system

Abstract

To provide a system for inputting the constitution and body type information of a user, and for proposing an optimal training menu.SOLUTION: A means for inputting user's constitution and body shape information, a means for transmitting the input constitution and body shape information to a server, a means for receiving user information and storing the user information in a database, a means for collecting and organizing training information from media and SNS, a means for combining the collected training information and user information to generate an optimal training menu, a means for transmitting the generated training menu to a user terminal, a means for displaying the optimal training menu, a means for receiving user's progress data and continuously updating the training menu, and a means for anonymizing user's ideal body shape data; Means for providing to an apparel maker.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] In recent years, with the rise of health consciousness, many people are trying to overcome lack of exercise and achieve their ideal body shape. However, it is difficult to choose a training menu that suits each individual's constitution and body type, making it difficult to train efficiently. Furthermore, the high cost of relying on personal gyms or professionals limits the number of convenient options available to many people. Furthermore, apparel manufacturers often produce clothing in unnecessary sizes to accommodate the diverse body types of consumers, resulting in increased costs and wasted resources. [Means for solving the problem]

[0005] The present invention is a system that inputs a user's physical constitution and body type information and proposes an optimal training menu. The present invention solves the problems by the following means: A means is provided for the user to input their physical constitution and body type information and send that information to a server using a terminal. The server stores the received user information in a database and further has means for collecting and organizing training information from media and social networking sites. An AI model is used to realize a means for combining the collected training information with the user information to generate an optimal training menu. The generated training menu is sent to the user's terminal, and the optimal training menu is displayed. Furthermore, a means is provided for receiving the user's progress data and continuously updating the training menu, and the user's ideal body type data is anonymized and provided to apparel manufacturers, thereby realizing efficient production without waste.

[0006] "User information" refers to data relating to physical constitution and body type, such as height, weight, age, and target body type, entered by the user.

[0007] "Constitution" refers to a user's physiological characteristics and health condition, and is usually composed of data such as height, weight, and age.

[0008] "Body shape" refers to the appearance and structure of the body as it changes with the user's exercise, including measurements such as muscle mass and fat mass.

[0009] The "server" is a central processing unit that receives user information and stores it in a database, and is responsible for generating training menus using AI models.

[0010] A "database" is a system for efficiently managing data such as user information and training information stored on a server.

[0011] An "AI model" is an algorithm or program that uses artificial intelligence technology to analyze data and generate an optimal training menu.

[0012] A "training menu" is a specific exercise plan or list of exercises suggested to help a user achieve their desired body shape.

[0013] "Progress data" refers to data that records the results and current status of the exercises that a user has performed in accordance with a training menu.

[0014] An "apparel manufacturer" is a company that manufactures, designs, and sells clothing and other fashion products.

[0015] "Anonymization" refers to the technology and methods used to process data so that individual users cannot be identified, and is used to protect privacy. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system that utilizes AI technology to provide an optimal training menu based on the user's physical constitution and body type information. The system sends the physical constitution and body type information entered by the user to a server, which then generates and distributes the optimal training menu based on that information. It also has the function of monitoring the user's progress and continuously updating the training menu. Furthermore, the system anonymizes the user's ideal body type data and provides it to apparel manufacturers, supporting efficient clothing production.

[0038] Entering and submitting user information

[0039] Users use the application to input their basic information (height, weight, age, and desired body shape). For example, if a user is 170 cm tall, weighs 70 kg, is 30 years old, and aims to increase muscle mass as their desired body shape, they can input this information on the application screen. This allows the proposed training menu to be precisely customized.

[0040] Data transmission and storage on the server

[0041] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database, which is then used for AI analysis.

[0042] Collecting and organizing training data

[0043] The server uses AI models to collect and organize training information from various sources, including media and social media, making it possible to reference the latest and most diverse training methods, including popular training videos on social media and training menus by athletes.

[0044] Training menu generation

[0045] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, the AI ​​will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[0046] Distribution and display of training menus

[0047] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[0048] Collecting progress data and updating training menus

[0049] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[0050] Providing data to apparel manufacturers

[0051] The server aggregates and anonymizes users' ideal body data before providing it to apparel manufacturers. This data is used as the foundation for apparel manufacturers to efficiently produce clothes that meet customer needs. For example, it can plan designs in advance to fit the body shapes desired by many users.

[0052] In this way, the present invention is a system that not only provides optimal training to users but also supports efficient clothing production in the apparel industry.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user starts the training application using the terminal and inputs information about his / her constitution and body type, such as his / her height, weight, age, and target body type.

[0056] Step 2:

[0057] The terminal sends the entered user's constitution and body type information to the server.

[0058] Step 3:

[0059] The server stores the received user information in a database.

[0060] Step 4:

[0061] The server collects the latest training information from media and social media, and organizes the data using an AI model, including famous training programs and athletes' body stats.

[0062] Step 5:

[0063] The server combines the organized training data with the saved user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to improve muscle strength, a menu including three strength training sessions and two aerobic exercise sessions per week will be suggested.

[0064] Step 6:

[0065] The server sends the generated training menu to the user's terminal.

[0066] Step 7:

[0067] The device receives the training menu and displays it to the user, who can then perform the training according to the displayed menu.

[0068] Step 8:

[0069] The user performs training and enters their progress into the application, including data such as the type and duration of training performed and the calories burned.

[0070] Step 9:

[0071] The terminal transmits the input progress data to the server.

[0072] Step 10:

[0073] The server analyzes the progress data received and continuously optimizes the training menu according to the user's progress. For example, if muscle strength is improving, the server will suggest higher-intensity training aimed at further improving muscle strength.

[0074] Step 11:

[0075] The server again transmits the latest training menu to the user's terminal, and the terminal displays it to the user.

[0076] Step 12:

[0077] The server collects data on users' ideal body types, anonymizes the data, and provides it to apparel manufacturers, enabling them to produce clothes efficiently and without waste.

[0078] Example 1

[0079] 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."

[0080] Traditional training programs were not optimized for each user's individual physique or goals, making it difficult to provide an effective training plan for a specific user. Furthermore, progress monitoring and continuous training menu updates were manually performed, resulting in inefficiencies. Furthermore, there was no efficient way to collect and provide customer body shape information to meet the needs of the apparel industry.

[0081] 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.

[0082] In this invention, the server includes: [means for a user to input their own basic physical constitution and body type information;] [means for transmitting the input physical constitution and body type information to the server via the Internet;] [means for the server to receive the user information and store it in a database;] [means for the server to collect and organize training data from the Internet;] [means for the server to generate an optimal training plan based on the collected training data and user information;] [means for transmitting the generated training plan to the user's terminal;] [means for displaying the optimal training plan on the user's terminal;] [means for the user to input training progress data and transmit the progress data to the server;] [means for the server to continuously update the training plan based on the user's progress data; and [means for the server to anonymize the user's target body type data and provide it to the apparel industry.] This makes it possible to provide an optimal training plan tailored to the user's individual physical constitution and goals, and to continuously optimize training menus based on the progress data. It also makes it possible to efficiently provide customer body type information to the apparel industry.

[0083] "User information" refers to the user's basic constitution and body type information, including height, weight, age, and target body type.

[0084] A "server" is a computer system that receives data from users via the Internet, stores it in a database, and generates and updates training plans.

[0085] "Terminal" refers to a device through which a user inputs data and receives and displays the training plan sent from the server, and includes a smartphone, computer, etc.

[0086] "Training data" refers to information about various types of training collected from the internet, and is data that has been organized from content obtained from the media and social networking sites.

[0087] A "training plan" is an optimal training menu created based on the user's physical constitution, body type information, and training data.

[0088] "Progress data" refers to data entered by the user regarding the status of training and the level of achievement, and is information used by the server when updating the training plan.

[0089] "Target body data" is information about the ideal body shape that a user is aiming for, and is anonymized data provided to the apparel industry.

[0090] "Anonymization" is a technical method of protecting data by removing personally identifiable information.

[0091] The present invention is a system that utilizes AI technology to provide an optimal training menu based on a user's physical constitution and body type information. The main components of the system include a user terminal, a server, and an AI model. Specific embodiments of this system are described below.

[0092] Hardware and Software Configuration

[0093] Users access the system using devices such as smartphones or computers. Applications developed using frameworks such as React and Flutter (registered trademark) run on the devices. Through these applications, users can input information about their physical constitution and body type.

[0094] On the server side, data collection and analysis are performed using programming languages ​​such as Python and R. Database management software such as MySQL (registered trademark) and PostgreSQL is used for the database, and user information and training data are stored. The server also uses generative AI models such as TENSORFLOW (registered trademark) and PyTorch to generate and optimize training menus.

[0095] Data processing and calculation

[0096] The physical constitution and body type information entered by the user through the application is sent from the device to the server, which receives the information and stores it in a database. This stored data is used for subsequent analysis and the generation of training menus.

[0097] The server utilizes web scraping technology to collect the latest training techniques and methods from online media and social media, and this collected data is converted into a well-organized format that is later analyzed by the AI ​​model.

[0098] The server uses the collected training data and the user's physical constitution and body type information to generate an optimal training menu. The generated training menu is then sent back to the user's device from the server and displayed via the application. The user then trains according to this training menu.

[0099] When users enter their training progress data into the app, the data is sent from the device to the server, which then uses the progress data to continuously optimize the training menu and provide the user with the latest menu.

[0100] Examples of concrete examples and prompts

[0101] For example, consider a situation where a user enters basic information through an application, such as:

[0102] My current height is 170cm, my weight is 70kg, and I'm 30 years old. My goal is to increase muscle mass. Please generate the optimal training menu for me.

[0103] Based on this prompt, the system can generate the following training menu:

[0104] Strength training three times a week

[0105] Weightlifting

[0106] bench press

[0107] Deadlift

[0108] Aerobic exercise twice a week

[0109] 30 minutes of jogging

[0110] 45 minutes cycling

[0111] After training, the user can enter information into the app, such as "I did 30 minutes of weightlifting today," and the system will use this information to update the next training menu.

[0112] In this way, the system of the present invention can continuously provide users with individually optimized training programs. Also, by anonymizing the user's goal body shape data and providing it to the apparel industry, apparel manufacturers can efficiently develop products that meet customer needs.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] The user launches the application and enters their basic information (height, weight, age, and desired body shape).

[0116] Input: User-entered height, weight, age, and body shape goals.

[0117] Output: The terminal collects these data and prepares them for transmission.

[0118] Specific operations: The user enters information such as "height 170 cm, weight 70 kg, age 30, goal body shape is to increase muscle mass" into a form on the application screen of a smartphone or computer, and clicks the submit button.

[0119] Step 2:

[0120] The device sends the user's input data to the server.

[0121] Input: Physical and body type information entered by the user.

[0122] Output: Data is sent to the server via the HTTPS protocol.

[0123] Specific operation: The device converts the data entered by the user into JSON format and sends it to the server.

[0124] Step 3:

[0125] The server receives the user information and stores it in a database.

[0126] Input: Physical and body type information sent from the device.

[0127] Output: User information stored in the database.

[0128] What happens: The server parses the received JSON data and inserts it into the appropriate table in the database.

[0129] Step 4:

[0130] The server collects and organizes training data from online media and social networking sites.

[0131] Input: Training-related information obtained from media and social media.

[0132] Output: Training data stored in an organized format.

[0133] Specific operation: The server uses web scraping technology to analyze and collect text and video information using Python or R programs.

[0134] Step 5:

[0135] The server generates an optimal training plan based on the collected training data and user information.

[0136] Input: Organized training data, user physical and body type information.

[0137] Output: The generated optimal training plan.

[0138] How it works: The server runs an AI model using TensorFlow or PyTorch to generate a training plan that includes, for example, "three strength training sessions and two cardio sessions per week."

[0139] Step 6:

[0140] The server sends the generated training plan to the user's device.

[0141] Input: The generated training plan.

[0142] Output: The training plan is sent to the user's device.

[0143] Specific operation: The server converts the generated training plan into JSON format and sends it to the device.

[0144] Step 7:

[0145] The device will display the optimal training plan to the user.

[0146] Input: The training plan sent from the server.

[0147] Output: The training plan that is displayed to the user.

[0148] Specific operation: The device analyzes the received training plan and displays it in a list format on the application screen. The user adds the event to their calendar.

[0149] Step 8:

[0150] The user enters training progress data into the app, and the device sends the progress data to the server.

[0151] Input: Training progress data entered by the user.

[0152] Output: Progress data sent to the server.

[0153] Specific behavior: The user enters "I lifted weights for 30 minutes today" and sends that data to the server.

[0154] Step 9:

[0155] The server continuously updates the training plan based on the user's progress.

[0156] Input: User progress data stored on the server.

[0157] Output: Continuously optimized training plans.

[0158] What it does: The server analyzes the new progress data, recreates the next training plan using the AI ​​model, and sends it to the user's device.

[0159] Step 10:

[0160] The server anonymizes the user's target body shape data and provides it to the apparel industry.

[0161] Input: User's goal body data.

[0162] Output: Anonymized data will be provided to the apparel industry.

[0163] Specific operation: The server collects the user's target body shape data, removes personal information, converts it into JSON format, and sends it to the apparel industry.

[0164] (Application example 1)

[0165] 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."

[0166] Conventional training menu provision systems cannot accurately reflect the user's physical constitution and body type information, making it difficult to provide an optimized menu for each user. Furthermore, they lack the functionality to update the menu in real time according to the user's training progress, and have not achieved the improvement of apparel production efficiency by utilizing the user's ideal body type data. The present invention aims to solve these problems and provide a training menu provision system that can be effectively used in physical stores.

[0167] 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.

[0168] In this invention, the server includes: [means for inputting a user's physical constitution and body type information and performing a body type scan;] [means for collecting and organizing training information using a generative AI model; and] [means for combining the collected training information and user information to generate an optimal training menu.] This makes it possible [to automatically generate a training menu optimized for each user and update it in real time according to progress, and further improve production efficiency by anonymizing ideal body type data and providing it to clothing manufacturers.]

[0169] "User's constitution and body type information" refers to data such as height, weight, age, body type scan results, and target body type entered by the user, and is the basic information for generating the optimal training menu for each individual user.

[0170] "Body scanning" is the process of using smart glasses and other sensors to collect detailed information about a user's body shape.

[0171] "Database" means an electronic storage system that systematically stores and manages user and training information.

[0172] "Media and SNS" is a collective term for various information media and social networking services used as sources of training information.

[0173] A "generative AI model" is an artificial intelligence model that analyzes data collected from media and social media to generate training menus, and automatically generates optimal training menus.

[0174] A "training menu" is a specific exercise and training plan created based on the user's physical constitution and body type information.

[0175] "Smart glasses" are eyeglass-type devices equipped with internet connectivity and a built-in camera that can acquire and display information in real time when worn by the user.

[0176] "Progress data" is data that indicates the results and progress of a user's training, and is information that forms the basis for updating the training menu.

[0177] "Clothing manufacturers" is a general term for companies that use ideal body data to produce clothes that are best suited to their users.

[0178] "Anonymization" is the process of removing information that identifies individual users and converting them into a form that makes it impossible to identify individual users.

[0179] The system of the present invention generates an optimal training menu based on the user's physical constitution and body type information, and supports use in physical stores. The system monitors the user's progress and continuously updates the training menu. The system also anonymizes the collected ideal body type data and provides it to clothing manufacturers, supporting efficient clothing production.

[0180] Program processing explanation

[0181] Hardware used

[0182] Smart glasses: Devices with built-in cameras, displays, and internet connectivity.

[0183] Server: Stores user information and generates training menus using AI models.

[0184] Device (smartphone or tablet): Used to input and check training progress.

[0185] Software used

[0186] scikit-learn: A Python machine learning library used to implement AI models.

[0187] TensorFlow: An open-source machine learning framework used to generate complex training menus.

[0188] PostgreSQL: An open-source relational database used to store user information and training data.

[0189] React Native: A cross-platform mobile application framework for applications on smart glasses and devices.

[0190] Data flow and processing procedures

[0191] 1. Input and transmission of user information: The user uses the smart glasses' camera to scan their body and input their height, weight, age, and desired body shape. The input data is then sent from the smart glasses to the server.

[0192] 2. Data storage: The server stores the received data in a PostgreSQL database. This stored data serves as the basis for generating training menus using the AI ​​model.

[0193] 3. Collecting and organizing training information: The server uses a generative AI model to collect and organize training information from media and social media, including the latest training methods and training menus of famous athletes.

[0194] 4. Training menu generation: The AI ​​model combines the collected training information with user information to generate the optimal training menu for the user.

[0195] 5. Menu distribution and display: The generated training menu is displayed in real time on the smart glasses display, and the user follows the instructions on the smart glasses to perform the training.

[0196] 6. Progress data collection and update: After completing training, users input their progress data into the smart glasses or terminal, which is then sent to the server, where the AI ​​model continuously optimizes the training menu.

[0197] 7. Providing ideal body data: The server anonymizes the user's ideal body data and provides it to clothing manufacturers, allowing them to efficiently produce clothes that meet customer needs.

[0198] Examples of concrete examples and prompts

[0199] Examples:

[0200] A fitness club trainer uses smart glasses to scan the body shape of a new client, and the system immediately generates and displays a training menu that is optimal for the client's goals.

[0201] Example prompt:

[0202] Generative AI model prompt example:

[0203] Input data: User's body scan, basic information (height, weight, age, desired body shape)

[0204] Goal: Optimal 4-day-per-week training schedule for users looking to gain muscle mass

[0205] The present invention not only provides an optimized training menu for each individual user, but also updates it in real time according to the user's progress, and further anonymizes ideal body shape data and provides it to clothing manufacturers, thereby improving the efficiency of clothing production.

[0206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0207] Step 1:

[0208] The user puts on the smart glasses and undergoes a body scan. The smart glasses' built-in camera scans the user's body and inputs information such as height, weight, age, and desired body shape. This input data is stored in the smart glasses. Input data: body scan results (image data), height, weight, age, and desired body shape.

[0209] Step 2:

[0210] The smart glasses send the collected user's physical constitution and body shape information to the server. The sent data is received by the server and stored in a database. After the data is sent, a confirmation message is displayed on the smart glasses. Input data: User's physical constitution and body shape information, Output data: Data sending confirmation message.

[0211] Step 3:

[0212] The server collects and organizes training information from media and social media. A generative AI model is used for this process, analyzing and organizing the collected information. Input data: training information obtained from media and social media. Output data: organized training information.

[0213] Step 4:

[0214] The server combines the collected training information with the user's physical constitution and body type information and generates an optimal training menu using a generative AI model. This generation process plans the type of exercise, frequency, and time that are suited to the user's goal body type. Input data: User information, organized training information, generative AI model. Output data: Optimal training menu.

[0215] Step 5:

[0216] The generated training menu is sent from the server to the smart glasses and displayed in real time. The user follows the instructions on the smart glasses to begin training. Input data: optimal training menu, Output data: training menu displayed on the smart glasses.

[0217] Step 6:

[0218] After completing the training, the user inputs their progress data using smart glasses or a terminal. This progress data is sent to the server. Input data: user's progress data. Output data: server receipt confirmation message.

[0219] Step 7:

[0220] The server analyzes the received progress data and continuously optimizes the training menu. The generative AI model takes in new data and updates the next training menu. Input data: user progress data, Output data: updated training menu.

[0221] Step 8:

[0222] The server anonymizes the user's ideal body data and provides it to clothing manufacturers. The anonymization process protects the user's personal information. The manufacturer uses this data to design products. Input data: User's ideal body data, Output data: Anonymized body data.

[0223] 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.

