system

Abstract

Provide a system. A means for inputting physical information and lifestyle information of a user; means for receiving the user's information and generating a training plan and a nutrition plan; means for presenting the generated training and nutrition plans to a user; A system including:

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] Amateur athletes need comprehensive management of not only training but also appropriate diet, recovery, sleep, and mental health. However, support generally provided is partial, making it difficult to incorporate these elements in a balanced manner. In particular, a lack of comprehensive support for balancing work, daily life, and athletics makes it difficult to achieve optimal performance. The objective of this invention is to support amateur athletes in achieving peak performance by providing comprehensive training plans, nutrition plans, recovery programs, sleep suggestions, and even mental health care tailored to each user's individual condition. [Means for solving the problem]

[0005] To solve this problem, the present invention provides a system that includes the following means: means for inputting a user's physical information and lifestyle information, means for receiving the user's information and generating a training plan and nutrition plan, and means for presenting the generated training plan and nutrition plan to the user. The system also includes means for generating a recovery program and sleep suggestions based on the user's physical information and lifestyle information, and means for measuring the user's stress level and suggesting appropriate relaxation techniques and mental conditioning methods, thereby providing a system that allows the user to receive comprehensive support.

[0006] "User's physical information" refers to information that indicates the user's physical characteristics and condition, such as age, gender, athletic history, and recent injuries.

[0007] "Lifestyle information" is information about the user's daily life and athletics, such as the date of the next match, daily stress levels, etc.

[0008] A "training plan" is a training program created based on the user's physical information and lifestyle information.

[0009] A "nutritional plan" is a meal program that suggests optimal nutritional balance and intake timing based on the user's physical and lifestyle information.

[0010] The "recovery program" is a program that takes into account the user's physical information and post-training recovery and supports recovery from injury and fatigue.

[0011] "Sleep suggestions" are advice to improve the quality of a user's sleep by proposing optimal sleeping environments and methods based on the user's lifestyle information.

[0012] "Mental care" refers to supporting the user's psychological health by measuring the user's stress level and providing appropriate relaxation techniques and mental conditioning methods. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The system of the present invention provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[0035] Entering user information

[0036] The user inputs information using the terminal.

[0037] The user enters information such as age, gender, athletic history, recent injuries, next game date, daily stress level, etc. into the input form on the device. The entered information is sent to the server by pressing the send button.

[0038] User data analysis

[0039] The server receives and analyzes the user data

[0040] The server stores the received data in a database and invokes a generative AI model, which analyzes the data and creates a user-specific profile that is then used to generate subsequent plans.

[0041] Generate training and nutrition plans

[0042] The server generates optimal training and nutrition plans

[0043] Based on the user's profile information, the server uses AI models to generate personalized training and nutrition plans, allowing users to train efficiently and consume proper nutrition.

[0044] Providing recovery menus and sleep suggestions

[0045] The server generates recovery menus and sleep suggestions

[0046] The system generates a recovery program to support the user's recovery, taking into account the presence or absence of injuries and the level of fatigue from training. It also generates advice on how to get quality sleep and sends this to the user's device.

[0047] Mental care support

[0048] Servers provide mental health care

[0049] The system suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server receives it and the AI ​​model generates an appropriate answer and sends it back.

[0050] As a concrete example, consider a 25-year-old male amateur basketball player using the system. He sprained his ankle during a recent practice, and his next game is in two weeks. He enters his daily stress level as 5.

[0051] in this case,

[0052] Training Plan: A light training menu is generated, encouraging upper body training and water exercises to avoid straining your ankles.

[0053] Nutrition plan: A protein-rich diet and anti-inflammatory foods are suggested.

[0054] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[0055] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[0056] Mental care: For stress level 5, meditation and deep breathing exercises are recommended, and support is provided via chat.

[0057] In this way, this system provides comprehensive support based on multifaceted data about the user, helping athletes to perform at their best.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The user inputs information using the terminal.

[0061] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. After completing the input, the user presses the send button to send this information to the server.

[0062] Step 2:

[0063] The server receives the user data

[0064] The server receives the user information sent from the device and stores it in a database, which aggregates the user's physical and lifestyle information.

[0065] Step 3:

[0066] The server analyzes the user data and creates a profile

[0067] The server invokes the generative AI model to analyze the received user data, and based on the analysis results, a personalized profile is created for the user, which contains the information needed to generate training and nutrition plans.

[0068] Step 4:

[0069] The server generates the training plan.

[0070] Based on the user profile, the server uses an AI model to generate a personalized training plan that includes specific training instructions tailored to the user's characteristics and goals.

[0071] Step 5:

[0072] The server generates the nutrition plan

[0073] The server also generates a nutrition plan based on the user profile, taking into account nutritional balance and timing of intake. This plan indicates what foods the user should eat and when.

[0074] Step 6:

[0075] The server sends the training plan and nutrition plan to the device and displays it.

[0076] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[0077] Step 7:

[0078] The server generates a recovery program

[0079] The server takes into account the user's injury information and fatigue level and uses an AI model to generate a recovery program to support recovery, including specific menus to help with injury recovery and reduce muscle fatigue.

[0080] Step 8:

[0081] The server generates sleep suggestions

[0082] The server generates recommendations for getting a good night's sleep based on the user's lifestyle information, including bedding selection and pre-sleep relaxation routines.

[0083] Step 9:

[0084] The server sends and displays recovery programs and sleep suggestions on the device.

[0085] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[0086] Step 10:

[0087] Servers provide mental health care

[0088] The server measures the user's daily stress level and suggests relaxation techniques and mental conditioning methods based on that. When the user inputs a question or inquiry from their device, the server generates an appropriate answer and returns it.

[0089] Step 11:

[0090] Users implement suggestions and provide feedback

[0091] Users implement the training, nutrition, recovery program, sleep, and mental care programs they receive through their device, and then send feedback from their device to the server, which allows the AI ​​to make further improvements.

[0092] Example 1

[0093] 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."

[0094] In today's busy lifestyles, individually optimized advice is necessary for efficient and effective training, nutritional management, recovery, and mental care. However, existing systems have difficulty accurately collecting user information and providing comprehensive support. Furthermore, advanced data analysis is required to create appropriate plans based on the user's physical and lifestyle information. This calls for a more advanced and integrated system.

[0095] 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.

[0096] In this invention, the server includes a means for inputting the user's physical information and lifestyle information, a means for creating a user profile using a generative AI model, and a means for generating individually optimized training and nutrition plans, which makes it possible to provide individually optimized training plans, nutrition plans, recovery programs, mental care, etc. to the user.

[0097] "User's physical information" refers to information about the user's body, such as age, gender, athletic history, and injury history.

[0098] "Lifestyle information" is information about the user's daily life, such as their daily stress level, lifestyle habits, and the date of their next game.

[0099] A "generative AI model" is an artificial intelligence model used to analyze a user's physical and lifestyle information and generate an optimal plan.

[0100] A "user profile" is an individualized collection of data created based on the user's physical and lifestyle information analyzed by a generative AI model.

[0101] A "training plan" is a schedule and content of exercises and physical activities recommended for a user.

[0102] A "nutritional plan" is a guideline for dietary and nutritional intake recommended for a user.

[0103] A "recovery program" is a method or procedure for supporting a user in recovering from fatigue or healing from an injury.

[0104] "Sleep suggestions" are advice and methods for ensuring quality sleep.

[0105] "Relaxation techniques" are methods such as meditation and deep breathing exercises that help users reduce stress.

[0106] A "mental conditioning method" is an approach or technique for maintaining a user's mental state in good condition.

[0107] The system of the present invention provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[0108] Users use devices such as smartphones or PCs to enter detailed information such as age, gender, athletic history, recent injuries, next game date, and daily stress level. This information is collected through an input form on the device and sent to the server by pressing a submit button. The submitted data is transferred using a secure encryption protocol (e.g., HTTPS).

[0109] The server stores the received data in a database. This database uses a relational database system such as MySQL (registered trademark) or PostgreSQL. The stored data is used as the basis for subsequent analysis and plan generation.

[0110] The server then invokes a generative AI model, built using Python®-based libraries (e.g., TENSORFLOW® and PyTorch), to analyze the stored user data. The AI ​​model then creates an individually optimized user profile based on the user's physical and lifestyle information.

[0111] Based on the created user profile, the server then uses the generative AI model to generate individually optimized training and nutrition plans. For example, a user with a sprained ankle might be recommended upper-body training and water exercises, and the nutrition plan might include anti-inflammatory foods and a diet high in protein. These plans are then sent to the user's device, where they can be viewed.

[0112] Furthermore, the server generates recovery programs and sleep recommendations based on the user's training data and injury information, including icing and stretching techniques, and the selection of bedding for quality sleep. The generated information is also sent to the user's device and made available to the user.

[0113] Finally, the server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or inquiry from their device, the server uses a generative AI model to generate an appropriate answer and provides it to the user in chat format. This mental care function allows users to receive comprehensive support.

[0114] As a specific example, by entering the prompt "30-year-old male, marathon runner as a hobby. My legs have been feeling tired recently, but my next race is in a month. My stress level is 3," an individually optimized training plan, nutrition plan, recovery program, sleep suggestions, and mental care will be generated and provided to the user.

[0115] In this way, the system of the present invention provides comprehensive support based on multifaceted data of the user, encouraging athletes to perform at their best.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] The user enters information using a device. Using a smartphone or PC, the user accesses a dedicated application or web page. There, they enter information such as age, gender, athletic history, recent injuries, next game date, and daily stress level into an input form. After the data is entered, they press the "Submit" button to proceed to the next step.

[0119] Input: User's physical and lifestyle information

[0120] Output: Data sent from the terminal

[0121] Step 2:

[0122] The device sends data to the server, which receives it. When the user presses the "Send" button, the device sends the entered data to the server using an encrypted communication protocol (e.g., HTTPS). The server receives this data and temporarily stores it. This process ensures the safety of the data.

[0123] Input: Data sent from the terminal

[0124] Output: User data temporarily stored on the server

[0125] Step 3:

[0126] The server stores the received user data in a database. The server analyzes the received data and stores it in a relational database system (e.g., MySQL or PostgreSQL). This database has tables, and each user's information is managed individually.

[0127] Input: User data temporarily stored on the server

[0128] Output: User data stored in the database

[0129] Step 4:

[0130] The server invokes a generative AI model to create a user profile. The server invokes a generative AI model (e.g., TensorFlow or PyTorch) based on the stored user data. The AI ​​model analyzes the user's physical and lifestyle information and creates an individually optimized user profile based on that information.

[0131] Input: User data stored in the database

[0132] Output: Generated user profile

[0133] Step 5:

[0134] The server generates personalized training and nutrition plans based on the user profile. The generative AI model analyzes the user profile and generates optimal training and nutrition plans for the user. For example, if a person has an ankle injury, upper body training and water exercises will be recommended, and the nutrition plan will include anti-inflammatory and high-protein foods.

[0135] Input: Generated user profile

[0136] Output: Training and nutrition plans

[0137] Step 6:

[0138] The server sends the training plan and nutrition plan to the user's device. The generated plan is sent from the server to the user's device, where the user can view it on their own device, allowing the user to put the plan into action.

[0139] Input: Training plan and nutrition plan

[0140] Output: The plan sent to the user's device

[0141] Step 7:

[0142] The server generates a recovery program and sleep suggestions and sends them to the user's device. Based on the user's training data and injury information, the server uses a generative AI model to create a recovery program and sleep suggestions. This includes, for example, advice on icing and stretching the ankle and ensuring quality sleep. The generated plan is sent to the user's device, where the user can review it.

[0143] Input: Training data and injury information

[0144] Output: Recovery program and sleep suggestions sent to the user's device

[0145] Step 8:

[0146] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server uses a generative AI model to generate an appropriate answer and provide mental care. This information is also sent to the user's device, allowing for communication in chat format.

[0147] Input: User's daily stress level, questions and inquiries from the user

[0148] Output: Relaxation techniques and mental conditioning methods sent to the device

[0149] These processing steps allow users to receive comprehensive and individually optimized support.

[0150] (Application example 1)

[0151] 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."

[0152] In traditional factory work environments, workers' health is not adequately managed, which can lead to problems with work efficiency and safety. It is also difficult to provide individually optimized training plans, nutrition plans, and recovery programs, and many workers do not receive appropriate feedback. This increases the physical and mental burden on workers and can lead to reduced productivity.

[0153] 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.

[0154] In this invention, the server includes means for inputting a user's physical information and lifestyle information, means for receiving the user's information and generating a training plan and a nutrition plan, means for presenting the generated training plan and nutrition plan to the user, means for collecting the user's health information and providing health management feedback, means for collecting data from the wearable device and generating individually optimized reminders and notifications using a generative AI model, and means for transmitting the collected data to the server and generating a customized plan, thereby enabling the health status of workers to be grasped in real time and individually optimized feedback to be provided.

[0155] "User's physical information" refers to physiological data such as the user's heart rate, blood pressure, and body temperature, as well as information related to the user's health condition.

[0156] "Lifestyle information" refers to information such as the user's daily behavior patterns, eating habits, sleeping hours, and exercise habits.

[0157] "Input means" refers to the interface or device through which a user provides physical and lifestyle information.

[0158] The "means for receiving and generating training and nutrition plans" is the part of the system that receives information from the user and creates optimal training and nutrition plans based on that information.

[0159] "Presenting means" refers to a device or software that displays the generated training plan and nutrition plan so that the user can check them.

[0160] "Means for collecting health information" refers to a system that automatically collects users' health data through wearable devices and sensors.

[0161] A "means for providing health management feedback" is a device or software for providing specific advice or feedback to a user based on collected health data.

[0162] A "wearable device" refers to an electronic device worn by a user that can constantly monitor and collect health information.

[0163] "Generative AI Model" refers to an artificial intelligence model used to analyze collected data and generate individually optimized training plans, nutrition plans, reminders and notifications.

[0164] The "server" is a central processing unit that receives information from users, stores and analyzes the data, and generates the optimal plan.

[0165] The "means for generating a customized plan" is part of a system that creates an individually optimized health management plan based on data collected from a wearable device.

[0166] The present invention is a system that provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[0167] Entering user information

[0168] The user uses the terminal to input physical information (physiological data such as heart rate, blood pressure, and body temperature) and lifestyle information (daily behavioral patterns, eating habits, sleep time, exercise habits, etc.). For example, the user enters information such as age, gender, experience, recent injuries, next shift time, and daily stress level into an input form on the terminal. The input information is sent to the server by pressing the send button.

[0169] User data analysis

[0170] The server stores the received data in a database and invokes a generative AI model, which analyzes the data and creates a user-specific profile that is then used to generate subsequent plans.

[0171] Generate training and nutrition plans

[0172] The server uses a generative AI model based on the user's profile information to generate personalized training and nutrition plans, allowing users to train efficiently and consume appropriate nutrition. For example, based on a user's data, the server may recommend "10 minutes of light exercise" and "a high-protein meal."

[0173] Providing recovery menus and sleep suggestions

[0174] The server generates a recovery program to support the user's recovery, taking into account the presence or absence of injuries and the level of fatigue from training. It also generates advice on how to get quality sleep and sends it to the user's device. For example, it recommends "20 minutes of cooling and stretching" and suggests "7-8 hours of sleep."

[0175] Mental care support

[0176] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server receives it and the generative AI model generates an appropriate answer and sends it back. For example, a message such as, "Your recent stress level is 6. We recommend taking five minutes of deep breaths" is provided.

[0177] Examples of concrete examples and prompts

[0178] For example, consider a 35-year-old male factory worker using the system. His recent heart rate was 85 and his daily stress level was 6. His next shift is one day away. In this case, the system will provide the following feedback:

[0179] "Your current stress level is 6. I suggest you take 5 minutes of deep breathing."

[0180] "Your heart rate has been high lately. Don't forget to stay hydrated at the water cooler."

[0181] An example of a prompt is as follows:

[0182] "Please use the AI ​​model to determine what health care I should do before my next shift:\n- Heart rate: 85\n- Stress level: 6\n- Sleep time: 6 hours\n"

[0183] In this way, this system provides comprehensive support based on multifaceted user data, helping workers to perform at their best.

[0184] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0185] Step 1:

[0186] The user uses the terminal to input physical and lifestyle information. The user enters data such as age, gender, experience, recent injuries, next shift time, and daily stress level into the input form, and presses the send button to send it to the server. Based on the input data, the server obtains data to generate the user's basic profile.

[0187] Step 2:

[0188] The server receives the user's input data and stores it in a database. The stored data becomes input data for calling the generative AI model. The database uses SQL or NoSQL, allowing for data storage and high-speed search.

[0189] Step 3:

[0190] The server invokes a generative AI model based on the received data. The generative AI model analyzes the data to create a profile specific to the user. This analysis uses machine learning libraries (e.g., TensorFlow and PyTorch). The profile includes the user's physical information, lifestyle information, and health status.

[0191] Step 4:

[0192] The server uses generative AI models to generate personalized training and nutrition plans. For example, it might recommend 10 minutes of light exercise and a high-protein meal based on the user's age, gender, heart rate, and exercise habits. These plans are then stored in a database on the server.

[0193] Step 5:

[0194] The server references the user's profile and generates recovery programs and sleep recommendations based on the user's injury status and training fatigue level. For example, if a user has a sprained ankle, the server might suggest "20 minutes of cooling and stretching" and "7-8 hours of sleep." These recommendations are also generated by an AI model and stored in the server's database.

[0195] Step 6:

[0196] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level, such as "take five minutes of deep breaths." This information is analyzed by a generative AI model to generate suggestions.

[0197] Step 7:

[0198] The generated training plan, nutrition plan, recovery program, sleep suggestions, and relaxation suggestions are sent to the user's device, which then visually displays this information. Feedback is provided via a smartphone or smart glasses, for example, and the user can review and put the information into practice.

[0199] Step 8:

[0200] A user's wearable device (such as a smartwatch or fitness tracker) continuously collects real-time health data, such as heart rate, stress level, and sleep data, and automatically transmits this data to a server. This data is then used as input for generative AI models, providing updated feedback.

[0201] Step 9:

[0202] The server continuously receives health data from the user and updates feedback based on the latest generative AI model. This allows the user to constantly receive optimal advice based on their health status. For example, a notification might be sent saying, "Your heart rate has been high recently. Don't forget to drink water at the water cooler."

[0203] Through the above processing steps, the user can receive individually optimized feedback in real time, enabling them to efficiently manage their own health.

[0204] 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.

[0205] The system of the present invention analyzes a user's physical information, lifestyle information, and emotions, and based on that information, provides optimal training plans, nutrition plans, recovery programs, sleep suggestions, and even mental care. This system is realized by exchanging information between the terminal, server, and user's device, and providing individually optimized advice.

[0206] Entering user information

[0207] The user inputs information using the terminal.

[0208] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. In addition, the user provides emotional information to the emotion engine using facial recognition or voice input. After completing the input, the user presses the send button to send this information to the server.

[0209] User data analysis

[0210] The server receives the user data

[0211] The server receives the user information and emotion information sent from the device and stores them in a database, thereby aggregating the user's physical information, lifestyle information, and emotion information.

[0212] The server analyzes user data and emotional information to create a profile

[0213] The server then invokes the generative AI model and emotion engine to analyze the received data, and a personalized profile is created based on the results of the analysis. This profile contains the information needed to generate training and nutrition plans.

[0214] Generate training and nutrition plans

[0215] The server generates the optimal training plan

[0216] Based on the user profile and emotional information, the server uses an AI model to generate a personalized training plan that includes specific training instructions tailored to the user's characteristics, goals, and current emotional state.

[0217] The server generates the nutrition plan

[0218] The server also generates a nutrition plan based on the user profile and emotional information, taking into account nutritional balance and timing of intake. This plan indicates what meals the user should eat and when.

