system

Abstract

We provide the system. [Solution] A means for receiving and storing input data from athletes, Means for using a generative model to analyze the aforementioned input data and generate individual training programs and nutritional plans, Means for transmitting the generated training program and nutrition plan to the user's terminal, A system including means for receiving the training program and nutrition plan displayed on the terminal.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] [[ID=2,1]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Problems include a decline in the quality of training due to a shortage of sports trainers and a lack of support tailored to the individual needs of athletes. In particular, it is difficult to provide appropriate training programs, injury prevention, rehabilitation, tactical improvement, and psychological support across a wide range of levels from professional athletes to amateur athletes. By solving these problems, it is required to improve the performance of athletes and realize a safe training environment.

Means for Solving the Problems

[0005] The present invention is a system comprising means for receiving and storing input data of an athlete; means for using a generative model to analyze the input data and generate an individual training program and nutrition plan; means for transmitting the generated training program and nutrition plan to a user's terminal; and means for receiving the training program and nutrition plan displayed on the terminal. Furthermore, the system can receive and store the athlete's training data and health status, analyze this data to generate injury prevention advice and rehabilitation programs, and transmit and display them on the user's terminal.

[0006] Furthermore, the system can receive athlete performance data, analyze it to generate tactical advice and optimized tactical plans, and send them to the user's device for display. By providing multifaceted support to athletes throughout the entire system in this way, the problem of trainer shortages can be resolved, and high-quality training and support can be realized.

[0007] The term "athlete" refers to an individual who specializes in sports or physical activities.

[0008] "Input data" refers to all information provided to the system, including an athlete's physical characteristics, performance data, and health status.

[0009] "Receiving" refers to the act of a system taking in data and information provided by a user.

[0010] "Saving" refers to the act of storing received data on a recording medium such as a database.

[0011] "Analysis" refers to the process of thoroughly examining received data and extracting potential patterns and useful information.

[0012] A "generative model" refers to an artificial intelligence or machine learning algorithm used to generate individualized training programs and other support plans based on an athlete's input data.

[0013] A "training program" refers to a set of exercises and activities designed to improve an athlete's performance and help them achieve their goals.

[0014] A "nutrition plan" refers to a guideline for customized meals and supplements designed to improve an athlete's performance and maintain their health.

[0015] "Injury prevention advice" refers to instructions and recommendations aimed at preventing injuries that may occur during training or competition.

[0016] A "rehabilitation program" refers to a training and treatment plan aimed at accelerating the recovery of already-occurring injuries and restoring normal function.

[0017] "Performance data" refers to information that expresses an athlete's results and achievements in training and competitive activities as numerical values ​​or records.

[0018] "Tactical advice" refers to information that suggests specific tactics and movements that athletes or teams should take during a competition.

[0019] A "tactical optimization plan" refers to a plan for optimizing tactics and strategies in order to achieve effective performance in a competition.

[0020] "Users" refer to stakeholders such as athletes, their coaches, and trainers who use the system.

[0021] A "terminal" refers to a computer or smart device that a user uses to input data or receive generated programs and advice. [Brief explanation of the drawing]

[0022] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0023] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0024] First, let's explain the terminology used in the following explanation.

[0025] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0026] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0027] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0028] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0030] [First Embodiment]

[0031] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0032] As shown in Figure 1, the 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.

[0033] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0035] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0036] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0038] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0042] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0043] This invention is a system that personalizes training programs for sports athletes, providing injury prevention, performance optimization, tactical advice, and psychological support. The system uses a generative AI model to analyze the athlete's input data and generate optimal training plans and advice.

[0044] First, when a user uses this system, they input basic information such as their physical characteristics, performance data, and health status on their device. This information is then sent from the device to the server. The server stores the received data in a database and passes it to a generating AI model for analysis.

[0045] The server uses a generative AI model to generate optimal training programs and nutrition plans for individual athletes. For example, if an amateur runner inputs their height, weight, and past runtime data into the system, the generative AI model analyzes this data and proposes a three-times-a-week running program and a high-protein meal plan for that runner. This generated plan is sent from the server to the user's terminal for review and execution.

[0046] Next, athletes can input training data and health status into the system, enabling it to provide injury prevention advice and support for rehabilitation programs. When a basketball player inputs recent training data and knee pain, the server uses a generative AI model to suggest stretches and muscle-strengthening exercises to reduce stress on the knee. This rehabilitation plan is also sent to the user's device and used in actual rehabilitation.

[0047] Furthermore, the system provides tactical advice and optimization plans based on performance data for athletes in team sports and individual competitions. For example, when a soccer team inputs post-match data into the system, a generating AI model analyzes it and suggests formation changes or instructions for specific players in the next match. This tactical advice is then sent from the server to the coach's terminal.

[0048] The system also provides psychological support. When golfers input data on the pressure and stress they feel before a tournament, a generating AI model analyzes this data and provides guidance on breathing techniques and visualization exercises. This mental support advice is also sent from the server to the user's device, allowing players to use it before the tournament.

[0049] Furthermore, the system receives user training and performance data, evaluates its effectiveness, and provides feedback. For example, when a club tennis player inputs daily practice data and match results, the server analyzes it using a generative AI model, evaluates serve success rate and backhand accuracy, and provides feedback for further improvement. This feedback is then sent from the server to the user's device.

[0050] This system makes it possible to provide highly personalized support to a wide range of athletes, from amateurs to professionals. This addresses the modern challenge of trainer shortages and effectively supports athletes in improving their performance and maintaining their health.

[0051] The following describes the processing flow.

[0052] Specific process flow for generating personalized training programs

[0053] Step 1:

[0054] User: Sends a training program generation request to the system. Specifically, enters basic information such as name, age, gender, height, weight, past performance data, and health status into the terminal.

[0055] Step 2:

[0056] Terminal: Receives user input data and verifies that the data is in the correct format and includes all necessary fields. After verification, it sends the data to the server in the appropriate format.

[0057] Step 3:

[0058] Server: Receives data and saves it to the database. This creates a separate data entry for each athlete, which is then used for subsequent analysis.

[0059] Step 4:

[0060] Server: Passes stored data to the generating AI model. The generating AI model generates an optimal training program and nutrition plan based on the input data.

[0061] Step 5:

[0062] Server: Sends the generated training program and nutrition plan to the user's terminal. It is sent in the appropriate format, linked to the user ID.

[0063] Step 6:

[0064] Terminal: Displays the training program and nutrition plan received on the user's terminal. The user can review and implement this.

[0065] Specific procedures for injury prevention and rehabilitation support

[0066] Step 1:

[0067] User: Submit a rehabilitation support request to the system. Specifically, enter recent training data, pain symptoms, and past health history into the terminal.

[0068] Step 2:

[0069] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[0070] Step 3:

[0071] Server: Receives data and saves it to a database. This records the user's health history and training data.

[0072] Step 4:

[0073] Server: The server passes the aforementioned data to the generating AI model to generate injury prevention advice and rehabilitation programs. The model identifies high-risk movements and suggests necessary advice and rehabilitation exercises.

[0074] Step 5:

[0075] Server: Sends generated injury prevention advice and rehabilitation programs to the user's device. Customizes the content sent based on the user ID.

[0076] Step 6:

[0077] Terminal: Displays injury prevention advice and rehabilitation programs received on the user's terminal. The user then performs training and rehabilitation according to these instructions.

[0078] Specific processing flow for performance optimization and tactical advice

[0079] Step 1:

[0080] User: Enter post-competition performance data into the system. Specifically, enter detailed match results and practice data into the terminal.

[0081] Step 2:

[0082] Terminal: Receives user input data and verifies the data format. After verification, converts it to the appropriate format for transmission to the server.

[0083] Step 3:

[0084] Server: Receives performance data and saves it to the database. This allows for the accumulation of data for each team and individual.

[0085] Step 4:

[0086] Server: Passes data to the generated AI model for analysis. The model suggests improvements and new tactics based on the match data.

[0087] Step 5:

[0088] Server: Sends generated tactical advice and optimized tactical plans to the user's terminal. Provides specific instructions and strategies to the coach's terminal.

[0089] Step 6:

[0090] Terminal: Displays tactical advice and tactical optimization plans received on the user's terminal. Coaches and team members use this to prepare for the next competition.

[0091] Specific steps for providing psychological support and improving motivation

[0092] Step 1:

[0093] User: Inputs information for psychological support into the system. Specifically, they input details such as stress levels and psychological state into the terminal.

[0094] Step 2:

[0095] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[0096] Step 3:

[0097] Server: Receives data and saves it to the database. This allows psychological data to be recorded for each athlete.

[0098] Step 4:

[0099] Server: Provides data to the generation AI model, which then generates advice for psychological support and motivation improvement. The model suggests techniques for stress management and improving concentration.

[0100] Step 5:

[0101] Server: Sends generated psychological support advice to the user's terminal. Provides the most appropriate advice based on the user ID.

[0102] Step 6:

[0103] Terminal: Displays psychological support advice received on the user's terminal. The user then implements relaxation methods and motivation-boosting techniques based on this advice.

[0104] Specific process flow for data analysis and evaluation of training effectiveness

[0105] Step 1:

[0106] User: Input training data and performance data into the system. Specifically, input detailed information about training results and match results into the terminal.

[0107] Step 2:

[0108] Terminal: Receives user input data and verifies data consistency and accuracy. After verification, converts the data to an appropriate format for transmission to the server.

[0109] Step 3:

[0110] Server: Receives data and saves it to the database. This allows for comparison with past data.

[0111] Step 4:

[0112] Server: Provides data to the generated AI model and evaluates the effectiveness of the training. The model compares past data with new data to measure its effectiveness.

[0113] Step 5:

[0114] Server: Sends generated evaluation results and feedback to the user's terminal. Provides appropriate feedback and strategic advice based on the user ID.

[0115] Step 6:

[0116] Terminal: Displays evaluation results and feedback received on the user's terminal. The user uses this to adjust their training.

[0117] In this way, by providing athletes with multifaceted and high-quality support throughout the entire system, we can resolve the problem of trainer shortages and realize high-quality training and support.

[0118] (Example 1)

[0119] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0120] Traditional training systems for athletes only provide general training plans and fail to comprehensively offer personalized training plans and nutrition plans based on individual athletes' physical characteristics, performance data, and health status, as well as injury prevention advice, rehabilitation programs, tactical advice, and psychological support. As a result, athletes do not receive sufficient support for improving their performance or maintaining their health.

[0121] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0122] In this invention, the server includes means for inputting basic information, performance data, and health status of an athlete via a terminal; means for transmitting the input data to the server; means for receiving the transmitted data on the server and storing it in a database; means for passing the stored data to a generating AI model for analysis; means for generating individual training programs and nutrition plans using the generating AI model; means for transmitting the generated training programs and nutrition plans to a user's terminal; and means for receiving the training programs and nutrition plans displayed on the user's terminal. This enables the user to receive highly personalized support based on their individual physical characteristics and performance data.

[0123] An "athlete" is an individual who trains to improve their physical performance.

[0124] "Basic information" refers to physical characteristics and personal data about individual athletes, such as height, weight, and age.

[0125] "Performance data" refers to data related to an athlete's athletic performance, such as training frequency, runtime, and training records.

[0126] "Health status" refers to information about an athlete's health, such as fatigue, pain, and general malaise.

[0127] A "device" refers to an electronic device used by a user, such as a smartphone, tablet, or personal computer.

[0128] A "server" is a computer system that receives, stores, analyzes, and runs generative models on data.

[0129] A "database" is a system that allows a server to structurally store and manage data.

[0130] A "generative AI model" is an artificial intelligence model used to analyze input data and generate training programs, advice, and plans.

[0131] A "training program" is an individualized exercise plan designed to improve an athlete's physical performance.

[0132] A "nutrition plan" is a meal plan designed to optimize an athlete's health and performance.

[0133] "Injury prevention advice" refers to specific guidance for athletes to reduce strain during training and prevent injuries.

[0134] A "rehabilitation program" is an exercise and treatment plan designed to help athletes recover from injuries and pain.

[0135] "Tactical advice" refers to strategic guidance provided in team sports or individual competitions for the upcoming match or competition.

[0136] A "tactical optimization plan" is a tactical plan designed to maximize performance in a match or competition.

[0137] "Psychological support" refers to advice and training that helps athletes manage mental pressure and stress.

[0138] This invention is a system that provides personalized training programs, nutrition plans, injury prevention measures, rehabilitation programs, tactical advice, and psychological support for sports athletes. The system uses a generative AI model to analyze the athlete's basic information, performance data, and health status. The system primarily operates with three parties: a server, a terminal, and the user.

[0139] First, the user uses a device (smartphone, tablet, PC, etc.) to input the athlete's basic information (e.g., height, weight, age), performance data (e.g., runtime, training frequency), and health status (e.g., fatigue level, presence or absence of pain). The device then sends this data to the server in a structured format (e.g., JSON). A secure protocol (e.g., HTTPS) is used for this communication.

[0140] The server stores the received data in a database (e.g., MySQL® or PostgreSQL). The server checks the integrity of the data and saves only the data that passes validation. After that, the server passes the saved data to a generating AI model (e.g., OpenAI® GPT model) for analysis.

[0141] Examples of prompt statements used for analysis are as follows:

[0142] "When generating optimal training and nutrition plans for amateur runners, please consider the user's height, weight, and past runtime data as input data."

[0143] "Please suggest stretches and strengthening exercises to alleviate knee pain in a basketball player. Input data includes recent training data and pain location information."

[0144] "Analyze the post-match data of the soccer team and suggest formation changes and instructions for specific players for the next match."

[0145] Based on the analysis results obtained from the generating AI model, the server generates personalized training programs, nutrition plans, injury prevention advice, rehabilitation programs, tactical advice, and psychological support. For example, if an amateur runner inputs their height (170cm), weight (65kg), and past runtime data into the system, the server uses the generating AI model to suggest a three-times-a-week running program and a high-protein meal plan.

[0146] Next, the server sends the generated plan and advice to the user's device. The device receives this data and displays it within the app. For example, if a basketball player enters knee pain, the server sends a rehabilitation plan of stretches and muscle strengthening exercises, which is then displayed on the device. As a result, athletes can receive personalized feedback in real time, which they can use for their daily training and health management.

[0147] This system addresses the modern challenge of trainer shortages by providing advanced personalized support to a wide range of athletes, from amateurs to professionals, effectively assisting in improving athlete performance and maintaining health. Users can easily obtain personalized training plans and advice simply by entering specific data.

[0148] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0149] Step 1:

[0150] The user enters basic information into the device.

[0151] Users enter basic information such as height, weight, age, runtime, training frequency, and health status using a dedicated app on their device or a web form. The entered data is created in a structured format (e.g., JSON). This data will later be used as input for analysis.

[0152] Step 2:

[0153] The device sends data to the server.

[0154] The device sends the entered data to the server using a secure protocol (e.g., HTTPS). The input data is sent in JSON format, and the server receives it. Specifically, when the send button is pressed, the device's app sends an HTTP POST request to the server.

[0155] Step 3:

[0156] The server saves the data to the database.

[0157] The server parses the received JSON data and saves it to a database (e.g., MySQL or PostgreSQL). Before saving, it validates the data to remove incomplete or invalid data. For example, the server checks that the data is in the correct format (e.g., whether all required fields are filled in).

[0158] Step 4:

[0159] The server passes data to the generated AI model.

[0160] The server extracts the necessary data from the database and passes it to the AI ​​model. During this process, it generates prompts, formats the data, and then sends it. For example, it might pass data along with a prompt such as, "Generate an optimal training plan and nutrition plan for an amateur runner." The input data is provided in JSON format.

[0161] Step 5:

[0162] The server receives results from the generated AI model.

[0163] The generative AI model analyzes the provided prompt text and data to generate an optimal training plan, nutrition plan, and advice. The server receives these results and stores them in JSON format. Specifically, it receives the analysis results returned by the generative AI model as an HTTP response.

[0164] Step 6:

[0165] The server sends the results to the user's terminal.

[0166] The server sends the generated training plan and advice results to the user's device. The device receives this data and displays it within the app. For example, it applies the data received from the server to the calendar and notification functions of the app on the device.

[0167] Step 7:

[0168] The user checks and executes the results on their device.

[0169] Users can review training and nutrition plans generated on their devices and use them to guide their actual training and meal planning. Specifically, users check the plan in a particular section of the app and act according to the schedule. For example, they might receive push notifications about their daily training.

[0170] By clearly defining the specific processes performed at each step, it becomes possible to understand in detail how this system works and how it provides value to the user.

[0171] (Application Example 1)

[0172] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0173] There is a need for a system that maximizes the effectiveness of training for factory robot operators, supports injury prevention and improved work performance, and provides appropriate feedback and optimization in real time. Conventional training systems have struggled to provide personalized suggestions based on the individual health status and performance data of operators.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0175] In this invention, the server includes means for receiving and storing operator input data; means for using a generative model to analyze the input data and generate individual training programs and work plans; means for transmitting the generated training programs and work plans to a user's terminal; means for receiving training results and using a generative model to optimize the plan in real time; means for transmitting the optimized training programs and work plans to a user's terminal; and means for receiving the optimized training programs and work plans displayed on the terminal. This maximizes the effectiveness of operator training, enabling injury prevention and improved work performance.

[0176] "Operator input data" refers to information entered by factory robot operators regarding their physical characteristics, operational experience, work performance data, and health status.

[0177] A "generative model" is an AI system that includes algorithms for analyzing input data and generating individual training programs and work plans.

[0178] A "training program" is a set of specific training content and schedules created by a generative model that operators should undergo.

[0179] A "work plan" is a set of guidelines and procedures created by a generative model that specify what tasks an operator should perform.

[0180] "User terminal" refers to electronic devices such as smartphones, tablets, and head-mounted displays that operators use to view and execute training programs and work plans.

[0181] "Training outcomes" refer to data obtained as a result of operators performing training and tasks, including information on performance improvements and health improvements.

[0182] "Optimizing the plan in real time" means that the generative model continuously re-evaluates the training program and work plan based on the training results, and adjusts the content immediately as needed.

[0183] "Injury prevention advice" refers to suggestions from a generative model regarding specific actions and precautions that operators should take to prevent injuries.

[0184] A "rehabilitation program" is a training and exercise plan that injured operators should follow to recover.

[0185] "Work tactical advice" refers to suggestions regarding specific work methods and strategies, provided based on generative models.

[0186] An "optimization plan" is a set of improvement suggestions and specific procedures generated to enable operators to perform their tasks more efficiently.

[0187] Modes for carrying out the invention

[0188] Embodiments of this invention are shown below. The configuration of hardware and software for realizing the invention, as well as the data processing and calculation methods, will be described.

[0189] System Configuration

[0190] The system consists of the following main elements:

[0191] 1. User devices: Smartphones (iOS or Android®), head-mounted displays (HoloLens®, Oculus, etc.).

[0192] 2. Server: Backend for data storage and generation, and for data analysis using AI models.

[0193] 3. Software: Mobile application development kits (Flutter®, React Native, etc.), generative AI model backend (TENSORFLOW®, PyTorch).

[0194] Data flow

[0195] 1. Receiving and saving input data:

[0196] Operators use a terminal to input their physical characteristics, operational experience, work performance data, and health status.

[0197] This data is sent from the terminal to the server and stored in the database.

[0198] 2. Analysis using generative AI models:

[0199] The server passes the stored data to the generating AI model, which then performs analysis using Google Cloud AI and AWS Machine Learning.

[0200] The generative AI model generates individual training programs and work plans based on the analysis results.

[0201] 3. Submission of training program and work plan:

[0202] The server sends the generated training program and work plan to the user's terminal.

[0203] The user's device displays the transmitted content, which the operator can then review and execute.

[0204] 4. Input of training results and real-time optimization:

[0205] The operator enters the training results into a terminal, and this data is also sent to the server.

[0206] Based on the training results, the server generates an AI model in real time, which then re-evaluates and optimizes the training program and work plan.

[0207] 5. Injury prevention and rehabilitation program:

[0208] When an operator inputs information about their health status and injuries, the AI ​​model generates injury prevention advice and rehabilitation programs.

[0209] The server sends these programs to the user's terminal, where they are displayed.

[0210] 6. Advice and optimization of work tactics:

[0211] Once the operator inputs work data, the generated AI model produces work tactical advice and optimization plans.

[0212] This information is sent to the user's terminal via the server and displayed on the terminal.

[0213] Examples

[0214] Specific example 1:

[0215] Factory robot operators open an app and input their current work performance (operation speed, accuracy, failure rate, etc.). For example, if an operator inputs their arm fatigue level and the success rate of precision operations, the generated AI will use this information to suggest arm muscle training and rest plans.

[0216] Specific example 2:

[0217] When a new factory operator joins the company, they input their experience level and physical data through an app, and an AI generates an optimal training program based on that data. For example, beginners are provided with a step-by-step training plan that starts with basic operations and gradually increases in difficulty.

[0218] Example of a prompt

[0219] AI for training factory robot operators:

[0220] 1. Basic information: Height, weight, operating experience, work data

[0221] 2. Specific operational performance: accuracy, speed, failure rate

[0222] 3. Training plan generation: Recommendations for repetitive training of maneuvers, strength training, etc.

[0223] "What is the best training plan for when my arms are experiencing high levels of fatigue?"

[0224] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0225] Step 1:

[0226] Operators use a terminal to input their physical characteristics, operational experience, work performance data, and health status. The entered data is reviewed on the terminal and sent to the server. This step provides an interface for collecting operator data and sending it to the server.

[0227] Step 2:

[0228] The server stores the data transmitted by the operator in a database. The stored data is then used for analysis by a subsequent generative AI model. In this step, the server efficiently manages the data using a data storage system.

[0229] Step 3:

[0230] The server passes the stored operator data to the generating AI model and begins the analysis. The generating AI model generates individual training programs and work plans based on the input data. For example, it performs data analysis using TensorFlow or PyTorch. In this step, the AI ​​model analyzes the data and designs an appropriate training program and work plan.

[0231] Step 4:

[0232] The generated training program and work plan are sent from the server to the user's terminal. The terminal displays the received training program and work plan, making them ready for the operator to review and execute. In this step, the server sends data using the appropriate communication protocol, and the terminal receives it.

[0233] Step 5:

[0234] Operators conduct training and tasks, and input the results into a terminal. The entered training results are then sent back to the server. In this step, a user interface for operators to provide feedback is crucial.

