System

Abstract

A system is provided.SOLUTION: A system comprising: input means for inputting user health information; program generation means for generating an exercise program based on the user health information; analysis means for analyzing a user's exercise based on the exercise program and providing feedback; program generation means for generating a nutrition plan based on the user health information; and display means for displaying the results of the exercise program and the nutrition plan.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] Traditional personal training is generally expensive, making it difficult to obtain exercise programs and nutritional guidance tailored to individual needs. Furthermore, few systems are capable of analyzing individual exercise movements in real time and providing appropriate feedback, preventing many users from training effectively and safely at home. Furthermore, they lack personalized support features to maintain motivation. To address these issues, a system is needed that provides personalized training and nutritional guidance at low cost and improves users' exercise efficiency and safety. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. A system is provided that includes an input means for inputting a user's health information, a program generation means for generating an exercise program based on the user's health information, an analysis means for analyzing the user's exercise based on the exercise program and providing feedback, a program generation means for generating a nutritional plan based on the user's health information, and a display means for displaying the results of the exercise program and nutritional plan. The analysis means includes a means for capturing and analyzing the user's exercise movements in real time. Furthermore, the system also includes a customization means that allows the user to customize the appearance and personality of the virtual trainer, thereby contributing to maintaining motivation. This enables training and nutritional guidance that matches individual needs at low cost, improving the user's exercise efficiency and safety as well as increasing motivation.

[0006] "User Health Information" means information about a user's personal health and lifestyle, such as height, weight, goals, and lifestyle habits.

[0007] "Input means" refers to the user interface or device through which a user inputs their health information.

[0008] "Program Generation Means" refers to software and algorithms for generating an optimal exercise program and nutrition plan for a user based on input health information.

[0009] "Analysis means" refers to hardware and software for analyzing the user's exercise and health data and providing appropriate feedback.

[0010] "Display means" refers to a display or application that visually presents the generated exercise program or nutrition plan to the user.

[0011] "Real-time capture" means instantly capturing the user's movements as video data and analyzing them on the spot.

[0012] "Feedback" refers to information such as corrections and advice provided to users based on the analysis results.

[0013] "Customization Method" refers to the interface or tools that allow a user to customize the appearance and personality of their virtual trainer to suit their preferences.

[0014] A "virtual trainer" is a digital character that interacts with users and supports them with their training and nutrition plans. [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] The present invention is a system for providing personalized training and nutritional advice to individual users. Specific embodiments of the system will be described below.

[0037] Collection of User's Health Information

[0038] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0039] The terminal transmits the input information to the server.

[0040] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[0041] Initial data analysis and program generation

[0042] The server generates an optimal exercise program and nutrition plan for the user based on the user's stored health information, including historical data analysis, the latest fitness theories, and nutritional knowledge.

[0043] Training and nutrition plan delivery

[0044] The server transmits the generated training program and nutrition plan to the terminal.

[0045] The terminal displays the received plan to the user.

[0046] The user exercises according to the displayed training plan and eats according to the nutrition plan.

[0047] Real-time motion analysis

[0048] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0049] The terminal transmits the captured video data to the server in real time.

[0050] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0051] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[0052] The terminal displays real-time feedback to the user.

[0053] Customization features

[0054] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0055] Users can select and save their preferred appearance and personality.

[0056] This customization feature helps users increase motivation and encourage them to continue their training.

[0057] Paid Plans and Monetization

[0058] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and monetizes by providing users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[0059] Specific examples

[0060] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[0061] The above is a specific embodiment of the present invention, which allows individual users to receive professional training and nutritional guidance at low cost, enabling them to achieve their goals effectively and safely.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0065] Step 2:

[0066] The terminal transmits the input health information to the server.

[0067] Step 3:

[0068] The server stores the received health information in a database and prepares to generate an initial training and nutrition plan.

[0069] Step 4:

[0070] The server analyzes the user's stored health information and generates an optimal training program and nutrition plan for the user.

[0071] Step 5:

[0072] The server transmits the generated training program and nutrition plan to the terminal.

[0073] Step 6:

[0074] The terminal displays the received training program and nutrition plan to the user.

[0075] Step 7:

[0076] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[0077] Step 8:

[0078] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0079] Step 9:

[0080] The terminal transmits the captured video data to the server in real time.

[0081] Step 10:

[0082] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0083] Step 11:

[0084] The server generates feedback based on the analysis results and sends corrections and advice to the device.

[0085] Step 12:

[0086] The terminal displays real-time feedback to the user.

[0087] Step 13:

[0088] The user modifies the athletic performance according to the displayed feedback.

[0089] Step 14:

[0090] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0091] Step 15:

[0092] Users select their preferred appearance and personality and save their settings.

[0093] Step 16:

[0094] The server manages paid plans and provides detailed exercise analysis reports and expert advice.

[0095] Step 17:

[0096] The server aims to generate revenue by providing users with information on rental and purchase of exercise equipment and affiliated gyms through affiliate partnerships.

[0097] Example 1

[0098] 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."

[0099] Conventional training and nutritional guidance systems have difficulty providing personalized programs based on individual users' health information. Furthermore, delays in real-time exercise analysis and feedback can prevent users from maintaining proper exercise form, increasing the risk of injury. Furthermore, the lack of functionality for customizing the virtual trainer makes it difficult to maintain user motivation.

[0100] 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.

[0101] In this invention, the server includes an input device for inputting a user's health information, a program generation device for generating an exercise program and a nutrition plan based on the user's health information, an analysis device for analyzing the user's exercise based on the exercise program and providing feedback, a device for the analysis device to input prompts and perform data analysis using a generated AI model, and a display device for displaying the results of the exercise program and nutrition plan. This enables personalized training and nutritional guidance for each user, and provides accurate feedback in real time, resulting in safer and more effective exercise guidance. Furthermore, adding a virtual trainer customization function can maintain user motivation and improve training continuity.

[0102] "User health information" refers to information entered by the user, such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0103] "Input means" refers to devices such as smartphones, tablets, and computers that users use to input health information into the application, as well as dedicated apps or web apps.

[0104] "Program generation means" refers to a program, algorithm, or analysis system for generating an exercise program and nutrition plan based on the user's health information.

[0105] The "analysis means" is a means for analyzing the user's exercise movements based on the exercise program, detecting incorrect movements or movements that pose a risk of injury, and providing feedback.

[0106] A "generative AI model" is an artificial intelligence model (e.g., a model built with TensorFlow or PyTorch) that is trained based on large amounts of data and generates a response to a specific task when a prompt is input.

[0107] A "prompt" is a series of sentences or commands that are input to a generative AI model and contain instructions for completing a specific task.

[0108] "Display means" refers to a device such as a smartphone, tablet, or PC and its display screen that displays the results and feedback of the exercise program and nutrition plan to the user.

[0109] "Real-time capture" means capturing the user's movements in real time (with almost no delay) with a camera while they are exercising, and acquiring the data.

[0110] "Feedback" refers to information that provides the user with corrections and advice regarding incorrect movements or injury risks detected by the analysis means.

[0111] A "virtual trainer" is a character that virtually guides and supports users during training, and its appearance and personality can be customized.

[0112] The present invention is a system for providing personalized training and nutritional advice to individual users. Specific embodiments for carrying out the invention are described below.

[0113] System configuration

[0114] The system includes an input means for inputting a user's health information, a program generation means for generating a training and nutrition plan based on the user's health information, an analysis means for analyzing the user's exercise movements and providing feedback, a data analysis means using the generated AI model, and a display means for displaying the results to the user.

[0115] Input Method

[0116] Users use a smartphone, tablet, or computer to launch an application or web app and enter health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0117] Data transmission

[0118] The device sends the entered health information to a server via the Internet, using security protocols such as SSL / TLS.

[0119] Data storage and program generation

[0120] The server stores the received health information in a database (for example, Amazon Web Services (AWS) Relational Database Service (RDS)). Based on the stored information, the server performs data analysis using programming languages ​​such as Python and R to generate training programs and nutrition plans. This process also utilizes existing knowledge bases based on fitness theory and nutrition as well as external APIs.

[0121] View Plans

[0122] The server sends the generated training program and nutrition plan to the device, which displays the program and plan to the user using a dedicated native app or web app.

[0123] Real-time motion analysis

[0124] When a user exercises, they activate the camera function on their device (smartphone or tablet) to capture their own exercise movements. The captured video data is compressed in real time and sent to a server.

[0125] Feedback Generation

[0126] The server analyzes the received video data using AI models (for example, models built with TensorFlow or PyTorch) to detect incorrect movements or movements that pose a risk of injury. After the analysis is complete, the server generates feedback and sends corrections or advice to the device. The feedback can be in the form of text, graphics, animation, or audio.

[0127] View Feedback

[0128] The device provides real-time feedback to the user, allowing them to instantly see corrections they need to make during exercise and continue exercising with correct form.

[0129] Customization features

[0130] In addition, the device provides users with the option to customize the appearance and personality of their virtual trainer. Users can select their preferred appearance and personality using a dedicated UI, and this customization information is saved on the server. This feature helps users stay motivated during training.

[0131] Paid plans available

[0132] The server manages paid plans and provides detailed exercise analysis reports and advice from experts. Payments are made via common payment services such as Stripe and PayPal. Users can also earn revenue through affiliate partnerships, renting or purchasing exercise equipment and providing information on affiliated gyms.

[0133] Specific examples

[0134] For example, if a user selects a program to exercise three times a week to lose 5 kg, the server will follow these specific steps:

[0135] The server reads the user's health information from the database.

[0136] The server uses a Python script to analyze the data and prompt the generative AI model with statements like:

[0137] "Please suggest a three-times-a-week exercise program to lose 5 kg and a balanced nutrition plan."

[0138] The server receives the exercise program and nutrition plan returned by the AI ​​model and optimizes it for the user.

[0139] The server sends this plan to the device, which displays it to the user.

[0140] As users exercise, a camera captures their movements and they receive real-time feedback, which is expected to include specific advice such as "bend your knees more" or "straighten your spine."

[0141] As a result, this system can provide personalized support to individual users and achieve effective and safe training and nutritional guidance.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1: Enter your health information

[0144] Users use a smartphone, tablet, or PC to launch an application or web app and enter health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). The entered information is temporarily stored in the device's local storage, allowing specific health data to be collected.

[0145] Step 2: Send and store your health information

[0146] The device sends the entered health information to a server via the Internet. The communication protocol used is SSL / TLS, ensuring secure communication. The server then stores the received health information in a database such as Amazon Web Services' (AWS) Relational Database Service (RDS). This ensures that the user's health data is stored securely and can be used for subsequent data analysis.

[0147] Step 3: Create a training and nutrition plan

[0148] The server analyzes the user's health information stored in the database using programming languages ​​such as Python and R. In particular, it preprocesses the data using libraries such as Pandas and Numpy. At this stage, the server inputs prompts like the following into the generative AI model to generate a detailed exercise program and nutrition plan:

[0149] "Please suggest a three-times-a-week exercise program to lose 5 kg and a balanced nutrition plan."

[0150] Based on the input prompt, the generative AI model generates the optimal exercise program and nutrition plan for the user and outputs it to the server, thereby creating a personalized plan for each individual user.

[0151] Step 4: Deploy and view your plan

[0152] The server sends the generated training program and nutrition plan to the device. This communication is also via HTTPS protocol. The device displays the received plan to the user. The user can check the daily training and meal plan. This allows the user to take appropriate action based on the plan.

[0153] Step 5: Real-time behavior analysis

[0154] When a user starts training, the device's camera is used to capture their exercise movements. Specifically, the camera app is launched to take a video of the exercise, and the video data is sent to the server in real time. The transmitted data is compressed to optimize transmission efficiency. This allows the exercise movement data to be sent to the server in real time.

[0155] Step 6: Feedback generation

[0156] The server analyzes the received video data using an AI model (for example, a model built with TensorFlow or PyTorch). This analyzes the user's exercise movements frame by frame to detect incorrect movements or movements that pose a risk of injury. Based on the analysis results, the server generates specific feedback (e.g., "Bend your knees more" or "Straighten your spine"). This feedback is output in the form of voice, text, animation, etc., providing the user with useful correction information in real time.

[0157] Step 7: View your feedback

[0158] The device receives feedback from the server and displays it to the user in real time, including audio prompts, on-screen animations, or text messages, allowing the user to instantly identify any exercise form that needs correction and make appropriate adjustments.

[0159] Step 8: Use customization features

[0160] The device provides users with a UI that allows them to customize the appearance and personality of their virtual trainer. Using a dedicated UI, users select the appearance and personality of their virtual trainer and save that information. The saved customization information is sent to a server and recorded in a database. This allows users to train with their preferred trainer, making it easier to stay motivated.

[0161] Step 9: Offer paid plans and monetize

[0162] The server manages paid plans, which include detailed exercise analysis reports and advice from experts. Users can sign up for paid plans using a specific payment service (e.g., Stripe or PayPal). The server also monetizes through affiliate partnerships, providing users with information on rental and purchase of exercise equipment and affiliated gyms. This improves the sustainability and profitability of the entire system.

[0163] (Application example 1)

[0164] 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."

[0165] Conventional training and nutritional guidance systems have difficulty providing effective exercise and nutritional guidance because they are unable to fully reflect the individual health information of users. Additionally, the introduction of real-time feedback functions and customizable virtual trainers has been limited, making it difficult to motivate users and ensure their continued training.

[0166] 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.

[0167] In this invention, the server includes an input device for inputting a user's health information, a program generation device for generating an exercise program based on the user's health information, an analysis device for analyzing the user's exercise based on the exercise program and providing feedback, a program generation device for generating a nutrition plan based on the user's health information, a display device for displaying the results of the exercise program and nutrition plan, a real-time display device for displaying the feedback in real time, and a motion capture device for capturing and analyzing the user's movements during training in real time. This enables personalized fitness and nutritional guidance based on the user's individual health information, and provides accurate real-time motion analysis and feedback to improve the user's training effectiveness and safety. Furthermore, the ability to customize the virtual trainer's appearance and personality can increase the user's motivation and encourage them to continue training.

[0168] "User Health Information" means individual data about a User's health, such as the User's height, weight, goals, and lifestyle habits.

[0169] "Input means" refers to a device or interface that allows a user to input health information.

[0170] "Program generation means" refers to a device or software that has the function of generating an exercise program, nutrition plan, etc. based on the user's health information.

[0171] "Analysis means" refers to a device or software that has the function of analyzing a user's exercise based on an exercise program and providing feedback.

[0172] "Display Means" means a device or interface for displaying the results and feedback of an exercise program and nutrition plan to a user.

[0173] "Real-time display means" refers to a device or software that has the function of analyzing the user's exercise movements in real time and displaying the results to the user as immediate feedback.

[0174] "Movement capture means" refers to a device or software that records a user's movements while they are training, transmits the recorded movements to a server in real time, and analyzes them.

[0175] "Virtual Trainer" refers to a virtual instructor who supports users in their training and is a character whose appearance and personality can be customized.

[0176] The present invention is a system that provides personalized training and nutritional guidance to individual users. The system uses hardware such as smartphones, tablets, and cameras, and software such as cloud servers, AI models, and databases.

[0177] Collection of User's Health Information

[0178] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0179] The terminal transmits the input information to the server.

[0180] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[0181] Initial data analysis and program generation

[0182] The server then generates an optimal exercise and nutrition plan for the user based on the user's stored health information. This process utilizes historical data analysis, the latest fitness theories, and nutritional knowledge. The plan is then sent back to the device.

[0183] Training and nutrition plan delivery

[0184] The terminal displays the received plan to the user.

[0185] The user exercises according to the displayed training plan and eats according to the nutrition plan.

[0186] Real-time motion analysis

[0187] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0188] The terminal transmits the captured video data to the server in real time.

[0189] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0190] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[0191] The terminal displays real-time feedback to the user.

[0192] Customization features

[0193] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0194] Users can select and save their preferred appearance and personality.

[0195] This customization feature helps users increase motivation and encourage them to continue their training.

[0196] Paid Plans and Monetization

[0197] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and provides users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[0198] Program Overview

[0199] The entire system is built using programming languages ​​and libraries such as Python and OpenCV. Data is transmitted over secure protocols such as HTTPS, and frameworks such as TensorFlow and PyTorch are used for AI models. Analyzed data is fed back to the user in real time, supporting safe and effective training.

[0200] Specific examples and examples of prompts for generative AI models

[0201] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[0202] Example prompt for a generative AI model:

[0203] Please provide a training plan for 3 days a week to lose 5 kg. Generate the best plan based on the following health information: Height: 175 cm, Weight: 68 kg, Goal: Lose 5 kg, Lifestyle: Exercise 3 times a week.

[0204] The above is a specific embodiment of the present invention, which allows users to receive personalized training and nutritional guidance based on their individual health information, and achieve their goals effectively and safely with real-time feedback.

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] Entering and submitting user health information

[0208] A user launches the smartphone application and inputs health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). The input health information is sent by the device to a server, where the user's individual information is stored in a database and used as the basis for subsequent processing.

[0209] Input: User's health information (height, weight, goals, lifestyle habits)

[0210] Output: JSON format data containing health data is sent to the server.

[0211] Step 2:

[0212] Save your health information and generate your initial plan

[0213] The server stores the received health information in a database. It then generates an exercise program and nutrition plan based on the stored information. This program utilizes past data analysis, the latest fitness theories, and nutritional knowledge. The plan is then sent to the device.

[0214] Input: JSON format data containing user health information

[0215] Output: Generates exercise programs and nutrition plans

[0216] Step 3:

[0217] View exercise programs and nutrition plans

[0218] The terminal displays the exercise program and nutrition plan received from the server to the user, who then begins exercising and eating according to the plan.

[0219] Input: Exercise program and nutrition plan data

[0220] Output: Training and nutrition plan displayed on the user's smartphone screen

[0221] Step 4:

[0222] Capture and transmit athletic movements

[0223] When a user trains, their movements are captured using a smartphone camera, and the captured video data is sent to a server in real time.

[0224] Input: Video data captured by a smartphone camera

[0225] Output: Real-time video data sent to the server

[0226] Step 5:

[0227] Analyzing video data and generating feedback

[0228] The server analyzes the received video data using an AI model, detecting incorrect movements or movements that pose a risk of injury, and generates feedback based on the results, which is sent to the device.

[0229] Input: Real-time video data

[0230] Output: Feedback (corrections and advice for incorrect behavior)

[0231] Step 6:

[0232] View real-time feedback

[0233] The device receives feedback from the server and displays it to the user in real time, allowing the user to modify their movements based on the feedback and train safely and effectively.

[0234] Input: Feedback data

[0235] Output: Real-time feedback displayed on the user's smartphone screen

[0236] Step 7:

[0237] Customizing your virtual trainer

[0238] The device provides the user with the option to customize the appearance and personality of the virtual trainer. The user selects the appearance and personality of their choice and saves the virtual trainer settings.

[0239] Input: User customization settings

[0240] Output: Customized virtual trainer data

[0241] Step 8:

[0242] Manage and monetize paid plans

[0243] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and also provides information on related products and services, aiming to generate revenue through affiliate programs.

[0244] Input: User's paid plan contract information

[0245] Output: Detailed exercise analysis report, expert advice, and related product information

[0246] 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.

[0247] The present invention is a system that provides personalized training and nutritional guidance to individual users. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions and adjusts the training and nutrition plan based on those emotions, thereby providing more effective and tailored support to the user.

[0248] Collection of User's Health Information

[0249] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0250] The terminal transmits the input information to the server.