[0219] The server sends the training plan and nutrition plan to the device and displays it.

[0220] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[0221] Providing recovery menus and sleep suggestions

[0222] The server generates a recovery program

[0223] The server takes into account the user's injury information, fatigue level, and emotional state, and uses an AI model and emotion engine to generate a recovery program to support recovery, including specific menus to help with injury recovery and muscle fatigue reduction.

[0224] The server generates sleep suggestions

[0225] The server generates recommendations for getting a good night's sleep based on the user's lifestyle and emotional information, including recommendations for bedding selection and pre-sleep relaxation routines.

[0226] The server sends and displays recovery programs and sleep suggestions on the device.

[0227] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[0228] Mental care support

[0229] The server uses an emotion engine to analyze the user's emotions.

[0230] The emotion engine analyzes the emotion information provided by the user and recognizes the current emotional state.

[0231] Servers provide mental health care

[0232] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information. When the user inputs a question or request for advice from the device, the server generates an appropriate answer and returns it.

[0233] As a concrete example, consider a 25-year-old male amateur basketball player using the system. He sprained his ankle during a recent practice, and his next game is two weeks away. He enters his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety."

[0234] in this case,

[0235] Training Plan: A light training menu is generated. Upper body training and water exercises are recommended to avoid straining the ankles. The Emotion Engine also adds training to reduce anxiety.

[0236] Nutrition plan: Protein-rich meals and anti-inflammatory foods are recommended, along with mental health foods to reduce anxiety.

[0237] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[0238] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[0239] Mental care: Meditation and deep breathing exercises are recommended for anxiety, and support is provided via chat.

[0240] In this way, this system provides comprehensive support based on the user's multifaceted data and emotional information, helping athletes to perform at their best.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] The user inputs information using the terminal.

[0244] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. The emotion engine also uses facial recognition and voice input to provide emotional information. Once input is complete, the user presses the send button to send the information to the server.

[0245] Step 2:

[0246] The server receives the user data

[0247] The server receives the user information and emotion information sent from the device and stores them in a database, thereby aggregating the user's physical information, lifestyle information, and emotion information.

[0248] Step 3:

[0249] The server analyzes user data and emotional information to create a profile

[0250] The server then invokes the generative AI model and emotion engine to analyze the received data, and creates a personalized profile for the user based on the results of this analysis. This profile contains the information needed to generate training and nutrition plans.

[0251] Step 4:

[0252] The server generates the optimal training plan

[0253] The server uses an AI model to generate a personalized training plan based on the user's profile and emotional information. The emotional engine adjusts the training content according to the user's current emotional state. The generated plan includes specific training instructions tailored to the user's characteristics and goals.

[0254] Step 5:

[0255] The server generates the nutrition plan

[0256] The server also generates a nutrition plan based on the user profile and emotional information, taking into account nutritional balance and timing. This plan indicates what meals the user should eat and when. The emotional engine also provides nutritional instructions based on the user's emotional state.

[0257] Step 6:

[0258] The server sends the training plan and nutrition plan to the device and displays it.

[0259] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[0260] Step 7:

[0261] The server generates a recovery program

[0262] The server takes into account the user's injury information, fatigue level, and emotional state, and uses an AI model and emotion engine to generate a recovery program to support recovery, including specific menus to help with injury recovery and reduce muscle fatigue.

[0263] Step 8:

[0264] The server generates sleep suggestions

[0265] The server generates recommendations for getting a good night's sleep based on the user's lifestyle and emotional information, including recommendations for bedding selection and pre-sleep relaxation routines.

[0266] Step 9:

[0267] The server sends and displays recovery programs and sleep suggestions on the device.

[0268] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[0269] Step 10:

[0270] The server uses an emotion engine to analyze the user's emotions.

[0271] The server uses an emotion engine to analyze the emotional information provided by the user and recognize their current emotional state, and adjusts the suggestions accordingly.

[0272] Step 11:

[0273] Servers provide mental health care

[0274] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information. When the user inputs a question or request for advice from the device, the server generates an appropriate answer and returns it.

[0275] Step 12:

[0276] Users implement suggestions and provide feedback

[0277] Users implement the training, nutrition, recovery program, sleep, and mental care programs they receive through their device, and then send feedback from their device to the server, which allows the AI ​​to make further improvements.

[0278] This process allows users to receive personalized, comprehensive support to improve their physical and mental performance. As a concrete example, consider a 25-year-old male amateur basketball player using the system. He recently sprained his ankle during practice, and his next game is two weeks away. He enters his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety."

[0279] in this case,

[0280] Training Plan: A light training menu is generated. Upper body training and water exercises are recommended to avoid straining the ankles. The Emotion Engine also adds training to reduce anxiety.

[0281] Nutrition plan: Protein-rich meals and anti-inflammatory foods are recommended, as well as mental health foods to reduce anxiety.

[0282] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[0283] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[0284] Mental care: Meditation and deep breathing exercises are recommended for anxiety, and support is provided via chat.

[0285] In this way, this system provides comprehensive support based on the user's multifaceted data and emotional information, helping athletes to perform at their best.

[0286] Example 2

[0287] 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."

[0288] Conventional training systems only consider a user's physical and lifestyle information, but are unable to reflect data such as their emotional state or daily stress level. This makes it difficult to provide training and nutritional plans that adequately reduce the user's psychological burden and risk of injury. Furthermore, comprehensive support for recovery programs, sleep recommendations, and mental health support is lacking. Furthermore, there is a need for a means to enable athletes to train more safely and efficiently by providing recovery programs that take into account the user's injury information and fatigue level.

[0289] 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.

[0290] In this invention, the server includes means for inputting a user's physical information and lifestyle information, means for inputting and analyzing the user's emotional information, means for receiving the user's information and analyzed emotional information and generating a training plan and nutrition plan, means for presenting the generated training plan, nutrition plan, recovery program, and sleep suggestions to the user, means for suggesting appropriate relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information, and means for analyzing the emotional information and providing mental care support in chat format. This makes it possible to comprehensively analyze a user's physical information, lifestyle information, and emotional information and provide individually optimized training plans, nutrition plans, recovery programs, sleep suggestions, and mental care.

[0291] "User's physical information" refers to information such as the user's age, gender, athletic history, and recent injuries.

[0292] "Lifestyle information" refers to information such as a user's daily habits, the date of their next game, and their daily stress level.

[0293] "Emotion information" refers to information indicating the user's emotional state obtained through facial recognition or voice input.

[0294] "Training plan" refers to a plan that includes specific exercise instructions that are individually optimized and generated based on the user's physical and emotional information.

[0295] A "nutritional plan" refers to a plan that includes specific dietary instructions regarding nutritional balance and intake timing, generated based on the user's physical and emotional information.

[0296] A "recovery program" refers to a program that includes a specific menu to support recovery, which is generated taking into account the user's injury information and fatigue level.

[0297] "Sleep suggestions" refer to specific advice for getting quality sleep based on the user's lifestyle and emotional information.

[0298] "Generative AI model" refers to an artificial intelligence model that uses a user's physical, lifestyle, and emotional information as input to generate training plans, nutrition plans, recovery programs, and sleep suggestions.

[0299] "Emotion engine" refers to an analysis engine that analyzes the emotion information provided by the user and recognizes the current emotional state.

[0300] "Terminal" refers to a device used by a user to input information. Specifically, this includes smartphones, tablets, and personal computers.

[0301] "Server" refers to the device that receives, stores, and analyzes user-submitted information. It is also where the generative AI model and emotion engine run.

[0302] "Mental care support in chat format" refers to a support function that provides appropriate answers in text message format to users' questions and inquiries.

[0303] A "prompt" is an input sentence used to leverage a generative AI model to derive a specific answer or outcome.

[0304] The system of the present invention analyzes a user's physical information, lifestyle information, and emotions, and based on that, provides optimal training plans, nutrition plans, recovery programs, sleep suggestions, and even mental care. This system is realized by exchanging information between the terminal, server, and user's device, and providing individually optimized advice.

[0305] Entering user information

[0306] The user inputs information using the device. The user enters their physical and lifestyle information (e.g., age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. In addition, the user provides emotional information using facial recognition or voice input. When the user has completed input and pressed the send button, the device sends this information to the server.

[0307] Receiving and storing user data

[0308] The server receives the user information and emotion information sent from the terminal, and stores the received information in a database.

[0309] Analyzing user data and generating profiles

[0310] The server analyzes the incoming data using generative AI models and an emotion engine. Based on the analysis, a personalized profile is created for the user. This profile serves as the basis for generating training and nutrition plans.

[0311] Generate training and nutrition plans

[0312] The server generates an individually optimized training plan based on the user profile and emotional information. The generated training plan includes specific exercise instructions. Similarly, the server also generates a nutrition plan, which includes specific dietary instructions regarding nutritional balance and intake timing. The generated training plan and nutrition plan are sent to the device, which displays them to the user.

[0313] Providing recovery programs and sleep suggestions

[0314] The server generates a recovery program based on the user's injury information, fatigue level, and emotional state. This includes specific menus to support recovery. The server also generates sleep suggestions to help users get a good night's sleep. These suggestions include how to select bedding and a pre-sleep relaxation routine. The generated recovery program and sleep suggestions are sent to the device, which then displays them to the user.

[0315] Mental care support

[0316] The server uses an emotion engine to analyze the user's emotional information and recognize their current emotional state. Based on their daily stress level and emotional information, the server suggests relaxation techniques and mental conditioning methods. When the user inputs a question or request for advice on their device, the server generates and replies with an appropriate answer.

[0317] As a concrete example, consider the case where a 25-year-old male amateur basketball player uses the system. He recently sprained his ankle during practice, and his next game is two weeks away. He has entered his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety." In this case, the system operates as follows:

[0318] Training Plan

[0319] A light training menu is generated, encouraging upper body training and water exercises to avoid straining the ankles, and the emotion engine adds training to reduce anxiety.

[0320] Nutrition Plan

[0321] High-protein diets and anti-inflammatory foods are suggested, along with mental health foods to reduce anxiety.

[0322] Recovery Program

[0323] Ankle recovery is included, and icing and stretching are recommended.

[0324] sleep suggestions

[0325] To ensure quality sleep, recommendations are made on bedding selection and stretching routines.

[0326] Mental care

[0327] Meditation and deep breathing exercises are recommended to combat anxiety, and support is provided via chat.

[0328] An example prompt is, "A 25-year-old male amateur basketball player recently sprained his ankle, and his next game is in two weeks. He has entered his daily stress level as 5, and the emotion engine has given him a facial recognition result indicating 'anxiety.' Please generate the optimal training plan, nutrition plan, recovery program, sleep suggestions, and mental care for this case."

[0329] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0330] Step 1:

[0331] The user uses the device to input information. The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. They also provide emotional information through facial recognition and voice input. When the user has completed input and presses the send button, the device sends this information to the server.

[0332] Input: Physical information, lifestyle information, emotional information

[0333] Output: Sending information from the device to the server

[0334] Specific actions: The user enters information using the device's keyboard, provides emotion information using the facial recognition camera or microphone, and clicks the send button.

[0335] Step 2:

[0336] The server receives the user information and emotion information sent from the device and stores them in a database. At this point, the server aggregates all of the user's physical information, lifestyle information, and emotion information.

[0337] Input: User information and emotional information sent from the device

[0338] Output: Information stored in the database

[0339] What it does: The server receives the data using a network communication module and executes an "INSERT" query in a database such as MySQL to store the information.

[0340] Step 3:

[0341] The server analyzes the incoming data using generative AI models and an emotion engine. Based on the analysis, a personalized profile is created for the user, which contains the basis for generating training and nutrition plans.

[0342] Input: User information and emotion information stored in the database

[0343] Output: Analysis results and user profile

[0344] How it works: The server calls the generative AI model and performs analysis using the stored data as input. The emotion engine analyzes the emotional information, creates a profile based on both results, and stores it in the database.

[0345] Step 4:

[0346] The server generates a personalized training plan based on the user profile and emotional information. The generated training plan includes specific exercise instructions. Similarly, the server also generates a nutrition plan, including specific dietary instructions regarding nutritional balance and intake timing. This plan is sent to the device and displayed to the user.

[0347] Input: User profile and emotional information

[0348] Output: Training and nutrition plans

[0349] How it works: The server uses the generative AI model to generate training and nutrition plans, which are then sent to the device in JSON format, where the device interprets them and presents them to the user.

[0350] Step 5:

[0351] The server generates a recovery program based on the user's injury information, fatigue level, and emotional state. It also generates sleep suggestions to help the user get quality sleep. The recovery program includes specific menus to support recovery. The sleep suggestions include how to select bedding and suggestions for a pre-sleep relaxation routine. The generated recovery program and sleep suggestions are sent to the device and displayed to the user.

[0352] Input: User profile, injury information, fatigue level, emotional information

[0353] Output: Recovery program and sleep suggestions

[0354] How it works: The server uses the AI ​​model to generate recovery programs and sleep suggestions, which are then sent to the device, which displays them to the user.

[0355] Step 6:

[0356] The server uses an emotion engine to analyze the user's emotional information and recognize their current emotional state. Based on their daily stress level and emotional information, the server suggests relaxation techniques and mental conditioning methods. When the user inputs a question or request for advice on their device, the server generates and replies with an appropriate answer.

[0357] Input: User's emotional information and daily stress level

[0358] Output: Relaxation techniques and mental conditioning methods, answers to user questions

[0359] Specific operation: The server calls the emotion engine and analyzes emotional information. Based on the analysis results, it generates relaxation techniques and mental conditioning methods, and uses the chatbot function to answer questions from users in real time.

[0360] Through the above processing steps, the system comprehensively analyzes the user's multifaceted data and emotional information, and provides optimal training, nutrition, recovery, sleep, and mental care.

[0361] (Application example 2)

[0362] 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."

[0363] While conventional fitness and health management systems can provide advice based on personal physical and lifestyle information, it is difficult to input and display user information in real time in physical stores, and to comprehensively provide optimal training plans, nutrition plans, recovery programs, sleep suggestions, and mental care on the spot. This requires users to use different tools and devices individually, making it difficult to achieve high convenience.

[0364] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0365] In this invention, the server includes a means for inputting physical information and lifestyle information of a user, a means for receiving the user information and generating a training plan and a nutrition plan, a means for presenting the generated training plan and nutrition plan to the user, and a means for inputting and presenting the user information via a device installed in a physical store, thereby enabling comprehensive health management advice that responds to the user's needs in real time to be provided in the physical store.

[0366] "User's physical information" refers to data about the user's body, such as the user's age, gender, weight, height, BMI, muscle mass, body fat percentage, and health condition (pre-existing conditions, allergies, etc.).

[0367] "Lifestyle information" refers to data about a user's daily life, such as the user's daily activities and habits, work content and hours, sleep patterns, eating habits, and stress levels.

[0368] A "training plan" refers to a proposal for an exercise program that is individually optimized based on the user's physical information and lifestyle information, and includes specific exercise content, frequency, intensity, time, etc.

[0369] A "nutritional plan" refers to a personalized, optimized meal plan based on the user's physical and lifestyle information, including specific ingredients, cooking methods, nutrients to be consumed, and timing of intake.

[0370] "Devices installed in physical stores" refers to terminals or robots installed in fitness gyms or physical stores for health management purposes, and refers to devices that input and display user information and provide fitness advice.

[0371] A "recovery program" refers to a program designed to support recovery from injury or fatigue, and specifically includes icing, stretching, massage, rehabilitation exercises, etc.

[0372] "Sleep suggestions" refers to advice on how to get quality sleep, including how to select bedding, relaxation routines before sleep, and adjusting bedtime.

[0373] "Mental care" refers to advice and techniques to support the user's mental health, and specifically includes meditation, deep breathing, relaxation techniques, mental conditioning methods, etc.

[0374] MODE FOR CARRYING OUT THE INVENTION

[0375] To implement this invention, a user inputs their own physical and lifestyle information using a smartphone or a device installed in a physical store. The terminal then transmits this information to a server.

[0376] The server receives the user's input data and uses a generative AI model to generate personalized training and nutrition plans using machine learning libraries such as TensorFlow. The generated training and nutrition plans are then sent to the user's smartphone or in-store device, where they are presented to the user.

[0377] The server also generates recovery programs and sleep suggestions based on the user's physical and lifestyle information. This process continues to utilize the generative AI model, taking into account the user's injury information and fatigue level. The generated recovery programs and sleep suggestions are also sent to the user's device and displayed.

[0378] Furthermore, the system measures the user's stress level and analyzes the emotional information using an emotion engine. Based on the analysis results, it proposes relaxation techniques and mental conditioning methods, thereby providing comprehensive mental care.

[0379] Devices and robots installed in physical stores input and display user information in real time, allowing them to receive immediate fitness advice and health management support within the store.

[0380] For example, consider the case of a 25-year-old male amateur basketball player accessing the system using his smartphone. He sprained his ankle during a recent practice, and his next game is in two weeks. He enters his daily stress level as 5, and the emotion engine detects facial recognition indicating "anxiety." Based on this information, the server generates a personalized optimization plan as follows:

[0381] Training plan: Include upper body training and water exercises to avoid ankle strain.

[0382] Nutrition plan: A diet high in protein and anti-inflammatory foods is recommended.

[0383] Recovery Program: Includes ankle recovery menu, including suggested icing and stretching.

[0384] Sleep Suggestions: Includes suggestions for getting quality sleep.

[0385] Mental care: Meditation and deep breathing exercises are recommended for anxiety.

[0386] The system can provide highly accurate health management advice based on specific prompts (e.g., "A 25-year-old male amateur basketball player recently sprained his ankle, has a daily stress level of 5, and feels anxious. Based on this information, generate an individualized training plan, nutrition plan, recovery program, sleep suggestions, and mental health advice that are optimal for him.").

[0387] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0388] Step 1:

[0389] Users enter their physical and lifestyle information using their smartphone or a device installed in a physical store. This includes age, gender, weight, height, athletic history, recent injuries, next game date, stress level, etc. Emotional information is also provided through facial recognition and voice input. The entered data is temporarily stored on the device.

[0390] Step 2:

[0391] The device sends the user's input data, including physical information, lifestyle information, and emotional information, to the server, which then stores the received data in a database for analysis.

[0392] Step 3:

[0393] The server uses a generative AI model based on the received data to generate an individually optimized training plan. Specific data processing and calculations involve determining the appropriate exercise intensity and training content, taking into account the user's physical information (e.g., injury status) and emotional information (e.g., anxiety level). The generated training plan includes specific exercise menus, repetitions, and number of sets.

[0394] Step 4:

[0395] The server then uses the generative AI model to generate a nutrition plan based on the user's physical and lifestyle information. The data processing and calculations take into account meal timing and required nutrients (e.g., protein, vitamins). The generated nutrition plan includes recommended ingredients, meal contents, and intake timing.

[0396] Step 5:

[0397] The server generates a recovery program and sleep suggestions. The recovery program takes into account injury information and fatigue level, and uses an AI model to suggest specific icing and stretching methods. Sleep suggestions include how to select bedding and how to relax before bed. These data processing and calculations take into account the user's injury, fatigue level, and sleep patterns.

[0398] Step 6:

[0399] The server sends the generated training plan, nutrition plan, recovery program, and sleep suggestions to the user's device. The device receives this data and displays it to the user. The displayed content includes training menus, meal plans, recovery menus, and sleep advice.

[0400] Step 7:

[0401] The server uses an emotion engine to analyze the user's emotional information and generates relaxation techniques and mental care methods based on the obtained data. These include deep breathing and meditation techniques. The generated mental care advice is sent to the device and displayed to the user.

[0402] Step 8:

[0403] Devices and robots installed in physical stores collect user information in real time and provide generated health management advice on the spot. The devices and robots guide users and support them in implementing training and recovery programs.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Second embodiment]

[0408] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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."

