System

Abstract

A system is provided.SOLUTION: A system including a data input means for an athlete to input his / her physical condition, goal, and past injury history, a data transmission means for transmitting the data input by the input means to a server, a program generation means for analyzing the data received by the server by using a generation AI and generating a program of individual optimal meals, training, rehabilitation, and rest, a program transmission means for transmitting the generated program to a terminal, and a notification means for notifying the user of the program by the terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, the commercialization of sports has progressed rapidly, increasing the physical and mental burden placed on athletes. This calls for a comprehensive system to manage athletes' physical and mental health and enable them to perform at their best. Conventional methods often provide centralized training plans and rehabilitation programs, which do not adequately address the individual athlete's condition. Furthermore, there are limited means to quickly and appropriately respond to athletes' questions and concerns. This makes it difficult for athletes to perform at their best. Therefore, a system is needed that provides individually tailored diet, training, rehabilitation, and rest programs, as well as enabling prompt consultation. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following solutions. First, a data input means is provided for athletes to input their physical condition, goals, and past injury history. A data transmission means is also incorporated to transmit the data input by the input means to a server. Next, a program generation means is provided for the server to analyze the data received using a generation AI and generate an individually optimized diet, training, rehabilitation, and rest program. A program transmission means is provided for transmitting the program generated by the program generation means to a terminal, and the terminal includes a notification means for notifying the user of the program. A consultation means is also provided for athletes to ask questions via a chatbot. The chatbot uses a generation AI to analyze the athlete's question and generate an appropriate answer, and an answer transmission means is provided for transmitting the generated answer to the terminal. The system further provides an answer display means for the terminal to display the answer to the athlete. A database acquisition means is also provided for the server to acquire the athlete's past data from a database and input the analysis results into the generation AI. In this way, an optimized plan for each athlete can be provided, and prompt consultation responses can be realized.

[0006] "Data input means" refers to a device or interface that allows athletes to input information such as their physical condition, goals, and past injury history.

[0007] The "data transmission means" is a device or protocol for transmitting information input by the data input means to the server.

[0008] "Program generation means" refers to a device or system that uses generation AI to analyze the data received by the server and generate individually optimized diet, training, rehabilitation, and rest programs.

[0009] The "program transmission means" is a device or protocol for transmitting the generated program to the terminal.

[0010] The "notification means" is a device or system for notifying the user of some information, and is a means for notifying the user of updates to programs or new programs.

[0011] The "consultation tool" is an interface that allows athletes to ask questions or ask for advice through the chatbot.

[0012] The "answer generation means" is a system in which the chatbot analyzes the athlete's question and generates an appropriate answer using generation AI.

[0013] The "answer sending means" is a device or protocol for sending the generated answer to the terminal.

[0014] The "answer display means" is a device or interface for displaying the answers received by the terminal to the user.

[0015] The "database acquisition means" is a means by which the server acquires past data of athletes from the database. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention uses generative AI to provide a system that provides optimal nutrition, training, rehabilitation, and rest programs for athletes to manage their physical and mental health and maximize their performance. It also has a chatbot function that responds to athlete inquiries.

[0038] System Configuration

[0039] The server plays a central role in the system, receiving and analyzing data sent by athletes. It also uses generative AI to generate optimization programs and send them to the devices. It also processes inquiries from athletes through a chatbot function, generating and sending appropriate answers.

[0040] The terminal is an interface where athletes can input data, check the program, and receive consultations. The data entered by the athlete is sent from the terminal to the server. The terminal receives the optimization program and answers sent from the server and notifies the athlete.

[0041] Athletes interact with the system using a dedicated application, allowing them to input data, check programs, and consult with chatbots.

[0042] Entering and Submitting Data

[0043] Users use a dedicated application to input their physical condition, goals, past injury history, etc. The input data is sent to a server via the device, and individual athlete data is then stored on the server.

[0044] Program generation by generative AI

[0045] The server uses AI to analyze the received athlete data. Based on this analysis, it generates an optimized diet, training, rehabilitation, and rest program for the athlete. Past data is also retrieved from the database for more precise analysis.

[0046] For example, it analyzes heart rate data and sleep patterns to adjust the next day's training and meal plan.

[0047] Program submission and notification

[0048] The optimized program generated by the server is sent to the device. The device receives the program and notifies the athlete. For example, a notification saying "A new training plan is available" is displayed. By clicking on this notification, the athlete can view the detailed program content.

[0049] Consultation response via chatbot

[0050] When a user uses the chatbot in the system to ask for advice, the content of the consultation is sent to the server. The server uses a generation AI to analyze the content and generate an appropriate answer. The generated answer is sent to the athlete's device and displayed.

[0051] For example, if a user asks, "My left knee is hurting. Is it okay to train today?", the chatbot will generate and provide an answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching."

[0052] This system allows athletes to receive the most appropriate program for their individual condition and provides prompt responses to any concerns or questions they may have, thereby creating an environment in which athletes can perform at their best as individuals.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user launches the dedicated application and inputs their physical condition, goals, past injury history, etc. through a data input means. Specifically, they input their name, age, height, weight, daily activity data (e.g., heart rate, sleep time), goals (e.g., preparation for a competition), and past injury history.

[0056] Step 2:

[0057] The terminal uses the data transmission means to transmit the data entered by the user to the server. Specifically, by pressing the "Send" button in the application, the input data is converted into JSON format and transmitted to the server.

[0058] Step 3:

[0059] The server receives the data transmitted from the terminal using the data receiving means, and temporarily stores the received data.

[0060] Step 4:

[0061] The server uses a database retrieval means to retrieve past data from the database, such as past training and injury history, relevant data for understanding the athlete's condition.

[0062] Step 5:

[0063] The server uses generated AI to analyze the received data and past data retrieved from the database, and this analysis provides a comprehensive assessment based on the user's current condition and goals.

[0064] Step 6:

[0065] The server uses a program generation means to generate an individualized optimal diet, training, rehabilitation, and rest program based on the analysis results. The generated program is customized taking into account the user's lifestyle, past results, and current condition.

[0066] Step 7:

[0067] The server uses the program transmission means to transmit the generated optimized program to the terminal, which exports the program in JSON format and transmits it to the terminal.

[0068] Step 8:

[0069] The device receives the optimization program and notifies the user using a notification means. Specifically, a push notification stating "A new training plan is available" is displayed.

[0070] Step 9:

[0071] The user clicks on the notification and sees the new optimization program within the app, which includes detailed meal plans, training menus, rehabilitation plans, and rest plans.

[0072] Step 10:

[0073] If a user wants to consult with a chatbot, they can use the chatbot functionality within the application, for example, by typing, "My left knee is hurting more. Will it be okay to train today?"

[0074] Step 11:

[0075] The server receives the chatbot's inquiry message, analyzes the content using the generation AI, and generates an appropriate answer based on the analysis results.

[0076] Step 12:

[0077] The server transmits the generated answer to the terminal using the answer transmitting means. For example, a generated answer may be, "If you have pain in your left knee, we recommend that you stop running and do some light stretching."

[0078] Step 13:

[0079] The device receives the reply and displays it in the chat window, allowing the user to receive appropriate instructions and advice.

[0080] By going through this process, athletes receive an individually optimized program and can also receive immediate consultation.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] Conventional athlete health management systems have had problems providing programs optimized for individual athletes, resulting in limited, generic advice. Furthermore, there was no environment in place where athletes could quickly consult about their physical condition or any questions they had, which meant that even when urgent action was required, responses were delayed. Furthermore, past data could not be fully utilized, making it difficult to manage health from a long-term perspective.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes a data input means for the athlete to input their own physical condition, goals, and past injury history, a data transmission means for transmitting the data input by the input means to the server, a program generation means for the server to analyze the received data using a generative AI model and generate an individually optimized program for diet, training, rehabilitation, and rest, a program transmission means for transmitting the generated program to a terminal, and a notification means for the terminal to notify the user of the program. This makes it possible to provide an optimized program based on the athlete's individual data.

[0086] "Data input means" refers to a device or software that allows athletes to input information such as their physical condition, goals, and past injury history into the system.

[0087] The "data transmission means" is a device or software having a function for transmitting data input by the data input means to the server.

[0088] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to analyze data and generate individually optimized diet, training, rehabilitation, and rest programs.

[0089] The "program generation means" is a device or software that has the function of analyzing the data received by the server and generating a program optimized for the athlete.

[0090] The "program transmission means" is a device or software having a function for transmitting the generated program to the terminal.

[0091] The "notification means" is a device or software having a function for notifying the user of a program received by the terminal.

[0092] A "consultation tool" is a device or software that has the function of allowing athletes to input and send questions via the chatbot.

[0093] "Answer generation means" refers to a device or software that has the function of analyzing an athlete's question using a generative AI model and generating an appropriate answer.

[0094] The "answer sending means" is a device or software having a function for sending the generated answer to the terminal.

[0095] The "answer display means" is a device or software that has the function of displaying the answers received by the terminal to the athlete.

[0096] "Database acquisition means" refers to a device or software that has the function of allowing the server to acquire an athlete's past data from the database and input it into the generative AI model along with the analysis results.

[0097] MODE FOR CARRYING OUT THE INVENTION

[0098] In this invention, a system for optimizing the health management of athletes is constructed. This system is realized by linking multiple pieces of hardware and software.

[0099] Hardware and software used

[0100] The server uses a cloud platform (general name: cloud computing service, e.g., Google (registered trademark) Cloud Platform, Amazon Web Services).

[0101] The terminals used include smartphones (commonly known as mobile devices, e.g., iPhone (registered trademark), ANDROID (registered trademark) terminals) and tablets (commonly known as tablet devices, e.g., iPad (registered trademark), Android tablets).

[0102] The generative AI model uses a machine learning algorithm (commonly known as a natural language generation model, e.g., GPT-4 (registered trademark)).

[0103] Overall system overview

[0104] The system has the following main functions:

[0105] 1. Data entry and submission

[0106] 2. Program generation using generative AI

[0107] 3. Program Submission and Notification

[0108] 4. Consultation response via chatbot

[0109] Data entry and submission

[0110] The user opens the dedicated application on a smartphone or tablet. The application contains a form where the user can enter information about their physical condition, goals, past injury history, etc. The entered data is converted to JSON format by the device and securely sent to the server using the HTTPS protocol. For example, data such as "This morning I weighed 70 kg, and I slept for 6 hours last night" can be entered.

[0111] Program generation by generative AI

[0112] The server stores the received data in a database (general name: associative database, e.g., MySQL (registered trademark), PostgreSQL). The stored data is then analyzed using a generative AI model (GPT-4) to generate an individually optimized diet, training, rehabilitation, and rest program. For example, a program might analyze a user's heart rate data and sleep patterns and suggest, "Since your heart rate has been high recently, today I suggest a light jog and a balanced meal." An example of a prompt might be, "Analyze an athlete's heart rate data and sleep patterns, and create a program that adjusts the next day's training and meal plan."

[0113] Program submission and notification

[0114] The program generated by the server is sent to the device in JSON format. The device receives this program and notifies the user. For example, a notification such as "A new training plan is available" may appear on the smartphone screen. When the user taps the notification, detailed program content is displayed. For example, it may say "30 minutes of light jogging for afternoon training, and a high-protein meal for today's meal."

[0115] Consultation response via chatbot

[0116] When a user enters a question using the chatbot function within the dedicated application, the question is sent by the device to the server. The server uses a generative AI model (GPT-4) to analyze the question and generate an appropriate answer. For example, in response to the question, "My left knee is hurting. Will it be okay to train today?", the server generates an answer such as, "If you are experiencing pain in your left knee, I recommend you stop running and do some light stretching." The answer is sent to the device and displayed to the user.

[0117] As described above, this system provides optimal health management programs and prompt consultations based on each athlete's individual data, thereby creating an environment in which athletes can perform at their best.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1: Data entry

[0120] The user opens the dedicated application on a smartphone or tablet. The user enters their physical condition, goals, and past injury history into the application's input form. Specifically, they enter their weight, heart rate, sleep time, etc. This data is necessary to understand their physical condition and progress toward achieving their goals. Input: Weight 70 kg, sleep time 6 hours. Output: Data in JSON format.

[0121] Step 2: Send data

[0122] The terminal converts the data entered by the user into JSON format and sends it to the server using the HTTPS protocol. The data undergoes serialization processing and is sent securely to the server. Input: User-entered data (weight, sleep time, etc.). Output: JSON-formatted data is sent to the server.

[0123] Step 3: Receiving and storing data

[0124] The server receives the data sent from the device, analyzes the HTTP request, and saves it in a database. Specifically, it uses a database such as MySQL or PostgreSQL and inserts the data into a table. Input: Data in JSON format. Output: Data saved in the database.

[0125] Step 4: Program Generation

[0126] The server retrieves data stored in the database and analyzes it using a generative AI model (GPT-4). This analysis generates a diet, training, rehabilitation, and rest program optimized for the user. Specifically, it analyzes heart rate data and sleep patterns and generates the program in text format. Example prompt: "Analyze an athlete's heart rate data and sleep patterns and create a program that adjusts the next day's training content and meal plan." Input: User data retrieved from the database. Output: The generated optimization program (text format).

[0127] Step 5: Send the program

[0128] The server converts the generated program into JSON format and sends it to the terminal using the HTTPS protocol. Input: The generated optimized program. Output: JSON format data is sent to the terminal.

[0129] Step 6: Program Notification

[0130] The device receives the program sent from the server and notifies the user. The notification is sent using push notifications, displaying a message such as "A new training plan is available." When the user taps the notification, detailed program content is displayed. Input: Program data in JSON format. Output: Notification to the user and program details displayed.

[0131] Step 7: Enter your consultation

[0132] The user inputs a question using the chatbot function in the dedicated application. For example, "My left knee is hurting more. Will it be okay to train today?" Input: Question text. Output: Question data in JSON format.

[0133] Step 8: Submit your question

[0134] The terminal converts the question entered by the user into JSON format and sends it to the server using the HTTPS protocol. Input: Question data (JSON format). Output: Question data sent to the server.

[0135] Step 9: Receiving questions and generating answers

[0136] The server analyzes the received question using a generative AI model (GPT-4) and generates an appropriate answer. For example, it generates an answer such as, "If you have pain in your left knee, we recommend you stop running and do some light stretching." Input: Question data. Output: Generated answer text.

[0137] Step 10: Submit your response

[0138] The server converts the generated answer into JSON format and sends it to the terminal using the HTTPS protocol. Input: Generated answer text. Output: JSON format answer data is sent to the terminal.

[0139] Step 11: Display answers

[0140] The device receives the answer sent from the server and displays it on the chatbot screen. For example, it might say, "If you have pain in your left knee, we recommend you stop running and do some light stretching." Input: Answer data in JSON format. Output: Display of answer to the user.

[0141] (Application example 1)

[0142] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0143] Currently, health management of industrial workers is not always thorough, resulting in delays in improving employee productivity and the working environment. Furthermore, there is a lack of systems that can quickly and individually address fatigue and health concerns felt by employees. This can result in reduced employee productivity and adversely affect the quality of work. Therefore, there is a need for a system that collects and analyzes the health data of industrial workers and provides optimal work schedules and rest plans.

[0144] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0145] In this invention, the server includes a data input means for industrial workers to input their physical condition, working hours, and rest times, a data transmission means for transmitting the data input by the input means to the server, a program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized work schedule and rest plan, a program transmission means for transmitting the generated program to a terminal, a notification means by the terminal notifying the user of the program, a consultation means by which industrial workers ask questions about their health condition via a chatbot, an answer generation means by the chatbot analyzing the industrial worker's question using a generation AI and generating an appropriate answer, an answer transmission means for transmitting the generated answer to the terminal, an answer display means by the terminal displaying the answer to the industrial worker, and a database acquisition means by the server to acquire the industrial worker's past data from a database and input it together with the analysis results into the generation AI.

[0146] This will enable efficient collection and analysis of industrial workers' health data and provide optimal individual work schedules and rest plans. Furthermore, by using chatbots to quickly respond to health consultations, it will be possible to manage employees' health status in real time and provide appropriate advice. This will lead to improved work efficiency and a better working environment.

[0147] "Data input means" refers to a device or interface that allows industrial workers to input information such as their physical condition, working hours, and rest times.

[0148] The "data transmission means" is a means for transmitting data obtained through the input means to the server.

[0149] A "server" is a device or system that uses generation AI to analyze the received data and generate individually optimized work schedules and break plans.

[0150] The "program generation means" refers to the processes and algorithms that analyze the data received by the server and generate optimal work schedules and rest plans.

[0151] The "program transmission means" is a means for transmitting the generated program to the terminal.

[0152] The "notification means" is an interface for notifying the user of the program received by the terminal.

[0153] The "consultation tool" is a tool that allows industrial workers to ask questions about their health via a chatbot.

[0154] A "chatbot" is a computer program designed to interact with users.

[0155] "Answer generation means" refers to the process or algorithm by which the chatbot uses generation AI to analyze the industrial worker's question and generate an appropriate answer.

[0156] The "answer transmission means" is a means for transmitting the generated answer to the terminal.

[0157] The "answer display means" is an interface for displaying the answers received by the terminal to the industrial worker.

[0158] The "database acquisition means" is a means by which the server acquires the industrial worker's past data from the database and inputs it into the generation AI along with the analysis results.

[0159] "Generative AI" is an artificial intelligence technology that analyzes received data and generates optimal programs and answers.

[0160] This invention is a system that efficiently collects and analyzes the health data of industrial workers and proposes individually optimized work schedules and rest plans. Furthermore, by providing a health consultation function using a chatbot, it is possible to manage the health status of industrial workers in real time, improving work efficiency and ensuring safety.

[0161] The server is equipped with a data input means for industrial workers to input data such as their physical condition, working hours, and rest times. Devices such as smartphones and PCs are used as the data input means. Industrial workers use these devices to input data, which is then transmitted to the server via a data transmission means.

[0162] The server analyzes the received data using a generation AI. For example, the generation AI uses Python's Scikit-learn library and predicts heart rate using a linear regression model. Based on this, an individually optimized work schedule and rest plan are generated and implemented as a program generation means. This generated program is sent to the terminal by a program transmission means.

[0163] The terminal is provided with a notification means for notifying the user of the received program. For example, a notification saying "A new work schedule is available" is displayed. The user can click on the notification to check the detailed program content.

[0164] Furthermore, the system responds to questions about the health status of industrial workers via a chatbot. When a worker uses the chatbot to input a question through the consultation means, the server analyzes the question using a generation AI and is equipped with an answer generation means that generates an appropriate answer. The generated answer is sent to the terminal and notified to the worker by the answer sending means through the display means. For example, in response to the question, "I have pain in my left knee. Is it safe to work today?", the chatbot will provide an answer based on the analysis results, such as, "If you have pain in your left knee, we recommend extending your break time and doing some light stretching."

[0165] The server also has a database acquisition means for retrieving past data from the database and inputting it into the generation AI, which enables more precise analysis based on past health data and work history, improving the accuracy of the individual optimization program.

[0166] A specific example is given below. For example, an industrial worker uses a smartphone to input data such as heart rate, working hours, and rest times every day. The server receives this data and predicts the heart rate using generative AI (Scikit-learn's Linear Regression model). It then generates optimal rest times and work schedules and sends the program to the device.

[0167] Examples of prompts include, "I've been losing concentration easily lately. Please give me some appropriate advice based on my health data," or "I've been having persistent pain in my left knee. Please tell me how to work safely."

[0168] The above system can improve the health management and work efficiency of industrial workers.

[0169] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0170] Step 1:

[0171] A user uses a device such as a smartphone or PC to input their own physical condition (heart rate, working hours, rest time, etc.) through data input means. The input data is sent to a server via data transmission means. The input data includes numerical data and written comments.

[0172] Input: User's physical condition data (heart rate, working hours, rest time, etc.)

[0173] Output: Data sent to the server

[0174] Step 2:

[0175] The server stores the received data and analyzes it using a generative AI model (for example, Scikit-learn's Linear Regression model). This analysis results in data calculations that predict heart rate and generate work schedules and rest plans. The server also retrieves past data from the database and uses it for analysis.

[0176] Input: Received user data, historical database data

[0177] Output: Optimal work schedule, break plan

[0178] Step 3:

[0179] The server transmits the generated work schedule and break plan to the terminal using a program transmission means, including specific start times, end times, and break times.

[0180] Input: Generated work schedule and break plan

[0181] Output: Program sent to terminal

[0182] Step 4:

[0183] The device notifies the user of the received program via a smartphone app or PC notification function, displaying a message such as "A new work schedule is available."