[0251] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[0252] Initial data analysis and program generation

[0253] The server generates a personalized training program and nutrition plan based on the user's stored health information, including historical data analysis, the latest fitness theories, and nutritional knowledge.

[0254] Training and nutrition plan delivery

[0255] The server transmits the generated training program and nutrition plan to the terminal.

[0256] The terminal displays the received plan to the user.

[0257] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[0258] Real-time motion analysis

[0259] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0260] The terminal transmits the captured video data to the server in real time.

[0261] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0262] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[0263] The terminal displays real-time feedback to the user.

[0264] Emotion recognition by emotion engine

[0265] The device collects the user's facial expression and voice data and sends it to the server.

[0266] The server analyzes the received data using an emotion engine to recognize the user's current emotional state (e.g., fatigue, motivation, stress).

[0267] Emotionally-driven plan adjustments

[0268] The server adjusts the training and nutrition plan based on the user's emotional state as determined by the emotion engine: for example, if the user is in a state of high stress, it may reduce the intensity of the training or suggest relaxation exercises.

[0269] The adjusted plan is sent back to the terminal and displayed to the user.

[0270] Customization features

[0271] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0272] Users can select and save their preferred appearance and personality.

[0273] This customization feature helps users increase motivation and encourage them to continue their training.

[0274] Paid Plans and Monetization

[0275] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and monetizes by providing users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[0276] Specific examples

[0277] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[0278] Furthermore, the system recognizes the user's emotional state based on their facial expressions and voice, and if they are feeling high levels of stress, it can flexibly adjust their training and nutrition plans, such as adding a relaxation menu.

[0279] The above is a specific embodiment of the present invention. This allows individual users to receive professional training and nutritional guidance at low cost, enabling them to achieve their goals effectively and safely. Furthermore, the introduction of an emotion engine makes it possible to support users mentally as well, achieving deeper personalization.

[0280] The processing flow will be explained below.

[0281] Step 1:

[0282] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0283] Step 2:

[0284] The terminal transmits the input health information to the server.

[0285] Step 3:

[0286] The server stores the received health information in a database and prepares to generate an initial training and nutrition plan.

[0287] Step 4:

[0288] The server analyzes the user's stored health information and generates the optimal training program and nutrition plan for the user.

[0289] Step 5:

[0290] The server transmits the generated training program and nutrition plan to the terminal.

[0291] Step 6:

[0292] The terminal displays the received training program and nutrition plan to the user.

[0293] Step 7:

[0294] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[0295] Step 8:

[0296] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0297] Step 9:

[0298] The terminal transmits the captured video data to the server in real time.

[0299] Step 10:

[0300] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0301] Step 11:

[0302] The server generates feedback based on the analysis results and sends corrections and advice to the device.

[0303] Step 12:

[0304] The terminal displays real-time feedback to the user.

[0305] Step 13:

[0306] The server analyzes the user's facial expression and voice data using an emotion engine to recognize their emotional state (e.g., fatigue, motivation, stress).

[0307] Step 14:

[0308] The server adjusts the training and nutrition plan based on the user's emotional state as determined by the emotion engine: for example, if the user is in a state of high stress, it may reduce the intensity of the training or suggest relaxation exercises.

[0309] Step 15:

[0310] The server transmits the tailored training and nutrition plan to the terminal.

[0311] Step 16:

[0312] The terminal displays the adjusted plan to the user.

[0313] Step 17:

[0314] The user follows the displayed adjustment plan and exercises and eats accordingly.

[0315] Step 18:

[0316] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0317] Step 19:

[0318] Users select their preferred appearance and personality and save their settings.

[0319] Step 20:

[0320] The server manages paid plans and provides detailed exercise analysis reports and expert advice.

[0321] Step 21:

[0322] The server aims to generate revenue by providing users with information on rental and purchase of exercise equipment and affiliated gyms through affiliate partnerships.

[0323] Example 2

[0324] 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."

[0325] Conventional training and nutritional guidance systems lack sufficient personalization to meet the individual needs of users and lack consideration of the user's emotional state, making it difficult to provide effective and safe programs. Furthermore, the lack of real-time motion analysis and adjustment of training and nutritional plans based on the user's emotional state raises concerns that user satisfaction and motivation may decrease.

[0326] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0327] In this invention, the server includes an input means for inputting a user's health information, a program generation means for generating an exercise program based on the user's health information, an analysis means for analyzing the user's exercise based on the exercise program and providing feedback, a program generation means for generating a nutrition plan based on the user's health information, a display means for displaying the results of the exercise program and the nutrition plan, an analysis means for collecting the user's facial expression data and voice data and analyzing their emotional state, and an adjustment means for adjusting the training and nutrition plan based on their emotional state. This enables more personalized training and nutritional guidance for each user and realizes flexible plan adjustments that take the user's emotional state into consideration.

[0328] "User's health information" refers to information such as height, weight, goals, and lifestyle habits that a user enters through the application.

[0329] An "input means" is an interface or device (e.g., smartphone, tablet, computer) through which a user inputs health information.

[0330] "Program generation means" refers to algorithms or software that automatically generate exercise programs and nutrition plans based on the user's health information.

[0331] "Analysis means" refers to a combination of hardware and software for analyzing a user's exercise in real time and providing feedback based on an exercise program.

[0332] "Display means" refers to a device or interface for displaying the generated exercise program, nutrition plan, and feedback results to the user.

[0333] "Analysis means" refers to technology that collects and analyzes a user's facial expression and voice data to recognize the user's emotional state.

[0334] The "adjustment means" is an algorithm or software for adjusting the training and nutrition plan based on the emotional state obtained by the analysis means.

[0335] The present invention is a system that provides users with personalized training and nutritional guidance. The system combines an emotion engine that recognizes the user's emotions and adjusts the training and nutrition plan based on those emotions to provide more effective and tailored support to the user.

[0336] First, the user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). This is done using a device such as a smartphone or tablet. The device then sends the entered information to the server via the HTTPS protocol. The server then stores this information in a database (e.g., MySQL or PostgreSQL).

[0337] The server then uses Python and R scripts to analyze the data based on the user's stored health information, incorporating historical data and the latest fitness and nutritional knowledge, and generates a training program and nutrition plan that is optimized for the user.

[0338] The generated training program and nutrition plan are sent to the device in JSON format from the server. The device parses this data and displays it on the application's UI. The user then trains according to the displayed plan and consumes meals based on the nutrition plan.

[0339] When a user exercises, the device's camera function is activated to capture the user's movements in real time. The captured video data is sent to a server in real time and analyzed using AI models such as TensorFlow and PyTorch. If an incorrect movement or risk of injury is detected, the server generates appropriate feedback and sends it to the device. The device then displays the feedback in real time, providing the user with specific corrections and advice.

[0340] The device also collects the user's facial expression and voice data and sends it to the server. The server then uses an emotion engine to analyze this data and recognize the user's emotional state (e.g., fatigue, motivation, stress). Based on the recognized emotional state, the server adjusts the training and nutrition plan, sends it back to the device, and displays it to the user. For example, if the user is in a high stress state, the server will reduce the intensity of the training or suggest relaxation exercises.

[0341] The device also provides users with the option to customize the appearance and personality of the virtual trainer. Users can select and save their preferred appearance and personality. This customization information is sent to the server and registered in a database. This allows future feedback and interactions to be tailored to the user's preferences.

[0342] The server also provides paid plan management functionality, offering detailed exercise analysis reports and the option to receive advice from experts. Users can easily upgrade to paid plans. The service also aims to generate revenue through affiliate partnerships, offering information on rental and purchase options for exercise equipment and affiliated gyms.

[0343] As a specific example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a meal plan that takes nutritional balance into account is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training while receiving real-time feedback. The system can also recognize the user's emotional state based on their facial expressions and voice, and flexibly adjust the training and nutrition plan, such as adding a relaxation menu if the user is feeling high stress.

[0344] Example prompt sentence:

[0345] "Please explain in detail how the system generates personalized training and nutrition plans based on users' input of health information such as height, weight, goals, and lifestyle habits, and combines real-time analysis and emotion recognition."

[0346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0347] Step 1:

[0348] The user launches the application and inputs health information such as height, weight, goals, and lifestyle habits. The device sends the input information to the server. Input data includes "height = 170 cm," "weight = 70 kg," "goal = lose 5 kg," and "lifestyle = exercise three times a week." The device sends the information to the server, which stores it in a database, making it ready for the next processing.

[0349] Step 2:

[0350] The server analyzes data based on the user's stored health information and generates a training program and nutrition plan. The input data is health information, and the output data is a personalized training program and nutrition plan. Specifically, it uses Python scripts and R scripts to analyze past data, the latest fitness theories, and nutritional knowledge. As a result, it generates recommendations such as "aerobic exercise = 30 minutes," "strength training = 20 minutes," and "daily calorie intake = 1800 kcal."

[0351] Step 3:

[0352] The server sends the generated training program and nutrition plan to the device. The device parses the received data and displays it on the application's UI. The input data is the generated program and plan, and the output data is the data in the format displayed to the user. Specifically, the device receives the JSON-formatted data, parses it, and displays it on the UI in the form of an "exercise menu," "meal plan," etc.

[0353] Step 4:

[0354] When a user exercises, they activate the camera function on their device to capture their movements. The input data is the captured video data, which is sent to the server in real time. The server receives the video data and analyzes it using AI models such as TensorFlow and PyTorch. Specific operations include "form check," "incorrect movement detection," and "injury risk analysis." The output data is corrections and advice, which are sent to the device and displayed to the user in real time.

[0355] Step 5:

[0356] The device collects the user's facial expression and voice data and sends it to the server. The input data is facial expression and voice data, which the server receives and analyzes using an emotion engine to obtain the user's emotional state as output data. Specific operations include "facial expression analysis" and "voice emotion analysis," and the device recognizes emotional states such as "fatigue," "motivation," and "stress."

[0357] Step 6:

[0358] The server adjusts the training and nutrition plan based on the recognized emotional state. The input data is the emotional state, and the output data is the adjusted plan. For example, if the user is in a high stress state, adjustments such as "reducing training intensity" or "adding relaxation exercises" will be made. The adjusted plan is then sent back to the device and displayed to the user.

[0359] Step 7:

[0360] The device provides options for users to customize the appearance and personality of their virtual trainer. Users select the appearance and personality of their favorite trainer through the UI, and the settings are sent from the device to the server. The input data is customization information, and the output data is setting information stored in the database. This allows future feedback and interactions to be tailored to the user's preferences.

[0361] Step 8:

[0362] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and also provides information on rental and purchase of exercise equipment and affiliate gyms through affiliate partnerships. The input data is the user's subscription status and affiliate information, and the output data is information on additional services and products. This allows for the expansion of services to users and monetization.

[0363] (Application example 2)

[0364] 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."

[0365] While existing systems provide exercise programs and nutritional plans based on users' health information, they are unable to provide feedback that takes into account the user's emotional state and work environment, resulting in low personalization accuracy and user satisfaction. Furthermore, there is a lack of systems specifically designed to manage the health of users working in factories, posing challenges for improving work efficiency and safety.

[0366] 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 an input means for inputting the user's health information, a program generation means for generating an exercise program based on the user's health information, an analysis means for analyzing the user's exercise and providing feedback, a program generation means for generating a nutrition plan based on the user's health information, a display means for displaying the results of the exercise program and the nutrition plan, an emotion recognition means for recognizing the user's emotional state, a means for adjusting the exercise program and the nutrition plan based on the emotion recognition means, and a means for monitoring the health information of users working in a factory in real time. This enables the health of users working in a factory to be managed in a highly personalized manner, improving work efficiency and safety.

[0367] "User's health information" refers to information related to the user's health, such as their height, weight, exercise habits, and diet.

[0368] An "exercise program" is an exercise plan generated based on the user's health information, and includes a training menu, intensity, and the like.

[0369] A "nutrition plan" is a nutrition plan generated based on the user's health information, and includes information such as meal content and calorie intake.

[0370] "Input means" refers to an interface for inputting the user's health information into the application, and includes a keyboard, touch screen, voice input, etc.

[0371] "Program generation means" refers to software or algorithms for generating exercise programs and nutrition plans based on input health information.

[0372] "Analysis means" refers to a system or software that captures the user's exercise movements, analyzes the movements, and provides feedback.

[0373] "Display means" refers to a device or interface for displaying the results of the generated exercise program or nutrition plan to the user, including a smartphone, head-mounted display, smart glasses, etc.

[0374] "Emotion recognition means" refers to a system or algorithm that analyzes a user's facial expression data and voice data to recognize the user's emotional state.

[0375] The "adjustment means" refers to an algorithm or system that appropriately adjusts the exercise program and nutrition plan based on the user's emotional state recognized by the emotion recognition means.

[0376] "Users working in factories" refers to operators and workers who work in the factory's working environment, and are the target of improving their health and work efficiency.

[0377] "Real-time monitoring means" refers to systems or devices that collect and analyze a user's health information in real time.

[0378] To implement the present invention, the following system configuration and program are required.

[0379] System Configuration

[0380] 1. Input Method

[0381] Users input health information (e.g., height, weight, exercise habits, and nutritional information) using a head-mounted display (HMD) or smart glasses, which can include keyboard, touchscreen, and voice input.

[0382] 2. Program Generation Method

[0383] The server generates the optimal exercise and nutritional plan for each individual user based on the health information entered by the user. The database contains past data, the latest fitness theories, and nutritional knowledge, and generates the program based on this information.

[0384] 3. Analysis method

[0385] When a user exercises, their movements are captured using the camera function of the HMD or smart glasses. The captured data is sent to a server in real time and analyzed using an AI model (e.g., TensorFlow / Keras). This detects incorrect movements or movements that pose a risk of injury.

[0386] 4. Display means

[0387] The generated exercise program and nutrition plan, as well as analytical feedback, are displayed on an HMD, smart glasses, smartphone, or tablet.

[0388] 5. Emotion recognition means

[0389] The user's facial expression and voice data are collected through a camera and microphone and sent to a server, which uses an emotion recognition algorithm to recognize the user's emotional state (e.g., fatigue, motivation, stress).

[0390] 6. Adjustment means

[0391] Based on the perceived emotional state, the server will adjust the exercise program and nutrition plan accordingly, for example reducing the intensity of training or suggesting relaxation exercises if the user is in a high stress state.

[0392] 7. Real-time monitoring methods

[0393] It includes a system for real-time monitoring of the health information and exercise status of users working in factories, which is expected to improve the health management and work efficiency of users working in factories.

[0394] Specific examples of hardware and software used

[0395] Hardware: HMD (head-mounted display), smart glasses, smartphone, tablet, camera, microphone

[0396] Software: TensorFlow / Keras (AI models), database management systems (e.g., Firebase, MySQL), software for real-time data analysis, emotion recognition algorithms

[0397] Specific examples

[0398] For example, if a user sets a goal of "losing 5 kg" and selects a program to exercise three times a week, the server will generate an appropriate training menu (e.g., aerobic exercise and strength training) and also suggest a nutritionally balanced meal plan. As the user exercises, the camera in the HMD or smart glasses captures the user's movements, and the server provides real-time feedback. Furthermore, the system recognizes the user's emotional state from their facial expressions and voice, and adjusts the program by, for example, adding relaxation options if fatigue increases.

[0399] Prompt Sentence Examples

[0400] "I would like to develop a healthcare application that captures the user's facial expressions, analyzes their emotions, and provides mental support. The user is an operator, and the application needs to detect fatigue in real time while working and suggest appropriate breaks and exercise plans. The technologies used include facial expression analysis using a camera and emotion recognition using machine learning. Please build such an application using Python and provide concrete examples of the necessary APIs and libraries."

[0401] The above is a detailed description of the embodiment of the present invention. This system enables highly personalized health management for users working in a factory, thereby improving efficiency and safety.

[0402] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0403] Step 1:

[0404] The user inputs health information (height, weight, exercise habits, nutritional information, etc.) using a head-mounted display (HMD) or smart glasses.

[0405] Input: User's health information

[0406] Output: Health information sent to the server

[0407] Specific operation: The user enters health information through a form on the HMD or smart glasses screen, and after completing the entry, the information is sent to the server.

[0408] Step 2:

[0409] The server generates a personalized exercise program and nutrition plan based on the user's input health information.

[0410] Input: User's health information submitted

[0411] Output: Generated exercise program and nutrition plan

[0412] How it works: The server pulls historical data, the latest fitness theories, and nutritional knowledge from a database, and uses algorithms to calculate the optimal exercise program and nutrition plan for the user.

[0413] Step 3:

[0414] The server transmits the generated exercise program and nutrition plan to the terminal.

[0415] Input: Generated exercise program and nutrition plan

[0416] Output: Exercise program and nutrition plan sent to the device

[0417] Specific operation: The generated exercise program and nutrition plan are sent to the terminal for display on the HMD or smart glasses.

[0418] Step 4:

[0419] The user exercises using an HMD or smart glasses. The device captures the user's movements in real time through a camera and transmits them to a server.

[0420] Input: Real-time video of the user's movements

[0421] Output: Real-time motion video sent to the server

[0422] Specific actions: The user starts exercising, and the device's camera captures the action and sends it to the server as video data.

[0423] Step 5:

[0424] The server analyzes the received real-time motion video using an AI model to detect incorrect movements and risk of injury, and generates feedback based on the analysis results and sends it to the device.

[0425] Input: Transmitted real-time motion video

[0426] Output: Analysis results and feedback

[0427] Specific operation: The server uses an AI model (e.g., TensorFlow / Keras) to analyze the motion video and detect incorrect movements or risk of injury. It then generates feedback based on the detection results.

[0428] Step 6:

[0429] The terminal displays the feedback sent from the server to the user in real time.

[0430] Input: Feedback from the server

[0431] Output: Displayed feedback

[0432] Specific operation: The device that receives the feedback displays it in real time on the HMD or smart glasses, providing the user with the correct exercise method and correction instructions.

[0433] Step 7:

[0434] The terminal collects the user's facial expression data and voice data and sends it to the server.

[0435] Input: User's facial expression data and voice data

[0436] Output: Facial expression data and voice data sent to the server

[0437] Specific operation: Uses a camera and microphone to collect the user's facial expressions and voice in real time.

[0438] Step 8:

[0439] The server performs emotion recognition using the transmitted facial expression data and voice data to recognize the user's current emotional state.

[0440] Input: facial expression data and voice data

[0441] Output: User's emotional state

[0442] Specific operation: The server uses an emotion recognition algorithm to analyze the transmitted data and recognize the user's emotional state (e.g., fatigue, motivation, stress).

[0443] Step 9:

[0444] The server adjusts the exercise program and nutrition plan accordingly based on the recognized emotional state and transmits them to the terminal.

[0445] Input: Perceived emotional state

[0446] Output: Tailored exercise program and nutrition plan

[0447] Specific operation: The server adjusts exercise programs and nutrition plans according to the emotional state, and takes measures such as suggesting relaxation exercises if the person is in a high-stress state.

[0448] Step 10:

[0449] The terminal displays the tailored exercise program and nutrition plan to the user.

[0450] Input: Tailored exercise program and nutrition plan

[0451] Output: Exercise program and nutrition plan with adjustments

[0452] Specific operation: The adjusted plan is displayed on the device, and the user can check the next exercise and meal plan they should do.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] [Second embodiment]

[0457] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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."

[0469] The present invention is a system for providing personalized training and nutritional advice to individual users. Specific embodiments of the system will be described below.