[0420] The system of the present invention provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[0421] Entering user information

[0422] The user inputs information using the terminal.

[0423] The user enters information such as age, gender, athletic history, recent injuries, next game date, daily stress level, etc. into the input form on the device. The entered information is sent to the server by pressing the send button.

[0424] User data analysis

[0425] The server receives and analyzes the user data

[0426] The server stores the received data in a database and invokes a generative AI model, which analyzes the data and creates a user-specific profile that is then used to generate subsequent plans.

[0427] Generate training and nutrition plans

[0428] The server generates optimal training and nutrition plans

[0429] Based on the user's profile information, the server uses AI models to generate personalized training and nutrition plans, allowing users to train efficiently and consume proper nutrition.

[0430] Providing recovery menus and sleep suggestions

[0431] The server generates recovery menus and sleep suggestions

[0432] The system generates a recovery program to support the user's recovery, taking into account the presence or absence of injuries and the level of fatigue from training. It also generates advice on how to get quality sleep and sends this to the user's device.

[0433] Mental care support

[0434] Servers provide mental health care

[0435] The system suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server receives it and the AI ​​model generates an appropriate answer and sends it back.

[0436] As a concrete example, consider a 25-year-old male amateur basketball player using the system. He sprained his ankle during a recent practice, and his next game is in two weeks. He enters his daily stress level as 5.

[0437] in this case,

[0438] Training Plan: A light training menu is generated, encouraging upper body training and water exercises to avoid straining your ankles.

[0439] Nutrition plan: A protein-rich diet and anti-inflammatory foods are suggested.

[0440] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[0441] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[0442] Mental care: For stress level 5, meditation and deep breathing exercises are recommended, and support is provided via chat.

[0443] In this way, this system provides comprehensive support based on multifaceted data about the user, helping athletes to perform at their best.

[0444] The processing flow will be explained below.

[0445] Step 1:

[0446] The user inputs information using the terminal.

[0447] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. After completing the input, the user presses the send button to send this information to the server.

[0448] Step 2:

[0449] The server receives the user data

[0450] The server receives the user information sent from the device and stores it in a database, which aggregates the user's physical and lifestyle information.

[0451] Step 3:

[0452] The server analyzes the user data and creates a profile

[0453] The server invokes the generative AI model to analyze the received user data, and based on the analysis results, a personalized profile is created for the user, which contains the information needed to generate training and nutrition plans.

[0454] Step 4:

[0455] The server generates the training plan.

[0456] Based on the user profile, the server uses an AI model to generate a personalized training plan that includes specific training instructions tailored to the user's characteristics and goals.

[0457] Step 5:

[0458] The server generates the nutrition plan

[0459] The server also generates a nutrition plan based on the user profile, taking into account nutritional balance and timing of intake. This plan indicates what foods the user should eat and when.

[0460] Step 6:

[0461] The server sends the training plan and nutrition plan to the device and displays it.

[0462] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[0463] Step 7:

[0464] The server generates a recovery program

[0465] The server takes into account the user's injury information and fatigue level and uses an AI model to generate a recovery program to support recovery, including specific menus to help with injury recovery and reduce muscle fatigue.

[0466] Step 8:

[0467] The server generates sleep suggestions

[0468] The server generates recommendations for getting a good night's sleep based on the user's lifestyle information, including bedding selection and pre-sleep relaxation routines.

[0469] Step 9:

[0470] The server sends and displays recovery programs and sleep suggestions on the device.

[0471] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[0472] Step 10:

[0473] Servers provide mental health care

[0474] The server measures the user's daily stress level and suggests relaxation techniques and mental conditioning methods based on that. When the user inputs a question or inquiry from their device, the server generates an appropriate answer and returns it.

[0475] Step 11:

[0476] Users implement suggestions and provide feedback

[0477] Users implement the training, nutrition, recovery program, sleep, and mental care programs they receive through their device, and then send feedback from their device to the server, which allows the AI ​​to make further improvements.

[0478] Example 1

[0479] 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."

[0480] In today's busy lifestyles, individually optimized advice is necessary for efficient and effective training, nutritional management, recovery, and mental care. However, existing systems have difficulty accurately collecting user information and providing comprehensive support. Furthermore, advanced data analysis is required to create appropriate plans based on the user's physical and lifestyle information. This calls for a more advanced and integrated system.

[0481] 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.

[0482] In this invention, the server includes a means for inputting the user's physical information and lifestyle information, a means for creating a user profile using a generative AI model, and a means for generating individually optimized training and nutrition plans, which makes it possible to provide individually optimized training plans, nutrition plans, recovery programs, mental care, etc. to the user.

[0483] "User's physical information" refers to information about the user's body, such as age, gender, athletic history, and injury history.

[0484] "Lifestyle information" is information about the user's daily life, such as their daily stress level, lifestyle habits, and the date of their next game.

[0485] A "generative AI model" is an artificial intelligence model used to analyze a user's physical and lifestyle information and generate an optimal plan.

[0486] A "user profile" is an individualized collection of data created based on the user's physical and lifestyle information analyzed by a generative AI model.

[0487] A "training plan" is a schedule and content of exercises and physical activities recommended for a user.

[0488] A "nutritional plan" is a guideline for dietary and nutritional intake recommended for a user.

[0489] A "recovery program" is a method or procedure for supporting a user in recovering from fatigue or healing from an injury.

[0490] "Sleep suggestions" are advice and methods for ensuring quality sleep.

[0491] "Relaxation techniques" are methods such as meditation and deep breathing exercises that help users reduce stress.

[0492] A "mental conditioning method" is an approach or technique for maintaining a user's mental state in good condition.

[0493] The system of the present invention provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[0494] Users use devices such as smartphones or PCs to enter detailed information such as age, gender, athletic history, recent injuries, next game date, and daily stress level. This information is collected through an input form on the device and sent to the server by pressing a submit button. The submitted data is transferred using a secure encryption protocol (e.g., HTTPS).

[0495] The server stores the received data in a database, typically a relational database system such as MySQL or PostgreSQL. The stored data is used as the basis for subsequent analysis and plan generation.

[0496] The server then invokes a generative AI model, built using Python-based libraries (e.g., TensorFlow and PyTorch), to analyze the stored user data and create an individually optimized user profile based on the user's physical and lifestyle information.

[0497] Based on the created user profile, the server then uses the generative AI model to generate individually optimized training and nutrition plans. For example, a user with a sprained ankle might be recommended upper-body training and water exercises, and the nutrition plan might include anti-inflammatory foods and a diet high in protein. These plans are then sent to the user's device, where they can be viewed.

[0498] Furthermore, the server generates recovery programs and sleep recommendations based on the user's training data and injury information, including icing and stretching techniques, and the selection of bedding for quality sleep. The generated information is also sent to the user's device and made available to the user.

[0499] Finally, the server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or inquiry from their device, the server uses a generative AI model to generate an appropriate answer and provides it to the user in chat format. This mental care function allows users to receive comprehensive support.

[0500] As a specific example, by entering the prompt "30-year-old male, marathon runner as a hobby. My legs have been feeling tired recently, but my next race is in a month. My stress level is 3," an individually optimized training plan, nutrition plan, recovery program, sleep suggestions, and mental care will be generated and provided to the user.

[0501] In this way, the system of the present invention provides comprehensive support based on multifaceted data of the user, encouraging athletes to perform at their best.

[0502] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0503] Step 1:

[0504] The user enters information using a device. Using a smartphone or PC, the user accesses a dedicated application or web page. There, they enter information such as age, gender, athletic history, recent injuries, next game date, and daily stress level into an input form. After the data is entered, they press the "Submit" button to proceed to the next step.

[0505] Input: User's physical and lifestyle information

[0506] Output: Data sent from the terminal

[0507] Step 2:

[0508] The device sends data to the server, which receives it. When the user presses the "Send" button, the device sends the entered data to the server using an encrypted communication protocol (e.g., HTTPS). The server receives this data and temporarily stores it. This process ensures the safety of the data.

[0509] Input: Data sent from the terminal

[0510] Output: User data temporarily stored on the server

[0511] Step 3:

[0512] The server stores the received user data in a database. The server analyzes the received data and stores it in a relational database system (e.g., MySQL or PostgreSQL). This database has tables, and each user's information is managed individually.

[0513] Input: User data temporarily stored on the server

[0514] Output: User data stored in the database

[0515] Step 4:

[0516] The server invokes a generative AI model to create a user profile. The server invokes a generative AI model (e.g., TensorFlow or PyTorch) based on the stored user data. The AI ​​model analyzes the user's physical and lifestyle information and creates an individually optimized user profile based on that information.

[0517] Input: User data stored in the database

[0518] Output: Generated user profile

[0519] Step 5:

[0520] The server generates personalized training and nutrition plans based on the user profile. The generative AI model analyzes the user profile and generates optimal training and nutrition plans for the user. For example, if a person has an ankle injury, upper body training and water exercises will be recommended, and the nutrition plan will include anti-inflammatory and high-protein foods.

[0521] Input: Generated user profile

[0522] Output: Training and nutrition plans

[0523] Step 6:

[0524] The server sends the training plan and nutrition plan to the user's device. The generated plan is sent from the server to the user's device, where the user can view it on their own device, allowing the user to put the plan into action.

[0525] Input: Training plan and nutrition plan

[0526] Output: The plan sent to the user's device

[0527] Step 7:

[0528] The server generates a recovery program and sleep suggestions and sends them to the user's device. Based on the user's training data and injury information, the server uses a generative AI model to create a recovery program and sleep suggestions. This includes, for example, advice on icing and stretching the ankle and ensuring quality sleep. The generated plan is sent to the user's device, where the user can review it.

[0529] Input: Training data and injury information

[0530] Output: Recovery program and sleep suggestions sent to the user's device

[0531] Step 8:

[0532] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server uses a generative AI model to generate an appropriate answer and provide mental care. This information is also sent to the user's device, allowing for communication in chat format.

[0533] Input: User's daily stress level, questions and inquiries from the user

[0534] Output: Relaxation techniques and mental conditioning methods sent to the device

[0535] These processing steps allow users to receive comprehensive and individually optimized support.

[0536] (Application example 1)

[0537] 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."

[0538] In traditional factory work environments, workers' health is not adequately managed, which can lead to problems with work efficiency and safety. It is also difficult to provide individually optimized training plans, nutrition plans, and recovery programs, and many workers do not receive appropriate feedback. This increases the physical and mental burden on workers and can lead to reduced productivity.

[0539] 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.

[0540] In this invention, the server includes means for inputting a user's physical information and lifestyle information, means for receiving the user's information and generating a training plan and a nutrition plan, means for presenting the generated training plan and nutrition plan to the user, means for collecting the user's health information and providing health management feedback, means for collecting data from the wearable device and generating individually optimized reminders and notifications using a generative AI model, and means for transmitting the collected data to the server and generating a customized plan, thereby enabling the health status of workers to be grasped in real time and individually optimized feedback to be provided.

[0541] "User's physical information" refers to physiological data such as the user's heart rate, blood pressure, and body temperature, as well as information related to the user's health condition.

[0542] "Lifestyle information" refers to information such as the user's daily behavior patterns, eating habits, sleeping hours, and exercise habits.

[0543] "Input means" refers to the interface or device through which a user provides physical and lifestyle information.

[0544] The "means for receiving and generating training and nutrition plans" is the part of the system that receives information from the user and creates optimal training and nutrition plans based on that information.

[0545] "Presenting means" refers to a device or software that displays the generated training plan and nutrition plan so that the user can check them.

[0546] "Means for collecting health information" refers to a system that automatically collects users' health data through wearable devices and sensors.

[0547] A "means for providing health management feedback" is a device or software for providing specific advice or feedback to a user based on collected health data.

[0548] A "wearable device" refers to an electronic device worn by a user that can constantly monitor and collect health information.

[0549] "Generative AI Model" refers to an artificial intelligence model used to analyze collected data and generate individually optimized training plans, nutrition plans, reminders and notifications.

[0550] The "server" is a central processing unit that receives information from users, stores and analyzes the data, and generates the optimal plan.

[0551] The "means for generating a customized plan" is part of a system that creates an individually optimized health management plan based on data collected from a wearable device.

[0552] The present invention is a system that provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[0553] Entering user information

[0554] The user uses the terminal to input physical information (physiological data such as heart rate, blood pressure, and body temperature) and lifestyle information (daily behavioral patterns, eating habits, sleep time, exercise habits, etc.). For example, the user enters information such as age, gender, experience, recent injuries, next shift time, and daily stress level into an input form on the terminal. The input information is sent to the server by pressing the send button.

[0555] User data analysis

[0556] The server stores the received data in a database and invokes a generative AI model, which analyzes the data and creates a user-specific profile that is then used to generate subsequent plans.

[0557] Generate training and nutrition plans

[0558] The server uses a generative AI model based on the user's profile information to generate personalized training and nutrition plans, allowing users to train efficiently and consume appropriate nutrition. For example, based on a user's data, the server may recommend "10 minutes of light exercise" and "a high-protein meal."

[0559] Providing recovery menus and sleep suggestions

[0560] The server generates a recovery program to support the user's recovery, taking into account the presence or absence of injuries and the level of fatigue from training. It also generates advice on how to get quality sleep and sends it to the user's device. For example, it recommends "20 minutes of cooling and stretching" and suggests "7-8 hours of sleep."

[0561] Mental care support

[0562] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server receives it and the generative AI model generates an appropriate answer and sends it back. For example, a message such as, "Your recent stress level is 6. We recommend taking five minutes of deep breaths" is provided.

[0563] Examples of concrete examples and prompts

[0564] For example, consider a 35-year-old male factory worker using the system. His recent heart rate was 85 and his daily stress level was 6. His next shift is one day away. In this case, the system will provide the following feedback:

[0565] "Your current stress level is 6. I suggest you take 5 minutes of deep breathing."

[0566] "Your heart rate has been high lately. Don't forget to stay hydrated at the water cooler."

[0567] An example of a prompt is as follows:

[0568] "Please use the AI ​​model to determine what health care I should do before my next shift:\n- Heart rate: 85\n- Stress level: 6\n- Sleep time: 6 hours\n"

[0569] In this way, this system provides comprehensive support based on multifaceted user data, helping workers to perform at their best.

[0570] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0571] Step 1:

[0572] The user uses the terminal to input physical and lifestyle information. The user enters data such as age, gender, experience, recent injuries, next shift time, and daily stress level into the input form, and presses the send button to send it to the server. Based on the input data, the server obtains data to generate the user's basic profile.

[0573] Step 2:

[0574] The server receives the user's input data and stores it in a database. The stored data becomes input data for calling the generative AI model. The database uses SQL or NoSQL, allowing for data storage and high-speed search.

[0575] Step 3:

[0576] The server invokes a generative AI model based on the received data. The generative AI model analyzes the data to create a profile specific to the user. This analysis uses machine learning libraries (e.g., TensorFlow and PyTorch). The profile includes the user's physical information, lifestyle information, and health status.

[0577] Step 4:

[0578] The server uses generative AI models to generate personalized training and nutrition plans. For example, it might recommend 10 minutes of light exercise and a high-protein meal based on the user's age, gender, heart rate, and exercise habits. These plans are then stored in a database on the server.

[0579] Step 5:

[0580] The server references the user's profile and generates recovery programs and sleep recommendations based on the user's injury status and training fatigue level. For example, if a user has a sprained ankle, the server might suggest "20 minutes of cooling and stretching" and "7-8 hours of sleep." These recommendations are also generated by an AI model and stored in the server's database.

[0581] Step 6:

[0582] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level, such as "take five minutes of deep breaths." This information is analyzed by a generative AI model to generate suggestions.

[0583] Step 7:

[0584] The generated training plan, nutrition plan, recovery program, sleep suggestions, and relaxation suggestions are sent to the user's device, which then visually displays this information. Feedback is provided via a smartphone or smart glasses, for example, and the user can review and put the information into practice.

[0585] Step 8:

[0586] A user's wearable device (such as a smartwatch or fitness tracker) continuously collects real-time health data, such as heart rate, stress level, and sleep data, and automatically transmits this data to a server. This data is then used as input for generative AI models, providing updated feedback.

[0587] Step 9:

[0588] The server continuously receives health data from the user and updates feedback based on the latest generative AI model. This allows the user to constantly receive optimal advice based on their health status. For example, a notification might be sent saying, "Your heart rate has been high recently. Don't forget to drink water at the water cooler."

[0589] Through the above processing steps, the user can receive individually optimized feedback in real time, enabling them to efficiently manage their own health.

[0590] 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.

[0591] The system of the present invention analyzes a user's physical information, lifestyle information, and emotions, and based on that information, provides optimal training plans, nutrition plans, recovery programs, sleep suggestions, and even mental care. This system is realized by exchanging information between the terminal, server, and user's device, and providing individually optimized advice.

[0592] Entering user information

[0593] The user inputs information using the terminal.

[0594] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. In addition, the user provides emotional information to the emotion engine using facial recognition or voice input. After completing the input, the user presses the send button to send this information to the server.

[0595] User data analysis

[0596] The server receives the user data

[0597] The server receives the user information and emotion information sent from the device and stores them in a database, thereby aggregating the user's physical information, lifestyle information, and emotion information.

[0598] The server analyzes user data and emotional information to create a profile

[0599] The server then invokes the generative AI model and emotion engine to analyze the received data, and a personalized profile is created based on the results of the analysis. This profile contains the information needed to generate training and nutrition plans.

[0600] Generate training and nutrition plans

[0601] The server generates the optimal training plan

[0602] Based on the user profile and emotional information, the server uses an AI model to generate a personalized training plan that includes specific training instructions tailored to the user's characteristics, goals, and current emotional state.

[0603] The server generates the nutrition plan

[0604] The server also generates a nutrition plan based on the user profile and emotional information, taking into account nutritional balance and timing of intake. This plan indicates what meals the user should eat and when.

[0605] The server sends the training plan and nutrition plan to the device and displays it.

[0606] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[0607] Providing recovery menus and sleep suggestions

[0608] The server generates a recovery program

[0609] The server takes into account the user's injury information, fatigue level, and emotional state, and uses an AI model and emotion engine to generate a recovery program to support recovery, including specific menus to help with injury recovery and muscle fatigue reduction.

[0610] The server generates sleep suggestions

[0611] The server generates recommendations for getting a good night's sleep based on the user's lifestyle and emotional information, including recommendations for bedding selection and pre-sleep relaxation routines.

[0612] The server sends and displays recovery programs and sleep suggestions on the device.

[0613] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[0614] Mental care support

[0615] The server uses an emotion engine to analyze the user's emotions.

[0616] The emotion engine analyzes the emotion information provided by the user and recognizes the current emotional state.

[0617] Servers provide mental health care

[0618] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information. When the user inputs a question or request for advice from the device, the server generates an appropriate answer and returns it.

[0619] As a concrete example, consider a 25-year-old male amateur basketball player using the system. He sprained his ankle during a recent practice, and his next game is two weeks away. He enters his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety."

[0620] in this case,

[0621] Training Plan: A light training menu is generated. Upper body training and water exercises are recommended to avoid straining the ankles. The Emotion Engine also adds training to reduce anxiety.

[0622] Nutrition plan: Protein-rich meals and anti-inflammatory foods are recommended, along with mental health foods to reduce anxiety.

[0623] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[0624] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[0625] Mental care: Meditation and deep breathing exercises are recommended for anxiety, and support is provided via chat.

[0626] In this way, this system provides comprehensive support based on the user's multifaceted data and emotional information, helping athletes to perform at their best.

[0627] The processing flow will be explained below.

[0628] Step 1:

[0629] The user inputs information using the terminal.

[0630] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. The emotion engine also uses facial recognition and voice input to provide emotional information. Once input is complete, the user presses the send button to send the information to the server.