[0224] This invention is a system that utilizes AI technology based on the user's physical constitution and physique information, and further incorporates an emotion engine to provide an optimal training menu that recognizes the user's emotions.By utilizing the user's emotional data, this system maximizes the effectiveness of the training menu and improves motivation.

[0225] Entering and submitting user information

[0226] Users use the application to input their basic information (height, weight, age, and desired body shape). For example, if a user is 170 cm tall, weighs 70 kg, is 30 years old, and aims to increase muscle mass as their desired body shape, they can input this information on the application screen. This allows the proposed training menu to be precisely customized.

[0227] Data transmission and storage on the server

[0228] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database, which is then used for AI analysis.

[0229] Collecting and organizing training data

[0230] The server uses AI models to collect and organize training information from various sources, including media and social media, making it possible to reference the latest and most diverse training methods, including popular training videos on social media and training menus by athletes.

[0231] Training menu generation

[0232] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, the AI ​​will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[0233] Incorporating an emotion engine

[0234] The device uses the emotion engine to identify the user's emotional state (e.g., stress level or excitement level) in real time, allowing the emotion engine to reflect the user's emotional data in generating a training menu.

[0235] Reflecting emotional data

[0236] The server further customizes the training menu based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest light exercises for relaxation, while if the user is highly motivated, it will suggest high-intensity training.

[0237] Providing messages to improve motivation

[0238] The device displays customized messages to motivate users based on their emotional data, which is recognized by the emotion engine. This allows users to receive not only the optimal training menu but also psychological support.

[0239] Distribution and display of training menus

[0240] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[0241] Collecting progress data and updating training menus

[0242] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[0243] Providing data to apparel manufacturers

[0244] The server aggregates and anonymizes users' ideal body data before providing it to apparel manufacturers. This data is used as the foundation for apparel manufacturers to efficiently produce clothes that meet customer needs. For example, it can plan designs in advance to fit the body shapes desired by many users.

[0245] In this way, the present invention is a system that not only provides optimal training to users, but also provides psychological support and can also support efficient clothing production in the apparel industry.

[0246] The processing flow will be explained below.

[0247] Step 1:

[0248] The user starts the training application using the terminal and inputs information about his / her constitution and body type, such as his / her height, weight, age, and target body type.

[0249] Step 2:

[0250] The terminal sends the entered user's constitution and body type information to the server.

[0251] Step 3:

[0252] The server stores the received user information in a database.

[0253] Step 4:

[0254] The server collects the latest training information from media and social media, and organizes the data using an AI model, including famous training programs and athletes' body stats.

[0255] Step 5:

[0256] The server combines the organized training data with the saved user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to improve muscle strength, a menu including three strength training sessions and two aerobic exercise sessions per week will be suggested.

[0257] Step 6:

[0258] The server sends the generated training menu to the user's terminal.

[0259] Step 7:

[0260] The device receives the training menu and displays it to the user, who can then perform the training according to the displayed menu.

[0261] Step 8:

[0262] The device uses an emotion engine to recognize the user's emotional state. For example, the emotion engine analyzes data such as the user's facial expressions, voice tone, and heart rate to determine whether the user is feeling stressed or motivated.

[0263] Step 9:

[0264] The server receives the user's emotional data recognized by the emotion engine and reflects that data in the generation of training menus. For example, if the user is feeling stressed, it will suggest a menu that includes gentle stretches to promote relaxation.

[0265] Step 10:

[0266] The device will display customized motivational messages based on the user's emotional state: for example, if the user is tired, an encouraging message will be displayed.

[0267] Step 11:

[0268] The user performs training and inputs progress (type of training performed, duration, calories burned, etc.) into the application.

[0269] Step 12:

[0270] The terminal transmits the input progress data to the server.

[0271] Step 13:

[0272] The server analyzes the progress data received and continuously updates the training menu according to the user's progress. For example, if muscle strength is improving, the server will suggest more intense training to further improve muscle strength.

[0273] Step 14:

[0274] The server again transmits the latest training menu to the user's terminal, and the terminal displays it to the user.

[0275] Step 15:

[0276] The server collects data on users' ideal body types, anonymizes the data, and provides it to apparel manufacturers, enabling them to produce clothes efficiently and without waste.

[0277] Example 2

[0278] 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."

[0279] Currently, most training systems only provide training menus based on basic information about the user's physical constitution and body type, and lack detailed customization based on the user's emotions and progress. Furthermore, they lack mechanisms for effectively increasing user motivation, resulting in a lack of training sustainability. Furthermore, there are no systems that effectively utilize data based on the user's ideal body type to benefit the apparel industry. To solve these problems, a comprehensive training system is needed that reflects the user's emotions and progress in real time, maximizes training efficiency, and increases user motivation.

[0280] 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.

[0281] In this invention, the server includes: [means for inputting a user's physical constitution and body type information;] [means for transmitting the input physical constitution and body type information to the server;] [means for receiving user information and saving it in a database;] [means for collecting and organizing training information from media and social networking services;] [means for combining the collected training information and user information to generate an optimal training menu;] [means for acquiring emotional data using an emotion engine that identifies the user's emotional state in real time;] [means for customizing the training menu based on the acquired emotional data;] [means for transmitting the generated training menu to the user's terminal;] [means for displaying the optimal training menu;] [means for receiving progress data and continuously updating the training menu;] [means for providing customized motivational messages; and [means for anonymizing the user's ideal body type data and providing it to clothing manufacturers.] This enables the provision of detailed training menus based on the user's emotions and progress, psychological support to improve motivation, and efficient product development by providing data to the apparel industry.

[0282] "User's constitution and body type information" refers to personal physical information such as height, weight, age, and target body type input by the user.

[0283] "Server" refers to a computer system used to manage and operate user information and training data.

[0284] A "database" is a storage device for storing and managing data such as user information and training menus.

[0285] "Media and Social Networking Services" means websites and social media platforms on which training information is made public.

[0286] "Training information" refers to information about training methods and menus that can be optimized to suit the user's body type and constitution.

[0287] The "optimal training menu" is a training plan generated using an AI model based on the user's physical constitution and body type information.

[0288] An "emotion engine" is a software technology that analyzes and identifies user emotions in real time.

[0289] "Emotion data" refers to information such as the user's stress level and motivation status identified by the emotion engine.

[0290] "Motivational Messages" are customized messages based on the user's emotional state to motivate them to train.

[0291] "Progress data" refers to performance data such as the calories burned and number of sets completed when a user trains.

[0292] A "clothing manufacturer" is a company that designs and produces clothing based on a user's ideal body data.

[0293] "Anonymization" is a technique for processing data so that users cannot be personally identified.

[0294] "User Device" means the device used by a User to enter information and receive training menus and motivational messages.

[0295] This invention utilizes AI technology based on the user's physical constitution and physique information, and incorporates an emotion engine to recognize the user's emotions and provide an optimal training menu. By utilizing the user's emotional data, this system maximizes the effectiveness of the training menu and improves motivation.

[0296] System configuration

[0297] The user uses a dedicated application to input and submit their own physical constitution and body type information (height, weight, age, and desired body type). This information is stored in JSON format on the device. The device then sends the information entered by the user to the server using the HTTPS protocol. The server receives the data, verifies it, and stores it in a secure database (such as PostgreSQL).

[0298] Data collection and organization

[0299] The server collects training information using web scraping tools (such as Beautiful Soup) and social media APIs (such as Twitter API and Instagram API), and organizes this data into categories using AI models (such as Google® TensorFlow and OpenAI® API).

[0300] Training menu generation

[0301] The server generates an optimal training menu using a generative AI model (e.g., GPT-3 (registered trademark)) based on the saved user information and organized training information. The generated menu is temporarily stored in the server's cache.

[0302] Incorporating an emotion engine

[0303] The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, and analyzes them with an emotion engine (e.g., Microsoft® Azure® Cognitive Services). Identified emotion data is temporarily stored on the device and then sent to a server.

[0304] Reflecting emotional data

[0305] The server receives the emotional data sent from the device and reflects it in the generated training menu. For example, if stress is high, relaxation menus are added, and if motivation is high, high-intensity menus are added. The updated menu is then saved back in the database.

[0306] Providing messages to improve motivation

[0307] The device displays customized motivational messages according to the identified user's emotional state. For example, if the user shows a lack of motivation, the device displays the message "You're making great progress! Keep it up!". The messages are retrieved from the server as appropriate through an internal API.

[0308] Distribution and display of training menus

[0309] The server sends the latest training menu generated by the AI ​​model to the user's device, which displays it on the screen. The user can then check the menu through the app and begin training.

[0310] Collecting progress data and updating training menus

[0311] After training, users enter progress data (e.g., calories burned, number of sets completed) into the app, which then sends it to the server, which uses AI models to optimize the next training menu.

[0312] Providing data to apparel manufacturers

[0313] The server aggregates the collected data on users' ideal body shapes, anonymizes it so that individuals cannot be identified, and then provides it to partner apparel manufacturers via API. The manufacturers use this information to design new products and plan production.

[0314] Examples of prompt statements

[0315] Here are some example prompts that can be fed into a generative AI model:

[0316] Prompt: Generate the optimal training menu for a 30-year-old user who is 170cm tall, weighs 70kg, and wants to increase muscle mass when they are feeling stressed.

[0317] In this way, the present invention is a system that provides users with an optimal training menu, provides support according to the user's emotional state, and even supports efficient product development by providing data to the apparel industry.

[0318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0319] Step 1:

[0320] The user uses a dedicated application to input their own physical constitution and physique information (height, weight, age, and desired body shape) and presses the send button. The input data is saved in JSON format. For example, a user might input "height 170cm, weight 70kg, age 30, desired body shape: increased muscle mass." This provides the basic data needed to generate a training menu specific to the user.

[0321] Step 2:

[0322] The device sends the input data to the server using the HTTPS protocol. The sent data includes the user's physical constitution and body type information. This data is saved in a database. Specifically, the server receives a POST request to https: / / api.example.com / userdata and saves the data. This allows the server to accumulate basic data for generating training menus.

[0323] Step 3:

[0324] The server collects training information using web scraping tools (e.g., Beautiful Soup) and social media APIs (e.g., Twitter API, Instagram API). It then organizes the collected data into categories. This is done using AI models (e.g., Google's TensorFlow or OpenAI's API) to efficiently process large amounts of data. The organized training information is then stored in a database as output. For example, it can collect popular training methods and training menus used by professional athletes.

[0325] Step 4:

[0326] The server combines the saved user information with the collected training information and generates an optimal training menu using a generative AI model (e.g., GPT-3). The AI ​​model customizes the training menu based on the user's goal body shape and current physical condition. The generated training menu is temporarily stored in the server's cache. For example, if the user's goal is to improve muscle strength, the server will suggest three sessions of strength training and two sessions of aerobic exercise per week.

[0327] Step 5:

[0328] The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, which are then analyzed by an emotion engine (e.g., Microsoft's Azure Cognitive Services). Input data includes the user's facial expressions and voice information. The emotion engine analyzes this data to identify the user's stress level and motivation state. The identified emotion data is temporarily stored on the device and later sent to a server. This allows the user's emotional state to be reflected in the training menu.

[0329] Step 6:

[0330] The server receives the emotional data sent from the device and reflects it in the generated training menu. The information is sent in JSON format, and the server analyzes the received data and makes changes to the training menu. For example, if the user is feeling stressed, it can add relaxation exercises. The revised training menu is then saved back to the database.

[0331] Step 7:

[0332] The device displays customized motivational messages based on the recognized emotional state. The input includes the emotional data identified by the emotion engine. As an output, the message to be displayed is retrieved from the server. By displaying messages such as "You are making great progress!", the user's motivation is improved.

[0333] Step 8:

[0334] The server sends the latest training menu to the user's device, which displays it on the screen. The menu is then made available to the user through the app. Input includes the training menu generated by the AI ​​model, allowing the user to have the latest training menu tailored to them.

[0335] Step 9:

[0336] After training, the user inputs progress data (e.g., calories burned, number of sets completed) into the app. The device sends this data to the server, which then uses that data to optimize the next training menu. The input includes the user's progress data, which allows the server to suggest the next training menu tailored to the user's condition.

[0337] Step 10:

[0338] The server aggregates the collected data on users' ideal body shapes and anonymizes it to prevent personal identification. The data is then provided to partner clothing manufacturers via API. The data includes users' ideal body shapes as input, allowing the clothing manufacturers to develop efficient product development and production plans.

[0339] (Application example 2)

[0340] 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."

[0341] Conventional training menu generation systems provide optimal menus based on the user's physical constitution and body type information, but do not flexibly adjust to the user's emotional state or motivation. As a result, it is difficult to provide training menus that respond to the user's psychological changes, leading to issues such as a decrease in motivation and maximizing training effectiveness. Furthermore, it is not sufficient to continuously update menus based on the user's progress data.

[0342] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's physical constitution and body type information, means for transmitting the input physical constitution and body type information to the server, means for receiving the user information and saving it in a database, means for collecting and organizing training information from media and SNS, means for combining the collected training information and the user information to generate an optimal training menu, means for transmitting the generated training menu to the user's device, means for displaying the optimal training menu, means for receiving the user's progress data and continuously updating the training menu, means for analyzing the user's emotional state and adjusting the training menu based on the emotional data, and means for providing motivational messages based on the user's emotional state. This makes it possible to provide an individually optimized training menu that takes the user's psychological state into consideration and to continuously motivate the user.

[0343] "User's constitution and body type information" means biological and physical characteristic data such as the user's height, weight, age, and target body type.

[0344] "Server" refers to a computer system that collects and organizes data and training information sent by users and stores them in a database.

[0345] "Media and SNS" refers to media platforms, including online news sites, video sharing sites, and social networking services.

[0346] "Training information" refers to information about various training methods and menus, such as strength training and aerobic exercise.

[0347] "User Information" means personal data entered by a User, including physical constitution and body type information and progress data.

[0348] "Training menu" refers to a systematic exercise program that is optimal for the user, and includes specific details of strength training and aerobic exercise.

[0349] "User's device" refers to electronic devices used by the user, such as smartphones, tablets, and personal computers.

[0350] "Progress Data" refers to data that shows a user's training progress, such as training history and weight changes.

[0351] "Emotional state" refers to a user's psychological state, such as stress level or excitement level.

[0352] "Motivational Messages" refers to customized messages provided to motivate a user to train based on the user's emotional state.

[0353] This invention is a system that provides a training menu based on the user's physical constitution, body type information, and emotional state. The system analyzes the information entered by the user on the server and generates and provides the optimal training menu using an AI model and emotion engine. Furthermore, the system updates the training menu based on the user's progress data and provides messages to increase motivation.

[0354] Entering and submitting user information

[0355] Users input their physical constitution and body type information (e.g., height, weight, age, and desired body shape) using a device such as a smartphone. This information is sent to the server via the application. For example, if a 30-year-old user aims to increase muscle mass, they would input information such as a height of 170 cm and a weight of 70 kg.

[0356] Data transmission and storage on the server

[0357] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database for later analysis by AI.

[0358] Collecting and organizing training information

[0359] The server uses AI models to collect the latest training information from various sources, such as media and social media, and organizes it, including training videos from famous trainers and training menus for athletes on social media.

[0360] Training menu generation

[0361] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, it will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[0362] Incorporating an emotion engine

[0363] The device uses an emotion engine to identify the user's emotional state (e.g., stress level or excitement level) in real time. This allows the emotion engine to reflect the user's emotional data in the generation of training menus. For example, if the user is feeling stressed, the device will suggest relaxation training.

[0364] Reflecting emotional data

[0365] The server further customizes the training menu based on the user's emotional data recognized by the emotion engine. If the user is feeling stressed, it will suggest a light exercise menu, and if the user is highly motivated, it will suggest a more intense menu.

[0366] Providing messages to improve motivation

[0367] The device displays customized messages aimed at improving motivation based on the user's emotional data recognized by the emotion engine, allowing users to receive psychological support.

[0368] Distribution and display of training menus

[0369] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[0370] Collecting progress data and updating training menus

[0371] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[0372] Specific examples

[0373] For example, if a 30-year-old user is trying to gain muscle mass and the emotion engine determines that the user is feeling stressed, the generation system will provide a menu of light exercises aimed at relaxation and display the message, "Relax and do some light exercise."

[0374] Prompt Sentence Examples

[0375] Below are some example prompts to input to the generative AI model:

[0376] "The user is 30 years old and has a goal of increasing muscle mass. The user's emotional state is analyzed using a camera, and the results show that the stress level is high. The AI ​​model should provide a light training menu for relaxation and a message saying, 'Relax and do some light exercise.'"

[0377] Using this prompt, the AI ​​model generates a training menu that meets the user's needs and displays appropriate motivational messages.

[0378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0379] Step 1:

[0380] Users use devices such as smartphones and tablets to input their physical constitution and body type information (e.g., height, weight, age, and desired body type). This information is saved on the device.

[0381] Input: User information such as height, weight, age, and desired body shape.

[0382] Output: User information stored on the device.

[0383] Step 2:

[0384] The terminal sends the entered physical constitution and body type information to the server, where the data is protected using the HTTPS protocol.

[0385] Input: User information stored on the device.

[0386] Output: The user information sent to the server.

[0387] Step 3:

[0388] The server stores the received user information in a database, and the stored data is assigned a user ID for identification.

[0389] Input: User information sent to the server.

[0390] Output: User information stored in the database.

[0391] Step 4:

[0392] The server uses an AI model to collect and organize training information from media and social media, for example, analyzing training videos on social media and storing the type and intensity of training in a database.

[0393] Input: Training information from media and social media.

[0394] Output: Organized training information stored in a database.

[0395] Step 5:

[0396] The server combines the collected training information with user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to increase muscle strength, an appropriate strength training menu will be generated.

[0397] Input: User and training information stored in the database.

[0398] Output: The generated optimal training menu.

[0399] Step 6:

[0400] The generated training menu is sent from the server to the user's device, where it is formatted for viewing by the user.

[0401] Input: The generated training menu.

[0402] Output: The training menu sent to the device.

[0403] Step 7:

[0404] The device displays the training menu to the user, including specific training content and number of repetitions.

[0405] Input: Training menu sent to the device.

[0406] Output: The training menu that is displayed to the user.

[0407] Step 8:

[0408] The emotion engine uses cameras and sensors to analyze the user's emotional state (e.g., stress level, excitement level) in real time. The analysis results are processed on the device and sent to the server.

[0409] Input: User facial expressions and vital data obtained from cameras and sensors.

[0410] Output: Parsed user sentiment data.

[0411] Step 9:

[0412] The server receives the emotional data and reflects it in the training menu. For example, if the user is in a high stress state, the training menu will be customized to a lighter one aimed at relaxation.

[0413] Input: Received user sentiment data.

[0414] Output: A customized training menu.

[0415] Step 10:

[0416] The device displays customized training menus along with motivational messages based on the user's emotional state.

[0417] Input: Customized training menu and emotional data.

[0418] Output: The motivational message that is displayed to the user.

[0419] Step 11:

[0420] Users input their training progress data (e.g., training results, weight changes) into the app, which is then sent from the device to the server.

[0421] Input: Progress data entered by the user.

[0422] Output: Progress data sent to the server.

[0423] Step 12:

[0424] The server receives the progress data and continuously updates the training menu based on that data. The system tracks the user's progress and provides the latest and most optimal menu.

[0425] Input: Received user progress data.

[0426] Output: Continuously updated training menu.

[0427] Prompt Sentence Examples

[0428] Below are some example prompts to input to the generative AI model:

[0429] "The user is 30 years old and has a goal of increasing muscle mass. The user's emotional state is analyzed using a camera, and the results show that the stress level is high. The AI ​​model should provide a light training menu for relaxation and a message saying, 'Relax and do some light exercise.'"