[0235] Step 6:

[0236] The server receives the training results, and the generated AI model re-evaluates and optimizes the training program and work plan in real time. This optimization is performed based on the latest data from the operators. In this step, the AI ​​model has the ability to dynamically update and adapt the plan.

[0237] Step 7:

[0238] The server resends the optimized training program and work plan to the user's terminal. The terminal receives this, the operator reviews the content, and they can continue working according to the latest training and work plan. In this step, the efficiency of data communication between the server and the terminal is crucial.

[0239] Specific example

[0240] For example, a factory robot operator opens an app and inputs current work performance data (operating speed, accuracy, failure rate, etc.). This data is sent to a server, where a generated AI model analyzes it and suggests arm muscle training and rest plans. This information is then sent back to the terminal, and the operator can review and implement the plan, which is expected to improve performance.

[0241] Example of a prompt

[0242] AI for training factory robot operators:

[0243] 1. Basic information: Height, weight, operating experience, work data

[0244] 2. Specific operational performance: accuracy, speed, failure rate

[0245] 3. Training plan generation: Recommendations for repetitive training of maneuvers, strength training, etc.

[0246] "What is the best training plan for when my arms are experiencing high levels of fatigue?"

[0247] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0248] This invention combines a system that provides athletes with personalized training programs, injury prevention, rehabilitation, tactical advice, and psychological support with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out this invention are described below.

[0249] First, when a user uses the system, they input their physical characteristics, performance data, health status, and emotional data using a terminal. This data is sent from the terminal to the server. The server stores the received data in a database and passes it to a generative AI model and emotion engine for analysis.

[0250] The server uses a generative AI model to analyze the user's physical and performance data and generate personalized training programs and nutrition plans. Additionally, an emotion engine analyzes the user's emotional data to understand their current mental state and stress level. For example, if an amateur runner inputs their height, weight, past runtime data, and emotional data into the system, the generative AI model and emotion engine analyze this data to generate an appropriate training program, nutrition plan, and motivational advice for that runner. This generated plan and advice are then sent from the server to the user's terminal, where they can review and implement it.

[0251] To support injury prevention and rehabilitation, the system allows users to input training data, health status, and emotional data. The generating AI model then generates injury prevention advice and rehabilitation programs, while the emotional engine provides advice aimed at mental stability. For example, if a basketball player inputs recent training data, knee pain, and anxiety, the generating AI model suggests stretches and strength training exercises to reduce knee stress, while the emotional engine proposes relaxation methods to reduce stress. This rehabilitation plan and mental support advice are sent to the user's device and effectively utilized in actual rehabilitation.

[0252] Regarding tactical advice, when a soccer team inputs post-match data and emotional data into the system, a generative AI model analyzes the performance data and suggests tactical improvements, while an emotional engine supports the team's overall tactical adaptation based on player motivation and teamwork. This allows new tactics to be executed more effectively. This tactical advice is sent from the server to the coach's terminal and displayed along with specific tactical details.

[0253] By using an enhanced system for psychological support, feedback tailored to the user's mental state and emotions is provided. For example, if a golfer inputs data on the pressure and stress they feel before a tournament, a generative AI model generates specific training and advice for mental support, and an emotion engine suggests relaxation techniques and imagery training. This helps to promote the player's psychological stability. This support is sent to the user's terminal via the server and can be used by the player before the tournament.

[0254] Finally, regarding data analysis and evaluation of training effectiveness, emotional data is analyzed along with training and performance data. This allows for the evaluation of not only the athlete's training effectiveness but also their psychological stability. For example, when a club tennis player inputs daily practice data, match results, and emotional data, a generative AI model evaluates the effectiveness of the training, and an emotional engine provides feedback that also takes into account the state of mental health. This feedback is sent from the server to the user's terminal, and the user uses it to adjust their training and mental care.

[0255] This system provides comprehensive support for improving athletes' physical and mental performance. By combining it with an emotional engine, it becomes possible to provide even more personalized support than before, and is expected to significantly improve the quality of athlete training, injury prevention, rehabilitation, tactical improvement, and psychological support.

[0256] The following describes the processing flow.

[0257] Personalized training program generation and the specific processing flow of the emotion engine

[0258] Step 1:

[0259] User: Sends a training program generation request to the system. Specifically, enters name, age, gender, height, weight, past performance data, health status, and emotional data (e.g., stress level, emotional score) into the terminal.

[0260] Step 2:

[0261] Terminal: Receives user input data and verifies that the data is in the correct format and all required fields are present. After verification, it sends the data to the server in the appropriate format.

[0262] Step 3:

[0263] Server: Receives data and saves it to the database. This creates a unique data entry for each user, which is then used for subsequent analysis.

[0264] Step 4:

[0265] Server: Passes stored data to the generating AI model and emotion engine. The generating AI model generates an optimal training program and nutrition plan based on the input physical and performance data.

[0266] Step 5:

[0267] Server: The emotion engine analyzes the user's emotional data and generates motivational advice based on their mental state. For example, if the user shows a high stress level, it suggests relaxation techniques or exercises to reduce stress.

[0268] Step 6:

[0269] Server: Sends the generated training program, nutrition plan, and emotional engine-driven advice to the user's device. It links the information to the user ID and sends it in the appropriate format.

[0270] Step 7:

[0271] Terminal: Displays training programs, nutrition plans, and emotional engine advice received on the user's terminal. The user can review and implement these.

[0272] Specific procedures for injury prevention and rehabilitation support

[0273] Step 1:

[0274] User: Send a rehabilitation support request to the system. Specifically, input recent training data, health status, and emotional data (e.g., pain level, emotional score, etc.) into the terminal.

[0275] Step 2:

[0276] Terminal: Receive the user's input data and verify the data accuracy. After verification, send it to the server in an appropriate format.

[0277] Step 3:

[0278] Server: Save the received data in the database. This records the health history and training data.

[0279] Step 4:

[0280] Server: Pass the data to the generated AI model to generate injury prevention advice and a rehabilitation program. The model identifies high-risk movements and proposes necessary injury prevention measures.

[0281] Step 5:

[0282] Server: The emotion engine analyzes the user's emotional data and generates advice aimed at mental stability. For example, if the user shows signs of anxiety, propose specific relaxation methods to reduce that anxiety.

[0283] Step 6:

[0284] Server: Send the generated injury prevention advice and rehabilitation program and the advice from the emotion engine to the user's terminal. Customize the sent content based on the user ID.

[0285] Step 7:

[0286] <第0000905号>Terminal: Displays injury prevention advice, rehabilitation programs, and advice from the emotion engine received on the user's terminal. The user conducts training and rehabilitation according to this.

[0287] Flow of specific processing for performance optimization and tactical advice

[0288] Step 1:

[0289] User: Inputs performance data and emotion data after the competition into the system. Specifically, details such as the results of the game, data during practice, as well as emotion scores and stress levels are input.

[0290] Step 2:

[0291] Terminal: Receives the user's input data, checks the data format. After confirmation, it sends it to the server in the appropriate format.

[0292] Step 3:

[0293] Server: Saves the received performance data and emotion data in the database. In this way, data for each team and each individual is accumulated.

[0294] Step 4:

[0295] Server: Passes the data to the generated AI model, analyzes the performance, and generates tactical improvement points and a tactical optimization plan. For example, if a specific player's performance is declining, identify the cause and propose countermeasures.

[0296] Step 5:

[0297] Server: The emotion engine analyzes the data and generates tactical advice considering the motivation and mental state of the players. For example, if the stress level of the whole team is increasing, propose leadership training and team-building exercises.

[0298] Step 6:

[0299] Server: Transmit the generated tactical advice and the tactical optimization plan to the user's terminal. Provide specific instructions and strategies to the coach's terminal.

[0300] Step 7:

[0301] Terminal: Display the tactical advice and the tactical optimization plan received by the user's terminal. Based on this, the coach prepares for the next competition.

[0302] Specific process flow for psychological support and motivation improvement

[0303] Step 1:

[0304] User: Input information for psychological support into the system. Specifically, input details such as stress level, emotion score, and recent psychological state.

[0305] Step 2:

[0306] Terminal: Receive the user's input data, confirm the accuracy of the data, and convert it into an appropriate format for transmission to the server.

[0307] Step 3:

[0308] [[ID=4​​​​​​​​​​​Step 5:

[0312] Server: The emotion engine analyzes data and provides relaxation methods and motivation-boosting techniques tailored to the user's psychological state.

[0313] Step 6:

[0314] Server: Sends generated psychological support advice to the user's terminal. Provides the most appropriate advice based on the user ID.

[0315] Step 7:

[0316] Terminal: Displays psychological support advice received on the user's terminal. The user then implements relaxation methods and motivation-boosting techniques based on this advice.

[0317] Specific process flow for data analysis and evaluation of training effectiveness

[0318] Step 1:

[0319] User: Inputs training data, performance data, and emotional data into the system. Specifically, this involves detailed input of training results, match results, and emotional data (e.g., emotional score, stress level, etc.).

[0320] Step 2:

[0321] Terminal: Receives user input data and verifies data consistency and accuracy. After verification, sends it to the server in the appropriate format.

[0322] Step 3:

[0323] Server: Receives data and saves it to the database. This allows for comparison with past data.

[0324] Step 4:

[0325] Server: Provides data to the generated AI model and evaluates the effectiveness of the training. The model compares past data with new data to measure the effectiveness of the training.

[0326] Step 5:

[0327] Server: The emotion engine analyzes the data and generates comprehensive feedback, including the user's psychological stability. For example, it presents not only the effectiveness of the training but also the user's mental health status.

[0328] Step 6:

[0329] Server: Sends generated evaluation results and feedback to the user's terminal. Provides appropriate feedback and strategic advice based on the user ID.

[0330] Step 7:

[0331] Terminal: Displays evaluation results and feedback received on the user's terminal. The user uses this to adjust their training and mental care.

[0332] In this way, by providing athletes with multifaceted, high-quality support throughout the entire system, the problem of trainer shortages can be resolved, and high-quality training and support can be realized. By combining this with an emotional engine, even more precise individualized support becomes possible, effectively supporting improved athletic performance and psychological stability.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0335] Conventional athlete support systems focused on providing physical training programs and nutritional plans, but lacked an element of emotional support, failing to address the user's mental state and stress levels. As a result, training effectiveness, injury prevention, rehabilitation processes, and tactical advice were sometimes not fully realized. This invention aims to solve these problems and provide comprehensive support for athletes' physical and mental performance.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] In this invention, the server includes means for receiving and storing user input data, means for using a generative model and an emotion engine to analyze the input data and generate individual training programs, nutrition plans, and emotional support advice, and means for transmitting the generated training programs, nutrition plans, and emotional support advice to the user's terminal. This makes it possible to achieve not only improved physical performance of athletes but also mental stability and stress management simultaneously.

[0338] A "user" is a person who uses the system to input their physical characteristics, performance data, health status, and emotional data.

[0339] "Input data" refers to information that users provide to the system, such as physical characteristics, performance data, health status, and emotional data.

[0340] A "generative model" is an artificial intelligence model that analyzes a user's physical characteristics and performance data to generate individualized training programs and nutrition plans.

[0341] An "emotion engine" is software that analyzes a user's emotional data to understand their mental state and stress level, and then generates advice.

[0342] A "training program" is an individualized exercise plan designed based on the user's physical characteristics and performance data.

[0343] A "nutrition plan" is a plan that provides instructions on the optimal diet and nutrient intake methods based on the user's physical characteristics and performance data.

[0344] "Emotional support advice" refers to advice that provides mental support, such as relaxation methods and motivation-enhancing techniques, based on the user's emotional data.

[0345] "Injury prevention advice" is advice that analyzes the user's training data and health status and suggests measures to reduce the risk of injury.

[0346] A "rehabilitation program" is a program that directs injured users through rehabilitation activities aimed at their return to activity.

[0347] "Performance data" refers to data on exercise results recorded by users during sports or training.

[0348] "Tactical advice" is advice that analyzes the user's performance data and suggests tactical improvements and new strategies in the game.

[0349] A "tactical optimization plan" is a specific action plan based on tactical advice, designed to maximize team and individual performance.

[0350] A "terminal" is a device used by users to input data and receive training programs, nutrition plans, and advice from the server.

[0351] A "server" is a system that receives, stores, and analyzes data from users, and generates training programs and advice.

[0352] This invention is a system that provides personalized training programs, nutritional plans, and emotional support advice based on user input data. The system mainly consists of a server, terminals, and a generative AI model and emotion engine.

[0353] Hardware and software configuration:

[0354] 1. Terminal

[0355] Hardware: Input devices such as smartphones, tablets, and personal computers.

[0356] Software: A dedicated application or web browser is used to input user data and communicate with the system.

[0357] 2. Server

[0358] Hardware: A high-performance server computer.

[0359] Software: Applications that integrate database management systems (e.g., MySQL, PostgreSQL) and analysis engines.

[0360] 3. Generative AI Models

[0361] Software: Uses machine learning algorithms (e.g., TensorFlow, PyTorch) to analyze user data and generate training programs and nutrition plans.

[0362] 4. Emotional Engine

[0363] Software: Uses emotion analysis programs (e.g., Affectiva, IBM Watson®) to analyze user emotion data and generate emotional support advice.

[0364] System processing description:

[0365] Users input their physical characteristics, performance data, health status, and emotional data using a device. This data is sent from the device to a server, which stores the received data in a database. The stored data is then passed to a generative AI model and an emotion engine for analysis.

[0366] The generative AI model generates personalized training programs and nutrition plans based on the user's physical and performance data. Meanwhile, the emotional engine analyzes mental state and stress levels based on emotional data, providing advice on relaxation methods and motivation enhancement.

[0367] For example, the generative AI model performs analysis using the following specific prompt:

[0368] User's height: 175cm

[0369] User's weight: 70kg

[0370] Past runtime data: 5km 30 minutes

[0371] Emotional data: High stress level

[0372] Generate a training program and nutrition plan tailored to the user.

[0373] Specific example:

[0374] Example 1: In the case of an amateur runner

[0375] Input data: Height 175cm, weight 70kg, runtime data for the past week, high stress level.

[0376] Output of the generated AI model: Interval training, 3 times a week.

[0377] Emotional engine output: Deep breathing and relaxation techniques for motivation enhancement.

[0378] Example 2: In the case of a basketball player

[0379] Input data: Recent training data, knee pain, anxiety.

[0380] Output of the generated AI model: Stretching and muscle strengthening exercises to reduce stress on the knees.

[0381] Emotional Engine Output: Meditation Methods for Stress Reduction.

[0382] The generated training program, nutrition plan, and emotional support advice are sent from the server to the user's terminal, where the user reviews and implements this information. This entire process allows the user to improve their physical and mental performance.

[0383] This system is extremely useful for athletes because it not only optimizes training but also provides injury prevention, rehabilitation, and tactical advice.

[0384] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0385] Step 1:

[0386] The user enters input data using a terminal.

[0387] Operation: The user opens a dedicated application and enters their physical characteristics (e.g., height, weight), performance data (e.g., runtime data), health status (e.g., knee pain), and emotional data (e.g., stress level). They then review the entered data and press the submit button.

[0388] Input: Physical characteristics, performance data, health status, emotional data

[0389] Output: Input data (data formatted in JSON format, etc.)

[0390] Step 2:

[0391] The terminal sends the input data to the server.

[0392] Operation: When the send button on the device is pressed, the input data is sent to the server as an HTTP request. An internet connection is required for transmission.

[0393] Input: Input data (data formatted in JSON format, etc.)

[0394] Output: Input data sent to the server (HTTP request)

[0395] Step 3:

[0396] The server receives the input data and saves it to the database.

[0397] Operation: The server retrieves input data from received HTTP requests and stores it in the database. Each piece of data is stored in the database for each user.

[0398] Input: Input data extracted from the HTTP request

[0399] Output: User data stored in the database

[0400] Step 4:

[0401] The server passes the stored data to the generating AI model and emotion engine.

[0402] Operation: The server retrieves the necessary data from the database and converts it into a data format for passing to the generating AI model and emotion engine. This may involve using formats such as Python's DataFrame.

[0403] Input: User data stored in the database

[0404] Output: Data to be passed to the generative AI model and emotion engine.

[0405] Step 5:

[0406] The generative AI model generates training programs and nutrition plans.

[0407] Operation: The generative AI model analyzes the user's physical characteristics and performance data to generate a personalized training program and nutrition plan. An example using prompts is shown below:

[0408] User's height: 175cm

[0409] User's weight: 70kg

[0410] Past runtime data: 5km 30 minutes

[0411] Please generate a training program and nutrition plan tailored to this user.

[0412] Input: User's physical characteristics, performance data (in prompt format)

[0413] Output: Individualized training programs and nutrition plans

[0414] Step 6:

[0415] The emotion engine analyzes emotional data and generates advice on relaxation methods and motivation enhancement.

[0416] Operation: The emotion engine analyzes the user's emotional data to understand their current mental state and stress level. Based on the analysis results, it generates specific advice on relaxation methods and ways to improve motivation.

[0417] Input: User sentiment data

[0418] Output: Relaxation methods and motivation-boosting advice

[0419] Step 7:

[0420] The server sends the generated training program, nutrition plan, and emotional advice to the user's device.

[0421] Operation: The server compiles the analysis results obtained from the generated AI model and emotion engine and sends them to the user's terminal. This transmission is also done via HTTP response.

[0422] Input: Individual training programs, nutrition plans, relaxation methods, and motivational advice.

[0423] Output: Analysis results (HTTP response) sent to the user's terminal.

[0424] Step 8:

[0425] Users can view analysis results on their devices and receive training and mental care based on those results.

[0426] Operation: The user opens a dedicated application on their device and reviews the training program, nutrition plan, and emotional support advice sent from the server. After reviewing, they proceed to implement the training and mental care.

[0427] Input: Analysis results sent from the server (training program, nutrition plan, relaxation methods, motivation improvement advice)

[0428] Output: User action plan (training implementation, mental care implementation)

[0429] (Application Example 2)

[0430] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0431] Traditional training systems for athletes could provide physical training programs and nutritional plans, but they failed to consider the user's mental state and emotional data. This resulted in a lack of mental support, hindering the achievement of optimal training results. Furthermore, the lack of mental support in rehabilitation and tactical advice made it difficult to improve the overall performance of athletes.

[0432] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0433] In this invention, the server includes means for receiving and storing input data of athletes; means for using a generative model to analyze the input data and generate individual training programs and nutrition plans; means for transmitting the generated training programs and nutrition plans to the user's terminal; means for using an emotion engine to analyze the user's emotional data and provide mental support; means for transmitting the mental support to the user's terminal; and means for receiving the mental support. This makes it possible to provide comprehensive training programs and support that take into account not only the user's physical health but also their mental health.

[0434] "Input data" refers to information that athletes provide to the system, such as physical characteristics, performance data, health status, and emotional data.

[0435] A "generative model" is an artificial intelligence algorithm that analyzes received input data to generate individual training programs, nutrition plans, injury prevention advice, rehabilitation programs, tactical advice, and tactical optimization plans.

[0436] An "emotion engine" is an analytical engine that analyzes users' emotional data and provides emotional support.

[0437] A "training program" is a plan of exercises and workouts aimed at improving an athlete's performance.

[0438] A "nutrition plan" is a guideline for meals and supplements tailored to an athlete's physical condition and goals.

[0439] "Injury prevention advice" refers to specific instructions and recommendations for athletes to avoid injuries.

[0440] A "rehabilitation program" is a training and treatment plan for athletes to recover from injuries.

[0441] "Tactical advice" refers to instructions and recommendations for athletes or teams to effectively execute tactics in competition.

[0442] A "tactical optimization plan" is a specific plan for optimizing a team's tactics based on match and competition data.

[0443] "User's device" refers to electronic devices such as smartphones and tablets used by athletes.

[0444] "Mental support" refers to assistance aimed at promoting the psychological stability of athletes, such as relaxation and stress management.

[0445] This invention is a system that provides athletes with personalized training programs, injury prevention, rehabilitation, tactical advice, and psychological support. The system is implemented by combining an emotion engine that recognizes the user's emotions with a generative AI model.

[0446] Initially, when a user uses the system, they input their physical characteristics, performance data, health status, and emotional data using a device such as a smartphone. This data is then sent from the device to the server. The server stores the received data in a database and passes it to a generative AI model and emotion engine for analysis.

[0447] Next, the server uses a generative AI model to analyze the user's physical and performance data and generate a personalized training program and nutrition plan. Additionally, the emotion engine analyzes the user's emotional data to understand their current mental state and stress level.

[0448] The generated AI model and emotion engine analyze the user's data, and the resulting training program, nutrition plan, and emotional support content are then sent back from the server to the user's device and displayed there. This allows the user to receive specific training programs and emotional support.

[0449] For example, when a gym user accesses the system and inputs their current physical and emotional data, the generative AI model creates a training program, and the emotional engine provides mental support. For instance, they might receive advice such as, "Your stress level is high, so let's focus on stretching today to help you relax."

[0450] The hardware used includes smartphones, servers, and databases. The software includes generative AI models, emotion engines, and frontends (applications that run on the user's device). Specifically, it utilizes server-side APIs using Python and Flask, the EmotionEngine library, and AI model libraries.

[0451] Examples of prompt messages include the following:

[0452] "Based on the user's health and emotional data, we will provide personalized training programs and mental support. Please generate specific advice based on the following data."

[0453] Height: 175cm

[0454] Weight: 65kg

[0455] Performance data: [10, 15, 12]

[0456] Health condition: Good

[0457] Emotional data: {'happiness': 0.8, 'stress': 0.2}''

[0458] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0459] Step 1:

[0460] Users input physical characteristics, performance data, health status, and emotional data using a smartphone or other device. This allows the system to collect necessary information from the user and prepare it for further processing. Input includes height, weight, historical runtime data, and emotional status (happiness level, stress level, etc.).

[0461] Step 2:

[0462] The terminal sends the data entered by the user to the server. The server stores the received data in a database. This ensures that the data is securely managed and ready for analysis. The data stored in the database includes the user ID and various input data.

[0463] Step 3:

[0464] The server uses a generative AI model to analyze stored physical and performance data and generate an optimal training program and nutrition plan for the user. The analysis employs machine learning algorithms to evaluate current data in comparison to historical data. The output is a individually customized training program and nutrition plan.

[0465] Step 4:

[0466] The server uses an emotion engine to analyze the user's emotional data. This allows the server to understand the user's mental state and stress level, generating data to provide appropriate emotional support. The analysis includes trend analysis of emotional data and assessment of psychological state. The output is specific advice on emotional support based on the user's mental state.

