system

Abstract

Provide a system. A login means; A data input means for inputting the player's weight, body fat, muscle mass, body parts to be trained, and target weight; an AI analysis means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration; a menu display means for displaying the generated menu to the user; A means for users to confirm, modify, and approve the menu; A means of communicating the final approved menu to the cafeteria; A means for cooking and providing nutritious meals based on the linked menu; A system including:

Description

[Technical Field]

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

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

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

[0004] Nutritional management for athletes is extremely important for improving performance and maintaining health, so it is necessary to hire a professional registered dietitian. However, not all teams or athletes can afford to hire a professional nutritionist, and it is also difficult to maintain consistent nutritional management. Therefore, there is a need for a method to propose the optimal nutritional balance based on each athlete's condition and goals, and to implement it efficiently and consistently. [Means for solving the problem]

[0005] The present invention provides an AI analysis means that inputs an athlete's weight, body fat, muscle mass, body parts to be trained, and target weight using a data input means, analyzes the data using an AI model, and generates a menu that takes nutritional balance into consideration. The generated menu is displayed to the user using a menu display means, and after passing through a confirmation / modification means that allows the user to confirm, modify, and approve the menu, a linking means is provided that links the final approved menu to the cafeteria system. This makes it possible to provide nutritionally optimized meals for each athlete, providing a system that achieves consistent nutritional management.

[0006] A "login means" is a function that performs an authentication process for a user to access a system.

[0007] The "data input means" is an interface for inputting information such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight.

[0008] The "AI analysis means" is an artificial intelligence model that analyzes input data and generates a menu that takes into account optimal nutritional balance.

[0009] The "menu display means" is a function for visually presenting the generated menu to the user.

[0010] The "verification and modification means" is an interface that allows the user to review the proposed menu, modify it if necessary, and finally approve it.

[0011] The "linking means" is a function for transmitting the final approved menu to the cafeteria system.

[0012] "Provision means" is a function that prepares and serves meals based on the linked menu. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] This invention relates to a system for efficiently managing the nutritional intake of athletes. This system automatically generates appropriate menus based on the athletes' current physical data and goals, and then provides individually optimized nutritional meals to the cafeteria.

[0035] Main system components

[0036] Login method

[0037] To access the system, users log in by entering their username and password, which is then authenticated by the server. If authentication is successful, the user is given access to their personal dashboard.

[0038] Data Entry Method

[0039] An interface is provided for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to the server.

[0040] AI analysis means

[0041] The server analyzes the received data and inputs it into an AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete data and proposes the optimal menu.

[0042] Menu display method

[0043] The device displays the generated menu to the user, and the user's dashboard displays the detailed menu contents, allowing the user to check them.

[0044] Check and corrective measures

[0045] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu.

[0046] Collaboration methods

[0047] The terminal sends the final approved menu to the server, which then links it to the cafeteria system. The menu is provided to the cafeteria as an individual nutrition plan for each player.

[0048] Providing means

[0049] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[0050] Specific examples

[0051] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[0052] Breakfast: Oatmeal, chicken breast, banana

[0053] Lunch: Brown rice, grilled salmon, salad

[0054] Dinner: Chicken breast steak, vegetable soup, protein shake

[0055] This menu is displayed on the user's dashboard, where the user can review, modify, and finally approve it. The approved menu is linked to the cafeteria system via the server, and the cafeteria prepares and serves the food based on it.

[0056] The system of the present invention can efficiently meet the individual nutritional needs of athletes, contributing to improved performance and health maintenance.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user logs in to the terminal, enters the username and password, and clicks the "Submit" button. The terminal sends the entered authentication information to the server.

[0060] Step 2:

[0061] The server receives the authentication information and checks it against the user information in its database. If it matches, authentication is successful and a login success message is returned to the user. If authentication fails, an error message is returned.

[0062] Step 3:

[0063] After successful authentication, the user accesses the dashboard and enters physical data such as weight, body fat, muscle mass, areas to train, and target weight. Once the input is complete, the user clicks the "Submit" button.

[0064] Step 4:

[0065] The terminal encodes the input data in JSON format or similar and sends it to the server using an HTTP POST request.

[0066] Step 5:

[0067] The server parses the received data, converts it into the appropriate format, and then feeds the data into the AI ​​model, starting the analysis process.

[0068] Step 6:

[0069] The AI ​​model on the server analyzes the player's data and generates a menu that takes into account the optimal nutritional balance. The generated menu is then converted into JSON format.

[0070] Step 7:

[0071] The server sends the generated menu in JSON format to the terminal, which returns the menu as an HTTP response.

[0072] Step 8:

[0073] The device analyzes the menu received and displays it on the user interface. An individual menu for each player is displayed on the dashboard.

[0074] Step 9:

[0075] The user checks the displayed menu and makes any necessary corrections. Once the corrections are complete, the user clicks the "Approve" button.

[0076] Step 10:

[0077] The terminal encodes the final approved menu, reflecting the user's modifications, in JSON format again and sends it to the server.

[0078] Step 11:

[0079] The server receives the final approved menu and connects it to the cafeteria system. Data is also sent in JSON format when connecting.