[0430] Using this prompt, the AI ​​model generates a training menu that meets the user's needs and displays appropriate motivational messages.

[0431] 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.

[0432] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0433] 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.

[0434] [Second embodiment]

[0435] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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. 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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."

[0447] This invention is a system that utilizes AI technology to provide an optimal training menu based on the user's physical constitution and body type information. The system sends the physical constitution and body type information entered by the user to a server, which then generates and distributes the optimal training menu based on that information. It also has the function of monitoring the user's progress and continuously updating the training menu. Furthermore, the system anonymizes the user's ideal body type data and provides it to apparel manufacturers, supporting efficient clothing production.

[0448] Entering and submitting user information

[0449] Users use the application to input their basic information (height, weight, age, and desired body shape). For example, if a user is 170 cm tall, weighs 70 kg, is 30 years old, and aims to increase muscle mass as their desired body shape, they can input this information on the application screen. This allows the proposed training menu to be precisely customized.

[0450] Data transmission and storage on the server

[0451] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database, which is then used for AI analysis.

[0452] Collecting and organizing training data

[0453] The server uses AI models to collect and organize training information from various sources, including media and social media, making it possible to reference the latest and most diverse training methods, including popular training videos on social media and training menus by athletes.

[0454] Training menu generation

[0455] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, the AI ​​will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[0456] Distribution and display of training menus

[0457] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[0458] Collecting progress data and updating training menus

[0459] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[0460] Providing data to apparel manufacturers

[0461] The server aggregates and anonymizes users' ideal body data before providing it to apparel manufacturers. This data is used as the foundation for apparel manufacturers to efficiently produce clothes that meet customer needs. For example, it can plan designs in advance to fit the body shapes desired by many users.

[0462] In this way, the present invention is a system that not only provides optimal training to users but also supports efficient clothing production in the apparel industry.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] The user starts the training application using the terminal and inputs information about his / her constitution and body type, such as his / her height, weight, age, and target body type.

[0466] Step 2:

[0467] The terminal sends the entered user's constitution and body type information to the server.

[0468] Step 3:

[0469] The server stores the received user information in a database.

[0470] Step 4:

[0471] The server collects the latest training information from media and social media, and organizes the data using an AI model, including famous training programs and athletes' body stats.

[0472] Step 5:

[0473] The server combines the organized training data with the saved user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to improve muscle strength, a menu including three strength training sessions and two aerobic exercise sessions per week will be suggested.

[0474] Step 6:

[0475] The server sends the generated training menu to the user's terminal.

[0476] Step 7:

[0477] The device receives the training menu and displays it to the user, who can then perform the training according to the displayed menu.

[0478] Step 8:

[0479] The user performs training and enters their progress into the application, including data such as the type and duration of training performed and the calories burned.

[0480] Step 9:

[0481] The terminal transmits the input progress data to the server.

[0482] Step 10:

[0483] The server analyzes the progress data received and continuously optimizes the training menu according to the user's progress. For example, if muscle strength is improving, the server will suggest higher-intensity training aimed at further improving muscle strength.

[0484] Step 11:

[0485] The server again transmits the latest training menu to the user's terminal, and the terminal displays it to the user.

[0486] Step 12:

[0487] The server collects data on users' ideal body types, anonymizes the data, and provides it to apparel manufacturers, enabling them to produce clothes efficiently and without waste.

[0488] Example 1

[0489] 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."

[0490] Traditional training programs were not optimized for each user's individual physique or goals, making it difficult to provide an effective training plan for a specific user. Furthermore, progress monitoring and continuous training menu updates were manually performed, resulting in inefficiencies. Furthermore, there was no efficient way to collect and provide customer body shape information to meet the needs of the apparel industry.

[0491] 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.

[0492] In this invention, the server includes: [means for a user to input their own basic physical constitution and body type information;] [means for transmitting the input physical constitution and body type information to the server via the Internet;] [means for the server to receive the user information and store it in a database;] [means for the server to collect and organize training data from the Internet;] [means for the server to generate an optimal training plan based on the collected training data and user information;] [means for transmitting the generated training plan to the user's terminal;] [means for displaying the optimal training plan on the user's terminal;] [means for the user to input training progress data and transmit the progress data to the server;] [means for the server to continuously update the training plan based on the user's progress data; and [means for the server to anonymize the user's target body type data and provide it to the apparel industry.] This makes it possible to provide an optimal training plan tailored to the user's individual physical constitution and goals, and to continuously optimize training menus based on the progress data. It also makes it possible to efficiently provide customer body type information to the apparel industry.

[0493] "User information" refers to the user's basic constitution and body type information, including height, weight, age, and target body type.

[0494] A "server" is a computer system that receives data from users via the Internet, stores it in a database, and generates and updates training plans.

[0495] "Terminal" refers to a device through which a user inputs data and receives and displays the training plan sent from the server, and includes a smartphone, computer, etc.

[0496] "Training data" refers to information about various types of training collected from the internet, and is data that has been organized from content obtained from the media and social networking sites.

[0497] A "training plan" is an optimal training menu created based on the user's physical constitution, body type information, and training data.

[0498] "Progress data" refers to data entered by the user regarding the status of training and the level of achievement, and is information used by the server when updating the training plan.

[0499] "Target body data" is information about the ideal body shape that a user is aiming for, and is anonymized data provided to the apparel industry.

[0500] "Anonymization" is a technical method of protecting data by removing personally identifiable information.

[0501] The present invention is a system that utilizes AI technology to provide an optimal training menu based on a user's physical constitution and body type information. The main components of the system include a user terminal, a server, and an AI model. Specific embodiments of this system are described below.

[0502] Hardware and Software Configuration

[0503] Users access the system using devices such as smartphones or computers. Applications developed using frameworks such as React and Flutter run on the devices. Through these applications, users can input information about their physical constitution and body type.

[0504] On the server side, data collection and analysis are performed using programming languages ​​such as Python and R. Database management software such as MySQL or PostgreSQL is used for the database, storing user information and training data. The server also generates and optimizes training menus using generative AI models such as TensorFlow and PyTorch.

[0505] Data processing and calculation

[0506] The physical constitution and body type information entered by the user through the application is sent from the device to the server, which receives the information and stores it in a database. This stored data is used for subsequent analysis and the generation of training menus.

[0507] The server utilizes web scraping technology to collect the latest training techniques and methods from online media and social media, and this collected data is converted into a well-organized format that is later analyzed by the AI ​​model.

[0508] The server uses the collected training data and the user's physical constitution and body type information to generate an optimal training menu. The generated training menu is then sent back to the user's device from the server and displayed via the application. The user then performs their training according to this training menu.

[0509] When users enter their training progress data into the app, the data is sent from the device to the server, which then uses the progress data to continuously optimize the training menu and provide the user with the latest menu.

[0510] Examples of concrete examples and prompts

[0511] For example, consider a situation where a user enters basic information through an application, such as:

[0512] My current height is 170cm, my weight is 70kg, and I'm 30 years old. My goal is to increase muscle mass. Please generate the optimal training menu for me.

[0513] Based on this prompt, the system can generate the following training menu:

[0514] Strength training three times a week

[0515] Weightlifting

[0516] bench press

[0517] Deadlift

[0518] Aerobic exercise twice a week

[0519] 30 minutes of jogging

[0520] 45 minutes cycling

[0521] After training, the user can enter information into the app, such as "I did 30 minutes of weightlifting today," and the system will use this information to update the next training menu.

[0522] In this way, the system of the present invention can continuously provide users with individually optimized training programs. Also, by anonymizing the user's goal body shape data and providing it to the apparel industry, apparel manufacturers can efficiently develop products that meet customer needs.

[0523] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0524] Step 1:

[0525] The user launches the application and enters their basic information (height, weight, age, and desired body shape).

[0526] Input: User-entered height, weight, age, and body shape goals.

[0527] Output: The terminal collects these data and prepares them for transmission.

[0528] Specific operations: The user enters information such as "height 170 cm, weight 70 kg, age 30, goal body shape is to increase muscle mass" into a form on the application screen of a smartphone or computer, and clicks the submit button.

[0529] Step 2:

[0530] The device sends the user's input data to the server.

[0531] Input: Physical and body type information entered by the user.

[0532] Output: Data is sent to the server via the HTTPS protocol.

[0533] Specific operation: The device converts the data entered by the user into JSON format and sends it to the server.

[0534] Step 3:

[0535] The server receives the user information and stores it in a database.

[0536] Input: Physical and body type information sent from the device.

[0537] Output: User information stored in the database.

[0538] What happens: The server parses the received JSON data and inserts it into the appropriate table in the database.

[0539] Step 4:

[0540] The server collects and organizes training data from online media and social networking sites.

[0541] Input: Training-related information obtained from media and social media.

[0542] Output: Training data stored in an organized format.

[0543] Specific operation: The server uses web scraping technology to analyze and collect text and video information using Python or R programs.

[0544] Step 5:

[0545] The server generates an optimal training plan based on the collected training data and user information.

[0546] Input: Organized training data, user physical and body type information.

[0547] Output: The generated optimal training plan.

[0548] How it works: The server runs an AI model using TensorFlow or PyTorch to generate a training plan that includes, for example, "three strength training sessions and two cardio sessions per week."

[0549] Step 6:

[0550] The server sends the generated training plan to the user's device.

[0551] Input: The generated training plan.

[0552] Output: The training plan is sent to the user's device.

[0553] Specific operation: The server converts the generated training plan into JSON format and sends it to the device.

[0554] Step 7:

[0555] The device will display the optimal training plan to the user.

[0556] Input: The training plan sent from the server.

[0557] Output: The training plan that is displayed to the user.

[0558] Specific operation: The device analyzes the received training plan and displays it in a list format on the application screen. The user adds the event to their calendar.

[0559] Step 8:

[0560] The user enters training progress data into the app, and the device sends the progress data to the server.

[0561] Input: Training progress data entered by the user.

[0562] Output: Progress data sent to the server.

[0563] Specific behavior: The user enters "I lifted weights for 30 minutes today" and sends that data to the server.

[0564] Step 9:

[0565] The server continuously updates the training plan based on the user's progress.

[0566] Input: User progress data stored on the server.

[0567] Output: Continuously optimized training plans.

[0568] What it does: The server analyzes the new progress data, recreates the next training plan using the AI ​​model, and sends it to the user's device.

[0569] Step 10:

[0570] The server anonymizes the user's target body shape data and provides it to the apparel industry.

[0571] Input: User's goal body data.

[0572] Output: Anonymized data will be provided to the apparel industry.

[0573] Specific operation: The server collects the user's target body shape data, removes personal information, converts it into JSON format, and sends it to the apparel industry.

[0574] (Application example 1)

[0575] 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."

[0576] Conventional training menu provision systems cannot accurately reflect the user's physical constitution and body type information, making it difficult to provide an optimized menu for each user. Furthermore, they lack the functionality to update the menu in real time according to the user's training progress, and have not achieved the improvement of apparel production efficiency by utilizing the user's ideal body type data. The present invention aims to solve these problems and provide a training menu provision system that can be effectively used in physical stores.

[0577] 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.

[0578] In this invention, the server includes: [means for inputting a user's physical constitution and body type information and performing a body type scan;] [means for collecting and organizing training information using a generative AI model; and] [means for combining the collected training information and user information to generate an optimal training menu.] This makes it possible [to automatically generate a training menu optimized for each user and update it in real time according to progress, and further improve production efficiency by anonymizing ideal body type data and providing it to clothing manufacturers.]

[0579] "User's constitution and body type information" refers to data such as height, weight, age, body type scan results, and target body type entered by the user, and is the basic information for generating the optimal training menu for each individual user.

[0580] "Body scanning" is the process of using smart glasses and other sensors to collect detailed information about a user's body shape.

[0581] "Database" means an electronic storage system that systematically stores and manages user and training information.

[0582] "Media and SNS" is a collective term for various information media and social networking services used as sources of training information.

[0583] A "generative AI model" is an artificial intelligence model that analyzes data collected from media and social media to generate training menus, and automatically generates optimal training menus.

[0584] A "training menu" is a specific exercise and training plan created based on the user's physical constitution and body type information.

[0585] "Smart glasses" are eyeglass-type devices equipped with internet connectivity and a built-in camera that can acquire and display information in real time when worn by the user.

[0586] "Progress data" is data that indicates the results and progress of a user's training, and is information that forms the basis for updating the training menu.

[0587] "Clothing manufacturers" is a general term for companies that use ideal body data to produce clothes that are best suited to their users.

[0588] "Anonymization" is the process of removing information that identifies individual users and converting them into a form that makes it impossible to identify individual users.

[0589] The system of the present invention generates an optimal training menu based on the user's physical constitution and body type information, and supports use in physical stores. The system monitors the user's progress and continuously updates the training menu. The system also anonymizes the collected ideal body type data and provides it to clothing manufacturers, supporting efficient clothing production.

[0590] Program processing explanation

[0591] Hardware used

[0592] Smart glasses: Devices with built-in cameras, displays, and internet connectivity.

[0593] Server: Stores user information and generates training menus using AI models.

[0594] Device (smartphone or tablet): Used to input and check training progress.

[0595] Software used

[0596] scikit-learn: A Python machine learning library used to implement AI models.

[0597] TensorFlow: An open-source machine learning framework used to generate complex training menus.

[0598] PostgreSQL: An open-source relational database used to store user information and training data.

[0599] React Native: A cross-platform mobile application framework for applications on smart glasses and devices.

[0600] Data flow and processing procedures

[0601] 1. Input and transmission of user information: The user uses the smart glasses' camera to scan their body and input their height, weight, age, and desired body shape. The input data is then sent from the smart glasses to the server.

[0602] 2. Data storage: The server stores the received data in a PostgreSQL database. This stored data serves as the basis for generating training menus using the AI ​​model.

[0603] 3. Collecting and organizing training information: The server uses a generative AI model to collect and organize training information from media and social media, including the latest training methods and training menus of famous athletes.

[0604] 4. Training menu generation: The AI ​​model combines the collected training information with user information to generate the optimal training menu for the user.

[0605] 5. Menu distribution and display: The generated training menu is displayed in real time on the smart glasses display, and the user follows the instructions on the smart glasses to perform the training.

[0606] 6. Progress data collection and update: After completing training, users input their progress data into the smart glasses or terminal, which is then sent to the server, where the AI ​​model continuously optimizes the training menu.

[0607] 7. Providing ideal body data: The server anonymizes the user's ideal body data and provides it to clothing manufacturers, allowing them to efficiently produce clothes that meet customer needs.

[0608] Examples of concrete examples and prompts

[0609] Examples:

[0610] A fitness club trainer uses smart glasses to scan the body shape of a new client, and the system immediately generates and displays a training menu that is optimal for the client's goals.

[0611] Example prompt:

[0612] Generative AI model prompt example:

[0613] Input data: User's body scan, basic information (height, weight, age, desired body shape)

[0614] Goal: Optimal 4-day-per-week training schedule for users looking to gain muscle mass

[0615] The present invention not only provides an optimized training menu for each individual user, but also updates it in real time according to the user's progress, and further anonymizes ideal body shape data and provides it to clothing manufacturers, thereby improving the efficiency of clothing production.

[0616] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0617] Step 1:

[0618] The user puts on the smart glasses and undergoes a body scan. The smart glasses' built-in camera scans the user's body and inputs information such as height, weight, age, and desired body shape. This input data is stored in the smart glasses. Input data: body scan results (image data), height, weight, age, and desired body shape.

[0619] Step 2:

[0620] The smart glasses send the collected user's physical constitution and body shape information to the server. The sent data is received by the server and stored in a database. After the data is sent, a confirmation message is displayed on the smart glasses. Input data: User's physical constitution and body shape information, Output data: Data sending confirmation message.

[0621] Step 3:

[0622] The server collects and organizes training information from media and social media. A generative AI model is used for this process, analyzing and organizing the collected information. Input data: training information obtained from media and social media. Output data: organized training information.

[0623] Step 4:

[0624] The server combines the collected training information with the user's physical constitution and body type information and generates an optimal training menu using a generative AI model. This generation process plans the type of exercise, frequency, and time that are suited to the user's goal body type. Input data: User information, organized training information, generative AI model. Output data: Optimal training menu.

[0625] Step 5:

[0626] The generated training menu is sent from the server to the smart glasses and displayed in real time. The user follows the instructions on the smart glasses to begin training. Input data: optimal training menu, Output data: training menu displayed on the smart glasses.

[0627] Step 6:

[0628] After completing the training, the user inputs their progress data using smart glasses or a terminal. This progress data is sent to the server. Input data: user's progress data. Output data: server receipt confirmation message.

[0629] Step 7:

[0630] The server analyzes the received progress data and continuously optimizes the training menu. The generative AI model takes in new data and updates the next training menu. Input data: user progress data, Output data: updated training menu.

[0631] Step 8:

[0632] The server anonymizes the user's ideal body data and provides it to clothing manufacturers. The anonymization process protects the user's personal information. The manufacturer uses this data to design products. Input data: User's ideal body data, Output data: Anonymized body data.

[0633] 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.

[0634] This invention is a system that utilizes AI technology based on the user's physical constitution and physique information, and further incorporates an emotion engine to provide an optimal training menu that recognizes the user's emotions.By utilizing the user's emotional data, this system maximizes the effectiveness of the training menu and improves motivation.

[0635] Entering and submitting user information

[0636] Users use the application to input their basic information (height, weight, age, and desired body shape). For example, if a user is 170 cm tall, weighs 70 kg, is 30 years old, and aims to increase muscle mass as their desired body shape, they can input this information on the application screen. This allows the proposed training menu to be precisely customized.

[0637] Data transmission and storage on the server

[0638] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database, which is then used for AI analysis.

[0639] Collecting and organizing training data

[0640] The server uses AI models to collect and organize training information from various sources, including media and social media, making it possible to reference the latest and most diverse training methods, including popular training videos on social media and training menus by athletes.

[0641] Training menu generation

[0642] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, the AI ​​will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[0643] Incorporating an emotion engine

[0644] The device uses the emotion engine to identify the user's emotional state (e.g., stress level or excitement level) in real time, allowing the emotion engine to reflect the user's emotional data in generating a training menu.

[0645] Reflecting emotional data

[0646] The server further customizes the training menu based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest light exercises for relaxation, while if the user is highly motivated, it will suggest high-intensity training.

[0647] Providing messages to improve motivation

[0648] The device displays customized messages to motivate users based on their emotional data, which is recognized by the emotion engine. This allows users to receive not only the optimal training menu but also psychological support.

[0649] Distribution and display of training menus

[0650] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[0651] Collecting progress data and updating training menus

[0652] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[0653] Providing data to apparel manufacturers

[0654] The server aggregates and anonymizes users' ideal body data before providing it to apparel manufacturers. This data is used as the foundation for apparel manufacturers to efficiently produce clothes that meet customer needs. For example, it can plan designs in advance to fit the body shapes desired by many users.

[0655] In this way, the present invention is a system that not only provides optimal training to users, but also provides psychological support and can also support efficient clothing production in the apparel industry.

[0656] The processing flow will be explained below.

[0657] Step 1:

[0658] The user starts the training application using the terminal and inputs information about his / her constitution and body type, such as his / her height, weight, age, and target body type.

[0659] Step 2:

[0660] The terminal sends the entered user's constitution and body type information to the server.

[0661] Step 3:

[0662] The server stores the received user information in a database.

[0663] Step 4:

[0664] The server collects the latest training information from media and social media, and organizes the data using an AI model, including famous training programs and athletes' body stats.