[0470] Collection of User's Health Information

[0471] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0472] The terminal transmits the input information to the server.

[0473] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[0474] Initial data analysis and program generation

[0475] The server generates an optimal exercise program and nutrition plan for the user based on the user's stored health information, including historical data analysis, the latest fitness theories, and nutritional knowledge.

[0476] Training and nutrition plan delivery

[0477] The server transmits the generated training program and nutrition plan to the terminal.

[0478] The terminal displays the received plan to the user.

[0479] The user exercises according to the displayed training plan and eats according to the nutrition plan.

[0480] Real-time motion analysis

[0481] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0482] The terminal transmits the captured video data to the server in real time.

[0483] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0484] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[0485] The terminal displays real-time feedback to the user.

[0486] Customization features

[0487] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0488] Users can select and save their preferred appearance and personality.

[0489] This customization feature helps users increase motivation and encourage them to continue their training.

[0490] Paid Plans and Monetization

[0491] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and monetizes by providing users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[0492] Specific examples

[0493] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[0494] The above is a specific embodiment of the present invention, which allows individual users to receive professional training and nutritional guidance at low cost, enabling them to achieve their goals effectively and safely.

[0495] The processing flow will be explained below.

[0496] Step 1:

[0497] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0498] Step 2:

[0499] The terminal transmits the input health information to the server.

[0500] Step 3:

[0501] The server stores the received health information in a database and prepares to generate an initial training and nutrition plan.

[0502] Step 4:

[0503] The server analyzes the user's stored health information and generates an optimal training program and nutrition plan for the user.

[0504] Step 5:

[0505] The server transmits the generated training program and nutrition plan to the terminal.

[0506] Step 6:

[0507] The terminal displays the received training program and nutrition plan to the user.

[0508] Step 7:

[0509] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[0510] Step 8:

[0511] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0512] Step 9:

[0513] The terminal transmits the captured video data to the server in real time.

[0514] Step 10:

[0515] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0516] Step 11:

[0517] The server generates feedback based on the analysis results and sends corrections and advice to the device.

[0518] Step 12:

[0519] The terminal displays real-time feedback to the user.

[0520] Step 13:

[0521] The user modifies the athletic performance according to the displayed feedback.

[0522] Step 14:

[0523] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0524] Step 15:

[0525] Users select their preferred appearance and personality and save their settings.

[0526] Step 16:

[0527] The server manages paid plans and provides detailed exercise analysis reports and expert advice.

[0528] Step 17:

[0529] The server aims to generate revenue by providing users with information on rental and purchase of exercise equipment and affiliated gyms through affiliate partnerships.

[0530] Example 1

[0531] 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."

[0532] Conventional training and nutritional guidance systems have difficulty providing personalized programs based on individual users' health information. Furthermore, delays in real-time exercise analysis and feedback can prevent users from maintaining proper exercise form, increasing the risk of injury. Furthermore, the lack of functionality for customizing the virtual trainer makes it difficult to maintain user motivation.

[0533] 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.

[0534] In this invention, the server includes an input device for inputting a user's health information, a program generation device for generating an exercise program and a nutrition plan based on the user's health information, an analysis device for analyzing the user's exercise based on the exercise program and providing feedback, a device for the analysis device to input prompts and perform data analysis using a generated AI model, and a display device for displaying the results of the exercise program and nutrition plan. This enables personalized training and nutritional guidance for each user, and provides accurate feedback in real time, resulting in safer and more effective exercise guidance. Furthermore, adding a virtual trainer customization function can maintain user motivation and improve training continuity.

[0535] "User health information" refers to information entered by the user, such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0536] "Input means" refers to devices such as smartphones, tablets, and computers that users use to input health information into the application, as well as dedicated apps or web apps.

[0537] "Program generation means" refers to a program, algorithm, or analysis system for generating an exercise program and nutrition plan based on the user's health information.

[0538] The "analysis means" is a means for analyzing the user's exercise movements based on the exercise program, detecting incorrect movements or movements that pose a risk of injury, and providing feedback.

[0539] A "generative AI model" is an artificial intelligence model (e.g., a model built with TensorFlow or PyTorch) that is trained based on large amounts of data and generates a response to a specific task when a prompt is input.

[0540] A "prompt" is a series of sentences or commands that are input to a generative AI model and contain instructions for completing a specific task.

[0541] "Display means" refers to a device such as a smartphone, tablet, or PC and its display screen that displays the results and feedback of the exercise program and nutrition plan to the user.

[0542] "Real-time capture" means capturing the user's movements in real time (with almost no delay) with a camera while they are exercising, and acquiring the data.

[0543] "Feedback" refers to information that provides the user with corrections and advice regarding incorrect movements or injury risks detected by the analysis means.

[0544] A "virtual trainer" is a character that virtually guides and supports users during training, and its appearance and personality can be customized.

[0545] The present invention is a system for providing personalized training and nutritional advice to individual users. Specific embodiments for carrying out the invention are described below.

[0546] System configuration

[0547] The system includes an input means for inputting a user's health information, a program generation means for generating a training and nutrition plan based on the user's health information, an analysis means for analyzing the user's exercise movements and providing feedback, a data analysis means using the generated AI model, and a display means for displaying the results to the user.

[0548] Input Method

[0549] Users use a smartphone, tablet, or computer to launch an application or web app and enter health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0550] Data transmission

[0551] The device sends the entered health information to a server via the Internet, using security protocols such as SSL / TLS.

[0552] Data storage and program generation

[0553] The server stores the received health information in a database (for example, Amazon Web Services (AWS) Relational Database Service (RDS)). Based on the stored information, the server performs data analysis using programming languages ​​such as Python and R to generate training programs and nutrition plans. This process also utilizes existing knowledge bases based on fitness theory and nutrition as well as external APIs.

[0554] View Plans

[0555] The server sends the generated training program and nutrition plan to the device, which displays the program and plan to the user using a dedicated native app or web app.

[0556] Real-time motion analysis

[0557] When a user exercises, they activate the camera function on their device (smartphone or tablet) to capture their own exercise movements. The captured video data is compressed in real time and sent to a server.

[0558] Feedback Generation

[0559] The server analyzes the received video data using AI models (for example, models built with TensorFlow or PyTorch) to detect incorrect movements or movements that pose a risk of injury. After the analysis is complete, the server generates feedback and sends corrections or advice to the device. The feedback can be in the form of text, graphics, animation, or audio.

[0560] View Feedback

[0561] The device provides real-time feedback to the user, allowing them to instantly see corrections they need to make during exercise and continue exercising with correct form.

[0562] Customization features

[0563] In addition, the device provides users with the option to customize the appearance and personality of their virtual trainer. Users can select their preferred appearance and personality using a dedicated UI, and this customization information is saved on the server. This feature helps users stay motivated during training.

[0564] Paid plans available

[0565] The server manages paid plans and provides detailed exercise analysis reports and advice from experts. Payments are made via common payment services such as Stripe and PayPal. Users can also earn revenue through affiliate partnerships, renting or purchasing exercise equipment and providing information on affiliated gyms.

[0566] Specific examples

[0567] For example, if a user selects a program to exercise three times a week to lose 5 kg, the server will follow these specific steps:

[0568] The server reads the user's health information from the database.

[0569] The server uses a Python script to analyze the data and prompt the generative AI model with statements like:

[0570] "Please suggest a three-times-a-week exercise program to lose 5 kg and a balanced nutrition plan."

[0571] The server receives the exercise program and nutrition plan returned by the AI ​​model and optimizes it for the user.

[0572] The server sends this plan to the device, which displays it to the user.

[0573] As users exercise, a camera captures their movements and they receive real-time feedback, which is expected to include specific advice such as "bend your knees more" or "straighten your spine."

[0574] As a result, this system can provide personalized support to individual users and achieve effective and safe training and nutritional guidance.

[0575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0576] Step 1: Enter your health information

[0577] Users use a smartphone, tablet, or PC to launch an application or web app and enter health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). The entered information is temporarily stored in the device's local storage, allowing specific health data to be collected.

[0578] Step 2: Send and store your health information

[0579] The device sends the entered health information to a server via the Internet. The communication protocol used is SSL / TLS, ensuring secure communication. The server then stores the received health information in a database such as Amazon Web Services' (AWS) Relational Database Service (RDS). This ensures that the user's health data is stored securely and can be used for subsequent data analysis.

[0580] Step 3: Create a training and nutrition plan

[0581] The server analyzes the user's health information stored in the database using programming languages ​​such as Python and R. In particular, it preprocesses the data using libraries such as Pandas and Numpy. At this stage, the server inputs prompts like the following into the generative AI model to generate a detailed exercise program and nutrition plan:

[0582] "Please suggest a three-times-a-week exercise program to lose 5 kg and a balanced nutrition plan."

[0583] Based on the input prompt, the generative AI model generates the optimal exercise program and nutrition plan for the user and outputs it to the server, thereby creating a personalized plan for each individual user.

[0584] Step 4: Deploy and view your plan

[0585] The server sends the generated training program and nutrition plan to the device. This communication is also via HTTPS protocol. The device displays the received plan to the user. The user can check the daily training and meal plan. This allows the user to take appropriate action based on the plan.

[0586] Step 5: Real-time behavior analysis

[0587] When a user starts training, the device's camera is used to capture their exercise movements. Specifically, the camera app is launched to take a video of the exercise, and the video data is sent to the server in real time. The transmitted data is compressed to optimize transmission efficiency. This allows the exercise movement data to be sent to the server in real time.

[0588] Step 6: Feedback generation

[0589] The server analyzes the received video data using an AI model (for example, a model built with TensorFlow or PyTorch). This analyzes the user's exercise movements frame by frame to detect incorrect movements or movements that pose a risk of injury. Based on the analysis results, the server generates specific feedback (e.g., "Bend your knees more" or "Straighten your spine"). This feedback is output in the form of voice, text, animation, etc., providing the user with useful correction information in real time.

[0590] Step 7: View your feedback

[0591] The device receives feedback from the server and displays it to the user in real time, including audio prompts, on-screen animations, or text messages, allowing the user to instantly identify any exercise form that needs correction and make appropriate adjustments.

[0592] Step 8: Use customization features

[0593] The device provides users with a UI that allows them to customize the appearance and personality of their virtual trainer. Using a dedicated UI, users select the appearance and personality of their virtual trainer and save that information. The saved customization information is sent to a server and recorded in a database. This allows users to train with their preferred trainer, making it easier to stay motivated.

[0594] Step 9: Offer paid plans and monetize

[0595] The server manages paid plans, which include detailed exercise analysis reports and advice from experts. Users can sign up for paid plans using a specific payment service (e.g., Stripe or PayPal). The server also monetizes through affiliate partnerships, providing users with information on rental and purchase of exercise equipment and affiliated gyms. This improves the sustainability and profitability of the entire system.

[0596] (Application example 1)

[0597] 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."

[0598] Conventional training and nutritional guidance systems have difficulty providing effective exercise and nutritional guidance because they are unable to fully reflect the individual health information of users. Additionally, the introduction of real-time feedback functions and customizable virtual trainers has been limited, making it difficult to motivate users and ensure their continued training.

[0599] 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.

[0600] In this invention, the server includes an input device for inputting a user's health information, a program generation device for generating an exercise program based on the user's health information, an analysis device for analyzing the user's exercise based on the exercise program and providing feedback, a program generation device for generating a nutrition plan based on the user's health information, a display device for displaying the results of the exercise program and nutrition plan, a real-time display device for displaying the feedback in real time, and a motion capture device for capturing and analyzing the user's movements during training in real time. This enables personalized fitness and nutritional guidance based on the user's individual health information, and provides accurate real-time motion analysis and feedback to improve the user's training effectiveness and safety. Furthermore, the ability to customize the virtual trainer's appearance and personality can increase the user's motivation and encourage them to continue training.

[0601] "User Health Information" means individual data about a User's health, such as the User's height, weight, goals, and lifestyle habits.

[0602] "Input means" refers to a device or interface that allows a user to input health information.

[0603] "Program generation means" refers to a device or software that has the function of generating an exercise program, nutrition plan, etc. based on the user's health information.

[0604] "Analysis means" refers to a device or software that has the function of analyzing a user's exercise based on an exercise program and providing feedback.

[0605] "Display Means" means a device or interface for displaying the results and feedback of an exercise program and nutrition plan to a user.

[0606] "Real-time display means" refers to a device or software that has the function of analyzing the user's exercise movements in real time and displaying the results to the user as immediate feedback.

[0607] "Movement capture means" refers to a device or software that records a user's movements while they are training, transmits the recorded movements to a server in real time, and analyzes them.

[0608] "Virtual Trainer" refers to a virtual instructor who supports users in their training and is a character whose appearance and personality can be customized.

[0609] The present invention is a system that provides personalized training and nutritional guidance to individual users. The system uses hardware such as smartphones, tablets, and cameras, and software such as cloud servers, AI models, and databases.

[0610] Collection of User's Health Information

[0611] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0612] The terminal transmits the input information to the server.

[0613] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[0614] Initial data analysis and program generation

[0615] The server then generates an optimal exercise and nutrition plan for the user based on the user's stored health information. This process utilizes historical data analysis, the latest fitness theories, and nutritional knowledge. The plan is then sent back to the device.

[0616] Training and nutrition plan delivery

[0617] The terminal displays the received plan to the user.

[0618] The user exercises according to the displayed training plan and eats according to the nutrition plan.

[0619] Real-time motion analysis

[0620] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0621] The terminal transmits the captured video data to the server in real time.

[0622] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0623] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[0624] The terminal displays real-time feedback to the user.

[0625] Customization features

[0626] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0627] Users can select and save their preferred appearance and personality.

[0628] This customization feature helps users increase motivation and encourage them to continue their training.

[0629] Paid Plans and Monetization

[0630] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and provides users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[0631] Program Overview

[0632] The entire system is built using programming languages ​​and libraries such as Python and OpenCV. Data is transmitted over secure protocols such as HTTPS, and frameworks such as TensorFlow and PyTorch are used for AI models. Analyzed data is fed back to the user in real time, supporting safe and effective training.

[0633] Specific examples and examples of prompts for generative AI models

[0634] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[0635] Example prompt for a generative AI model:

[0636] Please provide a training plan for 3 days a week to lose 5 kg. Generate the best plan based on the following health information: Height: 175 cm, Weight: 68 kg, Goal: Lose 5 kg, Lifestyle: Exercise 3 times a week.

[0637] The above is a specific embodiment of the present invention, which allows users to receive personalized training and nutritional guidance based on their individual health information, and achieve their goals effectively and safely with real-time feedback.

[0638] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0639] Step 1:

[0640] Entering and submitting user health information

[0641] A user launches the smartphone application and inputs health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). The input health information is sent by the device to a server, where the user's individual information is stored in a database and used as the basis for subsequent processing.

[0642] Input: User's health information (height, weight, goals, lifestyle habits)

[0643] Output: JSON format data containing health data is sent to the server.

[0644] Step 2:

[0645] Save your health information and generate your initial plan

[0646] The server stores the received health information in a database. It then generates an exercise program and nutrition plan based on the stored information. This program utilizes past data analysis, the latest fitness theories, and nutritional knowledge. The plan is then sent to the device.

[0647] Input: JSON format data containing user health information

[0648] Output: Generates exercise programs and nutrition plans

[0649] Step 3:

[0650] View exercise programs and nutrition plans

[0651] The terminal displays the exercise program and nutrition plan received from the server to the user, who then begins exercising and eating according to the plan.

[0652] Input: Exercise program and nutrition plan data

[0653] Output: Training and nutrition plan displayed on the user's smartphone screen

[0654] Step 4:

[0655] Capture and transmit athletic movements

[0656] When a user trains, their movements are captured using a smartphone camera, and the captured video data is sent to a server in real time.

[0657] Input: Video data captured by a smartphone camera

[0658] Output: Real-time video data sent to the server

[0659] Step 5:

[0660] Analyzing video data and generating feedback

[0661] The server analyzes the received video data using an AI model, detecting incorrect movements or movements that pose a risk of injury, and generates feedback based on the results, which is sent to the device.

[0662] Input: Real-time video data

[0663] Output: Feedback (corrections and advice for incorrect behavior)

[0664] Step 6:

[0665] View real-time feedback

[0666] The device receives feedback from the server and displays it to the user in real time, allowing the user to modify their movements based on the feedback and train safely and effectively.

[0667] Input: Feedback data

[0668] Output: Real-time feedback displayed on the user's smartphone screen

[0669] Step 7:

[0670] Customizing your virtual trainer

[0671] The device provides the user with the option to customize the appearance and personality of the virtual trainer. The user selects the appearance and personality of their choice and saves the virtual trainer settings.

[0672] Input: User customization settings

[0673] Output: Customized virtual trainer data

[0674] Step 8:

[0675] Manage and monetize paid plans

[0676] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and also provides information on related products and services, aiming to generate revenue through affiliate programs.

[0677] Input: User's paid plan contract information

[0678] Output: Detailed exercise analysis report, expert advice, and related product information

[0679] 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.

[0680] The present invention is a system that provides personalized training and nutritional guidance to individual users. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions and adjusts the training and nutrition plan based on those emotions, thereby providing more effective and tailored support to the user.

[0681] Collection of User's Health Information

[0682] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0683] The terminal transmits the input information to the server.

[0684] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[0685] Initial data analysis and program generation

[0686] The server generates a personalized training program and nutrition plan based on the user's stored health information, including historical data analysis, the latest fitness theories, and nutritional knowledge.

[0687] Training and nutrition plan delivery

[0688] The server transmits the generated training program and nutrition plan to the terminal.

[0689] The terminal displays the received plan to the user.

[0690] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[0691] Real-time motion analysis

[0692] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0693] The terminal transmits the captured video data to the server in real time.

[0694] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0695] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[0696] The terminal displays real-time feedback to the user.

[0697] Emotion recognition by emotion engine

[0698] The device collects the user's facial expression and voice data and sends it to the server.

[0699] The server analyzes the received data using an emotion engine to recognize the user's current emotional state (e.g., fatigue, motivation, stress).

[0700] Emotionally-driven plan adjustments

[0701] The server adjusts the training and nutrition plan based on the user's emotional state as determined by the emotion engine: for example, if the user is in a state of high stress, it may reduce the intensity of the training or suggest relaxation exercises.

[0702] The adjusted plan is sent back to the terminal and displayed to the user.

[0703] Customization features

[0704] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0705] Users can select and save their preferred appearance and personality.

[0706] This customization feature helps users increase motivation and encourage them to continue their training.

[0707] Paid Plans and Monetization

[0708] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and monetizes by providing users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[0709] Specific examples

[0710] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[0711] Furthermore, the system recognizes the user's emotional state based on their facial expressions and voice, and if they are feeling high levels of stress, it can flexibly adjust their training and nutrition plans, such as adding a relaxation menu.

[0712] The above is a specific embodiment of the present invention. This allows individual users to receive professional training and nutritional guidance at low cost, enabling them to achieve their goals effectively and safely. Furthermore, the introduction of an emotion engine makes it possible to support users mentally as well, achieving deeper personalization.

[0713] The processing flow will be explained below.

[0714] Step 1:

[0715] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0716] Step 2:

[0717] The terminal transmits the input health information to the server.

[0718] Step 3:

[0719] The server stores the received health information in a database and prepares to generate an initial training and nutrition plan.

[0720] Step 4:

[0721] The server analyzes the user's stored health information and generates the optimal training program and nutrition plan for the user.

[0722] Step 5:

[0723] The server transmits the generated training program and nutrition plan to the terminal.

