] As described above, the system of the present invention can comprehensively support the user's walking and maximize the health benefits.

[1978] The processing flow will be explained below.

[1979] User registration and initial evaluation

[1980] Step 1:

[1981] Device: The user installs the application and enters basic information (age, gender, health status, walking experience, etc.).

[1982] Step 2:

[1983] Terminal: Sends the entered information to the server.

[1984] Server: Stores the received basic information in a database.

[1985] Step 3:

[1986] Server: Based on the user's basic information, an initial evaluation is performed and reference data appropriate for the user is generated.

[1987] Optimizing your walking program

[1988] Step 1:

[1989] Device: The user sets walking goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[1990] Step 2:

[1991] Terminal: Sends goal settings to the server.

[1992] Server: Based on goals and initial assessments, an AI model is used to generate a personalized training program.

[1993] Step 3:

[1994] Server: Sends the generated training program (number of times per week, walking time per session, walking speed, timing of rest, etc.) to the user.

[1995] Terminal: displays the training program to the user.

[1996] Form Improvements

[1997] Step 1:

[1998] Device: Instruct the user to capture their posture and movements while walking.

[1999] Step 2:

[2000] User: Take a video while walking and save it on the device.

[2001] Step 3:

[2002] Device: Upload the captured video to the server.

[2003] Step 4:

[2004] Server: Analyzes the video and identifies areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[2005] Step 5:

[2006] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[2007] Terminal: Display feedback to the user.

[2008] Nutritional guidance

[2009] Step 1:

[2010] Terminal: Provides a screen where users can input their daily dietary information.

[2011] Step 2:

[2012] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[2013] Step 3:

[2014] Terminal: Sends the entered meal data to the server.

[2015] Step 4:

[2016] Server: Analyzes the received dietary data using a nutritional management AI model and generates nutritional guidance that matches the user's goals.

[2017] Step 5:

[2018] Server: Based on the analysis results, it provides specific nutritional advice to users (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[2019] Terminal: Notifies the user of nutritional advice.

[2020] Psychological support

[2021] Step 1:

[2022] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[2023] Step 2:

[2024] User: Enter your thoughts and stress about walking.

[2025] Step 3:

[2026] Terminal: Sends the input emotion data to the server.

[2027] Step 4:

[2028] Server: Analyzes the emotional data and generates customized psychological support suggestions for the user.

[2029] Step 5:

[2030] Server: Based on the analysis results, the server provides users with effective methods for managing stress and maintaining motivation (e.g., "practice deep breathing before walking" or "exercises that encourage positive thinking").

[2031] Terminal: Displays psychological support advice to the user.

[2032] In this way, the server, terminal, and user work together at each processing step to provide support to maximize the effect of the user's walking.

[2033] Example 1

[2034] 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."

[2035] In modern society, while personalized health management and exercise guidance are important, providing such guidance requires significant resources. Furthermore, users often find it difficult to manage themselves, making it difficult to consistently receive effective exercise and dietary guidance and psychological support. Therefore, there is a demand for a system that can comprehensively support users' health status and provide individually optimized exercise programs, nutritional guidance, and psychological support.

[2036] 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.

[2037] In this invention, the server includes means for receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing an exercise program based on the transmitted goals, means for analyzing photographed user movements and providing improvements, means for analyzing input dietary records and providing nutritional guidance, and means for analyzing input emotion data and providing psychological support. This makes it possible to comprehensively support the user's health condition and consistently provide individually optimized exercise programs, nutritional guidance, and psychological support.

[2038] "Basic information sent by the user" refers to basic data such as age, sex, health condition, and exercise experience that the user provides to the system.

[2039] "Initial evaluation" is a process of comprehensively evaluating the user's health condition and fitness for exercise based on basic information.

[2040] "Goals" are specific objectives or hopes that users wish to achieve through the system.

[2041] An "exercise program" refers to an exercise schedule and content customized to a user's individual goals.

[2042] "Movement" refers to the posture and movements of the user when exercising.

[2043] "Analysis" is the process by which the server processes the data sent by the user and extracts meaningful information.

[2044] "Points for improvement" are specific points that should be corrected in order for the user to exercise more effectively.

[2045] "Diet record" refers to the data that a user enters into the system about their daily diet.

[2046] "Nutrition guidance" is specific advice provided to users on how to lead a healthy diet based on their food records.

[2047] "Emotion data" is information about a user's own emotions and mental state that is input into the system by the user.

[2048] "Psychological support" refers to advice and support provided to users to help them maintain their mental health.

[2049] "Health status" refers to the overall state of a user's physical and mental health.

[2050] "Exercise experience" refers to information such as the history of exercise the user has done up to now, the frequency of exercise, and the details of exercise.

[2051] A "training schedule" refers to a schedule or plan for a user to exercise systematically.

[2052] "Posture" refers to the position and shape of the body that a user assumes during exercise or daily life.