[0184] Input: Program sent from the server

[0185] Output: Message notified to the user

[0186] Step 5:

[0187] The user inputs a question about their health condition via the chatbot. The question (e.g., "I have pain in my left knee. Is it safe to work today?") is sent to the server via the consultation tool.

[0188] Input: User's question

[0189] Output: Question data sent to the server

[0190] Step 6:

[0191] The server analyzes the received question data and generates an appropriate answer using the generative AI model. For example, "If you have pain in your left knee, we recommend extending your rest time and doing some light stretching." After generating the answer, the server sends it to the device via the answer sending means.

[0192] Input: User question data

[0193] Output: The generated answer

[0194] Step 7:

[0195] The device displays the received answers to the user via a chatbot app or notification function, allowing the user to immediately check the detailed answers.

[0196] Input: The answer sent by the server

[0197] Output: The answer displayed to the user

[0198] The above processing steps make it possible to provide optimal work schedules and rest plans based on the health data of industrial workers, and to provide prompt health consultations.

[0199] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0200] This invention will provide a system that uses generative AI and an emotion engine to manage the mental and physical health of athletes and provide individually optimized diet, training, rehabilitation, and rest programs to help them perform at their best.It will also have a chatbot function that can respond to athletes' inquiries.

[0201] System Configuration

[0202] The server plays a central role in the system, receiving and analyzing data sent by athletes. It also uses generative AI to generate optimization programs and send them to the devices. It also processes inquiries from athletes through a chatbot function, generating and sending appropriate answers. It also incorporates and analyzes emotional data recognized by the emotion engine.

[0203] The terminal is an interface through which athletes can input data, check programs, and receive consultations. The data entered by the athlete is sent from the terminal to the server. The terminal receives the optimization program and answers sent from the server and notifies the athlete. In addition, the terminal is equipped with an emotion engine that recognizes the user's emotions.

[0204] Athletes interact with the system using a dedicated application, allowing them to input data, check programs, and consult with chatbots.

[0205] Entering and Submitting Data

[0206] Users use a dedicated application to input their physical condition, goals, past injury history, etc. The input data is sent to a server via the device, and individual athlete data is then stored on the server.

[0207] Utilizing the Emotion Engine

[0208] The device is equipped with an emotion engine that analyzes the user's emotions based on their voice and facial expressions. The analysis results are sent to the server by the data transmission means.

[0209] Program generation by generative AI

[0210] The server uses AI to analyze the received athlete data and emotional data. Based on this analysis, it generates an optimized diet, training, rehabilitation, and rest program for the athlete. Past data is also retrieved from the database for more precise analysis.

[0211] For example, it analyzes heart rate data, sleep patterns, and emotional data to adjust the next day's training and meal plan.

[0212] Program submission and notification

[0213] The optimized program generated by the server is sent to the device. The device receives the program and notifies the athlete. For example, a notification saying "A new training plan is available" is displayed. By clicking on this notification, the athlete can view the detailed program content.

[0214] Consultation response via chatbot

[0215] When a user uses the chatbot in the system to ask for advice, the content of the consultation is sent to the server. The server then uses generative AI to analyze the content and generate an appropriate response, taking into account the emotional data recognized by the emotion engine. The generated response is then sent to the athlete's device and displayed.

[0216] For example, if a user asks, "My left knee is hurting more. Is it okay to train today?", the chatbot will generate and provide an answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, please consult a specialist." If the emotional data indicates "anxiety," a response that particularly emphasizes a sense of security will be generated.

[0217] This system allows athletes to receive the most appropriate program tailored to their individual state and emotions, and provides prompt and appropriate responses to any anxieties or questions they may have, thereby creating an environment in which athletes can perform at their best as individuals.

[0218] The processing flow will be explained below.

[0219] Step 1:

[0220] The user launches the dedicated application and inputs their physical condition, goals, past injury history, etc. through a data input means. Specifically, they input their name, age, height, weight, daily activity data (e.g., heart rate, sleep time), goals (e.g., preparation for a competition), and past injury history.

[0221] Step 2:

[0222] The terminal uses the data transmission means to transmit the data entered by the user to the server. Specifically, by pressing the "Send" button in the application, the input data is converted into JSON format and transmitted to the server.

[0223] Step 3:

[0224] The device uses an emotion engine to analyze the user's emotions. Specifically, it analyzes the user's voice and facial expressions to recognize emotions such as "happiness," "anxiety," and "fatigue."

[0225] Step 4:

[0226] The device transmits the emotion data analyzed by the emotion engine to the server using the data transmission means. The analysis results are transmitted in JSON format.

[0227] Step 5:

[0228] The server receives the physical data and emotional data transmitted from the terminal using the data receiving means, and temporarily stores the received data.

[0229] Step 6:

[0230] The server retrieves past data from the database using the database retrieval means, specifically, retrieves related data such as past training history and injury history.

[0231] Step 7:

[0232] The server uses the generated AI to comprehensively analyze the received physical data, emotional data, and past data, and evaluates the user's current condition and emotional state.

[0233] Step 8:

[0234] The server uses the program generation means to generate an individualized optimal diet, training, rehabilitation, and rest program based on the analysis results. The generated program is customized taking into account the user's physical and emotional state.

[0235] Step 9:

[0236] The server uses the program transmission means to transmit the generated optimized program to the terminal, which exports the program in JSON format and transmits it to the terminal.

[0237] Step 10:

[0238] The device receives the optimization program and notifies the user using a notification means. Specifically, a push notification stating "A new training plan is available" is displayed.

[0239] Step 11:

[0240] The user clicks on the notification and sees the new optimization program within the app, which includes detailed meal plans, training menus, rehabilitation plans, and rest plans.

[0241] Step 12:

[0242] If a user wants to consult with a chatbot, they can use the chatbot functionality within the application, for example, by typing, "My left knee is hurting more. Will it be okay to train today?"

[0243] Step 13:

[0244] The server receives the chatbot's consultation message and uses the generation AI to analyze the content, including emotional data. Based on the analysis results, an appropriate response is generated.

[0245] Step 14:

[0246] The server sends the generated answer to the terminal using the answer sending means. For example, an answer such as "If you have pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist." If the emotion data indicates "anxiety," a response that particularly emphasizes a sense of relief is generated.

[0247] Step 15:

[0248] The device receives the reply and displays it in the chat window, allowing the user to receive appropriate instructions and advice.

[0249] Example 2

[0250] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0251] It is difficult for today's athletes to easily obtain optimal diet, training, rehabilitation, and rest programs tailored to their individual physical condition and goals. It is also difficult for them to receive more appropriate and reassuring guidance and advice using data on their emotional and mental state. Furthermore, even when using chatbots for consultation, there is a problem in that they cannot provide answers that take emotional changes into account.

[0252] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data input means for the athlete to input their physical condition, goals, and past injury history; a data transmission means for transmitting the data input by the input means to the server; a program generation means for the server to analyze the data received by the server using a generation AI and generate an individually optimized program for diet, training, rehabilitation, and rest; a program transmission means for transmitting the generated program to a terminal; a notification means for the terminal to notify the user of the program; an emotion analysis means incorporated in the terminal for recognizing and analyzing the user's emotions; and an emotion data transmission means for transmitting the emotion data acquired by the emotion analysis means to the server. This allows users to receive an optimal program tailored to their individual physical condition and emotions. Furthermore, appropriate responses that take emotions into consideration can be obtained when consulting with a chatbot.

[0253] "Data Entry Method" means a device or software method through which an athlete enters information about their physical condition, goals, and past injury history.

[0254] "Data transmission means" refers to a device or software method that transmits data input by the data input means to the server.

[0255] "Program generation means" refers to a device or software method that uses generation AI to analyze data received by the server and generate individually optimized diet, training, rehabilitation, and rest programs.

[0256] "Program transmission means" refers to a device or software method for transmitting the generated program to the terminal.

[0257] "Notification means" refers to a device or software method for notifying the user of a program received by the terminal.

[0258] "Emotion analysis means" refers to a device or software method that is incorporated into a terminal and that recognizes and analyzes the user's emotions.

[0259] "Emotion data transmission means" refers to a device or software method that transmits the emotion data acquired by the emotion analysis means to the server.

[0260] The present invention is a system for managing the mental and physical health of athletes and enabling them to perform at their best. Embodiments of the invention include the following elements.

[0261] The server plays a central role in receiving and analyzing data sent by athletes, such as their physical condition, goals, and past injury history. The server then analyzes the received data using generative AI to generate a diet, training, rehabilitation, and rest program appropriate for the athlete. The hardware and software used include a standard server computer and Google's TENSORFLOW (registered trademark).

[0262] The terminal provides an interface for athletes to input data, review the programs generated by the generative AI, and even consult with a chatbot. The terminal also incorporates an emotion engine that analyzes the user's voice and facial expressions to extract emotional data and send it to a server. This emotional data analysis is powered by Microsoft® Azure® Cognitive Services.

[0263] Athletes access the system through a dedicated application and input data, such as their weight, target weight, and details of past injuries. Users can also use the application to receive generated programs and check notifications. Clicking on the notification displays detailed program content. Athletes can also use the chatbot function to consult about their current physical condition and training.

[0264] For example, if a user sends a question to a chatbot such as "My left knee is hurting more. Will it be okay to train today?", the generative AI will analyze the question and generate an appropriate response for knee pain (e.g., recommend stopping running and doing some light stretching). If emotion analysis indicates a strong feeling of "anxiety," the generative AI will generate an unambiguous answer that puts the user at ease.

[0265] An example of a prompt is as follows:

[0266] User: "I'm having increasing pain in my left knee. Is it okay to train today?"

[0267] The prompt sentence for the generative AI model in response to this is as follows:

[0268] Generative AI model: "If you have pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist."

[0269] In this way, the user can receive the most suitable program according to his / her individual condition, and can also receive appropriate support based on his / her emotions.

[0270] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0271] Step 1:

[0272] The user enters data

[0273] Users open the dedicated application and enter their physical condition, goals, and past injury history. For example, they enter detailed data such as "weight 70 kg, goal weight 65 kg, injured knee last night."

[0274] Input: User's physical condition data (weight, target weight, injury history)

[0275] Output: The entered data is saved on the device and ready to be sent to the server in the next step.

[0276] Step 2:

[0277] The device sends data

[0278] The device sends the user's input data to the server. HTTPS communication is used to ensure data security. For example, data such as "weight 70 kg, target weight 65 kg, knee pain" is sent in JSON format.

[0279] Input: User-entered data

[0280] Data processing: Convert data into JSON format

[0281] Output: Data is sent to the server

[0282] Step 3:

[0283] The device analyzes the emotional data

[0284] When a user launches an application, the device uses the camera and microphone to capture the user's facial expressions and voice.

[0285] Input: User's facial expressions and voice data

[0286] Data processing: Extracting emotion data through facial expression and voice analysis

[0287] Output: Emotion data (e.g., "Current emotion: Anxiety" and "Emotion level: 85%)

[0288] Step 4:

[0289] The device sends emotion data

[0290] Emotion data analyzed by the emotion engine is sent to the server in real time. For example, data such as "Emotion: Anxiety, Level: 85%" is sent.

[0291] Input: Emotion data

[0292] Output: Emotion data is sent to the server

[0293] Step 5:

[0294] The server analyzes the data

[0295] The server analyzes the received physical and emotional data, including historical data, using generative AI models such as Google's TensorFlow.

[0296] Input: Physical data, Emotional data, Historical data

[0297] Data arithmetic: Data correlation analysis and pattern recognition

[0298] Output: Individually optimized program for athletes

[0299] Step 6:

[0300] The server generates the optimization program.

[0301] Based on the analysis results, the server generates an optimal diet, training, rehabilitation, and rest program for the athlete, including specific instructions such as "Due to knee pain, light stretching is recommended today" and "Eat a high-protein diet."

[0302] Input: Analysis results

[0303] Data processing: generating individual instructions

[0304] Output: Individual optimization program

[0305] Step 7:

[0306] The server sends the program

[0307] The generated optimization program is sent from the server to the terminal, often in JSON or XML format.

[0308] Input: Optimizer

[0309] Output: The program is sent to the terminal

[0310] Step 8:

[0311] The terminal notifies the program

[0312] The device notifies the user of the program received from the server. For example, a push notification saying "A new training plan is available" is sent. When the user clicks on the notification, detailed program content is displayed.

[0313] Input: Received optimization program

[0314] Output: Notify user, display details

[0315] Step 9:

[0316] The user makes a consultation

[0317] Users can use the chatbot function of the dedicated application to ask for advice, for example, by sending a question such as, "My left knee is hurting more. Will it be okay to train today?"

[0318] Input: User's consultation question

[0319] Output: The consultation question is entered into the terminal and sent to the server in the next step.

[0320] Step 10:

[0321] The device sends the consultation content

[0322] The terminal sends the user's consultation to the server, and it is recommended that this consultation be sent in a structured format.

[0323] Input: Consultation question

[0324] Output: The consultation content is sent to the server

[0325] Step 11:

[0326] The server analyzes the consultation content

[0327] The server uses generative AI to analyze the content of the consultation and generate a response that takes into account emotional data.

[0328] Input: Consultation content, emotion data

[0329] Data calculation: Analyzing questions and generating appropriate answers

[0330] Output: The generated answer

[0331] Step 12:

[0332] The server sends the answer

[0333] The server then sends the generated answer to the device, providing a specific answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist."

[0334] Input: Generated answer

[0335] Output: The answer is sent to the terminal

[0336] Step 13:

[0337] The device displays the answer

[0338] The terminal receives the answer from the server and displays it to the user. The answer is displayed on the chatbot's screen, allowing the user to view it and decide how to respond.

[0339] Input: Response received

[0340] Output: Display the answer to the user

[0341] (Application example 2)

[0342] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0343] Factory workers' physical and mental health can deteriorate due to long working hours and excessive stress. This not only reduces work efficiency but can also threaten the safety of the workers. Current systems have the problem of making it difficult to provide optimal health management programs for individual workers and to respond to the workers' emotions and conditions.

[0344] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means for the worker to input their own physical condition, goals, and past physical pain history; a data transmission means for transmitting the data input by the input means to a cloud server; a program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized meal, rest, and training program; a program transmission means for transmitting the generated program to the smart glasses; an emotion analysis means for analyzing emotion data from the worker's voice and facial expressions using a camera and microphone installed in the smart glasses; and an emotion data transmission means for transmitting the analyzed emotion data to the cloud server. This enables an individually optimized health management program that takes into account the worker's mental and physical health condition and emotions, and enables prompt and appropriate responses.

[0345] "Worker" refers to a person who performs work in a factory or other facility.

[0346] "Physical status" refers to data about an individual's health and physical condition, such as heart rate, sleep patterns, and whether or not they are in pain.

[0347] "Goal" refers to the goal or business objective that a worker is trying to achieve.

[0348] "Past physical pain history" refers to information about physical pain that a worker has experienced in the past and the parts of the body that have experienced pain.

[0349] "Data input means" refers to a method or device that allows the worker to input his or her physical condition, goals, and past history of physical pain.

[0350] "Cloud server" refers to a remote server for storing and managing data via the Internet.

[0351] "Data transmission means" refers to a method or device for transmitting input data to a cloud server.

[0352] "Generative AI" refers to algorithms that use machine learning and artificial intelligence techniques to analyze data and generate appropriate results or programs.

[0353] "Program generation means" refers to a method or device for creating individually optimized diet, rest, and training programs from data analyzed using generative AI.

[0354] "Smart glasses" refers to a wearable device that has a built-in display, camera, and microphone for displaying information.

[0355] "Program transmission means" refers to a method or device for transmitting the generated program to the smart glasses.

[0356] "Notification means" refers to a method or device for notifying a worker of a program or notification through the smart glasses.

[0357] "Emotion analysis means" refers to a method or device for analyzing emotions from a worker's voice and facial expressions using the camera and microphone of smart glasses.

[0358] "Emotion data transmission means" refers to a method or device for transmitting analyzed emotion data to a cloud server.

[0359] An embodiment of the present invention will be described below. The purpose of this system is to manage the physical and mental health of workers and provide an individually optimized health management program. Specifically, workers input their physical condition, goals, and past pain history, and then send that data to a cloud server.

[0360] System Configuration

[0361] 1. Data entry method

[0362] The smart glasses allow workers to input their physical condition, goals, and past history of physical pain. The smart glasses provide an intuitive user interface for the input process.

[0363] 2. Data transmission method

[0364] The data entered by the worker is transmitted from the smart glasses to a cloud server using a secure communication protocol (e.g., HTTPS).

[0365] 3. Program Generation Methods Using Generative AI

[0366] The cloud server analyzes the received data using generative AI (e.g., the GPT-4 model), which generates an individualized diet, rest, and training program.

[0367] 4. Program Transmission Method

[0368] The generated program is sent from the cloud server to the smart glasses, where the worker receives a notification and can check the contents through the smart glasses' display.

[0369] 5. Emotion analysis method

[0370] The camera and microphone installed in the smart glasses are used to capture the worker's voice and facial expressions, and the emotional data is analyzed using emotion analysis software (e.g., Affectiva SDK).

[0371] 6. Means of transmitting emotional data

[0372] The analyzed emotional data is sent to a cloud server in real time and is used by the generative AI to generate programs that provide optimal responses based on the worker's emotions.

[0373] 7. Consultation methods and answer generation methods

[0374] Workers can ask questions or ask for advice using the chatbot function in the smart glasses. The chatbot uses generative AI to analyze the question and generate an appropriate answer, which is also displayed on the smart glasses.

[0375] Example

[0376] As a specific example, the following scene can be considered.

[0377] The worker asks the smart glasses, "I'm feeling a little tired today. What should I eat?" This question is sent to a cloud server, and the generative AI (GPT-4 model) responds, "Eating high-protein foods will help you recover from fatigue." At this time, the emotion analysis means detects "fatigue," and this information is included in the analysis.

[0378] Example prompt sentence:

[0379] Worker's heart rate data: 200 BPM, past pain history: "Pain in right shoulder", emotion: "Fatigue"

[0380] Use this information to generate the best training and meal plan for you.

[0381] result:

[0382] Today's training:

[0383] Light stretching (to avoid straining the right shoulder)

[0384] Rehabilitation exercises

[0385] Today's meal:

[0386] High protein diet (chicken breast, beans)

[0387] Stay hydrated

[0388] In this way, the system of the present invention can not only analyze the physical and mental health of workers and provide individually optimized programs, but also respond in real time to the worker's emotions, thereby ensuring the safety and efficient performance of workers' work.

[0389] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0390] Program processing steps

[0391] Step 1:

[0392] Through the smart glasses, workers input their physical condition (heart rate, sleep patterns, etc.), goals, and past history of physical pain. Using the smart glasses' interface, data is entered by selecting options on the screen or by voice input.

[0393] Input: Physical condition data, goals, past pain history

[0394] Output: The input data is stored in the internal memory of the smart glasses.

[0395] Step 2:

[0396] The smart glasses send the input data to a cloud server using HTTPS, a secure communication protocol.

[0397] Input: Data entered into the smart glasses

[0398] Output: Data is transferred to the cloud server.

[0399] Step 3:

[0400] The server then analyzes the received data using a generative AI model (e.g., GPT-4), which generates a personalized diet, rest, and training program based on health data, goals, and past pain data.

[0401] Input: Data sent to the cloud server

[0402] Output: Analyzed individual optimization programs

[0403] Step 4:

[0404] The server transmits the generated program to the smart glasses.

[0405] Input: Analyzed individual optimization program

[0406] Output: Program transferred to smart glasses

[0407] Step 5:

[0408] The smart glasses notify the worker of the generated program, which is then displayed on the screen so the worker can check the contents.

[0409] Input: Program sent from the cloud server

[0410] Output: Notification displayed to the worker

[0411] Step 6:

[0412] The camera and microphone installed in the smart glasses are used to capture the worker's voice and facial expressions, and the Affectiva SDK is used to analyze the emotional data in real time.

[0413] Input: Worker's voice and facial expression data

[0414] Output: Parsed emotion data

[0415] Step 7:

[0416] The smart glasses send the analyzed emotion data to a cloud server.

[0417] Input: Parsed emotion data

[0418] Output: Emotion data sent to the cloud server

[0419] Step 8:

[0420] Workers use smart glasses to ask questions or ask questions to a chatbot, either through voice or text input.

[0421] Input: Questions and inquiries from workers

[0422] Output: Consultation content sent to the cloud server

[0423] Step 9:

[0424] The generative AI model on the cloud server analyzes the received question or consultation content and generates an appropriate answer, taking emotional data into account during the analysis.

[0425] Input: consultation details, emotional data

[0426] Output: The generated answer

[0427] Step 10:

[0428] The generated answer is sent from the cloud server to the smart glasses.