[0724] Step 6:

[0725] The terminal displays the received training program and nutrition plan to the user.

[0726] Step 7:

[0727] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[0728] Step 8:

[0729] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0730] Step 9:

[0731] The terminal transmits the captured video data to the server in real time.

[0732] Step 10:

[0733] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0734] Step 11:

[0735] The server generates feedback based on the analysis results and sends corrections and advice to the device.

[0736] Step 12:

[0737] The terminal displays real-time feedback to the user.

[0738] Step 13:

[0739] The server analyzes the user's facial expression and voice data using an emotion engine to recognize their emotional state (e.g., fatigue, motivation, stress).

[0740] Step 14:

[0741] The server adjusts the training and nutrition plan based on the user's emotional state as determined by the emotion engine: for example, if the user is in a state of high stress, it may reduce the intensity of the training or suggest relaxation exercises.

[0742] Step 15:

[0743] The server transmits the tailored training and nutrition plan to the terminal.

[0744] Step 16:

[0745] The terminal displays the adjusted plan to the user.

[0746] Step 17:

[0747] The user follows the displayed adjustment plan and exercises and eats accordingly.

[0748] Step 18:

[0749] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0750] Step 19:

[0751] Users select their preferred appearance and personality and save their settings.

[0752] Step 20:

[0753] The server manages paid plans and provides detailed exercise analysis reports and expert advice.

[0754] Step 21:

[0755] The server aims to generate revenue by providing users with information on rental and purchase of exercise equipment and affiliated gyms through affiliate partnerships.

[0756] Example 2

[0757] 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."

[0758] Conventional training and nutritional guidance systems lack sufficient personalization to meet the individual needs of users and lack consideration of the user's emotional state, making it difficult to provide effective and safe programs. Furthermore, the lack of real-time motion analysis and adjustment of training and nutritional plans based on the user's emotional state raises concerns that user satisfaction and motivation may decrease.

[0759] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0760] In this invention, the server includes an input means for inputting a user's health information, a program generation means for generating an exercise program based on the user's health information, an analysis means for analyzing the user's exercise based on the exercise program and providing feedback, a program generation means for generating a nutrition plan based on the user's health information, a display means for displaying the results of the exercise program and the nutrition plan, an analysis means for collecting the user's facial expression data and voice data and analyzing their emotional state, and an adjustment means for adjusting the training and nutrition plan based on their emotional state. This enables more personalized training and nutritional guidance for each user and realizes flexible plan adjustments that take the user's emotional state into consideration.

[0761] "User's health information" refers to information such as height, weight, goals, and lifestyle habits that a user enters through the application.

[0762] An "input means" is an interface or device (e.g., smartphone, tablet, computer) through which a user inputs health information.

[0763] "Program generation means" refers to algorithms or software that automatically generate exercise programs and nutrition plans based on the user's health information.

[0764] "Analysis means" refers to a combination of hardware and software for analyzing a user's exercise in real time and providing feedback based on an exercise program.

[0765] "Display means" refers to a device or interface for displaying the generated exercise program, nutrition plan, and feedback results to the user.

[0766] "Analysis means" refers to technology that collects and analyzes a user's facial expression and voice data to recognize the user's emotional state.

[0767] The "adjustment means" is an algorithm or software for adjusting the training and nutrition plan based on the emotional state obtained by the analysis means.

[0768] The present invention is a system that provides users with personalized training and nutritional guidance. The system combines an emotion engine that recognizes the user's emotions and adjusts the training and nutrition plan based on those emotions to provide more effective and tailored support to the user.

[0769] First, the user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). This is done using a device such as a smartphone or tablet. The device then sends the entered information to the server via the HTTPS protocol. The server then stores this information in a database (e.g., MySQL or PostgreSQL).

[0770] The server then uses Python and R scripts to analyze the data based on the user's stored health information, incorporating historical data and the latest fitness and nutritional knowledge, and generates a training program and nutrition plan that is optimized for the user.

[0771] The generated training program and nutrition plan are sent to the device in JSON format from the server. The device parses this data and displays it on the application's UI. The user then trains according to the displayed plan and consumes meals based on the nutrition plan.

[0772] When a user exercises, the device's camera function is activated to capture the user's movements in real time. The captured video data is sent to a server in real time and analyzed using AI models such as TensorFlow and PyTorch. If an incorrect movement or risk of injury is detected, the server generates appropriate feedback and sends it to the device. The device then displays the feedback in real time, providing the user with specific corrections and advice.

[0773] The device also collects the user's facial expression and voice data and sends it to the server. The server then uses an emotion engine to analyze this data and recognize the user's emotional state (e.g., fatigue, motivation, stress). Based on the recognized emotional state, the server adjusts the training and nutrition plan, sends it back to the device, and displays it to the user. For example, if the user is in a high stress state, the server will reduce the intensity of the training or suggest relaxation exercises.

[0774] The device also provides users with the option to customize the appearance and personality of the virtual trainer. Users can select and save their preferred appearance and personality. This customization information is sent to the server and registered in a database. This allows future feedback and interactions to be tailored to the user's preferences.

[0775] The server also provides paid plan management functionality, offering detailed exercise analysis reports and the option to receive advice from experts. Users can easily upgrade to paid plans. The service also aims to generate revenue through affiliate partnerships, offering information on rental and purchase options for exercise equipment and affiliated gyms.

[0776] As a specific example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a meal plan that takes nutritional balance into account is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training while receiving real-time feedback. The system can also recognize the user's emotional state based on their facial expressions and voice, and flexibly adjust the training and nutrition plan, such as adding a relaxation menu if the user is feeling high stress.

[0777] Example prompt sentence:

[0778] "Please explain in detail how the system generates personalized training and nutrition plans based on users' input of health information such as height, weight, goals, and lifestyle habits, and combines real-time analysis and emotion recognition."

[0779] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0780] Step 1:

[0781] The user launches the application and inputs health information such as height, weight, goals, and lifestyle habits. The device sends the input information to the server. Input data includes "height = 170 cm," "weight = 70 kg," "goal = lose 5 kg," and "lifestyle = exercise three times a week." The device sends the information to the server, which stores it in a database, making it ready for the next processing.

[0782] Step 2:

[0783] The server analyzes data based on the user's stored health information and generates a training program and nutrition plan. The input data is health information, and the output data is a personalized training program and nutrition plan. Specifically, it uses Python scripts and R scripts to analyze past data, the latest fitness theories, and nutritional knowledge. As a result, it generates recommendations such as "aerobic exercise = 30 minutes," "strength training = 20 minutes," and "daily calorie intake = 1800 kcal."

[0784] Step 3:

[0785] The server sends the generated training program and nutrition plan to the device. The device parses the received data and displays it on the application's UI. The input data is the generated program and plan, and the output data is the data in the format displayed to the user. Specifically, the device receives the JSON-formatted data, parses it, and displays it on the UI in the form of an "exercise menu," "meal plan," etc.

[0786] Step 4:

[0787] When a user exercises, they activate the camera function on their device to capture their movements. The input data is the captured video data, which is sent to the server in real time. The server receives the video data and analyzes it using AI models such as TensorFlow and PyTorch. Specific operations include "form check," "incorrect movement detection," and "injury risk analysis." The output data is corrections and advice, which are sent to the device and displayed to the user in real time.

[0788] Step 5:

[0789] The device collects the user's facial expression and voice data and sends it to the server. The input data is facial expression and voice data, which the server receives and analyzes using an emotion engine to obtain the user's emotional state as output data. Specific operations include "facial expression analysis" and "voice emotion analysis," and the device recognizes emotional states such as "fatigue," "motivation," and "stress."

[0790] Step 6:

[0791] The server adjusts the training and nutrition plan based on the recognized emotional state. The input data is the emotional state, and the output data is the adjusted plan. For example, if the user is in a high stress state, adjustments such as "reducing training intensity" or "adding relaxation exercises" will be made. The adjusted plan is then sent back to the device and displayed to the user.

[0792] Step 7:

[0793] The device provides options for users to customize the appearance and personality of their virtual trainer. Users select the appearance and personality of their favorite trainer through the UI, and the settings are sent from the device to the server. The input data is customization information, and the output data is setting information stored in the database. This allows future feedback and interactions to be tailored to the user's preferences.

[0794] Step 8:

[0795] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and also provides information on rental and purchase of exercise equipment and affiliate gyms through affiliate partnerships. The input data is the user's subscription status and affiliate information, and the output data is information on additional services and products. This allows for the expansion of services to users and monetization.

[0796] (Application example 2)

[0797] 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."

[0798] While existing systems provide exercise programs and nutritional plans based on users' health information, they are unable to provide feedback that takes into account the user's emotional state and work environment, resulting in low personalization accuracy and user satisfaction. Furthermore, there is a lack of systems specifically designed to manage the health of users working in factories, posing challenges for improving work efficiency and safety.

[0799] 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 an input means for inputting the user's health information, a program generation means for generating an exercise program based on the user's health information, an analysis means for analyzing the user's exercise and providing feedback, a program generation means for generating a nutrition plan based on the user's health information, a display means for displaying the results of the exercise program and the nutrition plan, an emotion recognition means for recognizing the user's emotional state, a means for adjusting the exercise program and the nutrition plan based on the emotion recognition means, and a means for monitoring the health information of users working in a factory in real time. This enables the health of users working in a factory to be managed in a highly personalized manner, improving work efficiency and safety.

[0800] "User's health information" refers to information related to the user's health, such as their height, weight, exercise habits, and diet.

[0801] An "exercise program" is an exercise plan generated based on the user's health information, and includes a training menu, intensity, and the like.

[0802] A "nutrition plan" is a nutrition plan generated based on the user's health information, and includes information such as meal content and calorie intake.

[0803] "Input means" refers to an interface for inputting the user's health information into the application, and includes a keyboard, touch screen, voice input, etc.

[0804] "Program generation means" refers to software or algorithms for generating exercise programs and nutrition plans based on input health information.

[0805] "Analysis means" refers to a system or software that captures the user's exercise movements, analyzes the movements, and provides feedback.

[0806] "Display means" refers to a device or interface for displaying the results of the generated exercise program or nutrition plan to the user, including a smartphone, head-mounted display, smart glasses, etc.

[0807] "Emotion recognition means" refers to a system or algorithm that analyzes a user's facial expression data and voice data to recognize the user's emotional state.

[0808] The "adjustment means" refers to an algorithm or system that appropriately adjusts the exercise program and nutrition plan based on the user's emotional state recognized by the emotion recognition means.

[0809] "Users working in factories" refers to operators and workers who work in the factory's working environment, and are the target of improving their health and work efficiency.

[0810] "Real-time monitoring means" refers to systems or devices that collect and analyze a user's health information in real time.

[0811] To implement the present invention, the following system configuration and program are required.

[0812] System Configuration

[0813] 1. Input Method

[0814] Users input health information (e.g., height, weight, exercise habits, and nutritional information) using a head-mounted display (HMD) or smart glasses, which can include keyboard, touchscreen, and voice input.

[0815] 2. Program Generation Method

[0816] The server generates the optimal exercise and nutritional plan for each individual user based on the health information entered by the user. The database contains past data, the latest fitness theories, and nutritional knowledge, and generates the program based on this information.

[0817] 3. Analysis method

[0818] When a user exercises, their movements are captured using the camera function of the HMD or smart glasses. The captured data is sent to a server in real time and analyzed using an AI model (e.g., TensorFlow / Keras). This detects incorrect movements or movements that pose a risk of injury.

[0819] 4. Display means

[0820] The generated exercise program and nutrition plan, as well as analytical feedback, are displayed on an HMD, smart glasses, smartphone, or tablet.

[0821] 5. Emotion recognition means

[0822] The user's facial expression and voice data are collected through a camera and microphone and sent to a server, which uses an emotion recognition algorithm to recognize the user's emotional state (e.g., fatigue, motivation, stress).

[0823] 6. Adjustment means

[0824] Based on the perceived emotional state, the server will adjust the exercise program and nutrition plan accordingly, for example reducing the intensity of training or suggesting relaxation exercises if the user is in a high stress state.

[0825] 7. Real-time monitoring methods

[0826] It includes a system for real-time monitoring of the health information and exercise status of users working in factories, which is expected to improve the health management and work efficiency of users working in factories.

[0827] Specific examples of hardware and software used

[0828] Hardware: HMD (head-mounted display), smart glasses, smartphone, tablet, camera, microphone

[0829] Software: TensorFlow / Keras (AI models), database management systems (e.g., Firebase, MySQL), software for real-time data analysis, emotion recognition algorithms

[0830] Specific examples

[0831] For example, if a user sets a goal of "losing 5 kg" and selects a program to exercise three times a week, the server will generate an appropriate training menu (e.g., aerobic exercise and strength training) and also suggest a nutritionally balanced meal plan. As the user exercises, the camera in the HMD or smart glasses captures the user's movements, and the server provides real-time feedback. Furthermore, the system recognizes the user's emotional state from their facial expressions and voice, and adjusts the program by, for example, adding relaxation options if fatigue increases.

[0832] Prompt Sentence Examples

[0833] "I would like to develop a healthcare application that captures the user's facial expressions, analyzes their emotions, and provides mental support. The user is an operator, and the application needs to detect fatigue in real time while working and suggest appropriate breaks and exercise plans. The technologies used include facial expression analysis using a camera and emotion recognition using machine learning. Please build such an application using Python and provide concrete examples of the necessary APIs and libraries."

[0834] The above is a detailed description of the embodiment of the present invention. This system enables highly personalized health management for users working in a factory, thereby improving efficiency and safety.

[0835] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0836] Step 1:

[0837] The user inputs health information (height, weight, exercise habits, nutritional information, etc.) using a head-mounted display (HMD) or smart glasses.

[0838] Input: User's health information

[0839] Output: Health information sent to the server

[0840] Specific operation: The user enters health information through a form on the HMD or smart glasses screen, and after completing the entry, the information is sent to the server.

[0841] Step 2:

[0842] The server generates a personalized exercise program and nutrition plan based on the user's input health information.

[0843] Input: User's health information submitted

[0844] Output: Generated exercise program and nutrition plan

[0845] How it works: The server pulls historical data, the latest fitness theories, and nutritional knowledge from a database, and uses algorithms to calculate the optimal exercise program and nutrition plan for the user.

[0846] Step 3:

[0847] The server transmits the generated exercise program and nutrition plan to the terminal.

[0848] Input: Generated exercise program and nutrition plan

[0849] Output: Exercise program and nutrition plan sent to the device

[0850] Specific operation: The generated exercise program and nutrition plan are sent to the terminal for display on the HMD or smart glasses.

[0851] Step 4:

[0852] The user exercises using an HMD or smart glasses. The device captures the user's movements in real time through a camera and transmits them to a server.

[0853] Input: Real-time video of the user's movements

[0854] Output: Real-time motion video sent to the server

[0855] Specific actions: The user starts exercising, and the device's camera captures the action and sends it to the server as video data.

[0856] Step 5:

[0857] The server analyzes the received real-time motion video using an AI model to detect incorrect movements and risk of injury, and generates feedback based on the analysis results and sends it to the device.

[0858] Input: Transmitted real-time motion video

[0859] Output: Analysis results and feedback

[0860] Specific operation: The server uses an AI model (e.g., TensorFlow / Keras) to analyze the motion video and detect incorrect movements or risk of injury. It then generates feedback based on the detection results.

[0861] Step 6:

[0862] The terminal displays the feedback sent from the server to the user in real time.

[0863] Input: Feedback from the server

[0864] Output: Displayed feedback

[0865] Specific operation: The device that receives the feedback displays it in real time on the HMD or smart glasses, providing the user with the correct exercise method and correction instructions.

[0866] Step 7:

[0867] The terminal collects the user's facial expression data and voice data and sends it to the server.

[0868] Input: User's facial expression data and voice data

[0869] Output: Facial expression data and voice data sent to the server

[0870] Specific operation: Uses a camera and microphone to collect the user's facial expressions and voice in real time.

[0871] Step 8:

[0872] The server performs emotion recognition using the transmitted facial expression data and voice data to recognize the user's current emotional state.

[0873] Input: facial expression data and voice data

[0874] Output: User's emotional state

[0875] Specific operation: The server uses an emotion recognition algorithm to analyze the transmitted data and recognize the user's emotional state (e.g., fatigue, motivation, stress).

[0876] Step 9:

[0877] The server adjusts the exercise program and nutrition plan accordingly based on the recognized emotional state and transmits them to the terminal.

[0878] Input: Perceived emotional state

[0879] Output: Tailored exercise program and nutrition plan

[0880] Specific operation: The server adjusts exercise programs and nutrition plans according to the emotional state, and takes measures such as suggesting relaxation exercises if the person is in a high-stress state.

[0881] Step 10:

[0882] The terminal displays the tailored exercise program and nutrition plan to the user.

[0883] Input: Tailored exercise program and nutrition plan

[0884] Output: Exercise program and nutrition plan with adjustments

[0885] Specific operation: The adjusted plan is displayed on the device, and the user can check the next exercise and meal plan they should do.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] [Third embodiment]

[0890] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0891] 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.

[0892] 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).

[0893] 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.

[0894] 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.

[0895] 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).

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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."

[0902] The present invention is a system for providing personalized training and nutritional advice to individual users. Specific embodiments of the system will be described below.

[0903] Collection of User's Health Information

[0904] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0905] The terminal transmits the input information to the server.

[0906] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[0907] Initial data analysis and program generation

[0908] The server generates an optimal exercise program and nutrition plan for the user based on the user's stored health information, including historical data analysis, the latest fitness theories, and nutritional knowledge.

[0909] Training and nutrition plan delivery

[0910] The server transmits the generated training program and nutrition plan to the terminal.

[0911] The terminal displays the received plan to the user.

[0912] The user exercises according to the displayed training plan and eats according to the nutrition plan.

[0913] Real-time motion analysis

[0914] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0915] The terminal transmits the captured video data to the server in real time.

[0916] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0917] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[0918] The terminal displays real-time feedback to the user.

[0919] Customization features

[0920] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0921] Users can select and save their preferred appearance and personality.

[0922] This customization feature helps users increase motivation and encourage them to continue their training.

[0923] Paid Plans and Monetization

[0924] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and monetizes by providing users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[0925] Specific examples

[0926] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[0927] The above is a specific embodiment of the present invention, which allows individual users to receive professional training and nutritional guidance at low cost, enabling them to achieve their goals effectively and safely.

[0928] The processing flow will be explained below.

[0929] Step 1:

[0930] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0931] Step 2:

[0932] The terminal transmits the input health information to the server.

[0933] Step 3:

[0934] The server stores the received health information in a database and prepares to generate an initial training and nutrition plan.

[0935] Step 4:

[0936] The server analyzes the user's stored health information and generates an optimal training program and nutrition plan for the user.

[0937] Step 5:

[0938] The server transmits the generated training program and nutrition plan to the terminal.

[0939] Step 6:

[0940] The terminal displays the received training program and nutrition plan to the user.

[0941] Step 7:

[0942] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[0943] Step 8:

[0944] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[0945] Step 9:

[0946] The terminal transmits the captured video data to the server in real time.

[0947] Step 10:

[0948] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[0949] Step 11:

[0950] The server generates feedback based on the analysis results and sends corrections and advice to the device.

[0951] Step 12:

[0952] The terminal displays real-time feedback to the user.

[0953] Step 13:

[0954] The user modifies the athletic performance according to the displayed feedback.