[2053] MODE FOR CARRYING OUT THE INVENTION

[2054] The present invention is a system for automating health management and exercise guidance for users. The system utilizes a server, a terminal, and a generative AI model to provide users with individually optimized exercise programs, form improvement, nutritional guidance, and psychological support.

[2055] Hardware and Software Configuration

[2056] Server: A high-performance server is used to run AI libraries such as Python, TensorFlow, Keras, OpenPose, and BERT. This server manages the database, performs initial evaluation, generates training programs, analyzes videos, analyzes food data, and analyzes emotion data.

[2057] Terminal: A device used by a user, such as a smartphone or tablet. The terminal provides an interface for the user to input information and sends the input data to a server.

[2058] User: Refers to an individual who uses the system and inputs the information necessary to manage their own health and receive exercise guidance.

[2059] System Operation Overview

[2060] 1. User registration and initial evaluation

[2061] The user uses the application to input basic information (age, gender, health status, exercise experience, etc.). The device sends this information to the server, which then performs an initial evaluation based on the received information and stores it in a database.

[2062] 2. Optimize your walking program

[2063] The user sets exercise goals (e.g., dieting) through the terminal. The server receives these goals and generates an individual training program based on the initial assessment data and the goals. The generated program is stored in a database and provided to the user.

[2064] 3. Improving your form

[2065] Users take videos of themselves walking with their smartphones and upload them to the server. The server analyzes the videos and identifies areas for improvement in posture and form. This is done using a posture analysis model using OpenPose. Based on the analysis results, specific improvement advice is generated and provided to the user.

[2066] 4. Nutritional guidance

[2067] Users enter their daily food records into the application. The device sends this data to the server. The server uses a nutrition management AI model to analyze the food data and provide nutritional guidance tailored to the user's goals. The analysis results are then sent to the device and presented to the user.

[2068] 5. Psychological support

[2069] Users enter their feelings and concerns about walking into the application in diary format. The device then sends this data to a server. The server then uses the natural language processing library BERT to analyze the emotional data and generate psychological support suggestions, providing users with methods for managing stress and maintaining motivation.

[2070] Specific examples

[2071] For example, suppose a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. Next, if the user sets "diet" as a goal, the server generates a walking program for 30 minutes, three times a week.

[2072] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides advice such as "straighten your back" or "correct the way you swing your arms." Additionally, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to your breakfast" or "add nuts to your lunch salad."

[2073] Furthermore, when users enter their feelings about walking in diary format, the server analyzes the data and provides psychological support such as "deep breathing techniques" and "positive thinking techniques."

[2074] The system aims to provide comprehensive support for users' walking and maximize the health benefits.

[2075] Prompt Sentence Examples

[2076] "When walking, be mindful of the following: keep your back straight and adjust your arm swing."

[2077] "Add more protein to your breakfast, such as eggs or yogurt."

[2078] "Practice deep breathing exercises before walking. This will help you relax and improve your workout."

[2079] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2080] Step 1:

[2081] The user opens the application on a device such as a smartphone or tablet and enters basic information such as age, gender, health status, and exercise experience. This information is then obtained.

[2082] Input: Basic information such as the user's age, gender, health status, and exercise experience

[2083] Output: Basic information data

[2084] Specific operation: The user enters information into the form and presses the "Complete" button to submit. The device temporarily saves the information and sends it to the server.

[2085] Step 2:

[2086] The terminal sends the basic information entered by the user to the server, which then stores the information in a database and performs an initial evaluation for each user.

[2087] Input: Basic information data

[2088] Output: Initial evaluation data

[2089] What happens: The server creates a new user record in the database and runs an initial evaluation script using Python. The evaluation results are saved in the database.

[2090] Step 3:

[2091] The user sets a specific walking goal (e.g., dieting) within the application. The set goal is sent as input data from the device to the server.

[2092] Input: Walking goal

[2093] Output: Target data

[2094] Specific operation: The user inputs a goal, and the device sends it to the server, which then stores the goal data in a database.

[2095] Step 4:

[2096] The server generates an individual training program using an AI model (using Keras) based on the received goal data and initial evaluation data, which is then stored in a database and provided to the user.

[2097] Input: Target data, initial evaluation data

[2098] Output: Individual training program

[2099] Specific operation: The server dynamically generates training programs using the Keras library and stores them in a database.

[2100] Step 5:

[2101] The user records their walking form with their smartphone and uploads the video to a server via the application.

[2102] Input: Video of walking

[2103] Output: Recorded video data

[2104] Specific operation: The user takes a video using the device's camera and sends it to the server using the upload function. This operation can be easily performed within the application.

[2105] Step 6:

[2106] The server receives the uploaded video and analyzes it using an AI model (a posture analysis model using OpenPose). As a result of the analysis, it identifies areas for improvement in the user's posture and form.