[0631] Step 2:

[0632] The server receives the user data

[0633] The server receives the user information and emotion information sent from the device and stores them in a database, thereby aggregating the user's physical information, lifestyle information, and emotion information.

[0634] Step 3:

[0635] The server analyzes user data and emotional information to create a profile

[0636] The server then invokes the generative AI model and emotion engine to analyze the received data, and creates a personalized profile for the user based on the results of this analysis. This profile contains the information needed to generate training and nutrition plans.

[0637] Step 4:

[0638] The server generates the optimal training plan

[0639] The server uses an AI model to generate a personalized training plan based on the user's profile and emotional information. The emotional engine adjusts the training content according to the user's current emotional state. The generated plan includes specific training instructions tailored to the user's characteristics and goals.

[0640] Step 5:

[0641] The server generates the nutrition plan

[0642] The server also generates a nutrition plan based on the user profile and emotional information, taking into account nutritional balance and timing. This plan indicates what meals the user should eat and when. The emotional engine also provides nutritional instructions based on the user's emotional state.

[0643] Step 6:

[0644] The server sends the training plan and nutrition plan to the device and displays it.

[0645] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[0646] Step 7:

[0647] The server generates a recovery program

[0648] The server takes into account the user's injury information, fatigue level, and emotional state, and uses an AI model and emotion engine to generate a recovery program to support recovery, including specific menus to help with injury recovery and reduce muscle fatigue.

[0649] Step 8:

[0650] The server generates sleep suggestions

[0651] The server generates recommendations for getting a good night's sleep based on the user's lifestyle and emotional information, including recommendations for bedding selection and pre-sleep relaxation routines.

[0652] Step 9:

[0653] The server sends and displays recovery programs and sleep suggestions on the device.

[0654] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[0655] Step 10:

[0656] The server uses an emotion engine to analyze the user's emotions.

[0657] The server uses an emotion engine to analyze the emotional information provided by the user and recognize their current emotional state, and adjusts the suggestions accordingly.

[0658] Step 11:

[0659] Servers provide mental health care

[0660] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information. When the user inputs a question or request for advice from the device, the server generates an appropriate answer and returns it.

[0661] Step 12:

[0662] Users implement suggestions and provide feedback

[0663] Users implement the training, nutrition, recovery program, sleep, and mental care programs they receive through their device, and then send feedback from their device to the server, which allows the AI ​​to make further improvements.

[0664] This process allows users to receive personalized, comprehensive support to improve their physical and mental performance. As a concrete example, consider a 25-year-old male amateur basketball player using the system. He recently sprained his ankle during practice, and his next game is two weeks away. He enters his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety."

[0665] in this case,

[0666] Training Plan: A light training menu is generated. Upper body training and water exercises are recommended to avoid straining the ankles. The Emotion Engine also adds training to reduce anxiety.

[0667] Nutrition plan: Protein-rich meals and anti-inflammatory foods are recommended, as well as mental health foods to reduce anxiety.

[0668] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[0669] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[0670] Mental care: Meditation and deep breathing exercises are recommended for anxiety, and support is provided via chat.

[0671] In this way, this system provides comprehensive support based on the user's multifaceted data and emotional information, helping athletes to perform at their best.

[0672] Example 2

[0673] 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."

[0674] Conventional training systems only consider a user's physical and lifestyle information, but are unable to reflect data such as their emotional state or daily stress level. This makes it difficult to provide training and nutritional plans that adequately reduce the user's psychological burden and risk of injury. Furthermore, comprehensive support for recovery programs, sleep recommendations, and mental health support is lacking. Furthermore, there is a need for a means to enable athletes to train more safely and efficiently by providing recovery programs that take into account the user's injury information and fatigue level.

[0675] 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.

[0676] In this invention, the server includes means for inputting a user's physical information and lifestyle information, means for inputting and analyzing the user's emotional information, means for receiving the user's information and analyzed emotional information and generating a training plan and nutrition plan, means for presenting the generated training plan, nutrition plan, recovery program, and sleep suggestions to the user, means for suggesting appropriate relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information, and means for analyzing the emotional information and providing mental care support in chat format. This makes it possible to comprehensively analyze a user's physical information, lifestyle information, and emotional information and provide individually optimized training plans, nutrition plans, recovery programs, sleep suggestions, and mental care.

[0677] "User's physical information" refers to information such as the user's age, gender, athletic history, and recent injuries.

[0678] "Lifestyle information" refers to information such as a user's daily habits, the date of their next game, and their daily stress level.

[0679] "Emotion information" refers to information indicating the user's emotional state obtained through facial recognition or voice input.

[0680] "Training plan" refers to a plan that includes specific exercise instructions that are individually optimized and generated based on the user's physical and emotional information.

[0681] A "nutritional plan" refers to a plan that includes specific dietary instructions regarding nutritional balance and intake timing, generated based on the user's physical and emotional information.

[0682] A "recovery program" refers to a program that includes a specific menu to support recovery, which is generated taking into account the user's injury information and fatigue level.

[0683] "Sleep suggestions" refer to specific advice for getting quality sleep based on the user's lifestyle and emotional information.

[0684] "Generative AI model" refers to an artificial intelligence model that uses a user's physical, lifestyle, and emotional information as input to generate training plans, nutrition plans, recovery programs, and sleep suggestions.

[0685] "Emotion engine" refers to an analysis engine that analyzes the emotion information provided by the user and recognizes the current emotional state.

[0686] "Terminal" refers to a device used by a user to input information. Specifically, this includes smartphones, tablets, and personal computers.

[0687] "Server" refers to the device that receives, stores, and analyzes user-submitted information. It is also where the generative AI model and emotion engine run.

[0688] "Mental care support in chat format" refers to a support function that provides appropriate answers in text message format to users' questions and inquiries.

[0689] A "prompt" is an input sentence used to leverage a generative AI model to derive a specific answer or outcome.

[0690] The system of the present invention analyzes a user's physical information, lifestyle information, and emotions, and based on that, provides optimal training plans, nutrition plans, recovery programs, sleep suggestions, and even mental care. This system is realized by exchanging information between the terminal, server, and user's device, and providing individually optimized advice.

[0691] Entering user information

[0692] The user inputs information using the device. The user enters their physical and lifestyle information (e.g., age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. In addition, the user provides emotional information using facial recognition or voice input. When the user has completed input and pressed the send button, the device sends this information to the server.

[0693] Receiving and storing user data

[0694] The server receives the user information and emotion information sent from the terminal, and stores the received information in a database.

[0695] Analyzing user data and generating profiles

[0696] The server analyzes the incoming data using generative AI models and an emotion engine. Based on the analysis, a personalized profile is created for the user. This profile serves as the basis for generating training and nutrition plans.

[0697] Generate training and nutrition plans

[0698] The server generates an individually optimized training plan based on the user profile and emotional information. The generated training plan includes specific exercise instructions. Similarly, the server also generates a nutrition plan, which includes specific dietary instructions regarding nutritional balance and intake timing. The generated training plan and nutrition plan are sent to the device, which displays them to the user.

[0699] Providing recovery programs and sleep suggestions

[0700] The server generates a recovery program based on the user's injury information, fatigue level, and emotional state. This includes specific menus to support recovery. The server also generates sleep suggestions to help users get a good night's sleep. These suggestions include how to select bedding and a pre-sleep relaxation routine. The generated recovery program and sleep suggestions are sent to the device, which then displays them to the user.

[0701] Mental care support

[0702] The server uses an emotion engine to analyze the user's emotional information and recognize their current emotional state. Based on their daily stress level and emotional information, the server suggests relaxation techniques and mental conditioning methods. When the user inputs a question or request for advice on their device, the server generates and replies with an appropriate answer.

[0703] As a concrete example, consider the case where a 25-year-old male amateur basketball player uses the system. He recently sprained his ankle during practice, and his next game is two weeks away. He has entered his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety." In this case, the system operates as follows:

[0704] Training Plan

[0705] A light training menu is generated, encouraging upper body training and water exercises to avoid straining the ankles, and the emotion engine adds training to reduce anxiety.

[0706] Nutrition Plan

[0707] High-protein diets and anti-inflammatory foods are suggested, along with mental health foods to reduce anxiety.

[0708] Recovery Program

[0709] Ankle recovery is included, and icing and stretching are recommended.

[0710] sleep suggestions

[0711] To ensure quality sleep, recommendations are made on bedding selection and stretching routines.

[0712] Mental care

[0713] Meditation and deep breathing exercises are recommended to combat anxiety, and support is provided via chat.

[0714] An example prompt is, "A 25-year-old male amateur basketball player recently sprained his ankle, and his next game is in two weeks. He has entered his daily stress level as 5, and the emotion engine has given him a facial recognition result indicating 'anxiety.' Please generate the optimal training plan, nutrition plan, recovery program, sleep suggestions, and mental care for this case."

[0715] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0716] Step 1:

[0717] The user uses the device to input information. The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. They also provide emotional information through facial recognition and voice input. When the user has completed input and presses the send button, the device sends this information to the server.

[0718] Input: Physical information, lifestyle information, emotional information

[0719] Output: Sending information from the device to the server

[0720] Specific actions: The user enters information using the device's keyboard, provides emotion information using the facial recognition camera or microphone, and clicks the send button.

[0721] Step 2:

[0722] The server receives the user information and emotion information sent from the device and stores them in a database. At this point, the server aggregates all of the user's physical information, lifestyle information, and emotion information.

[0723] Input: User information and emotional information sent from the device

[0724] Output: Information stored in the database

[0725] What it does: The server receives the data using a network communication module and executes an "INSERT" query in a database such as MySQL to store the information.

[0726] Step 3:

[0727] The server analyzes the incoming data using generative AI models and an emotion engine. Based on the analysis, a personalized profile is created for the user, which contains the basis for generating training and nutrition plans.

[0728] Input: User information and emotion information stored in the database

[0729] Output: Analysis results and user profile

[0730] How it works: The server calls the generative AI model and performs analysis using the stored data as input. The emotion engine analyzes the emotional information, creates a profile based on both results, and stores it in the database.

[0731] Step 4:

[0732] The server generates a personalized training plan based on the user profile and emotional information. The generated training plan includes specific exercise instructions. Similarly, the server also generates a nutrition plan, including specific dietary instructions regarding nutritional balance and intake timing. This plan is sent to the device and displayed to the user.

[0733] Input: User profile and emotional information

[0734] Output: Training and nutrition plans

[0735] How it works: The server uses the generative AI model to generate training and nutrition plans, which are then sent to the device in JSON format, where the device interprets them and presents them to the user.

[0736] Step 5:

[0737] The server generates a recovery program based on the user's injury information, fatigue level, and emotional state. It also generates sleep suggestions to help the user get quality sleep. The recovery program includes specific menus to support recovery. The sleep suggestions include how to select bedding and suggestions for a pre-sleep relaxation routine. The generated recovery program and sleep suggestions are sent to the device and displayed to the user.

[0738] Input: User profile, injury information, fatigue level, emotional information

[0739] Output: Recovery program and sleep suggestions

[0740] How it works: The server uses the AI ​​model to generate recovery programs and sleep suggestions, which are then sent to the device, which displays them to the user.

[0741] Step 6:

[0742] The server uses an emotion engine to analyze the user's emotional information and recognize their current emotional state. Based on their daily stress level and emotional information, the server suggests relaxation techniques and mental conditioning methods. When the user inputs a question or request for advice on their device, the server generates and replies with an appropriate answer.

[0743] Input: User's emotional information and daily stress level

[0744] Output: Relaxation techniques and mental conditioning methods, answers to user questions

[0745] Specific operation: The server calls the emotion engine and analyzes emotional information. Based on the analysis results, it generates relaxation techniques and mental conditioning methods, and uses the chatbot function to answer questions from users in real time.

[0746] Through the above processing steps, the system comprehensively analyzes the user's multifaceted data and emotional information, and provides optimal training, nutrition, recovery, sleep, and mental care.

[0747] (Application example 2)

[0748] 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."

[0749] While conventional fitness and health management systems can provide advice based on personal physical and lifestyle information, it is difficult to input and display user information in real time in physical stores, and to comprehensively provide optimal training plans, nutrition plans, recovery programs, sleep suggestions, and mental care on the spot. This requires users to use different tools and devices individually, making it difficult to achieve high convenience.

[0750] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0751] In this invention, the server includes a means for inputting physical information and lifestyle information of a user, a means for receiving the user information and generating a training plan and a nutrition plan, a means for presenting the generated training plan and nutrition plan to the user, and a means for inputting and presenting the user information via a device installed in a physical store, thereby enabling comprehensive health management advice that responds to the user's needs in real time to be provided in the physical store.

[0752] "User's physical information" refers to data about the user's body, such as the user's age, gender, weight, height, BMI, muscle mass, body fat percentage, and health condition (pre-existing conditions, allergies, etc.).

[0753] "Lifestyle information" refers to data about a user's daily life, such as the user's daily activities and habits, work content and hours, sleep patterns, eating habits, and stress levels.

[0754] A "training plan" refers to a proposal for an exercise program that is individually optimized based on the user's physical information and lifestyle information, and includes specific exercise content, frequency, intensity, time, etc.

[0755] A "nutritional plan" refers to a personalized, optimized meal plan based on the user's physical and lifestyle information, including specific ingredients, cooking methods, nutrients to be consumed, and timing of intake.

[0756] "Devices installed in physical stores" refers to terminals or robots installed in fitness gyms or physical stores for health management purposes, and refers to devices that input and display user information and provide fitness advice.

[0757] A "recovery program" refers to a program designed to support recovery from injury or fatigue, and specifically includes icing, stretching, massage, rehabilitation exercises, etc.

[0758] "Sleep suggestions" refers to advice on how to get quality sleep, including how to select bedding, relaxation routines before sleep, and adjusting bedtime.

[0759] "Mental care" refers to advice and techniques to support the user's mental health, and specifically includes meditation, deep breathing, relaxation techniques, mental conditioning methods, etc.

[0760] MODE FOR CARRYING OUT THE INVENTION

[0761] To implement this invention, a user inputs their own physical and lifestyle information using a smartphone or a device installed in a physical store. The terminal then transmits this information to a server.

[0762] The server receives the user's input data and uses a generative AI model to generate personalized training and nutrition plans using machine learning libraries such as TensorFlow. The generated training and nutrition plans are then sent to the user's smartphone or in-store device, where they are presented to the user.

[0763] The server also generates recovery programs and sleep suggestions based on the user's physical and lifestyle information. This process continues to utilize the generative AI model, taking into account the user's injury information and fatigue level. The generated recovery programs and sleep suggestions are also sent to the user's device and displayed.

[0764] Furthermore, the system measures the user's stress level and analyzes the emotional information using an emotion engine. Based on the analysis results, it proposes relaxation techniques and mental conditioning methods, thereby providing comprehensive mental care.

[0765] Devices and robots installed in physical stores input and display user information in real time, allowing them to receive immediate fitness advice and health management support within the store.

[0766] For example, consider the case of a 25-year-old male amateur basketball player accessing the system using his smartphone. He sprained his ankle during a recent practice, and his next game is in two weeks. He enters his daily stress level as 5, and the emotion engine detects facial recognition indicating "anxiety." Based on this information, the server generates a personalized optimization plan as follows:

[0767] Training plan: Include upper body training and water exercises to avoid ankle strain.

[0768] Nutrition plan: A diet high in protein and anti-inflammatory foods is recommended.

[0769] Recovery Program: Includes ankle recovery menu, including suggested icing and stretching.

[0770] Sleep Suggestions: Includes suggestions for getting quality sleep.

[0771] Mental care: Meditation and deep breathing exercises are recommended for anxiety.

[0772] The system can provide highly accurate health management advice based on specific prompts (e.g., "A 25-year-old male amateur basketball player recently sprained his ankle, has a daily stress level of 5, and feels anxious. Based on this information, generate an individualized training plan, nutrition plan, recovery program, sleep suggestions, and mental health advice that are optimal for him.").

[0773] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0774] Step 1:

[0775] Users enter their physical and lifestyle information using their smartphone or a device installed in a physical store. This includes age, gender, weight, height, athletic history, recent injuries, next game date, stress level, etc. Emotional information is also provided through facial recognition and voice input. The entered data is temporarily stored on the device.

[0776] Step 2:

[0777] The device sends the user's input data, including physical information, lifestyle information, and emotional information, to the server, which then stores the received data in a database for analysis.

[0778] Step 3:

[0779] The server uses a generative AI model based on the received data to generate an individually optimized training plan. Specific data processing and calculations involve determining the appropriate exercise intensity and training content, taking into account the user's physical information (e.g., injury status) and emotional information (e.g., anxiety level). The generated training plan includes specific exercise menus, repetitions, and number of sets.

[0780] Step 4:

[0781] The server then uses the generative AI model to generate a nutrition plan based on the user's physical and lifestyle information. The data processing and calculations take into account meal timing and required nutrients (e.g., protein, vitamins). The generated nutrition plan includes recommended ingredients, meal contents, and intake timing.

[0782] Step 5:

[0783] The server generates a recovery program and sleep suggestions. The recovery program takes into account injury information and fatigue level, and uses an AI model to suggest specific icing and stretching methods. Sleep suggestions include how to select bedding and how to relax before bed. These data processing and calculations take into account the user's injury, fatigue level, and sleep patterns.

[0784] Step 6:

[0785] The server sends the generated training plan, nutrition plan, recovery program, and sleep suggestions to the user's device. The device receives this data and displays it to the user. The displayed content includes training menus, meal plans, recovery menus, and sleep advice.

[0786] Step 7:

[0787] The server uses an emotion engine to analyze the user's emotional information and generates relaxation techniques and mental care methods based on the obtained data. These include deep breathing and meditation techniques. The generated mental care advice is sent to the device and displayed to the user.

[0788] Step 8:

[0789] Devices and robots installed in physical stores collect user information in real time and provide generated health management advice on the spot. The devices and robots guide users and support them in implementing training and recovery programs.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] [Third embodiment]

[0794] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0795] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0796] 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).

[0797] 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.

[0798] 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.

[0799] 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).

[0800] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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."

[0806] The system of the present invention provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[0807] Entering user information

[0808] The user inputs information using the terminal.

[0809] The user enters information such as age, gender, athletic history, recent injuries, next game date, daily stress level, etc. into the input form on the device. The entered information is sent to the server by pressing the send button.

[0810] User data analysis

[0811] The server receives and analyzes the user data

[0812] The server stores the received data in a database and invokes a generative AI model, which analyzes the data and creates a user-specific profile that is then used to generate subsequent plans.

[0813] Generate training and nutrition plans

[0814] The server generates optimal training and nutrition plans

[0815] Based on the user's profile information, the server uses AI models to generate personalized training and nutrition plans, allowing users to train efficiently and consume proper nutrition.

[0816] Providing recovery menus and sleep suggestions

[0817] The server generates recovery menus and sleep suggestions

[0818] The system generates a recovery program to support the user's recovery, taking into account the presence or absence of injuries and the level of fatigue from training. It also generates advice on how to get quality sleep and sends this to the user's device.

[0819] Mental care support

[0820] Servers provide mental health care

[0821] The system suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server receives it and the AI ​​model generates an appropriate answer and sends it back.

[0822] As a concrete example, consider a 25-year-old male amateur basketball player using the system. He sprained his ankle during a recent practice, and his next game is in two weeks. He enters his daily stress level as 5.

[0823] in this case,

[0824] Training Plan: A light training menu is generated, encouraging upper body training and water exercises to avoid straining your ankles.

[0825] Nutrition plan: A protein-rich diet and anti-inflammatory foods are suggested.

[0826] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[0827] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[0828] Mental care: For stress level 5, meditation and deep breathing exercises are recommended, and support is provided via chat.