[0429] Input: Generated Answer

[0430] Output: Answers transferred to smart glasses

[0431] Step 11:

[0432] The smart glasses display the generated answer to the worker, who then checks the chatbot's response on the display.

[0433] Input: Answer sent from the cloud server

[0434] Output: The answer that is displayed to the worker

[0435] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0436] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0437] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0438] [Second embodiment]

[0439] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0440] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0441] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0442] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0443] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0444] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0445] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0446] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0447] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0448] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0449] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0450] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0451] This invention uses generative AI to provide a system that provides optimal nutrition, training, rehabilitation, and rest programs for athletes to manage their physical and mental health and maximize their performance. It also has a chatbot function that responds to athlete inquiries.

[0452] System Configuration

[0453] The server plays a central role in the system, receiving and analyzing data sent by athletes. It also uses generative AI to generate optimization programs and send them to the devices. It also processes inquiries from athletes through a chatbot function, generating and sending appropriate answers.

[0454] The terminal is an interface where athletes can input data, check the program, and receive consultations. The data entered by the athlete is sent from the terminal to the server. The terminal receives the optimization program and answers sent from the server and notifies the athlete.

[0455] Athletes interact with the system using a dedicated application, allowing them to input data, check programs, and consult with chatbots.

[0456] Entering and Submitting Data

[0457] Users use a dedicated application to input their physical condition, goals, past injury history, etc. The input data is sent to a server via the device, and individual athlete data is then stored on the server.

[0458] Program generation by generative AI

[0459] The server uses AI to analyze the received athlete data. Based on this analysis, it generates an optimized diet, training, rehabilitation, and rest program for the athlete. Past data is also retrieved from the database for more precise analysis.

[0460] For example, it analyzes heart rate data and sleep patterns to adjust the next day's training and meal plan.

[0461] Program submission and notification

[0462] The optimized program generated by the server is sent to the device. The device receives the program and notifies the athlete. For example, a notification saying "A new training plan is available" is displayed. By clicking on this notification, the athlete can view the detailed program content.

[0463] Consultation response via chatbot

[0464] When a user uses the chatbot in the system to ask for advice, the content of the consultation is sent to the server. The server uses a generation AI to analyze the content and generate an appropriate answer. The generated answer is sent to the athlete's device and displayed.

[0465] For example, if a user asks, "My left knee is hurting. Is it okay to train today?", the chatbot will generate and provide an answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching."

[0466] This system allows athletes to receive the most appropriate program for their individual condition and provides prompt responses to any concerns or questions they may have, thereby creating an environment in which athletes can perform at their best as individuals.

[0467] The processing flow will be explained below.

[0468] Step 1:

[0469] The user launches the dedicated application and inputs their physical condition, goals, past injury history, etc. through a data input means. Specifically, they input their name, age, height, weight, daily activity data (e.g., heart rate, sleep time), goals (e.g., preparation for a competition), and past injury history.

[0470] Step 2:

[0471] The terminal uses the data transmission means to transmit the data entered by the user to the server. Specifically, by pressing the "Send" button in the application, the input data is converted into JSON format and transmitted to the server.

[0472] Step 3:

[0473] The server receives the data transmitted from the terminal using the data receiving means, and temporarily stores the received data.

[0474] Step 4:

[0475] The server uses a database retrieval means to retrieve past data from the database, such as past training and injury history, relevant data for understanding the athlete's condition.

[0476] Step 5:

[0477] The server uses generated AI to analyze the received data and past data retrieved from the database, and this analysis provides a comprehensive assessment based on the user's current condition and goals.

[0478] Step 6:

[0479] The server uses a program generation means to generate an individualized optimal diet, training, rehabilitation, and rest program based on the analysis results. The generated program is customized taking into account the user's lifestyle, past results, and current condition.

[0480] Step 7:

[0481] The server uses the program transmission means to transmit the generated optimized program to the terminal, which exports the program in JSON format and transmits it to the terminal.

[0482] Step 8:

[0483] The device receives the optimization program and notifies the user using a notification means. Specifically, a push notification stating "A new training plan is available" is displayed.

[0484] Step 9:

[0485] The user clicks on the notification and sees the new optimization program within the app, which includes detailed meal plans, training menus, rehabilitation plans, and rest plans.

[0486] Step 10:

[0487] If a user wants to consult with a chatbot, they can use the chatbot functionality within the application, for example, by typing, "My left knee is hurting more. Will it be okay to train today?"

[0488] Step 11:

[0489] The server receives the chatbot's inquiry message, analyzes the content using the generation AI, and generates an appropriate answer based on the analysis results.

[0490] Step 12:

[0491] The server transmits the generated answer to the terminal using the answer transmitting means. For example, a generated answer may be, "If you have pain in your left knee, we recommend that you stop running and do some light stretching."

[0492] Step 13:

[0493] The device receives the reply and displays it in the chat window, allowing the user to receive appropriate instructions and advice.

[0494] By going through this process, athletes receive an individually optimized program and can also receive immediate consultation.

[0495] Example 1

[0496] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0497] Conventional athlete health management systems have had problems providing programs optimized for individual athletes, resulting in limited, generic advice. Furthermore, there was no environment in place where athletes could quickly consult about their physical condition or any questions they had, which meant that even when urgent action was required, responses were delayed. Furthermore, past data could not be fully utilized, making it difficult to manage health from a long-term perspective.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0499] In this invention, the server includes a data input means for the athlete to input their own physical condition, goals, and past injury history, a data transmission means for transmitting the data input by the input means to the server, a program generation means for the server to analyze the received data using a generative AI model and generate an individually optimized program for diet, training, rehabilitation, and rest, a program transmission means for transmitting the generated program to a terminal, and a notification means for the terminal to notify the user of the program. This makes it possible to provide an optimized program based on the athlete's individual data.

[0500] "Data input means" refers to a device or software that allows athletes to input information such as their physical condition, goals, and past injury history into the system.

[0501] The "data transmission means" is a device or software having a function for transmitting data input by the data input means to the server.

[0502] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to analyze data and generate individually optimized diet, training, rehabilitation, and rest programs.

[0503] The "program generation means" is a device or software that has the function of analyzing the data received by the server and generating a program optimized for the athlete.

[0504] The "program transmission means" is a device or software having a function for transmitting the generated program to the terminal.

[0505] The "notification means" is a device or software having a function for notifying the user of a program received by the terminal.

[0506] A "consultation tool" is a device or software that has the function of allowing athletes to input and send questions via the chatbot.

[0507] "Answer generation means" refers to a device or software that has the function of analyzing an athlete's question using a generative AI model and generating an appropriate answer.

[0508] The "answer sending means" is a device or software having a function for sending the generated answer to the terminal.

[0509] The "answer display means" is a device or software that has the function of displaying the answers received by the terminal to the athlete.

[0510] "Database acquisition means" refers to a device or software that has the function of allowing the server to acquire an athlete's past data from the database and input it into the generative AI model along with the analysis results.

[0511] MODE FOR CARRYING OUT THE INVENTION

[0512] In this invention, a system for optimizing the health management of athletes is constructed. This system is realized by linking multiple pieces of hardware and software.

[0513] Hardware and software used

[0514] For the servers, a cloud platform (general name: cloud computing service, e.g., Google Cloud Platform, Amazon Web Services) is used.

[0515] The devices used include smartphones (commonly known as mobile devices, e.g., iPhones and Android devices) and tablets (commonly known as tablet devices, e.g., iPads and Android tablets).

[0516] Generative AI models use machine learning algorithms (commonly known as natural language generation models, e.g., GPT-4).

[0517] Overall system overview

[0518] The system has the following main functions:

[0519] 1. Data entry and submission

[0520] 2. Program generation using generative AI

[0521] 3. Program Submission and Notification

[0522] 4. Consultation response via chatbot

[0523] Data entry and submission

[0524] The user opens the dedicated application on a smartphone or tablet. The application contains a form where the user can enter information about their physical condition, goals, past injury history, etc. The entered data is converted to JSON format by the device and securely sent to the server using the HTTPS protocol. For example, data such as "This morning I weighed 70 kg, and I slept for 6 hours last night" can be entered.

[0525] Program generation by generative AI

[0526] The server stores the received data in a database (commonly known as an associative database, e.g., MySQL or PostgreSQL). The stored data is then analyzed using a generative AI model (GPT-4) to generate an individually optimized diet, training, rehabilitation, and rest program. For example, a program might analyze a user's heart rate data and sleep patterns and suggest, "Since your heart rate has been high recently, today I suggest a light jog and a balanced meal." An example of a prompt might be, "Analyze an athlete's heart rate data and sleep patterns, and create a program that adjusts the next day's training and meal plan."

[0527] Program submission and notification

[0528] The program generated by the server is sent to the device in JSON format. The device receives this program and notifies the user. For example, a notification such as "A new training plan is available" may appear on the smartphone screen. When the user taps the notification, detailed program content is displayed. For example, it may say "30 minutes of light jogging for afternoon training, and a high-protein meal for today's meal."

[0529] Consultation response via chatbot

[0530] When a user enters a question using the chatbot function within the dedicated application, the question is sent by the device to the server. The server uses a generative AI model (GPT-4) to analyze the question and generate an appropriate answer. For example, in response to the question, "My left knee is hurting. Will it be okay to train today?", the server generates an answer such as, "If you are experiencing pain in your left knee, I recommend you stop running and do some light stretching." The answer is sent to the device and displayed to the user.

[0531] As described above, this system provides optimal health management programs and prompt consultations based on each athlete's individual data, thereby creating an environment in which athletes can perform at their best.

[0532] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0533] Step 1: Data entry

[0534] The user opens the dedicated application on a smartphone or tablet. The user enters their physical condition, goals, and past injury history into the application's input form. Specifically, they enter their weight, heart rate, sleep time, etc. This data is necessary to understand their physical condition and progress toward achieving their goals. Input: Weight 70 kg, sleep time 6 hours. Output: Data in JSON format.

[0535] Step 2: Send data

[0536] The terminal converts the data entered by the user into JSON format and sends it to the server using the HTTPS protocol. The data undergoes serialization processing and is sent securely to the server. Input: User-entered data (weight, sleep time, etc.). Output: JSON-formatted data is sent to the server.

[0537] Step 3: Receiving and storing data

[0538] The server receives the data sent from the device, analyzes the HTTP request, and saves it in a database. Specifically, it uses a database such as MySQL or PostgreSQL and inserts the data into a table. Input: Data in JSON format. Output: Data saved in the database.

[0539] Step 4: Program Generation

[0540] The server retrieves data stored in the database and analyzes it using a generative AI model (GPT-4). This analysis generates a diet, training, rehabilitation, and rest program optimized for the user. Specifically, it analyzes heart rate data and sleep patterns and generates the program in text format. Example prompt: "Analyze an athlete's heart rate data and sleep patterns and create a program that adjusts the next day's training content and meal plan." Input: User data retrieved from the database. Output: The generated optimization program (text format).

[0541] Step 5: Send the program

[0542] The server converts the generated program into JSON format and sends it to the terminal using the HTTPS protocol. Input: The generated optimized program. Output: JSON format data is sent to the terminal.

[0543] Step 6: Program Notification

[0544] The device receives the program sent from the server and notifies the user. The notification is sent using push notifications, displaying a message such as "A new training plan is available." When the user taps the notification, detailed program content is displayed. Input: Program data in JSON format. Output: Notification to the user and program details displayed.

[0545] Step 7: Enter your consultation

[0546] The user inputs a question using the chatbot function in the dedicated application. For example, "My left knee is hurting more. Will it be okay to train today?" Input: Question text. Output: Question data in JSON format.

[0547] Step 8: Submit your question

[0548] The terminal converts the question entered by the user into JSON format and sends it to the server using the HTTPS protocol. Input: Question data (JSON format). Output: Question data sent to the server.

[0549] Step 9: Receiving questions and generating answers

[0550] The server analyzes the received question using a generative AI model (GPT-4) and generates an appropriate answer. For example, it generates an answer such as, "If you have pain in your left knee, we recommend you stop running and do some light stretching." Input: Question data. Output: Generated answer text.

[0551] Step 10: Submit your response

[0552] The server converts the generated answer into JSON format and sends it to the terminal using the HTTPS protocol. Input: Generated answer text. Output: JSON format answer data is sent to the terminal.

[0553] Step 11: Display answers

[0554] The device receives the answer sent from the server and displays it on the chatbot screen. For example, it might say, "If you have pain in your left knee, we recommend you stop running and do some light stretching." Input: Answer data in JSON format. Output: Display of answer to the user.

[0555] (Application example 1)

[0556] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0557] Currently, health management of industrial workers is not always thorough, resulting in delays in improving employee productivity and the working environment. Furthermore, there is a lack of systems that can quickly and individually address fatigue and health concerns felt by employees. This can result in reduced employee productivity and adversely affect the quality of work. Therefore, there is a need for a system that collects and analyzes the health data of industrial workers and provides optimal work schedules and rest plans.

[0558] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0559] In this invention, the server includes a data input means for industrial workers to input their physical condition, working hours, and rest times, a data transmission means for transmitting the data input by the input means to the server, a program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized work schedule and rest plan, a program transmission means for transmitting the generated program to a terminal, a notification means by the terminal notifying the user of the program, a consultation means by which industrial workers ask questions about their health condition via a chatbot, an answer generation means by the chatbot analyzing the industrial worker's question using a generation AI and generating an appropriate answer, an answer transmission means for transmitting the generated answer to the terminal, an answer display means by the terminal displaying the answer to the industrial worker, and a database acquisition means by the server to acquire the industrial worker's past data from a database and input it together with the analysis results into the generation AI.

[0560] This will enable efficient collection and analysis of industrial workers' health data and provide optimal individual work schedules and rest plans. Furthermore, by using chatbots to quickly respond to health consultations, it will be possible to manage employees' health status in real time and provide appropriate advice. This will lead to improved work efficiency and a better working environment.

[0561] "Data input means" refers to a device or interface that allows industrial workers to input information such as their physical condition, working hours, and rest times.

[0562] The "data transmission means" is a means for transmitting data obtained through the input means to the server.

[0563] A "server" is a device or system that uses generation AI to analyze the received data and generate individually optimized work schedules and break plans.

[0564] The "program generation means" refers to the processes and algorithms that analyze the data received by the server and generate optimal work schedules and rest plans.

[0565] The "program transmission means" is a means for transmitting the generated program to the terminal.

[0566] The "notification means" is an interface for notifying the user of the program received by the terminal.

[0567] The "consultation tool" is a tool that allows industrial workers to ask questions about their health via a chatbot.

[0568] A "chatbot" is a computer program designed to interact with users.

[0569] "Answer generation means" refers to the process or algorithm by which the chatbot uses generation AI to analyze the industrial worker's question and generate an appropriate answer.

[0570] The "answer transmission means" is a means for transmitting the generated answer to the terminal.

[0571] The "answer display means" is an interface for displaying the answers received by the terminal to the industrial worker.

[0572] The "database acquisition means" is a means by which the server acquires the industrial worker's past data from the database and inputs it into the generation AI along with the analysis results.

[0573] "Generative AI" is an artificial intelligence technology that analyzes received data and generates optimal programs and answers.

[0574] This invention is a system that efficiently collects and analyzes the health data of industrial workers and proposes individually optimized work schedules and rest plans. Furthermore, by providing a health consultation function using a chatbot, it is possible to manage the health status of industrial workers in real time, improving work efficiency and ensuring safety.

[0575] The server is equipped with a data input means for industrial workers to input data such as their physical condition, working hours, and rest times. Devices such as smartphones and PCs are used as the data input means. Industrial workers use these devices to input data, which is then transmitted to the server via a data transmission means.

[0576] The server analyzes the received data using a generation AI. For example, the generation AI uses Python's Scikit-learn library and predicts heart rate using a linear regression model. Based on this, an individually optimized work schedule and rest plan are generated and implemented as a program generation means. This generated program is sent to the terminal by a program transmission means.

[0577] The terminal is provided with a notification means for notifying the user of the received program. For example, a notification saying "A new work schedule is available" is displayed. The user can click on the notification to check the detailed program content.

[0578] Furthermore, the system responds to questions about the health status of industrial workers via a chatbot. When a worker uses the chatbot to input a question through the consultation means, the server analyzes the question using a generation AI and is equipped with an answer generation means that generates an appropriate answer. The generated answer is sent to the terminal and notified to the worker by the answer sending means through the display means. For example, in response to the question, "I have pain in my left knee. Is it safe to work today?", the chatbot will provide an answer based on the analysis results, such as, "If you have pain in your left knee, we recommend extending your break time and doing some light stretching."

[0579] The server also has a database acquisition means for retrieving past data from the database and inputting it into the generation AI, which enables more precise analysis based on past health data and work history, improving the accuracy of the individual optimization program.

[0580] A specific example is given below. For example, an industrial worker uses a smartphone to input data such as heart rate, working hours, and rest times every day. The server receives this data and predicts the heart rate using generative AI (Scikit-learn's Linear Regression model). It then generates optimal rest times and work schedules and sends the program to the device.

[0581] Examples of prompts include, "I've been losing concentration easily lately. Please give me some appropriate advice based on my health data," or "I've been having persistent pain in my left knee. Please tell me how to work safely."

[0582] The above system can improve the health management and work efficiency of industrial workers.

[0583] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0584] Step 1:

[0585] A user uses a device such as a smartphone or PC to input their own physical condition (heart rate, working hours, rest time, etc.) through data input means. The input data is sent to a server via data transmission means. The input data includes numerical data and written comments.

[0586] Input: User's physical condition data (heart rate, working hours, rest time, etc.)

[0587] Output: Data sent to the server

[0588] Step 2:

[0589] The server stores the received data and analyzes it using a generative AI model (for example, Scikit-learn's Linear Regression model). This analysis results in data calculations that predict heart rate and generate work schedules and rest plans. The server also retrieves past data from the database and uses it for analysis.

[0590] Input: Received user data, historical database data

[0591] Output: Optimal work schedule, break plan

[0592] Step 3:

[0593] The server transmits the generated work schedule and break plan to the terminal using a program transmission means, including specific start times, end times, and break times.

[0594] Input: Generated work schedule and break plan

[0595] Output: Program sent to terminal

[0596] Step 4:

[0597] The device notifies the user of the received program via a smartphone app or PC notification function, displaying a message such as "A new work schedule is available."

[0598] Input: Program sent from the server

[0599] Output: Message notified to the user

[0600] Step 5:

[0601] The user inputs a question about their health condition via the chatbot. The question (e.g., "I have pain in my left knee. Is it safe to work today?") is sent to the server via the consultation tool.

[0602] Input: User's question

[0603] Output: Question data sent to the server

[0604] Step 6:

[0605] The server analyzes the received question data and generates an appropriate answer using the generative AI model. For example, "If you have pain in your left knee, we recommend extending your rest time and doing some light stretching." After generating the answer, the server sends it to the device via the answer sending means.

[0606] Input: User question data

[0607] Output: The generated answer

[0608] Step 7:

[0609] The device displays the received answers to the user via a chatbot app or notification function, allowing the user to immediately check the detailed answers.

[0610] Input: The answer sent by the server

[0611] Output: The answer displayed to the user

[0612] The above processing steps make it possible to provide optimal work schedules and rest plans based on the health data of industrial workers, and to provide prompt health consultations.

[0613] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0614] This invention will provide a system that uses generative AI and an emotion engine to manage the mental and physical health of athletes and provide individually optimized diet, training, rehabilitation, and rest programs to help them perform at their best.It will also have a chatbot function that can respond to athletes' inquiries.

[0615] System Configuration

[0616] The server plays a central role in the system, receiving and analyzing data sent by athletes. It also uses generative AI to generate optimization programs and send them to the devices. It also processes inquiries from athletes through a chatbot function, generating and sending appropriate answers. It also incorporates and analyzes emotional data recognized by the emotion engine.

[0617] The terminal is an interface through which athletes can input data, check programs, and receive consultations. The data entered by the athlete is sent from the terminal to the server. The terminal receives the optimization program and answers sent from the server and notifies the athlete. In addition, the terminal is equipped with an emotion engine that recognizes the user's emotions.

[0618] Athletes interact with the system using a dedicated application, allowing them to input data, check programs, and consult with chatbots.

[0619] Entering and Submitting Data

[0620] Users use a dedicated application to input their physical condition, goals, past injury history, etc. The input data is sent to a server via the device, and individual athlete data is then stored on the server.

[0621] Utilizing the Emotion Engine

[0622] The device is equipped with an emotion engine that analyzes the user's emotions based on their voice and facial expressions. The analysis results are sent to the server by the data transmission means.