[0080] Step 12:

[0081] The cafeteria will prepare individual nutritional meals for each player based on the coordinated menu, and serve the cooked meals to the players.

[0082] Example 1

[0083] 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."

[0084] Nutritional management for athletes is extremely important for improving performance and maintaining health. However, planning daily menus that meet individual nutritional needs and efficiently delivering those meals is difficult. This difficulty is particularly exacerbated in environments without the knowledge of specialized nutritionists. Furthermore, it is necessary to quickly create optimal nutrition plans tailored to each athlete's different goals and physical constitutions. To solve this problem, a system is needed that can automatically generate personalized menus based on athletes' physical data and provide them in conjunction with the cafeteria system.

[0085] 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.

[0086] In this invention, the server includes a login means for users to access and log in to the system, a data input means for inputting the player's physical data, and an analysis means for analyzing the data received from the data input means and generating a menu that takes nutritional balance into consideration using a generative AI model. This allows the server to efficiently generate an optimal menu based on the player's individual nutritional needs, display the menu, and link it to the cafeteria system after modification and approval. This allows players to receive nutritional meals optimized for their goals while receiving professional nutritional management.

[0087] The "login means" is a function that allows a user to access the system, enter a user name and password for authentication, and then access a dedicated dashboard.

[0088] The "data input means" is an interface that allows the user to input physical data such as the player's weight, body fat, muscle mass, target weight, and body parts to be trained, and transmit this data from the terminal to the server.

[0089] The "analysis means" is a function that analyzes the physical data received from the data input means, and in particular includes generating a menu that takes nutritional balance into consideration using a generative AI model.

[0090] The "display means" is a function that displays the generated menu on the user's dashboard, allowing the user to check its contents.

[0091] The "confirmation and correction means" is a function that allows the user to confirm the contents of the displayed menu, correct them if necessary, and finally approve them.

[0092] The "linking means" is a function for sending the final approved menu to the cafeteria system and linking it.

[0093] The "means of provision" is a function for preparing nutritious food based on a menu linked to the cafeteria system and providing it to athletes.

[0094] The "generative AI model" is an artificial intelligence model that generates optimal menus based on athletes' physical data, and mimics the knowledge of professional nutritionists.

[0095] A "prompt" is a sentence containing specific data or a question that is input into a generative AI model, providing instructions for the model to generate appropriate output.

[0096] The present invention relates to a system for efficiently managing the individual nutritional needs of athletes. This system automatically generates appropriate menus based on the athlete's current physical data and goals, and provides individually optimized nutritional meals in cooperation with a cafeteria system. Specific embodiments for implementing the present invention are described below.

[0097] Main configuration

[0098] 1. Login method

[0099] A user logs in to access the system. They enter their username and password, and the server authenticates them. If authentication is successful, a dashboard dedicated to the user is displayed on the terminal. This login function allows users to securely access their own data.

[0100] 2. Data entry method

[0101] An interface is provided for users to input the player's physical data. Specifically, information such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight is input. This information is sent to the server via the terminal. For example, if a player weighs 75 kg, has 15% body fat, and is aiming to increase muscle mass and strengthen his legs, he would enter that data.

[0102] 3. Analysis method

[0103] The server analyzes the received data and inputs it into a generative AI model. The AI ​​model then uses the athlete's data to mimic the knowledge of professional nutritionists and proposes optimal menus. This analysis is performed quickly using a cloud service. For example, the following data could be input into the AI ​​model as a prompt: "Weight: 75kg," "Body fat: 15%," "Goal: Increase muscle mass and strengthen legs."

[0104] 4. Menu Creation

[0105] The server uses an AI model to automatically generate a menu that takes into account the optimal nutritional balance for the athlete. The generated menu is encoded in JSON format and sent to the device. For example, the following menu may be generated:

[0106] Breakfast: Oatmeal, chicken breast, banana

[0107] Lunch: Brown rice, grilled salmon, salad

[0108] Dinner: Chicken breast steak, vegetable soup, protein shake

[0109] 5. Display of menu contents

[0110] The device displays the generated menu to the user. The user's dashboard displays the detailed menu, allowing the user to review it. This feature allows the user to visualize their daily meal plan.

[0111] 6. Verification and correction methods

[0112] The user can check the displayed menu and make any necessary changes, for example, if they do not want to use a particular ingredient or want to adjust the calorie content. These changes are made via the device and are reflected in the dashboard in real time.

[0113] 7. Final approval of the menu

[0114] The user finally approves the revised menu. The approved menu is sent from the device to the server. This function allows the user to determine the optimal meal plan.

[0115] 8. Collaboration with the cafeteria system

[0116] The server then connects the final approved menu to the cafeteria system. Specifically, the menu is sent to the cafeteria system as an individual nutrition plan for each player. This connection enables the cafeteria to prepare the optimal meal for each player.

[0117] 9. Providing nutritious meals

[0118] The cafeteria prepares and serves nutritious meals based on the coordinated menu to the athletes. The meals are optimized for each athlete's individual goals, allowing them to efficiently improve their performance and maintain their health.

[0119] Prompt Sentence Examples

[0120] Based on the athlete's physical data (weight: 75kg, body fat: 15%, goal: increase muscle mass and strengthen legs), please propose a menu for each meal. Please include specific menus for breakfast, lunch, and dinner.