[0665] Step 5:

[0666] The server combines the organized training data with the saved user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to improve muscle strength, a menu including three strength training sessions and two aerobic exercise sessions per week will be suggested.

[0667] Step 6:

[0668] The server sends the generated training menu to the user's terminal.

[0669] Step 7:

[0670] The device receives the training menu and displays it to the user, who can then perform the training according to the displayed menu.

[0671] Step 8:

[0672] The device uses an emotion engine to recognize the user's emotional state. For example, the emotion engine analyzes data such as the user's facial expressions, voice tone, and heart rate to determine whether the user is feeling stressed or motivated.

[0673] Step 9:

[0674] The server receives the user's emotional data recognized by the emotion engine and reflects that data in the generation of training menus. For example, if the user is feeling stressed, it will suggest a menu that includes gentle stretches to promote relaxation.

[0675] Step 10:

[0676] The device will display customized motivational messages based on the user's emotional state: for example, if the user is tired, an encouraging message will be displayed.

[0677] Step 11:

[0678] The user performs training and inputs progress (type of training performed, duration, calories burned, etc.) into the application.

[0679] Step 12:

[0680] The terminal transmits the input progress data to the server.

[0681] Step 13:

[0682] The server analyzes the progress data received and continuously updates the training menu according to the user's progress. For example, if muscle strength is improving, the server will suggest more intense training to further improve muscle strength.

[0683] Step 14:

[0684] The server again transmits the latest training menu to the user's terminal, and the terminal displays it to the user.

[0685] Step 15:

[0686] The server collects data on users' ideal body types, anonymizes the data, and provides it to apparel manufacturers, enabling them to produce clothes efficiently and without waste.

[0687] Example 2

[0688] 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."

[0689] Currently, most training systems only provide training menus based on basic information about the user's physical constitution and body type, and lack detailed customization based on the user's emotions and progress. Furthermore, they lack mechanisms for effectively increasing user motivation, resulting in a lack of training sustainability. Furthermore, there are no systems that effectively utilize data based on the user's ideal body type to benefit the apparel industry. To solve these problems, a comprehensive training system is needed that reflects the user's emotions and progress in real time, maximizes training efficiency, and increases user motivation.

[0690] 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.

[0691] In this invention, the server includes: [means for inputting a user's physical constitution and body type information;] [means for transmitting the input physical constitution and body type information to the server;] [means for receiving user information and saving it in a database;] [means for collecting and organizing training information from media and social networking services;] [means for combining the collected training information and user information to generate an optimal training menu;] [means for acquiring emotional data using an emotion engine that identifies the user's emotional state in real time;] [means for customizing the training menu based on the acquired emotional data;] [means for transmitting the generated training menu to the user's terminal;] [means for displaying the optimal training menu;] [means for receiving progress data and continuously updating the training menu;] [means for providing customized motivational messages; and [means for anonymizing the user's ideal body type data and providing it to clothing manufacturers.] This enables the provision of detailed training menus based on the user's emotions and progress, psychological support to improve motivation, and efficient product development by providing data to the apparel industry.

[0692] "User's constitution and body type information" refers to personal physical information such as height, weight, age, and target body type input by the user.

[0693] "Server" refers to a computer system used to manage and operate user information and training data.

[0694] A "database" is a storage device for storing and managing data such as user information and training menus.

[0695] "Media and Social Networking Services" means websites and social media platforms on which training information is made public.

[0696] "Training information" refers to information about training methods and menus that can be optimized to suit the user's body type and constitution.

[0697] The "optimal training menu" is a training plan generated using an AI model based on the user's physical constitution and body type information.

[0698] An "emotion engine" is a software technology that analyzes and identifies user emotions in real time.

[0699] "Emotion data" refers to information such as the user's stress level and motivation status identified by the emotion engine.

[0700] "Motivational Messages" are customized messages based on the user's emotional state to motivate them to train.

[0701] "Progress data" refers to performance data such as the calories burned and number of sets completed when a user trains.

[0702] A "clothing manufacturer" is a company that designs and produces clothing based on a user's ideal body data.

[0703] "Anonymization" is a technique for processing data so that users cannot be personally identified.

[0704] "User Device" means the device used by a User to enter information and receive training menus and motivational messages.

[0705] This invention utilizes AI technology based on the user's physical constitution and physique information, and incorporates an emotion engine to recognize the user's emotions and provide an optimal training menu. By utilizing the user's emotional data, this system maximizes the effectiveness of the training menu and improves motivation.

[0706] System configuration

[0707] The user uses a dedicated application to input and submit their own physical constitution and body type information (height, weight, age, and desired body type). This information is stored in JSON format on the device. The device then sends the information entered by the user to the server using the HTTPS protocol. The server receives the data, verifies it, and stores it in a secure database (such as PostgreSQL).

[0708] Data collection and organization

[0709] The server collects training information using web scraping tools (such as Beautiful Soup) and social media APIs (such as Twitter API and Instagram API), and organizes this data into categories using AI models (such as Google's TensorFlow or OpenAI's API).

[0710] Training menu generation

[0711] The server generates an optimal training menu using a generative AI model (e.g., GPT-3) based on the saved user information and organized training information. The generated menu is temporarily stored in the server's cache.

[0712] Incorporating an emotion engine

[0713] The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, and analyzes them with an emotion engine (e.g., Microsoft's Azure Cognitive Services). Identified emotion data is temporarily stored on the device and then sent to a server.

[0714] Reflecting emotional data

[0715] The server receives the emotional data sent from the device and reflects it in the generated training menu. For example, if stress is high, relaxation menus are added, and if motivation is high, high-intensity menus are added. The updated menu is then saved back in the database.

[0716] Providing messages to improve motivation

[0717] The device displays customized motivational messages according to the identified user's emotional state. For example, if the user shows a lack of motivation, the device displays the message "You're making great progress! Keep it up!". The messages are retrieved from the server as appropriate through an internal API.

[0718] Distribution and display of training menus

[0719] The server sends the latest training menu generated by the AI ​​model to the user's device, which displays it on the screen. The user can then check the menu through the app and begin training.

[0720] Collecting progress data and updating training menus

[0721] After training, users enter progress data (e.g., calories burned, number of sets completed) into the app, which then sends it to the server, which uses AI models to optimize the next training menu.

[0722] Providing data to apparel manufacturers

[0723] The server aggregates the collected data on users' ideal body shapes, anonymizes it so that individuals cannot be identified, and then provides it to partner apparel manufacturers via API. The manufacturers use this information to design new products and plan production.

[0724] Examples of prompt statements

[0725] Here are some example prompts that can be fed into a generative AI model:

[0726] Prompt: Generate the optimal training menu for a 30-year-old user who is 170cm tall, weighs 70kg, and wants to increase muscle mass when they are feeling stressed.

[0727] In this way, the present invention is a system that provides users with an optimal training menu, provides support according to the user's emotional state, and even supports efficient product development by providing data to the apparel industry.

[0728] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0729] Step 1:

[0730] The user uses a dedicated application to input their own physical constitution and physique information (height, weight, age, and desired body shape) and presses the send button. The input data is saved in JSON format. For example, a user might input "height 170cm, weight 70kg, age 30, desired body shape: increased muscle mass." This provides the basic data needed to generate a training menu specific to the user.

[0731] Step 2:

[0732] The device sends the input data to the server using the HTTPS protocol. The sent data includes the user's physical constitution and body type information. This data is saved in a database. Specifically, the server receives a POST request to https: / / api.example.com / userdata and saves the data. This allows the server to accumulate basic data for generating training menus.

[0733] Step 3:

[0734] The server collects training information using web scraping tools (e.g., Beautiful Soup) and social media APIs (e.g., Twitter API, Instagram API). It then organizes the collected data into categories. This is done using AI models (e.g., Google's TensorFlow or OpenAI's API) to efficiently process large amounts of data. The organized training information is then stored in a database as output. For example, it can collect popular training methods and training menus used by professional athletes.

[0735] Step 4:

[0736] The server combines the saved user information with the collected training information and generates an optimal training menu using a generative AI model (e.g., GPT-3). The AI ​​model customizes the training menu based on the user's goal body shape and current physical condition. The generated training menu is temporarily stored in the server's cache. For example, if the user's goal is to improve muscle strength, the server will suggest three sessions of strength training and two sessions of aerobic exercise per week.

[0737] Step 5:

[0738] The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, which are then analyzed by an emotion engine (e.g., Microsoft's Azure Cognitive Services). Input data includes the user's facial expressions and voice information. The emotion engine analyzes this data to identify the user's stress level and motivation state. The identified emotion data is temporarily stored on the device and later sent to a server. This allows the user's emotional state to be reflected in the training menu.

[0739] Step 6:

[0740] The server receives the emotional data sent from the device and reflects it in the generated training menu. The information is sent in JSON format, and the server analyzes the received data and makes changes to the training menu. For example, if the user is feeling stressed, it can add relaxation exercises. The revised training menu is then saved back to the database.

[0741] Step 7:

[0742] The device displays customized motivational messages based on the recognized emotional state. The input includes the emotional data identified by the emotion engine. As an output, the message to be displayed is retrieved from the server. By displaying messages such as "You are making great progress!", the user's motivation is improved.

[0743] Step 8:

[0744] The server sends the latest training menu to the user's device, which displays it on the screen. The menu is then made available to the user through the app. Input includes the training menu generated by the AI ​​model, allowing the user to have the latest training menu tailored to them.

[0745] Step 9:

[0746] After training, the user inputs progress data (e.g., calories burned, number of sets completed) into the app. The device sends this data to the server, which then uses that data to optimize the next training menu. The input includes the user's progress data, which allows the server to suggest the next training menu tailored to the user's condition.

[0747] Step 10:

[0748] The server aggregates the collected data on users' ideal body shapes and anonymizes it to prevent personal identification. The data is then provided to partner clothing manufacturers via API. The data includes users' ideal body shapes as input, allowing the clothing manufacturers to develop efficient product development and production plans.

[0749] (Application example 2)

[0750] 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."

[0751] Conventional training menu generation systems provide optimal menus based on the user's physical constitution and body type information, but do not flexibly adjust to the user's emotional state or motivation. As a result, it is difficult to provide training menus that respond to the user's psychological changes, leading to issues such as a decrease in motivation and maximizing training effectiveness. Furthermore, it is not sufficient to continuously update menus based on the user's progress data.

[0752] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's physical constitution and body type information, means for transmitting the input physical constitution and body type information to the server, means for receiving the user information and saving it in a database, means for collecting and organizing training information from media and SNS, means for combining the collected training information and the user information to generate an optimal training menu, means for transmitting the generated training menu to the user's device, means for displaying the optimal training menu, means for receiving the user's progress data and continuously updating the training menu, means for analyzing the user's emotional state and adjusting the training menu based on the emotional data, and means for providing motivational messages based on the user's emotional state. This makes it possible to provide an individually optimized training menu that takes the user's psychological state into consideration and to continuously motivate the user.

[0753] "User's constitution and body type information" means biological and physical characteristic data such as the user's height, weight, age, and target body type.

[0754] "Server" refers to a computer system that collects and organizes data and training information sent by users and stores them in a database.

[0755] "Media and SNS" refers to media platforms, including online news sites, video sharing sites, and social networking services.

[0756] "Training information" refers to information about various training methods and menus, such as strength training and aerobic exercise.

[0757] "User Information" means personal data entered by a User, including physical constitution and body type information and progress data.

[0758] "Training menu" refers to a systematic exercise program that is optimal for the user, and includes specific details of strength training and aerobic exercise.

[0759] "User's device" refers to electronic devices used by the user, such as smartphones, tablets, and personal computers.

[0760] "Progress Data" refers to data that shows a user's training progress, such as training history and weight changes.

[0761] "Emotional state" refers to a user's psychological state, such as stress level or excitement level.

[0762] "Motivational Messages" refers to customized messages provided to motivate a user to train based on the user's emotional state.

[0763] This invention is a system that provides a training menu based on the user's physical constitution, body type information, and emotional state. The system analyzes the information entered by the user on the server and generates and provides the optimal training menu using an AI model and emotion engine. Furthermore, the system updates the training menu based on the user's progress data and provides messages to increase motivation.

[0764] Entering and submitting user information

[0765] Users input their physical constitution and body type information (e.g., height, weight, age, and desired body shape) using a device such as a smartphone. This information is sent to the server via the application. For example, if a 30-year-old user aims to increase muscle mass, they would input information such as a height of 170 cm and a weight of 70 kg.

[0766] Data transmission and storage on the server

[0767] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database for later analysis by AI.

[0768] Collecting and organizing training information

[0769] The server uses AI models to collect the latest training information from various sources, such as media and social media, and organizes it, including training videos from famous trainers and training menus for athletes on social media.

[0770] Training menu generation

[0771] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, it will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[0772] Incorporating an emotion engine

[0773] The device uses an emotion engine to identify the user's emotional state (e.g., stress level or excitement level) in real time. This allows the emotion engine to reflect the user's emotional data in the generation of training menus. For example, if the user is feeling stressed, the device will suggest relaxation training.

[0774] Reflecting emotional data

[0775] The server further customizes the training menu based on the user's emotional data recognized by the emotion engine. If the user is feeling stressed, it will suggest a light exercise menu, and if the user is highly motivated, it will suggest a more intense menu.

[0776] Providing messages to improve motivation

[0777] The device displays customized messages aimed at improving motivation based on the user's emotional data recognized by the emotion engine, allowing users to receive psychological support.

[0778] Distribution and display of training menus

[0779] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[0780] Collecting progress data and updating training menus

[0781] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[0782] Specific examples

[0783] For example, if a 30-year-old user is trying to gain muscle mass and the emotion engine determines that the user is feeling stressed, the generation system will provide a menu of light exercises aimed at relaxation and display the message, "Relax and do some light exercise."

[0784] Prompt Sentence Examples

[0785] Below are some example prompts to input to the generative AI model:

[0786] "The user is 30 years old and has a goal of increasing muscle mass. The user's emotional state is analyzed using a camera, and the results show that the stress level is high. The AI ​​model should provide a light training menu for relaxation and a message saying, 'Relax and do some light exercise.'"

[0787] Using this prompt, the AI ​​model generates a training menu that meets the user's needs and displays appropriate motivational messages.

[0788] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0789] Step 1:

[0790] Users use devices such as smartphones and tablets to input their physical constitution and body type information (e.g., height, weight, age, and desired body type). This information is saved on the device.

[0791] Input: User information such as height, weight, age, and desired body shape.

[0792] Output: User information stored on the device.

[0793] Step 2:

[0794] The terminal sends the entered physical constitution and body type information to the server, where the data is protected using the HTTPS protocol.

[0795] Input: User information stored on the device.

[0796] Output: The user information sent to the server.

[0797] Step 3:

[0798] The server stores the received user information in a database, and the stored data is assigned a user ID for identification.

[0799] Input: User information sent to the server.

[0800] Output: User information stored in the database.

[0801] Step 4:

[0802] The server uses an AI model to collect and organize training information from media and social media, for example, analyzing training videos on social media and storing the type and intensity of training in a database.

[0803] Input: Training information from media and social media.

[0804] Output: Organized training information stored in a database.

[0805] Step 5:

[0806] The server combines the collected training information with user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to increase muscle strength, an appropriate strength training menu will be generated.

[0807] Input: User and training information stored in the database.

[0808] Output: The generated optimal training menu.

[0809] Step 6:

[0810] The generated training menu is sent from the server to the user's device, where it is formatted for viewing by the user.

[0811] Input: The generated training menu.

[0812] Output: The training menu sent to the device.

[0813] Step 7:

[0814] The device displays the training menu to the user, including specific training content and number of repetitions.

[0815] Input: Training menu sent to the device.

[0816] Output: The training menu that is displayed to the user.

[0817] Step 8:

[0818] The emotion engine uses cameras and sensors to analyze the user's emotional state (e.g., stress level, excitement level) in real time. The analysis results are processed on the device and sent to the server.

[0819] Input: User facial expressions and vital data obtained from cameras and sensors.

[0820] Output: Parsed user sentiment data.

[0821] Step 9:

[0822] The server receives the emotional data and reflects it in the training menu. For example, if the user is in a high stress state, the training menu will be customized to a lighter one aimed at relaxation.

[0823] Input: Received user sentiment data.

[0824] Output: A customized training menu.

[0825] Step 10:

[0826] The device displays customized training menus along with motivational messages based on the user's emotional state.

[0827] Input: Customized training menu and emotional data.

[0828] Output: The motivational message that is displayed to the user.

[0829] Step 11:

[0830] Users input their training progress data (e.g., training results, weight changes) into the app, which is then sent from the device to the server.

[0831] Input: Progress data entered by the user.

[0832] Output: Progress data sent to the server.

[0833] Step 12:

[0834] The server receives the progress data and continuously updates the training menu based on that data. The system tracks the user's progress and provides the latest and most optimal menu.

[0835] Input: Received user progress data.

[0836] Output: Continuously updated training menu.

[0837] Prompt Sentence Examples

[0838] Below are some example prompts to input to the generative AI model:

[0839] "The user is 30 years old and has a goal of increasing muscle mass. The user's emotional state is analyzed using a camera, and the results show that the stress level is high. The AI ​​model should provide a light training menu for relaxation and a message saying, 'Relax and do some light exercise.'"

[0840] Using this prompt, the AI ​​model generates a training menu that meets the user's needs and displays appropriate motivational messages.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] [Third embodiment]

[0845] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0846] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0847] 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).

[0848] 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.

[0849] 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.

[0850] 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).

[0851] 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. 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] This invention is a system that utilizes AI technology to provide an optimal training menu based on the user's physical constitution and body type information. The system sends the physical constitution and body type information entered by the user to a server, which then generates and distributes the optimal training menu based on that information. It also has the function of monitoring the user's progress and continuously updating the training menu. Furthermore, the system anonymizes the user's ideal body type data and provides it to apparel manufacturers, supporting efficient clothing production.

[0858] Entering and submitting user information

[0859] Users use the application to input their basic information (height, weight, age, and desired body shape). For example, if a user is 170 cm tall, weighs 70 kg, is 30 years old, and aims to increase muscle mass as their desired body shape, they can input this information on the application screen. This allows the proposed training menu to be precisely customized.

[0860] Data transmission and storage on the server

[0861] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database, which is then used for AI analysis.

[0862] Collecting and organizing training data

[0863] The server uses AI models to collect and organize training information from various sources, including media and social media, making it possible to reference the latest and most diverse training methods, including popular training videos on social media and training menus by athletes.

[0864] Training menu generation

[0865] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, the AI ​​will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[0866] Distribution and display of training menus

[0867] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[0868] Collecting progress data and updating training menus

[0869] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[0870] Providing data to apparel manufacturers

[0871] The server aggregates and anonymizes users' ideal body data before providing it to apparel manufacturers. This data is used as the foundation for apparel manufacturers to efficiently produce clothes that meet customer needs. For example, it can plan designs in advance to fit the body shapes desired by many users.

[0872] In this way, the present invention is a system that not only provides optimal training to users but also supports efficient clothing production in the apparel industry.

[0873] The processing flow will be explained below.

[0874] Step 1:

[0875] The user starts the training application using the terminal and inputs information about his / her constitution and body type, such as his / her height, weight, age, and target body type.

[0876] Step 2:

[0877] The terminal sends the entered user's constitution and body type information to the server.

[0878] Step 3:

[0879] The server stores the received user information in a database.

[0880] Step 4:

[0881] The server collects the latest training information from media and social media, and organizes the data using an AI model, including famous training programs and athletes' body stats.