[0623] Program generation by generative AI

[0624] The server uses AI to analyze the received athlete data and emotional data. Based on this analysis, it generates an optimized diet, training, rehabilitation, and rest program for the athlete. Past data is also retrieved from the database for more precise analysis.

[0625] For example, it analyzes heart rate data, sleep patterns, and emotional data to adjust the next day's training and meal plan.

[0626] Program submission and notification

[0627] The optimized program generated by the server is sent to the device. The device receives the program and notifies the athlete. For example, a notification saying "A new training plan is available" is displayed. By clicking on this notification, the athlete can view the detailed program content.

[0628] Consultation response via chatbot

[0629] When a user uses the chatbot in the system to ask for advice, the content of the consultation is sent to the server. The server then uses generative AI to analyze the content and generate an appropriate response, taking into account the emotional data recognized by the emotion engine. The generated response is then sent to the athlete's device and displayed.

[0630] For example, if a user asks, "My left knee is hurting more. Is it okay to train today?", the chatbot will generate and provide an answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, please consult a specialist." If the emotional data indicates "anxiety," a response that particularly emphasizes a sense of security will be generated.

[0631] This system allows athletes to receive the most appropriate program tailored to their individual state and emotions, and provides prompt and appropriate responses to any anxieties or questions they may have, thereby creating an environment in which athletes can perform at their best as individuals.

[0632] The processing flow will be explained below.

[0633] Step 1:

[0634] The user launches the dedicated application and inputs their physical condition, goals, past injury history, etc. through a data input means. Specifically, they input their name, age, height, weight, daily activity data (e.g., heart rate, sleep time), goals (e.g., preparation for a competition), and past injury history.

[0635] Step 2:

[0636] The terminal uses the data transmission means to transmit the data entered by the user to the server. Specifically, by pressing the "Send" button in the application, the input data is converted into JSON format and transmitted to the server.

[0637] Step 3:

[0638] The device uses an emotion engine to analyze the user's emotions. Specifically, it analyzes the user's voice and facial expressions to recognize emotions such as "happiness," "anxiety," and "fatigue."

[0639] Step 4:

[0640] The device transmits the emotion data analyzed by the emotion engine to the server using the data transmission means. The analysis results are transmitted in JSON format.

[0641] Step 5:

[0642] The server receives the physical data and emotional data transmitted from the terminal using the data receiving means, and temporarily stores the received data.

[0643] Step 6:

[0644] The server retrieves past data from the database using the database retrieval means, specifically, retrieves related data such as past training history and injury history.

[0645] Step 7:

[0646] The server uses the generated AI to comprehensively analyze the received physical data, emotional data, and past data, and evaluates the user's current condition and emotional state.

[0647] Step 8:

[0648] The server uses the program generation means to generate an individualized optimal diet, training, rehabilitation, and rest program based on the analysis results. The generated program is customized taking into account the user's physical and emotional state.

[0649] Step 9:

[0650] The server uses the program transmission means to transmit the generated optimized program to the terminal, which exports the program in JSON format and transmits it to the terminal.

[0651] Step 10:

[0652] The device receives the optimization program and notifies the user using a notification means. Specifically, a push notification stating "A new training plan is available" is displayed.

[0653] Step 11:

[0654] The user clicks on the notification and sees the new optimization program within the app, which includes detailed meal plans, training menus, rehabilitation plans, and rest plans.

[0655] Step 12:

[0656] If a user wants to consult with a chatbot, they can use the chatbot functionality within the application, for example, by typing, "My left knee is hurting more. Will it be okay to train today?"

[0657] Step 13:

[0658] The server receives the chatbot's consultation message and uses the generation AI to analyze the content, including emotional data. Based on the analysis results, an appropriate response is generated.

[0659] Step 14:

[0660] The server sends the generated answer to the terminal using the answer sending means. For example, an answer such as "If you have pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist." If the emotion data indicates "anxiety," a response that particularly emphasizes a sense of relief is generated.

[0661] Step 15:

[0662] The device receives the reply and displays it in the chat window, allowing the user to receive appropriate instructions and advice.

[0663] Example 2

[0664] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0665] It is difficult for today's athletes to easily obtain optimal diet, training, rehabilitation, and rest programs tailored to their individual physical condition and goals. It is also difficult for them to receive more appropriate and reassuring guidance and advice using data on their emotional and mental state. Furthermore, even when using chatbots for consultation, there is a problem in that they cannot provide answers that take emotional changes into account.

[0666] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data input means for the athlete to input their physical condition, goals, and past injury history; a data transmission means for transmitting the data input by the input means to the server; a program generation means for the server to analyze the data received by the server using a generation AI and generate an individually optimized program for diet, training, rehabilitation, and rest; a program transmission means for transmitting the generated program to a terminal; a notification means for the terminal to notify the user of the program; an emotion analysis means incorporated in the terminal for recognizing and analyzing the user's emotions; and an emotion data transmission means for transmitting the emotion data acquired by the emotion analysis means to the server. This allows users to receive an optimal program tailored to their individual physical condition and emotions. Furthermore, appropriate responses that take emotions into consideration can be obtained when consulting with a chatbot.

[0667] "Data Entry Method" means a device or software method through which an athlete enters information about their physical condition, goals, and past injury history.

[0668] "Data transmission means" refers to a device or software method that transmits data input by the data input means to the server.

[0669] "Program generation means" refers to a device or software method that uses generation AI to analyze data received by the server and generate individually optimized diet, training, rehabilitation, and rest programs.

[0670] "Program transmission means" refers to a device or software method for transmitting the generated program to the terminal.

[0671] "Notification means" refers to a device or software method for notifying the user of a program received by the terminal.

[0672] "Emotion analysis means" refers to a device or software method that is incorporated into a terminal and that recognizes and analyzes the user's emotions.

[0673] "Emotion data transmission means" refers to a device or software method that transmits the emotion data acquired by the emotion analysis means to the server.

[0674] The present invention is a system for managing the mental and physical health of athletes and enabling them to perform at their best. Embodiments of the invention include the following elements.

[0675] The server will act as a central point for receiving and analyzing data sent by athletes, such as their physical condition, goals, and past injury history. The server will then use generative AI to analyze the data and generate a nutrition, training, rehabilitation, and rest program tailored to each athlete. The hardware and software used will include a standard server computer and Google's TensorFlow.

[0676] The terminal provides an interface for athletes to input data, review the programs generated by the generative AI, and even consult with a chatbot. The terminal also incorporates an emotion engine that analyzes the user's voice and facial expressions to extract emotional data and send it to a server. Microsoft Azure's Cognitive Services are used for this emotional data analysis.

[0677] Athletes access the system through a dedicated application and input data, such as their weight, target weight, and details of past injuries. Users can also use the application to receive generated programs and check notifications. Clicking on the notification displays detailed program content. Athletes can also use the chatbot function to consult about their current physical condition and training.

[0678] For example, if a user sends a question to a chatbot such as "My left knee is hurting more. Will it be okay to train today?", the generative AI will analyze the question and generate an appropriate response for knee pain (e.g., recommend stopping running and doing some light stretching). If emotion analysis indicates a strong feeling of "anxiety," the generative AI will generate an unambiguous answer that puts the user at ease.

[0679] An example of a prompt is as follows:

[0680] User: "I'm having increasing pain in my left knee. Is it okay to train today?"

[0681] The prompt sentence for the generative AI model in response to this is as follows:

[0682] Generative AI model: "If you have pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist."

[0683] In this way, the user can receive the most suitable program according to his / her individual condition, and can also receive appropriate support based on his / her emotions.

[0684] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0685] Step 1:

[0686] The user enters data

[0687] Users open the dedicated application and enter their physical condition, goals, and past injury history. For example, they enter detailed data such as "weight 70 kg, goal weight 65 kg, injured knee last night."

[0688] Input: User's physical condition data (weight, target weight, injury history)

[0689] Output: The entered data is saved on the device and ready to be sent to the server in the next step.

[0690] Step 2:

[0691] The device sends data

[0692] The device sends the user's input data to the server. HTTPS communication is used to ensure data security. For example, data such as "weight 70 kg, target weight 65 kg, knee pain" is sent in JSON format.

[0693] Input: User-entered data

[0694] Data processing: Convert data into JSON format

[0695] Output: Data is sent to the server

[0696] Step 3:

[0697] The device analyzes the emotional data

[0698] When a user launches an application, the device uses the camera and microphone to capture the user's facial expressions and voice.

[0699] Input: User's facial expressions and voice data

[0700] Data processing: Extracting emotion data through facial expression and voice analysis

[0701] Output: Emotion data (e.g., "Current emotion: Anxiety" and "Emotion level: 85%)

[0702] Step 4:

[0703] The device sends emotion data

[0704] Emotion data analyzed by the emotion engine is sent to the server in real time. For example, data such as "Emotion: Anxiety, Level: 85%" is sent.

[0705] Input: Emotion data

[0706] Output: Emotion data is sent to the server

[0707] Step 5:

[0708] The server analyzes the data

[0709] The server analyzes the received physical and emotional data, including historical data, using generative AI models such as Google's TensorFlow.

[0710] Input: Physical data, Emotional data, Historical data

[0711] Data arithmetic: Data correlation analysis and pattern recognition

[0712] Output: Individually optimized program for athletes

[0713] Step 6:

[0714] The server generates the optimization program.

[0715] Based on the analysis results, the server generates an optimal diet, training, rehabilitation, and rest program for the athlete, including specific instructions such as "Due to knee pain, light stretching is recommended today" and "Eat a high-protein diet."

[0716] Input: Analysis results

[0717] Data processing: generating individual instructions

[0718] Output: Individual optimization program

[0719] Step 7:

[0720] The server sends the program

[0721] The generated optimization program is sent from the server to the terminal, often in JSON or XML format.

[0722] Input: Optimizer

[0723] Output: The program is sent to the terminal

[0724] Step 8:

[0725] The terminal notifies the program

[0726] The device notifies the user of the program received from the server. For example, a push notification saying "A new training plan is available" is sent. When the user clicks on the notification, detailed program content is displayed.

[0727] Input: Received optimization program

[0728] Output: Notify user, display details

[0729] Step 9:

[0730] The user makes a consultation

[0731] Users can use the chatbot function of the dedicated application to ask for advice, for example, by sending a question such as, "My left knee is hurting more. Will it be okay to train today?"

[0732] Input: User's consultation question

[0733] Output: The consultation question is entered into the terminal and sent to the server in the next step.

[0734] Step 10:

[0735] The device sends the consultation content

[0736] The terminal sends the user's consultation to the server, and it is recommended that this consultation be sent in a structured format.

[0737] Input: Consultation question

[0738] Output: The consultation content is sent to the server

[0739] Step 11:

[0740] The server analyzes the consultation content

[0741] The server uses generative AI to analyze the content of the consultation and generate a response that takes into account emotional data.

[0742] Input: Consultation content, emotion data

[0743] Data calculation: Analyzing questions and generating appropriate answers

[0744] Output: The generated answer

[0745] Step 12:

[0746] The server sends the answer

[0747] The server then sends the generated answer to the device, providing a specific answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist."

[0748] Input: Generated answer

[0749] Output: The answer is sent to the terminal

[0750] Step 13:

[0751] The device displays the answer

[0752] The terminal receives the answer from the server and displays it to the user. The answer is displayed on the chatbot's screen, allowing the user to view it and decide how to respond.

[0753] Input: Response received

[0754] Output: Display the answer to the user

[0755] (Application example 2)

[0756] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0757] Factory workers' physical and mental health can deteriorate due to long working hours and excessive stress. This not only reduces work efficiency but can also threaten the safety of the workers. Current systems have the problem of making it difficult to provide optimal health management programs for individual workers and to respond to the workers' emotions and conditions.

[0758] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means for the worker to input their own physical condition, goals, and past physical pain history; a data transmission means for transmitting the data input by the input means to a cloud server; a program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized meal, rest, and training program; a program transmission means for transmitting the generated program to the smart glasses; an emotion analysis means for analyzing emotion data from the worker's voice and facial expressions using a camera and microphone installed in the smart glasses; and an emotion data transmission means for transmitting the analyzed emotion data to the cloud server. This enables an individually optimized health management program that takes into account the worker's mental and physical health condition and emotions, and enables prompt and appropriate responses.

[0759] "Worker" refers to a person who performs work in a factory or other facility.

[0760] "Physical status" refers to data about an individual's health and physical condition, such as heart rate, sleep patterns, and whether or not they are in pain.

[0761] "Goal" refers to the goal or business objective that a worker is trying to achieve.

[0762] "Past physical pain history" refers to information about physical pain that a worker has experienced in the past and the parts of the body that have experienced pain.

[0763] "Data input means" refers to a method or device that allows the worker to input his or her physical condition, goals, and past history of physical pain.

[0764] "Cloud server" refers to a remote server for storing and managing data via the Internet.

[0765] "Data transmission means" refers to a method or device for transmitting input data to a cloud server.

[0766] "Generative AI" refers to algorithms that use machine learning and artificial intelligence techniques to analyze data and generate appropriate results or programs.

[0767] "Program generation means" refers to a method or device for creating individually optimized diet, rest, and training programs from data analyzed using generative AI.

[0768] "Smart glasses" refers to a wearable device that has a built-in display, camera, and microphone for displaying information.

[0769] "Program transmission means" refers to a method or device for transmitting the generated program to the smart glasses.

[0770] "Notification means" refers to a method or device for notifying a worker of a program or notification through the smart glasses.

[0771] "Emotion analysis means" refers to a method or device for analyzing emotions from a worker's voice and facial expressions using the camera and microphone of smart glasses.

[0772] "Emotion data transmission means" refers to a method or device for transmitting analyzed emotion data to a cloud server.

[0773] An embodiment of the present invention will be described below. The purpose of this system is to manage the physical and mental health of workers and provide an individually optimized health management program. Specifically, workers input their physical condition, goals, and past pain history, and then send that data to a cloud server.

[0774] System Configuration

[0775] 1. Data entry method

[0776] The smart glasses allow workers to input their physical condition, goals, and past history of physical pain. The smart glasses provide an intuitive user interface for the input process.

[0777] 2. Data transmission method

[0778] The data entered by the worker is transmitted from the smart glasses to a cloud server using a secure communication protocol (e.g., HTTPS).

[0779] 3. Program Generation Methods Using Generative AI

[0780] The cloud server analyzes the received data using generative AI (e.g., the GPT-4 model), which generates an individualized diet, rest, and training program.

[0781] 4. Program Transmission Method

[0782] The generated program is sent from the cloud server to the smart glasses, where the worker receives a notification and can check the contents through the smart glasses' display.

[0783] 5. Emotion analysis method

[0784] The camera and microphone installed in the smart glasses are used to capture the worker's voice and facial expressions, and the emotional data is analyzed using emotion analysis software (e.g., Affectiva SDK).

[0785] 6. Means of transmitting emotional data

[0786] The analyzed emotional data is sent to a cloud server in real time and is used by the generative AI to generate programs that provide optimal responses based on the worker's emotions.

[0787] 7. Consultation methods and answer generation methods

[0788] Workers can ask questions or ask for advice using the chatbot function in the smart glasses. The chatbot uses generative AI to analyze the question and generate an appropriate answer, which is also displayed on the smart glasses.

[0789] Example

[0790] As a specific example, the following scene can be considered.

[0791] The worker asks the smart glasses, "I'm feeling a little tired today. What should I eat?" This question is sent to a cloud server, and the generative AI (GPT-4 model) responds, "Eating high-protein foods will help you recover from fatigue." At this time, the emotion analysis means detects "fatigue," and this information is included in the analysis.

[0792] Example prompt sentence:

[0793] Worker's heart rate data: 200 BPM, past pain history: "Pain in right shoulder", emotion: "Fatigue"

[0794] Use this information to generate the best training and meal plan for you.

[0795] result:

[0796] Today's training:

[0797] Light stretching (to avoid straining the right shoulder)

[0798] Rehabilitation exercises

[0799] Today's meal:

[0800] High protein diet (chicken breast, beans)

[0801] Stay hydrated

[0802] In this way, the system of the present invention can not only analyze the physical and mental health of workers and provide individually optimized programs, but also respond in real time to the worker's emotions, thereby ensuring the safety and efficient performance of workers' work.

[0803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0804] Program processing steps

[0805] Step 1:

[0806] Through the smart glasses, workers input their physical condition (heart rate, sleep patterns, etc.), goals, and past history of physical pain. Using the smart glasses' interface, data is entered by selecting options on the screen or by voice input.

[0807] Input: Physical condition data, goals, past pain history

[0808] Output: The input data is stored in the internal memory of the smart glasses.

[0809] Step 2:

[0810] The smart glasses send the input data to a cloud server using HTTPS, a secure communication protocol.

[0811] Input: Data entered into the smart glasses

[0812] Output: Data is transferred to the cloud server.

[0813] Step 3:

[0814] The server then analyzes the received data using a generative AI model (e.g., GPT-4), which generates a personalized diet, rest, and training program based on health data, goals, and past pain data.

[0815] Input: Data sent to the cloud server

[0816] Output: Analyzed individual optimization programs

[0817] Step 4:

[0818] The server transmits the generated program to the smart glasses.

[0819] Input: Analyzed individual optimization program

[0820] Output: Program transferred to smart glasses

[0821] Step 5:

[0822] The smart glasses notify the worker of the generated program, which is then displayed on the screen so the worker can check the contents.

[0823] Input: Program sent from the cloud server

[0824] Output: Notification displayed to the worker

[0825] Step 6:

[0826] The camera and microphone installed in the smart glasses are used to capture the worker's voice and facial expressions, and the Affectiva SDK is used to analyze the emotional data in real time.

[0827] Input: Worker's voice and facial expression data

[0828] Output: Parsed emotion data

[0829] Step 7:

[0830] The smart glasses send the analyzed emotion data to a cloud server.

[0831] Input: Parsed emotion data

[0832] Output: Emotion data sent to the cloud server

[0833] Step 8:

[0834] Workers use smart glasses to ask questions or ask questions to a chatbot, either through voice or text input.

[0835] Input: Questions and inquiries from workers

[0836] Output: Consultation content sent to the cloud server

[0837] Step 9:

[0838] The generative AI model on the cloud server analyzes the received question or consultation content and generates an appropriate answer, taking emotional data into account during the analysis.

[0839] Input: consultation details, emotional data

[0840] Output: The generated answer

[0841] Step 10:

[0842] The generated answer is sent from the cloud server to the smart glasses.

[0843] Input: Generated Answer

[0844] Output: Answers transferred to smart glasses

[0845] Step 11:

[0846] The smart glasses display the generated answer to the worker, who then checks the chatbot's response on the display.

[0847] Input: Answer sent from the cloud server

[0848] Output: The answer that is displayed to the worker

[0849] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0850] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0851] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0852] [Third embodiment]

[0853] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0854] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0855] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0856] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0857] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0858] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0859] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0860] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0861] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0862] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0863] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0864] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0865] This invention uses generative AI to provide a system that provides optimal nutrition, training, rehabilitation, and rest programs for athletes to manage their physical and mental health and maximize their performance. It also has a chatbot function that responds to athlete inquiries.

[0866] System Configuration

[0867] The server plays a central role in the system, receiving and analyzing data sent by athletes. It also uses generative AI to generate optimization programs and send them to the devices. It also processes inquiries from athletes through a chatbot function, generating and sending appropriate answers.

[0868] The terminal is an interface where athletes can input data, check the program, and receive consultations. The data entered by the athlete is sent from the terminal to the server. The terminal receives the optimization program and answers sent from the server and notifies the athlete.

[0869] Athletes interact with the system using a dedicated application, allowing them to input data, check programs, and consult with chatbots.

[0870] Entering and Submitting Data

[0871] Users use a dedicated application to input their physical condition, goals, past injury history, etc. The input data is sent to a server via the device, and individual athlete data is then stored on the server.

[0872] Program generation by generative AI

[0873] The server uses AI to analyze the received athlete data. Based on this analysis, it generates an optimized diet, training, rehabilitation, and rest program for the athlete. Past data is also retrieved from the database for more precise analysis.

[0874] For example, it analyzes heart rate data and sleep patterns to adjust the next day's training and meal plan.

[0875] Program submission and notification

[0876] The optimized program generated by the server is sent to the device. The device receives the program and notifies the athlete. For example, a notification saying "A new training plan is available" is displayed. By clicking on this notification, the athlete can view the detailed program content.

[0877] Consultation response via chatbot

[0878] When a user uses the chatbot in the system to ask for advice, the content of the consultation is sent to the server. The server uses a generation AI to analyze the content and generate an appropriate answer. The generated answer is sent to the athlete's device and displayed.