[0467] Step 5:

[0468] The server then sends the generated training program, nutrition plan, and mental support back to the user's device. This allows the user to view a concrete action plan on their device. The server sends the data to the user's device in the appropriate format, so the user can receive the information in a visually easy-to-understand way.

[0469] Step 6:

[0470] The user's device displays the received data, allowing them to review the provided plans and support. This enables users to concretely implement their daily training, health management, and mental support. The display includes a training program shown chronologically, and a nutrition plan that includes recommended meals.

[0471] For example, when a user starts a workout at the gym, if emotional data indicates a high stress level, the system will provide advice such as, "Today, let's focus on light stretching and relaxation." This advice is displayed on the user's device and can be referenced during the workout.

[0472] Example of a prompt:

[0473] "Based on the user's health and emotional data, we will provide personalized training programs and mental support. Please generate specific advice based on the following data."

[0474] Height: 175cm

[0475] Weight: 65kg

[0476] Performance data: [10, 15, 12]

[0477] Health condition: Good

[0478] Emotional data: {'happiness': 0.8, 'stress': 0.2}''

[0479] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0480] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0481] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0482] [Second Embodiment]

[0483] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0484] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0485] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0486] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0487] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0488] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0489] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0490] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0491] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0492] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0493] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0494] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0495] This invention is a system that personalizes training programs for sports athletes, providing injury prevention, performance optimization, tactical advice, and psychological support. The system uses a generative AI model to analyze the athlete's input data and generate optimal training plans and advice.

[0496] First, when a user uses this system, they input basic information such as their physical characteristics, performance data, and health status on their device. This information is then sent from the device to the server. The server stores the received data in a database and passes it to a generating AI model for analysis.

[0497] The server uses a generative AI model to generate optimal training programs and nutrition plans for individual athletes. For example, if an amateur runner inputs their height, weight, and past runtime data into the system, the generative AI model analyzes this data and proposes a three-times-a-week running program and a high-protein meal plan for that runner. This generated plan is sent from the server to the user's terminal for review and execution.

[0498] Next, athletes can input training data and health status into the system, enabling it to provide injury prevention advice and support for rehabilitation programs. When a basketball player inputs recent training data and knee pain, the server uses a generative AI model to suggest stretches and muscle-strengthening exercises to reduce stress on the knee. This rehabilitation plan is also sent to the user's device and used in actual rehabilitation.

[0499] Furthermore, the system provides tactical advice and optimization plans based on performance data for athletes in team sports and individual competitions. For example, when a soccer team inputs post-match data into the system, a generating AI model analyzes it and suggests formation changes or instructions for specific players in the next match. This tactical advice is then sent from the server to the coach's terminal.

[0500] The system also provides psychological support. When golfers input data on the pressure and stress they feel before a tournament, a generating AI model analyzes this data and provides guidance on breathing techniques and visualization exercises. This mental support advice is also sent from the server to the user's device, allowing players to use it before the tournament.

[0501] Furthermore, the system receives user training and performance data, evaluates its effectiveness, and provides feedback. For example, when a club tennis player inputs daily practice data and match results, the server analyzes it using a generative AI model, evaluates serve success rate and backhand accuracy, and provides feedback for further improvement. This feedback is then sent from the server to the user's device.

[0502] This system makes it possible to provide highly personalized support to a wide range of athletes, from amateurs to professionals. This addresses the modern challenge of trainer shortages and effectively supports athletes in improving their performance and maintaining their health.

[0503] The following describes the processing flow.

[0504] Specific process flow for generating personalized training programs

[0505] Step 1:

[0506] User: Sends a training program generation request to the system. Specifically, enters basic information such as name, age, gender, height, weight, past performance data, and health status into the terminal.

[0507] Step 2:

[0508] Terminal: Receives user input data and verifies that the data is in the correct format and includes all necessary fields. After verification, it sends the data to the server in the appropriate format.

[0509] Step 3:

[0510] Server: Receives data and saves it to the database. This creates a separate data entry for each athlete, which is then used for subsequent analysis.

[0511] Step 4:

[0512] Server: Passes stored data to the generating AI model. The generating AI model generates an optimal training program and nutrition plan based on the input data.

[0513] Step 5:

[0514] Server: Sends the generated training program and nutrition plan to the user's terminal. It is sent in the appropriate format, linked to the user ID.

[0515] Step 6:

[0516] Terminal: Displays the training program and nutrition plan received on the user's terminal. The user can review and implement this.

[0517] Specific procedures for injury prevention and rehabilitation support

[0518] Step 1:

[0519] User: Submit a rehabilitation support request to the system. Specifically, enter recent training data, pain symptoms, and past health history into the terminal.

[0520] Step 2:

[0521] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[0522] Step 3:

[0523] Server: Receives data and saves it to a database. This records the user's health history and training data.

[0524] Step 4:

[0525] Server: The server passes the aforementioned data to the generating AI model to generate injury prevention advice and rehabilitation programs. The model identifies high-risk movements and suggests necessary advice and rehabilitation exercises.

[0526] Step 5:

[0527] Server: Sends generated injury prevention advice and rehabilitation programs to the user's device. Customizes the content sent based on the user ID.

[0528] Step 6:

[0529] Terminal: Displays injury prevention advice and rehabilitation programs received on the user's terminal. The user then performs training and rehabilitation according to these instructions.

[0530] Specific processing flow for performance optimization and tactical advice

[0531] Step 1:

[0532] User: Enter post-competition performance data into the system. Specifically, enter detailed match results and practice data into the terminal.

[0533] Step 2:

[0534] Terminal: Receives user input data and verifies the data format. After verification, converts it to the appropriate format for transmission to the server.

[0535] Step 3:

[0536] Server: Receives performance data and saves it to the database. This allows for the accumulation of data for each team and individual.

[0537] Step 4:

[0538] Server: Passes data to the generated AI model for analysis. The model suggests improvements and new tactics based on the match data.

[0539] Step 5:

[0540] Server: Sends generated tactical advice and optimized tactical plans to the user's terminal. Provides specific instructions and strategies to the coach's terminal.

[0541] Step 6:

[0542] Terminal: Displays tactical advice and tactical optimization plans received on the user's terminal. Coaches and team members use this to prepare for the next competition.

[0543] Specific steps for providing psychological support and improving motivation

[0544] Step 1:

[0545] User: Inputs information for psychological support into the system. Specifically, they input details such as stress levels and psychological state into the terminal.

[0546] Step 2:

[0547] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[0548] Step 3:

[0549] Server: Receives data and saves it to the database. This allows psychological data to be recorded for each athlete.

[0550] Step 4:

[0551] Server: Provides data to the generation AI model, which then generates advice for psychological support and motivation improvement. The model suggests techniques for stress management and improving concentration.

[0552] Step 5:

[0553] Server: Sends generated psychological support advice to the user's terminal. Provides the most appropriate advice based on the user ID.

[0554] Step 6:

[0555] Terminal: Displays psychological support advice received on the user's terminal. The user then implements relaxation methods and motivation-boosting techniques based on this advice.

[0556] Specific process flow for data analysis and evaluation of training effectiveness

[0557] Step 1:

[0558] User: Input training data and performance data into the system. Specifically, input detailed information about training results and match results into the terminal.

[0559] Step 2:

[0560] Terminal: Receives user input data and verifies data consistency and accuracy. After verification, converts the data to an appropriate format for transmission to the server.

[0561] Step 3:

[0562] Server: Receives data and saves it to the database. This allows for comparison with past data.

[0563] Step 4:

[0564] Server: Provides data to the generated AI model and evaluates the effectiveness of the training. The model compares past data with new data to measure its effectiveness.

[0565] Step 5:

[0566] Server: Sends generated evaluation results and feedback to the user's terminal. Provides appropriate feedback and strategic advice based on the user ID.

[0567] Step 6:

[0568] Terminal: Displays evaluation results and feedback received on the user's terminal. The user uses this to adjust their training.

[0569] In this way, by providing athletes with multifaceted and high-quality support throughout the entire system, we can resolve the problem of trainer shortages and realize high-quality training and support.

[0570] (Example 1)

[0571] Next, we will describe Example 1. 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".

[0572] Traditional training systems for athletes only provide general training plans and fail to comprehensively offer personalized training plans and nutrition plans based on individual athletes' physical characteristics, performance data, and health status, as well as injury prevention advice, rehabilitation programs, tactical advice, and psychological support. As a result, athletes do not receive sufficient support for improving their performance or maintaining their health.

[0573] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0574] In this invention, the server includes means for inputting basic information, performance data, and health status of an athlete via a terminal; means for transmitting the input data to the server; means for receiving the transmitted data on the server and storing it in a database; means for passing the stored data to a generating AI model for analysis; means for generating individual training programs and nutrition plans using the generating AI model; means for transmitting the generated training programs and nutrition plans to a user's terminal; and means for receiving the training programs and nutrition plans displayed on the user's terminal. This enables the user to receive highly personalized support based on their individual physical characteristics and performance data.

[0575] An "athlete" is an individual who trains to improve their physical performance.

[0576] "Basic information" refers to physical characteristics and personal data about individual athletes, such as height, weight, and age.

[0577] "Performance data" refers to data related to an athlete's athletic performance, such as training frequency, runtime, and training records.

[0578] "Health status" refers to information about an athlete's health, such as fatigue, pain, and general malaise.

[0579] A "device" refers to an electronic device used by a user, such as a smartphone, tablet, or personal computer.

[0580] A "server" is a computer system that receives, stores, analyzes, and runs generative models on data.

[0581] A "database" is a system that allows a server to structurally store and manage data.

[0582] A "generative AI model" is an artificial intelligence model used to analyze input data and generate training programs, advice, and plans.

[0583] A "training program" is an individualized exercise plan designed to improve an athlete's physical performance.

[0584] A "nutrition plan" is a meal plan designed to optimize an athlete's health and performance.

[0585] "Injury prevention advice" refers to specific guidance for athletes to reduce strain during training and prevent injuries.

[0586] A "rehabilitation program" is an exercise and treatment plan designed to help athletes recover from injuries and pain.

[0587] "Tactical advice" refers to strategic guidance provided in team sports or individual competitions for the upcoming match or competition.

[0588] A "tactical optimization plan" is a tactical plan designed to maximize performance in a match or competition.

[0589] "Psychological support" refers to advice and training that helps athletes manage mental pressure and stress.

[0590] This invention is a system that provides personalized training programs, nutrition plans, injury prevention measures, rehabilitation programs, tactical advice, and psychological support for sports athletes. The system uses a generative AI model to analyze the athlete's basic information, performance data, and health status. The system primarily operates with three parties: a server, a terminal, and the user.

[0591] First, the user uses a device (smartphone, tablet, PC, etc.) to input the athlete's basic information (e.g., height, weight, age), performance data (e.g., runtime, training frequency), and health status (e.g., fatigue level, presence or absence of pain). The device then sends this data to the server in a structured format (e.g., JSON). A secure protocol (e.g., HTTPS) is used for this communication.

[0592] The server stores the received data in a database (e.g., MySQL or PostgreSQL). The server checks the data's integrity and saves only the data that passes validation. After that, the server passes the saved data to a generating AI model (e.g., OpenAI GPT model) for analysis.

[0593] Examples of prompt statements used for analysis are as follows:

[0594] "When generating optimal training and nutrition plans for amateur runners, please consider the user's height, weight, and past runtime data as input data."

[0595] "Please suggest stretches and strengthening exercises to alleviate knee pain in a basketball player. Input data includes recent training data and pain location information."

[0596] "Analyze the post-match data of the soccer team and suggest formation changes and instructions for specific players for the next match."

[0597] Based on the analysis results obtained from the generating AI model, the server generates personalized training programs, nutrition plans, injury prevention advice, rehabilitation programs, tactical advice, and psychological support. For example, if an amateur runner inputs their height (170cm), weight (65kg), and past runtime data into the system, the server uses the generating AI model to suggest a three-times-a-week running program and a high-protein meal plan.

[0598] Next, the server sends the generated plan and advice to the user's device. The device receives this data and displays it within the app. For example, if a basketball player enters knee pain, the server sends a rehabilitation plan of stretches and muscle strengthening exercises, which is then displayed on the device. As a result, athletes can receive personalized feedback in real time, which they can use for their daily training and health management.

[0599] This system addresses the modern challenge of trainer shortages by providing advanced personalized support to a wide range of athletes, from amateurs to professionals, effectively assisting in improving athlete performance and maintaining health. Users can easily obtain personalized training plans and advice simply by entering specific data.

[0600] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0601] Step 1:

[0602] The user enters basic information into the device.

[0603] Users enter basic information such as height, weight, age, runtime, training frequency, and health status using a dedicated app on their device or a web form. The entered data is created in a structured format (e.g., JSON). This data will later be used as input for analysis.

[0604] Step 2:

[0605] The device sends data to the server.

[0606] The device sends the entered data to the server using a secure protocol (e.g., HTTPS). The input data is sent in JSON format, and the server receives it. Specifically, when the send button is pressed, the device's app sends an HTTP POST request to the server.

[0607] Step 3:

[0608] The server saves the data to the database.

[0609] The server parses the received JSON data and saves it to a database (e.g., MySQL or PostgreSQL). Before saving, it validates the data to remove incomplete or invalid data. For example, the server checks that the data is in the correct format (e.g., whether all required fields are filled in).

[0610] Step 4:

[0611] The server passes data to the generated AI model.

[0612] The server extracts the necessary data from the database and passes it to the AI ​​model. During this process, it generates prompts, formats the data, and then sends it. For example, it might pass data along with a prompt such as, "Generate an optimal training plan and nutrition plan for an amateur runner." The input data is provided in JSON format.

[0613] Step 5:

[0614] The server receives results from the generated AI model.

[0615] The generative AI model analyzes the provided prompt text and data to generate an optimal training plan, nutrition plan, and advice. The server receives these results and stores them in JSON format. Specifically, it receives the analysis results returned by the generative AI model as an HTTP response.

[0616] Step 6:

[0617] The server sends the results to the user's terminal.

[0618] The server sends the generated training plan and advice results to the user's device. The device receives this data and displays it within the app. For example, it applies the data received from the server to the calendar and notification functions of the app on the device.

[0619] Step 7:

[0620] The user checks and executes the results on their device.

[0621] Users can review training and nutrition plans generated on their devices and use them to guide their actual training and meal planning. Specifically, users check the plan in a particular section of the app and act according to the schedule. For example, they might receive push notifications about their daily training.

[0622] By clearly defining the specific processes performed at each step, it becomes possible to understand in detail how this system works and how it provides value to the user.

[0623] (Application Example 1)

[0624] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0625] There is a need for a system that maximizes the effectiveness of training for factory robot operators, supports injury prevention and improved work performance, and provides appropriate feedback and optimization in real time. Conventional training systems have struggled to provide personalized suggestions based on the individual health status and performance data of operators.

[0626] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0627] In this invention, the server includes means for receiving and storing operator input data; means for using a generative model to analyze the input data and generate individual training programs and work plans; means for transmitting the generated training programs and work plans to a user's terminal; means for receiving training results and using a generative model to optimize the plan in real time; means for transmitting the optimized training programs and work plans to a user's terminal; and means for receiving the optimized training programs and work plans displayed on the terminal. This maximizes the effectiveness of operator training, enabling injury prevention and improved work performance.

[0628] "Operator input data" refers to information entered by factory robot operators regarding their physical characteristics, operational experience, work performance data, and health status.

[0629] A "generative model" is an AI system that includes algorithms for analyzing input data and generating individual training programs and work plans.

[0630] A "training program" is a set of specific training content and schedules created by a generative model that operators should undergo.

[0631] A "work plan" is a set of guidelines and procedures created by a generative model that specify what tasks an operator should perform.

[0632] "User terminal" refers to electronic devices such as smartphones, tablets, and head-mounted displays that operators use to view and execute training programs and work plans.

[0633] "Training outcomes" refer to data obtained as a result of operators performing training and tasks, including information on performance improvements and health improvements.

[0634] "Optimizing the plan in real time" means that the generative model continuously re-evaluates the training program and work plan based on the training results, and adjusts the content immediately as needed.

[0635] "Injury prevention advice" refers to suggestions from a generative model regarding specific actions and precautions that operators should take to prevent injuries.

[0636] A "rehabilitation program" is a training and exercise plan that injured operators should follow to recover.

[0637] "Work tactical advice" refers to suggestions regarding specific work methods and strategies, provided based on generative models.

[0638] An "optimization plan" is a set of improvement suggestions and specific procedures generated to enable operators to perform their tasks more efficiently.

[0639] Modes for carrying out the invention

[0640] Embodiments of this invention are shown below. The configuration of hardware and software for realizing the invention, as well as the data processing and calculation methods, will be described.

[0641] System Configuration

[0642] The system consists of the following main elements:

[0643] 1. User devices: Smartphones (iOS or Android), head-mounted displays (HoloLens, Oculus, etc.).

[0644] 2. Server: Backend for data storage and generation, and for data analysis using AI models.

[0645] 3. Software: Mobile application development kits (Flutter, React Native, etc.), generative AI model backend (TensorFlow, PyTorch).

[0646] Data flow

[0647] 1. Receiving and saving input data:

[0648] Operators use a terminal to input their physical characteristics, operational experience, work performance data, and health status.

[0649] This data is sent from the terminal to the server and stored in the database.

[0650] 2. Analysis using generative AI models:

[0651] The server passes the stored data to a generating AI model, which then performs analysis using Google Cloud AI or AWS Machine Learning.

[0652] The generative AI model generates individual training programs and work plans based on the analysis results.

[0653] 3. Submission of training program and work plan:

[0654] The server sends the generated training program and work plan to the user's terminal.

[0655] The user's device displays the transmitted content, which the operator can then review and execute.

[0656] 4. Input of training results and real-time optimization:

[0657] The operator enters the training results into a terminal, and this data is also sent to the server.

[0658] Based on the training results, the server generates an AI model in real time, which then re-evaluates and optimizes the training program and work plan.

[0659] 5. Injury prevention and rehabilitation program:

[0660] When an operator inputs information about their health status and injuries, the AI ​​model generates injury prevention advice and rehabilitation programs.

[0661] The server sends these programs to the user's terminal, where they are displayed.

[0662] 6. Advice and optimization of work tactics:

[0663] Once the operator inputs work data, the generated AI model produces work tactical advice and optimization plans.

[0664] This information is sent to the user's terminal via the server and displayed on the terminal.

[0665] Examples

[0666] Specific example 1:

[0667] Factory robot operators open an app and input their current work performance (operation speed, accuracy, failure rate, etc.). For example, if an operator inputs their arm fatigue level and the success rate of precision operations, the generated AI will use this information to suggest arm muscle training and rest plans.

[0668] Specific example 2:

[0669] When a new factory operator joins the company, they input their experience level and physical data through an app, and an AI generates an optimal training program based on that data. For example, beginners are provided with a step-by-step training plan that starts with basic operations and gradually increases in difficulty.

[0670] Example of a prompt

[0671] AI for training factory robot operators:

[0672] 1. Basic information: Height, weight, operating experience, work data

[0673] 2. Specific operational performance: accuracy, speed, failure rate

[0674] 3. Training plan generation: Recommendations for repetitive training of maneuvers, strength training, etc.

[0675] "What is the best training plan for when my arms are experiencing high levels of fatigue?"

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] Operators use a terminal to input their physical characteristics, operational experience, work performance data, and health status. The entered data is reviewed on the terminal and sent to the server. This step provides an interface for collecting operator data and sending it to the server.

[0679] Step 2:

[0680] The server stores the data transmitted by the operator in a database. The stored data is then used for analysis by a subsequent generative AI model. In this step, the server efficiently manages the data using a data storage system.

[0681] Step 3:

[0682] The server passes the stored operator data to the generating AI model and begins the analysis. The generating AI model generates individual training programs and work plans based on the input data. For example, it performs data analysis using TensorFlow or PyTorch. In this step, the AI ​​model analyzes the data and designs an appropriate training program and work plan.

[0683] Step 4:

[0684] The generated training program and work plan are sent from the server to the user's terminal. The terminal displays the received training program and work plan, making them ready for the operator to review and execute. In this step, the server sends data using the appropriate communication protocol, and the terminal receives it.

[0685] Step 5:

[0686] Operators conduct training and tasks, and input the results into a terminal. The entered training results are then sent back to the server. In this step, a user interface for operators to provide feedback is crucial.

[0687] Step 6:

[0688] The server receives the training results, and the generated AI model re-evaluates and optimizes the training program and work plan in real time. This optimization is performed based on the latest data from the operators. In this step, the AI ​​model has the ability to dynamically update and adapt the plan.

[0689] Step 7:

[0690] The server resends the optimized training program and work plan to the user's terminal. The terminal receives this, the operator reviews the content, and they can continue working according to the latest training and work plan. In this step, the efficiency of data communication between the server and the terminal is crucial.

[0691] Specific example

[0692] For example, a factory robot operator opens an app and inputs current work performance data (operating speed, accuracy, failure rate, etc.). This data is sent to a server, where a generated AI model analyzes it and suggests arm muscle training and rest plans. This information is then sent back to the terminal, and the operator can review and implement the plan, which is expected to improve performance.

[0693] Example of a prompt

[0694] AI for training factory robot operators:

[0695] 1. Basic information: Height, weight, operating experience, work data

[0696] 2. Specific operational performance: accuracy, speed, failure rate

[0697] 3. Training plan generation: Recommendations for repetitive training of maneuvers, strength training, etc.

[0698] "What is the best training plan for when my arms are experiencing high levels of fatigue?"

[0699] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0700] This invention combines a system that provides athletes with personalized training programs, injury prevention, rehabilitation, tactical advice, and psychological support with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out this invention are described below.

[0701] First, when a user uses the system, they input their physical characteristics, performance data, health status, and emotional data using a terminal. This data is sent from the terminal to the server. The server stores the received data in a database and passes it to a generative AI model and emotion engine for analysis.

[0702] The server uses a generative AI model to analyze the user's physical and performance data and generate personalized training programs and nutrition plans. Additionally, an emotion engine analyzes the user's emotional data to understand their current mental state and stress level. For example, if an amateur runner inputs their height, weight, past runtime data, and emotional data into the system, the generative AI model and emotion engine analyze this data to generate an appropriate training program, nutrition plan, and motivational advice for that runner. This generated plan and advice are then sent from the server to the user's terminal, where they can review and implement it.