[0882] Step 5:

[0883] The server combines the organized training data with the saved user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to improve muscle strength, a menu including three strength training sessions and two aerobic exercise sessions per week will be suggested.

[0884] Step 6:

[0885] The server sends the generated training menu to the user's terminal.

[0886] Step 7:

[0887] The device receives the training menu and displays it to the user, who can then perform the training according to the displayed menu.

[0888] Step 8:

[0889] The user performs training and enters their progress into the application, including data such as the type and duration of training performed and the calories burned.

[0890] Step 9:

[0891] The terminal transmits the input progress data to the server.

[0892] Step 10:

[0893] The server analyzes the progress data received and continuously optimizes the training menu according to the user's progress. For example, if muscle strength is improving, the server will suggest higher-intensity training aimed at further improving muscle strength.

[0894] Step 11:

[0895] The server again transmits the latest training menu to the user's terminal, and the terminal displays it to the user.

[0896] Step 12:

[0897] The server collects data on users' ideal body types, anonymizes the data, and provides it to apparel manufacturers, enabling them to produce clothes efficiently and without waste.

[0898] Example 1

[0899] 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."

[0900] Traditional training programs were not optimized for each user's individual physique or goals, making it difficult to provide an effective training plan for a specific user. Furthermore, progress monitoring and continuous training menu updates were manually performed, resulting in inefficiencies. Furthermore, there was no efficient way to collect and provide customer body shape information to meet the needs of the apparel industry.

[0901] 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.

[0902] In this invention, the server includes: [means for a user to input their own basic physical constitution and body type information;] [means for transmitting the input physical constitution and body type information to the server via the Internet;] [means for the server to receive the user information and store it in a database;] [means for the server to collect and organize training data from the Internet;] [means for the server to generate an optimal training plan based on the collected training data and user information;] [means for transmitting the generated training plan to the user's terminal;] [means for displaying the optimal training plan on the user's terminal;] [means for the user to input training progress data and transmit the progress data to the server;] [means for the server to continuously update the training plan based on the user's progress data; and [means for the server to anonymize the user's target body type data and provide it to the apparel industry.] This makes it possible to provide an optimal training plan tailored to the user's individual physical constitution and goals, and to continuously optimize training menus based on the progress data. It also makes it possible to efficiently provide customer body type information to the apparel industry.

[0903] "User information" refers to the user's basic constitution and body type information, including height, weight, age, and target body type.

[0904] A "server" is a computer system that receives data from users via the Internet, stores it in a database, and generates and updates training plans.

[0905] "Terminal" refers to a device through which a user inputs data and receives and displays the training plan sent from the server, and includes a smartphone, computer, etc.

[0906] "Training data" refers to information about various types of training collected from the internet, and is data that has been organized from content obtained from the media and social networking sites.

[0907] A "training plan" is an optimal training menu created based on the user's physical constitution, body type information, and training data.

[0908] "Progress data" refers to data entered by the user regarding the status of training and the level of achievement, and is information used by the server when updating the training plan.

[0909] "Target body data" is information about the ideal body shape that a user is aiming for, and is anonymized data provided to the apparel industry.

[0910] "Anonymization" is a technical method of protecting data by removing personally identifiable information.

[0911] The present invention is a system that utilizes AI technology to provide an optimal training menu based on a user's physical constitution and body type information. The main components of the system include a user terminal, a server, and an AI model. Specific embodiments of this system are described below.

[0912] Hardware and Software Configuration

[0913] Users access the system using devices such as smartphones or computers. Applications developed using frameworks such as React and Flutter run on the devices. Through these applications, users can input information about their physical constitution and body type.

[0914] On the server side, data collection and analysis are performed using programming languages ​​such as Python and R. Database management software such as MySQL or PostgreSQL is used for the database, storing user information and training data. The server also generates and optimizes training menus using generative AI models such as TensorFlow and PyTorch.

[0915] Data processing and calculation

[0916] The physical constitution and body type information entered by the user through the application is sent from the device to the server, which receives the information and stores it in a database. This stored data is used for subsequent analysis and the generation of training menus.

[0917] The server utilizes web scraping technology to collect the latest training techniques and methods from online media and social media, and this collected data is converted into a well-organized format that is later analyzed by the AI ​​model.

[0918] The server uses the collected training data and the user's physical constitution and body type information to generate an optimal training menu. The generated training menu is then sent back to the user's device from the server and displayed via the application. The user then performs their training according to this training menu.

[0919] When users enter their training progress data into the app, the data is sent from the device to the server, which then uses the progress data to continuously optimize the training menu and provide the user with the latest menu.

[0920] Examples of concrete examples and prompts

[0921] For example, consider a situation where a user enters basic information through an application, such as:

[0922] My current height is 170cm, my weight is 70kg, and I'm 30 years old. My goal is to increase muscle mass. Please generate the optimal training menu for me.

[0923] Based on this prompt, the system can generate the following training menu:

[0924] Strength training three times a week

[0925] Weightlifting

[0926] bench press

[0927] Deadlift

[0928] Aerobic exercise twice a week

[0929] 30 minutes of jogging

[0930] 45 minutes cycling

[0931] After training, the user can enter information into the app, such as "I did 30 minutes of weightlifting today," and the system will use this information to update the next training menu.

[0932] In this way, the system of the present invention can continuously provide users with individually optimized training programs. Also, by anonymizing the user's goal body shape data and providing it to the apparel industry, apparel manufacturers can efficiently develop products that meet customer needs.

[0933] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0934] Step 1:

[0935] The user launches the application and enters their basic information (height, weight, age, and desired body shape).

[0936] Input: User-entered height, weight, age, and body shape goals.

[0937] Output: The terminal collects these data and prepares them for transmission.

[0938] Specific operations: The user enters information such as "height 170 cm, weight 70 kg, age 30, goal body shape is to increase muscle mass" into a form on the application screen of a smartphone or computer, and clicks the submit button.

[0939] Step 2:

[0940] The device sends the user's input data to the server.

[0941] Input: Physical and body type information entered by the user.

[0942] Output: Data is sent to the server via the HTTPS protocol.

[0943] Specific operation: The device converts the data entered by the user into JSON format and sends it to the server.

[0944] Step 3:

[0945] The server receives the user information and stores it in a database.

[0946] Input: Physical and body type information sent from the device.

[0947] Output: User information stored in the database.

[0948] What happens: The server parses the received JSON data and inserts it into the appropriate table in the database.

[0949] Step 4:

[0950] The server collects and organizes training data from online media and social networking sites.

[0951] Input: Training-related information obtained from media and social media.

[0952] Output: Training data stored in an organized format.

[0953] Specific operation: The server uses web scraping technology to analyze and collect text and video information using Python or R programs.

[0954] Step 5:

[0955] The server generates an optimal training plan based on the collected training data and user information.

[0956] Input: Organized training data, user physical and body type information.

[0957] Output: The generated optimal training plan.

[0958] How it works: The server runs an AI model using TensorFlow or PyTorch to generate a training plan that includes, for example, "three strength training sessions and two cardio sessions per week."

[0959] Step 6:

[0960] The server sends the generated training plan to the user's device.

[0961] Input: The generated training plan.

[0962] Output: The training plan is sent to the user's device.

[0963] Specific operation: The server converts the generated training plan into JSON format and sends it to the device.

[0964] Step 7:

[0965] The device will display the optimal training plan to the user.

[0966] Input: The training plan sent from the server.

[0967] Output: The training plan that is displayed to the user.

[0968] Specific operation: The device analyzes the received training plan and displays it in a list format on the application screen. The user adds the event to their calendar.

[0969] Step 8:

[0970] The user enters training progress data into the app, and the device sends the progress data to the server.

[0971] Input: Training progress data entered by the user.

[0972] Output: Progress data sent to the server.

[0973] Specific behavior: The user enters "I lifted weights for 30 minutes today" and sends that data to the server.

[0974] Step 9:

[0975] The server continuously updates the training plan based on the user's progress.

[0976] Input: User progress data stored on the server.

[0977] Output: Continuously optimized training plans.

[0978] What it does: The server analyzes the new progress data, recreates the next training plan using the AI ​​model, and sends it to the user's device.

[0979] Step 10:

[0980] The server anonymizes the user's target body shape data and provides it to the apparel industry.

[0981] Input: User's goal body data.

[0982] Output: Anonymized data will be provided to the apparel industry.

[0983] Specific operation: The server collects the user's target body shape data, removes personal information, converts it into JSON format, and sends it to the apparel industry.

[0984] (Application example 1)

[0985] 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."

[0986] Conventional training menu provision systems cannot accurately reflect the user's physical constitution and body type information, making it difficult to provide an optimized menu for each user. Furthermore, they lack the functionality to update the menu in real time according to the user's training progress, and have not achieved the improvement of apparel production efficiency by utilizing the user's ideal body type data. The present invention aims to solve these problems and provide a training menu provision system that can be effectively used in physical stores.

[0987] 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.

[0988] In this invention, the server includes: [means for inputting a user's physical constitution and body type information and performing a body type scan;] [means for collecting and organizing training information using a generative AI model; and] [means for combining the collected training information and user information to generate an optimal training menu.] This makes it possible [to automatically generate a training menu optimized for each user and update it in real time according to progress, and further improve production efficiency by anonymizing ideal body type data and providing it to clothing manufacturers.]

[0989] "User's constitution and body type information" refers to data such as height, weight, age, body type scan results, and target body type entered by the user, and is the basic information for generating the optimal training menu for each individual user.

[0990] "Body scanning" is the process of using smart glasses and other sensors to collect detailed information about a user's body shape.

[0991] "Database" means an electronic storage system that systematically stores and manages user and training information.

[0992] "Media and SNS" is a collective term for various information media and social networking services used as sources of training information.

[0993] A "generative AI model" is an artificial intelligence model that analyzes data collected from media and social media to generate training menus, and automatically generates optimal training menus.

[0994] A "training menu" is a specific exercise and training plan created based on the user's physical constitution and body type information.

[0995] "Smart glasses" are eyeglass-type devices equipped with internet connectivity and a built-in camera that can acquire and display information in real time when worn by the user.

[0996] "Progress data" is data that indicates the results and progress of a user's training, and is information that forms the basis for updating the training menu.

[0997] "Clothing manufacturers" is a general term for companies that use ideal body data to produce clothes that are best suited to their users.

[0998] "Anonymization" is the process of removing information that identifies individual users and converting them into a form that makes it impossible to identify individual users.

[0999] The system of the present invention generates an optimal training menu based on the user's physical constitution and body type information, and supports use in physical stores. The system monitors the user's progress and continuously updates the training menu. The system also anonymizes the collected ideal body type data and provides it to clothing manufacturers, supporting efficient clothing production.

[1000] Program processing explanation

[1001] Hardware used

[1002] Smart glasses: Devices with built-in cameras, displays, and internet connectivity.

[1003] Server: Stores user information and generates training menus using AI models.

[1004] Device (smartphone or tablet): Used to input and check training progress.

[1005] Software used

[1006] scikit-learn: A Python machine learning library used to implement AI models.

[1007] TensorFlow: An open-source machine learning framework used to generate complex training menus.

[1008] PostgreSQL: An open-source relational database used to store user information and training data.

[1009] React Native: A cross-platform mobile application framework for applications on smart glasses and devices.

[1010] Data flow and processing procedures

[1011] 1. Input and transmission of user information: The user uses the smart glasses' camera to scan their body and input their height, weight, age, and desired body shape. The input data is then sent from the smart glasses to the server.

[1012] 2. Data storage: The server stores the received data in a PostgreSQL database. This stored data serves as the basis for generating training menus using the AI ​​model.

[1013] 3. Collecting and organizing training information: The server uses a generative AI model to collect and organize training information from media and social media, including the latest training methods and training menus of famous athletes.

[1014] 4. Training menu generation: The AI ​​model combines the collected training information with user information to generate the optimal training menu for the user.

[1015] 5. Menu distribution and display: The generated training menu is displayed in real time on the smart glasses display, and the user follows the instructions on the smart glasses to perform the training.

[1016] 6. Progress data collection and update: After completing training, users input their progress data into the smart glasses or terminal, which is then sent to the server, where the AI ​​model continuously optimizes the training menu.

[1017] 7. Providing ideal body data: The server anonymizes the user's ideal body data and provides it to clothing manufacturers, allowing them to efficiently produce clothes that meet customer needs.

[1018] Examples of concrete examples and prompts

[1019] Examples:

[1020] A fitness club trainer uses smart glasses to scan the body shape of a new client, and the system immediately generates and displays a training menu that is optimal for the client's goals.

[1021] Example prompt:

[1022] Generative AI model prompt example:

[1023] Input data: User's body scan, basic information (height, weight, age, desired body shape)

[1024] Goal: Optimal 4-day-per-week training schedule for users looking to gain muscle mass

[1025] The present invention not only provides an optimized training menu for each individual user, but also updates it in real time according to the user's progress, and further anonymizes ideal body shape data and provides it to clothing manufacturers, thereby improving the efficiency of clothing production.

[1026] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1027] Step 1:

[1028] The user puts on the smart glasses and undergoes a body scan. The smart glasses' built-in camera scans the user's body and inputs information such as height, weight, age, and desired body shape. This input data is stored in the smart glasses. Input data: body scan results (image data), height, weight, age, and desired body shape.

[1029] Step 2:

[1030] The smart glasses send the collected user's physical constitution and body shape information to the server. The sent data is received by the server and stored in a database. After the data is sent, a confirmation message is displayed on the smart glasses. Input data: User's physical constitution and body shape information, Output data: Data sending confirmation message.

[1031] Step 3:

[1032] The server collects and organizes training information from media and social media. A generative AI model is used for this process, analyzing and organizing the collected information. Input data: training information obtained from media and social media. Output data: organized training information.

[1033] Step 4:

[1034] The server combines the collected training information with the user's physical constitution and body type information and generates an optimal training menu using a generative AI model. This generation process plans the type of exercise, frequency, and time that are suited to the user's goal body type. Input data: User information, organized training information, generative AI model. Output data: Optimal training menu.

[1035] Step 5:

[1036] The generated training menu is sent from the server to the smart glasses and displayed in real time. The user follows the instructions on the smart glasses to begin training. Input data: optimal training menu, Output data: training menu displayed on the smart glasses.

[1037] Step 6:

[1038] After completing the training, the user inputs their progress data using smart glasses or a terminal. This progress data is sent to the server. Input data: user's progress data. Output data: server receipt confirmation message.

[1039] Step 7:

[1040] The server analyzes the received progress data and continuously optimizes the training menu. The generative AI model takes in new data and updates the next training menu. Input data: user progress data, Output data: updated training menu.

[1041] Step 8:

[1042] The server anonymizes the user's ideal body data and provides it to clothing manufacturers. The anonymization process protects the user's personal information. The manufacturer uses this data to design products. Input data: User's ideal body data, Output data: Anonymized body data.

[1043] 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.

[1044] This invention is a system that utilizes AI technology based on the user's physical constitution and physique information, and further incorporates an emotion engine to provide an optimal training menu that recognizes the user's emotions.By utilizing the user's emotional data, this system maximizes the effectiveness of the training menu and improves motivation.

[1045] Entering and submitting user information

[1046] Users use the application to input their basic information (height, weight, age, and desired body shape). For example, if a user is 170 cm tall, weighs 70 kg, is 30 years old, and aims to increase muscle mass as their desired body shape, they can input this information on the application screen. This allows the proposed training menu to be precisely customized.

[1047] Data transmission and storage on the server

[1048] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database, which is then used for AI analysis.

[1049] Collecting and organizing training data

[1050] The server uses AI models to collect and organize training information from various sources, including media and social media, making it possible to reference the latest and most diverse training methods, including popular training videos on social media and training menus by athletes.

[1051] Training menu generation

[1052] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, the AI ​​will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[1053] Incorporating an emotion engine

[1054] The device uses the emotion engine to identify the user's emotional state (e.g., stress level or excitement level) in real time, allowing the emotion engine to reflect the user's emotional data in generating a training menu.

[1055] Reflecting emotional data

[1056] The server further customizes the training menu based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest light exercises for relaxation, while if the user is highly motivated, it will suggest high-intensity training.

[1057] Providing messages to improve motivation

[1058] The device displays customized messages to motivate users based on their emotional data, which is recognized by the emotion engine. This allows users to receive not only the optimal training menu but also psychological support.

[1059] Distribution and display of training menus

[1060] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[1061] Collecting progress data and updating training menus

[1062] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[1063] Providing data to apparel manufacturers

[1064] The server aggregates and anonymizes users' ideal body data before providing it to apparel manufacturers. This data is used as the foundation for apparel manufacturers to efficiently produce clothes that meet customer needs. For example, it can plan designs in advance to fit the body shapes desired by many users.

[1065] In this way, the present invention is a system that not only provides optimal training to users, but also provides psychological support and can also support efficient clothing production in the apparel industry.

[1066] The processing flow will be explained below.

[1067] Step 1:

[1068] The user starts the training application using the terminal and inputs information about his / her constitution and body type, such as his / her height, weight, age, and target body type.

[1069] Step 2:

[1070] The terminal sends the entered user's constitution and body type information to the server.

[1071] Step 3:

[1072] The server stores the received user information in a database.

[1073] Step 4:

[1074] The server collects the latest training information from media and social media, and organizes the data using an AI model, including famous training programs and athletes' body stats.

[1075] Step 5:

[1076] The server combines the organized training data with the saved user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to improve muscle strength, a menu including three strength training sessions and two aerobic exercise sessions per week will be suggested.

[1077] Step 6:

[1078] The server sends the generated training menu to the user's terminal.

[1079] Step 7:

[1080] The device receives the training menu and displays it to the user, who can then perform the training according to the displayed menu.

[1081] Step 8:

[1082] The device uses an emotion engine to recognize the user's emotional state. For example, the emotion engine analyzes data such as the user's facial expressions, voice tone, and heart rate to determine whether the user is feeling stressed or motivated.

[1083] Step 9:

[1084] The server receives the user's emotional data recognized by the emotion engine and reflects that data in the generation of training menus. For example, if the user is feeling stressed, it will suggest a menu that includes gentle stretches to promote relaxation.

[1085] Step 10:

[1086] The device will display customized motivational messages based on the user's emotional state: for example, if the user is tired, an encouraging message will be displayed.

[1087] Step 11:

[1088] The user performs training and inputs progress (type of training performed, duration, calories burned, etc.) into the application.

[1089] Step 12:

[1090] The terminal transmits the input progress data to the server.

[1091] Step 13:

[1092] The server analyzes the progress data received and continuously updates the training menu according to the user's progress. For example, if muscle strength is improving, the server will suggest more intense training to further improve muscle strength.

[1093] Step 14:

[1094] The server again transmits the latest training menu to the user's terminal, and the terminal displays it to the user.

[1095] Step 15:

[1096] The server collects data on users' ideal body types, anonymizes the data, and provides it to apparel manufacturers, enabling them to produce clothes efficiently and without waste.

[1097] Example 2

[1098] 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."

[1099] Currently, most training systems only provide training menus based on basic information about the user's physical constitution and body type, and lack detailed customization based on the user's emotions and progress. Furthermore, they lack mechanisms for effectively increasing user motivation, resulting in a lack of training sustainability. Furthermore, there are no systems that effectively utilize data based on the user's ideal body type to benefit the apparel industry. To solve these problems, a comprehensive training system is needed that reflects the user's emotions and progress in real time, maximizes training efficiency, and increases user motivation.

[1100] 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.