[0879] For example, if a user asks, "My left knee is hurting. Is it okay to train today?", the chatbot will generate and provide an answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching."

[0880] This system allows athletes to receive the most appropriate program for their individual condition and provides prompt responses to any concerns or questions they may have, thereby creating an environment in which athletes can perform at their best as individuals.

[0881] The processing flow will be explained below.

[0882] Step 1:

[0883] The user launches the dedicated application and inputs their physical condition, goals, past injury history, etc. through a data input means. Specifically, they input their name, age, height, weight, daily activity data (e.g., heart rate, sleep time), goals (e.g., preparation for a competition), and past injury history.

[0884] Step 2:

[0885] The terminal uses the data transmission means to transmit the data entered by the user to the server. Specifically, by pressing the "Send" button in the application, the input data is converted into JSON format and transmitted to the server.

[0886] Step 3:

[0887] The server receives the data transmitted from the terminal using the data receiving means, and temporarily stores the received data.

[0888] Step 4:

[0889] The server uses a database retrieval means to retrieve past data from the database, such as past training and injury history, relevant data for understanding the athlete's condition.

[0890] Step 5:

[0891] The server uses generated AI to analyze the received data and past data retrieved from the database, and this analysis provides a comprehensive assessment based on the user's current condition and goals.

[0892] Step 6:

[0893] The server uses a program generation means to generate an individualized optimal diet, training, rehabilitation, and rest program based on the analysis results. The generated program is customized taking into account the user's lifestyle, past results, and current condition.

[0894] Step 7:

[0895] The server uses the program transmission means to transmit the generated optimized program to the terminal, which exports the program in JSON format and transmits it to the terminal.

[0896] Step 8:

[0897] The device receives the optimization program and notifies the user using a notification means. Specifically, a push notification stating "A new training plan is available" is displayed.

[0898] Step 9:

[0899] The user clicks on the notification and sees the new optimization program within the app, which includes detailed meal plans, training menus, rehabilitation plans, and rest plans.

[0900] Step 10:

[0901] If a user wants to consult with a chatbot, they can use the chatbot functionality within the application, for example, by typing, "My left knee is hurting more. Will it be okay to train today?"

[0902] Step 11:

[0903] The server receives the chatbot's inquiry message, analyzes the content using the generation AI, and generates an appropriate answer based on the analysis results.

[0904] Step 12:

[0905] The server transmits the generated answer to the terminal using the answer transmitting means. For example, a generated answer may be, "If you have pain in your left knee, we recommend that you stop running and do some light stretching."

[0906] Step 13:

[0907] The device receives the reply and displays it in the chat window, allowing the user to receive appropriate instructions and advice.

[0908] By going through this process, athletes receive an individually optimized program and can also receive immediate consultation.

[0909] Example 1

[0910] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0911] Conventional athlete health management systems have had problems providing programs optimized for individual athletes, resulting in limited, generic advice. Furthermore, there was no environment in place where athletes could quickly consult about their physical condition or any questions they had, which meant that even when urgent action was required, responses were delayed. Furthermore, past data could not be fully utilized, making it difficult to manage health from a long-term perspective.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0913] In this invention, the server includes a data input means for the athlete to input their own physical condition, goals, and past injury history, a data transmission means for transmitting the data input by the input means to the server, a program generation means for the server to analyze the received data using a generative AI model and generate an individually optimized program for diet, training, rehabilitation, and rest, a program transmission means for transmitting the generated program to a terminal, and a notification means for the terminal to notify the user of the program. This makes it possible to provide an optimized program based on the athlete's individual data.

[0914] "Data input means" refers to a device or software that allows athletes to input information such as their physical condition, goals, and past injury history into the system.

[0915] The "data transmission means" is a device or software having a function for transmitting data input by the data input means to the server.

[0916] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to analyze data and generate individually optimized diet, training, rehabilitation, and rest programs.

[0917] The "program generation means" is a device or software that has the function of analyzing the data received by the server and generating a program optimized for the athlete.

[0918] The "program transmission means" is a device or software having a function for transmitting the generated program to the terminal.

[0919] The "notification means" is a device or software having a function for notifying the user of a program received by the terminal.

[0920] A "consultation tool" is a device or software that has the function of allowing athletes to input and send questions via the chatbot.

[0921] "Answer generation means" refers to a device or software that has the function of analyzing an athlete's question using a generative AI model and generating an appropriate answer.

[0922] The "answer sending means" is a device or software having a function for sending the generated answer to the terminal.

[0923] The "answer display means" is a device or software that has the function of displaying the answers received by the terminal to the athlete.

[0924] "Database acquisition means" refers to a device or software that has the function of allowing the server to acquire an athlete's past data from the database and input it into the generative AI model along with the analysis results.

[0925] MODE FOR CARRYING OUT THE INVENTION

[0926] In this invention, a system for optimizing the health management of athletes is constructed. This system is realized by linking multiple pieces of hardware and software.

[0927] Hardware and software used

[0928] For the servers, a cloud platform (general name: cloud computing service, e.g., Google Cloud Platform, Amazon Web Services) is used.

[0929] The devices used include smartphones (commonly known as mobile devices, e.g., iPhones and Android devices) and tablets (commonly known as tablet devices, e.g., iPads and Android tablets).

[0930] Generative AI models use machine learning algorithms (commonly known as natural language generation models, e.g., GPT-4).

[0931] Overall system overview

[0932] The system has the following main functions:

[0933] 1. Data entry and submission

[0934] 2. Program generation using generative AI

[0935] 3. Program Submission and Notification

[0936] 4. Consultation response via chatbot

[0937] Data entry and submission

[0938] The user opens the dedicated application on a smartphone or tablet. The application contains a form where the user can enter information about their physical condition, goals, past injury history, etc. The entered data is converted to JSON format by the device and securely sent to the server using the HTTPS protocol. For example, data such as "This morning I weighed 70 kg, and I slept for 6 hours last night" can be entered.

[0939] Program generation by generative AI

[0940] The server stores the received data in a database (commonly known as an associative database, e.g., MySQL or PostgreSQL). The stored data is then analyzed using a generative AI model (GPT-4) to generate an individually optimized diet, training, rehabilitation, and rest program. For example, a program might analyze a user's heart rate data and sleep patterns and suggest, "Since your heart rate has been high recently, today I suggest a light jog and a balanced meal." An example of a prompt might be, "Analyze an athlete's heart rate data and sleep patterns, and create a program that adjusts the next day's training and meal plan."

[0941] Program submission and notification

[0942] The program generated by the server is sent to the device in JSON format. The device receives this program and notifies the user. For example, a notification such as "A new training plan is available" may appear on the smartphone screen. When the user taps the notification, detailed program content is displayed. For example, it may say "30 minutes of light jogging for afternoon training, and a high-protein meal for today's meal."

[0943] Consultation response via chatbot

[0944] When a user enters a question using the chatbot function within the dedicated application, the question is sent by the device to the server. The server uses a generative AI model (GPT-4) to analyze the question and generate an appropriate answer. For example, in response to the question, "My left knee is hurting. Will it be okay to train today?", the server generates an answer such as, "If you are experiencing pain in your left knee, I recommend you stop running and do some light stretching." The answer is sent to the device and displayed to the user.

[0945] As described above, this system provides optimal health management programs and prompt consultations based on each athlete's individual data, thereby creating an environment in which athletes can perform at their best.

[0946] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0947] Step 1: Data entry

[0948] The user opens the dedicated application on a smartphone or tablet. The user enters their physical condition, goals, and past injury history into the application's input form. Specifically, they enter their weight, heart rate, sleep time, etc. This data is necessary to understand their physical condition and progress toward achieving their goals. Input: Weight 70 kg, sleep time 6 hours. Output: Data in JSON format.

[0949] Step 2: Send data

[0950] The terminal converts the data entered by the user into JSON format and sends it to the server using the HTTPS protocol. The data undergoes serialization processing and is sent securely to the server. Input: User-entered data (weight, sleep time, etc.). Output: JSON-formatted data is sent to the server.

[0951] Step 3: Receiving and storing data

[0952] The server receives the data sent from the device, analyzes the HTTP request, and saves it in a database. Specifically, it uses a database such as MySQL or PostgreSQL and inserts the data into a table. Input: Data in JSON format. Output: Data saved in the database.

[0953] Step 4: Program Generation

[0954] The server retrieves data stored in the database and analyzes it using a generative AI model (GPT-4). This analysis generates a diet, training, rehabilitation, and rest program optimized for the user. Specifically, it analyzes heart rate data and sleep patterns and generates the program in text format. Example prompt: "Analyze an athlete's heart rate data and sleep patterns and create a program that adjusts the next day's training content and meal plan." Input: User data retrieved from the database. Output: The generated optimization program (text format).

[0955] Step 5: Send the program

[0956] The server converts the generated program into JSON format and sends it to the terminal using the HTTPS protocol. Input: The generated optimized program. Output: JSON format data is sent to the terminal.

[0957] Step 6: Program Notification

[0958] The device receives the program sent from the server and notifies the user. The notification is sent using push notifications, displaying a message such as "A new training plan is available." When the user taps the notification, detailed program content is displayed. Input: Program data in JSON format. Output: Notification to the user and program details displayed.

[0959] Step 7: Enter your consultation

[0960] The user inputs a question using the chatbot function in the dedicated application. For example, "My left knee is hurting more. Will it be okay to train today?" Input: Question text. Output: Question data in JSON format.

[0961] Step 8: Submit your question

[0962] The terminal converts the question entered by the user into JSON format and sends it to the server using the HTTPS protocol. Input: Question data (JSON format). Output: Question data sent to the server.

[0963] Step 9: Receiving questions and generating answers

[0964] The server analyzes the received question using a generative AI model (GPT-4) and generates an appropriate answer. For example, it generates an answer such as, "If you have pain in your left knee, we recommend you stop running and do some light stretching." Input: Question data. Output: Generated answer text.

[0965] Step 10: Submit your response

[0966] The server converts the generated answer into JSON format and sends it to the terminal using the HTTPS protocol. Input: Generated answer text. Output: JSON format answer data is sent to the terminal.

[0967] Step 11: Display answers

[0968] The device receives the answer sent from the server and displays it on the chatbot screen. For example, it might say, "If you have pain in your left knee, we recommend you stop running and do some light stretching." Input: Answer data in JSON format. Output: Display of answer to the user.

[0969] (Application example 1)

[0970] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0971] Currently, health management of industrial workers is not always thorough, resulting in delays in improving employee productivity and the working environment. Furthermore, there is a lack of systems that can quickly and individually address fatigue and health concerns felt by employees. This can result in reduced employee productivity and adversely affect the quality of work. Therefore, there is a need for a system that collects and analyzes the health data of industrial workers and provides optimal work schedules and rest plans.

[0972] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0973] In this invention, the server includes a data input means for industrial workers to input their physical condition, working hours, and rest times, a data transmission means for transmitting the data input by the input means to the server, a program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized work schedule and rest plan, a program transmission means for transmitting the generated program to a terminal, a notification means by the terminal notifying the user of the program, a consultation means by which industrial workers ask questions about their health condition via a chatbot, an answer generation means by the chatbot analyzing the industrial worker's question using a generation AI and generating an appropriate answer, an answer transmission means for transmitting the generated answer to the terminal, an answer display means by the terminal displaying the answer to the industrial worker, and a database acquisition means by the server to acquire the industrial worker's past data from a database and input it together with the analysis results into the generation AI.

[0974] This will enable efficient collection and analysis of industrial workers' health data and provide optimal individual work schedules and rest plans. Furthermore, by using chatbots to quickly respond to health consultations, it will be possible to manage employees' health status in real time and provide appropriate advice. This will lead to improved work efficiency and a better working environment.

[0975] "Data input means" refers to a device or interface that allows industrial workers to input information such as their physical condition, working hours, and rest times.

[0976] The "data transmission means" is a means for transmitting data obtained through the input means to the server.

[0977] A "server" is a device or system that uses generation AI to analyze the received data and generate individually optimized work schedules and break plans.

[0978] The "program generation means" refers to the processes and algorithms that analyze the data received by the server and generate optimal work schedules and rest plans.

[0979] The "program transmission means" is a means for transmitting the generated program to the terminal.

[0980] The "notification means" is an interface for notifying the user of the program received by the terminal.

[0981] The "consultation tool" is a tool that allows industrial workers to ask questions about their health via a chatbot.

[0982] A "chatbot" is a computer program designed to interact with users.

[0983] "Answer generation means" refers to the process or algorithm by which the chatbot uses generation AI to analyze the industrial worker's question and generate an appropriate answer.

[0984] The "answer transmission means" is a means for transmitting the generated answer to the terminal.

[0985] The "answer display means" is an interface for displaying the answers received by the terminal to the industrial worker.

[0986] The "database acquisition means" is a means by which the server acquires the industrial worker's past data from the database and inputs it into the generation AI along with the analysis results.

[0987] "Generative AI" is an artificial intelligence technology that analyzes received data and generates optimal programs and answers.

[0988] This invention is a system that efficiently collects and analyzes the health data of industrial workers and proposes individually optimized work schedules and rest plans. Furthermore, by providing a health consultation function using a chatbot, it is possible to manage the health status of industrial workers in real time, improving work efficiency and ensuring safety.

[0989] The server is equipped with a data input means for industrial workers to input data such as their physical condition, working hours, and rest times. Devices such as smartphones and PCs are used as the data input means. Industrial workers use these devices to input data, which is then transmitted to the server via a data transmission means.

[0990] The server analyzes the received data using a generation AI. For example, the generation AI uses Python's Scikit-learn library and predicts heart rate using a linear regression model. Based on this, an individually optimized work schedule and rest plan are generated and implemented as a program generation means. This generated program is sent to the terminal by a program transmission means.

[0991] The terminal is provided with a notification means for notifying the user of the received program. For example, a notification saying "A new work schedule is available" is displayed. The user can click on the notification to check the detailed program content.

[0992] Furthermore, the system responds to questions about the health status of industrial workers via a chatbot. When a worker uses the chatbot to input a question through the consultation means, the server analyzes the question using a generation AI and is equipped with an answer generation means that generates an appropriate answer. The generated answer is sent to the terminal and notified to the worker by the answer sending means through the display means. For example, in response to the question, "I have pain in my left knee. Is it safe to work today?", the chatbot will provide an answer based on the analysis results, such as, "If you have pain in your left knee, we recommend extending your break time and doing some light stretching."

[0993] The server also has a database acquisition means for retrieving past data from the database and inputting it into the generation AI, which enables more precise analysis based on past health data and work history, improving the accuracy of the individual optimization program.

[0994] A specific example is given below. For example, an industrial worker uses a smartphone to input data such as heart rate, working hours, and rest times every day. The server receives this data and predicts the heart rate using generative AI (Scikit-learn's Linear Regression model). It then generates optimal rest times and work schedules and sends the program to the device.

[0995] Examples of prompts include, "I've been losing concentration easily lately. Please give me some appropriate advice based on my health data," or "I've been having persistent pain in my left knee. Please tell me how to work safely."

[0996] The above system can improve the health management and work efficiency of industrial workers.

[0997] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0998] Step 1:

[0999] A user uses a device such as a smartphone or PC to input their own physical condition (heart rate, working hours, rest time, etc.) through data input means. The input data is sent to a server via data transmission means. The input data includes numerical data and written comments.

[1000] Input: User's physical condition data (heart rate, working hours, rest time, etc.)

[1001] Output: Data sent to the server

[1002] Step 2:

[1003] The server stores the received data and analyzes it using a generative AI model (for example, Scikit-learn's Linear Regression model). This analysis results in data calculations that predict heart rate and generate work schedules and rest plans. The server also retrieves past data from the database and uses it for analysis.

[1004] Input: Received user data, historical database data

[1005] Output: Optimal work schedule, break plan

[1006] Step 3:

[1007] The server transmits the generated work schedule and break plan to the terminal using a program transmission means, including specific start times, end times, and break times.

[1008] Input: Generated work schedule and break plan

[1009] Output: Program sent to terminal

[1010] Step 4:

[1011] The device notifies the user of the received program via a smartphone app or PC notification function, displaying a message such as "A new work schedule is available."

[1012] Input: Program sent from the server

[1013] Output: Message notified to the user

[1014] Step 5:

[1015] The user inputs a question about their health condition via the chatbot. The question (e.g., "I have pain in my left knee. Is it safe to work today?") is sent to the server via the consultation tool.

[1016] Input: User's question

[1017] Output: Question data sent to the server

[1018] Step 6:

[1019] The server analyzes the received question data and generates an appropriate answer using the generative AI model. For example, "If you have pain in your left knee, we recommend extending your rest time and doing some light stretching." After generating the answer, the server sends it to the device via the answer sending means.

[1020] Input: User question data

[1021] Output: The generated answer

[1022] Step 7:

[1023] The device displays the received answers to the user via a chatbot app or notification function, allowing the user to immediately check the detailed answers.

[1024] Input: The answer sent by the server

[1025] Output: The answer displayed to the user

[1026] The above processing steps make it possible to provide optimal work schedules and rest plans based on the health data of industrial workers, and to provide prompt health consultations.

[1027] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1028] This invention will provide a system that uses generative AI and an emotion engine to manage the mental and physical health of athletes and provide individually optimized diet, training, rehabilitation, and rest programs to help them perform at their best.It will also have a chatbot function that can respond to athletes' inquiries.

[1029] System Configuration

[1030] The server plays a central role in the system, receiving and analyzing data sent by athletes. It also uses generative AI to generate optimization programs and send them to the devices. It also processes inquiries from athletes through a chatbot function, generating and sending appropriate answers. It also incorporates and analyzes emotional data recognized by the emotion engine.

[1031] The terminal is an interface through which athletes can input data, check programs, and receive consultations. The data entered by the athlete is sent from the terminal to the server. The terminal receives the optimization program and answers sent from the server and notifies the athlete. In addition, the terminal is equipped with an emotion engine that recognizes the user's emotions.

[1032] Athletes interact with the system using a dedicated application, allowing them to input data, check programs, and consult with chatbots.

[1033] Entering and Submitting Data

[1034] Users use a dedicated application to input their physical condition, goals, past injury history, etc. The input data is sent to a server via the device, and individual athlete data is then stored on the server.

[1035] Utilizing the Emotion Engine

[1036] The device is equipped with an emotion engine that analyzes the user's emotions based on their voice and facial expressions. The analysis results are sent to the server by the data transmission means.

[1037] Program generation by generative AI

[1038] The server uses AI to analyze the received athlete data and emotional data. Based on this analysis, it generates an optimized diet, training, rehabilitation, and rest program for the athlete. Past data is also retrieved from the database for more precise analysis.

[1039] For example, it analyzes heart rate data, sleep patterns, and emotional data to adjust the next day's training and meal plan.

[1040] Program submission and notification

[1041] The optimized program generated by the server is sent to the device. The device receives the program and notifies the athlete. For example, a notification saying "A new training plan is available" is displayed. By clicking on this notification, the athlete can view the detailed program content.

[1042] Consultation response via chatbot

[1043] When a user uses the chatbot in the system to ask for advice, the content of the consultation is sent to the server. The server then uses generative AI to analyze the content and generate an appropriate response, taking into account the emotional data recognized by the emotion engine. The generated response is then sent to the athlete's device and displayed.

[1044] For example, if a user asks, "My left knee is hurting more. Is it okay to train today?", the chatbot will generate and provide an answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, please consult a specialist." If the emotional data indicates "anxiety," a response that particularly emphasizes a sense of security will be generated.

[1045] This system allows athletes to receive the most appropriate program tailored to their individual state and emotions, and provides prompt and appropriate responses to any anxieties or questions they may have, thereby creating an environment in which athletes can perform at their best as individuals.

[1046] The processing flow will be explained below.

[1047] Step 1:

[1048] The user launches the dedicated application and inputs their physical condition, goals, past injury history, etc. through a data input means. Specifically, they input their name, age, height, weight, daily activity data (e.g., heart rate, sleep time), goals (e.g., preparation for a competition), and past injury history.

[1049] Step 2:

[1050] The terminal uses the data transmission means to transmit the data entered by the user to the server. Specifically, by pressing the "Send" button in the application, the input data is converted into JSON format and transmitted to the server.

[1051] Step 3:

[1052] The device uses an emotion engine to analyze the user's emotions. Specifically, it analyzes the user's voice and facial expressions to recognize emotions such as "happiness," "anxiety," and "fatigue."

[1053] Step 4:

[1054] The device transmits the emotion data analyzed by the emotion engine to the server using the data transmission means. The analysis results are transmitted in JSON format.

[1055] Step 5:

[1056] The server receives the physical data and emotional data transmitted from the terminal using the data receiving means, and temporarily stores the received data.