[0121] This system allows athletes to efficiently manage their individual nutritional needs, helping them improve their performance and maintain their health.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] A user accesses the system and logs in. Specifically, they enter their username and password and send them to the server. The server receives them and authenticates them by checking the database. The input data is the username and password, and the output is the authentication result. If authentication is successful, a dashboard dedicated to the user is displayed on the terminal.

[0125] Step 2:

[0126] The user inputs the athlete's physical data. Input items include weight, body fat, muscle mass, body parts to train, and target weight. The device sends this data to the server. The input data is physical data, and the output is data sent to the server for analysis. For example, "Weight: 75 kg," "Body fat: 15%," and "Goal: Increase muscle mass and strengthen legs" are input.

[0127] Step 3:

[0128] The server analyzes the received physical data. Specifically, the data is input into a generative AI model to generate a menu that takes into account optimal nutritional balance. The input data is the user's physical data, and the output is the menu generated by the AI ​​model. For example, the prompt sentences "Weight: 75 kg," "Body fat: 15%," and "Goal: Increase muscle mass and strengthen legs" are input.

[0129] Step 4:

[0130] The server encodes the generated menu in JSON format and sends it to the terminal. The input data is the generated menu, and the output is the encoded JSON data. For example, JSON data containing specific menus for breakfast, lunch, and dinner is generated.

[0131] Step 5:

[0132] The device displays the generated menu to the user. The displayed data is a detailed menu encoded in JSON format. The user can check the contents. For example, specific menu items such as oatmeal, chicken breast, and bananas are displayed on the dashboard.

[0133] Step 6:

[0134] The user checks the displayed menu and makes any necessary changes. Once the changes are complete, the menu is finally approved through the confirmation and correction process. The input data is the revised menu, and the output is the final approved menu. For example, a change can be made to change the salmon for lunch to pork.

[0135] Step 7:

[0136] The terminal sends the final approved menu to the server. The input data is the final approved menu, and the output is the data sent to the server. This data is then linked to the cafeteria system.

[0137] Step 8:

[0138] The server then sends the final approved menu to the cafeteria system. The input data is the final approved menu content, and the output is the data sent to the cafeteria system. For example, an individual nutrition plan for each player is sent to the cafeteria system.

[0139] Step 9:

[0140] The cafeteria prepares and serves nutritious meals to athletes based on the linked menu. The input data is the nutrition plan sent to the cafeteria system, and the output is the prepared nutritious meals. This allows athletes to receive meals optimized for their goals.

[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] Conventional nutrition management systems for athletes have a complicated process for providing athletes with individually optimized nutritional meals, and are often limited to specific facilities. This makes it difficult for athletes to quickly receive optimal nutritional meals no matter where they are. In addition, the process of linking data and modifying menus is complicated, making it difficult to carry out efficiently.

[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 login means, a data input means for inputting the athlete's weight, body fat, muscle mass, body parts to be trained, and target weight, an AI analysis means for analyzing the data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration, a menu display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu, a linking means for linking the finally approved menu to the cafeteria and delivery service, and a provision means for cooking and providing nutritious meals based on the linked menu. This enables athletes to quickly receive optimized nutritious meals wherever they are.

[0146] A "login means" is a means for performing the authentication required for a user to access the system.

[0147] The "data input means" is a means for inputting physical data such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight.

[0148] The "AI analysis means" is a means for generating a menu that takes into consideration nutritional balance using an AI model based on the physical data received from the data input means.

[0149] The "menu display means" is a means for visually presenting the generated menu to the user.

[0150] The "Means for Confirmation and Correction" is a means by which the user can confirm the displayed menu and make corrections and final approval as necessary.

[0151] The "linking means" is a means for transmitting the final approved menu to the cafeteria and delivery service system and linking them together.

[0152] The "providing means" is a means for preparing nutritious dishes based on the menu provided by the linking means and providing them to the players.

[0153] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and is a format for structuring data and expressing it in a text-based format.

[0154] A "terminal" is a computer or mobile device that a user uses to operate the system.

[0155] A "server" is a computer system for storing data, processing data, and providing various system functions.

[0156] "Delivery Service" means a service for delivering nutritious meals prepared in a cafeteria or cooking facility to a location designated by the player.

[0157] This invention is a system for efficiently managing the nutritional needs of athletes. This system automatically generates optimal menus based on the athletes' physical data and goals, and then links these to cafeterias and delivery services to provide individually optimized nutritional meals. The main components of the system and their specific implementation methods are described below.

[0158] Login method

[0159] This is an authentication method for users to access the system. Users enter their username and password, and the server performs authentication. If authentication is successful, users can access their own dashboard.

[0160] Data Entry Method

[0161] It provides an interface for users to input physical data such as the player's weight, body fat, muscle mass, areas to train, target weight, etc. The input information is sent from the user's terminal to the server.

[0162] AI analysis means

[0163] The server analyzes the physical data received from the data input means and inputs it into the AI ​​model. The AI ​​model generates the optimal menu based on the athlete's data, imitating the knowledge of professional nutritionists. This AI analysis is implemented using the programming language Python and the machine learning libraries TENSORFLOW (registered trademark) and PyTorch.