[0829] In this way, this system provides comprehensive support based on multifaceted data about the user, helping athletes to perform at their best.

[0830] The processing flow will be explained below.

[0831] Step 1:

[0832] The user inputs information using the terminal.

[0833] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. After completing the input, the user presses the send button to send this information to the server.

[0834] Step 2:

[0835] The server receives the user data

[0836] The server receives the user information sent from the device and stores it in a database, which aggregates the user's physical and lifestyle information.

[0837] Step 3:

[0838] The server analyzes the user data and creates a profile

[0839] The server invokes the generative AI model to analyze the received user data, and based on the analysis results, a personalized profile is created for the user, which contains the information needed to generate training and nutrition plans.

[0840] Step 4:

[0841] The server generates the training plan.

[0842] Based on the user profile, the server uses an AI model to generate a personalized training plan that includes specific training instructions tailored to the user's characteristics and goals.

[0843] Step 5:

[0844] The server generates the nutrition plan

[0845] The server also generates a nutrition plan based on the user profile, taking into account nutritional balance and timing of intake. This plan indicates what foods the user should eat and when.

[0846] Step 6:

[0847] The server sends the training plan and nutrition plan to the device and displays it.

[0848] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[0849] Step 7:

[0850] The server generates a recovery program

[0851] The server takes into account the user's injury information and fatigue level and uses an AI model to generate a recovery program to support recovery, including specific menus to help with injury recovery and reduce muscle fatigue.

[0852] Step 8:

[0853] The server generates sleep suggestions

[0854] The server generates recommendations for getting a good night's sleep based on the user's lifestyle information, including bedding selection and pre-sleep relaxation routines.

[0855] Step 9:

[0856] The server sends and displays recovery programs and sleep suggestions on the device.

[0857] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[0858] Step 10:

[0859] Servers provide mental health care

[0860] The server measures the user's daily stress level and suggests relaxation techniques and mental conditioning methods based on that. When the user inputs a question or inquiry from their device, the server generates an appropriate answer and returns it.

[0861] Step 11:

[0862] Users implement suggestions and provide feedback

[0863] Users implement the training, nutrition, recovery program, sleep, and mental care programs they receive through their device, and then send feedback from their device to the server, which allows the AI ​​to make further improvements.

[0864] Example 1

[0865] 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."

[0866] In today's busy lifestyles, individually optimized advice is necessary for efficient and effective training, nutritional management, recovery, and mental care. However, existing systems have difficulty accurately collecting user information and providing comprehensive support. Furthermore, advanced data analysis is required to create appropriate plans based on the user's physical and lifestyle information. This calls for a more advanced and integrated system.

[0867] 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.

[0868] In this invention, the server includes a means for inputting the user's physical information and lifestyle information, a means for creating a user profile using a generative AI model, and a means for generating individually optimized training and nutrition plans, which makes it possible to provide individually optimized training plans, nutrition plans, recovery programs, mental care, etc. to the user.

[0869] "User's physical information" refers to information about the user's body, such as age, gender, athletic history, and injury history.

[0870] "Lifestyle information" is information about the user's daily life, such as their daily stress level, lifestyle habits, and the date of their next game.

[0871] A "generative AI model" is an artificial intelligence model used to analyze a user's physical and lifestyle information and generate an optimal plan.

[0872] A "user profile" is an individualized collection of data created based on the user's physical and lifestyle information analyzed by a generative AI model.

[0873] A "training plan" is a schedule and content of exercises and physical activities recommended for a user.

[0874] A "nutritional plan" is a guideline for dietary and nutritional intake recommended for a user.

[0875] A "recovery program" is a method or procedure for supporting a user in recovering from fatigue or healing from an injury.

[0876] "Sleep suggestions" are advice and methods for ensuring quality sleep.

[0877] "Relaxation techniques" are methods such as meditation and deep breathing exercises that help users reduce stress.

[0878] A "mental conditioning method" is an approach or technique for maintaining a user's mental state in good condition.

[0879] The system of the present invention provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[0880] Users use devices such as smartphones or PCs to enter detailed information such as age, gender, athletic history, recent injuries, next game date, and daily stress level. This information is collected through an input form on the device and sent to the server by pressing a submit button. The submitted data is transferred using a secure encryption protocol (e.g., HTTPS).

[0881] The server stores the received data in a database, typically a relational database system such as MySQL or PostgreSQL. The stored data is used as the basis for subsequent analysis and plan generation.

[0882] The server then invokes a generative AI model, built using Python-based libraries (e.g., TensorFlow and PyTorch), to analyze the stored user data and create an individually optimized user profile based on the user's physical and lifestyle information.

[0883] Based on the created user profile, the server then uses the generative AI model to generate individually optimized training and nutrition plans. For example, a user with a sprained ankle might be recommended upper-body training and water exercises, and the nutrition plan might include anti-inflammatory foods and a diet high in protein. These plans are then sent to the user's device, where they can be viewed.

[0884] Furthermore, the server generates recovery programs and sleep recommendations based on the user's training data and injury information, including icing and stretching techniques, and the selection of bedding for quality sleep. The generated information is also sent to the user's device and made available to the user.

[0885] Finally, the server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or inquiry from their device, the server uses a generative AI model to generate an appropriate answer and provides it to the user in chat format. This mental care function allows users to receive comprehensive support.

[0886] As a specific example, by entering the prompt "30-year-old male, marathon runner as a hobby. My legs have been feeling tired recently, but my next race is in a month. My stress level is 3," an individually optimized training plan, nutrition plan, recovery program, sleep suggestions, and mental care will be generated and provided to the user.

[0887] In this way, the system of the present invention provides comprehensive support based on multifaceted data of the user, encouraging athletes to perform at their best.

[0888] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0889] Step 1:

[0890] The user enters information using a device. Using a smartphone or PC, the user accesses a dedicated application or web page. There, they enter information such as age, gender, athletic history, recent injuries, next game date, and daily stress level into an input form. After the data is entered, they press the "Submit" button to proceed to the next step.

[0891] Input: User's physical and lifestyle information

[0892] Output: Data sent from the terminal

[0893] Step 2:

[0894] The device sends data to the server, which receives it. When the user presses the "Send" button, the device sends the entered data to the server using an encrypted communication protocol (e.g., HTTPS). The server receives this data and temporarily stores it. This process ensures the safety of the data.

[0895] Input: Data sent from the terminal

[0896] Output: User data temporarily stored on the server

[0897] Step 3:

[0898] The server stores the received user data in a database. The server analyzes the received data and stores it in a relational database system (e.g., MySQL or PostgreSQL). This database has tables, and each user's information is managed individually.

[0899] Input: User data temporarily stored on the server

[0900] Output: User data stored in the database

[0901] Step 4:

[0902] The server invokes a generative AI model to create a user profile. The server invokes a generative AI model (e.g., TensorFlow or PyTorch) based on the stored user data. The AI ​​model analyzes the user's physical and lifestyle information and creates an individually optimized user profile based on that information.

[0903] Input: User data stored in the database

[0904] Output: Generated user profile

[0905] Step 5:

[0906] The server generates personalized training and nutrition plans based on the user profile. The generative AI model analyzes the user profile and generates optimal training and nutrition plans for the user. For example, if a person has an ankle injury, upper body training and water exercises will be recommended, and the nutrition plan will include anti-inflammatory and high-protein foods.

[0907] Input: Generated user profile

[0908] Output: Training and nutrition plans

[0909] Step 6:

[0910] The server sends the training plan and nutrition plan to the user's device. The generated plan is sent from the server to the user's device, where the user can view it on their own device, allowing the user to put the plan into action.

[0911] Input: Training plan and nutrition plan

[0912] Output: The plan sent to the user's device

[0913] Step 7:

[0914] The server generates a recovery program and sleep suggestions and sends them to the user's device. Based on the user's training data and injury information, the server uses a generative AI model to create a recovery program and sleep suggestions. This includes, for example, advice on icing and stretching the ankle and ensuring quality sleep. The generated plan is sent to the user's device, where the user can review it.

[0915] Input: Training data and injury information

[0916] Output: Recovery program and sleep suggestions sent to the user's device

[0917] Step 8:

[0918] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server uses a generative AI model to generate an appropriate answer and provide mental care. This information is also sent to the user's device, allowing for communication in chat format.

[0919] Input: User's daily stress level, questions and inquiries from the user

[0920] Output: Relaxation techniques and mental conditioning methods sent to the device

[0921] These processing steps allow users to receive comprehensive and individually optimized support.

[0922] (Application example 1)

[0923] 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."

[0924] In traditional factory work environments, workers' health is not adequately managed, which can lead to problems with work efficiency and safety. It is also difficult to provide individually optimized training plans, nutrition plans, and recovery programs, and many workers do not receive appropriate feedback. This increases the physical and mental burden on workers and can lead to reduced productivity.

[0925] 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.

[0926] In this invention, the server includes means for inputting a user's physical information and lifestyle information, means for receiving the user's information and generating a training plan and a nutrition plan, means for presenting the generated training plan and nutrition plan to the user, means for collecting the user's health information and providing health management feedback, means for collecting data from the wearable device and generating individually optimized reminders and notifications using a generative AI model, and means for transmitting the collected data to the server and generating a customized plan, thereby enabling the health status of workers to be grasped in real time and individually optimized feedback to be provided.

[0927] "User's physical information" refers to physiological data such as the user's heart rate, blood pressure, and body temperature, as well as information related to the user's health condition.

[0928] "Lifestyle information" refers to information such as the user's daily behavior patterns, eating habits, sleeping hours, and exercise habits.

[0929] "Input means" refers to the interface or device through which a user provides physical and lifestyle information.

[0930] The "means for receiving and generating training and nutrition plans" is the part of the system that receives information from the user and creates optimal training and nutrition plans based on that information.

[0931] "Presenting means" refers to a device or software that displays the generated training plan and nutrition plan so that the user can check them.

[0932] "Means for collecting health information" refers to a system that automatically collects users' health data through wearable devices and sensors.

[0933] A "means for providing health management feedback" is a device or software for providing specific advice or feedback to a user based on collected health data.

[0934] A "wearable device" refers to an electronic device worn by a user that can constantly monitor and collect health information.

[0935] "Generative AI Model" refers to an artificial intelligence model used to analyze collected data and generate individually optimized training plans, nutrition plans, reminders and notifications.

[0936] The "server" is a central processing unit that receives information from users, stores and analyzes the data, and generates the optimal plan.

[0937] The "means for generating a customized plan" is part of a system that creates an individually optimized health management plan based on data collected from a wearable device.

[0938] The present invention is a system that provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[0939] Entering user information

[0940] The user uses the terminal to input physical information (physiological data such as heart rate, blood pressure, and body temperature) and lifestyle information (daily behavioral patterns, eating habits, sleep time, exercise habits, etc.). For example, the user enters information such as age, gender, experience, recent injuries, next shift time, and daily stress level into an input form on the terminal. The input information is sent to the server by pressing the send button.

[0941] User data analysis

[0942] The server stores the received data in a database and invokes a generative AI model, which analyzes the data and creates a user-specific profile that is then used to generate subsequent plans.

[0943] Generate training and nutrition plans

[0944] The server uses a generative AI model based on the user's profile information to generate personalized training and nutrition plans, allowing users to train efficiently and consume appropriate nutrition. For example, based on a user's data, the server may recommend "10 minutes of light exercise" and "a high-protein meal."

[0945] Providing recovery menus and sleep suggestions

[0946] The server generates a recovery program to support the user's recovery, taking into account the presence or absence of injuries and the level of fatigue from training. It also generates advice on how to get quality sleep and sends it to the user's device. For example, it recommends "20 minutes of cooling and stretching" and suggests "7-8 hours of sleep."

[0947] Mental care support

[0948] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server receives it and the generative AI model generates an appropriate answer and sends it back. For example, a message such as, "Your recent stress level is 6. We recommend taking five minutes of deep breaths" is provided.

[0949] Examples of concrete examples and prompts

[0950] For example, consider a 35-year-old male factory worker using the system. His recent heart rate was 85 and his daily stress level was 6. His next shift is one day away. In this case, the system will provide the following feedback:

[0951] "Your current stress level is 6. I suggest you take 5 minutes of deep breathing."

[0952] "Your heart rate has been high lately. Don't forget to stay hydrated at the water cooler."

[0953] An example of a prompt is as follows:

[0954] "Please use the AI ​​model to determine what health care I should do before my next shift:\n- Heart rate: 85\n- Stress level: 6\n- Sleep time: 6 hours\n"

[0955] In this way, this system provides comprehensive support based on multifaceted user data, helping workers to perform at their best.

[0956] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0957] Step 1:

[0958] The user uses the terminal to input physical and lifestyle information. The user enters data such as age, gender, experience, recent injuries, next shift time, and daily stress level into the input form, and presses the send button to send it to the server. Based on the input data, the server obtains data to generate the user's basic profile.

[0959] Step 2:

[0960] The server receives the user's input data and stores it in a database. The stored data becomes input data for calling the generative AI model. The database uses SQL or NoSQL, allowing for data storage and high-speed search.

[0961] Step 3:

[0962] The server invokes a generative AI model based on the received data. The generative AI model analyzes the data to create a profile specific to the user. This analysis uses machine learning libraries (e.g., TensorFlow and PyTorch). The profile includes the user's physical information, lifestyle information, and health status.

[0963] Step 4:

[0964] The server uses generative AI models to generate personalized training and nutrition plans. For example, it might recommend 10 minutes of light exercise and a high-protein meal based on the user's age, gender, heart rate, and exercise habits. These plans are then stored in a database on the server.

[0965] Step 5:

[0966] The server references the user's profile and generates recovery programs and sleep recommendations based on the user's injury status and training fatigue level. For example, if a user has a sprained ankle, the server might suggest "20 minutes of cooling and stretching" and "7-8 hours of sleep." These recommendations are also generated by an AI model and stored in the server's database.

[0967] Step 6:

[0968] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level, such as "take five minutes of deep breaths." This information is analyzed by a generative AI model to generate suggestions.

[0969] Step 7:

[0970] The generated training plan, nutrition plan, recovery program, sleep suggestions, and relaxation suggestions are sent to the user's device, which then visually displays this information. Feedback is provided via a smartphone or smart glasses, for example, and the user can review and put the information into practice.

[0971] Step 8:

[0972] A user's wearable device (such as a smartwatch or fitness tracker) continuously collects real-time health data, such as heart rate, stress level, and sleep data, and automatically transmits this data to a server. This data is then used as input for generative AI models, providing updated feedback.

[0973] Step 9:

[0974] The server continuously receives health data from the user and updates feedback based on the latest generative AI model. This allows the user to constantly receive optimal advice based on their health status. For example, a notification might be sent saying, "Your heart rate has been high recently. Don't forget to drink water at the water cooler."

[0975] Through the above processing steps, the user can receive individually optimized feedback in real time, enabling them to efficiently manage their own health.

[0976] 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.

[0977] The system of the present invention analyzes a user's physical information, lifestyle information, and emotions, and based on that information, provides optimal training plans, nutrition plans, recovery programs, sleep suggestions, and even mental care. This system is realized by exchanging information between the terminal, server, and user's device, and providing individually optimized advice.

[0978] Entering user information

[0979] The user inputs information using the terminal.

[0980] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. In addition, the user provides emotional information to the emotion engine using facial recognition or voice input. After completing the input, the user presses the send button to send this information to the server.

[0981] User data analysis

[0982] The server receives the user data

[0983] The server receives the user information and emotion information sent from the device and stores them in a database, thereby aggregating the user's physical information, lifestyle information, and emotion information.

[0984] The server analyzes user data and emotional information to create a profile

[0985] The server then invokes the generative AI model and emotion engine to analyze the received data, and a personalized profile is created based on the results of the analysis. This profile contains the information needed to generate training and nutrition plans.

[0986] Generate training and nutrition plans

[0987] The server generates the optimal training plan

[0988] Based on the user profile and emotional information, the server uses an AI model to generate a personalized training plan that includes specific training instructions tailored to the user's characteristics, goals, and current emotional state.

[0989] The server generates the nutrition plan

[0990] The server also generates a nutrition plan based on the user profile and emotional information, taking into account nutritional balance and timing of intake. This plan indicates what meals the user should eat and when.

[0991] The server sends the training plan and nutrition plan to the device and displays it.

[0992] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[0993] Providing recovery menus and sleep suggestions

[0994] The server generates a recovery program

[0995] The server takes into account the user's injury information, fatigue level, and emotional state, and uses an AI model and emotion engine to generate a recovery program to support recovery, including specific menus to help with injury recovery and muscle fatigue reduction.

[0996] The server generates sleep suggestions

[0997] The server generates recommendations for getting a good night's sleep based on the user's lifestyle and emotional information, including recommendations for bedding selection and pre-sleep relaxation routines.

[0998] The server sends and displays recovery programs and sleep suggestions on the device.

[0999] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[1000] Mental care support

[1001] The server uses an emotion engine to analyze the user's emotions.

[1002] The emotion engine analyzes the emotion information provided by the user and recognizes the current emotional state.

[1003] Servers provide mental health care

[1004] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information. When the user inputs a question or request for advice from the device, the server generates an appropriate answer and returns it.

[1005] As a concrete example, consider a 25-year-old male amateur basketball player using the system. He sprained his ankle during a recent practice, and his next game is two weeks away. He enters his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety."

[1006] in this case,

[1007] Training Plan: A light training menu is generated. Upper body training and water exercises are recommended to avoid straining the ankles. The Emotion Engine also adds training to reduce anxiety.

[1008] Nutrition plan: Protein-rich meals and anti-inflammatory foods are recommended, along with mental health foods to reduce anxiety.

[1009] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[1010] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[1011] Mental care: Meditation and deep breathing exercises are recommended for anxiety, and support is provided via chat.

[1012] In this way, this system provides comprehensive support based on the user's multifaceted data and emotional information, helping athletes to perform at their best.

[1013] The processing flow will be explained below.

[1014] Step 1:

[1015] The user inputs information using the terminal.

[1016] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. The emotion engine also uses facial recognition and voice input to provide emotional information. Once input is complete, the user presses the send button to send the information to the server.

[1017] Step 2:

[1018] The server receives the user data

[1019] The server receives the user information and emotion information sent from the device and stores them in a database, thereby aggregating the user's physical information, lifestyle information, and emotion information.

[1020] Step 3:

[1021] The server analyzes user data and emotional information to create a profile

[1022] The server then invokes the generative AI model and emotion engine to analyze the received data, and creates a personalized profile for the user based on the results of this analysis. This profile contains the information needed to generate training and nutrition plans.

[1023] Step 4:

[1024] The server generates the optimal training plan

[1025] The server uses an AI model to generate a personalized training plan based on the user's profile and emotional information. The emotional engine adjusts the training content according to the user's current emotional state. The generated plan includes specific training instructions tailored to the user's characteristics and goals.

[1026] Step 5:

[1027] The server generates the nutrition plan

[1028] The server also generates a nutrition plan based on the user profile and emotional information, taking into account nutritional balance and timing. This plan indicates what meals the user should eat and when. The emotional engine also provides nutritional instructions based on the user's emotional state.

[1029] Step 6:

[1030] The server sends the training plan and nutrition plan to the device and displays it.

[1031] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[1032] Step 7:

[1033] The server generates a recovery program

[1034] The server takes into account the user's injury information, fatigue level, and emotional state, and uses an AI model and emotion engine to generate a recovery program to support recovery, including specific menus to help with injury recovery and reduce muscle fatigue.

[1035] Step 8:

[1036] The server generates sleep suggestions

[1037] The server generates recommendations for getting a good night's sleep based on the user's lifestyle and emotional information, including recommendations for bedding selection and pre-sleep relaxation routines.

[1038] Step 9:

[1039] The server sends and displays recovery programs and sleep suggestions on the device.