[2107] Input: Recorded video data

[2108] Output:Form Improvements

[2109] Specific behavior: The server uses the OpenPose library to analyze the video, analyze the data obtained, and identify problems with posture and behavior. This information is stored in a database.

[2110] Step 7:

[2111] Based on the analysis results, the server generates specific advice for the user on how to improve their form, and sends the advice to the terminal.

[2112] Input: Form Improvements

[2113] Output: Improvement advice

[2114] Specific operation: The server generates appropriate advice for the user based on the identified improvements. The generated advice is notified to the device and displayed to the user.

[2115] Step 8:

[2116] The user enters their daily food record into the application and sends the data to the server.

[2117] Input: Food log

[2118] Output: Food record data

[2119] Specific operation: When the user enters the meal details into the application form and presses the submit button, the data is sent from the terminal to the server.

[2120] Step 9:

[2121] The server analyzes the received dietary record data using a nutritional management AI model to generate data for providing nutritional guidance in line with goals. The analysis results are stored in a database.

[2122] Input: Food record data

[2123] Output: Nutritional guidance data

[2124] Specific operation: The server receives the food record data, analyzes it using a nutrition management AI model, and calculates nutritional balance and areas for improvement. This data is then stored in a database.

[2125] Step 10:

[2126] Based on the analysis results, the server generates nutritional guidance appropriate for the user and transmits the guidance content to the user's terminal.

[2127] Input: Nutritional guidance data

[2128] Output: Nutritional advice

[2129] Specific operation: The server generates nutritional advice appropriate for the user based on the analysis results obtained from the nutrition management AI model. This advice is sent to the user's device and displayed within the application.

[2130] Step 11:

[2131] The user enters their feelings and worries about walking into the application in diary format, and the data is sent to the server.

[2132] Input: Emotion data

[2133] Output: Emotion recording data

[2134] Specific operation: When the user enters their feelings or concerns into the application and presses the send button, the data is sent from the device to the server.

[2135] Step 12:

[2136] The server analyzes the received emotional data using the natural language processing library BERT and generates psychological support suggestions, including methods for managing stress and maintaining motivation.

[2137] Input: Emotion recording data

[2138] Output: Psychological support suggestions

[2139] How it works: The server uses the BERT library to analyze the sentiment data and extract patterns to understand the user's mental state. These data are then stored in a database.

[2140] Step 13:

[2141] Based on the analysis results, the server generates advice for providing psychological support to the user and sends it to the user's terminal.

[2142] Input: Psychological support suggestions

[2143] Output: Psychological support advice

[2144] Specific operation: Based on the acquired psychological support suggestions, the server generates appropriate advice for the user (e.g., "deep breathing" or "positive thinking") and sends it to the device. The device notifies and displays this to the user.

[2145] In this way, the system can comprehensively support the user's health and consistently provide individually optimized exercise programs, nutritional guidance, and psychological support.

[2146] (Application example 1)

[2147] 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."

[2148] Health management is becoming increasingly important in modern society, and many people are seeking a lifestyle that incorporates walking. However, there is no system that can comprehensively design and implement effective walking programs, improve one's form, maintain nutritional balance, and provide psychological support. Furthermore, linking these systems with food delivery services to easily provide individually optimized meal plans is also a challenge. Given this background, there is a need for a system that can provide users with comprehensive health support.

[2149] 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.

[2150] In this invention, the server includes means for receiving basic information sent by the user and making an initial evaluation, means for optimizing the walking program based on the sent goals, means for analyzing a photograph of the user's walking form and suggesting areas for improvement, means for analyzing the input meal record and providing nutritional advice, means for analyzing the input emotion data and providing psychological support, means for calculating calories burned based on the collected walking data and proposing an appropriate meal plan, and means for proposing a food delivery plan to provide a meal plan linked to the user's walking data. This allows for comprehensive support not only for the user's walking life but also for their daily meals and psychological health, enabling comprehensive health support.

[2151] "User information" refers to basic information provided by the user, such as age, gender, health status, walking experience, etc.

[2152] A "walking program" is a mobility training schedule that is individually customized based on the user's basic information and goals.

[2153] "Walking form" refers to the user's posture and movements while walking.

[2154] "Dietary records" are data on the contents and amounts of food consumed by a user each day.

[2155] "Emotion data" is information about emotions and worries about walking input by the user.

[2156] "Calories burned" refers to the amount of energy consumed through physical activity and is calculated based on walking data.

[2157] A "meal plan" is an appropriate meal plan and its composition that is suggested based on the user's walking data and nutritional analysis.

[2158] A "food delivery plan" is a meal delivery plan provided based on the user's optimal meal plan.

[2159] "Walking data" refers to information such as the number of steps taken and walking speed, and is collected through smart devices.

[2160] The "means for receiving basic information and making an initial evaluation" is a method in which the system receives basic information provided by the user and evaluates the initial state of the user based on that information.