[0703] To support injury prevention and rehabilitation, the system allows users to input training data, health status, and emotional data. The generating AI model then generates injury prevention advice and rehabilitation programs, while the emotional engine provides advice aimed at mental stability. For example, if a basketball player inputs recent training data, knee pain, and anxiety, the generating AI model suggests stretches and strength training exercises to reduce knee stress, while the emotional engine proposes relaxation methods to reduce stress. This rehabilitation plan and mental support advice are sent to the user's device and effectively utilized in actual rehabilitation.

[0704] Regarding tactical advice, when a soccer team inputs post-match data and emotional data into the system, a generative AI model analyzes the performance data and suggests tactical improvements, while an emotional engine supports the team's overall tactical adaptation based on player motivation and teamwork. This allows new tactics to be executed more effectively. This tactical advice is sent from the server to the coach's terminal and displayed along with specific tactical details.

[0705] By using an enhanced system for psychological support, feedback tailored to the user's mental state and emotions is provided. For example, if a golfer inputs data on the pressure and stress they feel before a tournament, a generative AI model generates specific training and advice for mental support, and an emotion engine suggests relaxation techniques and imagery training. This helps to promote the player's psychological stability. This support is sent to the user's terminal via the server and can be used by the player before the tournament.

[0706] Finally, regarding data analysis and evaluation of training effectiveness, emotional data is analyzed along with training and performance data. This allows for the evaluation of not only the athlete's training effectiveness but also their psychological stability. For example, when a club tennis player inputs daily practice data, match results, and emotional data, a generative AI model evaluates the effectiveness of the training, and an emotional engine provides feedback that also takes into account the state of mental health. This feedback is sent from the server to the user's terminal, and the user uses it to adjust their training and mental care.

[0707] This system provides comprehensive support for improving athletes' physical and mental performance. By combining it with an emotional engine, it becomes possible to provide even more personalized support than before, and is expected to significantly improve the quality of athlete training, injury prevention, rehabilitation, tactical improvement, and psychological support.

[0708] The following describes the processing flow.

[0709] Personalized training program generation and the specific processing flow of the emotion engine

[0710] Step 1:

[0711] User: Sends a training program generation request to the system. Specifically, enters name, age, gender, height, weight, past performance data, health status, and emotional data (e.g., stress level, emotional score) into the terminal.

[0712] Step 2:

[0713] Terminal: Receives user input data and verifies that the data is in the correct format and all required fields are present. After verification, it sends the data to the server in the appropriate format.

[0714] Step 3:

[0715] Server: Receives data and saves it to the database. This creates a unique data entry for each user, which is then used for subsequent analysis.

[0716] Step 4:

[0717] Server: Passes stored data to the generating AI model and emotion engine. The generating AI model generates an optimal training program and nutrition plan based on the input physical and performance data.

[0718] Step 5:

[0719] Server: The emotion engine analyzes the user's emotional data and generates motivational advice based on their mental state. For example, if the user shows a high stress level, it suggests relaxation techniques or exercises to reduce stress.

[0720] Step 6:

[0721] Server: Sends the generated training program, nutrition plan, and emotional engine-driven advice to the user's device. It links the information to the user ID and sends it in the appropriate format.

[0722] Step 7:

[0723] Terminal: Displays training programs, nutrition plans, and emotional engine advice received on the user's terminal. The user can review and implement these.

[0724] Specific procedures for injury prevention and rehabilitation support

[0725] Step 1:

[0726] User: Submit a rehabilitation support request to the system. Specifically, enter recent training data, health status, and emotional data (e.g., pain level, emotional score, etc.) into the terminal.

[0727] Step 2:

[0728] Terminal: Receives user input data and verifies its accuracy. After verification, it sends the data to the server in the appropriate format.

[0729] Step 3:

[0730] Server: Receives data and saves it to the database. This records health history and training data.

[0731] Step 4:

[0732] Server: The server passes the aforementioned data to the generating AI model to generate injury prevention advice and rehabilitation programs. The model identifies high-risk movements and suggests necessary injury prevention measures.

[0733] Step 5:

[0734] Server: The emotion engine analyzes the user's emotional data and generates advice aimed at promoting mental well-being. For example, if the user is showing signs of anxiety, it will suggest specific relaxation methods to alleviate that anxiety.

[0735] Step 6:

[0736] Server: Sends generated injury prevention advice, rehabilitation programs, and emotional engine advice to the user's device. Customizes the content sent based on the user ID.

[0737] Step 7:

[0738] Terminal: Displays injury prevention advice, rehabilitation programs, and emotional engine advice received on the user's terminal. The user then performs training and rehabilitation according to these recommendations.

[0739] Specific processing flow for performance optimization and tactical advice

[0740] Step 1:

[0741] User: Input post-competition performance and emotional data into the system. Specifically, input detailed information such as match results, training data, emotional scores, and stress levels.

[0742] Step 2:

[0743] Terminal: Receives user input data and verifies the data format. After verification, it sends the data to the server in the appropriate format.

[0744] Step 3:

[0745] Server: Stores received performance and sentiment data in a database. This allows for the accumulation of data for each team and individual.

[0746] Step 4:

[0747] Server: Passes data to the generated AI model, analyzes performance, and generates tactical improvements and optimization plans. For example, if a particular player's performance is declining, it identifies the cause and suggests countermeasures.

[0748] Step 5:

[0749] Server: The emotion engine analyzes data and generates tactical advice that takes into account the players' motivation and mental state. For example, if stress levels are high throughout the team, it might suggest leadership training or team-building exercises.

[0750] Step 6:

[0751] Server: Sends generated tactical advice and optimized tactical plans to the user's terminal. Provides specific instructions and strategies to the coach's terminal.

[0752] Step 7:

[0753] Terminal: Displays tactical advice and tactical optimization plans received on the user's terminal. The coach uses this to prepare for the next match.

[0754] Specific steps for providing psychological support and improving motivation

[0755] Step 1:

[0756] User: Enter information for psychological support into the system. Specifically, enter details about stress levels, emotion scores, and recent psychological states.

[0757] Step 2:

[0758] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[0759] Step 3:

[0760] Server: Stores received emotional data and related information in a database. This ensures that psychological data is recorded for each athlete.

[0761] Step 4:

[0762] Server: Passes data to the generation AI model and generates advice for psychological support and motivation improvement. For example, it provides training and advice for mental support.

[0763] Step 5:

[0764] Server: The emotion engine analyzes data and provides relaxation methods and motivation-boosting techniques tailored to the user's psychological state.

[0765] Step 6:

[0766] Server: Sends generated psychological support advice to the user's terminal. Provides the most appropriate advice based on the user ID.

[0767] Step 7:

[0768] Terminal: Displays psychological support advice received on the user's terminal. The user then implements relaxation methods and motivation-boosting techniques based on this advice.

[0769] Specific process flow for data analysis and evaluation of training effectiveness

[0770] Step 1:

[0771] User: Inputs training data, performance data, and emotional data into the system. Specifically, this involves detailed input of training results, match results, and emotional data (e.g., emotional score, stress level, etc.).

[0772] Step 2:

[0773] Terminal: Receives user input data and verifies data consistency and accuracy. After verification, sends it to the server in the appropriate format.

[0774] Step 3:

[0775] Server: Receives data and saves it to the database. This allows for comparison with past data.

[0776] Step 4:

[0777] Server: Provides data to the generated AI model and evaluates the effectiveness of the training. The model compares past data with new data to measure the effectiveness of the training.

[0778] Step 5:

[0779] Server: The emotion engine analyzes the data and generates comprehensive feedback, including the user's psychological stability. For example, it presents not only the effectiveness of the training but also the user's mental health status.

[0780] Step 6:

[0781] Server: Sends generated evaluation results and feedback to the user's terminal. Provides appropriate feedback and strategic advice based on the user ID.

[0782] Step 7:

[0783] Terminal: Displays evaluation results and feedback received on the user's terminal. The user uses this to adjust their training and mental care.

[0784] In this way, by providing athletes with multifaceted, high-quality support throughout the entire system, the problem of trainer shortages can be resolved, and high-quality training and support can be realized. By combining this with an emotional engine, even more precise individualized support becomes possible, effectively supporting improved athletic performance and psychological stability.

[0785] (Example 2)

[0786] Next, we will describe Example 2. 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".

[0787] Conventional athlete support systems focused on providing physical training programs and nutritional plans, but lacked an element of emotional support, failing to address the user's mental state and stress levels. As a result, training effectiveness, injury prevention, rehabilitation processes, and tactical advice were sometimes not fully realized. This invention aims to solve these problems and provide comprehensive support for athletes' physical and mental performance.

[0788] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0789] In this invention, the server includes means for receiving and storing user input data, means for using a generative model and an emotion engine to analyze the input data and generate individual training programs, nutrition plans, and emotional support advice, and means for transmitting the generated training programs, nutrition plans, and emotional support advice to the user's terminal. This makes it possible to achieve not only improved physical performance of athletes but also mental stability and stress management simultaneously.

[0790] A "user" is a person who uses the system to input their physical characteristics, performance data, health status, and emotional data.

[0791] "Input data" refers to information that users provide to the system, such as physical characteristics, performance data, health status, and emotional data.

[0792] A "generative model" is an artificial intelligence model that analyzes a user's physical characteristics and performance data to generate individualized training programs and nutrition plans.

[0793] An "emotion engine" is software that analyzes a user's emotional data to understand their mental state and stress level, and then generates advice.

[0794] A "training program" is an individualized exercise plan designed based on the user's physical characteristics and performance data.

[0795] A "nutrition plan" is a plan that provides instructions on the optimal diet and nutrient intake methods based on the user's physical characteristics and performance data.

[0796] "Emotional support advice" refers to advice that provides mental support, such as relaxation methods and motivation-enhancing techniques, based on the user's emotional data.

[0797] "Injury prevention advice" is advice that analyzes the user's training data and health status and suggests measures to reduce the risk of injury.

[0798] A "rehabilitation program" is a program that directs injured users through rehabilitation activities aimed at their return to activity.

[0799] "Performance data" refers to data on exercise results recorded by users during sports or training.

[0800] "Tactical advice" is advice that analyzes the user's performance data and suggests tactical improvements and new strategies in the game.

[0801] A "tactical optimization plan" is a specific action plan based on tactical advice, designed to maximize team and individual performance.

[0802] A "terminal" is a device used by users to input data and receive training programs, nutrition plans, and advice from the server.

[0803] A "server" is a system that receives, stores, and analyzes data from users, and generates training programs and advice.

[0804] This invention is a system that provides personalized training programs, nutritional plans, and emotional support advice based on user input data. The system mainly consists of a server, terminals, and a generative AI model and emotion engine.

[0805] Hardware and software configuration:

[0806] 1. Terminal

[0807] Hardware: Input devices such as smartphones, tablets, and personal computers.

[0808] Software: A dedicated application or web browser is used to input user data and communicate with the system.

[0809] 2. Server

[0810] Hardware: A high-performance server computer.

[0811] Software: Applications that integrate database management systems (e.g., MySQL, PostgreSQL) and analysis engines.

[0812] 3. Generative AI Models

[0813] Software: Uses machine learning algorithms (e.g., TensorFlow, PyTorch) to analyze user data and generate training programs and nutrition plans.

[0814] 4. Emotional Engine

[0815] Software: Use emotion analysis programs (e.g., Affectiva, IBM Watson) to analyze user emotion data and generate emotional support advice.

[0816] System processing description:

[0817] Users input their physical characteristics, performance data, health status, and emotional data using a device. This data is sent from the device to a server, which stores the received data in a database. The stored data is then passed to a generative AI model and an emotion engine for analysis.

[0818] The generative AI model generates personalized training programs and nutrition plans based on the user's physical and performance data. Meanwhile, the emotional engine analyzes mental state and stress levels based on emotional data, providing advice on relaxation methods and motivation enhancement.

[0819] For example, the generative AI model performs analysis using the following specific prompt:

[0820] User's height: 175cm

[0821] User's weight: 70kg

[0822] Past runtime data: 5km 30 minutes

[0823] Emotional data: High stress level

[0824] Generate a training program and nutrition plan tailored to the user.

[0825] Specific example:

[0826] Example 1: In the case of an amateur runner

[0827] Input data: Height 175cm, weight 70kg, runtime data for the past week, high stress level.

[0828] Output of the generated AI model: Interval training, 3 times a week.

[0829] Emotional engine output: Deep breathing and relaxation techniques for motivation enhancement.

[0830] Example 2: In the case of a basketball player

[0831] Input data: Recent training data, knee pain, anxiety.

[0832] Output of the generated AI model: Stretching and muscle strengthening exercises to reduce stress on the knees.

[0833] Emotional Engine Output: Meditation Methods for Stress Reduction.

[0834] The generated training program, nutrition plan, and emotional support advice are sent from the server to the user's terminal, where the user reviews and implements this information. This entire process allows the user to improve their physical and mental performance.

[0835] This system is extremely useful for athletes because it not only optimizes training but also provides injury prevention, rehabilitation, and tactical advice.

[0836] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0837] Step 1:

[0838] The user enters input data using a terminal.

[0839] Operation: The user opens a dedicated application and enters their physical characteristics (e.g., height, weight), performance data (e.g., runtime data), health status (e.g., knee pain), and emotional data (e.g., stress level). They then review the entered data and press the submit button.

[0840] Input: Physical characteristics, performance data, health status, emotional data

[0841] Output: Input data (data formatted in JSON format, etc.)

[0842] Step 2:

[0843] The terminal sends the input data to the server.

[0844] Operation: When the send button on the device is pressed, the input data is sent to the server as an HTTP request. An internet connection is required for transmission.

[0845] Input: Input data (data formatted in JSON format, etc.)

[0846] Output: Input data sent to the server (HTTP request)

[0847] Step 3:

[0848] The server receives the input data and saves it to the database.

[0849] Operation: The server retrieves input data from received HTTP requests and stores it in the database. Each piece of data is stored in the database for each user.

[0850] Input: Input data extracted from the HTTP request

[0851] Output: User data stored in the database

[0852] Step 4:

[0853] The server passes the stored data to the generating AI model and emotion engine.

[0854] Operation: The server retrieves the necessary data from the database and converts it into a data format for passing to the generating AI model and emotion engine. This may involve using formats such as Python's DataFrame.

[0855] Input: User data stored in the database

[0856] Output: Data to be passed to the generative AI model and emotion engine.

[0857] Step 5:

[0858] The generative AI model generates training programs and nutrition plans.

[0859] Operation: The generative AI model analyzes the user's physical characteristics and performance data to generate a personalized training program and nutrition plan. An example using prompts is shown below:

[0860] User's height: 175cm

[0861] User's weight: 70kg

[0862] Past runtime data: 5km 30 minutes

[0863] Please generate a training program and nutrition plan tailored to this user.

[0864] Input: User's physical characteristics, performance data (in prompt format)

[0865] Output: Individualized training programs and nutrition plans

[0866] Step 6:

[0867] The emotion engine analyzes emotional data and generates advice on relaxation methods and motivation enhancement.

[0868] Operation: The emotion engine analyzes the user's emotional data to understand their current mental state and stress level. Based on the analysis results, it generates specific advice on relaxation methods and ways to improve motivation.

[0869] Input: User sentiment data

[0870] Output: Relaxation methods and motivation-boosting advice

[0871] Step 7:

[0872] The server sends the generated training program, nutrition plan, and emotional advice to the user's device.

[0873] Operation: The server compiles the analysis results obtained from the generated AI model and emotion engine and sends them to the user's terminal. This transmission is also done via HTTP response.

[0874] Input: Individual training programs, nutrition plans, relaxation methods, and motivational advice.

[0875] Output: Analysis results (HTTP response) sent to the user's terminal.

[0876] Step 8:

[0877] Users can view analysis results on their devices and receive training and mental care based on those results.

[0878] Operation: The user opens a dedicated application on their device and reviews the training program, nutrition plan, and emotional support advice sent from the server. After reviewing, they proceed to implement the training and mental care.

[0879] Input: Analysis results sent from the server (training program, nutrition plan, relaxation methods, motivation improvement advice)

[0880] Output: User action plan (training implementation, mental care implementation)

[0881] (Application Example 2)

[0882] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0883] Traditional training systems for athletes could provide physical training programs and nutritional plans, but they failed to consider the user's mental state and emotional data. This resulted in a lack of mental support, hindering the achievement of optimal training results. Furthermore, the lack of mental support in rehabilitation and tactical advice made it difficult to improve the overall performance of athletes.

[0884] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0885] In this invention, the server includes means for receiving and storing input data of athletes; means for using a generative model to analyze the input data and generate individual training programs and nutrition plans; means for transmitting the generated training programs and nutrition plans to the user's terminal; means for using an emotion engine to analyze the user's emotional data and provide mental support; means for transmitting the mental support to the user's terminal; and means for receiving the mental support. This makes it possible to provide comprehensive training programs and support that take into account not only the user's physical health but also their mental health.

[0886] "Input data" refers to information that athletes provide to the system, such as physical characteristics, performance data, health status, and emotional data.

[0887] A "generative model" is an artificial intelligence algorithm that analyzes received input data to generate individual training programs, nutrition plans, injury prevention advice, rehabilitation programs, tactical advice, and tactical optimization plans.

[0888] An "emotion engine" is an analytical engine that analyzes users' emotional data and provides emotional support.

[0889] A "training program" is a plan of exercises and workouts aimed at improving an athlete's performance.

[0890] A "nutrition plan" is a guideline for meals and supplements tailored to an athlete's physical condition and goals.

[0891] "Injury prevention advice" refers to specific instructions and recommendations for athletes to avoid injuries.

[0892] A "rehabilitation program" is a training and treatment plan for athletes to recover from injuries.

[0893] "Tactical advice" refers to instructions and recommendations for athletes or teams to effectively execute tactics in competition.

[0894] A "tactical optimization plan" is a specific plan for optimizing a team's tactics based on match and competition data.

[0895] "User's device" refers to electronic devices such as smartphones and tablets used by athletes.

[0896] "Mental support" refers to assistance aimed at promoting the psychological stability of athletes, such as relaxation and stress management.

[0897] This invention is a system that provides athletes with personalized training programs, injury prevention, rehabilitation, tactical advice, and psychological support. The system is implemented by combining an emotion engine that recognizes the user's emotions with a generative AI model.

[0898] Initially, when a user uses the system, they input their physical characteristics, performance data, health status, and emotional data using a device such as a smartphone. This data is then sent from the device to the server. The server stores the received data in a database and passes it to a generative AI model and emotion engine for analysis.

[0899] Next, the server uses a generative AI model to analyze the user's physical and performance data and generate a personalized training program and nutrition plan. Additionally, the emotion engine analyzes the user's emotional data to understand their current mental state and stress level.

[0900] The generated AI model and emotion engine analyze the user's data, and the resulting training program, nutrition plan, and emotional support content are then sent back from the server to the user's device and displayed there. This allows the user to receive specific training programs and emotional support.

[0901] For example, when a gym user accesses the system and inputs their current physical and emotional data, the generative AI model creates a training program, and the emotional engine provides mental support. For instance, they might receive advice such as, "Your stress level is high, so let's focus on stretching today to help you relax."

[0902] The hardware used includes smartphones, servers, and databases. The software includes generative AI models, emotion engines, and frontends (applications that run on the user's device). Specifically, it utilizes server-side APIs using Python and Flask, the EmotionEngine library, and AI model libraries.

[0903] Examples of prompt messages include the following:

[0904] "Based on the user's health and emotional data, we will provide personalized training programs and mental support. Please generate specific advice based on the following data."

[0905] Height: 175cm

[0906] Weight: 65kg

[0907] Performance data: [10, 15, 12]

[0908] Health condition: Good

[0909] Emotional data: {'happiness': 0.8, 'stress': 0.2}''

[0910] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0911] Step 1:

[0912] Users input physical characteristics, performance data, health status, and emotional data using a smartphone or other device. This allows the system to collect necessary information from the user and prepare it for further processing. Input includes height, weight, historical runtime data, and emotional status (happiness level, stress level, etc.).

[0913] Step 2:

[0914] The terminal sends the data entered by the user to the server. The server stores the received data in a database. This ensures that the data is securely managed and ready for analysis. The data stored in the database includes the user ID and various input data.

[0915] Step 3:

[0916] The server uses a generative AI model to analyze stored physical and performance data and generate an optimal training program and nutrition plan for the user. The analysis employs machine learning algorithms to evaluate current data in comparison to historical data. The output is a individually customized training program and nutrition plan.

[0917] Step 4:

[0918] The server uses an emotion engine to analyze the user's emotional data. This allows the server to understand the user's mental state and stress level, generating data to provide appropriate emotional support. The analysis includes trend analysis of emotional data and assessment of psychological state. The output is specific advice on emotional support based on the user's mental state.

[0919] Step 5:

[0920] The server then sends the generated training program, nutrition plan, and mental support back to the user's device. This allows the user to view a concrete action plan on their device. The server sends the data to the user's device in the appropriate format, so the user can receive the information in a visually easy-to-understand way.

[0921] Step 6:

[0922] The user's device displays the received data, allowing them to review the provided plans and support. This enables users to concretely implement their daily training, health management, and mental support. The display includes a training program shown chronologically, and a nutrition plan that includes recommended meals.

[0923] For example, when a user starts a workout at the gym, if emotional data indicates a high stress level, the system will provide advice such as, "Today, let's focus on light stretching and relaxation." This advice is displayed on the user's device and can be referenced during the workout.

[0924] Example of a prompt:

[0925] "Based on the user's health and emotional data, we will provide personalized training programs and mental support. Please generate specific advice based on the following data."

[0926] Height: 175cm

[0927] Weight: 65kg

[0928] Performance data: [10, 15, 12]

[0929] Health condition: Good

[0930] Emotional data: {'happiness': 0.8, 'stress': 0.2}''

[0931] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0932] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0933] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0934] [Third Embodiment]

[0935] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0936] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0937] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0938] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0939] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0940] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0941] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0942] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0943] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0944] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0945] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0946] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0947] This invention is a system that personalizes training programs for sports athletes, providing injury prevention, performance optimization, tactical advice, and psychological support. The system uses a generative AI model to analyze the athlete's input data and generate optimal training plans and advice.

[0948] First, when a user uses this system, they input basic information such as their physical characteristics, performance data, and health status on their device. This information is then sent from the device to the server. The server stores the received data in a database and passes it to a generating AI model for analysis.

[0949] The server uses a generative AI model to generate optimal training programs and nutrition plans for individual athletes. For example, if an amateur runner inputs their height, weight, and past runtime data into the system, the generative AI model analyzes this data and proposes a three-times-a-week running program and a high-protein meal plan for that runner. This generated plan is sent from the server to the user's terminal for review and execution.