[1057] Step 6:

[1058] The server retrieves past data from the database using the database retrieval means, specifically, retrieves related data such as past training history and injury history.

[1059] Step 7:

[1060] The server uses the generated AI to comprehensively analyze the received physical data, emotional data, and past data, and evaluates the user's current condition and emotional state.

[1061] Step 8:

[1062] The server uses the program generation means to generate an individualized optimal diet, training, rehabilitation, and rest program based on the analysis results. The generated program is customized taking into account the user's physical and emotional state.

[1063] Step 9:

[1064] The server uses the program transmission means to transmit the generated optimized program to the terminal, which exports the program in JSON format and transmits it to the terminal.

[1065] Step 10:

[1066] The device receives the optimization program and notifies the user using a notification means. Specifically, a push notification stating "A new training plan is available" is displayed.

[1067] Step 11:

[1068] The user clicks on the notification and sees the new optimization program within the app, which includes detailed meal plans, training menus, rehabilitation plans, and rest plans.

[1069] Step 12:

[1070] If a user wants to consult with a chatbot, they can use the chatbot functionality within the application, for example, by typing, "My left knee is hurting more. Will it be okay to train today?"

[1071] Step 13:

[1072] The server receives the chatbot's consultation message and uses the generation AI to analyze the content, including emotional data. Based on the analysis results, an appropriate response is generated.

[1073] Step 14:

[1074] The server sends the generated answer to the terminal using the answer sending means. For example, an answer such as "If you have pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist." If the emotion data indicates "anxiety," a response that particularly emphasizes a sense of relief is generated.

[1075] Step 15:

[1076] The device receives the reply and displays it in the chat window, allowing the user to receive appropriate instructions and advice.

[1077] Example 2

[1078] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1079] It is difficult for today's athletes to easily obtain optimal diet, training, rehabilitation, and rest programs tailored to their individual physical condition and goals. It is also difficult for them to receive more appropriate and reassuring guidance and advice using data on their emotional and mental state. Furthermore, even when using chatbots for consultation, there is a problem in that they cannot provide answers that take emotional changes into account.

[1080] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data input means for the athlete to input their physical condition, goals, and past injury history; a data transmission means for transmitting the data input by the input means to the server; a program generation means for the server to analyze the data received by the server using a generation AI and generate an individually optimized program for diet, training, rehabilitation, and rest; a program transmission means for transmitting the generated program to a terminal; a notification means for the terminal to notify the user of the program; an emotion analysis means incorporated in the terminal for recognizing and analyzing the user's emotions; and an emotion data transmission means for transmitting the emotion data acquired by the emotion analysis means to the server. This allows users to receive an optimal program tailored to their individual physical condition and emotions. Furthermore, appropriate responses that take emotions into consideration can be obtained when consulting with a chatbot.

[1081] "Data Entry Method" means a device or software method through which an athlete enters information about their physical condition, goals, and past injury history.

[1082] "Data transmission means" refers to a device or software method that transmits data input by the data input means to the server.

[1083] "Program generation means" refers to a device or software method that uses generation AI to analyze data received by the server and generate individually optimized diet, training, rehabilitation, and rest programs.

[1084] "Program transmission means" refers to a device or software method for transmitting the generated program to the terminal.

[1085] "Notification means" refers to a device or software method for notifying the user of a program received by the terminal.

[1086] "Emotion analysis means" refers to a device or software method that is incorporated into a terminal and that recognizes and analyzes the user's emotions.

[1087] "Emotion data transmission means" refers to a device or software method that transmits the emotion data acquired by the emotion analysis means to the server.

[1088] The present invention is a system for managing the mental and physical health of athletes and enabling them to perform at their best. Embodiments of the invention include the following elements.

[1089] The server will act as a central point for receiving and analyzing data sent by athletes, such as their physical condition, goals, and past injury history. The server will then use generative AI to analyze the data and generate a nutrition, training, rehabilitation, and rest program tailored to each athlete. The hardware and software used will include a standard server computer and Google's TensorFlow.

[1090] The terminal provides an interface for athletes to input data, review the programs generated by the generative AI, and even consult with a chatbot. The terminal also incorporates an emotion engine that analyzes the user's voice and facial expressions to extract emotional data and send it to a server. Microsoft Azure's Cognitive Services are used for this emotional data analysis.

[1091] Athletes access the system through a dedicated application and input data, such as their weight, target weight, and details of past injuries. Users can also use the application to receive generated programs and check notifications. Clicking on the notification displays detailed program content. Athletes can also use the chatbot function to consult about their current physical condition and training.

[1092] For example, if a user sends a question to a chatbot such as "My left knee is hurting more. Will it be okay to train today?", the generative AI will analyze the question and generate an appropriate response for knee pain (e.g., recommend stopping running and doing some light stretching). If emotion analysis indicates a strong feeling of "anxiety," the generative AI will generate an unambiguous answer that puts the user at ease.

[1093] An example of a prompt is as follows:

[1094] User: "I'm having increasing pain in my left knee. Is it okay to train today?"

[1095] The prompt sentence for the generative AI model in response to this is as follows:

[1096] Generative AI model: "If you have pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist."

[1097] In this way, the user can receive the most suitable program according to his / her individual condition, and can also receive appropriate support based on his / her emotions.

[1098] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1099] Step 1:

[1100] The user enters data

[1101] Users open the dedicated application and enter their physical condition, goals, and past injury history. For example, they enter detailed data such as "weight 70 kg, goal weight 65 kg, injured knee last night."

[1102] Input: User's physical condition data (weight, target weight, injury history)

[1103] Output: The entered data is saved on the device and ready to be sent to the server in the next step.

[1104] Step 2:

[1105] The device sends data

[1106] The device sends the user's input data to the server. HTTPS communication is used to ensure data security. For example, data such as "weight 70 kg, target weight 65 kg, knee pain" is sent in JSON format.

[1107] Input: User-entered data

[1108] Data processing: Convert data into JSON format

[1109] Output: Data is sent to the server

[1110] Step 3:

[1111] The device analyzes the emotional data

[1112] When a user launches an application, the device uses the camera and microphone to capture the user's facial expressions and voice.

[1113] Input: User's facial expressions and voice data

[1114] Data processing: Extracting emotion data through facial expression and voice analysis

[1115] Output: Emotion data (e.g., "Current emotion: Anxiety" and "Emotion level: 85%)

[1116] Step 4:

[1117] The device sends emotion data

[1118] Emotion data analyzed by the emotion engine is sent to the server in real time. For example, data such as "Emotion: Anxiety, Level: 85%" is sent.

[1119] Input: Emotion data

[1120] Output: Emotion data is sent to the server

[1121] Step 5:

[1122] The server analyzes the data

[1123] The server analyzes the received physical and emotional data, including historical data, using generative AI models such as Google's TensorFlow.

[1124] Input: Physical data, Emotional data, Historical data

[1125] Data arithmetic: Data correlation analysis and pattern recognition

[1126] Output: Individually optimized program for athletes

[1127] Step 6:

[1128] The server generates the optimization program.

[1129] Based on the analysis results, the server generates an optimal diet, training, rehabilitation, and rest program for the athlete, including specific instructions such as "Due to knee pain, light stretching is recommended today" and "Eat a high-protein diet."

[1130] Input: Analysis results

[1131] Data processing: generating individual instructions

[1132] Output: Individual optimization program

[1133] Step 7:

[1134] The server sends the program

[1135] The generated optimization program is sent from the server to the terminal, often in JSON or XML format.

[1136] Input: Optimizer

[1137] Output: The program is sent to the terminal

[1138] Step 8:

[1139] The terminal notifies the program

[1140] The device notifies the user of the program received from the server. For example, a push notification saying "A new training plan is available" is sent. When the user clicks on the notification, detailed program content is displayed.

[1141] Input: Received optimization program

[1142] Output: Notify user, display details

[1143] Step 9:

[1144] The user makes a consultation

[1145] Users can use the chatbot function of the dedicated application to ask for advice, for example, by sending a question such as, "My left knee is hurting more. Will it be okay to train today?"

[1146] Input: User's consultation question

[1147] Output: The consultation question is entered into the terminal and sent to the server in the next step.

[1148] Step 10:

[1149] The device sends the consultation content

[1150] The terminal sends the user's consultation to the server, and it is recommended that this consultation be sent in a structured format.

[1151] Input: Consultation question

[1152] Output: The consultation content is sent to the server

[1153] Step 11:

[1154] The server analyzes the consultation content

[1155] The server uses generative AI to analyze the content of the consultation and generate a response that takes into account emotional data.

[1156] Input: Consultation content, emotion data

[1157] Data calculation: Analyzing questions and generating appropriate answers

[1158] Output: The generated answer

[1159] Step 12:

[1160] The server sends the answer

[1161] The server then sends the generated answer to the device, providing a specific answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist."

[1162] Input: Generated answer

[1163] Output: The answer is sent to the terminal

[1164] Step 13:

[1165] The device displays the answer

[1166] The terminal receives the answer from the server and displays it to the user. The answer is displayed on the chatbot's screen, allowing the user to view it and decide how to respond.

[1167] Input: Response received

[1168] Output: Display the answer to the user

[1169] (Application example 2)

[1170] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1171] Factory workers' physical and mental health can deteriorate due to long working hours and excessive stress. This not only reduces work efficiency but can also threaten the safety of the workers. Current systems have the problem of making it difficult to provide optimal health management programs for individual workers and to respond to the workers' emotions and conditions.

[1172] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means for the worker to input their own physical condition, goals, and past physical pain history; a data transmission means for transmitting the data input by the input means to a cloud server; a program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized meal, rest, and training program; a program transmission means for transmitting the generated program to the smart glasses; an emotion analysis means for analyzing emotion data from the worker's voice and facial expressions using a camera and microphone installed in the smart glasses; and an emotion data transmission means for transmitting the analyzed emotion data to the cloud server. This enables an individually optimized health management program that takes into account the worker's mental and physical health condition and emotions, and enables prompt and appropriate responses.

[1173] "Worker" refers to a person who performs work in a factory or other facility.

[1174] "Physical status" refers to data about an individual's health and physical condition, such as heart rate, sleep patterns, and whether or not they are in pain.

[1175] "Goal" refers to the goal or business objective that a worker is trying to achieve.

[1176] "Past physical pain history" refers to information about physical pain that a worker has experienced in the past and the parts of the body that have experienced pain.

[1177] "Data input means" refers to a method or device that allows the worker to input his or her physical condition, goals, and past history of physical pain.

[1178] "Cloud server" refers to a remote server for storing and managing data via the Internet.

[1179] "Data transmission means" refers to a method or device for transmitting input data to a cloud server.

[1180] "Generative AI" refers to algorithms that use machine learning and artificial intelligence techniques to analyze data and generate appropriate results or programs.

[1181] "Program generation means" refers to a method or device for creating individually optimized diet, rest, and training programs from data analyzed using generative AI.

[1182] "Smart glasses" refers to a wearable device that has a built-in display, camera, and microphone for displaying information.

[1183] "Program transmission means" refers to a method or device for transmitting the generated program to the smart glasses.

[1184] "Notification means" refers to a method or device for notifying a worker of a program or notification through the smart glasses.

[1185] "Emotion analysis means" refers to a method or device for analyzing emotions from a worker's voice and facial expressions using the camera and microphone of smart glasses.

[1186] "Emotion data transmission means" refers to a method or device for transmitting analyzed emotion data to a cloud server.

[1187] An embodiment of the present invention will be described below. The purpose of this system is to manage the physical and mental health of workers and provide an individually optimized health management program. Specifically, workers input their physical condition, goals, and past pain history, and then send that data to a cloud server.

[1188] System Configuration

[1189] 1. Data entry method

[1190] The smart glasses allow workers to input their physical condition, goals, and past history of physical pain. The smart glasses provide an intuitive user interface for the input process.

[1191] 2. Data transmission method

[1192] The data entered by the worker is transmitted from the smart glasses to a cloud server using a secure communication protocol (e.g., HTTPS).

[1193] 3. Program Generation Methods Using Generative AI

[1194] The cloud server analyzes the received data using generative AI (e.g., the GPT-4 model), which generates an individualized diet, rest, and training program.

[1195] 4. Program Transmission Method

[1196] The generated program is sent from the cloud server to the smart glasses, where the worker receives a notification and can check the contents through the smart glasses' display.

[1197] 5. Emotion analysis method

[1198] The camera and microphone installed in the smart glasses are used to capture the worker's voice and facial expressions, and the emotional data is analyzed using emotion analysis software (e.g., Affectiva SDK).

[1199] 6. Means of transmitting emotional data

[1200] The analyzed emotional data is sent to a cloud server in real time and is used by the generative AI to generate programs that provide optimal responses based on the worker's emotions.

[1201] 7. Consultation methods and answer generation methods

[1202] Workers can ask questions or ask for advice using the chatbot function in the smart glasses. The chatbot uses generative AI to analyze the question and generate an appropriate answer, which is also displayed on the smart glasses.

[1203] Example

[1204] As a specific example, the following scene can be considered.

[1205] The worker asks the smart glasses, "I'm feeling a little tired today. What should I eat?" This question is sent to a cloud server, and the generative AI (GPT-4 model) responds, "Eating high-protein foods will help you recover from fatigue." At this time, the emotion analysis means detects "fatigue," and this information is included in the analysis.

[1206] Example prompt sentence:

[1207] Worker's heart rate data: 200 BPM, past pain history: "Pain in right shoulder", emotion: "Fatigue"

[1208] Use this information to generate the best training and meal plan for you.

[1209] result:

[1210] Today's training:

[1211] Light stretching (to avoid straining the right shoulder)

[1212] Rehabilitation exercises

[1213] Today's meal:

[1214] High protein diet (chicken breast, beans)

[1215] Stay hydrated

[1216] In this way, the system of the present invention can not only analyze the physical and mental health of workers and provide individually optimized programs, but also respond in real time to the worker's emotions, thereby ensuring the safety and efficient performance of workers' work.

[1217] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1218] Program processing steps

[1219] Step 1:

[1220] Through the smart glasses, workers input their physical condition (heart rate, sleep patterns, etc.), goals, and past history of physical pain. Using the smart glasses' interface, data is entered by selecting options on the screen or by voice input.

[1221] Input: Physical condition data, goals, past pain history

[1222] Output: The input data is stored in the internal memory of the smart glasses.

[1223] Step 2:

[1224] The smart glasses send the input data to a cloud server using HTTPS, a secure communication protocol.

[1225] Input: Data entered into the smart glasses

[1226] Output: Data is transferred to the cloud server.

[1227] Step 3:

[1228] The server then analyzes the received data using a generative AI model (e.g., GPT-4), which generates a personalized diet, rest, and training program based on health data, goals, and past pain data.

[1229] Input: Data sent to the cloud server

[1230] Output: Analyzed individual optimization programs

[1231] Step 4:

[1232] The server transmits the generated program to the smart glasses.

[1233] Input: Analyzed individual optimization program

[1234] Output: Program transferred to smart glasses

[1235] Step 5:

[1236] The smart glasses notify the worker of the generated program, which is then displayed on the screen so the worker can check the contents.

[1237] Input: Program sent from the cloud server

[1238] Output: Notification displayed to the worker

[1239] Step 6:

[1240] The camera and microphone installed in the smart glasses are used to capture the worker's voice and facial expressions, and the Affectiva SDK is used to analyze the emotional data in real time.

[1241] Input: Worker's voice and facial expression data

[1242] Output: Parsed emotion data

[1243] Step 7:

[1244] The smart glasses send the analyzed emotion data to a cloud server.

[1245] Input: Parsed emotion data

[1246] Output: Emotion data sent to the cloud server

[1247] Step 8:

[1248] Workers use smart glasses to ask questions or ask questions to a chatbot, either through voice or text input.

[1249] Input: Questions and inquiries from workers

[1250] Output: Consultation content sent to the cloud server

[1251] Step 9:

[1252] The generative AI model on the cloud server analyzes the received question or consultation content and generates an appropriate answer, taking emotional data into account during the analysis.

[1253] Input: consultation details, emotional data

[1254] Output: The generated answer

[1255] Step 10:

[1256] The generated answer is sent from the cloud server to the smart glasses.

[1257] Input: Generated Answer

[1258] Output: Answers transferred to smart glasses

[1259] Step 11:

[1260] The smart glasses display the generated answer to the worker, who then checks the chatbot's response on the display.

[1261] Input: Answer sent from the cloud server

[1262] Output: The answer that is displayed to the worker

[1263] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1264] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1265] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1266] [Fourth embodiment]

[1267] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1268] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1269] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1270] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1271] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1272] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1273] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1274] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1275] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1276] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1277] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1278] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1279] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1280] This invention uses generative AI to provide a system that provides optimal nutrition, training, rehabilitation, and rest programs for athletes to manage their physical and mental health and maximize their performance. It also has a chatbot function that responds to athlete inquiries.

[1281] System Configuration

[1282] The server plays a central role in the system, receiving and analyzing data sent by athletes. It also uses generative AI to generate optimization programs and send them to the devices. It also processes inquiries from athletes through a chatbot function, generating and sending appropriate answers.

[1283] The terminal is an interface where athletes can input data, check the program, and receive consultations. The data entered by the athlete is sent from the terminal to the server. The terminal receives the optimization program and answers sent from the server and notifies the athlete.

[1284] Athletes interact with the system using a dedicated application, allowing them to input data, check programs, and consult with chatbots.

[1285] Entering and Submitting Data

[1286] Users use a dedicated application to input their physical condition, goals, past injury history, etc. The input data is sent to a server via the device, and individual athlete data is then stored on the server.

[1287] Program generation by generative AI

[1288] The server uses AI to analyze the received athlete data. Based on this analysis, it generates an optimized diet, training, rehabilitation, and rest program for the athlete. Past data is also retrieved from the database for more precise analysis.

[1289] For example, it analyzes heart rate data and sleep patterns to adjust the next day's training and meal plan.

[1290] Program submission and notification

[1291] The optimized program generated by the server is sent to the device. The device receives the program and notifies the athlete. For example, a notification saying "A new training plan is available" is displayed. By clicking on this notification, the athlete can view the detailed program content.

[1292] Consultation response via chatbot

[1293] When a user uses the chatbot in the system to ask for advice, the content of the consultation is sent to the server. The server uses a generation AI to analyze the content and generate an appropriate answer. The generated answer is sent to the athlete's device and displayed.

[1294] For example, if a user asks, "My left knee is hurting. Is it okay to train today?", the chatbot will generate and provide an answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching."

[1295] This system allows athletes to receive the most appropriate program for their individual condition and provides prompt responses to any concerns or questions they may have, thereby creating an environment in which athletes can perform at their best as individuals.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] The user launches the dedicated application and inputs their physical condition, goals, past injury history, etc. through a data input means. Specifically, they input their name, age, height, weight, daily activity data (e.g., heart rate, sleep time), goals (e.g., preparation for a competition), and past injury history.

[1299] Step 2:

[1300] The terminal uses the data transmission means to transmit the data entered by the user to the server. Specifically, by pressing the "Send" button in the application, the input data is converted into JSON format and transmitted to the server.

[1301] Step 3:

[1302] The server receives the data transmitted from the terminal using the data receiving means, and temporarily stores the received data.

[1303] Step 4:

[1304] The server uses a database retrieval means to retrieve past data from the database, such as past training and injury history, relevant data for understanding the athlete's condition.

[1305] Step 5:

[1306] The server uses generated AI to analyze the received data and past data retrieved from the database, and this analysis provides a comprehensive assessment based on the user's current condition and goals.

[1307] Step 6:

[1308] The server uses a program generation means to generate an individualized optimal diet, training, rehabilitation, and rest program based on the analysis results. The generated program is customized taking into account the user's lifestyle, past results, and current condition.

[1309] Step 7:

[1310] The server uses the program transmission means to transmit the generated optimized program to the terminal, which exports the program in JSON format and transmits it to the terminal.

[1311] Step 8:

[1312] The device receives the optimization program and notifies the user using a notification means. Specifically, a push notification stating "A new training plan is available" is displayed.

[1313] Step 9:

[1314] The user clicks on the notification and sees the new optimization program within the app, which includes detailed meal plans, training menus, rehabilitation plans, and rest plans.

[1315] Step 10:

[1316] If a user wants to consult with a chatbot, they can use the chatbot functionality within the application, for example, by typing, "My left knee is hurting more. Will it be okay to train today?"

[1317] Step 11:

[1318] The server receives the chatbot's inquiry message, analyzes the content using the generation AI, and generates an appropriate answer based on the analysis results.

[1319] Step 12:

[1320] The server transmits the generated answer to the terminal using the answer transmitting means. For example, a generated answer may be, "If you have pain in your left knee, we recommend that you stop running and do some light stretching."