[2161] The "means for optimizing a walking program based on goals" is a method for designing and generating an individually optimized walking program based on goals set by the user.

[2162] The "means of analyzing a user's filmed walking form and suggesting areas for improvement" is a method of analyzing a walking video filmed by the user and suggesting areas for improvement to the user's form based on the results of the analysis.

[2163] The "means for analyzing the input dietary record and providing nutritional guidance" is a method for analyzing the dietary details input by the user and providing nutritional guidance based on the analysis results.

[2164] The "means for analyzing input emotion data and providing psychological support" is a method for analyzing emotion data input by a user and providing psychological support based on the results.

[2165] "Means for calculating calories burned based on collected walking data and proposing an appropriate meal plan" refers to a method for calculating energy consumption using data collected during walking and proposing a meal plan suited to the user.

[2166] "Means for proposing food delivery plans to provide meal plans linked to the user's walking data" refers to a method for determining the optimal meal plan based on collected walking data and proposing a food delivery service based on that plan.

[2167] This system receives basic information sent by the user and optimizes walking programs based on that information. The system analyzes the user's walking data, food records, and emotional data to provide comprehensive health support. It also has a function to suggest optimal meal plans in conjunction with food delivery plans.

[2168] System configuration

[2169] This system uses the following hardware and software:

[2170] Programming language: Python

[2171] Generative AI model: GPT-4

[2172] Database: MySQL

[2173] Server: AWS EC2

[2174] App development platform: React Native (mobile app)

[2175] Processing flow

[2176] User information registration and initial evaluation

[2177] The user enters basic information into the terminal and sends it to the server. The server stores the received basic information in a MySQL database and performs an initial evaluation. Based on this initial evaluation, a walking program suitable for the user is generated.

[2178] Creating a walking program

[2179] The server uses a generative AI model to generate a customized walking program based on the user's basic information and goals, and inputs prompts such as the following into the generative AI model:

[2180] User age: 40

[2181] Gender: Female

[2182] Walking experience: Beginner

[2183] Goal: Lose weight

[2184] Based on this information, we will suggest the best walking program for you.

[2185] Improve your walking form

[2186] Users record their walking form and upload it to a server from their device. The server analyzes the video and identifies areas for improvement. This information is then analyzed using a generative AI model to provide appropriate advice to the user.

[2187] Food records and nutritional advice

[2188] The user inputs their daily dietary information into the device and sends it to the server. The server analyzes the received dietary data and generates nutritional advice. For example, the following prompt sentence is input into the generative AI model:

[2189] Breakfast: Toast, eggs, coffee

[2190] Lunch: Sandwich, salad

[2191] Dinner: Grilled fish, vegetable soup

[2192] Based on this food record, provide appropriate nutritional advice.

[2193] Emotional data and psychological support

[2194] The user inputs their feelings and concerns about walking into the device and sends them to the server, which analyzes the input emotional data and provides appropriate psychological support.

[2195] Food delivery plan proposal

[2196] The system calculates the calorie expenditure based on the user's walking data and generates a meal plan based on this. The server then connects the generated meal plan to a food delivery service and suggests the most suitable meal for the user. In this case, the following prompt is also input into the generative AI model:

[2197] Today's steps: 8000

[2198] Walking speed: 5km / h

[2199] This information will help you provide optimal nutritional advice and meal plans.

[2200] Specific examples

[2201] For example, if a 40-year-old woman uses this system to manage her daily walking and diet, the process would be as follows: The user enters initial information and walks according to the generated walking program. She records a video of herself walking to receive advice on improving her form, and then enters her daily food record to receive nutritional guidance. She follows the meal plan suggested based on the calories burned and orders appropriate meals from the food delivery plan. This series of processes allows the user to maintain a healthy lifestyle.

[2202] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2203] Step 1:

[2204] The user enters basic information (age, gender, health condition, walking experience, etc.) into the device. The entered basic information is sent from the device to the server. The basic information received by the server is stored in a MySQL database. Here, the data format is sent as JSON and inserted into the database using SQL statements.

[2205] Step 2:

[2206] The server performs an initial evaluation of the user based on the stored basic information. The results of the initial evaluation are generated using a generative AI model (GPT-4). The input data is user information, and an appropriate evaluation result is generated based on this. The server inputs the following prompt sentence into the generative AI model and obtains the evaluation result.

[2207] User age: 40

[2208] Gender: Female

[2209] Walking experience: Beginner

[2210] Use this information to make your initial assessment.

[2211] As a result, an initial evaluation report is generated for the user.

[2212] Step 3:

[2213] The user sets walking goals on the device. Possible goals include "dieting" or "improving physical fitness." The user's set goals are sent from the device to the server. The server generates an individually customized walking program based on the initial assessment and goals. This process also uses a generative AI model, and the following prompt is input:

[2214] User age: 40

[2215] Gender: Female

[2216] Walking experience: Beginner

[2217] Goal: Lose weight

[2218] Based on this information, we will suggest the best walking program for you.