[0955] Step 14:

[0956] The device provides options for users to customize the appearance and personality of their virtual trainer.

[0957] Step 15:

[0958] Users select their preferred appearance and personality and save their settings.

[0959] Step 16:

[0960] The server manages paid plans and provides detailed exercise analysis reports and expert advice.

[0961] Step 17:

[0962] The server aims to generate revenue by providing users with information on rental and purchase of exercise equipment and affiliated gyms through affiliate partnerships.

[0963] Example 1

[0964] 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."

[0965] Conventional training and nutritional guidance systems have difficulty providing personalized programs based on individual users' health information. Furthermore, delays in real-time exercise analysis and feedback can prevent users from maintaining proper exercise form, increasing the risk of injury. Furthermore, the lack of functionality for customizing the virtual trainer makes it difficult to maintain user motivation.

[0966] 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.

[0967] In this invention, the server includes an input device for inputting a user's health information, a program generation device for generating an exercise program and a nutrition plan based on the user's health information, an analysis device for analyzing the user's exercise based on the exercise program and providing feedback, a device for the analysis device to input prompts and perform data analysis using a generated AI model, and a display device for displaying the results of the exercise program and nutrition plan. This enables personalized training and nutritional guidance for each user, and provides accurate feedback in real time, resulting in safer and more effective exercise guidance. Furthermore, adding a virtual trainer customization function can maintain user motivation and improve training continuity.

[0968] "User health information" refers to information entered by the user, such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0969] "Input means" refers to devices such as smartphones, tablets, and computers that users use to input health information into the application, as well as dedicated apps or web apps.

[0970] "Program generation means" refers to a program, algorithm, or analysis system for generating an exercise program and nutrition plan based on the user's health information.

[0971] The "analysis means" is a means for analyzing the user's exercise movements based on the exercise program, detecting incorrect movements or movements that pose a risk of injury, and providing feedback.

[0972] A "generative AI model" is an artificial intelligence model (e.g., a model built with TensorFlow or PyTorch) that is trained based on large amounts of data and generates a response to a specific task when a prompt is input.

[0973] A "prompt" is a series of sentences or commands that are input to a generative AI model and contain instructions for completing a specific task.

[0974] "Display means" refers to a device such as a smartphone, tablet, or PC and its display screen that displays the results and feedback of the exercise program and nutrition plan to the user.

[0975] "Real-time capture" means capturing the user's movements in real time (with almost no delay) with a camera while they are exercising, and acquiring the data.

[0976] "Feedback" refers to information that provides the user with corrections and advice regarding incorrect movements or injury risks detected by the analysis means.

[0977] A "virtual trainer" is a character that virtually guides and supports users during training, and its appearance and personality can be customized.

[0978] The present invention is a system for providing personalized training and nutritional advice to individual users. Specific embodiments for carrying out the invention are described below.

[0979] System configuration

[0980] The system includes an input means for inputting a user's health information, a program generation means for generating a training and nutrition plan based on the user's health information, an analysis means for analyzing the user's exercise movements and providing feedback, a data analysis means using the generated AI model, and a display means for displaying the results to the user.

[0981] Input Method

[0982] Users use a smartphone, tablet, or computer to launch an application or web app and enter health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[0983] Data transmission

[0984] The device sends the entered health information to a server via the Internet, using security protocols such as SSL / TLS.

[0985] Data storage and program generation

[0986] The server stores the received health information in a database (for example, Amazon Web Services (AWS) Relational Database Service (RDS)). Based on the stored information, the server performs data analysis using programming languages ​​such as Python and R to generate training programs and nutrition plans. This process also utilizes existing knowledge bases based on fitness theory and nutrition as well as external APIs.

[0987] View Plans

[0988] The server sends the generated training program and nutrition plan to the device, which displays the program and plan to the user using a dedicated native app or web app.

[0989] Real-time motion analysis

[0990] When a user exercises, they activate the camera function on their device (smartphone or tablet) to capture their own exercise movements. The captured video data is compressed in real time and sent to a server.

[0991] Feedback Generation

[0992] The server analyzes the received video data using AI models (for example, models built with TensorFlow or PyTorch) to detect incorrect movements or movements that pose a risk of injury. After the analysis is complete, the server generates feedback and sends corrections or advice to the device. The feedback can be in the form of text, graphics, animation, or audio.

[0993] View Feedback

[0994] The device provides real-time feedback to the user, allowing them to instantly see corrections they need to make during exercise and continue exercising with correct form.

[0995] Customization features

[0996] In addition, the device provides users with the option to customize the appearance and personality of their virtual trainer. Users can select their preferred appearance and personality using a dedicated UI, and this customization information is saved on the server. This feature helps users stay motivated during training.

[0997] Paid plans available

[0998] The server manages paid plans and provides detailed exercise analysis reports and advice from experts. Payments are made via common payment services such as Stripe and PayPal. Users can also earn revenue through affiliate partnerships, renting or purchasing exercise equipment and providing information on affiliated gyms.

[0999] Specific examples

[1000] For example, if a user selects a program to exercise three times a week to lose 5 kg, the server will follow these specific steps:

[1001] The server reads the user's health information from the database.

[1002] The server uses a Python script to analyze the data and prompt the generative AI model with statements like:

[1003] "Please suggest a three-times-a-week exercise program to lose 5 kg and a balanced nutrition plan."

[1004] The server receives the exercise program and nutrition plan returned by the AI ​​model and optimizes it for the user.

[1005] The server sends this plan to the device, which displays it to the user.

[1006] As users exercise, a camera captures their movements and they receive real-time feedback, which is expected to include specific advice such as "bend your knees more" or "straighten your spine."

[1007] As a result, this system can provide personalized support to individual users and achieve effective and safe training and nutritional guidance.

[1008] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1009] Step 1: Enter your health information

[1010] Users use a smartphone, tablet, or PC to launch an application or web app and enter health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). The entered information is temporarily stored in the device's local storage, allowing specific health data to be collected.

[1011] Step 2: Send and store your health information

[1012] The device sends the entered health information to a server via the Internet. The communication protocol used is SSL / TLS, ensuring secure communication. The server then stores the received health information in a database such as Amazon Web Services' (AWS) Relational Database Service (RDS). This ensures that the user's health data is stored securely and can be used for subsequent data analysis.

[1013] Step 3: Create a training and nutrition plan

[1014] The server analyzes the user's health information stored in the database using programming languages ​​such as Python and R. In particular, it preprocesses the data using libraries such as Pandas and Numpy. At this stage, the server inputs prompts like the following into the generative AI model to generate a detailed exercise program and nutrition plan:

[1015] "Please suggest a three-times-a-week exercise program to lose 5 kg and a balanced nutrition plan."

[1016] Based on the input prompt, the generative AI model generates the optimal exercise program and nutrition plan for the user and outputs it to the server, thereby creating a personalized plan for each individual user.

[1017] Step 4: Deploy and view your plan

[1018] The server sends the generated training program and nutrition plan to the device. This communication is also via HTTPS protocol. The device displays the received plan to the user. The user can check the daily training and meal plan. This allows the user to take appropriate action based on the plan.

[1019] Step 5: Real-time behavior analysis

[1020] When a user starts training, the device's camera is used to capture their exercise movements. Specifically, the camera app is launched to take a video of the exercise, and the video data is sent to the server in real time. The transmitted data is compressed to optimize transmission efficiency. This allows the exercise movement data to be sent to the server in real time.

[1021] Step 6: Feedback generation

[1022] The server analyzes the received video data using an AI model (for example, a model built with TensorFlow or PyTorch). This analyzes the user's exercise movements frame by frame to detect incorrect movements or movements that pose a risk of injury. Based on the analysis results, the server generates specific feedback (e.g., "Bend your knees more" or "Straighten your spine"). This feedback is output in the form of voice, text, animation, etc., providing the user with useful correction information in real time.

[1023] Step 7: View your feedback

[1024] The device receives feedback from the server and displays it to the user in real time, including audio prompts, on-screen animations, or text messages, allowing the user to instantly identify any exercise form that needs correction and make appropriate adjustments.

[1025] Step 8: Use customization features

[1026] The device provides users with a UI that allows them to customize the appearance and personality of their virtual trainer. Using a dedicated UI, users select the appearance and personality of their virtual trainer and save that information. The saved customization information is sent to a server and recorded in a database. This allows users to train with their preferred trainer, making it easier to stay motivated.

[1027] Step 9: Offer paid plans and monetize

[1028] The server manages paid plans, which include detailed exercise analysis reports and advice from experts. Users can sign up for paid plans using a specific payment service (e.g., Stripe or PayPal). The server also monetizes through affiliate partnerships, providing users with information on rental and purchase of exercise equipment and affiliated gyms. This improves the sustainability and profitability of the entire system.

[1029] (Application example 1)

[1030] 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."

[1031] Conventional training and nutritional guidance systems have difficulty providing effective exercise and nutritional guidance because they are unable to fully reflect the individual health information of users. Additionally, the introduction of real-time feedback functions and customizable virtual trainers has been limited, making it difficult to motivate users and ensure their continued training.

[1032] 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.

[1033] In this invention, the server includes an input device for inputting a user's health information, a program generation device for generating an exercise program based on the user's health information, an analysis device for analyzing the user's exercise based on the exercise program and providing feedback, a program generation device for generating a nutrition plan based on the user's health information, a display device for displaying the results of the exercise program and nutrition plan, a real-time display device for displaying the feedback in real time, and a motion capture device for capturing and analyzing the user's movements during training in real time. This enables personalized fitness and nutritional guidance based on the user's individual health information, and provides accurate real-time motion analysis and feedback to improve the user's training effectiveness and safety. Furthermore, the ability to customize the virtual trainer's appearance and personality can increase the user's motivation and encourage them to continue training.

[1034] "User Health Information" means individual data about a User's health, such as the User's height, weight, goals, and lifestyle habits.

[1035] "Input means" refers to a device or interface that allows a user to input health information.

[1036] "Program generation means" refers to a device or software that has the function of generating an exercise program, nutrition plan, etc. based on the user's health information.

[1037] "Analysis means" refers to a device or software that has the function of analyzing a user's exercise based on an exercise program and providing feedback.

[1038] "Display Means" means a device or interface for displaying the results and feedback of an exercise program and nutrition plan to a user.

[1039] "Real-time display means" refers to a device or software that has the function of analyzing the user's exercise movements in real time and displaying the results to the user as immediate feedback.

[1040] "Movement capture means" refers to a device or software that records a user's movements while they are training, transmits the recorded movements to a server in real time, and analyzes them.

[1041] "Virtual Trainer" refers to a virtual instructor who supports users in their training and is a character whose appearance and personality can be customized.

[1042] The present invention is a system that provides personalized training and nutritional guidance to individual users. The system uses hardware such as smartphones, tablets, and cameras, and software such as cloud servers, AI models, and databases.

[1043] Collection of User's Health Information

[1044] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1045] The terminal transmits the input information to the server.

[1046] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[1047] Initial data analysis and program generation

[1048] The server then generates an optimal exercise and nutrition plan for the user based on the user's stored health information. This process utilizes historical data analysis, the latest fitness theories, and nutritional knowledge. The plan is then sent back to the device.

[1049] Training and nutrition plan delivery

[1050] The terminal displays the received plan to the user.

[1051] The user exercises according to the displayed training plan and eats according to the nutrition plan.

[1052] Real-time motion analysis

[1053] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[1054] The terminal transmits the captured video data to the server in real time.

[1055] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[1056] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[1057] The terminal displays real-time feedback to the user.

[1058] Customization features

[1059] The device provides options for users to customize the appearance and personality of their virtual trainer.

[1060] Users can select and save their preferred appearance and personality.

[1061] This customization feature helps users increase motivation and encourage them to continue their training.

[1062] Paid Plans and Monetization

[1063] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and provides users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[1064] Program Overview

[1065] The entire system is built using programming languages ​​and libraries such as Python and OpenCV. Data is transmitted over secure protocols such as HTTPS, and frameworks such as TensorFlow and PyTorch are used for AI models. Analyzed data is fed back to the user in real time, supporting safe and effective training.

[1066] Specific examples and examples of prompts for generative AI models

[1067] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[1068] Example prompt for a generative AI model:

[1069] Please provide a training plan for 3 days a week to lose 5 kg. Generate the best plan based on the following health information: Height: 175 cm, Weight: 68 kg, Goal: Lose 5 kg, Lifestyle: Exercise 3 times a week.

[1070] The above is a specific embodiment of the present invention, which allows users to receive personalized training and nutritional guidance based on their individual health information, and achieve their goals effectively and safely with real-time feedback.

[1071] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1072] Step 1:

[1073] Entering and submitting user health information

[1074] A user launches the smartphone application and inputs health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). The input health information is sent by the device to a server, where the user's individual information is stored in a database and used as the basis for subsequent processing.

[1075] Input: User's health information (height, weight, goals, lifestyle habits)

[1076] Output: JSON format data containing health data is sent to the server.

[1077] Step 2:

[1078] Save your health information and generate your initial plan

[1079] The server stores the received health information in a database. It then generates an exercise program and nutrition plan based on the stored information. This program utilizes past data analysis, the latest fitness theories, and nutritional knowledge. The plan is then sent to the device.

[1080] Input: JSON format data containing user health information

[1081] Output: Generates exercise programs and nutrition plans

[1082] Step 3:

[1083] View exercise programs and nutrition plans

[1084] The terminal displays the exercise program and nutrition plan received from the server to the user, who then begins exercising and eating according to the plan.

[1085] Input: Exercise program and nutrition plan data

[1086] Output: Training and nutrition plan displayed on the user's smartphone screen

[1087] Step 4:

[1088] Capture and transmit athletic movements

[1089] When a user trains, their movements are captured using a smartphone camera, and the captured video data is sent to a server in real time.

[1090] Input: Video data captured by a smartphone camera

[1091] Output: Real-time video data sent to the server

[1092] Step 5:

[1093] Analyzing video data and generating feedback

[1094] The server analyzes the received video data using an AI model, detecting incorrect movements or movements that pose a risk of injury, and generates feedback based on the results, which is sent to the device.

[1095] Input: Real-time video data

[1096] Output: Feedback (corrections and advice for incorrect behavior)

[1097] Step 6:

[1098] View real-time feedback

[1099] The device receives feedback from the server and displays it to the user in real time, allowing the user to modify their movements based on the feedback and train safely and effectively.

[1100] Input: Feedback data

[1101] Output: Real-time feedback displayed on the user's smartphone screen

[1102] Step 7:

[1103] Customizing your virtual trainer

[1104] The device provides the user with the option to customize the appearance and personality of the virtual trainer. The user selects the appearance and personality of their choice and saves the virtual trainer settings.

[1105] Input: User customization settings

[1106] Output: Customized virtual trainer data

[1107] Step 8:

[1108] Manage and monetize paid plans

[1109] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and also provides information on related products and services, aiming to generate revenue through affiliate programs.

[1110] Input: User's paid plan contract information

[1111] Output: Detailed exercise analysis report, expert advice, and related product information

[1112] 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.

[1113] The present invention is a system that provides personalized training and nutritional guidance to individual users. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions and adjusts the training and nutrition plan based on those emotions, thereby providing more effective and tailored support to the user.

[1114] Collection of User's Health Information

[1115] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1116] The terminal transmits the input information to the server.

[1117] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[1118] Initial data analysis and program generation

[1119] The server generates a personalized training program and nutrition plan based on the user's stored health information, including historical data analysis, the latest fitness theories, and nutritional knowledge.

[1120] Training and nutrition plan delivery

[1121] The server transmits the generated training program and nutrition plan to the terminal.

[1122] The terminal displays the received plan to the user.

[1123] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[1124] Real-time motion analysis

[1125] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[1126] The terminal transmits the captured video data to the server in real time.

[1127] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[1128] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[1129] The terminal displays real-time feedback to the user.

[1130] Emotion recognition by emotion engine

[1131] The device collects the user's facial expression and voice data and sends it to the server.

[1132] The server analyzes the received data using an emotion engine to recognize the user's current emotional state (e.g., fatigue, motivation, stress).

[1133] Emotionally-driven plan adjustments

[1134] The server adjusts the training and nutrition plan based on the user's emotional state as determined by the emotion engine: for example, if the user is in a state of high stress, it may reduce the intensity of the training or suggest relaxation exercises.

[1135] The adjusted plan is sent back to the terminal and displayed to the user.

[1136] Customization features

[1137] The device provides options for users to customize the appearance and personality of their virtual trainer.

[1138] Users can select and save their preferred appearance and personality.

[1139] This customization feature helps users increase motivation and encourage them to continue their training.

[1140] Paid Plans and Monetization

[1141] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and monetizes by providing users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[1142] Specific examples

[1143] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[1144] Furthermore, the system recognizes the user's emotional state based on their facial expressions and voice, and if they are feeling high levels of stress, it can flexibly adjust their training and nutrition plans, such as adding a relaxation menu.

[1145] The above is a specific embodiment of the present invention. This allows individual users to receive professional training and nutritional guidance at low cost, enabling them to achieve their goals effectively and safely. Furthermore, the introduction of an emotion engine makes it possible to support users mentally as well, achieving deeper personalization.

[1146] The processing flow will be explained below.

[1147] Step 1:

[1148] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1149] Step 2:

[1150] The terminal transmits the input health information to the server.

[1151] Step 3:

[1152] The server stores the received health information in a database and prepares to generate an initial training and nutrition plan.

[1153] Step 4:

[1154] The server analyzes the user's stored health information and generates the optimal training program and nutrition plan for the user.

[1155] Step 5:

[1156] The server transmits the generated training program and nutrition plan to the terminal.

[1157] Step 6:

[1158] The terminal displays the received training program and nutrition plan to the user.

[1159] Step 7:

[1160] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[1161] Step 8:

[1162] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[1163] Step 9:

[1164] The terminal transmits the captured video data to the server in real time.

[1165] Step 10:

[1166] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[1167] Step 11:

[1168] The server generates feedback based on the analysis results and sends corrections and advice to the device.

[1169] Step 12:

[1170] The terminal displays real-time feedback to the user.

[1171] Step 13:

[1172] The server analyzes the user's facial expression and voice data using an emotion engine to recognize their emotional state (e.g., fatigue, motivation, stress).

[1173] Step 14:

[1174] The server adjusts the training and nutrition plan based on the user's emotional state as determined by the emotion engine: for example, if the user is in a state of high stress, it may reduce the intensity of the training or suggest relaxation exercises.

[1175] Step 15:

[1176] The server transmits the tailored training and nutrition plan to the terminal.

[1177] Step 16:

[1178] The terminal displays the adjusted plan to the user.

[1179] Step 17:

[1180] The user follows the displayed adjustment plan and exercises and eats accordingly.

[1181] Step 18:

[1182] The device provides options for users to customize the appearance and personality of their virtual trainer.

[1183] Step 19:

[1184] Users select their preferred appearance and personality and save their settings.

[1185] Step 20:

[1186] The server manages paid plans and provides detailed exercise analysis reports and expert advice.

[1187] Step 21:

[1188] The server aims to generate revenue by providing users with information on rental and purchase of exercise equipment and affiliated gyms through affiliate partnerships.

[1189] Example 2

[1190] 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."

[1191] Conventional training and nutritional guidance systems lack sufficient personalization to meet the individual needs of users and lack consideration of the user's emotional state, making it difficult to provide effective and safe programs. Furthermore, the lack of real-time motion analysis and adjustment of training and nutritional plans based on the user's emotional state raises concerns that user satisfaction and motivation may decrease.