[0164] Menu display method

[0165] The generated menu is visually displayed on the user's device, allowing the user to check the menu details.

[0166] Check and corrective measures

[0167] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu. This procedure is designed to be intuitively operated on the dashboard.

[0168] Collaboration methods

[0169] The server then sends the final approved menu to the restaurant and delivery service system. The menu is encoded in JSON format and sent to the restaurant and delivery service. This communication is performed via the HTTP protocol.

[0170] Providing means

[0171] The cafeteria and delivery service will prepare and serve nutritious meals based on the coordinated menu, ensuring that players receive individually optimized meals.

[0172] Specific examples

[0173] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[0174] Breakfast: Oatmeal, chicken breast, banana

[0175] Lunch: Brown rice, grilled salmon, salad

[0176] Dinner: Chicken breast steak, vegetable soup, protein shake

[0177] This menu is displayed on the user's dashboard, where the user can review, modify, and approve it, and the menu data is then sent to the delivery service, which then delivers meals based on this menu to the athletes.

[0178] Prompt Sentence Examples

[0179] The prompt statement is entered as follows:

[0180] The athlete weighs 75kg, has 15% body fat, and is looking to increase muscle mass and strengthen his legs. Can you suggest optimal meals for breakfast, lunch, and dinner?

[0181] In this way, the system can efficiently meet the individual nutritional needs of athletes, contributing to improved performance and health maintenance.

[0182] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0183] Step 1:

[0184] The terminal obtains the user's login information. The user enters the username and password and sends them to the server. The server authenticates the received login information and, if successful, allows access to the user's dedicated dashboard. The input is the username and password, and the output is the authentication result.

[0185] Step 2:

[0186] The terminal obtains the player's physical data from the user. The user inputs the player's weight, body fat, muscle mass, body parts to train, target weight, etc., and sends this information to the server. The server stores the received information in a database. The input is the physical data, and the output is a message indicating that the data has been successfully saved to the database.

[0187] Step 3:

[0188] The server inputs the stored physical data into an AI model for analysis. The AI ​​model generates an optimal menu based on multiple nutritional balance data sets. As a result of the analysis, menus for each meal (breakfast, lunch, and dinner) are suggested. The input is physical data, and the output is a menu.

[0189] Step 4:

[0190] The terminal displays the menu returned from the server on the user dashboard. The user can check the menu in detail. The input is the menu, and the output is the display on the user dashboard.

[0191] Step 5:

[0192] The user checks the displayed menu and makes any necessary corrections. After the corrections are complete, the user finally approves the menu and sends it from the terminal to the server. The input is the revised menu, and the output is the approved menu.

[0193] Step 6:

[0194] The server encodes the approved menu in JSON format and sends it to the cafeteria and delivery service system, which then begins preparing the nutritional meals for the athletes. The input is the approved menu, and the output is the encoded data and a message that transmission was successful.

[0195] Step 7:

[0196] The cafeteria and delivery service prepares and serves nutritious meals to athletes based on the menu sent from the server. Athletes can receive their meals at the cafeteria or receive the nutritious meals at a designated location such as their home via a delivery service. The input is the menu, and the output is the prepared nutritious meals.

[0197] 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.

[0198] This invention relates to a system for efficient nutritional management of athletes. This system uses an AI model and an emotion engine to automatically generate appropriate menus based on the athlete's current physical data and goals, and then connects them to the cafeteria to provide individually optimized nutritional meals.

[0199] Main system components

[0200] Login method

[0201] To access the system, the user logs in by entering their username and password and clicking the "Submit" button. The terminal then sends the entered authentication information to the server.

[0202] Data Entry Method

[0203] An interface is provided for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to the server.

[0204] AI analysis means

[0205] The server analyzes the received data and inputs it into an AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete data and proposes the optimal menu.

[0206] Menu display method

[0207] The device displays the generated menu to the user, and the user's dashboard displays the detailed menu contents, allowing the user to check them.

[0208] Check and corrective measures

[0209] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu.

[0210] Collaboration methods

[0211] The terminal sends the final approved menu to the server, which then links it to the cafeteria system. The menu is provided to the cafeteria as an individual nutrition plan for each player.

[0212] Providing means

[0213] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[0214] Introducing the Emotion Engine

[0215] emotion recognition means

[0216] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice, detecting emotions from the user's facial expressions and voice as they check the menu.

[0217] Emotion analysis means

[0218] The server receives the emotional data sent from the emotion engine and analyzes whether the user is satisfied or dissatisfied. This emotional data is fed back to the AI ​​analysis means.

[0219] Feedback Adjustment Means

[0220] The server uses the emotional data to generate a new menu using the AI ​​model if necessary, allowing it to re-propose a more optimal menu that reflects the user's emotional state.

[0221] Specific examples

[0222] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[0223] Breakfast: Oatmeal, chicken breast, banana

[0224] Lunch: Brown rice, grilled salmon, salad

[0225] Dinner: Chicken breast steak, vegetable soup, protein shake

[0226] This menu is displayed on the user's dashboard, and when the user confirms it, the emotion engine analyzes the user's facial expressions and voice. For example, if the user is dissatisfied, the emotion data is sent to the server, and the AI ​​model regenerates the menu. The regenerated menu takes the user's emotions into account, resulting in a more satisfying menu.