[1321] Step 13:

[1322] The device receives the reply and displays it in the chat window, allowing the user to receive appropriate instructions and advice.

[1323] By going through this process, athletes receive an individually optimized program and can also receive immediate consultation.

[1324] Example 1

[1325] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1326] Conventional athlete health management systems have had problems providing programs optimized for individual athletes, resulting in limited, generic advice. Furthermore, there was no environment in place where athletes could quickly consult about their physical condition or any questions they had, which meant that even when urgent action was required, responses were delayed. Furthermore, past data could not be fully utilized, making it difficult to manage health from a long-term perspective.

[1327] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1328] In this invention, the server includes a data input means for the athlete to input their own physical condition, goals, and past injury history, a data transmission means for transmitting the data input by the input means to the server, a program generation means for the server to analyze the received data using a generative AI model and generate an individually optimized program for diet, training, rehabilitation, and rest, a program transmission means for transmitting the generated program to a terminal, and a notification means for the terminal to notify the user of the program. This makes it possible to provide an optimized program based on the athlete's individual data.

[1329] "Data input means" refers to a device or software that allows athletes to input information such as their physical condition, goals, and past injury history into the system.

[1330] The "data transmission means" is a device or software having a function for transmitting data input by the data input means to the server.

[1331] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to analyze data and generate individually optimized diet, training, rehabilitation, and rest programs.

[1332] The "program generation means" is a device or software that has the function of analyzing the data received by the server and generating a program optimized for the athlete.

[1333] The "program transmission means" is a device or software having a function for transmitting the generated program to the terminal.

[1334] The "notification means" is a device or software having a function for notifying the user of a program received by the terminal.

[1335] A "consultation tool" is a device or software that has the function of allowing athletes to input and send questions via the chatbot.

[1336] "Answer generation means" refers to a device or software that has the function of analyzing an athlete's question using a generative AI model and generating an appropriate answer.

[1337] The "answer sending means" is a device or software having a function for sending the generated answer to the terminal.

[1338] The "answer display means" is a device or software that has the function of displaying the answers received by the terminal to the athlete.

[1339] "Database acquisition means" refers to a device or software that has the function of allowing the server to acquire an athlete's past data from the database and input it into the generative AI model along with the analysis results.

[1340] MODE FOR CARRYING OUT THE INVENTION

[1341] In this invention, a system for optimizing the health management of athletes is constructed. This system is realized by linking multiple pieces of hardware and software.

[1342] Hardware and software used

[1343] For the servers, a cloud platform (general name: cloud computing service, e.g., Google Cloud Platform, Amazon Web Services) is used.

[1344] The devices used include smartphones (commonly known as mobile devices, e.g., iPhones and Android devices) and tablets (commonly known as tablet devices, e.g., iPads and Android tablets).

[1345] Generative AI models use machine learning algorithms (commonly known as natural language generation models, e.g., GPT-4).

[1346] Overall system overview

[1347] The system has the following main functions:

[1348] 1. Data entry and submission

[1349] 2. Program generation using generative AI

[1350] 3. Program Submission and Notification

[1351] 4. Consultation response via chatbot

[1352] Data entry and submission

[1353] The user opens the dedicated application on a smartphone or tablet. The application contains a form where the user can enter information about their physical condition, goals, past injury history, etc. The entered data is converted to JSON format by the device and securely sent to the server using the HTTPS protocol. For example, data such as "This morning I weighed 70 kg, and I slept for 6 hours last night" can be entered.

[1354] Program generation by generative AI

[1355] The server stores the received data in a database (commonly known as an associative database, e.g., MySQL or PostgreSQL). The stored data is then analyzed using a generative AI model (GPT-4) to generate an individually optimized diet, training, rehabilitation, and rest program. For example, a program might analyze a user's heart rate data and sleep patterns and suggest, "Since your heart rate has been high recently, today I suggest a light jog and a balanced meal." An example of a prompt might be, "Analyze an athlete's heart rate data and sleep patterns, and create a program that adjusts the next day's training and meal plan."

[1356] Program submission and notification

[1357] The program generated by the server is sent to the device in JSON format. The device receives this program and notifies the user. For example, a notification such as "A new training plan is available" may appear on the smartphone screen. When the user taps the notification, detailed program content is displayed. For example, it may say "30 minutes of light jogging for afternoon training, and a high-protein meal for today's meal."

[1358] Consultation response via chatbot

[1359] When a user enters a question using the chatbot function within the dedicated application, the question is sent by the device to the server. The server uses a generative AI model (GPT-4) to analyze the question and generate an appropriate answer. For example, in response to the question, "My left knee is hurting. Will it be okay to train today?", the server generates an answer such as, "If you are experiencing pain in your left knee, I recommend you stop running and do some light stretching." The answer is sent to the device and displayed to the user.

[1360] As described above, this system provides optimal health management programs and prompt consultations based on each athlete's individual data, thereby creating an environment in which athletes can perform at their best.

[1361] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1362] Step 1: Data entry

[1363] The user opens the dedicated application on a smartphone or tablet. The user enters their physical condition, goals, and past injury history into the application's input form. Specifically, they enter their weight, heart rate, sleep time, etc. This data is necessary to understand their physical condition and progress toward achieving their goals. Input: Weight 70 kg, sleep time 6 hours. Output: Data in JSON format.

[1364] Step 2: Send data

[1365] The terminal converts the data entered by the user into JSON format and sends it to the server using the HTTPS protocol. The data undergoes serialization processing and is sent securely to the server. Input: User-entered data (weight, sleep time, etc.). Output: JSON-formatted data is sent to the server.

[1366] Step 3: Receiving and storing data

[1367] The server receives the data sent from the device, analyzes the HTTP request, and saves it in a database. Specifically, it uses a database such as MySQL or PostgreSQL and inserts the data into a table. Input: Data in JSON format. Output: Data saved in the database.

[1368] Step 4: Program Generation

[1369] The server retrieves data stored in the database and analyzes it using a generative AI model (GPT-4). This analysis generates a diet, training, rehabilitation, and rest program optimized for the user. Specifically, it analyzes heart rate data and sleep patterns and generates the program in text format. Example prompt: "Analyze an athlete's heart rate data and sleep patterns and create a program that adjusts the next day's training content and meal plan." Input: User data retrieved from the database. Output: The generated optimization program (text format).

[1370] Step 5: Send the program

[1371] The server converts the generated program into JSON format and sends it to the terminal using the HTTPS protocol. Input: The generated optimized program. Output: JSON format data is sent to the terminal.

[1372] Step 6: Program Notification

[1373] The device receives the program sent from the server and notifies the user. The notification is sent using push notifications, displaying a message such as "A new training plan is available." When the user taps the notification, detailed program content is displayed. Input: Program data in JSON format. Output: Notification to the user and program details displayed.

[1374] Step 7: Enter your consultation

[1375] The user inputs a question using the chatbot function in the dedicated application. For example, "My left knee is hurting more. Will it be okay to train today?" Input: Question text. Output: Question data in JSON format.

[1376] Step 8: Submit your question

[1377] The terminal converts the question entered by the user into JSON format and sends it to the server using the HTTPS protocol. Input: Question data (JSON format). Output: Question data sent to the server.

[1378] Step 9: Receiving questions and generating answers

[1379] The server analyzes the received question using a generative AI model (GPT-4) and generates an appropriate answer. For example, it generates an answer such as, "If you have pain in your left knee, we recommend you stop running and do some light stretching." Input: Question data. Output: Generated answer text.

[1380] Step 10: Submit your response

[1381] The server converts the generated answer into JSON format and sends it to the terminal using the HTTPS protocol. Input: Generated answer text. Output: JSON format answer data is sent to the terminal.

[1382] Step 11: Display answers

[1383] The device receives the answer sent from the server and displays it on the chatbot screen. For example, it might say, "If you have pain in your left knee, we recommend you stop running and do some light stretching." Input: Answer data in JSON format. Output: Display of answer to the user.

[1384] (Application example 1)

[1385] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1386] Currently, health management of industrial workers is not always thorough, resulting in delays in improving employee productivity and the working environment. Furthermore, there is a lack of systems that can quickly and individually address fatigue and health concerns felt by employees. This can result in reduced employee productivity and adversely affect the quality of work. Therefore, there is a need for a system that collects and analyzes the health data of industrial workers and provides optimal work schedules and rest plans.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1388] In this invention, the server includes a data input means for industrial workers to input their physical condition, working hours, and rest times, a data transmission means for transmitting the data input by the input means to the server, a program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized work schedule and rest plan, a program transmission means for transmitting the generated program to a terminal, a notification means by the terminal notifying the user of the program, a consultation means by which industrial workers ask questions about their health condition via a chatbot, an answer generation means by the chatbot analyzing the industrial worker's question using a generation AI and generating an appropriate answer, an answer transmission means for transmitting the generated answer to the terminal, an answer display means by the terminal displaying the answer to the industrial worker, and a database acquisition means by the server to acquire the industrial worker's past data from a database and input it together with the analysis results into the generation AI.

[1389] This will enable efficient collection and analysis of industrial workers' health data and provide optimal individual work schedules and rest plans. Furthermore, by using chatbots to quickly respond to health consultations, it will be possible to manage employees' health status in real time and provide appropriate advice. This will lead to improved work efficiency and a better working environment.

[1390] "Data input means" refers to a device or interface that allows industrial workers to input information such as their physical condition, working hours, and rest times.

[1391] The "data transmission means" is a means for transmitting data obtained through the input means to the server.

[1392] A "server" is a device or system that uses generation AI to analyze the received data and generate individually optimized work schedules and break plans.

[1393] The "program generation means" refers to the processes and algorithms that analyze the data received by the server and generate optimal work schedules and rest plans.

[1394] The "program transmission means" is a means for transmitting the generated program to the terminal.

[1395] The "notification means" is an interface for notifying the user of the program received by the terminal.

[1396] The "consultation tool" is a tool that allows industrial workers to ask questions about their health via a chatbot.

[1397] A "chatbot" is a computer program designed to interact with users.

[1398] "Answer generation means" refers to the process or algorithm by which the chatbot uses generation AI to analyze the industrial worker's question and generate an appropriate answer.

[1399] The "answer transmission means" is a means for transmitting the generated answer to the terminal.

[1400] The "answer display means" is an interface for displaying the answers received by the terminal to the industrial worker.

[1401] The "database acquisition means" is a means by which the server acquires the industrial worker's past data from the database and inputs it into the generation AI along with the analysis results.

[1402] "Generative AI" is an artificial intelligence technology that analyzes received data and generates optimal programs and answers.

[1403] This invention is a system that efficiently collects and analyzes the health data of industrial workers and proposes individually optimized work schedules and rest plans. Furthermore, by providing a health consultation function using a chatbot, it is possible to manage the health status of industrial workers in real time, improving work efficiency and ensuring safety.

[1404] The server is equipped with a data input means for industrial workers to input data such as their physical condition, working hours, and rest times. Devices such as smartphones and PCs are used as the data input means. Industrial workers use these devices to input data, which is then transmitted to the server via a data transmission means.

[1405] The server analyzes the received data using a generation AI. For example, the generation AI uses Python's Scikit-learn library and predicts heart rate using a linear regression model. Based on this, an individually optimized work schedule and rest plan are generated and implemented as a program generation means. This generated program is sent to the terminal by a program transmission means.

[1406] The terminal is provided with a notification means for notifying the user of the received program. For example, a notification saying "A new work schedule is available" is displayed. The user can click on the notification to check the detailed program content.

[1407] Furthermore, the system responds to questions about the health status of industrial workers via a chatbot. When a worker uses the chatbot to input a question through the consultation means, the server analyzes the question using a generation AI and is equipped with an answer generation means that generates an appropriate answer. The generated answer is sent to the terminal and notified to the worker by the answer sending means through the display means. For example, in response to the question, "I have pain in my left knee. Is it safe to work today?", the chatbot will provide an answer based on the analysis results, such as, "If you have pain in your left knee, we recommend extending your break time and doing some light stretching."

[1408] The server also has a database acquisition means for retrieving past data from the database and inputting it into the generation AI, which enables more precise analysis based on past health data and work history, improving the accuracy of the individual optimization program.

[1409] A specific example is given below. For example, an industrial worker uses a smartphone to input data such as heart rate, working hours, and rest times every day. The server receives this data and predicts the heart rate using generative AI (Scikit-learn's Linear Regression model). It then generates optimal rest times and work schedules and sends the program to the device.

[1410] Examples of prompts include, "I've been losing concentration easily lately. Please give me some appropriate advice based on my health data," or "I've been having persistent pain in my left knee. Please tell me how to work safely."

[1411] The above system can improve the health management and work efficiency of industrial workers.

[1412] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1413] Step 1:

[1414] A user uses a device such as a smartphone or PC to input their own physical condition (heart rate, working hours, rest time, etc.) through data input means. The input data is sent to a server via data transmission means. The input data includes numerical data and written comments.

[1415] Input: User's physical condition data (heart rate, working hours, rest time, etc.)

[1416] Output: Data sent to the server

[1417] Step 2:

[1418] The server stores the received data and analyzes it using a generative AI model (for example, Scikit-learn's Linear Regression model). This analysis results in data calculations that predict heart rate and generate work schedules and rest plans. The server also retrieves past data from the database and uses it for analysis.

[1419] Input: Received user data, historical database data

[1420] Output: Optimal work schedule, break plan

[1421] Step 3:

[1422] The server transmits the generated work schedule and break plan to the terminal using a program transmission means, including specific start times, end times, and break times.

[1423] Input: Generated work schedule and break plan

[1424] Output: Program sent to terminal

[1425] Step 4:

[1426] The device notifies the user of the received program via a smartphone app or PC notification function, displaying a message such as "A new work schedule is available."

[1427] Input: Program sent from the server

[1428] Output: Message notified to the user

[1429] Step 5:

[1430] The user inputs a question about their health condition via the chatbot. The question (e.g., "I have pain in my left knee. Is it safe to work today?") is sent to the server via the consultation tool.

[1431] Input: User's question

[1432] Output: Question data sent to the server

[1433] Step 6:

[1434] The server analyzes the received question data and generates an appropriate answer using the generative AI model. For example, "If you have pain in your left knee, we recommend extending your rest time and doing some light stretching." After generating the answer, the server sends it to the device via the answer sending means.

[1435] Input: User question data

[1436] Output: The generated answer

[1437] Step 7:

[1438] The device displays the received answers to the user via a chatbot app or notification function, allowing the user to immediately check the detailed answers.

[1439] Input: The answer sent by the server

[1440] Output: The answer displayed to the user

[1441] The above processing steps make it possible to provide optimal work schedules and rest plans based on the health data of industrial workers, and to provide prompt health consultations.

[1442] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1443] This invention will provide a system that uses generative AI and an emotion engine to manage the mental and physical health of athletes and provide individually optimized diet, training, rehabilitation, and rest programs to help them perform at their best.It will also have a chatbot function that can respond to athletes' inquiries.

[1444] System Configuration

[1445] The server plays a central role in the system, receiving and analyzing data sent by athletes. It also uses generative AI to generate optimization programs and send them to the devices. It also processes inquiries from athletes through a chatbot function, generating and sending appropriate answers. It also incorporates and analyzes emotional data recognized by the emotion engine.

[1446] The terminal is an interface through which athletes can input data, check programs, and receive consultations. The data entered by the athlete is sent from the terminal to the server. The terminal receives the optimization program and answers sent from the server and notifies the athlete. In addition, the terminal is equipped with an emotion engine that recognizes the user's emotions.

[1447] Athletes interact with the system using a dedicated application, allowing them to input data, check programs, and consult with chatbots.

[1448] Entering and Submitting Data

[1449] Users use a dedicated application to input their physical condition, goals, past injury history, etc. The input data is sent to a server via the device, and individual athlete data is then stored on the server.

[1450] Utilizing the Emotion Engine

[1451] The device is equipped with an emotion engine that analyzes the user's emotions based on their voice and facial expressions. The analysis results are sent to the server by the data transmission means.

[1452] Program generation by generative AI

[1453] The server uses AI to analyze the received athlete data and emotional data. Based on this analysis, it generates an optimized diet, training, rehabilitation, and rest program for the athlete. Past data is also retrieved from the database for more precise analysis.

[1454] For example, it analyzes heart rate data, sleep patterns, and emotional data to adjust the next day's training and meal plan.

[1455] Program submission and notification

[1456] The optimized program generated by the server is sent to the device. The device receives the program and notifies the athlete. For example, a notification saying "A new training plan is available" is displayed. By clicking on this notification, the athlete can view the detailed program content.

[1457] Consultation response via chatbot

[1458] When a user uses the chatbot in the system to ask for advice, the content of the consultation is sent to the server. The server then uses generative AI to analyze the content and generate an appropriate response, taking into account the emotional data recognized by the emotion engine. The generated response is then sent to the athlete's device and displayed.

[1459] For example, if a user asks, "My left knee is hurting more. Is it okay to train today?", the chatbot will generate and provide an answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, please consult a specialist." If the emotional data indicates "anxiety," a response that particularly emphasizes a sense of security will be generated.

[1460] This system allows athletes to receive the most appropriate program tailored to their individual state and emotions, and provides prompt and appropriate responses to any anxieties or questions they may have, thereby creating an environment in which athletes can perform at their best as individuals.

[1461] The processing flow will be explained below.

[1462] Step 1:

[1463] The user launches the dedicated application and inputs their physical condition, goals, past injury history, etc. through a data input means. Specifically, they input their name, age, height, weight, daily activity data (e.g., heart rate, sleep time), goals (e.g., preparation for a competition), and past injury history.

[1464] Step 2:

[1465] The terminal uses the data transmission means to transmit the data entered by the user to the server. Specifically, by pressing the "Send" button in the application, the input data is converted into JSON format and transmitted to the server.

[1466] Step 3:

[1467] The device uses an emotion engine to analyze the user's emotions. Specifically, it analyzes the user's voice and facial expressions to recognize emotions such as "happiness," "anxiety," and "fatigue."

[1468] Step 4:

[1469] The device transmits the emotion data analyzed by the emotion engine to the server using the data transmission means. The analysis results are transmitted in JSON format.

[1470] Step 5:

[1471] The server receives the physical data and emotional data transmitted from the terminal using the data receiving means, and temporarily stores the received data.

[1472] Step 6:

[1473] The server retrieves past data from the database using the database retrieval means, specifically, retrieves related data such as past training history and injury history.

[1474] Step 7:

[1475] The server uses the generated AI to comprehensively analyze the received physical data, emotional data, and past data, and evaluates the user's current condition and emotional state.

[1476] Step 8:

[1477] The server uses the program generation means to generate an individualized optimal diet, training, rehabilitation, and rest program based on the analysis results. The generated program is customized taking into account the user's physical and emotional state.

[1478] Step 9:

[1479] The server uses the program transmission means to transmit the generated optimized program to the terminal, which exports the program in JSON format and transmits it to the terminal.

[1480] Step 10:

[1481] The device receives the optimization program and notifies the user using a notification means. Specifically, a push notification stating "A new training plan is available" is displayed.

[1482] Step 11:

[1483] The user clicks on the notification and sees the new optimization program within the app, which includes detailed meal plans, training menus, rehabilitation plans, and rest plans.

[1484] Step 12:

[1485] If a user wants to consult with a chatbot, they can use the chatbot functionality within the application, for example, by typing, "My left knee is hurting more. Will it be okay to train today?"

[1486] Step 13:

[1487] The server receives the chatbot's consultation message and uses the generation AI to analyze the content, including emotional data. Based on the analysis results, an appropriate response is generated.

[1488] Step 14:

[1489] The server sends the generated answer to the terminal using the answer sending means. For example, an answer such as "If you have pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist." If the emotion data indicates "anxiety," a response that particularly emphasizes a sense of relief is generated.

[1490] Step 15:

[1491] The device receives the reply and displays it in the chat window, allowing the user to receive appropriate instructions and advice.

[1492] Example 2

[1493] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1494] It is difficult for today's athletes to easily obtain optimal diet, training, rehabilitation, and rest programs tailored to their individual physical condition and goals. It is also difficult for them to receive more appropriate and reassuring guidance and advice using data on their emotional and mental state. Furthermore, even when using chatbots for consultation, there is a problem in that they cannot provide answers that take emotional changes into account.