[1192] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1193] In this invention, the server includes an input means for inputting a user's health information, a program generation means for generating an exercise program based on the user's health information, an analysis means for analyzing the user's exercise based on the exercise program and providing feedback, a program generation means for generating a nutrition plan based on the user's health information, a display means for displaying the results of the exercise program and the nutrition plan, an analysis means for collecting the user's facial expression data and voice data and analyzing their emotional state, and an adjustment means for adjusting the training and nutrition plan based on their emotional state. This enables more personalized training and nutritional guidance for each user and realizes flexible plan adjustments that take the user's emotional state into consideration.

[1194] "User's health information" refers to information such as height, weight, goals, and lifestyle habits that a user enters through the application.

[1195] An "input means" is an interface or device (e.g., smartphone, tablet, computer) through which a user inputs health information.

[1196] "Program generation means" refers to algorithms or software that automatically generate exercise programs and nutrition plans based on the user's health information.

[1197] "Analysis means" refers to a combination of hardware and software for analyzing a user's exercise in real time and providing feedback based on an exercise program.

[1198] "Display means" refers to a device or interface for displaying the generated exercise program, nutrition plan, and feedback results to the user.

[1199] "Analysis means" refers to technology that collects and analyzes a user's facial expression and voice data to recognize the user's emotional state.

[1200] The "adjustment means" is an algorithm or software for adjusting the training and nutrition plan based on the emotional state obtained by the analysis means.

[1201] The present invention is a system that provides users with personalized training and nutritional guidance. The system combines an emotion engine that recognizes the user's emotions and adjusts the training and nutrition plan based on those emotions to provide more effective and tailored support to the user.

[1202] First, the user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). This is done using a device such as a smartphone or tablet. The device then sends the entered information to the server via the HTTPS protocol. The server then stores this information in a database (e.g., MySQL or PostgreSQL).

[1203] The server then uses Python and R scripts to analyze the data based on the user's stored health information, incorporating historical data and the latest fitness and nutritional knowledge, and generates a training program and nutrition plan that is optimized for the user.

[1204] The generated training program and nutrition plan are sent to the device in JSON format from the server. The device parses this data and displays it on the application's UI. The user then trains according to the displayed plan and consumes meals based on the nutrition plan.

[1205] When a user exercises, the device's camera function is activated to capture the user's movements in real time. The captured video data is sent to a server in real time and analyzed using AI models such as TensorFlow and PyTorch. If an incorrect movement or risk of injury is detected, the server generates appropriate feedback and sends it to the device. The device then displays the feedback in real time, providing the user with specific corrections and advice.

[1206] The device also collects the user's facial expression and voice data and sends it to the server. The server then uses an emotion engine to analyze this data and recognize the user's emotional state (e.g., fatigue, motivation, stress). Based on the recognized emotional state, the server adjusts the training and nutrition plan, sends it back to the device, and displays it to the user. For example, if the user is in a high stress state, the server will reduce the intensity of the training or suggest relaxation exercises.

[1207] The device also provides users with the option to customize the appearance and personality of the virtual trainer. Users can select and save their preferred appearance and personality. This customization information is sent to the server and registered in a database. This allows future feedback and interactions to be tailored to the user's preferences.

[1208] The server also provides paid plan management functionality, offering detailed exercise analysis reports and the option to receive advice from experts. Users can easily upgrade to paid plans. The service also aims to generate revenue through affiliate partnerships, offering information on rental and purchase options for exercise equipment and affiliated gyms.

[1209] As a specific example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a meal plan that takes nutritional balance into account is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training while receiving real-time feedback. The system can also recognize the user's emotional state based on their facial expressions and voice, and flexibly adjust the training and nutrition plan, such as adding a relaxation menu if the user is feeling high stress.

[1210] Example prompt sentence:

[1211] "Please explain in detail how the system generates personalized training and nutrition plans based on users' input of health information such as height, weight, goals, and lifestyle habits, and combines real-time analysis and emotion recognition."

[1212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1213] Step 1:

[1214] The user launches the application and inputs health information such as height, weight, goals, and lifestyle habits. The device sends the input information to the server. Input data includes "height = 170 cm," "weight = 70 kg," "goal = lose 5 kg," and "lifestyle = exercise three times a week." The device sends the information to the server, which stores it in a database, making it ready for the next processing.

[1215] Step 2:

[1216] The server analyzes data based on the user's stored health information and generates a training program and nutrition plan. The input data is health information, and the output data is a personalized training program and nutrition plan. Specifically, it uses Python scripts and R scripts to analyze past data, the latest fitness theories, and nutritional knowledge. As a result, it generates recommendations such as "aerobic exercise = 30 minutes," "strength training = 20 minutes," and "daily calorie intake = 1800 kcal."

[1217] Step 3:

[1218] The server sends the generated training program and nutrition plan to the device. The device parses the received data and displays it on the application's UI. The input data is the generated program and plan, and the output data is the data in the format displayed to the user. Specifically, the device receives the JSON-formatted data, parses it, and displays it on the UI in the form of an "exercise menu," "meal plan," etc.

[1219] Step 4:

[1220] When a user exercises, they activate the camera function on their device to capture their movements. The input data is the captured video data, which is sent to the server in real time. The server receives the video data and analyzes it using AI models such as TensorFlow and PyTorch. Specific operations include "form check," "incorrect movement detection," and "injury risk analysis." The output data is corrections and advice, which are sent to the device and displayed to the user in real time.

[1221] Step 5:

[1222] The device collects the user's facial expression and voice data and sends it to the server. The input data is facial expression and voice data, which the server receives and analyzes using an emotion engine to obtain the user's emotional state as output data. Specific operations include "facial expression analysis" and "voice emotion analysis," and the device recognizes emotional states such as "fatigue," "motivation," and "stress."

[1223] Step 6:

[1224] The server adjusts the training and nutrition plan based on the recognized emotional state. The input data is the emotional state, and the output data is the adjusted plan. For example, if the user is in a high stress state, adjustments such as "reducing training intensity" or "adding relaxation exercises" will be made. The adjusted plan is then sent back to the device and displayed to the user.

[1225] Step 7:

[1226] The device provides options for users to customize the appearance and personality of their virtual trainer. Users select the appearance and personality of their favorite trainer through the UI, and the settings are sent from the device to the server. The input data is customization information, and the output data is setting information stored in the database. This allows future feedback and interactions to be tailored to the user's preferences.

[1227] Step 8:

[1228] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and also provides information on rental and purchase of exercise equipment and affiliate gyms through affiliate partnerships. The input data is the user's subscription status and affiliate information, and the output data is information on additional services and products. This allows for the expansion of services to users and monetization.

[1229] (Application example 2)

[1230] 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."

[1231] While existing systems provide exercise programs and nutritional plans based on users' health information, they are unable to provide feedback that takes into account the user's emotional state and work environment, resulting in low personalization accuracy and user satisfaction. Furthermore, there is a lack of systems specifically designed to manage the health of users working in factories, posing challenges for improving work efficiency and safety.

[1232] 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 an input means for inputting the user's health information, a program generation means for generating an exercise program based on the user's health information, an analysis means for analyzing the user's exercise and providing feedback, a program generation means for generating a nutrition plan based on the user's health information, a display means for displaying the results of the exercise program and the nutrition plan, an emotion recognition means for recognizing the user's emotional state, a means for adjusting the exercise program and the nutrition plan based on the emotion recognition means, and a means for monitoring the health information of users working in a factory in real time. This enables the health of users working in a factory to be managed in a highly personalized manner, improving work efficiency and safety.

[1233] "User's health information" refers to information related to the user's health, such as their height, weight, exercise habits, and diet.

[1234] An "exercise program" is an exercise plan generated based on the user's health information, and includes a training menu, intensity, and the like.

[1235] A "nutrition plan" is a nutrition plan generated based on the user's health information, and includes information such as meal content and calorie intake.

[1236] "Input means" refers to an interface for inputting the user's health information into the application, and includes a keyboard, touch screen, voice input, etc.

[1237] "Program generation means" refers to software or algorithms for generating exercise programs and nutrition plans based on input health information.

[1238] "Analysis means" refers to a system or software that captures the user's exercise movements, analyzes the movements, and provides feedback.

[1239] "Display means" refers to a device or interface for displaying the results of the generated exercise program or nutrition plan to the user, including a smartphone, head-mounted display, smart glasses, etc.

[1240] "Emotion recognition means" refers to a system or algorithm that analyzes a user's facial expression data and voice data to recognize the user's emotional state.

[1241] The "adjustment means" refers to an algorithm or system that appropriately adjusts the exercise program and nutrition plan based on the user's emotional state recognized by the emotion recognition means.

[1242] "Users working in factories" refers to operators and workers who work in the factory's working environment, and are the target of improving their health and work efficiency.

[1243] "Real-time monitoring means" refers to systems or devices that collect and analyze a user's health information in real time.

[1244] To implement the present invention, the following system configuration and program are required.

[1245] System Configuration

[1246] 1. Input Method

[1247] Users input health information (e.g., height, weight, exercise habits, and nutritional information) using a head-mounted display (HMD) or smart glasses, which can include keyboard, touchscreen, and voice input.

[1248] 2. Program Generation Method

[1249] The server generates the optimal exercise and nutritional plan for each individual user based on the health information entered by the user. The database contains past data, the latest fitness theories, and nutritional knowledge, and generates the program based on this information.

[1250] 3. Analysis method

[1251] When a user exercises, their movements are captured using the camera function of the HMD or smart glasses. The captured data is sent to a server in real time and analyzed using an AI model (e.g., TensorFlow / Keras). This detects incorrect movements or movements that pose a risk of injury.

[1252] 4. Display means

[1253] The generated exercise program and nutrition plan, as well as analytical feedback, are displayed on an HMD, smart glasses, smartphone, or tablet.

[1254] 5. Emotion recognition means

[1255] The user's facial expression and voice data are collected through a camera and microphone and sent to a server, which uses an emotion recognition algorithm to recognize the user's emotional state (e.g., fatigue, motivation, stress).

[1256] 6. Adjustment means

[1257] Based on the perceived emotional state, the server will adjust the exercise program and nutrition plan accordingly, for example reducing the intensity of training or suggesting relaxation exercises if the user is in a high stress state.

[1258] 7. Real-time monitoring methods

[1259] It includes a system for real-time monitoring of the health information and exercise status of users working in factories, which is expected to improve the health management and work efficiency of users working in factories.

[1260] Specific examples of hardware and software used

[1261] Hardware: HMD (head-mounted display), smart glasses, smartphone, tablet, camera, microphone

[1262] Software: TensorFlow / Keras (AI models), database management systems (e.g., Firebase, MySQL), software for real-time data analysis, emotion recognition algorithms

[1263] Specific examples

[1264] For example, if a user sets a goal of "losing 5 kg" and selects a program to exercise three times a week, the server will generate an appropriate training menu (e.g., aerobic exercise and strength training) and also suggest a nutritionally balanced meal plan. As the user exercises, the camera in the HMD or smart glasses captures the user's movements, and the server provides real-time feedback. Furthermore, the system recognizes the user's emotional state from their facial expressions and voice, and adjusts the program by, for example, adding relaxation options if fatigue increases.

[1265] Prompt Sentence Examples

[1266] "I would like to develop a healthcare application that captures the user's facial expressions, analyzes their emotions, and provides mental support. The user is an operator, and the application needs to detect fatigue in real time while working and suggest appropriate breaks and exercise plans. The technologies used include facial expression analysis using a camera and emotion recognition using machine learning. Please build such an application using Python and provide concrete examples of the necessary APIs and libraries."

[1267] The above is a detailed description of the embodiment of the present invention. This system enables highly personalized health management for users working in a factory, thereby improving efficiency and safety.

[1268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1269] Step 1:

[1270] The user inputs health information (height, weight, exercise habits, nutritional information, etc.) using a head-mounted display (HMD) or smart glasses.

[1271] Input: User's health information

[1272] Output: Health information sent to the server

[1273] Specific operation: The user enters health information through a form on the HMD or smart glasses screen, and after completing the entry, the information is sent to the server.

[1274] Step 2:

[1275] The server generates a personalized exercise program and nutrition plan based on the user's input health information.

[1276] Input: User's health information submitted

[1277] Output: Generated exercise program and nutrition plan

[1278] How it works: The server pulls historical data, the latest fitness theories, and nutritional knowledge from a database, and uses algorithms to calculate the optimal exercise program and nutrition plan for the user.

[1279] Step 3:

[1280] The server transmits the generated exercise program and nutrition plan to the terminal.

[1281] Input: Generated exercise program and nutrition plan

[1282] Output: Exercise program and nutrition plan sent to the device

[1283] Specific operation: The generated exercise program and nutrition plan are sent to the terminal for display on the HMD or smart glasses.

[1284] Step 4:

[1285] The user exercises using an HMD or smart glasses. The device captures the user's movements in real time through a camera and transmits them to a server.

[1286] Input: Real-time video of the user's movements

[1287] Output: Real-time motion video sent to the server

[1288] Specific actions: The user starts exercising, and the device's camera captures the action and sends it to the server as video data.

[1289] Step 5:

[1290] The server analyzes the received real-time motion video using an AI model to detect incorrect movements and risk of injury, and generates feedback based on the analysis results and sends it to the device.

[1291] Input: Transmitted real-time motion video

[1292] Output: Analysis results and feedback

[1293] Specific operation: The server uses an AI model (e.g., TensorFlow / Keras) to analyze the motion video and detect incorrect movements or risk of injury. It then generates feedback based on the detection results.

[1294] Step 6:

[1295] The terminal displays the feedback sent from the server to the user in real time.

[1296] Input: Feedback from the server

[1297] Output: Displayed feedback

[1298] Specific operation: The device that receives the feedback displays it in real time on the HMD or smart glasses, providing the user with the correct exercise method and correction instructions.

[1299] Step 7:

[1300] The terminal collects the user's facial expression data and voice data and sends it to the server.

[1301] Input: User's facial expression data and voice data

[1302] Output: Facial expression data and voice data sent to the server

[1303] Specific operation: Uses a camera and microphone to collect the user's facial expressions and voice in real time.

[1304] Step 8:

[1305] The server performs emotion recognition using the transmitted facial expression data and voice data to recognize the user's current emotional state.

[1306] Input: facial expression data and voice data

[1307] Output: User's emotional state

[1308] Specific operation: The server uses an emotion recognition algorithm to analyze the transmitted data and recognize the user's emotional state (e.g., fatigue, motivation, stress).

[1309] Step 9:

[1310] The server adjusts the exercise program and nutrition plan accordingly based on the recognized emotional state and transmits them to the terminal.

[1311] Input: Perceived emotional state

[1312] Output: Tailored exercise program and nutrition plan

[1313] Specific operation: The server adjusts exercise programs and nutrition plans according to the emotional state, and takes measures such as suggesting relaxation exercises if the person is in a high-stress state.

[1314] Step 10:

[1315] The terminal displays the tailored exercise program and nutrition plan to the user.

[1316] Input: Tailored exercise program and nutrition plan

[1317] Output: Exercise program and nutrition plan with adjustments

[1318] Specific operation: The adjusted plan is displayed on the device, and the user can check the next exercise and meal plan they should do.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] [Fourth embodiment]

[1323] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1324] 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.

[1325] 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).

[1326] 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.

[1327] 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.

[1328] 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).

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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."

[1336] The present invention is a system for providing personalized training and nutritional advice to individual users. Specific embodiments of the system will be described below.

[1337] Collection of User's Health Information

[1338] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1339] The terminal transmits the input information to the server.

[1340] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[1341] Initial data analysis and program generation

[1342] The server generates an optimal exercise program and nutrition plan for the user based on the user's stored health information, including historical data analysis, the latest fitness theories, and nutritional knowledge.

[1343] Training and nutrition plan delivery

[1344] The server transmits the generated training program and nutrition plan to the terminal.

[1345] The terminal displays the received plan to the user.

[1346] The user exercises according to the displayed training plan and eats according to the nutrition plan.

[1347] Real-time motion analysis

[1348] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[1349] The terminal transmits the captured video data to the server in real time.

[1350] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[1351] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[1352] The terminal displays real-time feedback to the user.

[1353] Customization features

[1354] The device provides options for users to customize the appearance and personality of their virtual trainer.

[1355] Users can select and save their preferred appearance and personality.

[1356] This customization feature helps users increase motivation and encourage them to continue their training.

[1357] Paid Plans and Monetization

[1358] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and monetizes by providing users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[1359] Specific examples

[1360] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[1361] The above is a specific embodiment of the present invention, which allows individual users to receive professional training and nutritional guidance at low cost, enabling them to achieve their goals effectively and safely.

[1362] The processing flow will be explained below.

[1363] Step 1:

[1364] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1365] Step 2:

[1366] The terminal transmits the input health information to the server.

[1367] Step 3:

[1368] The server stores the received health information in a database and prepares to generate an initial training and nutrition plan.

[1369] Step 4:

[1370] The server analyzes the user's stored health information and generates an optimal training program and nutrition plan for the user.

[1371] Step 5:

[1372] The server transmits the generated training program and nutrition plan to the terminal.

[1373] Step 6:

[1374] The terminal displays the received training program and nutrition plan to the user.

[1375] Step 7:

[1376] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[1377] Step 8:

[1378] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[1379] Step 9:

[1380] The terminal transmits the captured video data to the server in real time.

[1381] Step 10:

[1382] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[1383] Step 11:

[1384] The server generates feedback based on the analysis results and sends corrections and advice to the device.

[1385] Step 12:

[1386] The terminal displays real-time feedback to the user.

[1387] Step 13:

[1388] The user modifies the athletic performance according to the displayed feedback.

[1389] Step 14:

[1390] The device provides options for users to customize the appearance and personality of their virtual trainer.

[1391] Step 15:

[1392] Users select their preferred appearance and personality and save their settings.

[1393] Step 16:

[1394] The server manages paid plans and provides detailed exercise analysis reports and expert advice.

[1395] Step 17:

[1396] The server aims to generate revenue by providing users with information on rental and purchase of exercise equipment and affiliated gyms through affiliate partnerships.

[1397] Example 1

[1398] 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."

[1399] Conventional training and nutritional guidance systems have difficulty providing personalized programs based on individual users' health information. Furthermore, delays in real-time exercise analysis and feedback can prevent users from maintaining proper exercise form, increasing the risk of injury. Furthermore, the lack of functionality for customizing the virtual trainer makes it difficult to maintain user motivation.

[1400] 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.

[1401] In this invention, the server includes an input device for inputting a user's health information, a program generation device for generating an exercise program and a nutrition plan based on the user's health information, an analysis device for analyzing the user's exercise based on the exercise program and providing feedback, a device for the analysis device to input prompts and perform data analysis using a generated AI model, and a display device for displaying the results of the exercise program and nutrition plan. This enables personalized training and nutritional guidance for each user, and provides accurate feedback in real time, resulting in safer and more effective exercise guidance. Furthermore, adding a virtual trainer customization function can maintain user motivation and improve training continuity.

[1402] "User health information" refers to information entered by the user, such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1403] "Input means" refers to devices such as smartphones, tablets, and computers that users use to input health information into the application, as well as dedicated apps or web apps.

[1404] "Program generation means" refers to a program, algorithm, or analysis system for generating an exercise program and nutrition plan based on the user's health information.

[1405] The "analysis means" is a means for analyzing the user's exercise movements based on the exercise program, detecting incorrect movements or movements that pose a risk of injury, and providing feedback.