[1040] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[1041] Step 10:

[1042] The server uses an emotion engine to analyze the user's emotions.

[1043] The server uses an emotion engine to analyze the emotional information provided by the user and recognize their current emotional state, and adjusts the suggestions accordingly.

[1044] Step 11:

[1045] Servers provide mental health care

[1046] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information. When the user inputs a question or request for advice from the device, the server generates an appropriate answer and returns it.

[1047] Step 12:

[1048] Users implement suggestions and provide feedback

[1049] Users implement the training, nutrition, recovery program, sleep, and mental care programs they receive through their device, and then send feedback from their device to the server, which allows the AI ​​to make further improvements.

[1050] This process allows users to receive personalized, comprehensive support to improve their physical and mental performance. As a concrete example, consider a 25-year-old male amateur basketball player using the system. He recently sprained his ankle during practice, and his next game is two weeks away. He enters his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety."

[1051] in this case,

[1052] Training Plan: A light training menu is generated. Upper body training and water exercises are recommended to avoid straining the ankles. The Emotion Engine also adds training to reduce anxiety.

[1053] Nutrition plan: Protein-rich meals and anti-inflammatory foods are recommended, as well as mental health foods to reduce anxiety.

[1054] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[1055] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[1056] Mental care: Meditation and deep breathing exercises are recommended for anxiety, and support is provided via chat.

[1057] In this way, this system provides comprehensive support based on the user's multifaceted data and emotional information, helping athletes to perform at their best.

[1058] Example 2

[1059] 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."

[1060] Conventional training systems only consider a user's physical and lifestyle information, but are unable to reflect data such as their emotional state or daily stress level. This makes it difficult to provide training and nutritional plans that adequately reduce the user's psychological burden and risk of injury. Furthermore, comprehensive support for recovery programs, sleep recommendations, and mental health support is lacking. Furthermore, there is a need for a means to enable athletes to train more safely and efficiently by providing recovery programs that take into account the user's injury information and fatigue level.

[1061] 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.

[1062] In this invention, the server includes means for inputting a user's physical information and lifestyle information, means for inputting and analyzing the user's emotional information, means for receiving the user's information and analyzed emotional information and generating a training plan and nutrition plan, means for presenting the generated training plan, nutrition plan, recovery program, and sleep suggestions to the user, means for suggesting appropriate relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information, and means for analyzing the emotional information and providing mental care support in chat format. This makes it possible to comprehensively analyze a user's physical information, lifestyle information, and emotional information and provide individually optimized training plans, nutrition plans, recovery programs, sleep suggestions, and mental care.

[1063] "User's physical information" refers to information such as the user's age, gender, athletic history, and recent injuries.

[1064] "Lifestyle information" refers to information such as a user's daily habits, the date of their next game, and their daily stress level.

[1065] "Emotion information" refers to information indicating the user's emotional state obtained through facial recognition or voice input.

[1066] "Training plan" refers to a plan that includes specific exercise instructions that are individually optimized and generated based on the user's physical and emotional information.

[1067] A "nutritional plan" refers to a plan that includes specific dietary instructions regarding nutritional balance and intake timing, generated based on the user's physical and emotional information.

[1068] A "recovery program" refers to a program that includes a specific menu to support recovery, which is generated taking into account the user's injury information and fatigue level.

[1069] "Sleep suggestions" refer to specific advice for getting quality sleep based on the user's lifestyle and emotional information.

[1070] "Generative AI model" refers to an artificial intelligence model that uses a user's physical, lifestyle, and emotional information as input to generate training plans, nutrition plans, recovery programs, and sleep suggestions.

[1071] "Emotion engine" refers to an analysis engine that analyzes the emotion information provided by the user and recognizes the current emotional state.

[1072] "Terminal" refers to a device used by a user to input information. Specifically, this includes smartphones, tablets, and personal computers.

[1073] "Server" refers to the device that receives, stores, and analyzes user-submitted information. It is also where the generative AI model and emotion engine run.

[1074] "Mental care support in chat format" refers to a support function that provides appropriate answers in text message format to users' questions and inquiries.

[1075] A "prompt" is an input sentence used to leverage a generative AI model to derive a specific answer or outcome.

[1076] The system of the present invention analyzes a user's physical information, lifestyle information, and emotions, and based on that, provides optimal training plans, nutrition plans, recovery programs, sleep suggestions, and even mental care. This system is realized by exchanging information between the terminal, server, and user's device, and providing individually optimized advice.

[1077] Entering user information

[1078] The user inputs information using the device. The user enters their physical and lifestyle information (e.g., age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. In addition, the user provides emotional information using facial recognition or voice input. When the user has completed input and pressed the send button, the device sends this information to the server.

[1079] Receiving and storing user data

[1080] The server receives the user information and emotion information sent from the terminal, and stores the received information in a database.

[1081] Analyzing user data and generating profiles

[1082] The server analyzes the incoming data using generative AI models and an emotion engine. Based on the analysis, a personalized profile is created for the user. This profile serves as the basis for generating training and nutrition plans.

[1083] Generate training and nutrition plans

[1084] The server generates an individually optimized training plan based on the user profile and emotional information. The generated training plan includes specific exercise instructions. Similarly, the server also generates a nutrition plan, which includes specific dietary instructions regarding nutritional balance and intake timing. The generated training plan and nutrition plan are sent to the device, which displays them to the user.

[1085] Providing recovery programs and sleep suggestions

[1086] The server generates a recovery program based on the user's injury information, fatigue level, and emotional state. This includes specific menus to support recovery. The server also generates sleep suggestions to help users get a good night's sleep. These suggestions include how to select bedding and a pre-sleep relaxation routine. The generated recovery program and sleep suggestions are sent to the device, which then displays them to the user.

[1087] Mental care support

[1088] The server uses an emotion engine to analyze the user's emotional information and recognize their current emotional state. Based on their daily stress level and emotional information, the server suggests relaxation techniques and mental conditioning methods. When the user inputs a question or request for advice on their device, the server generates and replies with an appropriate answer.

[1089] As a concrete example, consider the case where a 25-year-old male amateur basketball player uses the system. He recently sprained his ankle during practice, and his next game is two weeks away. He has entered his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety." In this case, the system operates as follows:

[1090] Training Plan

[1091] A light training menu is generated, encouraging upper body training and water exercises to avoid straining the ankles, and the emotion engine adds training to reduce anxiety.

[1092] Nutrition Plan

[1093] High-protein diets and anti-inflammatory foods are suggested, along with mental health foods to reduce anxiety.

[1094] Recovery Program

[1095] Ankle recovery is included, and icing and stretching are recommended.

[1096] sleep suggestions

[1097] To ensure quality sleep, recommendations are made on bedding selection and stretching routines.

[1098] Mental care

[1099] Meditation and deep breathing exercises are recommended to combat anxiety, and support is provided via chat.

[1100] An example prompt is, "A 25-year-old male amateur basketball player recently sprained his ankle, and his next game is in two weeks. He has entered his daily stress level as 5, and the emotion engine has given him a facial recognition result indicating 'anxiety.' Please generate the optimal training plan, nutrition plan, recovery program, sleep suggestions, and mental care for this case."

[1101] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1102] Step 1:

[1103] The user uses the device to input information. The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. They also provide emotional information through facial recognition and voice input. When the user has completed input and presses the send button, the device sends this information to the server.

[1104] Input: Physical information, lifestyle information, emotional information

[1105] Output: Sending information from the device to the server

[1106] Specific actions: The user enters information using the device's keyboard, provides emotion information using the facial recognition camera or microphone, and clicks the send button.

[1107] Step 2:

[1108] The server receives the user information and emotion information sent from the device and stores them in a database. At this point, the server aggregates all of the user's physical information, lifestyle information, and emotion information.

[1109] Input: User information and emotional information sent from the device

[1110] Output: Information stored in the database

[1111] What it does: The server receives the data using a network communication module and executes an "INSERT" query in a database such as MySQL to store the information.

[1112] Step 3:

[1113] The server analyzes the incoming data using generative AI models and an emotion engine. Based on the analysis, a personalized profile is created for the user, which contains the basis for generating training and nutrition plans.

[1114] Input: User information and emotion information stored in the database

[1115] Output: Analysis results and user profile

[1116] How it works: The server calls the generative AI model and performs analysis using the stored data as input. The emotion engine analyzes the emotional information, creates a profile based on both results, and stores it in the database.

[1117] Step 4:

[1118] The server generates a personalized training plan based on the user profile and emotional information. The generated training plan includes specific exercise instructions. Similarly, the server also generates a nutrition plan, including specific dietary instructions regarding nutritional balance and intake timing. This plan is sent to the device and displayed to the user.

[1119] Input: User profile and emotional information

[1120] Output: Training and nutrition plans

[1121] How it works: The server uses the generative AI model to generate training and nutrition plans, which are then sent to the device in JSON format, where the device interprets them and presents them to the user.

[1122] Step 5:

[1123] The server generates a recovery program based on the user's injury information, fatigue level, and emotional state. It also generates sleep suggestions to help the user get quality sleep. The recovery program includes specific menus to support recovery. The sleep suggestions include how to select bedding and suggestions for a pre-sleep relaxation routine. The generated recovery program and sleep suggestions are sent to the device and displayed to the user.

[1124] Input: User profile, injury information, fatigue level, emotional information

[1125] Output: Recovery program and sleep suggestions

[1126] How it works: The server uses the AI ​​model to generate recovery programs and sleep suggestions, which are then sent to the device, which displays them to the user.

[1127] Step 6:

[1128] The server uses an emotion engine to analyze the user's emotional information and recognize their current emotional state. Based on their daily stress level and emotional information, the server suggests relaxation techniques and mental conditioning methods. When the user inputs a question or request for advice on their device, the server generates and replies with an appropriate answer.

[1129] Input: User's emotional information and daily stress level

[1130] Output: Relaxation techniques and mental conditioning methods, answers to user questions

[1131] Specific operation: The server calls the emotion engine and analyzes emotional information. Based on the analysis results, it generates relaxation techniques and mental conditioning methods, and uses the chatbot function to answer questions from users in real time.

[1132] Through the above processing steps, the system comprehensively analyzes the user's multifaceted data and emotional information, and provides optimal training, nutrition, recovery, sleep, and mental care.

[1133] (Application example 2)

[1134] 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."

[1135] While conventional fitness and health management systems can provide advice based on personal physical and lifestyle information, it is difficult to input and display user information in real time in physical stores, and to comprehensively provide optimal training plans, nutrition plans, recovery programs, sleep suggestions, and mental care on the spot. This requires users to use different tools and devices individually, making it difficult to achieve high convenience.

[1136] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1137] In this invention, the server includes a means for inputting physical information and lifestyle information of a user, a means for receiving the user information and generating a training plan and a nutrition plan, a means for presenting the generated training plan and nutrition plan to the user, and a means for inputting and presenting the user information via a device installed in a physical store, thereby enabling comprehensive health management advice that responds to the user's needs in real time to be provided in the physical store.

[1138] "User's physical information" refers to data about the user's body, such as the user's age, gender, weight, height, BMI, muscle mass, body fat percentage, and health condition (pre-existing conditions, allergies, etc.).

[1139] "Lifestyle information" refers to data about a user's daily life, such as the user's daily activities and habits, work content and hours, sleep patterns, eating habits, and stress levels.

[1140] A "training plan" refers to a proposal for an exercise program that is individually optimized based on the user's physical information and lifestyle information, and includes specific exercise content, frequency, intensity, time, etc.

[1141] A "nutritional plan" refers to a personalized, optimized meal plan based on the user's physical and lifestyle information, including specific ingredients, cooking methods, nutrients to be consumed, and timing of intake.

[1142] "Devices installed in physical stores" refers to terminals or robots installed in fitness gyms or physical stores for health management purposes, and refers to devices that input and display user information and provide fitness advice.

[1143] A "recovery program" refers to a program designed to support recovery from injury or fatigue, and specifically includes icing, stretching, massage, rehabilitation exercises, etc.

[1144] "Sleep suggestions" refers to advice on how to get quality sleep, including how to select bedding, relaxation routines before sleep, and adjusting bedtime.

[1145] "Mental care" refers to advice and techniques to support the user's mental health, and specifically includes meditation, deep breathing, relaxation techniques, mental conditioning methods, etc.

[1146] MODE FOR CARRYING OUT THE INVENTION

[1147] To implement this invention, a user inputs their own physical and lifestyle information using a smartphone or a device installed in a physical store. The terminal then transmits this information to a server.

[1148] The server receives the user's input data and uses a generative AI model to generate personalized training and nutrition plans using machine learning libraries such as TensorFlow. The generated training and nutrition plans are then sent to the user's smartphone or in-store device, where they are presented to the user.

[1149] The server also generates recovery programs and sleep suggestions based on the user's physical and lifestyle information. This process continues to utilize the generative AI model, taking into account the user's injury information and fatigue level. The generated recovery programs and sleep suggestions are also sent to the user's device and displayed.

[1150] Furthermore, the system measures the user's stress level and analyzes the emotional information using an emotion engine. Based on the analysis results, it proposes relaxation techniques and mental conditioning methods, thereby providing comprehensive mental care.

[1151] Devices and robots installed in physical stores input and display user information in real time, allowing them to receive immediate fitness advice and health management support within the store.

[1152] For example, consider the case of a 25-year-old male amateur basketball player accessing the system using his smartphone. He sprained his ankle during a recent practice, and his next game is in two weeks. He enters his daily stress level as 5, and the emotion engine detects facial recognition indicating "anxiety." Based on this information, the server generates a personalized optimization plan as follows:

[1153] Training plan: Include upper body training and water exercises to avoid ankle strain.

[1154] Nutrition plan: A diet high in protein and anti-inflammatory foods is recommended.

[1155] Recovery Program: Includes ankle recovery menu, including suggested icing and stretching.

[1156] Sleep Suggestions: Includes suggestions for getting quality sleep.

[1157] Mental care: Meditation and deep breathing exercises are recommended for anxiety.

[1158] The system can provide highly accurate health management advice based on specific prompts (e.g., "A 25-year-old male amateur basketball player recently sprained his ankle, has a daily stress level of 5, and feels anxious. Based on this information, generate an individualized training plan, nutrition plan, recovery program, sleep suggestions, and mental health advice that are optimal for him.").

[1159] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1160] Step 1:

[1161] Users enter their physical and lifestyle information using their smartphone or a device installed in a physical store. This includes age, gender, weight, height, athletic history, recent injuries, next game date, stress level, etc. Emotional information is also provided through facial recognition and voice input. The entered data is temporarily stored on the device.

[1162] Step 2:

[1163] The device sends the user's input data, including physical information, lifestyle information, and emotional information, to the server, which then stores the received data in a database for analysis.

[1164] Step 3:

[1165] The server uses a generative AI model based on the received data to generate an individually optimized training plan. Specific data processing and calculations involve determining the appropriate exercise intensity and training content, taking into account the user's physical information (e.g., injury status) and emotional information (e.g., anxiety level). The generated training plan includes specific exercise menus, repetitions, and number of sets.

[1166] Step 4:

[1167] The server then uses the generative AI model to generate a nutrition plan based on the user's physical and lifestyle information. The data processing and calculations take into account meal timing and required nutrients (e.g., protein, vitamins). The generated nutrition plan includes recommended ingredients, meal contents, and intake timing.

[1168] Step 5:

[1169] The server generates a recovery program and sleep suggestions. The recovery program takes into account injury information and fatigue level, and uses an AI model to suggest specific icing and stretching methods. Sleep suggestions include how to select bedding and how to relax before bed. These data processing and calculations take into account the user's injury, fatigue level, and sleep patterns.

[1170] Step 6:

[1171] The server sends the generated training plan, nutrition plan, recovery program, and sleep suggestions to the user's device. The device receives this data and displays it to the user. The displayed content includes training menus, meal plans, recovery menus, and sleep advice.

[1172] Step 7:

[1173] The server uses an emotion engine to analyze the user's emotional information and generates relaxation techniques and mental care methods based on the obtained data. These include deep breathing and meditation techniques. The generated mental care advice is sent to the device and displayed to the user.

[1174] Step 8:

[1175] Devices and robots installed in physical stores collect user information in real time and provide generated health management advice on the spot. The devices and robots guide users and support them in implementing training and recovery programs.

[1176] 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.

[1177] 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.

[1178] 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.

[1179] [Fourth embodiment]

[1180] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1181] 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.

[1182] 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).

[1183] 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.

[1184] 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.

[1185] 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).

[1186] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1187] 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.

[1188] 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.

[1189] 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.

[1190] 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.

[1191] 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.

[1192] 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."

[1193] The system of the present invention provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[1194] Entering user information

[1195] The user inputs information using the terminal.

[1196] The user enters information such as age, gender, athletic history, recent injuries, next game date, daily stress level, etc. into the input form on the device. The entered information is sent to the server by pressing the send button.

[1197] User data analysis

[1198] The server receives and analyzes the user data

[1199] The server stores the received data in a database and invokes a generative AI model, which analyzes the data and creates a user-specific profile that is then used to generate subsequent plans.

[1200] Generate training and nutrition plans

[1201] The server generates optimal training and nutrition plans

[1202] Based on the user's profile information, the server uses AI models to generate personalized training and nutrition plans, allowing users to train efficiently and consume proper nutrition.

[1203] Providing recovery menus and sleep suggestions

[1204] The server generates recovery menus and sleep suggestions

[1205] The system generates a recovery program to support the user's recovery, taking into account the presence or absence of injuries and the level of fatigue from training. It also generates advice on how to get quality sleep and sends this to the user's device.

[1206] Mental care support

[1207] Servers provide mental health care

[1208] The system suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server receives it and the AI ​​model generates an appropriate answer and sends it back.

[1209] As a concrete example, consider a 25-year-old male amateur basketball player using the system. He sprained his ankle during a recent practice, and his next game is in two weeks. He enters his daily stress level as 5.

[1210] in this case,

[1211] Training Plan: A light training menu is generated, encouraging upper body training and water exercises to avoid straining your ankles.

[1212] Nutrition plan: A protein-rich diet and anti-inflammatory foods are suggested.

[1213] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[1214] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[1215] Mental care: For stress level 5, meditation and deep breathing exercises are recommended, and support is provided via chat.

[1216] In this way, this system provides comprehensive support based on multifaceted data about the user, helping athletes to perform at their best.

[1217] The processing flow will be explained below.

[1218] Step 1:

[1219] The user inputs information using the terminal.

[1220] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. After completing the input, the user presses the send button to send this information to the server.

[1221] Step 2:

[1222] The server receives the user data

[1223] The server receives the user information sent from the device and stores it in a database, which aggregates the user's physical and lifestyle information.

[1224] Step 3:

[1225] The server analyzes the user data and creates a profile

[1226] The server invokes the generative AI model to analyze the received user data, and based on the analysis results, a personalized profile is created for the user, which contains the information needed to generate training and nutrition plans.

[1227] Step 4:

[1228] The server generates the training plan.

[1229] Based on the user profile, the server uses an AI model to generate a personalized training plan that includes specific training instructions tailored to the user's characteristics and goals.

[1230] Step 5:

[1231] The server generates the nutrition plan

[1232] The server also generates a nutrition plan based on the user profile, taking into account nutritional balance and timing of intake. This plan indicates what foods the user should eat and when.

[1233] Step 6:

[1234] The server sends the training plan and nutrition plan to the device and displays it.

[1235] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[1236] Step 7:

[1237] The server generates a recovery program

[1238] The server takes into account the user's injury information and fatigue level and uses an AI model to generate a recovery program to support recovery, including specific menus to help with injury recovery and reduce muscle fatigue.

[1239] Step 8:

[1240] The server generates sleep suggestions

[1241] The server generates recommendations for getting a good night's sleep based on the user's lifestyle information, including bedding selection and pre-sleep relaxation routines.