[0950] Next, athletes can input training data and health status into the system, enabling it to provide injury prevention advice and support for rehabilitation programs. When a basketball player inputs recent training data and knee pain, the server uses a generative AI model to suggest stretches and muscle-strengthening exercises to reduce stress on the knee. This rehabilitation plan is also sent to the user's device and used in actual rehabilitation.

[0951] Furthermore, the system provides tactical advice and optimization plans based on performance data for athletes in team sports and individual competitions. For example, when a soccer team inputs post-match data into the system, a generating AI model analyzes it and suggests formation changes or instructions for specific players in the next match. This tactical advice is then sent from the server to the coach's terminal.

[0952] The system also provides psychological support. When golfers input data on the pressure and stress they feel before a tournament, a generating AI model analyzes this data and provides guidance on breathing techniques and visualization exercises. This mental support advice is also sent from the server to the user's device, allowing players to use it before the tournament.

[0953] Furthermore, the system receives user training and performance data, evaluates its effectiveness, and provides feedback. For example, when a club tennis player inputs daily practice data and match results, the server analyzes it using a generative AI model, evaluates serve success rate and backhand accuracy, and provides feedback for further improvement. This feedback is then sent from the server to the user's device.

[0954] This system makes it possible to provide highly personalized support to a wide range of athletes, from amateurs to professionals. This addresses the modern challenge of trainer shortages and effectively supports athletes in improving their performance and maintaining their health.

[0955] The following describes the processing flow.

[0956] Specific process flow for generating personalized training programs

[0957] Step 1:

[0958] User: Sends a training program generation request to the system. Specifically, enters basic information such as name, age, gender, height, weight, past performance data, and health status into the terminal.

[0959] Step 2:

[0960] Terminal: Receives user input data and verifies that the data is in the correct format and includes all necessary fields. After verification, it sends the data to the server in the appropriate format.

[0961] Step 3:

[0962] Server: Receives data and saves it to the database. This creates a separate data entry for each athlete, which is then used for subsequent analysis.

[0963] Step 4:

[0964] Server: Passes stored data to the generating AI model. The generating AI model generates an optimal training program and nutrition plan based on the input data.

[0965] Step 5:

[0966] Server: Sends the generated training program and nutrition plan to the user's terminal. It is sent in the appropriate format, linked to the user ID.

[0967] Step 6:

[0968] Terminal: Displays the training program and nutrition plan received on the user's terminal. The user can review and implement this.

[0969] Specific procedures for injury prevention and rehabilitation support

[0970] Step 1:

[0971] User: Submit a rehabilitation support request to the system. Specifically, enter recent training data, pain symptoms, and past health history into the terminal.

[0972] Step 2:

[0973] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[0974] Step 3:

[0975] Server: Receives data and saves it to a database. This records the user's health history and training data.

[0976] Step 4:

[0977] Server: The server passes the aforementioned data to the generating AI model to generate injury prevention advice and rehabilitation programs. The model identifies high-risk movements and suggests necessary advice and rehabilitation exercises.

[0978] Step 5:

[0979] Server: Sends generated injury prevention advice and rehabilitation programs to the user's device. Customizes the content sent based on the user ID.

[0980] Step 6:

[0981] Terminal: Displays injury prevention advice and rehabilitation programs received on the user's terminal. The user then performs training and rehabilitation according to these instructions.

[0982] Specific processing flow for performance optimization and tactical advice

[0983] Step 1:

[0984] User: Enter post-competition performance data into the system. Specifically, enter detailed match results and practice data into the terminal.

[0985] Step 2:

[0986] Terminal: Receives user input data and verifies the data format. After verification, converts it to the appropriate format for transmission to the server.

[0987] Step 3:

[0988] Server: Receives performance data and saves it to the database. This allows for the accumulation of data for each team and individual.

[0989] Step 4:

[0990] Server: Passes data to the generated AI model for analysis. The model suggests improvements and new tactics based on the match data.

[0991] Step 5:

[0992] Server: Sends generated tactical advice and optimized tactical plans to the user's terminal. Provides specific instructions and strategies to the coach's terminal.

[0993] Step 6:

[0994] Terminal: Displays tactical advice and tactical optimization plans received on the user's terminal. Coaches and team members use this to prepare for the next competition.

[0995] Specific steps for providing psychological support and improving motivation

[0996] Step 1:

[0997] User: Inputs information for psychological support into the system. Specifically, they input details such as stress levels and psychological state into the terminal.

[0998] Step 2:

[0999] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[1000] Step 3:

[1001] Server: Receives data and saves it to the database. This allows psychological data to be recorded for each athlete.

[1002] Step 4:

[1003] Server: Provides data to the generation AI model, which then generates advice for psychological support and motivation improvement. The model suggests techniques for stress management and improving concentration.

[1004] Step 5:

[1005] Server: Sends generated psychological support advice to the user's terminal. Provides the most appropriate advice based on the user ID.

[1006] Step 6:

[1007] Terminal: Displays psychological support advice received on the user's terminal. The user then implements relaxation methods and motivation-boosting techniques based on this advice.

[1008] Specific process flow for data analysis and evaluation of training effectiveness

[1009] Step 1:

[1010] User: Input training data and performance data into the system. Specifically, input detailed information about training results and match results into the terminal.

[1011] Step 2:

[1012] Terminal: Receives user input data and verifies data consistency and accuracy. After verification, converts the data to an appropriate format for transmission to the server.

[1013] Step 3:

[1014] Server: Receives data and saves it to the database. This allows for comparison with past data.

[1015] Step 4:

[1016] Server: Provides data to the generated AI model and evaluates the effectiveness of the training. The model compares past data with new data to measure its effectiveness.

[1017] Step 5:

[1018] Server: Sends generated evaluation results and feedback to the user's terminal. Provides appropriate feedback and strategic advice based on the user ID.

[1019] Step 6:

[1020] Terminal: Displays evaluation results and feedback received on the user's terminal. The user uses this to adjust their training.

[1021] In this way, by providing athletes with multifaceted and high-quality support throughout the entire system, we can resolve the problem of trainer shortages and realize high-quality training and support.

[1022] (Example 1)

[1023] Next, we will describe Example 1. 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."

[1024] Traditional training systems for athletes only provide general training plans and fail to comprehensively offer personalized training plans and nutrition plans based on individual athletes' physical characteristics, performance data, and health status, as well as injury prevention advice, rehabilitation programs, tactical advice, and psychological support. As a result, athletes do not receive sufficient support for improving their performance or maintaining their health.

[1025] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1026] In this invention, the server includes means for inputting basic information, performance data, and health status of an athlete via a terminal; means for transmitting the input data to the server; means for receiving the transmitted data on the server and storing it in a database; means for passing the stored data to a generating AI model for analysis; means for generating individual training programs and nutrition plans using the generating AI model; means for transmitting the generated training programs and nutrition plans to a user's terminal; and means for receiving the training programs and nutrition plans displayed on the user's terminal. This enables the user to receive highly personalized support based on their individual physical characteristics and performance data.

[1027] An "athlete" is an individual who trains to improve their physical performance.

[1028] "Basic information" refers to physical characteristics and personal data about individual athletes, such as height, weight, and age.

[1029] "Performance data" refers to data related to an athlete's athletic performance, such as training frequency, runtime, and training records.

[1030] "Health status" refers to information about an athlete's health, such as fatigue, pain, and general malaise.

[1031] A "device" refers to an electronic device used by a user, such as a smartphone, tablet, or personal computer.

[1032] A "server" is a computer system that receives, stores, analyzes, and runs generative models on data.

[1033] A "database" is a system that allows a server to structurally store and manage data.

[1034] A "generative AI model" is an artificial intelligence model used to analyze input data and generate training programs, advice, and plans.

[1035] A "training program" is an individualized exercise plan designed to improve an athlete's physical performance.

[1036] A "nutrition plan" is a meal plan designed to optimize an athlete's health and performance.

[1037] "Injury prevention advice" refers to specific guidance for athletes to reduce strain during training and prevent injuries.

[1038] A "rehabilitation program" is an exercise and treatment plan designed to help athletes recover from injuries and pain.

[1039] "Tactical advice" refers to strategic guidance provided in team sports or individual competitions for the upcoming match or competition.

[1040] A "tactical optimization plan" is a tactical plan designed to maximize performance in a match or competition.

[1041] "Psychological support" refers to advice and training that helps athletes manage mental pressure and stress.

[1042] This invention is a system that provides personalized training programs, nutrition plans, injury prevention measures, rehabilitation programs, tactical advice, and psychological support for sports athletes. The system uses a generative AI model to analyze the athlete's basic information, performance data, and health status. The system primarily operates with three parties: a server, a terminal, and the user.

[1043] First, the user uses a device (smartphone, tablet, PC, etc.) to input the athlete's basic information (e.g., height, weight, age), performance data (e.g., runtime, training frequency), and health status (e.g., fatigue level, presence or absence of pain). The device then sends this data to the server in a structured format (e.g., JSON). A secure protocol (e.g., HTTPS) is used for this communication.

[1044] The server stores the received data in a database (e.g., MySQL or PostgreSQL). The server checks the data's integrity and saves only the data that passes validation. After that, the server passes the saved data to a generating AI model (e.g., OpenAI GPT model) for analysis.

[1045] Examples of prompt statements used for analysis are as follows:

[1046] "When generating optimal training and nutrition plans for amateur runners, please consider the user's height, weight, and past runtime data as input data."

[1047] "Please suggest stretches and strengthening exercises to alleviate knee pain in a basketball player. Input data includes recent training data and pain location information."

[1048] "Analyze the post-match data of the soccer team and suggest formation changes and instructions for specific players for the next match."

[1049] Based on the analysis results obtained from the generating AI model, the server generates personalized training programs, nutrition plans, injury prevention advice, rehabilitation programs, tactical advice, and psychological support. For example, if an amateur runner inputs their height (170cm), weight (65kg), and past runtime data into the system, the server uses the generating AI model to suggest a three-times-a-week running program and a high-protein meal plan.

[1050] Next, the server sends the generated plan and advice to the user's device. The device receives this data and displays it within the app. For example, if a basketball player enters knee pain, the server sends a rehabilitation plan of stretches and muscle strengthening exercises, which is then displayed on the device. As a result, athletes can receive personalized feedback in real time, which they can use for their daily training and health management.

[1051] This system addresses the modern challenge of trainer shortages by providing advanced personalized support to a wide range of athletes, from amateurs to professionals, effectively assisting in improving athlete performance and maintaining health. Users can easily obtain personalized training plans and advice simply by entering specific data.

[1052] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1053] Step 1:

[1054] The user enters basic information into the device.

[1055] Users enter basic information such as height, weight, age, runtime, training frequency, and health status using a dedicated app on their device or a web form. The entered data is created in a structured format (e.g., JSON). This data will later be used as input for analysis.

[1056] Step 2:

[1057] The device sends data to the server.

[1058] The device sends the entered data to the server using a secure protocol (e.g., HTTPS). The input data is sent in JSON format, and the server receives it. Specifically, when the send button is pressed, the device's app sends an HTTP POST request to the server.

[1059] Step 3:

[1060] The server saves the data to the database.

[1061] The server parses the received JSON data and saves it to a database (e.g., MySQL or PostgreSQL). Before saving, it validates the data to remove incomplete or invalid data. For example, the server checks that the data is in the correct format (e.g., whether all required fields are filled in).

[1062] Step 4:

[1063] The server passes data to the generated AI model.

[1064] The server extracts the necessary data from the database and passes it to the AI ​​model. During this process, it generates prompts, formats the data, and then sends it. For example, it might pass data along with a prompt such as, "Generate an optimal training plan and nutrition plan for an amateur runner." The input data is provided in JSON format.

[1065] Step 5:

[1066] The server receives results from the generated AI model.

[1067] The generative AI model analyzes the provided prompt text and data to generate an optimal training plan, nutrition plan, and advice. The server receives these results and stores them in JSON format. Specifically, it receives the analysis results returned by the generative AI model as an HTTP response.

[1068] Step 6:

[1069] The server sends the results to the user's terminal.

[1070] The server sends the generated training plan and advice results to the user's device. The device receives this data and displays it within the app. For example, it applies the data received from the server to the calendar and notification functions of the app on the device.

[1071] Step 7:

[1072] The user checks and executes the results on their device.

[1073] Users can review training and nutrition plans generated on their devices and use them to guide their actual training and meal planning. Specifically, users check the plan in a particular section of the app and act according to the schedule. For example, they might receive push notifications about their daily training.

[1074] By clearly defining the specific processes performed at each step, it becomes possible to understand in detail how this system works and how it provides value to the user.

[1075] (Application Example 1)

[1076] Next, we will explain Application Example 1. In the following explanation, 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."

[1077] There is a need for a system that maximizes the effectiveness of training for factory robot operators, supports injury prevention and improved work performance, and provides appropriate feedback and optimization in real time. Conventional training systems have struggled to provide personalized suggestions based on the individual health status and performance data of operators.

[1078] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1079] In this invention, the server includes means for receiving and storing operator input data; means for using a generative model to analyze the input data and generate individual training programs and work plans; means for transmitting the generated training programs and work plans to a user's terminal; means for receiving training results and using a generative model to optimize the plan in real time; means for transmitting the optimized training programs and work plans to a user's terminal; and means for receiving the optimized training programs and work plans displayed on the terminal. This maximizes the effectiveness of operator training, enabling injury prevention and improved work performance.

[1080] "Operator input data" refers to information entered by factory robot operators regarding their physical characteristics, operational experience, work performance data, and health status.

[1081] A "generative model" is an AI system that includes algorithms for analyzing input data and generating individual training programs and work plans.

[1082] A "training program" is a set of specific training content and schedules created by a generative model that operators should undergo.

[1083] A "work plan" is a set of guidelines and procedures created by a generative model that specify what tasks an operator should perform.

[1084] "User terminal" refers to electronic devices such as smartphones, tablets, and head-mounted displays that operators use to view and execute training programs and work plans.

[1085] "Training outcomes" refer to data obtained as a result of operators performing training and tasks, including information on performance improvements and health improvements.

[1086] "Optimizing the plan in real time" means that the generative model continuously re-evaluates the training program and work plan based on the training results, and adjusts the content immediately as needed.

[1087] "Injury prevention advice" refers to suggestions from a generative model regarding specific actions and precautions that operators should take to prevent injuries.

[1088] A "rehabilitation program" is a training and exercise plan that injured operators should follow to recover.

[1089] "Work tactical advice" refers to suggestions regarding specific work methods and strategies, provided based on generative models.

[1090] An "optimization plan" is a set of improvement suggestions and specific procedures generated to enable operators to perform their tasks more efficiently.

[1091] Modes for carrying out the invention

[1092] Embodiments of this invention are shown below. The configuration of hardware and software for realizing the invention, as well as the data processing and calculation methods, will be described.

[1093] System Configuration

[1094] The system consists of the following main elements:

[1095] 1. User devices: Smartphones (iOS or Android), head-mounted displays (HoloLens, Oculus, etc.).

[1096] 2. Server: Backend for data storage and generation, and for data analysis using AI models.

[1097] 3. Software: Mobile application development kits (Flutter, React Native, etc.), generative AI model backend (TensorFlow, PyTorch).

[1098] Data flow

[1099] 1. Receiving and saving input data:

[1100] Operators use a terminal to input their physical characteristics, operational experience, work performance data, and health status.

[1101] This data is sent from the terminal to the server and stored in the database.

[1102] 2. Analysis using generative AI models:

[1103] The server passes the stored data to a generating AI model, which then performs analysis using Google Cloud AI or AWS Machine Learning.

[1104] The generative AI model generates individual training programs and work plans based on the analysis results.

[1105] 3. Submission of training program and work plan:

[1106] The server sends the generated training program and work plan to the user's terminal.

[1107] The user's device displays the transmitted content, which the operator can then review and execute.

[1108] 4. Input of training results and real-time optimization:

[1109] The operator enters the training results into a terminal, and this data is also sent to the server.

[1110] Based on the training results, the server generates an AI model in real time, which then re-evaluates and optimizes the training program and work plan.

[1111] 5. Injury prevention and rehabilitation program:

[1112] When an operator inputs information about their health status and injuries, the AI ​​model generates injury prevention advice and rehabilitation programs.

[1113] The server sends these programs to the user's terminal, where they are displayed.

[1114] 6. Advice and optimization of work tactics:

[1115] Once the operator inputs work data, the generated AI model produces work tactical advice and optimization plans.

[1116] This information is sent to the user's terminal via the server and displayed on the terminal.

[1117] Examples

[1118] Specific example 1:

[1119] Factory robot operators open an app and input their current work performance (operation speed, accuracy, failure rate, etc.). For example, if an operator inputs their arm fatigue level and the success rate of precision operations, the generated AI will use this information to suggest arm muscle training and rest plans.

[1120] Specific example 2:

[1121] When a new factory operator joins the company, they input their experience level and physical data through an app, and an AI generates an optimal training program based on that data. For example, beginners are provided with a step-by-step training plan that starts with basic operations and gradually increases in difficulty.

[1122] Example of a prompt

[1123] AI for training factory robot operators:

[1124] 1. Basic information: Height, weight, operating experience, work data

[1125] 2. Specific operational performance: accuracy, speed, failure rate

[1126] 3. Training plan generation: Recommendations for repetitive training of maneuvers, strength training, etc.

[1127] "What is the best training plan for when my arms are experiencing high levels of fatigue?"

[1128] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1129] Step 1:

[1130] Operators use a terminal to input their physical characteristics, operational experience, work performance data, and health status. The entered data is reviewed on the terminal and sent to the server. This step provides an interface for collecting operator data and sending it to the server.

[1131] Step 2:

[1132] The server stores the data transmitted by the operator in a database. The stored data is then used for analysis by a subsequent generative AI model. In this step, the server efficiently manages the data using a data storage system.

[1133] Step 3:

[1134] The server passes the stored operator data to the generating AI model and begins the analysis. The generating AI model generates individual training programs and work plans based on the input data. For example, it performs data analysis using TensorFlow or PyTorch. In this step, the AI ​​model analyzes the data and designs an appropriate training program and work plan.

[1135] Step 4:

[1136] The generated training program and work plan are sent from the server to the user's terminal. The terminal displays the received training program and work plan, making them ready for the operator to review and execute. In this step, the server sends data using the appropriate communication protocol, and the terminal receives it.

[1137] Step 5:

[1138] Operators conduct training and tasks, and input the results into a terminal. The entered training results are then sent back to the server. In this step, a user interface for operators to provide feedback is crucial.

[1139] Step 6:

[1140] The server receives the training results, and the generated AI model re-evaluates and optimizes the training program and work plan in real time. This optimization is performed based on the latest data from the operators. In this step, the AI ​​model has the ability to dynamically update and adapt the plan.

[1141] Step 7:

[1142] The server resends the optimized training program and work plan to the user's terminal. The terminal receives this, the operator reviews the content, and they can continue working according to the latest training and work plan. In this step, the efficiency of data communication between the server and the terminal is crucial.

[1143] Specific example

[1144] For example, a factory robot operator opens an app and inputs current work performance data (operating speed, accuracy, failure rate, etc.). This data is sent to a server, where a generated AI model analyzes it and suggests arm muscle training and rest plans. This information is then sent back to the terminal, and the operator can review and implement the plan, which is expected to improve performance.

[1145] Example of a prompt

[1146] AI for training factory robot operators:

[1147] 1. Basic information: Height, weight, operating experience, work data

[1148] 2. Specific operational performance: accuracy, speed, failure rate

[1149] 3. Training plan generation: Recommendations for repetitive training of maneuvers, strength training, etc.

[1150] "What is the best training plan for when my arms are experiencing high levels of fatigue?"

[1151] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1152] This invention combines a system that provides athletes with personalized training programs, injury prevention, rehabilitation, tactical advice, and psychological support with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out this invention are described below.

[1153] First, when a user uses the system, they input their physical characteristics, performance data, health status, and emotional data using a terminal. This data is sent from the terminal to the server. The server stores the received data in a database and passes it to a generative AI model and emotion engine for analysis.

[1154] The server uses a generative AI model to analyze the user's physical and performance data and generate personalized training programs and nutrition plans. Additionally, an emotion engine analyzes the user's emotional data to understand their current mental state and stress level. For example, if an amateur runner inputs their height, weight, past runtime data, and emotional data into the system, the generative AI model and emotion engine analyze this data to generate an appropriate training program, nutrition plan, and motivational advice for that runner. This generated plan and advice are then sent from the server to the user's terminal, where they can review and implement it.

[1155] To support injury prevention and rehabilitation, the system allows users to input training data, health status, and emotional data. The generating AI model then generates injury prevention advice and rehabilitation programs, while the emotional engine provides advice aimed at mental stability. For example, if a basketball player inputs recent training data, knee pain, and anxiety, the generating AI model suggests stretches and strength training exercises to reduce knee stress, while the emotional engine proposes relaxation methods to reduce stress. This rehabilitation plan and mental support advice are sent to the user's device and effectively utilized in actual rehabilitation.

[1156] Regarding tactical advice, when a soccer team inputs post-match data and emotional data into the system, a generative AI model analyzes the performance data and suggests tactical improvements, while an emotional engine supports the team's overall tactical adaptation based on player motivation and teamwork. This allows new tactics to be executed more effectively. This tactical advice is sent from the server to the coach's terminal and displayed along with specific tactical details.

[1157] By using an enhanced system for psychological support, feedback tailored to the user's mental state and emotions is provided. For example, if a golfer inputs data on the pressure and stress they feel before a tournament, a generative AI model generates specific training and advice for mental support, and an emotion engine suggests relaxation techniques and imagery training. This helps to promote the player's psychological stability. This support is sent to the user's terminal via the server and can be used by the player before the tournament.

[1158] Finally, regarding data analysis and evaluation of training effectiveness, emotional data is analyzed along with training and performance data. This allows for the evaluation of not only the athlete's training effectiveness but also their psychological stability. For example, when a club tennis player inputs daily practice data, match results, and emotional data, a generative AI model evaluates the effectiveness of the training, and an emotional engine provides feedback that also takes into account the state of mental health. This feedback is sent from the server to the user's terminal, and the user uses it to adjust their training and mental care.

[1159] This system provides comprehensive support for improving athletes' physical and mental performance. By combining it with an emotional engine, it becomes possible to provide even more personalized support than before, and is expected to significantly improve the quality of athlete training, injury prevention, rehabilitation, tactical improvement, and psychological support.