[1406] A "generative AI model" is an artificial intelligence model (e.g., a model built with TensorFlow or PyTorch) that is trained based on large amounts of data and generates a response to a specific task when a prompt is input.

[1407] A "prompt" is a series of sentences or commands that are input to a generative AI model and contain instructions for completing a specific task.

[1408] "Display means" refers to a device such as a smartphone, tablet, or computer and its display screen that displays the results and feedback of the exercise program and nutrition plan to the user.

[1409] "Real-time capture" means capturing the user's movements in real time (with almost no delay) with a camera while they are exercising, and acquiring the data.

[1410] "Feedback" refers to information that provides the user with corrections and advice regarding incorrect movements or injury risks detected by the analysis means.

[1411] A "virtual trainer" is a character that virtually guides and supports users during training, and its appearance and personality can be customized.

[1412] The present invention is a system for providing personalized training and nutritional advice to individual users. Specific embodiments for carrying out the invention are described below.

[1413] System configuration

[1414] The system includes an input means for inputting a user's health information, a program generation means for generating a training and nutrition plan based on the user's health information, an analysis means for analyzing the user's exercise movements and providing feedback, a data analysis means using the generated AI model, and a display means for displaying the results to the user.

[1415] Input Method

[1416] Users use a smartphone, tablet, or computer to launch an application or web app and enter health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1417] Data transmission

[1418] The device sends the entered health information to a server via the Internet, using security protocols such as SSL / TLS.

[1419] Data storage and program generation

[1420] The server stores the received health information in a database (for example, Amazon Web Services (AWS) Relational Database Service (RDS)). Based on the stored information, the server performs data analysis using programming languages ​​such as Python and R to generate training programs and nutrition plans. This process also utilizes existing knowledge bases based on fitness theory and nutrition as well as external APIs.

[1421] View Plans

[1422] The server sends the generated training program and nutrition plan to the device, which displays the program and plan to the user using a dedicated native app or web app.

[1423] Real-time motion analysis

[1424] When a user exercises, they activate the camera function on their device (smartphone or tablet) to capture their own exercise movements. The captured video data is compressed in real time and sent to a server.

[1425] Feedback Generation

[1426] The server analyzes the received video data using AI models (for example, models built with TensorFlow or PyTorch) to detect incorrect movements or movements that pose a risk of injury. After the analysis is complete, the server generates feedback and sends corrections or advice to the device. The feedback can be in the form of text, graphics, animation, or audio.

[1427] View Feedback

[1428] The device provides real-time feedback to the user, allowing them to instantly see corrections during exercise and continue exercising with correct form.

[1429] Customization features

[1430] In addition, the device provides users with the option to customize the appearance and personality of their virtual trainer. Users can select their preferred appearance and personality using a dedicated UI, and this customization information is saved on the server. This feature helps users stay motivated during training.

[1431] Paid plans available

[1432] The server manages paid plans and provides detailed exercise analysis reports and advice from experts. Payments are made via common payment services such as Stripe and PayPal. Users can also earn revenue through affiliate partnerships, renting or purchasing exercise equipment and providing information on affiliated gyms.

[1433] Specific examples

[1434] For example, if a user selects a program to exercise three times a week to lose 5 kg, the server will follow these specific steps:

[1435] The server reads the user's health information from the database.

[1436] The server uses a Python script to analyze the data and prompt the generative AI model with statements like:

[1437] "Please suggest a three-times-a-week exercise program to lose 5 kg and a balanced nutrition plan."

[1438] The server receives the exercise program and nutrition plan returned by the AI ​​model and optimizes it for the user.

[1439] The server sends this plan to the device, which displays it to the user.

[1440] As users exercise, a camera captures their movements and they receive real-time feedback, which is expected to include specific advice such as "bend your knees more" or "straighten your spine."

[1441] As a result, this system can provide personalized support to individual users and achieve effective and safe training and nutritional guidance.

[1442] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1443] Step 1: Enter your health information

[1444] Users use a smartphone, tablet, or PC to launch an application or web app and enter health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). The entered information is temporarily stored in the device's local storage, allowing specific health data to be collected.

[1445] Step 2: Send and store your health information

[1446] The device sends the entered health information to a server via the Internet. The communication protocol used is SSL / TLS, ensuring secure communication. The server then stores the received health information in a database such as Amazon Web Services' (AWS) Relational Database Service (RDS). This ensures that the user's health data is stored securely and can be used for subsequent data analysis.

[1447] Step 3: Create a training and nutrition plan

[1448] The server analyzes the user's health information stored in the database using programming languages ​​such as Python and R. In particular, it preprocesses the data using libraries such as Pandas and Numpy. At this stage, the server inputs prompts like the following into the generative AI model to generate a detailed exercise program and nutrition plan:

[1449] "Please suggest a three-times-a-week exercise program to lose 5 kg and a balanced nutrition plan."

[1450] Based on the input prompt, the generative AI model generates the optimal exercise program and nutrition plan for the user and outputs it to the server, thereby creating a personalized plan for each individual user.

[1451] Step 4: Deploy and view your plan

[1452] The server sends the generated training program and nutrition plan to the device. This communication is also via HTTPS protocol. The device displays the received plan to the user. The user can check the daily training and meal plan. This allows the user to take appropriate action based on the plan.

[1453] Step 5: Real-time behavior analysis

[1454] When a user starts training, the device's camera is used to capture their exercise movements. Specifically, the camera app is launched to take a video of the exercise, and the video data is sent to the server in real time. The transmitted data is compressed to optimize transmission efficiency. This allows the exercise movement data to be sent to the server in real time.

[1455] Step 6: Feedback generation

[1456] The server analyzes the received video data using an AI model (for example, a model built with TensorFlow or PyTorch). This analyzes the user's exercise movements frame by frame to detect incorrect movements or movements that pose a risk of injury. Based on the analysis results, the server generates specific feedback (e.g., "Bend your knees more" or "Straighten your spine"). This feedback is output in the form of voice, text, animation, etc., providing the user with useful correction information in real time.

[1457] Step 7: View your feedback

[1458] The device receives feedback from the server and displays it to the user in real time, including audio prompts, on-screen animations, or text messages, allowing the user to instantly identify any exercise form that needs correction and make appropriate corrections.

[1459] Step 8: Use customization features

[1460] The device provides users with a UI that allows them to customize the appearance and personality of their virtual trainer. Using a dedicated UI, users select the appearance and personality of their virtual trainer and save that information. The saved customization information is sent to a server and recorded in a database. This allows users to train with their preferred trainer, making it easier to stay motivated.

[1461] Step 9: Offer paid plans and monetize

[1462] The server manages paid plans, which include detailed exercise analysis reports and advice from experts. Users can sign up for paid plans using a specific payment service (e.g., Stripe or PayPal). The server also monetizes through affiliate partnerships, providing users with information on rental and purchase of exercise equipment and affiliated gyms. This improves the sustainability and profitability of the entire system.

[1463] (Application example 1)

[1464] 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."

[1465] Conventional training and nutritional guidance systems have difficulty providing effective exercise and nutritional guidance because they are unable to fully reflect the individual health information of users. Additionally, the introduction of real-time feedback functions and customizable virtual trainers has been limited, making it difficult to motivate users and ensure their continued training.

[1466] 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.

[1467] In this invention, the server includes an input device for inputting a user's health information, a program generation device for generating an exercise program based on the user's health information, an analysis device for analyzing the user's exercise based on the exercise program and providing feedback, a program generation device for generating a nutrition plan based on the user's health information, a display device for displaying the results of the exercise program and nutrition plan, a real-time display device for displaying the feedback in real time, and a motion capture device for capturing and analyzing the user's movements during training in real time. This enables personalized fitness and nutritional guidance based on the user's individual health information, and provides accurate real-time motion analysis and feedback to improve the user's training effectiveness and safety. Furthermore, the ability to customize the virtual trainer's appearance and personality can increase the user's motivation and encourage them to continue training.

[1468] "User Health Information" means individual data about a User's health, such as the User's height, weight, goals, and lifestyle habits.

[1469] "Input means" refers to a device or interface that allows a user to input health information.

[1470] "Program generation means" refers to a device or software that has the function of generating an exercise program, nutrition plan, etc. based on the user's health information.

[1471] "Analysis means" refers to a device or software that has the function of analyzing a user's exercise based on an exercise program and providing feedback.

[1472] "Display Means" means a device or interface for displaying the results and feedback of an exercise program and nutrition plan to a user.

[1473] "Real-time display means" refers to a device or software that has the function of analyzing the user's exercise movements in real time and displaying the results to the user as immediate feedback.

[1474] "Movement capture means" refers to a device or software that records a user's movements while they are training, transmits the recorded movements to a server in real time, and analyzes them.

[1475] "Virtual Trainer" refers to a virtual instructor who supports users in their training and is a character whose appearance and personality can be customized.

[1476] The present invention is a system that provides personalized training and nutritional guidance to individual users. The system uses hardware such as smartphones, tablets, and cameras, and software such as cloud servers, AI models, and databases.

[1477] Collection of User's Health Information

[1478] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1479] The terminal transmits the input information to the server.

[1480] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[1481] Initial data analysis and program generation

[1482] The server then generates an optimal exercise and nutrition plan for the user based on the user's stored health information. This process utilizes historical data analysis, the latest fitness theories, and nutritional knowledge. The plan is then sent back to the device.

[1483] Training and nutrition plan delivery

[1484] The terminal displays the received plan to the user.

[1485] The user exercises according to the displayed training plan and eats according to the nutrition plan.

[1486] Real-time motion analysis

[1487] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[1488] The terminal transmits the captured video data to the server in real time.

[1489] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[1490] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[1491] The terminal displays real-time feedback to the user.

[1492] Customization features

[1493] The device provides options for users to customize the appearance and personality of their virtual trainer.

[1494] Users can select and save their preferred appearance and personality.

[1495] This customization feature helps users increase motivation and encourage them to continue their training.

[1496] Paid Plans and Monetization

[1497] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and provides users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[1498] Program Overview

[1499] The entire system is built using programming languages ​​and libraries such as Python and OpenCV. Data is transmitted over secure protocols such as HTTPS, and frameworks such as TensorFlow and PyTorch are used for AI models. Analyzed data is fed back to the user in real time, supporting safe and effective training.

[1500] Specific examples and examples of prompts for generative AI models

[1501] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[1502] Example prompt for a generative AI model:

[1503] Please provide a training plan for 3 days a week to lose 5 kg. Generate the best plan based on the following health information: Height: 175 cm, Weight: 68 kg, Goal: Lose 5 kg, Lifestyle: Exercise 3 times a week.

[1504] The above is a specific embodiment of the present invention, which allows users to receive personalized training and nutritional guidance based on their individual health information, and achieve their goals effectively and safely with real-time feedback.

[1505] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1506] Step 1:

[1507] Entering and submitting user health information

[1508] A user launches the smartphone application and inputs health information such as height, weight, goals (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). The input health information is sent by the device to a server, where the user's individual information is stored in a database and used as the basis for subsequent processing.

[1509] Input: User's health information (height, weight, goals, lifestyle habits)

[1510] Output: JSON format data containing health data is sent to the server.

[1511] Step 2:

[1512] Save your health information and generate your initial plan

[1513] The server stores the received health information in a database. It then generates an exercise program and nutrition plan based on the stored information. This program utilizes past data analysis, the latest fitness theories, and nutritional knowledge. The plan is then sent to the device.

[1514] Input: JSON format data containing user health information

[1515] Output: Generates exercise programs and nutrition plans

[1516] Step 3:

[1517] View exercise programs and nutrition plans

[1518] The terminal displays the exercise program and nutrition plan received from the server to the user, who then begins exercising and eating according to the plan.

[1519] Input: Exercise program and nutrition plan data

[1520] Output: Training and nutrition plan displayed on the user's smartphone screen

[1521] Step 4:

[1522] Capture and transmit athletic movements

[1523] When a user trains, their movements are captured using a smartphone camera, and the captured video data is sent to a server in real time.

[1524] Input: Video data captured by a smartphone camera

[1525] Output: Real-time video data sent to the server

[1526] Step 5:

[1527] Analyzing video data and generating feedback

[1528] The server analyzes the received video data using an AI model, detecting incorrect movements or movements that pose a risk of injury, and generates feedback based on the results, which is sent to the device.

[1529] Input: Real-time video data

[1530] Output: Feedback (corrections and advice for incorrect behavior)

[1531] Step 6:

[1532] View real-time feedback

[1533] The device receives feedback from the server and displays it to the user in real time, allowing the user to modify their movements based on the feedback and train safely and effectively.

[1534] Input: Feedback data

[1535] Output: Real-time feedback displayed on the user's smartphone screen

[1536] Step 7:

[1537] Customizing your virtual trainer

[1538] The device provides the user with the option to customize the appearance and personality of the virtual trainer. The user selects the appearance and personality of their choice and saves the virtual trainer settings.

[1539] Input: User customization settings

[1540] Output: Customized virtual trainer data

[1541] Step 8:

[1542] Manage and monetize your paid plans

[1543] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and also provides information on related products and services, aiming to generate revenue through affiliate programs.

[1544] Input: User's paid plan contract information

[1545] Output: Detailed exercise analysis report, expert advice, and related product information

[1546] 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.

[1547] The present invention is a system that provides personalized training and nutritional guidance to individual users. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions and adjusts the training and nutrition plan based on those emotions, thereby providing more effective and tailored support to the user.

[1548] Collection of User's Health Information

[1549] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1550] The terminal transmits the input information to the server.

[1551] The server stores the received information in a database and prepares to generate an initial training and nutrition plan.

[1552] Initial data analysis and program generation

[1553] The server generates a personalized training program and nutrition plan based on the user's stored health information, including historical data analysis, the latest fitness theories, and nutritional knowledge.

[1554] Training and nutrition plan delivery

[1555] The server transmits the generated training program and nutrition plan to the terminal.

[1556] The terminal displays the received plan to the user.

[1557] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[1558] Real-time motion analysis

[1559] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[1560] The terminal transmits the captured video data to the server in real time.

[1561] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[1562] Based on the analysis results, the server generates feedback and sends corrections and advice to the device.

[1563] The terminal displays real-time feedback to the user.

[1564] Emotion recognition by emotion engine

[1565] The device collects the user's facial expression and voice data and sends it to the server.

[1566] The server analyzes the received data using an emotion engine to recognize the user's current emotional state (e.g., fatigue, motivation, stress).

[1567] Emotionally-driven plan adjustments

[1568] The server adjusts the training and nutrition plan based on the user's emotional state as determined by the emotion engine: for example, if the user is in a state of high stress, it may reduce the intensity of the training or suggest relaxation exercises.

[1569] The adjusted plan is sent back to the terminal and displayed to the user.

[1570] Customization features

[1571] The device provides options for users to customize the appearance and personality of their virtual trainer.

[1572] Users can select and save their preferred appearance and personality.

[1573] This customization feature helps users increase motivation and encourage them to continue their training.

[1574] Paid Plans and Monetization

[1575] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and monetizes by providing users with information on affiliate gyms and the rental and purchase of exercise equipment through affiliate partnerships.

[1576] Specific examples

[1577] For example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a nutritionally balanced meal plan is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training with real-time feedback.

[1578] Furthermore, the system recognizes the user's emotional state based on their facial expressions and voice, and if they are feeling high levels of stress, it can flexibly adjust their training and nutrition plans, such as adding a relaxation menu.

[1579] The above is a specific embodiment of the present invention. This allows individual users to receive professional training and nutritional guidance at low cost, enabling them to achieve their goals effectively and safely. Furthermore, the introduction of an emotion engine makes it possible to support users mentally as well, achieving deeper personalization.

[1580] The processing flow will be explained below.

[1581] Step 1:

[1582] The user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week).

[1583] Step 2:

[1584] The terminal transmits the input health information to the server.

[1585] Step 3:

[1586] The server stores the received health information in a database and prepares to generate an initial training and nutrition plan.

[1587] Step 4:

[1588] The server analyzes the user's stored health information and generates the optimal training program and nutrition plan for the user.

[1589] Step 5:

[1590] The server transmits the generated training program and nutrition plan to the terminal.

[1591] Step 6:

[1592] The terminal displays the received training program and nutrition plan to the user.

[1593] Step 7:

[1594] The user exercises according to the displayed training program and eats meals based on the nutrition plan.

[1595] Step 8:

[1596] When a user exercises at home, the user activates the camera function on a device (such as a smartphone or tablet) to capture the exercise movements.

[1597] Step 9:

[1598] The terminal transmits the captured video data to the server in real time.

[1599] Step 10:

[1600] The server analyzes the received video data using an AI model to detect incorrect movements or movements that pose a risk of injury.

[1601] Step 11:

[1602] The server generates feedback based on the analysis results and sends corrections and advice to the device.

[1603] Step 12:

[1604] The terminal displays real-time feedback to the user.

[1605] Step 13:

[1606] The server analyzes the user's facial expression and voice data using an emotion engine to recognize their emotional state (e.g., fatigue, motivation, stress).

[1607] Step 14:

[1608] The server adjusts the training and nutrition plan based on the user's emotional state as determined by the emotion engine: for example, if the user is in a state of high stress, it may reduce the intensity of the training or suggest relaxation exercises.

[1609] Step 15:

[1610] The server transmits the tailored training and nutrition plan to the terminal.

[1611] Step 16:

[1612] The terminal displays the adjusted plan to the user.

[1613] Step 17:

[1614] The user follows the displayed adjustment plan and exercises and eats accordingly.

[1615] Step 18:

[1616] The device provides options for users to customize the appearance and personality of their virtual trainer.

[1617] Step 19:

[1618] Users select their preferred appearance and personality and save their settings.

[1619] Step 20:

[1620] The server manages paid plans and provides detailed exercise analysis reports and expert advice.

[1621] Step 21:

[1622] The server aims to generate revenue by providing users with information on rental and purchase of exercise equipment and affiliated gyms through affiliate partnerships.

[1623] Example 2

[1624] 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."

[1625] Conventional training and nutritional guidance systems lack sufficient personalization to meet the individual needs of users and lack consideration of the user's emotional state, making it difficult to provide effective and safe programs. Furthermore, the lack of real-time motion analysis and adjustment of training and nutritional plans based on the user's emotional state raises concerns that user satisfaction and motivation may decrease.

[1626] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1627] In this invention, the server includes an input means for inputting a user's health information, a program generation means for generating an exercise program based on the user's health information, an analysis means for analyzing the user's exercise based on the exercise program and providing feedback, a program generation means for generating a nutrition plan based on the user's health information, a display means for displaying the results of the exercise program and the nutrition plan, an analysis means for collecting the user's facial expression data and voice data and analyzing their emotional state, and an adjustment means for adjusting the training and nutrition plan based on their emotional state. This enables more personalized training and nutritional guidance for each user and realizes flexible plan adjustments that take the user's emotional state into consideration.

[1628] "User's health information" refers to information such as height, weight, goals, and lifestyle habits that a user enters through the application.

[1629] An "input means" is an interface or device (e.g., smartphone, tablet, computer) through which a user inputs health information.

[1630] "Program generation means" refers to algorithms or software that automatically generate exercise programs and nutrition plans based on the user's health information.

[1631] "Analysis means" refers to a combination of hardware and software for analyzing a user's exercise in real time and providing feedback based on an exercise program.

[1632] "Display means" refers to a device or interface for displaying the generated exercise program, nutrition plan, and feedback results to the user.

[1633] "Analysis means" refers to technology that collects and analyzes a user's facial expression and voice data to recognize the user's emotional state.