[1495] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data input means for the athlete to input their physical condition, goals, and past injury history; a data transmission means for transmitting the data input by the input means to the server; a program generation means for the server to analyze the data received by the server using a generation AI and generate an individually optimized program for diet, training, rehabilitation, and rest; a program transmission means for transmitting the generated program to a terminal; a notification means for the terminal to notify the user of the program; an emotion analysis means incorporated in the terminal for recognizing and analyzing the user's emotions; and an emotion data transmission means for transmitting the emotion data acquired by the emotion analysis means to the server. This allows users to receive an optimal program tailored to their individual physical condition and emotions. Furthermore, appropriate responses that take emotions into consideration can be obtained when consulting with a chatbot.

[1496] "Data Entry Method" means a device or software method through which an athlete enters information about their physical condition, goals, and past injury history.

[1497] "Data transmission means" refers to a device or software method that transmits data input by the data input means to the server.

[1498] "Program generation means" refers to a device or software method that uses generation AI to analyze data received by the server and generate individually optimized diet, training, rehabilitation, and rest programs.

[1499] "Program transmission means" refers to a device or software method for transmitting the generated program to the terminal.

[1500] "Notification means" refers to a device or software method for notifying the user of a program received by the terminal.

[1501] "Emotion analysis means" refers to a device or software method that is incorporated into a terminal and that recognizes and analyzes the user's emotions.

[1502] "Emotion data transmission means" refers to a device or software method that transmits the emotion data acquired by the emotion analysis means to the server.

[1503] The present invention is a system for managing the mental and physical health of athletes and enabling them to perform at their best. Embodiments of the invention include the following elements.

[1504] The server will act as a central point for receiving and analyzing data sent by athletes, such as their physical condition, goals, and past injury history. The server will then use generative AI to analyze the data and generate a nutrition, training, rehabilitation, and rest program tailored to each athlete. The hardware and software used will include a standard server computer and Google's TensorFlow.

[1505] The terminal provides an interface for athletes to input data, review the programs generated by the generative AI, and even consult with a chatbot. The terminal also incorporates an emotion engine that analyzes the user's voice and facial expressions to extract emotional data and send it to a server. Microsoft Azure's Cognitive Services are used for this emotional data analysis.

[1506] Athletes access the system through a dedicated application and input data, such as their weight, target weight, and details of past injuries. Users can also use the application to receive generated programs and check notifications. Clicking on the notification displays detailed program content. Athletes can also use the chatbot function to consult about their current physical condition and training.

[1507] For example, if a user sends a question to a chatbot such as "My left knee is hurting more. Will it be okay to train today?", the generative AI will analyze the question and generate an appropriate response for knee pain (e.g., recommend stopping running and doing some light stretching). If emotion analysis indicates a strong feeling of "anxiety," the generative AI will generate an unambiguous answer that puts the user at ease.

[1508] An example of a prompt is as follows:

[1509] User: "I'm having increasing pain in my left knee. Is it okay to train today?"

[1510] The prompt sentence for the generative AI model in response to this is as follows:

[1511] Generative AI model: "If you have pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist."

[1512] In this way, the user can receive the most suitable program according to his / her individual condition, and can also receive appropriate support based on his / her emotions.

[1513] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1514] Step 1:

[1515] The user enters data

[1516] Users open the dedicated application and enter their physical condition, goals, and past injury history. For example, they enter detailed data such as "weight 70 kg, goal weight 65 kg, injured knee last night."

[1517] Input: User's physical condition data (weight, target weight, injury history)

[1518] Output: The entered data is saved on the device and ready to be sent to the server in the next step.

[1519] Step 2:

[1520] The device sends data

[1521] The device sends the user's input data to the server. HTTPS communication is used to ensure data security. For example, data such as "weight 70 kg, target weight 65 kg, knee pain" is sent in JSON format.

[1522] Input: User-entered data

[1523] Data processing: Convert data into JSON format

[1524] Output: Data is sent to the server

[1525] Step 3:

[1526] The device analyzes the emotional data

[1527] When a user launches an application, the device uses the camera and microphone to capture the user's facial expressions and voice.

[1528] Input: User's facial expressions and voice data

[1529] Data processing: Extracting emotion data through facial expression and voice analysis

[1530] Output: Emotion data (e.g., "Current emotion: Anxiety" and "Emotion level: 85%)

[1531] Step 4:

[1532] The device sends emotion data

[1533] Emotion data analyzed by the emotion engine is sent to the server in real time. For example, data such as "Emotion: Anxiety, Level: 85%" is sent.

[1534] Input: Emotion data

[1535] Output: Emotion data is sent to the server

[1536] Step 5:

[1537] The server analyzes the data

[1538] The server analyzes the received physical and emotional data, including historical data, using generative AI models such as Google's TensorFlow.

[1539] Input: Physical data, Emotional data, Historical data

[1540] Data arithmetic: Data correlation analysis and pattern recognition

[1541] Output: Individually optimized program for athletes

[1542] Step 6:

[1543] The server generates the optimization program.

[1544] Based on the analysis results, the server generates an optimal diet, training, rehabilitation, and rest program for the athlete, including specific instructions such as "Due to knee pain, light stretching is recommended today" and "Eat a high-protein diet."

[1545] Input: Analysis results

[1546] Data processing: generating individual instructions

[1547] Output: Individual optimization program

[1548] Step 7:

[1549] The server sends the program

[1550] The generated optimization program is sent from the server to the terminal, often in JSON or XML format.

[1551] Input: Optimizer

[1552] Output: The program is sent to the terminal

[1553] Step 8:

[1554] The terminal notifies the program

[1555] The device notifies the user of the program received from the server. For example, a push notification saying "A new training plan is available" is sent. When the user clicks on the notification, detailed program content is displayed.

[1556] Input: Received optimization program

[1557] Output: Notify user, display details

[1558] Step 9:

[1559] The user makes a consultation

[1560] Users can use the chatbot function of the dedicated application to ask for advice, for example, by sending a question such as, "My left knee is hurting more. Will it be okay to train today?"

[1561] Input: User's consultation question

[1562] Output: The consultation question is entered into the terminal and sent to the server in the next step.

[1563] Step 10:

[1564] The device sends the consultation content

[1565] The terminal sends the user's consultation to the server, and it is recommended that this consultation be sent in a structured format.

[1566] Input: Consultation question

[1567] Output: The consultation content is sent to the server

[1568] Step 11:

[1569] The server analyzes the consultation content

[1570] The server uses generative AI to analyze the content of the consultation and generate a response that takes into account emotional data.

[1571] Input: Consultation content, emotion data

[1572] Data calculation: Analyzing questions and generating appropriate answers

[1573] Output: The generated answer

[1574] Step 12:

[1575] The server sends the answer

[1576] The server then sends the generated answer to the device, providing a specific answer such as, "If you are experiencing pain in your left knee, we recommend you stop running and do some light stretching. If the pain persists, consult a specialist."

[1577] Input: Generated answer

[1578] Output: The answer is sent to the terminal

[1579] Step 13:

[1580] The device displays the answer

[1581] The terminal receives the answer from the server and displays it to the user. The answer is displayed on the chatbot's screen, allowing the user to view it and decide how to respond.

[1582] Input: Response received

[1583] Output: Display the answer to the user

[1584] (Application example 2)

[1585] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1586] Factory workers' physical and mental health can deteriorate due to long working hours and excessive stress. This not only reduces work efficiency but can also threaten the safety of the workers. Current systems have the problem of making it difficult to provide optimal health management programs for individual workers and to respond to the workers' emotions and conditions.

[1587] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means for the worker to input their own physical condition, goals, and past physical pain history; a data transmission means for transmitting the data input by the input means to a cloud server; a program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized meal, rest, and training program; a program transmission means for transmitting the generated program to the smart glasses; an emotion analysis means for analyzing emotion data from the worker's voice and facial expressions using a camera and microphone installed in the smart glasses; and an emotion data transmission means for transmitting the analyzed emotion data to the cloud server. This enables an individually optimized health management program that takes into account the worker's mental and physical health condition and emotions, and enables prompt and appropriate responses.

[1588] "Worker" refers to a person who performs work in a factory or other facility.

[1589] "Physical status" refers to data about an individual's health and physical condition, such as heart rate, sleep patterns, and whether or not they are in pain.

[1590] "Goal" refers to the goal or business objective that a worker is trying to achieve.

[1591] "Past physical pain history" refers to information about physical pain that a worker has experienced in the past and the parts of the body that have experienced pain.

[1592] "Data input means" refers to a method or device that allows the worker to input his or her physical condition, goals, and past history of physical pain.

[1593] "Cloud server" refers to a remote server for storing and managing data via the Internet.

[1594] "Data transmission means" refers to a method or device for transmitting input data to a cloud server.

[1595] "Generative AI" refers to algorithms that use machine learning and artificial intelligence techniques to analyze data and generate appropriate results or programs.

[1596] "Program generation means" refers to a method or device for creating individually optimized diet, rest, and training programs from data analyzed using generative AI.

[1597] "Smart glasses" refers to a wearable device that has a built-in display, camera, and microphone for displaying information.

[1598] "Program transmission means" refers to a method or device for transmitting the generated program to the smart glasses.

[1599] "Notification means" refers to a method or device for notifying a worker of a program or notification through the smart glasses.

[1600] "Emotion analysis means" refers to a method or device for analyzing emotions from a worker's voice and facial expressions using the camera and microphone of smart glasses.

[1601] "Emotion data transmission means" refers to a method or device for transmitting analyzed emotion data to a cloud server.

[1602] An embodiment of the present invention will be described below. The purpose of this system is to manage the physical and mental health of workers and provide an individually optimized health management program. Specifically, workers input their physical condition, goals, and past pain history, and then send that data to a cloud server.

[1603] System Configuration

[1604] 1. Data entry method

[1605] The smart glasses allow workers to input their physical condition, goals, and past history of physical pain. The smart glasses provide an intuitive user interface for the input process.

[1606] 2. Data transmission method

[1607] The data entered by the worker is transmitted from the smart glasses to a cloud server using a secure communication protocol (e.g., HTTPS).

[1608] 3. Program Generation Methods Using Generative AI

[1609] The cloud server analyzes the received data using generative AI (e.g., the GPT-4 model), which generates an individualized diet, rest, and training program.

[1610] 4. Program Transmission Method

[1611] The generated program is sent from the cloud server to the smart glasses, where the worker receives a notification and can check the contents through the smart glasses' display.

[1612] 5. Emotion analysis method

[1613] The camera and microphone installed in the smart glasses are used to capture the worker's voice and facial expressions, and the emotional data is analyzed using emotion analysis software (e.g., Affectiva SDK).

[1614] 6. Means of transmitting emotional data

[1615] The analyzed emotional data is sent to a cloud server in real time and is used by the generative AI to generate programs that provide optimal responses based on the worker's emotions.

[1616] 7. Consultation methods and answer generation methods

[1617] Workers can ask questions or ask for advice using the chatbot function in the smart glasses. The chatbot uses generative AI to analyze the question and generate an appropriate answer, which is also displayed on the smart glasses.

[1618] Example

[1619] As a specific example, the following scene can be considered.

[1620] The worker asks the smart glasses, "I'm feeling a little tired today. What should I eat?" This question is sent to a cloud server, and the generative AI (GPT-4 model) responds, "Eating high-protein foods will help you recover from fatigue." At this time, the emotion analysis means detects "fatigue," and this information is included in the analysis.

[1621] Example prompt sentence:

[1622] Worker's heart rate data: 200 BPM, past pain history: "Pain in right shoulder", emotion: "Fatigue"

[1623] Use this information to generate the best training and meal plan for you.

[1624] result:

[1625] Today's training:

[1626] Light stretching (to avoid straining the right shoulder)

[1627] Rehabilitation exercises

[1628] Today's meal:

[1629] High protein diet (chicken breast, beans)

[1630] Stay hydrated

[1631] In this way, the system of the present invention can not only analyze the physical and mental health of workers and provide individually optimized programs, but also respond in real time to the worker's emotions, thereby ensuring the safety and efficient performance of workers' work.

[1632] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1633] Program processing steps

[1634] Step 1:

[1635] Through the smart glasses, workers input their physical condition (heart rate, sleep patterns, etc.), goals, and past history of physical pain. Using the smart glasses' interface, data is entered by selecting options on the screen or by voice input.

[1636] Input: Physical condition data, goals, past pain history

[1637] Output: The input data is stored in the internal memory of the smart glasses.

[1638] Step 2:

[1639] The smart glasses send the input data to a cloud server using HTTPS, a secure communication protocol.

[1640] Input: Data entered into the smart glasses

[1641] Output: Data is transferred to the cloud server.

[1642] Step 3:

[1643] The server then analyzes the received data using a generative AI model (e.g., GPT-4), which generates a personalized diet, rest, and training program based on health data, goals, and past pain data.

[1644] Input: Data sent to the cloud server

[1645] Output: Analyzed individual optimization programs

[1646] Step 4:

[1647] The server transmits the generated program to the smart glasses.

[1648] Input: Analyzed individual optimization program

[1649] Output: Program transferred to smart glasses

[1650] Step 5:

[1651] The smart glasses notify the worker of the generated program, which is then displayed on the screen so the worker can check the contents.

[1652] Input: Program sent from the cloud server

[1653] Output: Notification displayed to the worker

[1654] Step 6:

[1655] The camera and microphone installed in the smart glasses are used to capture the worker's voice and facial expressions, and the Affectiva SDK is used to analyze the emotional data in real time.

[1656] Input: Worker's voice and facial expression data

[1657] Output: Parsed emotion data

[1658] Step 7:

[1659] The smart glasses send the analyzed emotion data to a cloud server.

[1660] Input: Parsed emotion data

[1661] Output: Emotion data sent to the cloud server

[1662] Step 8:

[1663] Workers use smart glasses to ask questions or ask questions to a chatbot, either through voice or text input.

[1664] Input: Questions and inquiries from workers

[1665] Output: Consultation content sent to the cloud server

[1666] Step 9:

[1667] The generative AI model on the cloud server analyzes the received question or consultation content and generates an appropriate answer, taking emotional data into account during the analysis.

[1668] Input: consultation details, emotional data

[1669] Output: The generated answer

[1670] Step 10:

[1671] The generated answer is sent from the cloud server to the smart glasses.

[1672] Input: Generated Answer

[1673] Output: Answers transferred to smart glasses

[1674] Step 11:

[1675] The smart glasses display the generated answer to the worker, who then checks the chatbot's response on the display.

[1676] Input: Answer sent from the cloud server

[1677] Output: The answer that is displayed to the worker

[1678] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1679] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1680] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1681] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1682] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1683] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1684] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1685] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1686] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1687] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1688] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1689] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1690] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1691] 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.

[1692] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1693] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1694] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1695] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1696] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1697] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1698] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1699] The following is further disclosed regarding the above embodiment.

[1700] (Claim 1)

[1701] a data entry means for athletes to enter their physical condition, goals, and past injury history;

[1702] data transmission means for transmitting the data input by the input means to a server;

[1703] A program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized diet, training, rehabilitation, and rest program;

[1704] a program transmission means for transmitting the generated program to a terminal;

[1705] The terminal includes a notification means for notifying the user of the program.

[1706] (Claim 2)

[1707] A consultation channel for athletes to ask questions via a chatbot;

[1708] An answer generation means for the chatbot to analyze the athlete's question using a generation AI and generate an appropriate answer;

[1709] a response sending means for sending the generated response to a terminal;

[1710] 10. The system of claim 1, wherein the terminal further comprises an answer display means for displaying the answers to the athlete.

[1711] (Claim 3)

[1712] The system according to claim 1, further comprising a database acquisition means for the server to acquire the athlete's past data from a database and input the data together with the analysis results into the generation AI.

[1713] "Example 1"

[1714] (Claim 1)

[1715] a data entry means for athletes to enter their physical condition, goals, and past injury history;

[1716] data transmission means for transmitting the data input by the input means to a server;

[1717] A program generation means for analyzing the data received by the server using a generation AI model and generating an individually optimized diet, training, rehabilitation, and rest program;

[1718] a program transmission means for transmitting the generated program to a terminal;

[1719] The terminal includes a notification means for notifying the user of the program.

[1720] (Claim 2)

[1721] A consultation channel for athletes to ask questions via a chatbot;

[1722] An answer generation means for the chatbot to analyze the athlete's question using a generation AI model and generate an appropriate answer;

[1723] a response sending means for sending the generated response to a terminal;

[1724] 10. The system of claim 1, wherein the terminal further comprises an answer display means for displaying the answers to the athlete.

[1725] (Claim 3)

[1726] The system of claim 1, further comprising a database acquisition means for the server to acquire the athlete's past data from a database and input the data together with the analysis results into the generative AI model.

[1727] "Application Example 1"

[1728] (Claim 1)

[1729] data input means for industrial workers to input their physical condition, working hours and rest hours;

[1730] data transmission means for transmitting the data input by the input means to a server;

[1731] A program generating means for analyzing the data received by the server using a generating AI and generating an individually optimized work schedule and rest plan;

[1732] a program transmission means for transmitting the generated program to a terminal;

[1733] The terminal includes a notification means for notifying the user of the program.

[1734] (Claim 2)

[1735] A consultation tool for industrial workers to ask questions about their health status via a chatbot;

[1736] An answer generation means for the chatbot to analyze questions from industrial workers using a generation AI and generate appropriate answers;

[1737] a response sending means for sending the generated response to a terminal;

[1738] 2. The system of claim 1, wherein the terminal further comprises an answer display means for displaying the answer to the industrial worker.

[1739] (Claim 3)

[1740] 2. The system according to claim 1, further comprising a database acquisition means for the server to acquire past data of industrial workers from a database and input the data together with analysis results into the generation AI.

[1741] "Example 2: Combining Emotion Engines"

[1742] (Claim 1)

[1743] a data entry means for athletes to enter their physical condition, goals, and past injury history;

[1744] data transmission means for transmitting the data input by the input means to a server;

[1745] A program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized diet, training, rehabilitation, and rest program;

[1746] a program transmission means for transmitting the generated program to a terminal;

[1747] a notification means for the terminal to notify the user of the program;

[1748] emotion analysis means incorporated in the terminal for recognizing and analyzing the emotions of a user;

[1749] The system includes emotion data transmission means for transmitting the emotion data acquired by the emotion analysis means to a server.

[1750] (Claim 2)

[1751] A consultation channel for athletes to ask questions via a chatbot;

[1752] An answer generation means for the chatbot to analyze the athlete's question using a generation AI and generate an appropriate answer;

[1753] a response sending means for sending the generated response to a terminal;

[1754] an answer display means for displaying the answers to the athlete on the terminal;

[1755] 2. The system according to claim 1, further comprising an answer generating means for generating an answer based on emotion using the emotion data recognized by the emotion analyzing means.

[1756] (Claim 3)

[1757] The system according to claim 1, further comprising a database acquisition means for the server to acquire the athlete's past data from a database and input the data together with the analysis results into the generation AI.

[1758] "Application example 2 when combining emotion engines"

[1759] (Claim 1)

[1760] a data input means for the worker to input his / her physical condition, goals, and past history of physical pain;

[1761] a data transmission means for transmitting the data input by the input means to a cloud server;

[1762] A program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized meal, rest, and training program;

[1763] a program transmission means for transmitting the generated program to the smart glasses;

[1764] a notification means for notifying the operator of the program by the smart glasses;

[1765] An emotion analysis means that analyzes emotion data from the worker's voice and facial expression using a camera and microphone installed in the smart glasses;

[1766] The system includes an emotion data transmission means for transmitting the analyzed emotion data to a cloud server.

[1767] (Claim 2)

[1768] A consultation method for workers to ask questions via a chatbot;

[1769] An answer generation means for the chatbot to analyze the worker's question using a generation AI and generate an appropriate answer;

[1770] an answer sending means for sending the generated answer to the smart glasses;

[1771] 10. The system of claim 1, wherein the smart glasses further comprise an answer display means for displaying the answer to the worker.

[1772] (Claim 3)

[1773] The system according to claim 1, further comprising a database acquisition means for the cloud server to acquire the worker's past data from a database and input the data together with the analysis results into the generation AI. [Explanation of symbols]

[1774] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a data entry means for athletes to enter their physical condition, goals, and past injury history; data transmission means for transmitting the data input by the input means to a server; A program generation means for analyzing the data received by the server using a generation AI and generating an individually optimized diet, training, rehabilitation, and rest program; a program transmission means for transmitting the generated program to a terminal; The terminal includes a notification means for notifying the user of the program.

2. A consultation channel for athletes to ask questions via a chatbot; An answer generation means for the chatbot to analyze the athlete's question using a generation AI and generate an appropriate answer; a response sending means for sending the generated response to a terminal; 2. The system of claim 1, wherein the terminal further comprises an answer display means for displaying the answers to the athlete.

3. 2. The system according to claim 1, further comprising a database acquisition means for the server to acquire past data of athletes from a database and input the data together with analysis results into the generation AI.

Images