[0227] The system of the present invention realizes efficient nutritional management that takes into account the individual nutritional needs of athletes as well as their emotional state, contributing to improved performance and health maintenance.

[0228] The processing flow will be explained below.

[0229] Step 1:

[0230] The user logs in to the terminal, enters the username and password, and clicks the "Submit" button. The terminal sends the entered authentication information to the server.

[0231] Step 2:

[0232] The server receives the authentication information and checks it against the user information in its database. If it matches, authentication is successful and a login success message is returned to the user. If authentication fails, an error message is returned.

[0233] Step 3:

[0234] After successful authentication, the user accesses the dashboard and enters physical data such as weight, body fat, muscle mass, areas to train, and target weight. Once the input is complete, the user clicks the "Submit" button.

[0235] Step 4:

[0236] The terminal encodes the input data in JSON format or similar and sends it to the server using an HTTP POST request.

[0237] Step 5:

[0238] The server parses the received data, converts it into the appropriate format, and then feeds the data into the AI ​​model, starting the analysis process.

[0239] Step 6:

[0240] The AI ​​model on the server analyzes the player's data and generates a menu that takes into account the optimal nutritional balance. The generated menu is then converted into JSON format.

[0241] Step 7:

[0242] The server sends the generated menu in JSON format to the terminal, which returns the menu as an HTTP response.

[0243] Step 8:

[0244] The device analyzes the menu received and displays it on the user interface. An individual menu for each player is displayed on the dashboard.

[0245] Step 9:

[0246] The user checks the displayed menu. The emotion engine analyzes the user's facial expressions and voice via the device's camera and microphone. The analysis results are sent to the server.

[0247] Step 10:

[0248] The server receives the sentiment analysis results and determines whether the user is satisfied or dissatisfied. If dissatisfaction is detected, it sends feedback to the AI ​​model and instructs it to regenerate the menu. If the user is satisfied, it proceeds to the next step.

[0249] Step 11:

[0250] The server sends the regenerated menu to the terminal, which displays it again to the user, and the process is repeated until the user is satisfied.

[0251] Step 12:

[0252] The user checks the menu for the final time and makes any necessary corrections. After making any corrections, the user clicks the "Approve" button.

[0253] Step 13:

[0254] The terminal encodes the final approved menu again in JSON format and sends it to the server.

[0255] Step 14:

[0256] The server receives the final approved menu and connects it to the cafeteria system. Data is also sent in JSON format when connecting.

[0257] Step 15:

[0258] The cafeteria will prepare individual nutritional meals for each player based on the coordinated menu, and serve the cooked meals to the players.

[0259] Example 2

[0260] 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."

[0261] For modern athletes, efficient individual nutritional management is crucial for improving performance and maintaining health. However, traditional methods require nutritionists to manually create and individually manage menus for each athlete, which requires significant effort and time. It is also difficult for athletes to reflect their satisfaction with the proposed menu or their emotional state in real time, which can result in menus that are not optimal for the athlete. Furthermore, the lack of a system for smoothly changing menus makes it difficult to quickly incorporate athletes' feedback.

[0262] 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 login means, a data input means for inputting the athlete's weight, body fat, muscle mass, body part to be trained, and target weight, an AI analysis means for analyzing the data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration, a menu display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu, a linking means for linking the finally approved menu to the cafeteria system, a serving means for cooking and serving nutritious meals based on the linked menu, an emotion recognition means for analyzing the user's facial expressions and voice to recognize emotions, an emotion analysis means for analyzing the emotion data and evaluating satisfaction, and a feedback adjustment means for reanalyzing the emotion data as feedback and regenerating a menu as necessary. This enables efficient and optimal nutritional management that takes into account not only the individual nutritional needs of athletes but also their emotional state.

[0263] A "login means" is a means by which a user accesses the system and receives authentication by entering authentication information.

[0264] The "data input means" is an interface that allows the user to input physical data such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight.

[0265] The "AI analysis means" is a means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration.

[0266] The "menu display means" is a means for visually displaying the generated menu to the user.

[0267] The "Means for Confirmation and Correction" is a means by which the user can confirm the displayed menu, correct it if necessary, and finally approve it.

[0268] The "linking means" is a means for linking the final approved menu to the cafeteria system.

[0269] The "providing means" is a means for cooking based on the linked menu and providing nutritious food.

[0270] The "emotion recognition means" is a means for analyzing the user's facial expressions and voice and recognizing their emotions.

[0271] The "emotion analysis means" is a means for analyzing the emotion data obtained from the emotion recognition means and evaluating the user's satisfaction level.

[0272] The "feedback adjustment means" is a means for re-analyzing the emotion data and regenerating the menu as necessary.

[0273] This invention relates to a system for efficient nutritional management of athletes. This system uses an AI model and an emotion engine to automatically generate appropriate menus based on the athlete's current physical data and goals, and then connects them to the cafeteria to provide individually optimized nutritional meals.