[1160] The following describes the processing flow.

[1161] Personalized training program generation and the specific processing flow of the emotion engine

[1162] Step 1:

[1163] User: Sends a training program generation request to the system. Specifically, enters name, age, gender, height, weight, past performance data, health status, and emotional data (e.g., stress level, emotional score) into the terminal.

[1164] Step 2:

[1165] Terminal: Receives user input data and verifies that the data is in the correct format and all required fields are present. After verification, it sends the data to the server in the appropriate format.

[1166] Step 3:

[1167] Server: Receives data and saves it to the database. This creates a unique data entry for each user, which is then used for subsequent analysis.

[1168] Step 4:

[1169] Server: Passes stored data to the generating AI model and emotion engine. The generating AI model generates an optimal training program and nutrition plan based on the input physical and performance data.

[1170] Step 5:

[1171] Server: The emotion engine analyzes the user's emotional data and generates motivational advice based on their mental state. For example, if the user shows a high stress level, it suggests relaxation techniques or exercises to reduce stress.

[1172] Step 6:

[1173] Server: Sends the generated training program, nutrition plan, and emotional engine-driven advice to the user's device. It links the information to the user ID and sends it in the appropriate format.

[1174] Step 7:

[1175] Terminal: Displays training programs, nutrition plans, and emotional engine advice received on the user's terminal. The user can review and implement these.

[1176] Specific procedures for injury prevention and rehabilitation support

[1177] Step 1:

[1178] User: Submit a rehabilitation support request to the system. Specifically, enter recent training data, health status, and emotional data (e.g., pain level, emotional score, etc.) into the terminal.

[1179] Step 2:

[1180] Terminal: Receives user input data and verifies its accuracy. After verification, it sends the data to the server in the appropriate format.

[1181] Step 3:

[1182] Server: Receives data and saves it to the database. This records health history and training data.

[1183] Step 4:

[1184] Server: The server passes the aforementioned data to the generating AI model to generate injury prevention advice and rehabilitation programs. The model identifies high-risk movements and suggests necessary injury prevention measures.

[1185] Step 5:

[1186] Server: The emotion engine analyzes the user's emotional data and generates advice aimed at promoting mental well-being. For example, if the user is showing signs of anxiety, it will suggest specific relaxation methods to alleviate that anxiety.

[1187] Step 6:

[1188] Server: Sends generated injury prevention advice, rehabilitation programs, and emotional engine advice to the user's device. Customizes the content sent based on the user ID.

[1189] Step 7:

[1190] Terminal: Displays injury prevention advice, rehabilitation programs, and emotional engine advice received on the user's terminal. The user then performs training and rehabilitation according to these recommendations.

[1191] Specific processing flow for performance optimization and tactical advice

[1192] Step 1:

[1193] User: Input post-competition performance and emotional data into the system. Specifically, input detailed information such as match results, training data, emotional scores, and stress levels.

[1194] Step 2:

[1195] Terminal: Receives user input data and verifies the data format. After verification, it sends the data to the server in the appropriate format.

[1196] Step 3:

[1197] Server: Stores received performance and sentiment data in a database. This allows for the accumulation of data for each team and individual.

[1198] Step 4:

[1199] Server: Passes data to the generated AI model, analyzes performance, and generates tactical improvements and optimization plans. For example, if a particular player's performance is declining, it identifies the cause and suggests countermeasures.

[1200] Step 5:

[1201] Server: The emotion engine analyzes data and generates tactical advice that takes into account the players' motivation and mental state. For example, if stress levels are high throughout the team, it might suggest leadership training or team-building exercises.

[1202] Step 6:

[1203] Server: Sends generated tactical advice and optimized tactical plans to the user's terminal. Provides specific instructions and strategies to the coach's terminal.

[1204] Step 7:

[1205] Terminal: Displays tactical advice and tactical optimization plans received on the user's terminal. The coach uses this to prepare for the next match.

[1206] Specific steps for providing psychological support and improving motivation

[1207] Step 1:

[1208] User: Enter information for psychological support into the system. Specifically, enter details about stress levels, emotion scores, and recent psychological states.

[1209] Step 2:

[1210] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[1211] Step 3:

[1212] Server: Stores received emotional data and related information in a database. This ensures that psychological data is recorded for each athlete.

[1213] Step 4:

[1214] Server: Passes data to the generation AI model and generates advice for psychological support and motivation improvement. For example, it provides training and advice for mental support.

[1215] Step 5:

[1216] Server: The emotion engine analyzes data and provides relaxation methods and motivation-boosting techniques tailored to the user's psychological state.

[1217] Step 6:

[1218] Server: Sends generated psychological support advice to the user's terminal. Provides the most appropriate advice based on the user ID.

[1219] Step 7:

[1220] Terminal: Displays psychological support advice received on the user's terminal. The user then implements relaxation methods and motivation-boosting techniques based on this advice.

[1221] Specific process flow for data analysis and evaluation of training effectiveness

[1222] Step 1:

[1223] User: Inputs training data, performance data, and emotional data into the system. Specifically, this involves detailed input of training results, match results, and emotional data (e.g., emotional score, stress level, etc.).

[1224] Step 2:

[1225] Terminal: Receives user input data and verifies data consistency and accuracy. After verification, sends it to the server in the appropriate format.

[1226] Step 3:

[1227] Server: Receives data and saves it to the database. This allows for comparison with past data.

[1228] Step 4:

[1229] Server: Provides data to the generated AI model and evaluates the effectiveness of the training. The model compares past data with new data to measure the effectiveness of the training.

[1230] Step 5:

[1231] Server: The emotion engine analyzes the data and generates comprehensive feedback, including the user's psychological stability. For example, it presents not only the effectiveness of the training but also the user's mental health status.

[1232] Step 6:

[1233] Server: Sends generated evaluation results and feedback to the user's terminal. Provides appropriate feedback and strategic advice based on the user ID.

[1234] Step 7:

[1235] Terminal: Displays evaluation results and feedback received on the user's terminal. The user uses this to adjust their training and mental care.

[1236] In this way, by providing athletes with multifaceted, high-quality support throughout the entire system, the problem of trainer shortages can be resolved, and high-quality training and support can be realized. By combining this with an emotional engine, even more precise individualized support becomes possible, effectively supporting improved athletic performance and psychological stability.

[1237] (Example 2)

[1238] Next, we will describe Example 2. 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."

[1239] Conventional athlete support systems focused on providing physical training programs and nutritional plans, but lacked an element of emotional support, failing to address the user's mental state and stress levels. As a result, training effectiveness, injury prevention, rehabilitation processes, and tactical advice were sometimes not fully realized. This invention aims to solve these problems and provide comprehensive support for athletes' physical and mental performance.

[1240] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1241] In this invention, the server includes means for receiving and storing user input data, means for using a generative model and an emotion engine to analyze the input data and generate individual training programs, nutrition plans, and emotional support advice, and means for transmitting the generated training programs, nutrition plans, and emotional support advice to the user's terminal. This makes it possible to achieve not only improved physical performance of athletes but also mental stability and stress management simultaneously.

[1242] A "user" is a person who uses the system to input their physical characteristics, performance data, health status, and emotional data.

[1243] "Input data" refers to information that users provide to the system, such as physical characteristics, performance data, health status, and emotional data.

[1244] A "generative model" is an artificial intelligence model that analyzes a user's physical characteristics and performance data to generate individualized training programs and nutrition plans.

[1245] An "emotion engine" is software that analyzes a user's emotional data to understand their mental state and stress level, and then generates advice.

[1246] A "training program" is an individualized exercise plan designed based on the user's physical characteristics and performance data.

[1247] A "nutrition plan" is a plan that provides instructions on the optimal diet and nutrient intake methods based on the user's physical characteristics and performance data.

[1248] "Emotional support advice" refers to advice that provides mental support, such as relaxation methods and motivation-enhancing techniques, based on the user's emotional data.

[1249] "Injury prevention advice" is advice that analyzes the user's training data and health status and suggests measures to reduce the risk of injury.

[1250] A "rehabilitation program" is a program that directs injured users through rehabilitation activities aimed at their return to activity.

[1251] "Performance data" refers to data on exercise results recorded by users during sports or training.

[1252] "Tactical advice" is advice that analyzes the user's performance data and suggests tactical improvements and new strategies in the game.

[1253] A "tactical optimization plan" is a specific action plan based on tactical advice, designed to maximize team and individual performance.

[1254] A "terminal" is a device used by users to input data and receive training programs, nutrition plans, and advice from the server.

[1255] A "server" is a system that receives, stores, and analyzes data from users, and generates training programs and advice.

[1256] This invention is a system that provides personalized training programs, nutritional plans, and emotional support advice based on user input data. The system mainly consists of a server, terminals, and a generative AI model and emotion engine.

[1257] Hardware and software configuration:

[1258] 1. Terminal

[1259] Hardware: Input devices such as smartphones, tablets, and personal computers.

[1260] Software: A dedicated application or web browser is used to input user data and communicate with the system.

[1261] 2. Server

[1262] Hardware: A high-performance server computer.

[1263] Software: Applications that integrate database management systems (e.g., MySQL, PostgreSQL) and analysis engines.

[1264] 3. Generative AI Models

[1265] Software: Uses machine learning algorithms (e.g., TensorFlow, PyTorch) to analyze user data and generate training programs and nutrition plans.

[1266] 4. Emotional Engine

[1267] Software: Use emotion analysis programs (e.g., Affectiva, IBM Watson) to analyze user emotion data and generate emotional support advice.

[1268] System processing description:

[1269] Users input their physical characteristics, performance data, health status, and emotional data using a device. This data is sent from the device to a server, which stores the received data in a database. The stored data is then passed to a generative AI model and an emotion engine for analysis.

[1270] The generative AI model generates personalized training programs and nutrition plans based on the user's physical and performance data. Meanwhile, the emotional engine analyzes mental state and stress levels based on emotional data, providing advice on relaxation methods and motivation enhancement.

[1271] For example, the generative AI model performs analysis using the following specific prompt:

[1272] User's height: 175cm

[1273] User's weight: 70kg

[1274] Past runtime data: 5km 30 minutes

[1275] Emotional data: High stress level

[1276] Generate a training program and nutrition plan tailored to the user.

[1277] Specific example:

[1278] Example 1: In the case of an amateur runner

[1279] Input data: Height 175cm, weight 70kg, runtime data for the past week, high stress level.

[1280] Output of the generated AI model: Interval training, 3 times a week.

[1281] Emotional engine output: Deep breathing and relaxation techniques for motivation enhancement.

[1282] Example 2: In the case of a basketball player

[1283] Input data: Recent training data, knee pain, anxiety.

[1284] Output of the generated AI model: Stretching and muscle strengthening exercises to reduce stress on the knees.

[1285] Emotional Engine Output: Meditation Methods for Stress Reduction.

[1286] The generated training program, nutrition plan, and emotional support advice are sent from the server to the user's terminal, where the user reviews and implements this information. This entire process allows the user to improve their physical and mental performance.

[1287] This system is extremely useful for athletes because it not only optimizes training but also provides injury prevention, rehabilitation, and tactical advice.

[1288] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1289] Step 1:

[1290] The user enters input data using a terminal.

[1291] Operation: The user opens a dedicated application and enters their physical characteristics (e.g., height, weight), performance data (e.g., runtime data), health status (e.g., knee pain), and emotional data (e.g., stress level). They then review the entered data and press the submit button.

[1292] Input: Physical characteristics, performance data, health status, emotional data

[1293] Output: Input data (data formatted in JSON format, etc.)

[1294] Step 2:

[1295] The terminal sends the input data to the server.

[1296] Operation: When the send button on the device is pressed, the input data is sent to the server as an HTTP request. An internet connection is required for transmission.

[1297] Input: Input data (data formatted in JSON format, etc.)

[1298] Output: Input data sent to the server (HTTP request)

[1299] Step 3:

[1300] The server receives the input data and saves it to the database.

[1301] Operation: The server retrieves input data from received HTTP requests and stores it in the database. Each piece of data is stored in the database for each user.

[1302] Input: Input data extracted from the HTTP request

[1303] Output: User data stored in the database

[1304] Step 4:

[1305] The server passes the stored data to the generating AI model and emotion engine.

[1306] Operation: The server retrieves the necessary data from the database and converts it into a data format for passing to the generating AI model and emotion engine. This may involve using formats such as Python's DataFrame.

[1307] Input: User data stored in the database

[1308] Output: Data to be passed to the generative AI model and emotion engine.

[1309] Step 5:

[1310] The generative AI model generates training programs and nutrition plans.

[1311] Operation: The generative AI model analyzes the user's physical characteristics and performance data to generate a personalized training program and nutrition plan. An example using prompts is shown below:

[1312] User's height: 175cm

[1313] User's weight: 70kg

[1314] Past runtime data: 5km 30 minutes

[1315] Please generate a training program and nutrition plan tailored to this user.

[1316] Input: User's physical characteristics, performance data (in prompt format)

[1317] Output: Individualized training programs and nutrition plans

[1318] Step 6:

[1319] The emotion engine analyzes emotional data and generates advice on relaxation methods and motivation enhancement.

[1320] Operation: The emotion engine analyzes the user's emotional data to understand their current mental state and stress level. Based on the analysis results, it generates specific advice on relaxation methods and ways to improve motivation.

[1321] Input: User sentiment data

[1322] Output: Relaxation methods and motivation-boosting advice

[1323] Step 7:

[1324] The server sends the generated training program, nutrition plan, and emotional advice to the user's device.

[1325] Operation: The server compiles the analysis results obtained from the generated AI model and emotion engine and sends them to the user's terminal. This transmission is also done via HTTP response.

[1326] Input: Individual training programs, nutrition plans, relaxation methods, and motivational advice.

[1327] Output: Analysis results (HTTP response) sent to the user's terminal.

[1328] Step 8:

[1329] Users can view analysis results on their devices and receive training and mental care based on those results.

[1330] Operation: The user opens a dedicated application on their device and reviews the training program, nutrition plan, and emotional support advice sent from the server. After reviewing, they proceed to implement the training and mental care.

[1331] Input: Analysis results sent from the server (training program, nutrition plan, relaxation methods, motivation improvement advice)

[1332] Output: User action plan (training implementation, mental care implementation)

[1333] (Application Example 2)

[1334] Next, we will explain application example 2. In the following explanation, 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."

[1335] Traditional training systems for athletes could provide physical training programs and nutritional plans, but they failed to consider the user's mental state and emotional data. This resulted in a lack of mental support, hindering the achievement of optimal training results. Furthermore, the lack of mental support in rehabilitation and tactical advice made it difficult to improve the overall performance of athletes.

[1336] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1337] In this invention, the server includes means for receiving and storing input data of athletes; means for using a generative model to analyze the input data and generate individual training programs and nutrition plans; means for transmitting the generated training programs and nutrition plans to the user's terminal; means for using an emotion engine to analyze the user's emotional data and provide mental support; means for transmitting the mental support to the user's terminal; and means for receiving the mental support. This makes it possible to provide comprehensive training programs and support that take into account not only the user's physical health but also their mental health.

[1338] "Input data" refers to information that athletes provide to the system, such as physical characteristics, performance data, health status, and emotional data.

[1339] A "generative model" is an artificial intelligence algorithm that analyzes received input data to generate individual training programs, nutrition plans, injury prevention advice, rehabilitation programs, tactical advice, and tactical optimization plans.

[1340] An "emotion engine" is an analytical engine that analyzes users' emotional data and provides emotional support.

[1341] A "training program" is a plan of exercises and workouts aimed at improving an athlete's performance.

[1342] A "nutrition plan" is a guideline for meals and supplements tailored to an athlete's physical condition and goals.

[1343] "Injury prevention advice" refers to specific instructions and recommendations for athletes to avoid injuries.

[1344] A "rehabilitation program" is a training and treatment plan for athletes to recover from injuries.

[1345] "Tactical advice" refers to instructions and recommendations for athletes or teams to effectively execute tactics in competition.

[1346] A "tactical optimization plan" is a specific plan for optimizing a team's tactics based on match and competition data.

[1347] "User's device" refers to electronic devices such as smartphones and tablets used by athletes.

[1348] "Mental support" refers to assistance aimed at promoting the psychological stability of athletes, such as relaxation and stress management.

[1349] This invention is a system that provides athletes with personalized training programs, injury prevention, rehabilitation, tactical advice, and psychological support. The system is implemented by combining an emotion engine that recognizes the user's emotions with a generative AI model.

[1350] Initially, when a user uses the system, they input their physical characteristics, performance data, health status, and emotional data using a device such as a smartphone. This data is then sent from the device to the server. The server stores the received data in a database and passes it to a generative AI model and emotion engine for analysis.

[1351] Next, the server uses a generative AI model to analyze the user's physical and performance data and generate a personalized training program and nutrition plan. Additionally, the emotion engine analyzes the user's emotional data to understand their current mental state and stress level.

[1352] The generated AI model and emotion engine analyze the user's data, and the resulting training program, nutrition plan, and emotional support content are then sent back from the server to the user's device and displayed there. This allows the user to receive specific training programs and emotional support.

[1353] For example, when a gym user accesses the system and inputs their current physical and emotional data, the generative AI model creates a training program, and the emotional engine provides mental support. For instance, they might receive advice such as, "Your stress level is high, so let's focus on stretching today to help you relax."

[1354] The hardware used includes smartphones, servers, and databases. The software includes generative AI models, emotion engines, and frontends (applications that run on the user's device). Specifically, it utilizes server-side APIs using Python and Flask, the EmotionEngine library, and AI model libraries.

[1355] Examples of prompt messages include the following:

[1356] "Based on the user's health and emotional data, we will provide personalized training programs and mental support. Please generate specific advice based on the following data."

[1357] Height: 175cm

[1358] Weight: 65kg

[1359] Performance data: [10, 15, 12]

[1360] Health condition: Good

[1361] Emotional data: {'happiness': 0.8, 'stress': 0.2}''

[1362] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1363] Step 1:

[1364] Users input physical characteristics, performance data, health status, and emotional data using a smartphone or other device. This allows the system to collect necessary information from the user and prepare it for further processing. Input includes height, weight, historical runtime data, and emotional status (happiness level, stress level, etc.).

[1365] Step 2:

[1366] The terminal sends the data entered by the user to the server. The server stores the received data in a database. This ensures that the data is securely managed and ready for analysis. The data stored in the database includes the user ID and various input data.

[1367] Step 3:

[1368] The server uses a generative AI model to analyze stored physical and performance data and generate an optimal training program and nutrition plan for the user. The analysis employs machine learning algorithms to evaluate current data in comparison to historical data. The output is a individually customized training program and nutrition plan.

[1369] Step 4:

[1370] The server uses an emotion engine to analyze the user's emotional data. This allows the server to understand the user's mental state and stress level, generating data to provide appropriate emotional support. The analysis includes trend analysis of emotional data and assessment of psychological state. The output is specific advice on emotional support based on the user's mental state.

[1371] Step 5:

[1372] The server then sends the generated training program, nutrition plan, and mental support back to the user's device. This allows the user to view a concrete action plan on their device. The server sends the data to the user's device in the appropriate format, so the user can receive the information in a visually easy-to-understand way.

[1373] Step 6:

[1374] The user's device displays the received data, allowing them to review the provided plans and support. This enables users to concretely implement their daily training, health management, and mental support. The display includes a training program shown chronologically, and a nutrition plan that includes recommended meals.

[1375] For example, when a user starts a workout at the gym, if emotional data indicates a high stress level, the system will provide advice such as, "Today, let's focus on light stretching and relaxation." This advice is displayed on the user's device and can be referenced during the workout.

[1376] Example of a prompt:

[1377] "Based on the user's health and emotional data, we will provide personalized training programs and mental support. Please generate specific advice based on the following data."

[1378] Height: 175cm

[1379] Weight: 65kg

[1380] Performance data: [10, 15, 12]

[1381] Health condition: Good

[1382] Emotional data: {'happiness': 0.8, 'stress': 0.2}''

[1383] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1384] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1385] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1386] [Fourth Embodiment]

[1387] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1388] As shown in Figure 7, the 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.

[1389] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1390] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1391] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1393] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1394] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1395] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1396] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1398] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1400] This invention is a system that personalizes training programs for sports athletes, providing injury prevention, performance optimization, tactical advice, and psychological support. The system uses a generative AI model to analyze the athlete's input data and generate optimal training plans and advice.

[1401] First, when a user uses this system, they input basic information such as their physical characteristics, performance data, and health status on their device. This information is then sent from the device to the server. The server stores the received data in a database and passes it to a generating AI model for analysis.

[1402] The server uses a generative AI model to generate optimal training programs and nutrition plans for individual athletes. For example, if an amateur runner inputs their height, weight, and past runtime data into the system, the generative AI model analyzes this data and proposes a three-times-a-week running program and a high-protein meal plan for that runner. This generated plan is sent from the server to the user's terminal for review and execution.

[1403] Next, athletes can input training data and health status into the system, enabling it to provide injury prevention advice and support for rehabilitation programs. When a basketball player inputs recent training data and knee pain, the server uses a generative AI model to suggest stretches and muscle-strengthening exercises to reduce stress on the knee. This rehabilitation plan is also sent to the user's device and used in actual rehabilitation.

[1404] Furthermore, the system provides tactical advice and optimization plans based on performance data for athletes in team sports and individual competitions. For example, when a soccer team inputs post-match data into the system, a generating AI model analyzes it and suggests formation changes or instructions for specific players in the next match. This tactical advice is then sent from the server to the coach's terminal.

[1405] The system also provides psychological support. When golfers input data on the pressure and stress they feel before a tournament, a generating AI model analyzes this data and provides guidance on breathing techniques and visualization exercises. This mental support advice is also sent from the server to the user's device, allowing players to use it before the tournament.

[1406] Furthermore, the system receives user training and performance data, evaluates its effectiveness, and provides feedback. For example, when a club tennis player inputs daily practice data and match results, the server analyzes it using a generative AI model, evaluates serve success rate and backhand accuracy, and provides feedback for further improvement. This feedback is then sent from the server to the user's device.

[1407] This system makes it possible to provide highly personalized support to a wide range of athletes, from amateurs to professionals. This addresses the modern challenge of trainer shortages and effectively supports athletes in improving their performance and maintaining their health.

[1408] The following describes the processing flow.