[1242] Step 9:

[1243] The server sends and displays recovery programs and sleep suggestions on the device.

[1244] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[1245] Step 10:

[1246] Servers provide mental health care

[1247] The server measures the user's daily stress level and suggests relaxation techniques and mental conditioning methods based on that. When the user inputs a question or inquiry from their device, the server generates an appropriate answer and returns it.

[1248] Step 11:

[1249] Users implement suggestions and provide feedback

[1250] Users implement the training, nutrition, recovery program, sleep, and mental care programs they receive through their device, and then send feedback from their device to the server, which allows the AI ​​to make further improvements.

[1251] Example 1

[1252] 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."

[1253] In today's busy lifestyles, individually optimized advice is necessary for efficient and effective training, nutritional management, recovery, and mental care. However, existing systems have difficulty accurately collecting user information and providing comprehensive support. Furthermore, advanced data analysis is required to create appropriate plans based on the user's physical and lifestyle information. This calls for a more advanced and integrated system.

[1254] 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.

[1255] In this invention, the server includes a means for inputting the user's physical information and lifestyle information, a means for creating a user profile using a generative AI model, and a means for generating individually optimized training and nutrition plans, which makes it possible to provide individually optimized training plans, nutrition plans, recovery programs, mental care, etc. to the user.

[1256] "User's physical information" refers to information about the user's body, such as age, gender, athletic history, and injury history.

[1257] "Lifestyle information" is information about the user's daily life, such as their daily stress level, lifestyle habits, and the date of their next game.

[1258] A "generative AI model" is an artificial intelligence model used to analyze a user's physical and lifestyle information and generate an optimal plan.

[1259] A "user profile" is an individualized collection of data created based on the user's physical and lifestyle information analyzed by a generative AI model.

[1260] A "training plan" is a schedule and content of exercises and physical activities recommended for a user.

[1261] A "nutritional plan" is a guideline for dietary and nutritional intake recommended for a user.

[1262] A "recovery program" is a method or procedure for supporting a user in recovering from fatigue or healing from an injury.

[1263] "Sleep suggestions" are advice and methods for ensuring quality sleep.

[1264] "Relaxation techniques" are methods such as meditation and deep breathing exercises that help users reduce stress.

[1265] A "mental conditioning method" is an approach or technique for maintaining a user's mental state in good condition.

[1266] The system of the present invention provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[1267] Users use devices such as smartphones or PCs to enter detailed information such as age, gender, athletic history, recent injuries, next game date, and daily stress level. This information is collected through an input form on the device and sent to the server by pressing a submit button. The submitted data is transferred using a secure encryption protocol (e.g., HTTPS).

[1268] The server stores the received data in a database, typically a relational database system such as MySQL or PostgreSQL. The stored data is used as the basis for subsequent analysis and plan generation.

[1269] The server then invokes a generative AI model, built using Python-based libraries (e.g., TensorFlow and PyTorch), to analyze the stored user data and create an individually optimized user profile based on the user's physical and lifestyle information.

[1270] Based on the created user profile, the server then uses the generative AI model to generate individually optimized training and nutrition plans. For example, a user with a sprained ankle might be recommended upper-body training and water exercises, and the nutrition plan might include anti-inflammatory foods and a diet high in protein. These plans are then sent to the user's device, where they can be viewed.

[1271] Furthermore, the server generates recovery programs and sleep recommendations based on the user's training data and injury information, including icing and stretching techniques, and the selection of bedding for quality sleep. The generated information is also sent to the user's device and made available to the user.

[1272] Finally, the server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or inquiry from their device, the server uses a generative AI model to generate an appropriate answer and provides it to the user in chat format. This mental care function allows users to receive comprehensive support.

[1273] As a specific example, by entering the prompt "30-year-old male, marathon runner as a hobby. My legs have been feeling tired recently, but my next race is in a month. My stress level is 3," an individually optimized training plan, nutrition plan, recovery program, sleep suggestions, and mental care will be generated and provided to the user.

[1274] In this way, the system of the present invention provides comprehensive support based on multifaceted data of the user, encouraging athletes to perform at their best.

[1275] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1276] Step 1:

[1277] The user enters information using a device. Using a smartphone or PC, the user accesses a dedicated application or web page. There, they enter information such as age, gender, athletic history, recent injuries, next game date, and daily stress level into an input form. After the data is entered, they press the "Submit" button to proceed to the next step.

[1278] Input: User's physical and lifestyle information

[1279] Output: Data sent from the terminal

[1280] Step 2:

[1281] The device sends data to the server, which receives it. When the user presses the "Send" button, the device sends the entered data to the server using an encrypted communication protocol (e.g., HTTPS). The server receives this data and temporarily stores it. This process ensures the safety of the data.

[1282] Input: Data sent from the terminal

[1283] Output: User data temporarily stored on the server

[1284] Step 3:

[1285] The server stores the received user data in a database. The server analyzes the received data and stores it in a relational database system (e.g., MySQL or PostgreSQL). This database has tables, and each user's information is managed individually.

[1286] Input: User data temporarily stored on the server

[1287] Output: User data stored in the database

[1288] Step 4:

[1289] The server invokes a generative AI model to create a user profile. The server invokes a generative AI model (e.g., TensorFlow or PyTorch) based on the stored user data. The AI ​​model analyzes the user's physical and lifestyle information and creates an individually optimized user profile based on that information.

[1290] Input: User data stored in the database

[1291] Output: Generated user profile

[1292] Step 5:

[1293] The server generates personalized training and nutrition plans based on the user profile. The generative AI model analyzes the user profile and generates optimal training and nutrition plans for the user. For example, if a person has an ankle injury, upper body training and water exercises will be recommended, and the nutrition plan will include anti-inflammatory and high-protein foods.

[1294] Input: Generated user profile

[1295] Output: Training and nutrition plans

[1296] Step 6:

[1297] The server sends the training plan and nutrition plan to the user's device. The generated plan is sent from the server to the user's device, where the user can view it on their own device, allowing the user to put the plan into action.

[1298] Input: Training plan and nutrition plan

[1299] Output: The plan sent to the user's device

[1300] Step 7:

[1301] The server generates a recovery program and sleep suggestions and sends them to the user's device. Based on the user's training data and injury information, the server uses a generative AI model to create a recovery program and sleep suggestions. This includes, for example, advice on icing and stretching the ankle and ensuring quality sleep. The generated plan is sent to the user's device, where the user can review it.

[1302] Input: Training data and injury information

[1303] Output: Recovery program and sleep suggestions sent to the user's device

[1304] Step 8:

[1305] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server uses a generative AI model to generate an appropriate answer and provide mental care. This information is also sent to the user's device, allowing for communication in chat format.

[1306] Input: User's daily stress level, questions and inquiries from the user

[1307] Output: Relaxation techniques and mental conditioning methods sent to the device

[1308] These processing steps allow users to receive comprehensive and individually optimized support.

[1309] (Application example 1)

[1310] 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."

[1311] In traditional factory work environments, workers' health is not adequately managed, which can lead to problems with work efficiency and safety. It is also difficult to provide individually optimized training plans, nutrition plans, and recovery programs, and many workers do not receive appropriate feedback. This increases the physical and mental burden on workers and can lead to reduced productivity.

[1312] 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.

[1313] In this invention, the server includes means for inputting a user's physical information and lifestyle information, means for receiving the user's information and generating a training plan and a nutrition plan, means for presenting the generated training plan and nutrition plan to the user, means for collecting the user's health information and providing health management feedback, means for collecting data from the wearable device and generating individually optimized reminders and notifications using a generative AI model, and means for transmitting the collected data to the server and generating a customized plan, thereby enabling the health status of workers to be grasped in real time and individually optimized feedback to be provided.

[1314] "User's physical information" refers to physiological data such as the user's heart rate, blood pressure, and body temperature, as well as information related to the user's health condition.

[1315] "Lifestyle information" refers to information such as the user's daily behavior patterns, eating habits, sleeping hours, and exercise habits.

[1316] "Input means" refers to the interface or device through which a user provides physical and lifestyle information.

[1317] The "means for receiving and generating training and nutrition plans" is the part of the system that receives information from the user and creates optimal training and nutrition plans based on that information.

[1318] "Presenting means" refers to a device or software that displays the generated training plan and nutrition plan so that the user can check them.

[1319] "Means for collecting health information" refers to a system that automatically collects users' health data through wearable devices and sensors.

[1320] A "means for providing health management feedback" is a device or software for providing specific advice or feedback to a user based on collected health data.

[1321] A "wearable device" refers to an electronic device worn by a user that can constantly monitor and collect health information.

[1322] "Generative AI Model" refers to an artificial intelligence model used to analyze collected data and generate individually optimized training plans, nutrition plans, reminders and notifications.

[1323] The "server" is a central processing unit that receives information from users, stores and analyzes the data, and generates the optimal plan.

[1324] The "means for generating a customized plan" is part of a system that creates an individually optimized health management plan based on data collected from a wearable device.

[1325] The present invention is a system that provides an integrated training plan, nutrition plan, recovery program, sleep suggestions, and mental care based on the user's physical and lifestyle information. This system is realized by exchanging information between the terminal, server, and user's device to provide optimal advice.

[1326] Entering user information

[1327] The user uses the terminal to input physical information (physiological data such as heart rate, blood pressure, and body temperature) and lifestyle information (daily behavioral patterns, eating habits, sleep time, exercise habits, etc.). For example, the user enters information such as age, gender, experience, recent injuries, next shift time, and daily stress level into an input form on the terminal. The input information is sent to the server by pressing the send button.

[1328] User data analysis

[1329] The server stores the received data in a database and invokes a generative AI model, which analyzes the data and creates a user-specific profile that is then used to generate subsequent plans.

[1330] Generate training and nutrition plans

[1331] The server uses a generative AI model based on the user's profile information to generate personalized training and nutrition plans, allowing users to train efficiently and consume appropriate nutrition. For example, based on a user's data, the server may recommend "10 minutes of light exercise" and "a high-protein meal."

[1332] Providing recovery menus and sleep suggestions

[1333] The server generates a recovery program to support the user's recovery, taking into account the presence or absence of injuries and the level of fatigue from training. It also generates advice on how to get quality sleep and sends it to the user's device. For example, it recommends "20 minutes of cooling and stretching" and suggests "7-8 hours of sleep."

[1334] Mental care support

[1335] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level. When the user inputs a question or request for advice from their device, the server receives it and the generative AI model generates an appropriate answer and sends it back. For example, a message such as, "Your recent stress level is 6. We recommend taking five minutes of deep breaths" is provided.

[1336] Examples of concrete examples and prompts

[1337] For example, consider a 35-year-old male factory worker using the system. His recent heart rate was 85 and his daily stress level was 6. His next shift is one day away. In this case, the system will provide the following feedback:

[1338] "Your current stress level is 6. I suggest you take 5 minutes of deep breathing."

[1339] "Your heart rate has been high lately. Don't forget to stay hydrated at the water cooler."

[1340] An example of a prompt is as follows:

[1341] "Please use the AI ​​model to determine what health care I should do before my next shift:\n- Heart rate: 85\n- Stress level: 6\n- Sleep time: 6 hours\n"

[1342] In this way, this system provides comprehensive support based on multifaceted user data, helping workers to perform at their best.

[1343] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1344] Step 1:

[1345] The user uses the terminal to input physical and lifestyle information. The user enters data such as age, gender, experience, recent injuries, next shift time, and daily stress level into the input form, and presses the send button to send it to the server. Based on the input data, the server obtains data to generate the user's basic profile.

[1346] Step 2:

[1347] The server receives the user's input data and stores it in a database. The stored data becomes input data for calling the generative AI model. The database uses SQL or NoSQL, allowing for data storage and high-speed search.

[1348] Step 3:

[1349] The server invokes a generative AI model based on the received data. The generative AI model analyzes the data to create a profile specific to the user. This analysis uses machine learning libraries (e.g., TensorFlow and PyTorch). The profile includes the user's physical information, lifestyle information, and health status.

[1350] Step 4:

[1351] The server uses generative AI models to generate personalized training and nutrition plans. For example, it might recommend 10 minutes of light exercise and a high-protein meal based on the user's age, gender, heart rate, and exercise habits. These plans are then stored in a database on the server.

[1352] Step 5:

[1353] The server references the user's profile and generates recovery programs and sleep recommendations based on the user's injury status and training fatigue level. For example, if a user has a sprained ankle, the server might suggest "20 minutes of cooling and stretching" and "7-8 hours of sleep." These recommendations are also generated by an AI model and stored in the server's database.

[1354] Step 6:

[1355] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level, such as "take five minutes of deep breaths." This information is analyzed by a generative AI model to generate suggestions.

[1356] Step 7:

[1357] The generated training plan, nutrition plan, recovery program, sleep suggestions, and relaxation suggestions are sent to the user's device, which then visually displays this information. Feedback is provided via a smartphone or smart glasses, for example, and the user can review and put the information into practice.

[1358] Step 8:

[1359] A user's wearable device (such as a smartwatch or fitness tracker) continuously collects real-time health data, such as heart rate, stress level, and sleep data, and automatically transmits this data to a server. This data is then used as input for generative AI models, providing updated feedback.

[1360] Step 9:

[1361] The server continuously receives health data from the user and updates feedback based on the latest generative AI model. This allows the user to constantly receive optimal advice based on their health status. For example, a notification might be sent saying, "Your heart rate has been high recently. Don't forget to drink water at the water cooler."

[1362] Through the above processing steps, the user can receive individually optimized feedback in real time, enabling them to efficiently manage their own health.

[1363] 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.

[1364] The system of the present invention analyzes a user's physical information, lifestyle information, and emotions, and based on that information, provides optimal training plans, nutrition plans, recovery programs, sleep suggestions, and even mental care. This system is realized by exchanging information between the terminal, server, and user's device, and providing individually optimized advice.

[1365] Entering user information

[1366] The user inputs information using the terminal.

[1367] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. In addition, the user provides emotional information to the emotion engine using facial recognition or voice input. After completing the input, the user presses the send button to send this information to the server.

[1368] User data analysis

[1369] The server receives the user data

[1370] The server receives the user information and emotion information sent from the device and stores them in a database, thereby aggregating the user's physical information, lifestyle information, and emotion information.

[1371] The server analyzes user data and emotional information to create a profile

[1372] The server then invokes the generative AI model and emotion engine to analyze the received data, and a personalized profile is created based on the results of the analysis. This profile contains the information needed to generate training and nutrition plans.

[1373] Generate training and nutrition plans

[1374] The server generates the optimal training plan

[1375] Based on the user profile and emotional information, the server uses an AI model to generate a personalized training plan that includes specific training instructions tailored to the user's characteristics, goals, and current emotional state.

[1376] The server generates the nutrition plan

[1377] The server also generates a nutrition plan based on the user profile and emotional information, taking into account nutritional balance and timing of intake. This plan indicates what meals the user should eat and when.

[1378] The server sends the training plan and nutrition plan to the device and displays it.

[1379] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[1380] Providing recovery menus and sleep suggestions

[1381] The server generates a recovery program

[1382] The server takes into account the user's injury information, fatigue level, and emotional state, and uses an AI model and emotion engine to generate a recovery program to support recovery, including specific menus to help with injury recovery and muscle fatigue reduction.

[1383] The server generates sleep suggestions

[1384] The server generates recommendations for getting a good night's sleep based on the user's lifestyle and emotional information, including recommendations for bedding selection and pre-sleep relaxation routines.

[1385] The server sends and displays recovery programs and sleep suggestions on the device.

[1386] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[1387] Mental care support

[1388] The server uses an emotion engine to analyze the user's emotions.

[1389] The emotion engine analyzes the emotion information provided by the user and recognizes the current emotional state.

[1390] Servers provide mental health care

[1391] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information. When the user inputs a question or request for advice from the device, the server generates an appropriate answer and returns it.

[1392] As a concrete example, consider a 25-year-old male amateur basketball player using the system. He sprained his ankle during a recent practice, and his next game is two weeks away. He enters his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety."

[1393] in this case,

[1394] Training Plan: A light training menu is generated. Upper body training and water exercises are recommended to avoid straining the ankles. The Emotion Engine also adds training to reduce anxiety.

[1395] Nutrition plan: Protein-rich meals and anti-inflammatory foods are recommended, along with mental health foods to reduce anxiety.

[1396] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[1397] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[1398] Mental care: Meditation and deep breathing exercises are recommended for anxiety, and support is provided via chat.

[1399] In this way, this system provides comprehensive support based on the user's multifaceted data and emotional information, helping athletes to perform at their best.

[1400] The processing flow will be explained below.

[1401] Step 1:

[1402] The user inputs information using the terminal.

[1403] The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. The emotion engine also uses facial recognition and voice input to provide emotional information. Once input is complete, the user presses the send button to send the information to the server.

[1404] Step 2:

[1405] The server receives the user data

[1406] The server receives the user information and emotion information sent from the device and stores them in a database, thereby aggregating the user's physical information, lifestyle information, and emotion information.

[1407] Step 3:

[1408] The server analyzes user data and emotional information to create a profile

[1409] The server then invokes the generative AI model and emotion engine to analyze the received data, and creates a personalized profile for the user based on the results of this analysis. This profile contains the information needed to generate training and nutrition plans.

[1410] Step 4:

[1411] The server generates the optimal training plan

[1412] The server uses an AI model to generate a personalized training plan based on the user's profile and emotional information. The emotional engine adjusts the training content according to the user's current emotional state. The generated plan includes specific training instructions tailored to the user's characteristics and goals.

[1413] Step 5:

[1414] The server generates the nutrition plan

[1415] The server also generates a nutrition plan based on the user profile and emotional information, taking into account nutritional balance and timing. This plan indicates what meals the user should eat and when. The emotional engine also provides nutritional instructions based on the user's emotional state.

[1416] Step 6:

[1417] The server sends the training plan and nutrition plan to the device and displays it.

[1418] The server sends the generated training and nutrition plans to the user's device, which displays these plans to the user so that the user can put them into practice.

[1419] Step 7:

[1420] The server generates a recovery program

[1421] The server takes into account the user's injury information, fatigue level, and emotional state, and uses an AI model and emotion engine to generate a recovery program to support recovery, including specific menus to help with injury recovery and reduce muscle fatigue.

[1422] Step 8:

[1423] The server generates sleep suggestions

[1424] The server generates recommendations for getting a good night's sleep based on the user's lifestyle and emotional information, including recommendations for bedding selection and pre-sleep relaxation routines.

[1425] Step 9:

[1426] The server sends and displays recovery programs and sleep suggestions on the device.

[1427] The server sends the generated recovery program and sleep suggestions to the user's device, which displays them to the user and provides guidance on how to implement them.

[1428] Step 10:

[1429] The server uses an emotion engine to analyze the user's emotions.

[1430] The server uses an emotion engine to analyze the emotional information provided by the user and recognize their current emotional state, and adjusts the suggestions accordingly.

[1431] Step 11:

[1432] Servers provide mental health care

[1433] The server suggests relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information. When the user inputs a question or request for advice from the device, the server generates an appropriate answer and returns it.

[1434] Step 12:

[1435] Users implement suggestions and provide feedback

[1436] Users implement the training, nutrition, recovery program, sleep, and mental care programs they receive through their device, and then send feedback from their device to the server, which allows the AI ​​to make further improvements.

[1437] This process allows users to receive personalized, comprehensive support to improve their physical and mental performance. As a concrete example, consider a 25-year-old male amateur basketball player using the system. He recently sprained his ankle during practice, and his next game is two weeks away. He enters his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety."

[1438] in this case,

[1439] Training Plan: A light training menu is generated. Upper body training and water exercises are recommended to avoid straining the ankles. The Emotion Engine also adds training to reduce anxiety.