[0274] The system includes the following main means:

[0275] Login method

[0276] To access the system, the user logs in by entering a username and password and clicking the "Submit" button. The terminal sends the entered authentication information to the server, which then verifies the authentication information.

[0277] Data Entry Method

[0278] It provides an interface for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to a server, which then stores it in a database.

[0279] AI analysis means

[0280] The server analyzes the received data and inputs it into a generative AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete's data and proposes the optimal menu. Specifically, the deep learning model is run on Python.

[0281] Menu display method

[0282] The device displays the generated menu to the user. The user's dashboard displays the detailed menu contents in an HTML table format.

[0283] Check and corrective measures

[0284] The user can check the displayed menu and make any necessary changes. Once the changes are complete, the user finally approves the menu. The device sends the changes to the server, which stores them in the database.

[0285] Collaboration methods

[0286] This is the means by which the final approved menu is linked to the cafeteria system. The terminal sends the final approved menu to the server, and the server links the menu to the cafeteria system. This includes sending data via REST API.

[0287] Providing means

[0288] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[0289] emotion recognition means

[0290] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice. When the user is reviewing a menu, emotions are detected from their facial expressions and voice. The system is implemented using the OpenVINO toolkit.

[0291] Emotion analysis means

[0292] The server receives the emotional data sent from the emotion engine and analyzes whether the user is satisfied or dissatisfied. This emotional data is fed back to the AI ​​analysis means.

[0293] Feedback Adjustment Means

[0294] The server generates a menu using the generative AI model again if necessary based on the emotional data, allowing it to re-propose a more optimal menu that reflects the user's emotional state.

[0295] Specific examples

[0296] For example, consider an athlete who weighs 75kg, has 15% body fat, and wants to increase muscle mass and strengthen their legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts they want to train. The server receives the data, and the generative AI model proposes the following menu:

[0297] Breakfast: Oatmeal, chicken breast, banana

[0298] Lunch: Brown rice, grilled salmon, salad

[0299] Dinner: Chicken breast steak, vegetable soup, protein shake

[0300] Prompt Sentence Examples

[0301] "Example: Please suggest three meal plans that are ideal for a 75kg athlete with 15% body fat who wants to gain muscle mass and strengthen his legs."

[0302] When this prompt is input into the generative AI model, an appropriate menu is returned. In this way, the system realizes efficient nutritional management that takes into account the individual nutritional needs of athletes as well as their emotional state.

[0303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0304] Step 1: Log in

[0305] Specific actions

[0306] 1. The user accesses the login screen and enters their username and password.

[0307] 2. The user clicks the "Submit" button.

[0308] 3. The device sends the entered authentication information (user name and password) to the server.

[0309] 4. The server validates the credentials

[0310] Input: Username, Password

[0311] Data manipulation: Compare with authentication information stored in the database

[0312] Output: Authentication result (success or failure)

[0313] 5. If authentication is successful, the server starts a session and returns a login success message to the terminal, otherwise it returns an error message.

[0314] Step 2: Enter your physical data

[0315] Specific actions

[0316] 1. The user accesses the physical data entry screen.

[0317] 2. The user enters information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight.

[0318] 3. The user clicks the "Submit" button.

[0319] 4. The device sends the entered physical data to the server.

[0320] 5. The server stores the received body data in a database.

[0321] Input: weight, body fat, muscle mass, area to train, target weight

[0322] Data processing: format check, range check

[0323] Output: Message that data was successfully saved to the database

[0324] Step 3: Menu generation by AI

[0325] Specific actions

[0326] 1. The server acquires the player's physical data.

[0327] Input: Player's physical data

[0328] Data processing: Reading player data from the database

[0329] Output: Player data

[0330] 2. The server inputs the body data into the generative AI model.

[0331] Input: Player data

[0332] Data Computation: Analysis with Deep Learning Models

[0333] Output: Optimal menu

[0334] 3. The AI ​​model mimics the knowledge of professional nutritionists and generates optimal meal plans.

[0335] 4. The server saves the generated menu in the database.

[0336] Input: Menu

[0337] Data processing: Save the menu in the database

[0338] Output: Data saved successfully message

[0339] Step 4: Display the menu

[0340] Specific actions

[0341] 1. The user accesses the dashboard screen.

[0342] 2. The device retrieves the latest menu from the server.

[0343] Input: User ID

[0344] Data calculation: Get the latest menu from the database

[0345] Output: Menu

[0346] 3. The device displays the menu to the user.

[0347] Data processing: Convert to HTML format

[0348] Output: Display the menu on the dashboard screen

[0349] Step 5: Check and edit the menu

[0350] Specific actions

[0351] 1. The user checks the displayed menu.

[0352] 2. If corrections are required, the user enters the corrections.

[0353] 3. The user clicks the "Done" button.

[0354] 4. The device sends the modifications to the server.

[0355] 5. The server updates the changes and saves them in the database

[0356] Input: Correction details

[0357] Data processing: Update the menu in the database

[0358] Output: Data update successful message

[0359] Step 6: Menu Integration

[0360] Specific actions

[0361] 1. The user gives final approval to the menu that has been confirmed and modified.