[1409] Specific process flow for generating personalized training programs

[1410] Step 1:

[1411] User: Sends a training program generation request to the system. Specifically, enters basic information such as name, age, gender, height, weight, past performance data, and health status into the terminal.

[1412] Step 2:

[1413] Terminal: Receives user input data and verifies that the data is in the correct format and includes all necessary fields. After verification, it sends the data to the server in the appropriate format.

[1414] Step 3:

[1415] Server: Receives data and saves it to the database. This creates a separate data entry for each athlete, which is then used for subsequent analysis.

[1416] Step 4:

[1417] Server: Passes stored data to the generating AI model. The generating AI model generates an optimal training program and nutrition plan based on the input data.

[1418] Step 5:

[1419] Server: Sends the generated training program and nutrition plan to the user's terminal. It is sent in the appropriate format, linked to the user ID.

[1420] Step 6:

[1421] Terminal: Displays the training program and nutrition plan received on the user's terminal. The user can review and implement this.

[1422] Specific procedures for injury prevention and rehabilitation support

[1423] Step 1:

[1424] User: Submit a rehabilitation support request to the system. Specifically, enter recent training data, pain symptoms, and past health history into the terminal.

[1425] Step 2:

[1426] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[1427] Step 3:

[1428] Server: Receives data and saves it to a database. This records the user's health history and training data.

[1429] Step 4:

[1430] Server: The server passes the aforementioned data to the generating AI model to generate injury prevention advice and rehabilitation programs. The model identifies high-risk movements and suggests necessary advice and rehabilitation exercises.

[1431] Step 5:

[1432] Server: Sends generated injury prevention advice and rehabilitation programs to the user's device. Customizes the content sent based on the user ID.

[1433] Step 6:

[1434] Terminal: Displays injury prevention advice and rehabilitation programs received on the user's terminal. The user then performs training and rehabilitation according to these instructions.

[1435] Specific processing flow for performance optimization and tactical advice

[1436] Step 1:

[1437] User: Enter post-competition performance data into the system. Specifically, enter detailed match results and practice data into the terminal.

[1438] Step 2:

[1439] Terminal: Receives user input data and verifies the data format. After verification, converts it to the appropriate format for transmission to the server.

[1440] Step 3:

[1441] Server: Receives performance data and saves it to the database. This allows for the accumulation of data for each team and individual.

[1442] Step 4:

[1443] Server: Passes data to the generated AI model for analysis. The model suggests improvements and new tactics based on the match data.

[1444] Step 5:

[1445] Server: Sends generated tactical advice and optimized tactical plans to the user's terminal. Provides specific instructions and strategies to the coach's terminal.

[1446] Step 6:

[1447] Terminal: Displays tactical advice and tactical optimization plans received on the user's terminal. Coaches and team members use this to prepare for the next competition.

[1448] Specific steps for providing psychological support and improving motivation

[1449] Step 1:

[1450] User: Inputs information for psychological support into the system. Specifically, they input details such as stress levels and psychological state into the terminal.

[1451] Step 2:

[1452] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[1453] Step 3:

[1454] Server: Receives data and saves it to the database. This allows psychological data to be recorded for each athlete.

[1455] Step 4:

[1456] Server: Provides data to the generation AI model, which then generates advice for psychological support and motivation improvement. The model suggests techniques for stress management and improving concentration.

[1457] Step 5:

[1458] Server: Sends generated psychological support advice to the user's terminal. Provides the most appropriate advice based on the user ID.

[1459] Step 6:

[1460] Terminal: Displays psychological support advice received on the user's terminal. The user then implements relaxation methods and motivation-boosting techniques based on this advice.

[1461] Specific process flow for data analysis and evaluation of training effectiveness

[1462] Step 1:

[1463] User: Input training data and performance data into the system. Specifically, input detailed information about training results and match results into the terminal.

[1464] Step 2:

[1465] Terminal: Receives user input data and verifies data consistency and accuracy. After verification, converts the data to an appropriate format for transmission to the server.

[1466] Step 3:

[1467] Server: Receives data and saves it to the database. This allows for comparison with past data.

[1468] Step 4:

[1469] Server: Provides data to the generated AI model and evaluates the effectiveness of the training. The model compares past data with new data to measure its effectiveness.

[1470] Step 5:

[1471] Server: Sends generated evaluation results and feedback to the user's terminal. Provides appropriate feedback and strategic advice based on the user ID.

[1472] Step 6:

[1473] Terminal: Displays evaluation results and feedback received on the user's terminal. The user uses this to adjust their training.

[1474] In this way, by providing athletes with multifaceted and high-quality support throughout the entire system, we can resolve the problem of trainer shortages and realize high-quality training and support.

[1475] (Example 1)

[1476] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1477] Traditional training systems for athletes only provide general training plans and fail to comprehensively offer personalized training plans and nutrition plans based on individual athletes' physical characteristics, performance data, and health status, as well as injury prevention advice, rehabilitation programs, tactical advice, and psychological support. As a result, athletes do not receive sufficient support for improving their performance or maintaining their health.

[1478] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1479] In this invention, the server includes means for inputting basic information, performance data, and health status of an athlete via a terminal; means for transmitting the input data to the server; means for receiving the transmitted data on the server and storing it in a database; means for passing the stored data to a generating AI model for analysis; means for generating individual training programs and nutrition plans using the generating AI model; means for transmitting the generated training programs and nutrition plans to a user's terminal; and means for receiving the training programs and nutrition plans displayed on the user's terminal. This enables the user to receive highly personalized support based on their individual physical characteristics and performance data.

[1480] An "athlete" is an individual who trains to improve their physical performance.

[1481] "Basic information" refers to physical characteristics and personal data about individual athletes, such as height, weight, and age.

[1482] "Performance data" refers to data related to an athlete's athletic performance, such as training frequency, runtime, and training records.

[1483] "Health status" refers to information about an athlete's health, such as fatigue, pain, and general malaise.

[1484] A "device" refers to an electronic device used by a user, such as a smartphone, tablet, or personal computer.

[1485] A "server" is a computer system that receives, stores, analyzes, and runs generative models on data.

[1486] A "database" is a system that allows a server to structurally store and manage data.

[1487] A "generative AI model" is an artificial intelligence model used to analyze input data and generate training programs, advice, and plans.

[1488] A "training program" is an individualized exercise plan designed to improve an athlete's physical performance.

[1489] A "nutrition plan" is a meal plan designed to optimize an athlete's health and performance.

[1490] "Injury prevention advice" refers to specific guidance for athletes to reduce strain during training and prevent injuries.

[1491] A "rehabilitation program" is an exercise and treatment plan designed to help athletes recover from injuries and pain.

[1492] "Tactical advice" refers to strategic guidance provided in team sports or individual competitions for the upcoming match or competition.

[1493] A "tactical optimization plan" is a tactical plan designed to maximize performance in a match or competition.

[1494] "Psychological support" refers to advice and training that helps athletes manage mental pressure and stress.

[1495] This invention is a system that provides personalized training programs, nutrition plans, injury prevention measures, rehabilitation programs, tactical advice, and psychological support for sports athletes. The system uses a generative AI model to analyze the athlete's basic information, performance data, and health status. The system primarily operates with three parties: a server, a terminal, and the user.

[1496] First, the user uses a device (smartphone, tablet, PC, etc.) to input the athlete's basic information (e.g., height, weight, age), performance data (e.g., runtime, training frequency), and health status (e.g., fatigue level, presence or absence of pain). The device then sends this data to the server in a structured format (e.g., JSON). A secure protocol (e.g., HTTPS) is used for this communication.

[1497] The server stores the received data in a database (e.g., MySQL or PostgreSQL). The server checks the data's integrity and saves only the data that passes validation. After that, the server passes the saved data to a generating AI model (e.g., OpenAI GPT model) for analysis.

[1498] Examples of prompt statements used for analysis are as follows:

[1499] "When generating optimal training and nutrition plans for amateur runners, please consider the user's height, weight, and past runtime data as input data."

[1500] "Please suggest stretches and strengthening exercises to alleviate knee pain in a basketball player. Input data includes recent training data and pain location information."

[1501] "Analyze the post-match data of the soccer team and suggest formation changes and instructions for specific players for the next match."

[1502] Based on the analysis results obtained from the generating AI model, the server generates personalized training programs, nutrition plans, injury prevention advice, rehabilitation programs, tactical advice, and psychological support. For example, if an amateur runner inputs their height (170cm), weight (65kg), and past runtime data into the system, the server uses the generating AI model to suggest a three-times-a-week running program and a high-protein meal plan.

[1503] Next, the server sends the generated plan and advice to the user's device. The device receives this data and displays it within the app. For example, if a basketball player enters knee pain, the server sends a rehabilitation plan of stretches and muscle strengthening exercises, which is then displayed on the device. As a result, athletes can receive personalized feedback in real time, which they can use for their daily training and health management.

[1504] This system addresses the modern challenge of trainer shortages by providing advanced personalized support to a wide range of athletes, from amateurs to professionals, effectively assisting in improving athlete performance and maintaining health. Users can easily obtain personalized training plans and advice simply by entering specific data.

[1505] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1506] Step 1:

[1507] The user enters basic information into the device.

[1508] Users enter basic information such as height, weight, age, runtime, training frequency, and health status using a dedicated app on their device or a web form. The entered data is created in a structured format (e.g., JSON). This data will later be used as input for analysis.

[1509] Step 2:

[1510] The device sends data to the server.

[1511] The device sends the entered data to the server using a secure protocol (e.g., HTTPS). The input data is sent in JSON format, and the server receives it. Specifically, when the send button is pressed, the device's app sends an HTTP POST request to the server.

[1512] Step 3:

[1513] The server saves the data to the database.

[1514] The server parses the received JSON data and saves it to a database (e.g., MySQL or PostgreSQL). Before saving, it validates the data to remove incomplete or invalid data. For example, the server checks that the data is in the correct format (e.g., whether all required fields are filled in).

[1515] Step 4:

[1516] The server passes data to the generated AI model.

[1517] The server extracts the necessary data from the database and passes it to the AI ​​model. During this process, it generates prompts, formats the data, and then sends it. For example, it might pass data along with a prompt such as, "Generate an optimal training plan and nutrition plan for an amateur runner." The input data is provided in JSON format.

[1518] Step 5:

[1519] The server receives results from the generated AI model.

[1520] The generative AI model analyzes the provided prompt text and data to generate an optimal training plan, nutrition plan, and advice. The server receives these results and stores them in JSON format. Specifically, it receives the analysis results returned by the generative AI model as an HTTP response.

[1521] Step 6:

[1522] The server sends the results to the user's terminal.

[1523] The server sends the generated training plan and advice results to the user's device. The device receives this data and displays it within the app. For example, it applies the data received from the server to the calendar and notification functions of the app on the device.

[1524] Step 7:

[1525] The user checks and executes the results on their device.

[1526] Users can review training and nutrition plans generated on their devices and use them to guide their actual training and meal planning. Specifically, users check the plan in a particular section of the app and act according to the schedule. For example, they might receive push notifications about their daily training.

[1527] By clearly defining the specific processes performed at each step, it becomes possible to understand in detail how this system works and how it provides value to the user.

[1528] (Application Example 1)

[1529] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1530] There is a need for a system that maximizes the effectiveness of training for factory robot operators, supports injury prevention and improved work performance, and provides appropriate feedback and optimization in real time. Conventional training systems have struggled to provide personalized suggestions based on the individual health status and performance data of operators.

[1531] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1532] In this invention, the server includes means for receiving and storing operator input data; means for using a generative model to analyze the input data and generate individual training programs and work plans; means for transmitting the generated training programs and work plans to a user's terminal; means for receiving training results and using a generative model to optimize the plan in real time; means for transmitting the optimized training programs and work plans to a user's terminal; and means for receiving the optimized training programs and work plans displayed on the terminal. This maximizes the effectiveness of operator training, enabling injury prevention and improved work performance.

[1533] "Operator input data" refers to information entered by factory robot operators regarding their physical characteristics, operational experience, work performance data, and health status.

[1534] A "generative model" is an AI system that includes algorithms for analyzing input data and generating individual training programs and work plans.

[1535] A "training program" is a set of specific training content and schedules created by a generative model that operators should undergo.

[1536] A "work plan" is a set of guidelines and procedures created by a generative model that specify what tasks an operator should perform.

[1537] "User terminal" refers to electronic devices such as smartphones, tablets, and head-mounted displays that operators use to view and execute training programs and work plans.

[1538] "Training outcomes" refer to data obtained as a result of operators performing training and tasks, including information on performance improvements and health improvements.

[1539] "Optimizing the plan in real time" means that the generative model continuously re-evaluates the training program and work plan based on the training results, and adjusts the content immediately as needed.

[1540] "Injury prevention advice" refers to suggestions from a generative model regarding specific actions and precautions that operators should take to prevent injuries.

[1541] A "rehabilitation program" is a training and exercise plan that injured operators should follow to recover.

[1542] "Work tactical advice" refers to suggestions regarding specific work methods and strategies, provided based on generative models.

[1543] An "optimization plan" is a set of improvement suggestions and specific procedures generated to enable operators to perform their tasks more efficiently.

[1544] Modes for carrying out the invention

[1545] Embodiments of this invention are shown below. The configuration of hardware and software for realizing the invention, as well as the data processing and calculation methods, will be described.

[1546] System Configuration

[1547] The system consists of the following main elements:

[1548] 1. User devices: Smartphones (iOS or Android), head-mounted displays (HoloLens, Oculus, etc.).

[1549] 2. Server: Backend for data storage and generation, and for data analysis using AI models.

[1550] 3. Software: Mobile application development kits (Flutter, React Native, etc.), generative AI model backend (TensorFlow, PyTorch).

[1551] Data flow

[1552] 1. Receiving and saving input data:

[1553] Operators use a terminal to input their physical characteristics, operational experience, work performance data, and health status.

[1554] This data is sent from the terminal to the server and stored in the database.

[1555] 2. Analysis using generative AI models:

[1556] The server passes the stored data to a generating AI model, which then performs analysis using Google Cloud AI or AWS Machine Learning.

[1557] The generative AI model generates individual training programs and work plans based on the analysis results.

[1558] 3. Submission of training program and work plan:

[1559] The server sends the generated training program and work plan to the user's terminal.

[1560] The user's device displays the transmitted content, which the operator can then review and execute.

[1561] 4. Input of training results and real-time optimization:

[1562] The operator enters the training results into a terminal, and this data is also sent to the server.

[1563] Based on the training results, the server generates an AI model in real time, which then re-evaluates and optimizes the training program and work plan.

[1564] 5. Injury prevention and rehabilitation program:

[1565] When an operator inputs information about their health status and injuries, the AI ​​model generates injury prevention advice and rehabilitation programs.

[1566] The server sends these programs to the user's terminal, where they are displayed.

[1567] 6. Advice and optimization of work tactics:

[1568] Once the operator inputs work data, the generated AI model produces work tactical advice and optimization plans.

[1569] This information is sent to the user's terminal via the server and displayed on the terminal.

[1570] Examples

[1571] Specific example 1:

[1572] Factory robot operators open an app and input their current work performance (operation speed, accuracy, failure rate, etc.). For example, if an operator inputs their arm fatigue level and the success rate of precision operations, the generated AI will use this information to suggest arm muscle training and rest plans.

[1573] Specific example 2:

[1574] When a new factory operator joins the company, they input their experience level and physical data through an app, and an AI generates an optimal training program based on that data. For example, beginners are provided with a step-by-step training plan that starts with basic operations and gradually increases in difficulty.

[1575] Example of a prompt

[1576] AI for training factory robot operators:

[1577] 1. Basic information: Height, weight, operating experience, work data

[1578] 2. Specific operational performance: accuracy, speed, failure rate

[1579] 3. Training plan generation: Recommendations for repetitive training of maneuvers, strength training, etc.

[1580] "What is the best training plan for when my arms are experiencing high levels of fatigue?"

[1581] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1582] Step 1:

[1583] Operators use a terminal to input their physical characteristics, operational experience, work performance data, and health status. The entered data is reviewed on the terminal and sent to the server. This step provides an interface for collecting operator data and sending it to the server.

[1584] Step 2:

[1585] The server stores the data transmitted by the operator in a database. The stored data is then used for analysis by a subsequent generative AI model. In this step, the server efficiently manages the data using a data storage system.

[1586] Step 3:

[1587] The server passes the stored operator data to the generating AI model and begins the analysis. The generating AI model generates individual training programs and work plans based on the input data. For example, it performs data analysis using TensorFlow or PyTorch. In this step, the AI ​​model analyzes the data and designs an appropriate training program and work plan.

[1588] Step 4:

[1589] The generated training program and work plan are sent from the server to the user's terminal. The terminal displays the received training program and work plan, making them ready for the operator to review and execute. In this step, the server sends data using the appropriate communication protocol, and the terminal receives it.

[1590] Step 5:

[1591] Operators conduct training and tasks, and input the results into a terminal. The entered training results are then sent back to the server. In this step, a user interface for operators to provide feedback is crucial.

[1592] Step 6:

[1593] The server receives the training results, and the generated AI model re-evaluates and optimizes the training program and work plan in real time. This optimization is performed based on the latest data from the operators. In this step, the AI ​​model has the ability to dynamically update and adapt the plan.

[1594] Step 7:

[1595] The server resends the optimized training program and work plan to the user's terminal. The terminal receives this, the operator reviews the content, and they can continue working according to the latest training and work plan. In this step, the efficiency of data communication between the server and the terminal is crucial.

[1596] Specific example

[1597] For example, a factory robot operator opens an app and inputs current work performance data (operating speed, accuracy, failure rate, etc.). This data is sent to a server, where a generated AI model analyzes it and suggests arm muscle training and rest plans. This information is then sent back to the terminal, and the operator can review and implement the plan, which is expected to improve performance.

[1598] Example of a prompt

[1599] AI for training factory robot operators:

[1600] 1. Basic information: Height, weight, operating experience, work data

[1601] 2. Specific operational performance: accuracy, speed, failure rate

[1602] 3. Training plan generation: Recommendations for repetitive training of maneuvers, strength training, etc.

[1603] "What is the best training plan for when my arms are experiencing high levels of fatigue?"

[1604] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1605] This invention combines a system that provides athletes with personalized training programs, injury prevention, rehabilitation, tactical advice, and psychological support with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out this invention are described below.

[1606] First, when a user uses the system, they input their physical characteristics, performance data, health status, and emotional data using a terminal. This data is sent from the terminal to the server. The server stores the received data in a database and passes it to a generative AI model and emotion engine for analysis.

[1607] The server uses a generative AI model to analyze the user's physical and performance data and generate personalized training programs and nutrition plans. Additionally, an emotion engine analyzes the user's emotional data to understand their current mental state and stress level. For example, if an amateur runner inputs their height, weight, past runtime data, and emotional data into the system, the generative AI model and emotion engine analyze this data to generate an appropriate training program, nutrition plan, and motivational advice for that runner. This generated plan and advice are then sent from the server to the user's terminal, where they can review and implement it.

[1608] To support injury prevention and rehabilitation, the system allows users to input training data, health status, and emotional data. The generating AI model then generates injury prevention advice and rehabilitation programs, while the emotional engine provides advice aimed at mental stability. For example, if a basketball player inputs recent training data, knee pain, and anxiety, the generating AI model suggests stretches and strength training exercises to reduce knee stress, while the emotional engine proposes relaxation methods to reduce stress. This rehabilitation plan and mental support advice are sent to the user's device and effectively utilized in actual rehabilitation.

[1609] Regarding tactical advice, when a soccer team inputs post-match data and emotional data into the system, a generative AI model analyzes the performance data and suggests tactical improvements, while an emotional engine supports the team's overall tactical adaptation based on player motivation and teamwork. This allows new tactics to be executed more effectively. This tactical advice is sent from the server to the coach's terminal and displayed along with specific tactical details.

[1610] By using an enhanced system for psychological support, feedback tailored to the user's mental state and emotions is provided. For example, if a golfer inputs data on the pressure and stress they feel before a tournament, a generative AI model generates specific training and advice for mental support, and an emotion engine suggests relaxation techniques and imagery training. This helps to promote the player's psychological stability. This support is sent to the user's terminal via the server and can be used by the player before the tournament.

[1611] Finally, regarding data analysis and evaluation of training effectiveness, emotional data is analyzed along with training and performance data. This allows for the evaluation of not only the athlete's training effectiveness but also their psychological stability. For example, when a club tennis player inputs daily practice data, match results, and emotional data, a generative AI model evaluates the effectiveness of the training, and an emotional engine provides feedback that also takes into account the state of mental health. This feedback is sent from the server to the user's terminal, and the user uses it to adjust their training and mental care.

[1612] This system provides comprehensive support for improving athletes' physical and mental performance. By combining it with an emotional engine, it becomes possible to provide even more personalized support than before, and is expected to significantly improve the quality of athlete training, injury prevention, rehabilitation, tactical improvement, and psychological support.

[1613] The following describes the processing flow.

[1614] Personalized training program generation and the specific processing flow of the emotion engine

[1615] Step 1:

[1616] User: Sends a training program generation request to the system. Specifically, enters name, age, gender, height, weight, past performance data, health status, and emotional data (e.g., stress level, emotional score) into the terminal.

[1617] Step 2:

[1618] Terminal: Receives user input data and verifies that the data is in the correct format and all required fields are present. After verification, it sends the data to the server in the appropriate format.

[1619] Step 3:

[1620] Server: Receives data and saves it to the database. This creates a unique data entry for each user, which is then used for subsequent analysis.

[1621] Step 4:

[1622] Server: Passes stored data to the generating AI model and emotion engine. The generating AI model generates an optimal training program and nutrition plan based on the input physical and performance data.

[1623] Step 5:

[1624] Server: The emotion engine analyzes the user's emotional data and generates motivational advice based on their mental state. For example, if the user shows a high stress level, it suggests relaxation techniques or exercises to reduce stress.

[1625] Step 6:

[1626] Server: Sends the generated training program, nutrition plan, and emotional engine-driven advice to the user's device. It links the information to the user ID and sends it in the appropriate format.

[1627] Step 7:

[1628] Terminal: Displays training programs, nutrition plans, and emotional engine advice received on the user's terminal. The user can review and implement these.

[1629] Specific procedures for injury prevention and rehabilitation support

[1630] Step 1:

[1631] User: Submit a rehabilitation support request to the system. Specifically, enter recent training data, health status, and emotional data (e.g., pain level, emotional score, etc.) into the terminal.

[1632] Step 2:

[1633] Terminal: Receives user input data and verifies its accuracy. After verification, it sends the data to the server in the appropriate format.