[1101] In this invention, the server includes: [means for inputting a user's physical constitution and body type information;] [means for transmitting the input physical constitution and body type information to the server;] [means for receiving user information and saving it in a database;] [means for collecting and organizing training information from media and social networking services;] [means for combining the collected training information and user information to generate an optimal training menu;] [means for acquiring emotional data using an emotion engine that identifies the user's emotional state in real time;] [means for customizing the training menu based on the acquired emotional data;] [means for transmitting the generated training menu to the user's terminal;] [means for displaying the optimal training menu;] [means for receiving progress data and continuously updating the training menu;] [means for providing customized motivational messages; and [means for anonymizing the user's ideal body type data and providing it to clothing manufacturers.] This enables the provision of detailed training menus based on the user's emotions and progress, psychological support to improve motivation, and efficient product development by providing data to the apparel industry.

[1102] "User's constitution and body type information" refers to personal physical information such as height, weight, age, and target body type input by the user.

[1103] "Server" refers to a computer system used to manage and operate user information and training data.

[1104] A "database" is a storage device for storing and managing data such as user information and training menus.

[1105] "Media and Social Networking Services" means websites and social media platforms on which training information is made public.

[1106] "Training information" refers to information about training methods and menus that can be optimized to suit the user's body type and constitution.

[1107] The "optimal training menu" is a training plan generated using an AI model based on the user's physical constitution and body type information.

[1108] An "emotion engine" is a software technology that analyzes and identifies user emotions in real time.

[1109] "Emotion data" refers to information such as the user's stress level and motivation status identified by the emotion engine.

[1110] "Motivational Messages" are customized messages based on the user's emotional state to motivate them to train.

[1111] "Progress data" refers to performance data such as the calories burned and number of sets completed when a user trains.

[1112] A "clothing manufacturer" is a company that designs and produces clothing based on a user's ideal body data.

[1113] "Anonymization" is a technique for processing data so that users cannot be personally identified.

[1114] "User Device" means the device used by a User to enter information and receive training menus and motivational messages.

[1115] This invention utilizes AI technology based on the user's physical constitution and physique information, and incorporates an emotion engine to recognize the user's emotions and provide an optimal training menu. By utilizing the user's emotional data, this system maximizes the effectiveness of the training menu and improves motivation.

[1116] System configuration

[1117] The user uses a dedicated application to input and submit their own physical constitution and body type information (height, weight, age, and desired body type). This information is stored in JSON format on the device. The device then sends the information entered by the user to the server using the HTTPS protocol. The server receives the data, verifies it, and stores it in a secure database (such as PostgreSQL).

[1118] Data collection and organization

[1119] The server collects training information using web scraping tools (such as Beautiful Soup) and social media APIs (such as Twitter API and Instagram API), and organizes this data into categories using AI models (such as Google's TensorFlow or OpenAI's API).

[1120] Training menu generation

[1121] The server generates an optimal training menu using a generative AI model (e.g., GPT-3) based on the saved user information and organized training information. The generated menu is temporarily stored in the server's cache.

[1122] Incorporating an emotion engine

[1123] The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, and analyzes them with an emotion engine (e.g., Microsoft's Azure Cognitive Services). Identified emotion data is temporarily stored on the device and then sent to a server.

[1124] Reflecting emotional data

[1125] The server receives the emotional data sent from the device and reflects it in the generated training menu. For example, if stress is high, relaxation menus are added, and if motivation is high, high-intensity menus are added. The updated menu is then saved back in the database.

[1126] Providing messages to improve motivation

[1127] The device displays customized motivational messages according to the identified user's emotional state. For example, if the user shows a lack of motivation, the device displays the message "You're making great progress! Keep it up!". The messages are retrieved from the server as appropriate through an internal API.

[1128] Distribution and display of training menus

[1129] The server sends the latest training menu generated by the AI ​​model to the user's device, which displays it on the screen. The user can then check the menu through the app and begin training.

[1130] Collecting progress data and updating training menus

[1131] After training, users enter progress data (e.g., calories burned, number of sets completed) into the app, which then sends it to the server, which uses AI models to optimize the next training menu.

[1132] Providing data to apparel manufacturers

[1133] The server aggregates the collected data on users' ideal body shapes, anonymizes it so that individuals cannot be identified, and then provides it to partner apparel manufacturers via API. The manufacturers use this information to design new products and plan production.

[1134] Examples of prompt statements

[1135] Here are some example prompts that can be fed into a generative AI model:

[1136] Prompt: Generate the optimal training menu for a 30-year-old user who is 170cm tall, weighs 70kg, and wants to increase muscle mass when they are feeling stressed.

[1137] In this way, the present invention is a system that provides users with an optimal training menu, provides support according to the user's emotional state, and even supports efficient product development by providing data to the apparel industry.

[1138] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1139] Step 1:

[1140] The user uses a dedicated application to input their own physical constitution and physique information (height, weight, age, and desired body shape) and presses the send button. The input data is saved in JSON format. For example, a user might input "height 170cm, weight 70kg, age 30, desired body shape: increased muscle mass." This provides the basic data needed to generate a training menu specific to the user.

[1141] Step 2:

[1142] The device sends the input data to the server using the HTTPS protocol. The sent data includes the user's physical constitution and body type information. This data is saved in a database. Specifically, the server receives a POST request to https: / / api.example.com / userdata and saves the data. This allows the server to accumulate basic data for generating training menus.

[1143] Step 3:

[1144] The server collects training information using web scraping tools (e.g., Beautiful Soup) and social media APIs (e.g., Twitter API, Instagram API). It then organizes the collected data into categories. This is done using AI models (e.g., Google's TensorFlow or OpenAI's API) to efficiently process large amounts of data. The organized training information is then stored in a database as output. For example, it can collect popular training methods and training menus used by professional athletes.

[1145] Step 4:

[1146] The server combines the saved user information with the collected training information and generates an optimal training menu using a generative AI model (e.g., GPT-3). The AI ​​model customizes the training menu based on the user's goal body shape and current physical condition. The generated training menu is temporarily stored in the server's cache. For example, if the user's goal is to improve muscle strength, the server will suggest three sessions of strength training and two sessions of aerobic exercise per week.

[1147] Step 5:

[1148] The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, which are then analyzed by an emotion engine (e.g., Microsoft's Azure Cognitive Services). Input data includes the user's facial expressions and voice information. The emotion engine analyzes this data to identify the user's stress level and motivation state. The identified emotion data is temporarily stored on the device and later sent to a server. This allows the user's emotional state to be reflected in the training menu.

[1149] Step 6:

[1150] The server receives the emotional data sent from the device and reflects it in the generated training menu. The information is sent in JSON format, and the server analyzes the received data and makes changes to the training menu. For example, if the user is feeling stressed, it can add relaxation exercises. The revised training menu is then saved back to the database.

[1151] Step 7:

[1152] The device displays customized motivational messages based on the recognized emotional state. The input includes the emotional data identified by the emotion engine. As an output, the message to be displayed is retrieved from the server. By displaying messages such as "You are making great progress!", the user's motivation is improved.

[1153] Step 8:

[1154] The server sends the latest training menu to the user's device, which displays it on the screen. The menu is then made available to the user through the app. Input includes the training menu generated by the AI ​​model, allowing the user to have the latest training menu tailored to them.

[1155] Step 9:

[1156] After training, the user inputs progress data (e.g., calories burned, number of sets completed) into the app. The device sends this data to the server, which then uses that data to optimize the next training menu. The input includes the user's progress data, which allows the server to suggest the next training menu tailored to the user's condition.

[1157] Step 10:

[1158] The server aggregates the collected data on users' ideal body shapes and anonymizes it to prevent personal identification. The data is then provided to partner clothing manufacturers via API. The data includes users' ideal body shapes as input, allowing the clothing manufacturers to develop efficient product development and production plans.

[1159] (Application example 2)

[1160] 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."

[1161] Conventional training menu generation systems provide optimal menus based on the user's physical constitution and body type information, but do not flexibly adjust to the user's emotional state or motivation. As a result, it is difficult to provide training menus that respond to the user's psychological changes, leading to issues such as a decrease in motivation and maximizing training effectiveness. Furthermore, it is not sufficient to continuously update menus based on the user's progress data.

[1162] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's physical constitution and body type information, means for transmitting the input physical constitution and body type information to the server, means for receiving the user information and saving it in a database, means for collecting and organizing training information from media and SNS, means for combining the collected training information and the user information to generate an optimal training menu, means for transmitting the generated training menu to the user's device, means for displaying the optimal training menu, means for receiving the user's progress data and continuously updating the training menu, means for analyzing the user's emotional state and adjusting the training menu based on the emotional data, and means for providing motivational messages based on the user's emotional state. This makes it possible to provide an individually optimized training menu that takes the user's psychological state into consideration and to continuously motivate the user.

[1163] "User's constitution and body type information" means biological and physical characteristic data such as the user's height, weight, age, and target body type.

[1164] "Server" refers to a computer system that collects and organizes data and training information sent by users and stores them in a database.

[1165] "Media and SNS" refers to media platforms, including online news sites, video sharing sites, and social networking services.

[1166] "Training information" refers to information about various training methods and menus, such as strength training and aerobic exercise.

[1167] "User Information" means personal data entered by a User, including physical constitution and body type information and progress data.

[1168] "Training menu" refers to a systematic exercise program that is optimal for the user, and includes specific details of strength training and aerobic exercise.

[1169] "User's device" refers to electronic devices used by the user, such as smartphones, tablets, and personal computers.

[1170] "Progress Data" refers to data that shows a user's training progress, such as training history and weight changes.

[1171] "Emotional state" refers to a user's psychological state, such as stress level or excitement level.

[1172] "Motivational Messages" refers to customized messages provided to motivate a user to train based on the user's emotional state.

[1173] This invention is a system that provides a training menu based on the user's physical constitution, body type information, and emotional state. The system analyzes the information entered by the user on the server and generates and provides the optimal training menu using an AI model and emotion engine. Furthermore, the system updates the training menu based on the user's progress data and provides messages to increase motivation.

[1174] Entering and submitting user information

[1175] Users input their physical constitution and body type information (e.g., height, weight, age, and desired body shape) using a device such as a smartphone. This information is sent to the server via the application. For example, if a 30-year-old user aims to increase muscle mass, they would input information such as a height of 170 cm and a weight of 70 kg.

[1176] Data transmission and storage on the server

[1177] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database for later analysis by AI.

[1178] Collecting and organizing training information

[1179] The server uses AI models to collect the latest training information from various sources, such as media and social media, and organizes it, including training videos from famous trainers and training menus for athletes on social media.

[1180] Training menu generation

[1181] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, it will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[1182] Incorporating an emotion engine

[1183] The device uses an emotion engine to identify the user's emotional state (e.g., stress level or excitement level) in real time. This allows the emotion engine to reflect the user's emotional data in the generation of training menus. For example, if the user is feeling stressed, the device will suggest relaxation training.

[1184] Reflecting emotional data

[1185] The server further customizes the training menu based on the user's emotional data recognized by the emotion engine. If the user is feeling stressed, it will suggest a light exercise menu, and if the user is highly motivated, it will suggest a more intense menu.

[1186] Providing messages to improve motivation

[1187] The device displays customized messages aimed at improving motivation based on the user's emotional data recognized by the emotion engine, allowing users to receive psychological support.

[1188] Distribution and display of training menus

[1189] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[1190] Collecting progress data and updating training menus

[1191] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[1192] Specific examples

[1193] For example, if a 30-year-old user is trying to gain muscle mass and the emotion engine determines that the user is feeling stressed, the generation system will provide a menu of light exercises aimed at relaxation and display the message, "Relax and do some light exercise."

[1194] Prompt Sentence Examples

[1195] Below are some example prompts to input to the generative AI model:

[1196] "The user is 30 years old and has a goal of increasing muscle mass. The user's emotional state is analyzed using a camera, and the results show that the stress level is high. The AI ​​model should provide a light training menu for relaxation and a message saying, 'Relax and do some light exercise.'"

[1197] Using this prompt, the AI ​​model generates a training menu that meets the user's needs and displays appropriate motivational messages.

[1198] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1199] Step 1:

[1200] Users use devices such as smartphones and tablets to input their physical constitution and body type information (e.g., height, weight, age, and desired body type). This information is saved on the device.

[1201] Input: User information such as height, weight, age, and desired body shape.

[1202] Output: User information stored on the device.

[1203] Step 2:

[1204] The terminal sends the entered physical constitution and body type information to the server, where the data is protected using the HTTPS protocol.

[1205] Input: User information stored on the device.

[1206] Output: The user information sent to the server.

[1207] Step 3:

[1208] The server stores the received user information in a database, and the stored data is assigned a user ID for identification.

[1209] Input: User information sent to the server.

[1210] Output: User information stored in the database.

[1211] Step 4:

[1212] The server uses an AI model to collect and organize training information from media and social media, for example, analyzing training videos on social media and storing the type and intensity of training in a database.

[1213] Input: Training information from media and social media.

[1214] Output: Organized training information stored in a database.

[1215] Step 5:

[1216] The server combines the collected training information with user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to increase muscle strength, an appropriate strength training menu will be generated.

[1217] Input: User and training information stored in the database.

[1218] Output: The generated optimal training menu.

[1219] Step 6:

[1220] The generated training menu is sent from the server to the user's device, where it is formatted for viewing by the user.

[1221] Input: The generated training menu.

[1222] Output: The training menu sent to the device.

[1223] Step 7:

[1224] The device displays the training menu to the user, including specific training content and number of repetitions.

[1225] Input: Training menu sent to the device.

[1226] Output: The training menu that is displayed to the user.

[1227] Step 8:

[1228] The emotion engine uses cameras and sensors to analyze the user's emotional state (e.g., stress level, excitement level) in real time. The analysis results are processed on the device and sent to the server.

[1229] Input: User facial expressions and vital data obtained from cameras and sensors.

[1230] Output: Parsed user sentiment data.

[1231] Step 9:

[1232] The server receives the emotional data and reflects it in the training menu. For example, if the user is in a high stress state, the training menu will be customized to a lighter one aimed at relaxation.

[1233] Input: Received user sentiment data.

[1234] Output: A customized training menu.

[1235] Step 10:

[1236] The device displays customized training menus along with motivational messages based on the user's emotional state.

[1237] Input: Customized training menu and emotional data.

[1238] Output: The motivational message that is displayed to the user.

[1239] Step 11:

[1240] Users input their training progress data (e.g., training results, weight changes) into the app, which is then sent from the device to the server.

[1241] Input: Progress data entered by the user.

[1242] Output: Progress data sent to the server.

[1243] Step 12:

[1244] The server receives the progress data and continuously updates the training menu based on that data. The system tracks the user's progress and provides the latest and most optimal menu.

[1245] Input: Received user progress data.

[1246] Output: Continuously updated training menu.

[1247] Prompt Sentence Examples

[1248] Below are some example prompts to input to the generative AI model:

[1249] "The user is 30 years old and has a goal of increasing muscle mass. The user's emotional state is analyzed using a camera, and the results show that the stress level is high. The AI ​​model should provide a light training menu for relaxation and a message saying, 'Relax and do some light exercise.'"

[1250] Using this prompt, the AI ​​model generates a training menu that meets the user's needs and displays appropriate motivational messages.

[1251] 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.

[1252] 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.

[1253] 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.

[1254] [Fourth embodiment]

[1255] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1256] 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.

[1257] 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).

[1258] 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.

[1259] 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.

[1260] 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).

[1261] 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. 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.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] 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.

[1267] 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."

[1268] This invention is a system that utilizes AI technology to provide an optimal training menu based on the user's physical constitution and body type information. The system sends the physical constitution and body type information entered by the user to a server, which then generates and distributes the optimal training menu based on that information. It also has the function of monitoring the user's progress and continuously updating the training menu. Furthermore, the system anonymizes the user's ideal body type data and provides it to apparel manufacturers, supporting efficient clothing production.

[1269] Entering and submitting user information

[1270] Users use the application to input their basic information (height, weight, age, and desired body shape). For example, if a user is 170 cm tall, weighs 70 kg, is 30 years old, and aims to increase muscle mass as their desired body shape, they can input this information on the application screen. This allows the proposed training menu to be precisely customized.

[1271] Data transmission and storage on the server

[1272] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database, which is then used for AI analysis.

[1273] Collecting and organizing training data

[1274] The server uses AI models to collect and organize training information from various sources, including media and social media, making it possible to reference the latest and most diverse training methods, including popular training videos on social media and training menus by athletes.

[1275] Training menu generation

[1276] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, the AI ​​will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[1277] Distribution and display of training menus

[1278] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[1279] Collecting progress data and updating training menus

[1280] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[1281] Providing data to apparel manufacturers

[1282] The server aggregates and anonymizes users' ideal body data before providing it to apparel manufacturers. This data is used as the foundation for apparel manufacturers to efficiently produce clothes that meet customer needs. For example, it can plan designs in advance to fit the body shapes desired by many users.

[1283] In this way, the present invention is a system that not only provides optimal training to users but also supports efficient clothing production in the apparel industry.

[1284] The processing flow will be explained below.

[1285] Step 1:

[1286] The user starts the training application using the terminal and inputs information about his / her constitution and body type, such as his / her height, weight, age, and target body type.

[1287] Step 2:

[1288] The terminal sends the entered user's constitution and body type information to the server.

[1289] Step 3:

[1290] The server stores the received user information in a database.

[1291] Step 4:

[1292] The server collects the latest training information from media and social media, and organizes the data using an AI model, including famous training programs and athletes' body stats.

[1293] Step 5:

[1294] The server combines the organized training data with the saved user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to improve muscle strength, a menu including three strength training sessions and two aerobic exercise sessions per week will be suggested.

[1295] Step 6:

[1296] The server sends the generated training menu to the user's terminal.

[1297] Step 7:

[1298] The device receives the training menu and displays it to the user, who can then perform the training according to the displayed menu.

[1299] Step 8:

[1300] The user performs training and enters their progress into the application, including data such as the type and duration of training performed and the calories burned.

[1301] Step 9:

[1302] The terminal transmits the input progress data to the server.

[1303] Step 10:

[1304] The server analyzes the progress data received and continuously optimizes the training menu according to the user's progress. For example, if muscle strength is improving, the server will suggest higher-intensity training aimed at further improving muscle strength.

[1305] Step 11:

[1306] The server again transmits the latest training menu to the user's terminal, and the terminal displays it to the user.

[1307] Step 12:

[1308] The server collects data on users' ideal body types, anonymizes the data, and provides it to apparel manufacturers, enabling them to produce clothes efficiently and without waste.

[1309] Example 1

[1310] 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."

[1311] Traditional training programs were not optimized for each user's individual physique or goals, making it difficult to provide an effective training plan for a specific user. Furthermore, progress monitoring and continuous training menu updates were manually performed, resulting in inefficiencies. Furthermore, there was no efficient way to collect and provide customer body shape information to meet the needs of the apparel industry.

[1312] 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.

[1313] In this invention, the server includes: [means for a user to input their own basic physical constitution and body type information;] [means for transmitting the input physical constitution and body type information to the server via the Internet;] [means for the server to receive the user information and store it in a database;] [means for the server to collect and organize training data from the Internet;] [means for the server to generate an optimal training plan based on the collected training data and user information;] [means for transmitting the generated training plan to the user's terminal;] [means for displaying the optimal training plan on the user's terminal;] [means for the user to input training progress data and transmit the progress data to the server;] [means for the server to continuously update the training plan based on the user's progress data; and [means for the server to anonymize the user's target body type data and provide it to the apparel industry.] This makes it possible to provide an optimal training plan tailored to the user's individual physical constitution and goals, and to continuously optimize training menus based on the progress data. It also makes it possible to efficiently provide customer body type information to the apparel industry.