[2219] As a result, a walking program is generated that specifies the number of times per week, the duration of each walk, walking speed, and rest timing.

[2220] Step 4:

[2221] Users record their walking form with their device's camera. The video is then uploaded from the device to a server. The server then analyzes the video using a generative AI model to identify areas for improvement in posture and form. The following prompts are used:

[2222] Analyze the video while walking.

[2223] Based on the analysis results, the server generates specific feedback for improving form, such as straightening your back or correcting the way you swing your arms, and sends this to the device.

[2224] Step 5:

[2225] The user enters the details of their daily meals into the device and sends this meal record to the server. The server then analyzes the received meal record using an AI model for nutritional management and generates nutritional advice appropriate for the user. For example, the following prompt sentences are used:

[2226] Breakfast: Toast, eggs, coffee

[2227] Lunch: Sandwich, salad

[2228] Dinner: Grilled fish, vegetable soup

[2229] Based on this food record, provide appropriate nutritional advice.

[2230] The analysis results in specific advice, such as adding more protein to your breakfast or adding nuts to your lunch salad, which is displayed on the device.

[2231] Step 6:

[2232] The user enters their feelings and worries about walking in diary format into the device and sends it to the server. The server analyzes the received emotional data and suggests appropriate psychological support. The following prompts are used:

[2233] The user has entered their thoughts and concerns. Based on this information, please suggest psychological support.

[2234] As a result, specific psychological support advice, such as deep breathing techniques and positive thinking techniques, is generated and displayed on the device.

[2235] Step 7:

[2236] The server calculates the calories burned based on walking data collected from the user's steps and walking speed. Based on the calculated calories, it proposes an appropriate meal plan. Furthermore, it uses a generative AI model to generate a specific meal plan using prompt sentences such as the following:

[2237] Today's steps: 8000

[2238] Walking speed: 5km / h

[2239] This information will help you provide optimal nutritional advice and meal plans.

[2240] Based on this suggestion, a food delivery plan is created and presented to the user, who can then order appropriate meals from the suggested meal plan.

[2241] 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.

[2242] System Overview

[2243] This invention is a system that utilizes a generative AI model to maximize the health benefits of walking. The system performs an initial assessment based on the user's basic information and provides an individually customized walking program based on that assessment. It also analyzes the user's form, food records, and emotional data to provide appropriate support. The system also incorporates an emotion engine that recognizes the user's emotions.

[2244] Program processing and specific examples

[2245] User registration and initial evaluation

[2246] 1. Enter your user information

[2247] Device: The user enters their basic information (age, gender, health condition, walking experience, etc.) into the application.

[2248] Server: Receives this information and stores it in a database.

[2249] 2. Conducting an initial evaluation

[2250] Server: Performs an initial evaluation based on the user's basic information and generates reference data suitable for the user.

[2251] Optimizing your walking program

[2252] 1. Receiving goal setting

[2253] Device: The user sets walking goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[2254] Server: Receives the goals and generates a personalized training program based on the user's initial assessment and goals.

[2255] 2. Creating a customized program

[2256] Server: The AI ​​model creates a training schedule (number of times per week, walking time per session, walking speed, rest timing, etc.) needed to achieve the goal.

[2257] 3. Notice to Users

[2258] Server: Sends the generated training program to the user.

[2259] Terminal: displays the training program to the user.

[2260] Form Improvements

[2261] 1. Form recording instructions

[2262] Device: Instruct the user to capture their posture and movements while walking.

[2263] 2. Recording and uploading videos

[2264] User: Take a video while walking and save it on the device.

[2265] Device: Upload the captured video to the server.

[2266] 3. Video analysis and feedback

[2267] Server: Analyzes the video and identifies areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[2268] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[2269] Terminal: Display feedback to the user.

[2270] Nutritional guidance

[2271] 1. Enter your food record

[2272] Terminal: Provides a screen where the user can input their daily dietary information.

[2273] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[2274] 2. Submitting and analyzing dietary data

[2275] Terminal: Sends the entered meal data to the server.

[2276] Server: Analyzes the received dietary data using a nutritional management AI model and provides nutritional guidance in line with goals.

[2277] 3. Providing nutritional guidance

[2278] Server: Based on the analysis results, it provides the user with nutritional advice appropriate for their diet (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[2279] Terminal: Notifies the user of nutritional advice.

[2280] Psychological support and emotional engine

[2281] 1. Emotion Input and Recognition

[2282] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[2283] User: Enter your thoughts and stress about walking.

[2284] 2. Transmission and analysis of emotional data

[2285] Terminal: Sends the input emotion data to the server.

[2286] Server: The emotion engine analyzes the emotion data and evaluates the emotional state.