[1440] Nutrition plan: Protein-rich meals and anti-inflammatory foods are recommended, as well as mental health foods to reduce anxiety.

[1441] Recovery Program: Includes ankle recovery, with icing and stretching recommended.

[1442] Sleep Suggestions: Bedding selection and stretching routines are suggested to ensure quality sleep.

[1443] Mental care: Meditation and deep breathing exercises are recommended for anxiety, and support is provided via chat.

[1444] In this way, this system provides comprehensive support based on the user's multifaceted data and emotional information, helping athletes to perform at their best.

[1445] Example 2

[1446] 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."

[1447] Conventional training systems only consider a user's physical and lifestyle information, but are unable to reflect data such as their emotional state or daily stress level. This makes it difficult to provide training and nutritional plans that adequately reduce the user's psychological burden and risk of injury. Furthermore, comprehensive support for recovery programs, sleep recommendations, and mental health support is lacking. Furthermore, there is a need for a means to enable athletes to train more safely and efficiently by providing recovery programs that take into account the user's injury information and fatigue level.

[1448] 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.

[1449] In this invention, the server includes means for inputting a user's physical information and lifestyle information, means for inputting and analyzing the user's emotional information, means for receiving the user's information and analyzed emotional information and generating a training plan and nutrition plan, means for presenting the generated training plan, nutrition plan, recovery program, and sleep suggestions to the user, means for suggesting appropriate relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information, and means for analyzing the emotional information and providing mental care support in chat format. This makes it possible to comprehensively analyze a user's physical information, lifestyle information, and emotional information and provide individually optimized training plans, nutrition plans, recovery programs, sleep suggestions, and mental care.

[1450] "User's physical information" refers to information such as the user's age, gender, athletic history, and recent injuries.

[1451] "Lifestyle information" refers to information such as a user's daily habits, the date of their next game, and their daily stress level.

[1452] "Emotion information" refers to information indicating the user's emotional state obtained through facial recognition or voice input.

[1453] "Training plan" refers to a plan that includes specific exercise instructions that are individually optimized and generated based on the user's physical and emotional information.

[1454] A "nutritional plan" refers to a plan that includes specific dietary instructions regarding nutritional balance and intake timing, generated based on the user's physical and emotional information.

[1455] A "recovery program" refers to a program that includes a specific menu to support recovery, which is generated taking into account the user's injury information and fatigue level.

[1456] "Sleep suggestions" refer to specific advice for getting quality sleep based on the user's lifestyle and emotional information.

[1457] "Generative AI model" refers to an artificial intelligence model that uses a user's physical, lifestyle, and emotional information as input to generate training plans, nutrition plans, recovery programs, and sleep suggestions.

[1458] "Emotion engine" refers to an analysis engine that analyzes the emotion information provided by the user and recognizes the current emotional state.

[1459] "Terminal" refers to a device used by a user to input information. Specifically, this includes smartphones, tablets, and personal computers.

[1460] "Server" refers to the device that receives, stores, and analyzes user-submitted information. It is also where the generative AI model and emotion engine run.

[1461] "Mental care support in chat format" refers to a support function that provides appropriate answers in text message format to users' questions and inquiries.

[1462] A "prompt" is an input sentence used to leverage a generative AI model to derive a specific answer or outcome.

[1463] The system of the present invention analyzes a user's physical information, lifestyle information, and emotions, and based on that, provides optimal training plans, nutrition plans, recovery programs, sleep suggestions, and even mental care. This system is realized by exchanging information between the terminal, server, and user's device, and providing individually optimized advice.

[1464] Entering user information

[1465] The user inputs information using the device. The user enters their physical and lifestyle information (e.g., age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. In addition, the user provides emotional information using facial recognition or voice input. When the user has completed input and pressed the send button, the device sends this information to the server.

[1466] Receiving and storing user data

[1467] The server receives the user information and emotion information sent from the terminal, and stores the received information in a database.

[1468] Analyzing user data and generating profiles

[1469] The server analyzes the incoming data using generative AI models and an emotion engine. Based on the analysis, a personalized profile is created for the user. This profile serves as the basis for generating training and nutrition plans.

[1470] Generate training and nutrition plans

[1471] The server generates an individually optimized training plan based on the user profile and emotional information. The generated training plan includes specific exercise instructions. Similarly, the server also generates a nutrition plan, which includes specific dietary instructions regarding nutritional balance and intake timing. The generated training plan and nutrition plan are sent to the device, which displays them to the user.

[1472] Providing recovery programs and sleep suggestions

[1473] The server generates a recovery program based on the user's injury information, fatigue level, and emotional state. This includes specific menus to support recovery. The server also generates sleep suggestions to help users get a good night's sleep. These suggestions include how to select bedding and a pre-sleep relaxation routine. The generated recovery program and sleep suggestions are sent to the device, which then displays them to the user.

[1474] Mental care support

[1475] The server uses an emotion engine to analyze the user's emotional information and recognize their current emotional state. Based on their daily stress level and emotional information, the server suggests relaxation techniques and mental conditioning methods. When the user inputs a question or request for advice on their device, the server generates and replies with an appropriate answer.

[1476] As a concrete example, consider the case where a 25-year-old male amateur basketball player uses the system. He recently sprained his ankle during practice, and his next game is two weeks away. He has entered his daily stress level as 5, and the emotion engine detects facial recognition that indicates "anxiety." In this case, the system operates as follows:

[1477] Training Plan

[1478] A light training menu is generated, encouraging upper body training and water exercises to avoid straining the ankles, and the emotion engine adds training to reduce anxiety.

[1479] Nutrition Plan

[1480] High-protein diets and anti-inflammatory foods are suggested, along with mental health foods to reduce anxiety.

[1481] Recovery Program

[1482] Ankle recovery is included, and icing and stretching are recommended.

[1483] sleep suggestions

[1484] To ensure quality sleep, recommendations are made on bedding selection and stretching routines.

[1485] Mental care

[1486] Meditation and deep breathing exercises are recommended to combat anxiety, and support is provided via chat.

[1487] An example prompt is, "A 25-year-old male amateur basketball player recently sprained his ankle, and his next game is in two weeks. He has entered his daily stress level as 5, and the emotion engine has given him a facial recognition result indicating 'anxiety.' Please generate the optimal training plan, nutrition plan, recovery program, sleep suggestions, and mental care for this case."

[1488] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1489] Step 1:

[1490] The user uses the device to input information. The user enters their physical and lifestyle information (age, gender, athletic history, recent injuries, next game date, daily stress level, etc.) into an input form on the device. They also provide emotional information through facial recognition and voice input. When the user has completed input and presses the send button, the device sends this information to the server.

[1491] Input: Physical information, lifestyle information, emotional information

[1492] Output: Sending information from the device to the server

[1493] Specific actions: The user enters information using the device's keyboard, provides emotion information using the facial recognition camera or microphone, and clicks the send button.

[1494] Step 2:

[1495] The server receives the user information and emotion information sent from the device and stores them in a database. At this point, the server aggregates all of the user's physical information, lifestyle information, and emotion information.

[1496] Input: User information and emotional information sent from the device

[1497] Output: Information stored in the database

[1498] What it does: The server receives the data using a network communication module and executes an "INSERT" query in a database such as MySQL to store the information.

[1499] Step 3:

[1500] The server analyzes the incoming data using generative AI models and an emotion engine. Based on the analysis, a personalized profile is created for the user, which contains the basis for generating training and nutrition plans.

[1501] Input: User information and emotion information stored in the database

[1502] Output: Analysis results and user profile

[1503] How it works: The server calls the generative AI model and performs analysis using the stored data as input. The emotion engine analyzes the emotional information, creates a profile based on both results, and stores it in the database.

[1504] Step 4:

[1505] The server generates a personalized training plan based on the user profile and emotional information. The generated training plan includes specific exercise instructions. Similarly, the server also generates a nutrition plan, including specific dietary instructions regarding nutritional balance and intake timing. This plan is sent to the device and displayed to the user.

[1506] Input: User profile and emotional information

[1507] Output: Training and nutrition plans

[1508] How it works: The server uses the generative AI model to generate training and nutrition plans, which are then sent to the device in JSON format, where the device interprets them and presents them to the user.

[1509] Step 5:

[1510] The server generates a recovery program based on the user's injury information, fatigue level, and emotional state. It also generates sleep suggestions to help the user get quality sleep. The recovery program includes specific menus to support recovery. The sleep suggestions include how to select bedding and suggestions for a pre-sleep relaxation routine. The generated recovery program and sleep suggestions are sent to the device and displayed to the user.

[1511] Input: User profile, injury information, fatigue level, emotional information

[1512] Output: Recovery program and sleep suggestions

[1513] How it works: The server uses the AI ​​model to generate recovery programs and sleep suggestions, which are then sent to the device, which displays them to the user.

[1514] Step 6:

[1515] The server uses an emotion engine to analyze the user's emotional information and recognize their current emotional state. Based on their daily stress level and emotional information, the server suggests relaxation techniques and mental conditioning methods. When the user inputs a question or request for advice on their device, the server generates and replies with an appropriate answer.

[1516] Input: User's emotional information and daily stress level

[1517] Output: Relaxation techniques and mental conditioning methods, answers to user questions

[1518] Specific operation: The server calls the emotion engine and analyzes emotional information. Based on the analysis results, it generates relaxation techniques and mental conditioning methods, and uses the chatbot function to answer questions from users in real time.

[1519] Through the above processing steps, the system comprehensively analyzes the user's multifaceted data and emotional information, and provides optimal training, nutrition, recovery, sleep, and mental care.

[1520] (Application example 2)

[1521] 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."

[1522] While conventional fitness and health management systems can provide advice based on personal physical and lifestyle information, it is difficult to input and display user information in real time in physical stores, and to comprehensively provide optimal training plans, nutrition plans, recovery programs, sleep suggestions, and mental care on the spot. This requires users to use different tools and devices individually, making it difficult to achieve high convenience.

[1523] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1524] In this invention, the server includes a means for inputting physical information and lifestyle information of a user, a means for receiving the user information and generating a training plan and a nutrition plan, a means for presenting the generated training plan and nutrition plan to the user, and a means for inputting and presenting the user information via a device installed in a physical store, thereby enabling comprehensive health management advice that responds to the user's needs in real time to be provided in the physical store.

[1525] "User's physical information" refers to data about the user's body, such as the user's age, gender, weight, height, BMI, muscle mass, body fat percentage, and health condition (pre-existing conditions, allergies, etc.).

[1526] "Lifestyle information" refers to data about a user's daily life, such as the user's daily activities and habits, work content and hours, sleep patterns, eating habits, and stress levels.

[1527] A "training plan" refers to a proposal for an exercise program that is individually optimized based on the user's physical information and lifestyle information, and includes specific exercise content, frequency, intensity, time, etc.

[1528] A "nutritional plan" refers to a personalized, optimized meal plan based on the user's physical and lifestyle information, including specific ingredients, cooking methods, nutrients to be consumed, and timing of intake.

[1529] "Devices installed in physical stores" refers to terminals or robots installed in fitness gyms or physical stores for health management purposes, and refers to devices that input and display user information and provide fitness advice.

[1530] A "recovery program" refers to a program designed to support recovery from injury or fatigue, and specifically includes icing, stretching, massage, rehabilitation exercises, etc.

[1531] "Sleep suggestions" refers to advice on how to get quality sleep, including how to select bedding, relaxation routines before sleep, and adjusting bedtime.

[1532] "Mental care" refers to advice and techniques to support the user's mental health, and specifically includes meditation, deep breathing, relaxation techniques, mental conditioning methods, etc.

[1533] MODE FOR CARRYING OUT THE INVENTION

[1534] To implement this invention, a user inputs their own physical and lifestyle information using a smartphone or a device installed in a physical store. The terminal then transmits this information to a server.

[1535] The server receives the user's input data and uses a generative AI model to generate personalized training and nutrition plans using machine learning libraries such as TensorFlow. The generated training and nutrition plans are then sent to the user's smartphone or in-store device, where they are presented to the user.

[1536] The server also generates recovery programs and sleep suggestions based on the user's physical and lifestyle information. This process continues to utilize the generative AI model, taking into account the user's injury information and fatigue level. The generated recovery programs and sleep suggestions are also sent to the user's device and displayed.

[1537] Furthermore, the system measures the user's stress level and analyzes the emotional information using an emotion engine. Based on the analysis results, it proposes relaxation techniques and mental conditioning methods, thereby providing comprehensive mental care.

[1538] Devices and robots installed in physical stores input and display user information in real time, allowing them to receive immediate fitness advice and health management support within the store.

[1539] For example, consider the case of a 25-year-old male amateur basketball player accessing the system using his smartphone. He sprained his ankle during a recent practice, and his next game is in two weeks. He enters his daily stress level as 5, and the emotion engine detects facial recognition indicating "anxiety." Based on this information, the server generates a personalized optimization plan as follows:

[1540] Training plan: Include upper body training and water exercises to avoid ankle strain.

[1541] Nutrition plan: A diet high in protein and anti-inflammatory foods is recommended.

[1542] Recovery Program: Includes ankle recovery menu, including suggested icing and stretching.

[1543] Sleep Suggestions: Includes suggestions for getting quality sleep.

[1544] Mental care: Meditation and deep breathing exercises are recommended for anxiety.

[1545] The system can provide highly accurate health management advice based on specific prompts (e.g., "A 25-year-old male amateur basketball player recently sprained his ankle, has a daily stress level of 5, and feels anxious. Based on this information, generate an individualized training plan, nutrition plan, recovery program, sleep suggestions, and mental health advice that are optimal for him.").

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] Users enter their physical and lifestyle information using their smartphone or a device installed in a physical store. This includes age, gender, weight, height, athletic history, recent injuries, next game date, stress level, etc. Emotional information is also provided through facial recognition and voice input. The entered data is temporarily stored on the device.

[1549] Step 2:

[1550] The device sends the user's input data, including physical information, lifestyle information, and emotional information, to the server, which then stores the received data in a database for analysis.

[1551] Step 3:

[1552] The server uses a generative AI model based on the received data to generate an individually optimized training plan. Specific data processing and calculations involve determining the appropriate exercise intensity and training content, taking into account the user's physical information (e.g., injury status) and emotional information (e.g., anxiety level). The generated training plan includes specific exercise menus, repetitions, and number of sets.

[1553] Step 4:

[1554] The server then uses the generative AI model to generate a nutrition plan based on the user's physical and lifestyle information. The data processing and calculations take into account meal timing and required nutrients (e.g., protein, vitamins). The generated nutrition plan includes recommended ingredients, meal contents, and intake timing.

[1555] Step 5:

[1556] The server generates a recovery program and sleep suggestions. The recovery program takes into account injury information and fatigue level, and uses an AI model to suggest specific icing and stretching methods. Sleep suggestions include how to select bedding and how to relax before bed. These data processing and calculations take into account the user's injury, fatigue level, and sleep patterns.

[1557] Step 6:

[1558] The server sends the generated training plan, nutrition plan, recovery program, and sleep suggestions to the user's device. The device receives this data and displays it to the user. The displayed content includes training menus, meal plans, recovery menus, and sleep advice.

[1559] Step 7:

[1560] The server uses an emotion engine to analyze the user's emotional information and generates relaxation techniques and mental care methods based on the obtained data. These include deep breathing and meditation techniques. The generated mental care advice is sent to the device and displayed to the user.

[1561] Step 8:

[1562] Devices and robots installed in physical stores collect user information in real time and provide generated health management advice on the spot. The devices and robots guide users and support them in implementing training and recovery programs.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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).

[1570] 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.

[1571] 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."

[1572] 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.

[1573] 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).

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] 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.

[1584] The following is further disclosed regarding the above embodiment.

[1585] (Claim 1)

[1586] a means for inputting user physical and lifestyle information;

[1587] means for receiving the user's information and generating a training plan and a nutrition plan;

[1588] means for presenting the generated training and nutrition plans to a user;

[1589] A system including:

[1590] (Claim 2)

[1591] 10. The system of claim 1, further comprising means for generating recovery programs and sleep suggestions based on the user's physical and lifestyle information.

[1592] (Claim 3)

[1593] 10. The system of claim 1, further comprising means for measuring a user's stress level and suggesting appropriate relaxation techniques and mental conditioning methods.

[1594] "Example 1"

[1595] (Claim 1)

[1596] a means for inputting user physical and lifestyle information;

[1597] means for receiving the user's information and creating a user profile using a generative AI model;

[1598] means for generating an individually optimized training and nutrition plan based on said user profile;

[1599] means for presenting the generated training and nutrition plans to a user;

[1600] A system including:

[1601] (Claim 2)

[1602] 10. The system of claim 1, further comprising means for generating recovery programs and sleep suggestions based on the user's physical and lifestyle information.

[1603] (Claim 3)

[1604] 10. The system of claim 1, further comprising means for measuring a user's stress level and suggesting appropriate relaxation techniques and mental conditioning methods.

[1605] "Application Example 1"

[1606] (Claim 1)

[1607] a means for inputting user physical and lifestyle information;

[1608] means for receiving the user's information and generating a training plan and a nutrition plan;

[1609] means for presenting the generated training and nutrition plans to a user;

[1610] means for collecting health information of a user and providing health management feedback;

[1611] A means of collecting data from wearable devices and using generative AI models to generate personalized reminders and notifications;

[1612] means for transmitting the collected data to a server and generating a customized plan;

[1613] A system including:

[1614] (Claim 2)

[1615] 10. The system of claim 1, further comprising means for generating recovery programs and sleep suggestions based on the user's physical and lifestyle information.

[1616] (Claim 3)

[1617] 10. The system of claim 1, further comprising means for measuring a user's stress level and suggesting appropriate relaxation techniques and mental conditioning methods.

[1618] "Example 2: Combining Emotion Engines"

[1619] (Claim 1)

[1620] a means for inputting user physical and lifestyle information;

[1621] means for receiving the user's information and generating a training plan and a nutrition plan;

[1622] means for presenting the generated training and nutrition plans to a user;

[1623] A means for inputting and analyzing user emotion information;

[1624] means for optimizing training and nutritional plans based on the analyzed emotional information;

[1625] means for generating a recovery program and sleep suggestions taking into consideration the user's injury information and fatigue level;

[1626] means for transmitting the generated training plan, nutrition plan, recovery program, and sleep suggestion to a terminal and displaying the same;

[1627] A system including:

[1628] (Claim 2)

[1629] 2. The system according to claim 1, further comprising means for suggesting appropriate relaxation techniques and mental conditioning methods based on the user's daily stress level and emotional information.

[1630] (Claim 3)

[1631] 2. The system according to claim 1, further comprising means for analyzing the emotional information provided by the user and providing mental care support in a chat format.

[1632] "Application example 2 when combining emotion engines"

[1633] (Claim 1)

[1634] a means for inputting user physical and lifestyle information;

[1635] means for receiving the user's information and generating a training plan and a nutrition plan;

[1636] means for presenting the generated training and nutrition plans to a user;

[1637] A means for inputting and submitting user information via a device installed in a physical store;

[1638] A system including:

[1639] (Claim 2)

[1640] 10. The system of claim 1, further comprising means for generating recovery programs and sleep suggestions based on the user's physical and lifestyle information.

[1641] (Claim 3)

[1642] 10. The system of claim 1, further comprising means for measuring a user's stress level and suggesting appropriate relaxation techniques and mental conditioning methods. [Explanation of symbols]

[1643] 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 physical and lifestyle information; means for receiving the user's information and generating a training plan and a nutrition plan; means for presenting the generated training and nutrition plans to a user; A system including:

2. The system of claim 1 , further comprising means for generating recovery programs and sleep suggestions based on the user's physical and lifestyle information.

3. 10. The system according to claim 1, further comprising means for measuring a user's stress level and suggesting appropriate relaxation techniques and mental conditioning methods.

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