[1634] Step 3:

[1635] Server: Receives data and saves it to the database. This records health history and training data.

[1636] Step 4:

[1637] Server: The server passes the aforementioned data to the generating AI model to generate injury prevention advice and rehabilitation programs. The model identifies high-risk movements and suggests necessary injury prevention measures.

[1638] Step 5:

[1639] Server: The emotion engine analyzes the user's emotional data and generates advice aimed at promoting mental well-being. For example, if the user is showing signs of anxiety, it will suggest specific relaxation methods to alleviate that anxiety.

[1640] Step 6:

[1641] Server: Sends generated injury prevention advice, rehabilitation programs, and emotional engine advice to the user's device. Customizes the content sent based on the user ID.

[1642] Step 7:

[1643] Terminal: Displays injury prevention advice, rehabilitation programs, and emotional engine advice received on the user's terminal. The user then performs training and rehabilitation according to these recommendations.

[1644] Specific processing flow for performance optimization and tactical advice

[1645] Step 1:

[1646] User: Input post-competition performance and emotional data into the system. Specifically, input detailed information such as match results, training data, emotional scores, and stress levels.

[1647] Step 2:

[1648] Terminal: Receives user input data and verifies the data format. After verification, it sends the data to the server in the appropriate format.

[1649] Step 3:

[1650] Server: Stores received performance and sentiment data in a database. This allows for the accumulation of data for each team and individual.

[1651] Step 4:

[1652] Server: Passes data to the generated AI model, analyzes performance, and generates tactical improvements and optimization plans. For example, if a particular player's performance is declining, it identifies the cause and suggests countermeasures.

[1653] Step 5:

[1654] Server: The emotion engine analyzes data and generates tactical advice that takes into account the players' motivation and mental state. For example, if stress levels are high throughout the team, it might suggest leadership training or team-building exercises.

[1655] Step 6:

[1656] Server: Sends generated tactical advice and optimized tactical plans to the user's terminal. Provides specific instructions and strategies to the coach's terminal.

[1657] Step 7:

[1658] Terminal: Displays tactical advice and tactical optimization plans received on the user's terminal. The coach uses this to prepare for the next match.

[1659] Specific steps for providing psychological support and improving motivation

[1660] Step 1:

[1661] User: Enter information for psychological support into the system. Specifically, enter details about stress levels, emotion scores, and recent psychological states.

[1662] Step 2:

[1663] Terminal: Receives user input data and verifies its accuracy. After verification, it converts the data to an appropriate format for transmission to the server.

[1664] Step 3:

[1665] Server: Stores received emotional data and related information in a database. This ensures that psychological data is recorded for each athlete.

[1666] Step 4:

[1667] Server: Passes data to the generation AI model and generates advice for psychological support and motivation improvement. For example, it provides training and advice for mental support.

[1668] Step 5:

[1669] Server: The emotion engine analyzes data and provides relaxation methods and motivation-boosting techniques tailored to the user's psychological state.

[1670] Step 6:

[1671] Server: Sends generated psychological support advice to the user's terminal. Provides the most appropriate advice based on the user ID.

[1672] Step 7:

[1673] Terminal: Displays psychological support advice received on the user's terminal. The user then implements relaxation methods and motivation-boosting techniques based on this advice.

[1674] Specific process flow for data analysis and evaluation of training effectiveness

[1675] Step 1:

[1676] User: Inputs training data, performance data, and emotional data into the system. Specifically, this involves detailed input of training results, match results, and emotional data (e.g., emotional score, stress level, etc.).

[1677] Step 2:

[1678] Terminal: Receives user input data and verifies data consistency and accuracy. After verification, sends it to the server in the appropriate format.

[1679] Step 3:

[1680] Server: Receives data and saves it to the database. This allows for comparison with past data.

[1681] Step 4:

[1682] Server: Provides data to the generated AI model and evaluates the effectiveness of the training. The model compares past data with new data to measure the effectiveness of the training.

[1683] Step 5:

[1684] Server: The emotion engine analyzes the data and generates comprehensive feedback, including the user's psychological stability. For example, it presents not only the effectiveness of the training but also the user's mental health status.

[1685] Step 6:

[1686] Server: Sends generated evaluation results and feedback to the user's terminal. Provides appropriate feedback and strategic advice based on the user ID.

[1687] Step 7:

[1688] Terminal: Displays evaluation results and feedback received on the user's terminal. The user uses this to adjust their training and mental care.

[1689] In this way, by providing athletes with multifaceted, high-quality support throughout the entire system, the problem of trainer shortages can be resolved, and high-quality training and support can be realized. By combining this with an emotional engine, even more precise individualized support becomes possible, effectively supporting improved athletic performance and psychological stability.

[1690] (Example 2)

[1691] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1692] Conventional athlete support systems focused on providing physical training programs and nutritional plans, but lacked an element of emotional support, failing to address the user's mental state and stress levels. As a result, training effectiveness, injury prevention, rehabilitation processes, and tactical advice were sometimes not fully realized. This invention aims to solve these problems and provide comprehensive support for athletes' physical and mental performance.

[1693] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1694] In this invention, the server includes means for receiving and storing user input data, means for using a generative model and an emotion engine to analyze the input data and generate individual training programs, nutrition plans, and emotional support advice, and means for transmitting the generated training programs, nutrition plans, and emotional support advice to the user's terminal. This makes it possible to achieve not only improved physical performance of athletes but also mental stability and stress management simultaneously.

[1695] A "user" is a person who uses the system to input their physical characteristics, performance data, health status, and emotional data.

[1696] "Input data" refers to information that users provide to the system, such as physical characteristics, performance data, health status, and emotional data.

[1697] A "generative model" is an artificial intelligence model that analyzes a user's physical characteristics and performance data to generate individualized training programs and nutrition plans.

[1698] An "emotion engine" is software that analyzes a user's emotional data to understand their mental state and stress level, and then generates advice.

[1699] A "training program" is an individualized exercise plan designed based on the user's physical characteristics and performance data.

[1700] A "nutrition plan" is a plan that provides instructions on the optimal diet and nutrient intake methods based on the user's physical characteristics and performance data.

[1701] "Emotional support advice" refers to advice that provides mental support, such as relaxation methods and motivation-enhancing techniques, based on the user's emotional data.

[1702] "Injury prevention advice" is advice that analyzes the user's training data and health status and suggests measures to reduce the risk of injury.

[1703] A "rehabilitation program" is a program that directs injured users through rehabilitation activities aimed at their return to activity.

[1704] "Performance data" refers to data on exercise results recorded by users during sports or training.

[1705] "Tactical advice" is advice that analyzes the user's performance data and suggests tactical improvements and new strategies in the game.

[1706] A "tactical optimization plan" is a specific action plan based on tactical advice, designed to maximize team and individual performance.

[1707] A "terminal" is a device used by users to input data and receive training programs, nutrition plans, and advice from the server.

[1708] A "server" is a system that receives, stores, and analyzes data from users, and generates training programs and advice.

[1709] This invention is a system that provides personalized training programs, nutritional plans, and emotional support advice based on user input data. The system mainly consists of a server, terminals, and a generative AI model and emotion engine.

[1710] Hardware and software configuration:

[1711] 1. Terminal

[1712] Hardware: Input devices such as smartphones, tablets, and personal computers.

[1713] Software: A dedicated application or web browser is used to input user data and communicate with the system.

[1714] 2. Server

[1715] Hardware: A high-performance server computer.

[1716] Software: Applications that integrate database management systems (e.g., MySQL, PostgreSQL) and analysis engines.

[1717] 3. Generative AI Models

[1718] Software: Uses machine learning algorithms (e.g., TensorFlow, PyTorch) to analyze user data and generate training programs and nutrition plans.

[1719] 4. Emotional Engine

[1720] Software: Use emotion analysis programs (e.g., Affectiva, IBM Watson) to analyze user emotion data and generate emotional support advice.

[1721] System processing description:

[1722] Users input their physical characteristics, performance data, health status, and emotional data using a device. This data is sent from the device to a server, which stores the received data in a database. The stored data is then passed to a generative AI model and an emotion engine for analysis.

[1723] The generative AI model generates personalized training programs and nutrition plans based on the user's physical and performance data. Meanwhile, the emotional engine analyzes mental state and stress levels based on emotional data, providing advice on relaxation methods and motivation enhancement.

[1724] For example, the generative AI model performs analysis using the following specific prompt:

[1725] User's height: 175cm

[1726] User's weight: 70kg

[1727] Past runtime data: 5km 30 minutes

[1728] Emotional data: High stress level

[1729] Generate a training program and nutrition plan tailored to the user.

[1730] Specific example:

[1731] Example 1: In the case of an amateur runner

[1732] Input data: Height 175cm, weight 70kg, runtime data for the past week, high stress level.

[1733] Output of the generated AI model: Interval training, 3 times a week.

[1734] Emotional engine output: Deep breathing and relaxation techniques for motivation enhancement.

[1735] Example 2: In the case of a basketball player

[1736] Input data: Recent training data, knee pain, anxiety.

[1737] Output of the generated AI model: Stretching and muscle strengthening exercises to reduce stress on the knees.

[1738] Emotional Engine Output: Meditation Methods for Stress Reduction.

[1739] The generated training program, nutrition plan, and emotional support advice are sent from the server to the user's terminal, where the user reviews and implements this information. This entire process allows the user to improve their physical and mental performance.

[1740] This system is extremely useful for athletes because it not only optimizes training but also provides injury prevention, rehabilitation, and tactical advice.

[1741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1742] Step 1:

[1743] The user enters input data using a terminal.

[1744] Operation: The user opens a dedicated application and enters their physical characteristics (e.g., height, weight), performance data (e.g., runtime data), health status (e.g., knee pain), and emotional data (e.g., stress level). They then review the entered data and press the submit button.

[1745] Input: Physical characteristics, performance data, health status, emotional data

[1746] Output: Input data (data formatted in JSON format, etc.)

[1747] Step 2:

[1748] The terminal sends the input data to the server.

[1749] Operation: When the send button on the device is pressed, the input data is sent to the server as an HTTP request. An internet connection is required for transmission.

[1750] Input: Input data (data formatted in JSON format, etc.)

[1751] Output: Input data sent to the server (HTTP request)

[1752] Step 3:

[1753] The server receives the input data and saves it to the database.

[1754] Operation: The server retrieves input data from received HTTP requests and stores it in the database. Each piece of data is stored in the database for each user.

[1755] Input: Input data extracted from the HTTP request

[1756] Output: User data stored in the database

[1757] Step 4:

[1758] The server passes the stored data to the generating AI model and emotion engine.

[1759] Operation: The server retrieves the necessary data from the database and converts it into a data format for passing to the generating AI model and emotion engine. This may involve using formats such as Python's DataFrame.

[1760] Input: User data stored in the database

[1761] Output: Data to be passed to the generative AI model and emotion engine.

[1762] Step 5:

[1763] The generative AI model generates training programs and nutrition plans.

[1764] Operation: The generative AI model analyzes the user's physical characteristics and performance data to generate a personalized training program and nutrition plan. An example using prompts is shown below:

[1765] User's height: 175cm

[1766] User's weight: 70kg

[1767] Past runtime data: 5km 30 minutes

[1768] Please generate a training program and nutrition plan tailored to this user.

[1769] Input: User's physical characteristics, performance data (in prompt format)

[1770] Output: Individualized training programs and nutrition plans

[1771] Step 6:

[1772] The emotion engine analyzes emotional data and generates advice on relaxation methods and motivation enhancement.

[1773] Operation: The emotion engine analyzes the user's emotional data to understand their current mental state and stress level. Based on the analysis results, it generates specific advice on relaxation methods and ways to improve motivation.

[1774] Input: User sentiment data

[1775] Output: Relaxation methods and motivation-boosting advice

[1776] Step 7:

[1777] The server sends the generated training program, nutrition plan, and emotional advice to the user's device.

[1778] Operation: The server compiles the analysis results obtained from the generated AI model and emotion engine and sends them to the user's terminal. This transmission is also done via HTTP response.

[1779] Input: Individual training programs, nutrition plans, relaxation methods, and motivational advice.

[1780] Output: Analysis results (HTTP response) sent to the user's terminal.

[1781] Step 8:

[1782] Users can view analysis results on their devices and receive training and mental care based on those results.

[1783] Operation: The user opens a dedicated application on their device and reviews the training program, nutrition plan, and emotional support advice sent from the server. After reviewing, they proceed to implement the training and mental care.

[1784] Input: Analysis results sent from the server (training program, nutrition plan, relaxation methods, motivation improvement advice)

[1785] Output: User action plan (training implementation, mental care implementation)

[1786] (Application Example 2)

[1787] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1788] Traditional training systems for athletes could provide physical training programs and nutritional plans, but they failed to consider the user's mental state and emotional data. This resulted in a lack of mental support, hindering the achievement of optimal training results. Furthermore, the lack of mental support in rehabilitation and tactical advice made it difficult to improve the overall performance of athletes.

[1789] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1790] In this invention, the server includes means for receiving and storing input data of athletes; means for using a generative model to analyze the input data and generate individual training programs and nutrition plans; means for transmitting the generated training programs and nutrition plans to the user's terminal; means for using an emotion engine to analyze the user's emotional data and provide mental support; means for transmitting the mental support to the user's terminal; and means for receiving the mental support. This makes it possible to provide comprehensive training programs and support that take into account not only the user's physical health but also their mental health.

[1791] "Input data" refers to information that athletes provide to the system, such as physical characteristics, performance data, health status, and emotional data.

[1792] A "generative model" is an artificial intelligence algorithm that analyzes received input data to generate individual training programs, nutrition plans, injury prevention advice, rehabilitation programs, tactical advice, and tactical optimization plans.

[1793] An "emotion engine" is an analytical engine that analyzes users' emotional data and provides emotional support.

[1794] A "training program" is a plan of exercises and workouts aimed at improving an athlete's performance.

[1795] A "nutrition plan" is a guideline for meals and supplements tailored to an athlete's physical condition and goals.

[1796] "Injury prevention advice" refers to specific instructions and recommendations for athletes to avoid injuries.

[1797] A "rehabilitation program" is a training and treatment plan for athletes to recover from injuries.

[1798] "Tactical advice" refers to instructions and recommendations for athletes or teams to effectively execute tactics in competition.

[1799] A "tactical optimization plan" is a specific plan for optimizing a team's tactics based on match and competition data.

[1800] "User's device" refers to electronic devices such as smartphones and tablets used by athletes.

[1801] "Mental support" refers to assistance aimed at promoting the psychological stability of athletes, such as relaxation and stress management.

[1802] This invention is a system that provides athletes with personalized training programs, injury prevention, rehabilitation, tactical advice, and psychological support. The system is implemented by combining an emotion engine that recognizes the user's emotions with a generative AI model.

[1803] Initially, when a user uses the system, they input their physical characteristics, performance data, health status, and emotional data using a device such as a smartphone. This data is then sent from the device to the server. The server stores the received data in a database and passes it to a generative AI model and emotion engine for analysis.

[1804] Next, the server uses a generative AI model to analyze the user's physical and performance data and generate a personalized training program and nutrition plan. Additionally, the emotion engine analyzes the user's emotional data to understand their current mental state and stress level.

[1805] The generated AI model and emotion engine analyze the user's data, and the resulting training program, nutrition plan, and emotional support content are then sent back from the server to the user's device and displayed there. This allows the user to receive specific training programs and emotional support.

[1806] For example, when a gym user accesses the system and inputs their current physical and emotional data, the generative AI model creates a training program, and the emotional engine provides mental support. For instance, they might receive advice such as, "Your stress level is high, so let's focus on stretching today to help you relax."

[1807] The hardware used includes smartphones, servers, and databases. The software includes generative AI models, emotion engines, and frontends (applications that run on the user's device). Specifically, it utilizes server-side APIs using Python and Flask, the EmotionEngine library, and AI model libraries.

[1808] Examples of prompt messages include the following:

[1809] "Based on the user's health and emotional data, we will provide personalized training programs and mental support. Please generate specific advice based on the following data."

[1810] Height: 175cm

[1811] Weight: 65kg

[1812] Performance data: [10, 15, 12]

[1813] Health condition: Good

[1814] Emotional data: {'happiness': 0.8, 'stress': 0.2}''

[1815] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1816] Step 1:

[1817] Users input physical characteristics, performance data, health status, and emotional data using a smartphone or other device. This allows the system to collect necessary information from the user and prepare it for further processing. Input includes height, weight, historical runtime data, and emotional status (happiness level, stress level, etc.).

[1818] Step 2:

[1819] The terminal sends the data entered by the user to the server. The server stores the received data in a database. This ensures that the data is securely managed and ready for analysis. The data stored in the database includes the user ID and various input data.

[1820] Step 3:

[1821] The server uses a generative AI model to analyze stored physical and performance data and generate an optimal training program and nutrition plan for the user. The analysis employs machine learning algorithms to evaluate current data in comparison to historical data. The output is a individually customized training program and nutrition plan.

[1822] Step 4:

[1823] The server uses an emotion engine to analyze the user's emotional data. This allows the server to understand the user's mental state and stress level, generating data to provide appropriate emotional support. The analysis includes trend analysis of emotional data and assessment of psychological state. The output is specific advice on emotional support based on the user's mental state.

[1824] Step 5:

[1825] The server then sends the generated training program, nutrition plan, and mental support back to the user's device. This allows the user to view a concrete action plan on their device. The server sends the data to the user's device in the appropriate format, so the user can receive the information in a visually easy-to-understand way.

[1826] Step 6:

[1827] The user's device displays the received data, allowing them to review the provided plans and support. This enables users to concretely implement their daily training, health management, and mental support. The display includes a training program shown chronologically, and a nutrition plan that includes recommended meals.

[1828] For example, when a user starts a workout at the gym, if emotional data indicates a high stress level, the system will provide advice such as, "Today, let's focus on light stretching and relaxation." This advice is displayed on the user's device and can be referenced during the workout.

[1829] Example of a prompt:

[1830] "Based on the user's health and emotional data, we will provide personalized training programs and mental support. Please generate specific advice based on the following data."

[1831] Height: 175cm

[1832] Weight: 65kg

[1833] Performance data: [10, 15, 12]

[1834] Health condition: Good

[1835] Emotional data: {'happiness': 0.8, 'stress': 0.2}''

[1836] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1837] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1838] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1839] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1840] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1841] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1842] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1843] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1844] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1845] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1846] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1847] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1848] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1849] 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.

[1850] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1851] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1852] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1853] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1854] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1855] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1856] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1857] The following is further disclosed regarding the embodiments described above.

[1858] (Claim 1)

[1859] A means of receiving and storing athlete input data,

[1860] Means for using a generative model to analyze the aforementioned input data and generate individual training programs and nutritional plans,

[1861] Means for transmitting the generated training program and nutrition plan to the user's terminal,

[1862] A system including means for receiving the training program and nutrition plan displayed on the terminal.

[1863] (Claim 2)

[1864] A means for receiving and storing athlete training data and health status,

[1865] Means for using a generative model to analyze the aforementioned training data and health status to generate injury prevention advice and rehabilitation programs,

[1866] Means for transmitting the generated injury prevention advice and rehabilitation program to the user's terminal,

[1867] The system according to claim 1, further comprising means for receiving the injury prevention advice and rehabilitation program displayed on the terminal.

[1868] (Claim 3)

[1869] A means of receiving and storing athlete performance data,

[1870] Means for using a generative model to analyze the performance data and generate tactical advice and tactical optimization plans,

[1871] Means for transmitting the generated tactical advice and tactical optimization plan to the user's terminal,

[1872] The system according to claim 1, further comprising means for receiving the tactical advice and tactical optimization plan displayed on the terminal.

[1873] "Example 1"

[1874] (Claim 1)

[1875] A means of inputting basic information, performance data, and health status of athletes using a terminal,

[1876] Means for transmitting the input data to the server,

[1877] A means for receiving the transmitted data on a server and storing it in a database,

[1878] A means for passing the aforementioned stored data to a generating AI model for analysis,

[1879] A means for generating individual training programs and nutritional plans using the aforementioned AI model,

[1880] Means for transmitting the generated training program and nutrition plan to the user's terminal,

[1881] A system including means for receiving the training program and nutrition plan displayed on the user's terminal.

[1882] (Claim 2)

[1883] A means for receiving and storing athlete training data and health status,

[1884] Means for using a generative AI model to analyze the aforementioned training data and health status to generate injury prevention advice and rehabilitation programs,

[1885] Means for transmitting the generated injury prevention advice and rehabilitation program to the user's terminal,

[1886] The system according to claim 1, further comprising means for receiving the injury prevention advice and rehabilitation program displayed on the terminal.

[1887] (Claim 3)

[1888] A means of receiving and storing athlete performance data,

[1889] Means for using a generative AI model to analyze the aforementioned performance data and generate tactical advice and tactical optimization plans,

[1890] Means for transmitting the generated tactical advice and tactical optimization plan to the user's terminal,

[1891] The system according to claim 1, further comprising means for receiving the tactical advice and tactical optimization plan displayed on the terminal.

[1892] "Application Example 1"

[1893] (Claim 1)

[1894] A means for receiving and storing operator input data,

[1895] Means for using a generative model to analyze the aforementioned input data and generate individual training programs and work plans,

[1896] Means for transmitting the generated training program and work plan to the user's terminal,

[1897] Means for receiving the training program and work plan displayed on the terminal,

[1898] A means of receiving training results and using a generative model to optimize the plan in real time,

[1899] Means for transmitting the optimized training program and work plan to the user...

Claims

1. A means of receiving and storing athlete input data, Means for using a generative model to analyze the aforementioned input data and generate individual training programs and nutritional plans, Means for transmitting the generated training program and nutrition plan to the user's terminal, A system including means for receiving the training program and nutrition plan displayed on the terminal.

2. A means for receiving and storing athlete training data and health status, Means for using a generative model to analyze the aforementioned training data and health status to generate injury prevention advice and rehabilitation programs, Means for transmitting the generated injury prevention advice and rehabilitation program to the user's terminal, The system according to claim 1, further comprising means for receiving the injury prevention advice and rehabilitation program displayed on the terminal.

3. A means of receiving and storing athlete performance data, Means for using a generative model to analyze the performance data and generate tactical advice and tactical optimization plans, Means for transmitting the generated tactical advice and tactical optimization plan to the user's terminal, The system according to claim 1, further comprising means for receiving the tactical advice and tactical optimization plan displayed on the terminal.

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