[1634] The "adjustment means" is an algorithm or software for adjusting the training and nutrition plan based on the emotional state obtained by the analysis means.

[1635] The present invention is a system that provides users with personalized training and nutritional guidance. The system combines an emotion engine that recognizes the user's emotions and adjusts the training and nutrition plan based on those emotions to provide more effective and tailored support to the user.

[1636] First, the user launches the application and enters health information such as height, weight, goal (e.g., lose 5 kg), and lifestyle habits (e.g., exercise three times a week). This is done using a device such as a smartphone or tablet. The device then sends the entered information to the server via the HTTPS protocol. The server then stores this information in a database (e.g., MySQL or PostgreSQL).

[1637] The server then uses Python and R scripts to analyze the data based on the user's stored health information, incorporating historical data and the latest fitness and nutritional knowledge, and generates a training program and nutrition plan that is optimized for the user.

[1638] The generated training program and nutrition plan are sent to the device in JSON format from the server. The device parses this data and displays it on the application's UI. The user then trains according to the displayed plan and consumes meals based on the nutrition plan.

[1639] When a user exercises, the device's camera function is activated to capture the user's movements in real time. The captured video data is sent to a server in real time and analyzed using AI models such as TensorFlow and PyTorch. If an incorrect movement or risk of injury is detected, the server generates appropriate feedback and sends it to the device. The device then displays the feedback in real time, providing the user with specific corrections and advice.

[1640] The device also collects the user's facial expression and voice data and sends it to the server. The server then uses an emotion engine to analyze this data and recognize the user's emotional state (e.g., fatigue, motivation, stress). Based on the recognized emotional state, the server adjusts the training and nutrition plan, sends it back to the device, and displays it to the user. For example, if the user is in a high stress state, the server will reduce the intensity of the training or suggest relaxation exercises.

[1641] The device also provides users with the option to customize the appearance and personality of the virtual trainer. Users can select and save their preferred appearance and personality. This customization information is sent to the server and registered in a database. This allows future feedback and interactions to be tailored to the user's preferences.

[1642] The server also provides paid plan management functionality, offering detailed exercise analysis reports and the option to receive advice from experts. Users can easily upgrade to paid plans. The service also aims to generate revenue through affiliate partnerships, offering information on rental and purchase options for exercise equipment and affiliated gyms.

[1643] As a specific example, if a user selects an exercise program to lose 5 kg three times a week, the server generates an appropriate training menu (e.g., aerobic exercise, strength training) based on the user's profile. At the same time, a meal plan that takes nutritional balance into account is also suggested. When the user exercises, the smartphone camera can be activated to capture the movements, allowing for effective training while receiving real-time feedback. The system can also recognize the user's emotional state based on their facial expressions and voice, and flexibly adjust the training and nutrition plan, such as adding a relaxation menu if the user is feeling high stress.

[1644] Example prompt sentence:

[1645] "Please explain in detail how the system generates personalized training and nutrition plans based on users' input of health information such as height, weight, goals, and lifestyle habits, and combines real-time analysis and emotion recognition."

[1646] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1647] Step 1:

[1648] The user launches the application and inputs health information such as height, weight, goals, and lifestyle habits. The device sends the input information to the server. Input data includes "height = 170 cm," "weight = 70 kg," "goal = lose 5 kg," and "lifestyle = exercise three times a week." The device sends the information to the server, which stores it in a database, making it ready for the next processing.

[1649] Step 2:

[1650] The server analyzes data based on the user's stored health information and generates a training program and nutrition plan. The input data is health information, and the output data is a personalized training program and nutrition plan. Specifically, it uses Python scripts and R scripts to analyze past data, the latest fitness theories, and nutritional knowledge. As a result, it generates recommendations such as "aerobic exercise = 30 minutes," "strength training = 20 minutes," and "daily calorie intake = 1800 kcal."

[1651] Step 3:

[1652] The server sends the generated training program and nutrition plan to the device. The device parses the received data and displays it on the application's UI. The input data is the generated program and plan, and the output data is the data in the format displayed to the user. Specifically, the device receives the JSON-formatted data, parses it, and displays it on the UI in the form of an "exercise menu," "meal plan," etc.

[1653] Step 4:

[1654] When a user exercises, they activate the camera function on their device to capture their movements. The input data is the captured video data, which is sent to the server in real time. The server receives the video data and analyzes it using AI models such as TensorFlow and PyTorch. Specific operations include "form check," "incorrect movement detection," and "injury risk analysis." The output data is corrections and advice, which are sent to the device and displayed to the user in real time.

[1655] Step 5:

[1656] The device collects the user's facial expression and voice data and sends it to the server. The input data is facial expression and voice data, which the server receives and analyzes using an emotion engine to obtain the user's emotional state as output data. Specific operations include "facial expression analysis" and "voice emotion analysis," and the device recognizes emotional states such as "fatigue," "motivation," and "stress."

[1657] Step 6:

[1658] The server adjusts the training and nutrition plan based on the recognized emotional state. The input data is the emotional state, and the output data is the adjusted plan. For example, if the user is in a high stress state, adjustments such as "reducing training intensity" or "adding relaxation exercises" will be made. The adjusted plan is then sent back to the device and displayed to the user.

[1659] Step 7:

[1660] The device provides options for users to customize the appearance and personality of their virtual trainer. Users select the appearance and personality of their favorite trainer through the UI, and the settings are sent from the device to the server. The input data is customization information, and the output data is setting information stored in the database. This allows future feedback and interactions to be tailored to the user's preferences.

[1661] Step 8:

[1662] The server manages paid plans, provides detailed exercise analysis reports and advice from experts, and also provides information on rental and purchase of exercise equipment and affiliate gyms through affiliate partnerships. The input data is the user's subscription status and affiliate information, and the output data is information on additional services and products. This allows for the expansion of services to users and monetization.

[1663] (Application example 2)

[1664] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1665] While existing systems provide exercise programs and nutritional plans based on users' health information, they are unable to provide feedback that takes into account the user's emotional state and work environment, resulting in low personalization accuracy and user satisfaction. Furthermore, there is a lack of systems specifically designed to manage the health of users working in factories, posing challenges for improving work efficiency and safety.

[1666] 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 an input means for inputting the user's health information, a program generation means for generating an exercise program based on the user's health information, an analysis means for analyzing the user's exercise and providing feedback, a program generation means for generating a nutrition plan based on the user's health information, a display means for displaying the results of the exercise program and the nutrition plan, an emotion recognition means for recognizing the user's emotional state, a means for adjusting the exercise program and the nutrition plan based on the emotion recognition means, and a means for monitoring the health information of users working in a factory in real time. This enables the health of users working in a factory to be managed in a highly personalized manner, improving work efficiency and safety.

[1667] "User's health information" refers to information related to the user's health, such as their height, weight, exercise habits, and diet.

[1668] An "exercise program" is an exercise plan generated based on the user's health information, and includes a training menu, intensity, and the like.

[1669] A "nutrition plan" is a nutrition plan generated based on the user's health information, and includes information such as meal content and calorie intake.

[1670] "Input means" refers to an interface for inputting the user's health information into the application, and includes a keyboard, touch screen, voice input, etc.

[1671] "Program generation means" refers to software or algorithms for generating exercise programs and nutrition plans based on input health information.

[1672] "Analysis means" refers to a system or software that captures the user's exercise movements, analyzes the movements, and provides feedback.

[1673] "Display means" refers to a device or interface for displaying the results of the generated exercise program or nutrition plan to the user, including a smartphone, head-mounted display, smart glasses, etc.

[1674] "Emotion recognition means" refers to a system or algorithm that analyzes a user's facial expression data and voice data to recognize the user's emotional state.

[1675] The "adjustment means" refers to an algorithm or system that appropriately adjusts the exercise program and nutrition plan based on the user's emotional state recognized by the emotion recognition means.

[1676] "Users working in factories" refers to operators and workers who work in the factory's working environment, and are the target of improving their health and work efficiency.

[1677] "Real-time monitoring means" refers to systems or devices that collect and analyze a user's health information in real time.

[1678] To implement the present invention, the following system configuration and program are required.

[1679] System Configuration

[1680] 1. Input Method

[1681] Users input health information (e.g., height, weight, exercise habits, and nutritional information) using a head-mounted display (HMD) or smart glasses, which can include keyboard, touchscreen, and voice input.

[1682] 2. Program Generation Method

[1683] The server generates the optimal exercise and nutritional plan for each individual user based on the health information entered by the user. The database contains past data, the latest fitness theories, and nutritional knowledge, and generates the program based on this information.

[1684] 3. Analysis method

[1685] When a user exercises, their movements are captured using the camera function of the HMD or smart glasses. The captured data is sent to a server in real time and analyzed using an AI model (e.g., TensorFlow / Keras). This detects incorrect movements or movements that pose a risk of injury.

[1686] 4. Display means

[1687] The generated exercise program and nutrition plan, as well as analytical feedback, are displayed on an HMD, smart glasses, smartphone, or tablet.

[1688] 5. Emotion recognition means

[1689] The user's facial expression and voice data are collected through a camera and microphone and sent to a server, which uses an emotion recognition algorithm to recognize the user's emotional state (e.g., fatigue, motivation, stress).

[1690] 6. Adjustment means

[1691] Based on the perceived emotional state, the server will adjust the exercise program and nutrition plan accordingly, for example reducing the intensity of training or suggesting relaxation exercises if the user is in a high stress state.

[1692] 7. Real-time monitoring methods

[1693] It includes a system for real-time monitoring of the health information and exercise status of users working in factories, which is expected to improve the health management and work efficiency of users working in factories.

[1694] Specific examples of hardware and software used

[1695] Hardware: HMD (head-mounted display), smart glasses, smartphone, tablet, camera, microphone

[1696] Software: TensorFlow / Keras (AI models), database management systems (e.g., Firebase, MySQL), software for real-time data analysis, emotion recognition algorithms

[1697] Specific examples

[1698] For example, if a user sets a goal of "losing 5 kg" and selects a program to exercise three times a week, the server will generate an appropriate training menu (e.g., aerobic exercise and strength training) and also suggest a nutritionally balanced meal plan. As the user exercises, the camera in the HMD or smart glasses captures the user's movements, and the server provides real-time feedback. Furthermore, the system recognizes the user's emotional state from their facial expressions and voice, and adjusts the program by, for example, adding relaxation options if fatigue increases.

[1699] Prompt Sentence Examples

[1700] "I would like to develop a healthcare application that captures the user's facial expressions, analyzes their emotions, and provides mental support. The user is an operator, and the application needs to detect fatigue in real time while working and suggest appropriate breaks and exercise plans. The technologies used include facial expression analysis using a camera and emotion recognition using machine learning. Please build such an application using Python and provide concrete examples of the necessary APIs and libraries."

[1701] The above is a detailed description of the embodiment of the present invention. This system enables highly personalized health management for users working in a factory, thereby improving efficiency and safety.

[1702] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1703] Step 1:

[1704] The user inputs health information (height, weight, exercise habits, nutritional information, etc.) using a head-mounted display (HMD) or smart glasses.

[1705] Input: User's health information

[1706] Output: Health information sent to the server

[1707] Specific operation: The user enters health information through a form on the HMD or smart glasses screen, and after completing the entry, the information is sent to the server.

[1708] Step 2:

[1709] The server generates a personalized exercise program and nutrition plan based on the user's input health information.

[1710] Input: User's health information submitted

[1711] Output: Generated exercise program and nutrition plan

[1712] How it works: The server pulls historical data, the latest fitness theories, and nutritional knowledge from a database, and uses algorithms to calculate the optimal exercise program and nutrition plan for the user.

[1713] Step 3:

[1714] The server transmits the generated exercise program and nutrition plan to the terminal.

[1715] Input: Generated exercise program and nutrition plan

[1716] Output: Exercise program and nutrition plan sent to the device

[1717] Specific operation: The generated exercise program and nutrition plan are sent to the terminal for display on the HMD or smart glasses.

[1718] Step 4:

[1719] The user exercises using an HMD or smart glasses. The device captures the user's movements in real time through a camera and transmits them to a server.

[1720] Input: Real-time video of the user's movements

[1721] Output: Real-time motion video sent to the server

[1722] Specific actions: The user starts exercising, and the device's camera captures the action and sends it to the server as video data.

[1723] Step 5:

[1724] The server analyzes the received real-time motion video using an AI model to detect incorrect movements and risk of injury, and generates feedback based on the analysis results and sends it to the device.

[1725] Input: Transmitted real-time motion video

[1726] Output: Analysis results and feedback

[1727] Specific operation: The server uses an AI model (e.g., TensorFlow / Keras) to analyze the motion video and detect incorrect movements or risk of injury. It then generates feedback based on the detection results.

[1728] Step 6:

[1729] The terminal displays the feedback sent from the server to the user in real time.

[1730] Input: Feedback from the server

[1731] Output: Displayed feedback

[1732] Specific operation: The device that receives the feedback displays it in real time on the HMD or smart glasses, providing the user with the correct exercise method and correction instructions.

[1733] Step 7:

[1734] The terminal collects the user's facial expression data and voice data and sends it to the server.

[1735] Input: User's facial expression data and voice data

[1736] Output: Facial expression data and voice data sent to the server

[1737] Specific operation: Uses a camera and microphone to collect the user's facial expressions and voice in real time.

[1738] Step 8:

[1739] The server performs emotion recognition using the transmitted facial expression data and voice data to recognize the user's current emotional state.

[1740] Input: facial expression data and voice data

[1741] Output: User's emotional state

[1742] Specific operation: The server uses an emotion recognition algorithm to analyze the transmitted data and recognize the user's emotional state (e.g., fatigue, motivation, stress).

[1743] Step 9:

[1744] The server adjusts the exercise program and nutrition plan accordingly based on the recognized emotional state and transmits them to the terminal.

[1745] Input: Perceived emotional state

[1746] Output: Tailored exercise program and nutrition plan

[1747] Specific operation: The server adjusts exercise programs and nutrition plans according to the emotional state, and takes measures such as suggesting relaxation exercises if the person is in a high-stress state.

[1748] Step 10:

[1749] The terminal displays the tailored exercise program and nutrition plan to the user.

[1750] Input: Tailored exercise program and nutrition plan

[1751] Output: Exercise program and nutrition plan with adjustments

[1752] Specific operation: The adjusted plan is displayed on the device, and the user can check the next exercise and meal plan they should do.

[1753] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1754] 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.

[1755] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1756] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1757] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1758] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1759] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1760] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1761] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1762] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1763] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1764] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1765] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1766] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1767] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1768] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1769] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1770] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1771] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1772] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1773] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1774] The following is further disclosed regarding the above embodiment.

[1775] (Claim 1)

[1776] an input means for inputting the user's health information;

[1777] a program generation means for generating an exercise program based on the user's health information;

[1778] analysis means for analyzing the user's exercise based on the exercise program and providing feedback;

[1779] a program generating means for generating a nutrition plan based on the user's health information;

[1780] a display means for displaying the results of said exercise program and nutrition plan;

[1781] A system including:

[1782] (Claim 2)

[1783] 10. The system of claim 1, wherein the analyzing means includes means for capturing and analyzing the user's athletic movements in real time.

[1784] (Claim 3)

[1785] 10. The system of claim 1, further comprising a customization means for a user to customize the appearance and personality of the virtual trainer.

[1786] "Example 1"

[1787] (Claim 1)

[1788] an input means for inputting the user's health information;

[1789] a program generating means for generating an exercise program and a nutrition plan based on the user's health information;

[1790] analysis means for analyzing the user's exercise based on the exercise program and providing feedback;

[1791] The analysis means uses a generative AI model to input a prompt sentence and perform data analysis;

[1792] a display means for displaying the results of said exercise program and nutrition plan;

[1793] A system including:

[1794] (Claim 2)

[1795] 10. The system of claim 1, wherein the analyzing means includes means for capturing and analyzing the user's athletic movements in real time.

[1796] (Claim 3)

[1797] 10. The system of claim 1, further comprising a customization means for a user to customize the appearance and personality of the virtual trainer.

[1798] "Application Example 1"

[1799] (Claim 1)

[1800] an input means for inputting the user's health information;

[1801] a program generation means for generating an exercise program based on the user's health information;

[1802] analysis means for analyzing the user's exercise based on the exercise program and providing feedback;

[1803] a program generating means for generating a nutrition plan based on the user's health information;

[1804] a display means for displaying the results of said exercise program and nutrition plan;

[1805] real-time display means for displaying the feedback in real time;

[1806] A motion capture means for capturing and analyzing the user's motions in real time while the user is training;

[1807] A system including:

[1808] (Claim 2)

[1809] 10. The system of claim 1, wherein the motion capture means includes means for capturing and analyzing the user's athletic motion in real time.

[1810] (Claim 3)

[1811] 10. The system of claim 1, further comprising a customization means for a user to customize the appearance and personality of the virtual trainer.

[1812] "Example 2: Combining Emotion Engines"

[1813] (Claim 1)

[1814] an input means for inputting the user's health information;

[1815] a program generation means for generating an exercise program based on the user's health information;

[1816] analysis means for analyzing the user's exercise based on the exercise program and providing feedback;

[1817] a program generating means for generating a nutrition plan based on the user's health information;

[1818] a display means for displaying the results of said exercise program and nutrition plan;

[1819] an analytical means for collecting facial expression data and voice data of the user and analyzing the emotional state;

[1820] adjusting means for adjusting a training and nutrition plan based on said emotional state;

[1821] A system including:

[1822] (Claim 2)

[1823] 10. The system of claim 1, wherein the analyzing means includes means for capturing and analyzing the user's athletic movements in real time.

[1824] (Claim 3)

[1825] 10. The system of claim 1, further comprising a customization means for a user to customize the appearance and personality of the virtual trainer.

[1826] "Application example 2 when combining emotion engines"

[1827] (Claim 1)

[1828] an input means for inputting the user's health information;

[1829] a program generation means for generating an exercise program based on the user's health information;

[1830] analysis means for analyzing the user's exercise based on the exercise program and providing feedback;

[1831] a program generating means for generating a nutrition plan based on the user's health information;

[1832] a display means for displaying the results of said exercise program and nutrition plan;

[1833] emotion recognition means for recognizing an emotional state of the user;

[1834] means for adjusting an exercise program and a nutrition plan based on said emotion recognition means;

[1835] A system including:

[1836] (Claim 2)

[1837] 10. The system of claim 1, wherein the analyzing means includes means for capturing and analyzing the user's athletic movements in real time.

[1838] (Claim 3)

[1839] 10. The system of claim 1, further comprising a customization means for a user to customize the appearance and personality of the virtual trainer.

[1840] (Claim 4)

[1841] 10. The system of claim 1, further comprising means for monitoring said health information in real time, for providing health information and exercise and nutrition plans to users working in a factory.

[1842] (Claim 5)

[1843] The system according to claim 4, wherein the display means includes a head-mounted display or smart glasses and is applied in a factory. [Explanation of symbols]

[1844] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting the user's health information; a program generation means for generating an exercise program based on the user's health information; analysis means for analyzing the user's exercise based on the exercise program and providing feedback; a program generating means for generating a nutrition plan based on the user's health information; a display means for displaying the results of said exercise program and nutrition plan; A system including:

2. 2. The system of claim 1, wherein the analyzing means includes means for capturing and analyzing the user's athletic movements in real time.

3. 10. The system of claim 1, further comprising a customization means for a user to customize the appearance and personality of the virtual trainer.

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