[0362] 2. The terminal sends the final approved menu to the server.

[0363] 3. The server connects the menu to the cafeteria system

[0364] Input: Final approved menu

[0365] Data Calculation: Send data via REST API

[0366] Output: Integration success message

[0367] Step 7: Serve the food

[0368] Specific actions

[0369] 1. The restaurant receives the linked menu from the server

[0370] Input: Menu

[0371] Data processing: Converting menus into a cooking format

[0372] Output: Cooking instructions

[0373] 2. The cafeteria will prepare nutritious meals based on the menu.

[0374] 3. The cafeteria will provide prepared meals to the athletes.

[0375] Step 8: Implementing the Emotion Engine

[0376] Specific actions

[0377] 1. When a user checks the menu, the device analyzes the user's facial expressions and voice.

[0378] Input: facial expression data, voice data

[0379] Data Computing: Applying Emotion Recognition Algorithms (Using the OpenVINO Toolkit)

[0380] Output: Emotion data

[0381] 2. The device sends the emotion data to the server.

[0382] 3. The server analyzes the emotional data and evaluates the user's satisfaction.

[0383] Input: Emotion data

[0384] Data Computing: Applying Sentiment Analysis Algorithms

[0385] Output: Satisfaction rating data

[0386] 4. If necessary, the server will feed back the emotion data and generate a new menu using the AI ​​model.

[0387] Input: Satisfaction rating data

[0388] Data calculation: Executing regenerative AI models

[0389] Output: Regenerated menu

[0390] Thus, by carrying out these processing steps, optimal nutritional management can be achieved, taking into account the individual nutritional needs and emotional state of the athlete.

[0391] (Application example 2)

[0392] 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."

[0393] For athletes, proper nutritional management is important for improving performance and maintaining health. However, it is difficult to create effective menus based on each athlete's individual nutritional needs and goals, while also incorporating emotional feedback. Similarly, factory robots require the automatic generation and management of optimal maintenance schedules and energy supply plans based on their operating status and performance goals. Using conventional methods, it is difficult to efficiently manage such complex tasks, requiring a great deal of time and effort.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0395] In this invention, the server includes a login means, a data input means, an AI analysis means, a display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu and schedule, a linking means for linking the final approved menu and plan, and a supply means. This enables the automation of nutritional management, energy supply, and maintenance optimized to the individual needs of athletes and factory robots.

[0396] A "login means" is an authentication means for a user to access the system.

[0397] The "data input means" is an interface that allows system users to input data such as the athlete's weight, body fat, muscle mass, areas to be trained, and target weight.

[0398] The "AI analysis means" is an artificial intelligence engine that analyzes data received from the data input means and generates optimal menus and maintenance plans.

[0399] The "menu display means" is a means for displaying the generated menu to the user.

[0400] "Means for confirmation and correction" refers to the means by which users can check the menu and schedule, and correct or approve them as necessary.

[0401] The "linking means" is a means for linking the final approved menu and maintenance plan to the related cooking equipment and automated equipment.

[0402] "Supply means" refers to a means for providing nutritious meals based on a linked menu, and for carrying out energy supply and maintenance based on a linked schedule and plan.

[0403] The embodiments of the present invention will be described in detail below.

[0404] System Configuration

[0405] 1. Login method

[0406] The server provides authentication functionality for users to access the system. Users enter their username and password and click the "Submit" button to send the authentication information to the server.

[0407] 2. Data entry method

[0408] The server provides an interface for inputting physical data such as the athlete's weight, body fat, muscle mass, areas to be trained, and target weight. This data is sent from the terminal to the server.

[0409] 3. AI analysis means

[0410] The server analyzes the data received from the data input means and generates an optimal menu using an AI model. It is desirable to use a machine learning library such as TensorFlow as the AI ​​model used for analysis. Here, the server proposes an optimal menu based on the player's data, imitating the knowledge of a nutritionist.

[0411] 4. Menu display method

[0412] The server provides a means to display the generated menu to the user. This menu display is performed through a web dashboard or a smartphone application. The user can check the detailed menu contents.

[0413] 5. Verification and correction methods

[0414] The server provides an interface through which the user can review the displayed menu and make any necessary modifications, and through which the user can finally approve the menu.

[0415] 6. Collaboration Methods

[0416] The server provides a means for synchronizing the final approved menu with the food preparation facility, which then provides the nutrition plan individually optimized for each athlete.

[0417] 7. Means of supply

[0418] The kitchen facilities will prepare and serve nutritious meals to athletes based on coordinated menus.

[0419] Factory robot applications

[0420] 1. Data entry method

[0421] The server provides an interface for inputting data such as factory robot operating hours, energy consumption, and performance targets, which are then sent from the terminal to the server.

[0422] 2. AI analysis means

[0423] The server analyzes the input data and generates optimal maintenance schedules and energy supply plans using an AI model. It is desirable to use a machine learning library such as TensorFlow as the AI ​​model used for analysis.

[0424] 3. Plan display method

[0425] The server provides a means to display the generated maintenance schedule and energy supply plan to the user. This plan display is performed via a web dashboard or a smartphone application, and the user can check the detailed plan contents.