[2287] 3. Providing psychological support

[2288] Server: Based on the analysis results, it generates customized psychological support suggestions for the user (e.g., "practice deep breathing before walking" or "exercises that promote positive thinking").

[2289] Terminal: Displays psychological support advice to the user.

[2290] Specific examples

[2291] Let's say a user installs the application and enters basic information such as age 40, gender female, and beginner walking. The server receives this information and performs an initial evaluation. After that, if the user sets a goal of dieting, the server generates a walking program of 30 minutes, 3 times a week.

[2292] When a user takes a video of themselves walking and uploads it to the server, the server analyzes the video and provides specific advice such as "straighten your back" or "correct the way you swing your arms."

[2293] Furthermore, when a user enters a daily food record, the server analyzes the dietary data and provides nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad."

[2294] When users enter their feelings about walking in diary format, the server's emotion engine analyzes them and provides psychological support such as "deep breathing techniques" and "positive thinking methods."

[2295] By utilizing the emotion engine, more personalized support can be provided by providing care appropriate to the user's individual emotional state. This system can comprehensively support the user's walking and maximize health benefits.

[2296] The processing flow will be explained below.

[2297] User registration and initial evaluation

[2298] Step 1:

[2299] Device: The user installs the application and enters basic information (age, gender, health status, walking experience, etc.).

[2300] Step 2:

[2301] Terminal: Sends the entered information to the server.

[2302] Step 3:

[2303] Server: Stores the received basic information in a database.

[2304] Step 4:

[2305] Server: Performs an initial evaluation based on the user's basic information and generates reference data suitable for the user.

[2306] Optimizing your walking program

[2307] Step 1:

[2308] Terminal: The user inputs the walking goal (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.).

[2309] Step 2:

[2310] Terminal: Sends goal settings to the server.

[2311] Step 3:

[2312] Server: Uses AI models to generate personalized training programs based on goals and initial assessment results.

[2313] Step 4:

[2314] Server: Sends the generated training program to the user.

[2315] Step 5:

[2316] Terminal: displays the training program to the user.

[2317] Form Improvements

[2318] Step 1:

[2319] Device: Instruct the user to capture their posture and movements while walking.

[2320] Step 2:

[2321] User: Take a video while walking and save it on the device.

[2322] Step 3:

[2323] Device: Upload the captured video to the server.

[2324] Step 4:

[2325] Server: Analyzes the video and detects areas for improvement in posture and form. An AI model performs detailed analysis and generates improvement instructions.

[2326] Step 5:

[2327] Server: Based on the analysis results, the server provides the user with specific advice on improving their form (e.g., "straighten your back" or "correct the way you swing your arms").

[2328] Step 6:

[2329] Terminal: Display feedback to the user.

[2330] Nutritional guidance

[2331] Step 1:

[2332] Terminal: Provides a screen where users can input their daily dietary information.

[2333] Step 2:

[2334] User: Enter the contents and amounts of breakfast, lunch, and dinner.

[2335] Step 3:

[2336] Terminal: Sends the entered meal data to the server.

[2337] Step 4:

[2338] Server: Analyzes the received dietary data using a nutritional management AI model and generates nutritional guidance that matches the user's goals.

[2339] Step 5:

[2340] Server: Based on the analysis results, it provides specific nutritional advice to users (e.g., "add more protein to breakfast" or "add nuts to your lunch salad").

[2341] Step 6:

[2342] Terminal: Notifies the user of nutritional advice.

[2343] Psychological support and emotional engine

[2344] Step 1:

[2345] Terminal: Provides a screen where users can enter their feelings and worries about walking in diary format.

[2346] Step 2:

[2347] User: Enter your thoughts and stress about walking.

[2348] Step 3:

[2349] Terminal: Sends the input emotion data to the server.

[2350] Step 4:

[2351] Server: The emotion engine analyzes the emotion data and evaluates the emotional state.

[2352] Step 5:

[2353] Server: Based on the analysis results, it generates customized psychological support suggestions for the user (e.g., "practice deep breathing before walking" or "exercises that promote positive thinking").

[2354] Step 6:

[2355] Terminal: Displays psychological support advice to the user.

[2356] In this way, the server, terminal, and user work together at each processing step to provide comprehensive support to maximize the effectiveness of the user's walking.

[2357] Example 2

[2358] 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."

[2359] Conventional health management systems lack a personalized approach, making it difficult to respond appropriately to individual users' situations and goals. Furthermore, there were no systems capable of providing advanced support, such as improving walking form, analyzing food records, or providing psychological support based on emotional data. This made it difficult to maximize the health benefits for users.

[2360] 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 receiving basic information transmitted by the user and performing an initial evaluation, means for optimizing the exercise program based on the transmitted goals, means for analyzing the user's photographed exercise form and providing improvements, means for analyzing the input dietary record and providing nutritional advice, means for analyzing the input emotional data and providing psychological support, means for instructing the user to record their exercise form, and means for notifying the user of the generated program. This enables a personalized approach according to the user's individual situation and goals, and makes it possible to maximize the user's health benefits through improvements in walking form, analysis of the dietary record, and advanced psychological support based on the emotional data.