[1314] "User information" refers to the user's basic constitution and body type information, including height, weight, age, and target body type.

[1315] A "server" is a computer system that receives data from users via the Internet, stores it in a database, and generates and updates training plans.

[1316] "Terminal" refers to a device through which a user inputs data and receives and displays the training plan sent from the server, and includes a smartphone, computer, etc.

[1317] "Training data" refers to information about various types of training collected from the internet, and is data that has been organized from content obtained from the media and social networking sites.

[1318] A "training plan" is an optimal training menu created based on the user's physical constitution, body type information, and training data.

[1319] "Progress data" refers to data entered by the user regarding the status of training and the level of achievement, and is information used by the server when updating the training plan.

[1320] "Target body data" is information about the ideal body shape that a user is aiming for, and is anonymized data provided to the apparel industry.

[1321] "Anonymization" is a technical method of protecting data by removing personally identifiable information.

[1322] The present invention is a system that utilizes AI technology to provide an optimal training menu based on a user's physical constitution and body type information. The main components of the system include a user terminal, a server, and an AI model. Specific embodiments of this system are described below.

[1323] Hardware and Software Configuration

[1324] Users access the system using devices such as smartphones or computers. Applications developed using frameworks such as React and Flutter run on the devices. Through these applications, users can input information about their physical constitution and body type.

[1325] On the server side, data collection and analysis are performed using programming languages ​​such as Python and R. Database management software such as MySQL or PostgreSQL is used for the database, storing user information and training data. The server also generates and optimizes training menus using generative AI models such as TensorFlow and PyTorch.

[1326] Data processing and calculation

[1327] The physical constitution and body type information entered by the user through the application is sent from the device to the server, which receives the information and stores it in a database. This stored data is used for subsequent analysis and the generation of training menus.

[1328] The server utilizes web scraping technology to collect the latest training techniques and methods from online media and social media, and this collected data is converted into a well-organized format that is later analyzed by the AI ​​model.

[1329] The server uses the collected training data and the user's physical constitution and body type information to generate an optimal training menu. The generated training menu is then sent back to the user's device from the server and displayed via the application. The user then performs their training according to this training menu.

[1330] When users enter their training progress data into the app, the data is sent from the device to the server, which then uses the progress data to continuously optimize the training menu and provide the user with the latest menu.

[1331] Examples of concrete examples and prompts

[1332] For example, consider a situation where a user enters basic information through an application, such as:

[1333] My current height is 170cm, my weight is 70kg, and I'm 30 years old. My goal is to increase muscle mass. Please generate the optimal training menu for me.

[1334] Based on this prompt, the system can generate the following training menu:

[1335] Strength training three times a week

[1336] Weightlifting

[1337] bench press

[1338] Deadlift

[1339] Aerobic exercise twice a week

[1340] 30 minutes of jogging

[1341] 45 minutes cycling

[1342] After training, the user can enter information into the app, such as "I did 30 minutes of weightlifting today," and the system will use this information to update the next training menu.

[1343] In this way, the system of the present invention can continuously provide users with individually optimized training programs. Also, by anonymizing the user's goal body shape data and providing it to the apparel industry, apparel manufacturers can efficiently develop products that meet customer needs.

[1344] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1345] Step 1:

[1346] The user launches the application and enters their basic information (height, weight, age, and desired body shape).

[1347] Input: User-entered height, weight, age, and body shape goals.

[1348] Output: The terminal collects these data and prepares them for transmission.

[1349] Specific operations: The user enters information such as "height 170 cm, weight 70 kg, age 30, goal body shape is to increase muscle mass" into a form on the application screen of a smartphone or computer, and clicks the submit button.

[1350] Step 2:

[1351] The device sends the user's input data to the server.

[1352] Input: Physical and body type information entered by the user.

[1353] Output: Data is sent to the server via the HTTPS protocol.

[1354] Specific operation: The device converts the data entered by the user into JSON format and sends it to the server.

[1355] Step 3:

[1356] The server receives the user information and stores it in a database.

[1357] Input: Physical and body type information sent from the device.

[1358] Output: User information stored in the database.

[1359] What happens: The server parses the received JSON data and inserts it into the appropriate table in the database.

[1360] Step 4:

[1361] The server collects and organizes training data from online media and social networking sites.

[1362] Input: Training-related information obtained from media and social media.

[1363] Output: Training data stored in an organized format.

[1364] Specific operation: The server uses web scraping technology to analyze and collect text and video information using Python or R programs.

[1365] Step 5:

[1366] The server generates an optimal training plan based on the collected training data and user information.

[1367] Input: Organized training data, user physical and body type information.

[1368] Output: The generated optimal training plan.

[1369] How it works: The server runs an AI model using TensorFlow or PyTorch to generate a training plan that includes, for example, "three strength training sessions and two cardio sessions per week."

[1370] Step 6:

[1371] The server sends the generated training plan to the user's device.

[1372] Input: The generated training plan.

[1373] Output: The training plan is sent to the user's device.

[1374] Specific operation: The server converts the generated training plan into JSON format and sends it to the device.

[1375] Step 7:

[1376] The device will display the optimal training plan to the user.

[1377] Input: The training plan sent from the server.

[1378] Output: The training plan that is displayed to the user.

[1379] Specific operation: The device analyzes the received training plan and displays it in a list format on the application screen. The user adds the event to their calendar.

[1380] Step 8:

[1381] The user enters training progress data into the app, and the device sends the progress data to the server.

[1382] Input: Training progress data entered by the user.

[1383] Output: Progress data sent to the server.

[1384] Specific behavior: The user enters "I lifted weights for 30 minutes today" and sends that data to the server.

[1385] Step 9:

[1386] The server continuously updates the training plan based on the user's progress.

[1387] Input: User progress data stored on the server.

[1388] Output: Continuously optimized training plans.

[1389] What it does: The server analyzes the new progress data, recreates the next training plan using the AI ​​model, and sends it to the user's device.

[1390] Step 10:

[1391] The server anonymizes the user's target body shape data and provides it to the apparel industry.

[1392] Input: User's goal body data.

[1393] Output: Anonymized data will be provided to the apparel industry.

[1394] Specific operation: The server collects the user's target body shape data, removes personal information, converts it into JSON format, and sends it to the apparel industry.

[1395] (Application example 1)

[1396] 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."

[1397] Conventional training menu provision systems cannot accurately reflect the user's physical constitution and body type information, making it difficult to provide an optimized menu for each user. Furthermore, they lack the functionality to update the menu in real time according to the user's training progress, and have not achieved the improvement of apparel production efficiency by utilizing the user's ideal body type data. The present invention aims to solve these problems and provide a training menu provision system that can be effectively used in physical stores.

[1398] 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.

[1399] In this invention, the server includes: [means for inputting a user's physical constitution and body type information and performing a body type scan;] [means for collecting and organizing training information using a generative AI model; and] [means for combining the collected training information and user information to generate an optimal training menu.] This makes it possible [to automatically generate a training menu optimized for each user and update it in real time according to progress, and further improve production efficiency by anonymizing ideal body type data and providing it to clothing manufacturers.]

[1400] "User's constitution and body type information" refers to data such as height, weight, age, body type scan results, and target body type entered by the user, and is the basic information for generating the optimal training menu for each individual user.

[1401] "Body scanning" is the process of using smart glasses and other sensors to collect detailed information about a user's body shape.

[1402] "Database" means an electronic storage system that systematically stores and manages user and training information.

[1403] "Media and SNS" is a collective term for various information media and social networking services used as sources of training information.

[1404] A "generative AI model" is an artificial intelligence model that analyzes data collected from media and social media to generate training menus, and automatically generates optimal training menus.

[1405] A "training menu" is a specific exercise and training plan created based on the user's physical constitution and body type information.

[1406] "Smart glasses" are eyeglass-type devices equipped with internet connectivity and a built-in camera that can acquire and display information in real time when worn by the user.

[1407] "Progress data" is data that indicates the results and progress of a user's training, and is information that forms the basis for updating the training menu.

[1408] "Clothing manufacturers" is a general term for companies that use ideal body data to produce clothes that are best suited to their users.

[1409] "Anonymization" is the process of removing information that identifies individual users and converting them into a form that makes it impossible to identify individual users.

[1410] The system of the present invention generates an optimal training menu based on the user's physical constitution and body type information, and supports use in physical stores. The system monitors the user's progress and continuously updates the training menu. The system also anonymizes the collected ideal body type data and provides it to clothing manufacturers, supporting efficient clothing production.

[1411] Program processing explanation

[1412] Hardware used

[1413] Smart glasses: Devices with built-in cameras, displays, and internet connectivity.

[1414] Server: Stores user information and generates training menus using AI models.

[1415] Device (smartphone or tablet): Used to input and check training progress.

[1416] Software used

[1417] scikit-learn: A Python machine learning library used to implement AI models.

[1418] TensorFlow: An open-source machine learning framework used to generate complex training menus.

[1419] PostgreSQL: An open-source relational database used to store user information and training data.

[1420] React Native: A cross-platform mobile application framework for applications on smart glasses and devices.

[1421] Data flow and processing procedures

[1422] 1. Input and transmission of user information: The user uses the smart glasses' camera to scan their body and input their height, weight, age, and desired body shape. The input data is then sent from the smart glasses to the server.

[1423] 2. Data storage: The server stores the received data in a PostgreSQL database. This stored data serves as the basis for generating training menus using the AI ​​model.

[1424] 3. Collecting and organizing training information: The server uses a generative AI model to collect and organize training information from media and social media, including the latest training methods and training menus of famous athletes.

[1425] 4. Training menu generation: The AI ​​model combines the collected training information with user information to generate the optimal training menu for the user.

[1426] 5. Menu distribution and display: The generated training menu is displayed in real time on the smart glasses display, and the user follows the instructions on the smart glasses to perform the training.

[1427] 6. Progress data collection and update: After completing training, users input their progress data into the smart glasses or terminal, which is then sent to the server, where the AI ​​model continuously optimizes the training menu.

[1428] 7. Providing ideal body data: The server anonymizes the user's ideal body data and provides it to clothing manufacturers, allowing them to efficiently produce clothes that meet customer needs.

[1429] Examples of concrete examples and prompts

[1430] Examples:

[1431] A fitness club trainer uses smart glasses to scan the body shape of a new client, and the system immediately generates and displays a training menu that is optimal for the client's goals.

[1432] Example prompt:

[1433] Generative AI model prompt example:

[1434] Input data: User's body scan, basic information (height, weight, age, desired body shape)

[1435] Goal: Optimal 4-day-per-week training schedule for users looking to gain muscle mass

[1436] The present invention not only provides an optimized training menu for each individual user, but also updates it in real time according to the user's progress, and further anonymizes ideal body shape data and provides it to clothing manufacturers, thereby improving the efficiency of clothing production.

[1437] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1438] Step 1:

[1439] The user puts on the smart glasses and undergoes a body scan. The smart glasses' built-in camera scans the user's body and inputs information such as height, weight, age, and desired body shape. This input data is stored in the smart glasses. Input data: body scan results (image data), height, weight, age, and desired body shape.

[1440] Step 2:

[1441] The smart glasses send the collected user's physical constitution and body shape information to the server. The sent data is received by the server and stored in a database. After the data is sent, a confirmation message is displayed on the smart glasses. Input data: User's physical constitution and body shape information, Output data: Data sending confirmation message.

[1442] Step 3:

[1443] The server collects and organizes training information from media and social media. A generative AI model is used for this process, analyzing and organizing the collected information. Input data: training information obtained from media and social media. Output data: organized training information.

[1444] Step 4:

[1445] The server combines the collected training information with the user's physical constitution and body type information and generates an optimal training menu using a generative AI model. This generation process plans the type of exercise, frequency, and time that are suited to the user's goal body type. Input data: User information, organized training information, generative AI model. Output data: Optimal training menu.

[1446] Step 5:

[1447] The generated training menu is sent from the server to the smart glasses and displayed in real time. The user follows the instructions on the smart glasses to begin training. Input data: optimal training menu, Output data: training menu displayed on the smart glasses.

[1448] Step 6:

[1449] After completing the training, the user inputs their progress data using smart glasses or a terminal. This progress data is sent to the server. Input data: user's progress data. Output data: server receipt confirmation message.

[1450] Step 7:

[1451] The server analyzes the received progress data and continuously optimizes the training menu. The generative AI model takes in new data and updates the next training menu. Input data: user progress data, Output data: updated training menu.

[1452] Step 8:

[1453] The server anonymizes the user's ideal body data and provides it to clothing manufacturers. The anonymization process protects the user's personal information. The manufacturer uses this data to design products. Input data: User's ideal body data, Output data: Anonymized body data.

[1454] 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.

[1455] This invention is a system that utilizes AI technology based on the user's physical constitution and physique information, and further incorporates an emotion engine to provide an optimal training menu that recognizes the user's emotions.By utilizing the user's emotional data, this system maximizes the effectiveness of the training menu and improves motivation.

[1456] Entering and submitting user information

[1457] Users use the application to input their basic information (height, weight, age, and desired body shape). For example, if a user is 170 cm tall, weighs 70 kg, is 30 years old, and aims to increase muscle mass as their desired body shape, they can input this information on the application screen. This allows the proposed training menu to be precisely customized.

[1458] Data transmission and storage on the server

[1459] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database, which is then used for AI analysis.

[1460] Collecting and organizing training data

[1461] The server uses AI models to collect and organize training information from various sources, including media and social media, making it possible to reference the latest and most diverse training methods, including popular training videos on social media and training menus by athletes.

[1462] Training menu generation

[1463] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, the AI ​​will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[1464] Incorporating an emotion engine

[1465] The device uses the emotion engine to identify the user's emotional state (e.g., stress level or excitement level) in real time, allowing the emotion engine to reflect the user's emotional data in generating a training menu.

[1466] Reflecting emotional data

[1467] The server further customizes the training menu based on the user's emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest light exercises for relaxation, while if the user is highly motivated, it will suggest high-intensity training.

[1468] Providing messages to improve motivation

[1469] The device displays customized messages to motivate users based on their emotional data, which is recognized by the emotion engine. This allows users to receive not only the optimal training menu but also psychological support.

[1470] Distribution and display of training menus

[1471] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[1472] Collecting progress data and updating training menus

[1473] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[1474] Providing data to apparel manufacturers

[1475] The server aggregates and anonymizes users' ideal body data before providing it to apparel manufacturers. This data is used as the foundation for apparel manufacturers to efficiently produce clothes that meet customer needs. For example, it can plan designs in advance to fit the body shapes desired by many users.

[1476] In this way, the present invention is a system that not only provides optimal training to users, but also provides psychological support and can also support efficient clothing production in the apparel industry.

[1477] The processing flow will be explained below.

[1478] Step 1:

[1479] The user starts the training application using the terminal and inputs information about his / her constitution and body type, such as his / her height, weight, age, and target body type.

[1480] Step 2:

[1481] The terminal sends the entered user's constitution and body type information to the server.

[1482] Step 3:

[1483] The server stores the received user information in a database.

[1484] Step 4:

[1485] The server collects the latest training information from media and social media, and organizes the data using an AI model, including famous training programs and athletes' body stats.

[1486] Step 5:

[1487] The server combines the organized training data with the saved user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to improve muscle strength, a menu including three strength training sessions and two aerobic exercise sessions per week will be suggested.

[1488] Step 6:

[1489] The server sends the generated training menu to the user's terminal.

[1490] Step 7:

[1491] The device receives the training menu and displays it to the user, who can then perform the training according to the displayed menu.

[1492] Step 8:

[1493] The device uses an emotion engine to recognize the user's emotional state. For example, the emotion engine analyzes data such as the user's facial expressions, voice tone, and heart rate to determine whether the user is feeling stressed or motivated.

[1494] Step 9:

[1495] The server receives the user's emotional data recognized by the emotion engine and reflects that data in the generation of training menus. For example, if the user is feeling stressed, it will suggest a menu that includes gentle stretches to promote relaxation.

[1496] Step 10:

[1497] The device will display customized motivational messages based on the user's emotional state: for example, if the user is tired, an encouraging message will be displayed.

[1498] Step 11:

[1499] The user performs training and inputs progress (type of training performed, duration, calories burned, etc.) into the application.

[1500] Step 12:

[1501] The terminal transmits the input progress data to the server.

[1502] Step 13:

[1503] The server analyzes the progress data received and continuously updates the training menu according to the user's progress. For example, if muscle strength is improving, the server will suggest more intense training to further improve muscle strength.

[1504] Step 14:

[1505] The server again transmits the latest training menu to the user's terminal, and the terminal displays it to the user.

[1506] Step 15:

[1507] The server collects data on users' ideal body types, anonymizes the data, and provides it to apparel manufacturers, enabling them to produce clothes efficiently and without waste.

[1508] Example 2

[1509] 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."

[1510] Currently, most training systems only provide training menus based on basic information about the user's physical constitution and body type, and lack detailed customization based on the user's emotions and progress. Furthermore, they lack mechanisms for effectively increasing user motivation, resulting in a lack of training sustainability. Furthermore, there are no systems that effectively utilize data based on the user's ideal body type to benefit the apparel industry. To solve these problems, a comprehensive training system is needed that reflects the user's emotions and progress in real time, maximizes training efficiency, and increases user motivation.

[1511] 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.

[1512] In this invention, the server includes: [means for inputting a user's physical constitution and body type information;] [means for transmitting the input physical constitution and body type information to the server;] [means for receiving user information and saving it in a database;] [means for collecting and organizing training information from media and social networking services;] [means for combining the collected training information and user information to generate an optimal training menu;] [means for acquiring emotional data using an emotion engine that identifies the user's emotional state in real time;] [means for customizing the training menu based on the acquired emotional data;] [means for transmitting the generated training menu to the user's terminal;] [means for displaying the optimal training menu;] [means for receiving progress data and continuously updating the training menu;] [means for providing customized motivational messages; and [means for anonymizing the user's ideal body type data and providing it to clothing manufacturers.] This enables the provision of detailed training menus based on the user's emotions and progress, psychological support to improve motivation, and efficient product development by providing data to the apparel industry.

[1513] "User's constitution and body type information" refers to personal physical information such as height, weight, age, and target body type input by the user.

[1514] "Server" refers to a computer system used to manage and operate user information and training data.

[1515] A "database" is a storage device for storing and managing data such as user information and training menus.

[1516] "Media and Social Networking Services" means websites and social media platforms on which training information is made public.

[1517] "Training information" refers to information about training methods and menus that can be optimized to suit the user's body type and constitution.

[1518] The "optimal training menu" is a training plan generated using an AI model based on the user's physical constitution and body type information.

[1519] An "emotion engine" is a software technology that analyzes and identifies user emotions in real time.

[1520] "Emotion data" refers to information such as the user's stress level and motivation status identified by the emotion engine.

[1521] "Motivational Messages" are customized messages based on the user's emotional state to motivate them to train.

[1522] "Progress data" refers to performance data such as the calories burned and number of sets completed when a user trains.

[1523] A "clothing manufacturer" is a company that designs and produces clothing based on a user's ideal body data.

[1524] "Anonymization" is a technique for processing data so that users cannot be personally identified.

[1525] "User Device" means the device used by a User to enter information and receive training menus and motivational messages.

[1526] This invention utilizes AI technology based on the user's physical constitution and physique information, and incorporates an emotion engine to recognize the user's emotions and provide an optimal training menu. By utilizing the user's emotional data, this system maximizes the effectiveness of the training menu and improves motivation.