[2361] "Basic information" refers to initial data about an individual user, such as the user's age, gender, health condition, and exercise experience.

[2362] An "initial assessment" is an assessment based on basic information about the user, and this assessment confirms the user's health condition and exercise experience.

[2363] An "exercise program" is a customized exercise plan based on the goals set by the user, specifically including the number of walking sessions, duration, and speed.

[2364] "Exercise form" refers to the posture and movements of the user when exercising, and by analyzing this, areas for improvement can be identified.

[2365] "Nutrition guidance" refers to advice based on data entered by the user about their daily diet, and indicates nutritional intake methods that are suitable for the user's goals.

[2366] "Emotion data" refers to data entered in diary format by the user about emotions and worries felt during exercise or daily life.

[2367] "Psychological support" refers to support provided based on an assessment of the user's emotional state through analysis of emotional data, and includes, for example, stress relief and promotion of positive thinking.

[2368] The "recording instruction means" is a system function that instructs the user to record their exercise form.

[2369] "Notification means" refers to the system's function of informing users of information such as exercise programs generated by the server, analysis results, nutritional guidance, and psychological support.

[2370] Overall system overview

[2371] This invention is a system for managing a user's health by analyzing the user's exercise program, exercise form, diet record, and emotional data, and providing personalized advice. The system consists of three entities: a server, a terminal, and the user.

[2372] Hardware and software used

[2373] Hardware

[2374] Terminals: Smartphones and personal computers are mainly used.

[2375] Server: Cloud or dedicated servers are used.

[2376] Camera: Smartphones and webcams are used.

[2377] software

[2378] Database: A relational database management system such as MySQL or PostgreSQL.

[2379] Statistical analysis model: R was used.

[2380] AI model: Built using TensorFlow and PyTorch.

[2381] Video analysis: Using OpenCV.

[2382] Natural Language Processing (NLP): Uses SpaCy, BERT, etc.

[2383] User information and initial evaluation

[2384] The user inputs basic information (e.g., age, gender, health status, exercise experience, etc.) via the terminal. This information is sent to the server, which stores it in a database.

[2385] The server performs an initial assessment using a statistical analysis model implemented in R based on the stored basic information, and generates reference data according to the user's health condition and exercise experience. This reference data will serve as the basis for a customized program that will be generated later.

[2386] Generating a customized exercise program

[2387] The user sets exercise goals (e.g., dieting, improving cardiopulmonary function, relieving stress, etc.) using the device. These goals are sent to the server, which compares them with the initial assessment data and generates an individualized exercise program using an AI model built in Python (using TensorFlow and PyTorch). For example, a program for walking three times a week for 30 minutes each time is generated.

[2388] The generated exercise program is sent to the terminal by the server, and the user can check it and put it into practice.

[2389] Recording and analyzing exercise form

[2390] While the exercise program is running, the user is prompted to record their walking form. The user can record the video of their exercise using a smartphone or webcam and save it on their device.

[2391] The device uploads the saved video to a server. The server uses OpenCV to analyze the video and uses an AI model to identify areas for improvement in posture and form. Based on the analysis results, specific advice such as "straighten your back" or "correct your arm swing" is generated and sent to the device.

[2392] Food record entry and nutritional advice

[2393] The terminal provides a screen for the user to input daily meal plans, such as bread and eggs for breakfast, salad and chicken for lunch, and fish and rice for dinner.

[2394] The device sends the input data to a server, where the server's nutritional management AI model (using SpaCy and other deep learning technologies) analyzes the data. Based on this, nutritional advice such as "add more protein to breakfast" or "add nuts to your lunch salad" is generated and sent to the device.

[2395] Emotional data input and psychological support

[2396] The user uses the emotion input screen of the device to enter their feelings and worries about walking in diary format. For example, they might enter something like, "I felt really good after walking today."

[2397] The device sends the input emotional data to the server, where it is analyzed by the server's emotion engine (using NLP models such as BERT). Based on the analysis results, psychological support suggestions such as "practice deep breathing before walking" and "exercises to promote positive thinking" are generated and notified to the device as mentioned above.

[2398] Example prompts for generative AI models

[2399] Here are some example prompts to input to the generative AI model:

[2400] "What kind of training schedule would be best to create a walking program suitable for a 40-year-old woman who is new to walking?"

[2401] "Please provide users with advice on balanced nutrition based on their dietary data."

[2402] "How can I analyze video of my walking to identify areas for improvement?"

[2403] The above is a detailed description of an embodiment of the present invention, which allows the user to maximize their health benefits.