[1527] System configuration

[1528] The user uses a dedicated application to input and submit their own physical constitution and body type information (height, weight, age, and desired body type). This information is stored in JSON format on the device. The device then sends the information entered by the user to the server using the HTTPS protocol. The server receives the data, verifies it, and stores it in a secure database (such as PostgreSQL).

[1529] Data collection and organization

[1530] The server collects training information using web scraping tools (such as Beautiful Soup) and social media APIs (such as Twitter API and Instagram API), and organizes this data into categories using AI models (such as Google's TensorFlow or OpenAI's API).

[1531] Training menu generation

[1532] The server generates an optimal training menu using a generative AI model (e.g., GPT-3) based on the saved user information and organized training information. The generated menu is temporarily stored in the server's cache.

[1533] Incorporating an emotion engine

[1534] The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, and analyzes them with an emotion engine (e.g., Microsoft's Azure Cognitive Services). Identified emotion data is temporarily stored on the device and then sent to a server.

[1535] Reflecting emotional data

[1536] The server receives the emotional data sent from the device and reflects it in the generated training menu. For example, if stress is high, relaxation menus are added, and if motivation is high, high-intensity menus are added. The updated menu is then saved back in the database.

[1537] Providing messages to improve motivation

[1538] The device displays customized motivational messages according to the identified user's emotional state. For example, if the user shows a lack of motivation, the device displays the message "You're making great progress! Keep it up!". The messages are retrieved from the server as appropriate through an internal API.

[1539] Distribution and display of training menus

[1540] The server sends the latest training menu generated by the AI ​​model to the user's device, which displays it on the screen. The user can then check the menu through the app and begin training.

[1541] Collecting progress data and updating training menus

[1542] After training, users enter progress data (e.g., calories burned, number of sets completed) into the app, which then sends it to the server, which uses AI models to optimize the next training menu.

[1543] Providing data to apparel manufacturers

[1544] The server aggregates the collected data on users' ideal body shapes, anonymizes it so that individuals cannot be identified, and then provides it to partner apparel manufacturers via API. The manufacturers use this information to design new products and plan production.

[1545] Examples of prompt statements

[1546] Here are some example prompts that can be fed into a generative AI model:

[1547] Prompt: Generate the optimal training menu for a 30-year-old user who is 170cm tall, weighs 70kg, and wants to increase muscle mass when they are feeling stressed.

[1548] In this way, the present invention is a system that provides users with an optimal training menu, provides support according to the user's emotional state, and even supports efficient product development by providing data to the apparel industry.

[1549] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1550] Step 1:

[1551] The user uses a dedicated application to input their own physical constitution and physique information (height, weight, age, and desired body shape) and presses the send button. The input data is saved in JSON format. For example, a user might input "height 170cm, weight 70kg, age 30, desired body shape: increased muscle mass." This provides the basic data needed to generate a training menu specific to the user.

[1552] Step 2:

[1553] The device sends the input data to the server using the HTTPS protocol. The sent data includes the user's physical constitution and body type information. This data is saved in a database. Specifically, the server receives a POST request to https: / / api.example.com / userdata and saves the data. This allows the server to accumulate basic data for generating training menus.

[1554] Step 3:

[1555] The server collects training information using web scraping tools (e.g., Beautiful Soup) and social media APIs (e.g., Twitter API, Instagram API). It then organizes the collected data into categories. This is done using AI models (e.g., Google's TensorFlow or OpenAI's API) to efficiently process large amounts of data. The organized training information is then stored in a database as output. For example, it can collect popular training methods and training menus used by professional athletes.

[1556] Step 4:

[1557] The server combines the saved user information with the collected training information and generates an optimal training menu using a generative AI model (e.g., GPT-3). The AI ​​model customizes the training menu based on the user's goal body shape and current physical condition. The generated training menu is temporarily stored in the server's cache. For example, if the user's goal is to improve muscle strength, the server will suggest three sessions of strength training and two sessions of aerobic exercise per week.

[1558] Step 5:

[1559] The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, which are then analyzed by an emotion engine (e.g., Microsoft's Azure Cognitive Services). Input data includes the user's facial expressions and voice information. The emotion engine analyzes this data to identify the user's stress level and motivation state. The identified emotion data is temporarily stored on the device and later sent to a server. This allows the user's emotional state to be reflected in the training menu.

[1560] Step 6:

[1561] The server receives the emotional data sent from the device and reflects it in the generated training menu. The information is sent in JSON format, and the server analyzes the received data and makes changes to the training menu. For example, if the user is feeling stressed, it can add relaxation exercises. The revised training menu is then saved back to the database.

[1562] Step 7:

[1563] The device displays customized motivational messages based on the recognized emotional state. The input includes the emotional data identified by the emotion engine. As an output, the message to be displayed is retrieved from the server. By displaying messages such as "You are making great progress!", the user's motivation is improved.

[1564] Step 8:

[1565] The server sends the latest training menu to the user's device, which displays it on the screen. The menu is then made available to the user through the app. Input includes the training menu generated by the AI ​​model, allowing the user to have the latest training menu tailored to them.

[1566] Step 9:

[1567] After training, the user inputs progress data (e.g., calories burned, number of sets completed) into the app. The device sends this data to the server, which then uses that data to optimize the next training menu. The input includes the user's progress data, which allows the server to suggest the next training menu tailored to the user's condition.

[1568] Step 10:

[1569] The server aggregates the collected data on users' ideal body shapes and anonymizes it to prevent personal identification. The data is then provided to partner clothing manufacturers via API. The data includes users' ideal body shapes as input, allowing the clothing manufacturers to develop efficient product development and production plans.

[1570] (Application example 2)

[1571] 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."

[1572] Conventional training menu generation systems provide optimal menus based on the user's physical constitution and body type information, but do not flexibly adjust to the user's emotional state or motivation. As a result, it is difficult to provide training menus that respond to the user's psychological changes, leading to issues such as a decrease in motivation and maximizing training effectiveness. Furthermore, it is not sufficient to continuously update menus based on the user's progress data.

[1573] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's physical constitution and body type information, means for transmitting the input physical constitution and body type information to the server, means for receiving the user information and saving it in a database, means for collecting and organizing training information from media and SNS, means for combining the collected training information and the user information to generate an optimal training menu, means for transmitting the generated training menu to the user's device, means for displaying the optimal training menu, means for receiving the user's progress data and continuously updating the training menu, means for analyzing the user's emotional state and adjusting the training menu based on the emotional data, and means for providing motivational messages based on the user's emotional state. This makes it possible to provide an individually optimized training menu that takes the user's psychological state into consideration and to continuously motivate the user.

[1574] "User's constitution and body type information" means biological and physical characteristic data such as the user's height, weight, age, and target body type.

[1575] "Server" refers to a computer system that collects and organizes data and training information sent by users and stores them in a database.

[1576] "Media and SNS" refers to media platforms, including online news sites, video sharing sites, and social networking services.

[1577] "Training information" refers to information about various training methods and menus, such as strength training and aerobic exercise.

[1578] "User Information" means personal data entered by a User, including physical constitution and body type information and progress data.

[1579] "Training menu" refers to a systematic exercise program that is optimal for the user, and includes specific details of strength training and aerobic exercise.

[1580] "User's device" refers to electronic devices used by the user, such as smartphones, tablets, and personal computers.

[1581] "Progress Data" refers to data that shows a user's training progress, such as training history and weight changes.

[1582] "Emotional state" refers to a user's psychological state, such as stress level or excitement level.

[1583] "Motivational Messages" refers to customized messages provided to motivate a user to train based on the user's emotional state.

[1584] This invention is a system that provides a training menu based on the user's physical constitution, body type information, and emotional state. The system analyzes the information entered by the user on the server and generates and provides the optimal training menu using an AI model and emotion engine. Furthermore, the system updates the training menu based on the user's progress data and provides messages to increase motivation.

[1585] Entering and submitting user information

[1586] Users input their physical constitution and body type information (e.g., height, weight, age, and desired body shape) using a device such as a smartphone. This information is sent to the server via the application. For example, if a 30-year-old user aims to increase muscle mass, they would input information such as a height of 170 cm and a weight of 70 kg.

[1587] Data transmission and storage on the server

[1588] The device collects data entered by the user and sends it to a server, which receives the data and stores it in a database for later analysis by AI.

[1589] Collecting and organizing training information

[1590] The server uses AI models to collect the latest training information from various sources, such as media and social media, and organizes it, including training videos from famous trainers and training menus for athletes on social media.

[1591] Training menu generation

[1592] The server uses an AI model to combine the collected training information with user information to generate an optimal training menu. For example, if a user is aiming to improve their muscle strength, it will suggest a menu that includes three strength training sessions and two aerobic sessions per week.

[1593] Incorporating an emotion engine

[1594] The device uses an emotion engine to identify the user's emotional state (e.g., stress level or excitement level) in real time. This allows the emotion engine to reflect the user's emotional data in the generation of training menus. For example, if the user is feeling stressed, the device will suggest relaxation training.

[1595] Reflecting emotional data

[1596] The server further customizes the training menu based on the user's emotional data recognized by the emotion engine. If the user is feeling stressed, it will suggest a light exercise menu, and if the user is highly motivated, it will suggest a more intense menu.

[1597] Providing messages to improve motivation

[1598] The device displays customized messages aimed at improving motivation based on the user's emotional data recognized by the emotion engine, allowing users to receive psychological support.

[1599] Distribution and display of training menus

[1600] The server transmits the generated training menu to the user's terminal, which then displays the menu to the user, allowing the user to effectively perform the training menu that is best suited to them.

[1601] Collecting progress data and updating training menus

[1602] Users input their training progress data into the app, which then sends it to the server, which then uses that data to continually optimize the training menu, providing users with an up-to-date training menu that adapts to their changing physical condition.

[1603] Specific examples

[1604] For example, if a 30-year-old user is trying to gain muscle mass and the emotion engine determines that the user is feeling stressed, the generation system will provide a menu of light exercises aimed at relaxation and display the message, "Relax and do some light exercise."

[1605] Prompt Sentence Examples

[1606] Below are some example prompts to input to the generative AI model:

[1607] "The user is 30 years old and has a goal of increasing muscle mass. The user's emotional state is analyzed using a camera, and the results show that the stress level is high. The AI ​​model should provide a light training menu for relaxation and a message saying, 'Relax and do some light exercise.'"

[1608] Using this prompt, the AI ​​model generates a training menu that meets the user's needs and displays appropriate motivational messages.

[1609] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1610] Step 1:

[1611] Users use devices such as smartphones and tablets to input their physical constitution and body type information (e.g., height, weight, age, and desired body type). This information is saved on the device.

[1612] Input: User information such as height, weight, age, and desired body shape.

[1613] Output: User information stored on the device.

[1614] Step 2:

[1615] The terminal sends the entered physical constitution and body type information to the server, where the data is protected using the HTTPS protocol.

[1616] Input: User information stored on the device.

[1617] Output: The user information sent to the server.

[1618] Step 3:

[1619] The server stores the received user information in a database, and the stored data is assigned a user ID for identification.

[1620] Input: User information sent to the server.

[1621] Output: User information stored in the database.

[1622] Step 4:

[1623] The server uses an AI model to collect and organize training information from media and social media, for example, analyzing training videos on social media and storing the type and intensity of training in a database.

[1624] Input: Training information from media and social media.

[1625] Output: Organized training information stored in a database.

[1626] Step 5:

[1627] The server combines the collected training information with user information and uses an AI model to generate an optimal training menu. For example, if a user's goal is to increase muscle strength, an appropriate strength training menu will be generated.

[1628] Input: User and training information stored in the database.

[1629] Output: The generated optimal training menu.

[1630] Step 6:

[1631] The generated training menu is sent from the server to the user's device, where it is formatted for viewing by the user.

[1632] Input: The generated training menu.

[1633] Output: The training menu sent to the device.

[1634] Step 7:

[1635] The device displays the training menu to the user, including specific training content and number of repetitions.

[1636] Input: Training menu sent to the device.

[1637] Output: The training menu that is displayed to the user.

[1638] Step 8:

[1639] The emotion engine uses cameras and sensors to analyze the user's emotional state (e.g., stress level, excitement level) in real time. The analysis results are processed on the device and sent to the server.

[1640] Input: User facial expressions and vital data obtained from cameras and sensors.

[1641] Output: Parsed user sentiment data.

[1642] Step 9:

[1643] The server receives the emotional data and reflects it in the training menu. For example, if the user is in a high stress state, the training menu will be customized to a lighter one aimed at relaxation.

[1644] Input: Received user sentiment data.

[1645] Output: A customized training menu.

[1646] Step 10:

[1647] The device displays customized training menus along with motivational messages based on the user's emotional state.

[1648] Input: Customized training menu and emotional data.

[1649] Output: The motivational message that is displayed to the user.

[1650] Step 11:

[1651] Users input their training progress data (e.g., training results, weight changes) into the app, which is then sent from the device to the server.

[1652] Input: Progress data entered by the user.

[1653] Output: Progress data sent to the server.

[1654] Step 12:

[1655] The server receives the progress data and continuously updates the training menu based on that data. The system tracks the user's progress and provides the latest and most optimal menu.

[1656] Input: Received user progress data.

[1657] Output: Continuously updated training menu.

[1658] Prompt Sentence Examples

[1659] Below are some example prompts to input to the generative AI model:

[1660] "The user is 30 years old and has a goal of increasing muscle mass. The user's emotional state is analyzed using a camera, and the results show that the stress level is high. The AI ​​model should provide a light training menu for relaxation and a message saying, 'Relax and do some light exercise.'"

[1661] Using this prompt, the AI ​​model generates a training menu that meets the user's needs and displays appropriate motivational messages.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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).

[1669] 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.

[1670] 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."

[1671] 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.

[1672] 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).

[1673] 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.

[1674] 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.

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] The following is further disclosed regarding the above embodiment.

[1684] (Claim 1)

[1685] [Means for inputting user's constitution and body type information;

[1686] [Means for transmitting the inputted constitution and body type information to a server;

[1687] [Means for receiving and storing user information in a database;

[1688] [Means of collecting and organizing training information from the media and social media,

[1689] [Means for generating optimal training menus by combining collected training information and user information; and

[1690] [Means for sending the generated training menu to the user's terminal;

[1691] [Method to display the optimal training menu,

[1692] [Means of receiving user progress data and continually updating the training menu;

[1693] [Method to anonymize users' ideal body data and provide it to apparel manufacturers,

[1694] A system including:

[1695] (Claim 2)

[1696] The system according to claim 1, wherein the physical constitution and body type information input by the user includes height, weight, age, and target body type.

[1697] (Claim 3)

[1698] [The system of claim 1, wherein the server uses the AI ​​model to collect and organize training information.

[1699] "Example 1"

[1700] (Claim 1)

[1701] [Means for users to input their basic physical constitution and body type information;

[1702] [Means for transmitting the inputted constitution and body type information to a server via the Internet;

[1703] [Means for the server to receive and store user information in a database;

[1704] [Means for the server to collect and organize training data from the Internet; and

[1705] [Means for the server to generate an optimal training plan based on the collected training data and user information;

[1706] [Means for sending the generated training plan to the user's device;

[1707] [Means to display the optimal training plan on the user's device,

[1708] [means for a user to input training progress data and transmit the progress data to a server;

[1709] [Means for the server to continually update the training plan based on the user's progress data; and

[1710] [Means for the server to anonymize the user's target body shape data and provide it to the apparel industry;

[1711] A system including:

[1712] (Claim 2)

[1713] The system according to claim 1, wherein the physical constitution and body type information input by the user includes height, weight, age, and target body type.

[1714] (Claim 3)

[1715] The system of claim 1, wherein the server collects and organizes training data using the generative AI model.

[1716] "Application Example 1"

[1717] (Claim 1)

[1718] [Means for inputting user's constitution and body type information and performing body type scan;

[1719] [Means for transmitting the inputted constitution and body type information to a server;

[1720] [Means for receiving and storing user information in a database;

[1721] [Means of collecting and organizing training information from the media and social media,

[1722] [Means for generating optimal training menus by combining collected training information and user information; and

[1723] [Means for transmitting the generated training menu to the user's terminal and displaying it on the smart glasses;

[1724] [Means of receiving user progress data and continually updating the training menu;

[1725] [Means to anonymize users' ideal body data and provide it to clothing manufacturers,

[1726] A system including:

[1727] (Claim 2)

[1728] [The system of claim 1, wherein the physical constitution and body type information input by the user includes height, weight, age, and target body type, and incorporates body type scan results.

[1729] (Claim 3)

[1730] [The system of claim 1, wherein the server uses the generative AI model to collect and organize training information.

[1731] "Example 2: Combining Emotion Engines"

[1732] (Claim 1)

[1733] [Means for inputting user's constitution and body type information;

[1734] [Means for transmitting the inputted constitution and body type information to a server;

[1735] [Means for receiving and storing user information in a database;

[1736] [Means for collecting and organizing training information from media and social networking services;

[1737] [Means for generating optimal training menus by combining collected training information and user information; and

[1738] [Means for acquiring emotional data using an emotional engine that identifies the emotional state of a user in real time;

[1739] [Means for customizing training menus based on acquired emotional data,

[1740] [Means for sending the generated training menu to the user's terminal;

[1741] [Method to display the optimal training menu,

[1742] [Means to receive progress data and continually update your training menu;

[1743] [Means for providing customized motivational messages;

[1744] [Means of anonymizing users' ideal body data and providing it to clothing manufacturers;

[1745] A system including:

[1746] (Claim 2)

[1747] The system according to claim 1, wherein the physical constitution and body type information input by the user includes height, weight, age, and target body type.

[1748] (Claim 3)

[1749] The system of claim 1, wherein the server uses an artificial intelligence model to collect and organize training information.

[1750] (Claim 4)

[1751] [The system of claim 1, wherein the emotion engine analyzes the user's facial expressions and voice using a camera and microphone.

[1752] (Claim 5)

[1753] The system of claim 1, wherein the progress data includes calories burned and number of sets completed.

[1754] "Application example 2 when combining emotion engines"

[1755] (Claim 1)

[1756] [Means for inputting user's constitution and body type information;

[1757] [Means for transmitting the inputted constitution and body type information to a server;

[1758] [Means for receiving and storing user information in a database;

[1759] [Means of collecting and organizing training information from the media and social media,

[1760] [Means for generating optimal training menus by combining collected training information and user information; and

[1761] [Means for sending the generated training menu to the user's terminal;

[1762] [Method to display the optimal training menu,

[1763] [Means of receiving user progress data and continually updating the training menu;

[1764] [Means for analyzing the user's emotional state and adjusting the training menu based on the emotional data;

[1765] [Means for providing motivational messages based on the user's emotional state;

[1766] A system including:

[1767] (Claim 2)

[1768] The system according to claim 1, wherein the physical constitution and body type information input by the user includes height, weight, age, and target body type.

[1769] (Claim 3)

[1770] [The system of claim 1, wherein the server uses the AI ​​model to collect and organize training information. [Explanation of symbols]

[1771] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting user's constitution and body type information; means for transmitting the inputted constitution and body type information to a server; a means for receiving and storing user information in a database; A means of collecting and organizing training information from the media and social media, A means for combining collected training information and user information to generate an optimal training menu; A means for transmitting the generated training menu to a user's terminal; A means to display the optimal training menu, A means of receiving user progress data and continually updating the training menu; A method to anonymize users' ideal body data and provide it to apparel manufacturers, A system including:

2. 2. The system according to claim 1, wherein the physical constitution and body type information input by the user includes height, weight, age, and target body type.

3. The system of claim 1 , wherein the server uses the AI ​​model to collect and organize training information.

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