[2404] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2405] Step 1:

[2406] Enter basic information

[2407] Terminal: Displays a screen for the user to enter basic information (e.g., age, gender, health status, exercise experience, etc.). As a specific example of input, the user enters "Age: 40 years old," "Gender: Female," and "Exercise experience: Beginner."

[2408] User: Enter basic information and press the send button.

[2409] Terminal: Sends the entered basic information to the server.

[2410] Input: User basic information

[2411] Output: Basic information about the user sent to the server

[2412] Step 2:

[2413] Conducting an initial evaluation

[2414] Server: Receives basic information sent by the user and stores it in a database.

[2415] Server: Analyzes basic information using a statistical analysis model implemented in R and performs an initial evaluation. Here, baseline data is generated based on the user's age, health status, etc.

[2416] Input: User basic information

[2417] Output: Baseline data for initial evaluation

[2418] Step 3:

[2419] Receiving goal setting

[2420] Device: Displays a screen for setting exercise goals. The user sets "diet" as an example.

[2421] User: Enter a goal and press the submit button.

[2422] Terminal: Sends the entered goal to the server.

[2423] Input: User-defined exercise goals

[2424] Output: Movement goal sent to the server

[2425] Step 4:

[2426] Generate customized programs

[2427] Server: Based on the baseline data from the initial evaluation and the user's exercise goals, a generative AI model built in Python (using TensorFlow and PyTorch) is used to generate an individual exercise program. Specifically, a walking program of 30 minutes, three times a week, is generated.

[2428] Server: Sends the generated exercise program to the terminal.

[2429] Input: Initial evaluation data, user's exercise goals

[2430] Output: Customized exercise program

[2431] Step 5:

[2432] Exercise program notification

[2433] Terminal: Receives the customized exercise program sent from the server and notifies the user.

[2434] User: Check notifications and view exercise program details.

[2435] Input: Customized exercise program

[2436] Output: An exercise program that can be viewed by the user

[2437] Step 6:

[2438] Form recording instructions

[2439] Device: Notifies the user at the set time to record a video of their exercise form.

[2440] User: Uses the camera function on a smartphone to take a photo of their exercise form.

[2441] Input: Recording timing instructions

[2442] Output: Video of exercise form taken by the user

[2443] Step 7:

[2444] Video recording and uploading

[2445] User: Record a video of your exercise form and save it on your device.

[2446] Terminal: Presents the user with the option to upload, and when they press the upload button, the video is sent to the server.

[2447] Input: Video of exercise form taken by the user

[2448] Output: Video of exercise form uploaded to the server

[2449] Step 8:

[2450] Video analysis and feedback

[2451] Server: Receives the uploaded video and analyzes it using OpenCV. Using a generative AI model, it identifies areas for improvement in posture and form (e.g., "straighten your back" or "correct your arm swing").

[2452] Server: Based on the analysis results, specific feedback for the user is generated and sent to the device.

[2453] Terminal: Notifies the user of the generated feedback.

[2454] Input: Video of exercise form

[2455] Output: Specific form feedback

[2456] Step 9:

[2457] Entering a food record

[2458] Terminal: Provides a screen for the user to input meal details, such as bread and eggs for breakfast, salad and chicken for lunch, and fish and rice for dinner.

[2459] User: Enter the meal details and press the send button.

[2460] Terminal: Sends the entered meal details to the server.

[2461] Input: User's diet

[2462] Output: Meal details sent to the server

[2463] Step 10:

[2464] Analysis of dietary data

[2465] Server: Receives meal data and analyzes it using a nutrition management AI model (using SpaCy and other deep learning technologies). As a result of the analysis, it generates specific nutritional advice such as "add more protein to breakfast" or "add nuts to lunch."

[2466] Server: Sends the analysis results to the device.

[2467] Terminal: Notifies the user of the generated nutritional advice.

[2468] Input: Meal data

[2469] Output: Nutritional guidance

[2470] Step 11:

[2471] Entering emotion data

[2472] Device: Provides an emotion input screen and allows the user to input their emotions and worries. For example, they can input "I felt great after walking today."

[2473] User: Enter emotion data and press the send button.

[2474] Terminal: Sends the input emotion data to the server.

[2475] Input: User emotion data

[2476] Output: Emotion data sent to the server

[2477] Step 12:

[2478] Emotional data analysis and psychological support

[2479] Server: Receives emotional data and analyzes it using an emotion engine (using NLP models such as BERT). Based o...

Claims

1. means for receiving basic information sent by a user and performing an initial evaluation; means for optimizing a walking program based on the transmitted goals; A means for analyzing the user's walking form and providing suggestions for improvement; A means for analyzing the input dietary records and providing nutritional guidance; A means for analyzing input emotional data and providing psychological support; A system including:

2. 2. The system of claim 1, wherein the walking program optimization means generates a training schedule according to the user's health condition and walking experience.

3. 2. The system according to claim 1, wherein the form analysis means analyzes the user's walking posture and suggests specific methods for improving the posture.

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