[0426] 4. Verification and correction methods

[0427] The server provides an interface through which the user can review the displayed schedule and plan, modify it if necessary, and finally approve the plan.

[0428] 5. Collaboration Methods

[0429] The server provides a means to link the final approved maintenance schedule and energy supply plan to the automated equipment, and the linked plan is then provided to the robots.

[0430] 6. Means of supply

[0431] Automated equipment performs energy supply and maintenance based on coordinated schedules and plans.

[0432] Specific examples

[0433] For example, if factory robot A operates for eight hours every day and shows signs of a decline in performance, the user can input the data into the system. The AI ​​engine uses that data to generate an optimal maintenance schedule and energy supply plan. For example, it may suggest performing maintenance twice a week and increasing energy supply by 20%. If the user is satisfied with the suggestion, the emotion recognition engine will apply the plan as is and link it to factory robot A.

[0434] Prompt Sentence Examples

[0435] Factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Please propose an optimal maintenance schedule and energy supply plan based on the most recent performance report.

[0436] The above is a description of specific embodiments of the present invention.

[0437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0438] Step 1:

[0439] Implementing login methods

[0440] (Subject: Terminal) The user accesses the system login screen on the terminal.

[0441] (Input) Username and password.

[0442] (Process) Enter your username and password and click the "Send" button. The device will send the authentication information to the server.

[0443] (Output) The authentication information is sent to the server and once the user is authenticated the dashboard is displayed.

[0444] Step 2:

[0445] Implementing data entry methods

[0446] (Subject: User) The user enters the necessary data for the player or factory robot into the system.

[0447] (Input) Athlete's weight, body fat, muscle mass, body part to be trained, target weight, or factory robot operating hours, energy consumption, and performance goals.

[0448] (Processing) Enter these values ​​through the data input interface and click the send button. The terminal will send these data to the server.

[0449] (Output) The input data is saved on the server.

[0450] Step 3:

[0451] Implementing AI analysis methods

[0452] (Subject: Server) The server analyzes the received data and generates an optimal menu or maintenance plan.

[0453] (Input) Data sent from the data input means.

[0454] (Processing) AI models (e.g., TensorFlow) are used to generate optimal menus for athletes, and optimal maintenance schedules and energy supply plans for factory robots.

[0455] (Output) The generated menu or maintenance plan.

[0456] Step 4:

[0457] Implementing menu display means

[0458] (Subject: Server) The server displays the generated menu or maintenance plan to the user.

[0459] (Input) The generated menu or maintenance plan.

[0460] The (processed) data is displayed to the user via a web dashboard or smartphone application.

[0461] (Output) The menu or maintenance plan is displayed on the user's screen.

[0462] Step 5:

[0463] Implementing verification and correction measures

[0464] (Subject: User) The user checks the displayed menu or maintenance plan and modifies it if necessary.

[0465] (Input) The displayed menu or maintenance plan.

[0466] (Processing) The user checks the points that need to be corrected, makes the corrections, and clicks the Confirm button when the corrections are complete.

[0467] (Output) The modified data is sent to the server.

[0468] Step 6:

[0469] Implementation of collaboration measures

[0470] (Subject: Server) The server communicates the final approved menu or maintenance plan to the cooking equipment or automation equipment.

[0471] (Input) Final approved menu or maintenance plan.

[0472] (Processing) Sending the final approved data to cooking equipment and automation equipment.

[0473] (Output) Data is sent and reflected in cooking equipment and automated equipment.

[0474] Step 7:

[0475] Implementation of supply measures

[0476] (Subject: cooking equipment or automated equipment) Carry out actual actions based on a coordinated plan.

[0477] (Input) The linked menu or maintenance plan.

[0478] (Processing) The cooking equipment prepares and serves nutritious meals based on the menu. The automated equipment supplies energy and performs maintenance based on the maintenance plan.

[0479] (Output) Optimized nutritional meals are provided to athletes, or optimal energy supply and maintenance is provided to factory robots.

[0480] This enables the system to efficiently manage the nutritional intake of athletes and the maintenance of factory robots.

[0481] Hardware and software used:

[0482] Devices: Smartphones, tablets, PCs.

[0483] Server: Data analysis and AI model execution (e.g., TensorFlow, MySQL, MongoDB).

[0484] Sensors: Sensors that monitor the operating status of factory robots (e.g. temperature sensors, energy consumption sensors).

[0485] Emotion recognition engine: An engine that analyzes user emotions (e.g., Microsoft® Azure® emotion recognition API).

[0486] Examples of concrete examples and prompts

[0487] As a concrete example, suppose that factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Based on the robot's most recent performance report, the following prompts will be used to propose an optimal maintenance schedule and energy supply plan:

[0488] Factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Please propose an optimal maintenance schedule and energy supply plan based on the most recent performance report.

[0489] The above is the specific processing flow of the system that realizes the application example.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] [Second embodiment]

[0494] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0495] 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.

[0496] 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).

[0497] 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.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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.

[0503] 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.

[0504] 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.

[0505] 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."

[0506] This invention relates to a system for efficiently managing the nutritional intake of athletes. This system automatically generates appropriate menus based on the athletes' current physical data and goals, and then provides individually optimized nutritional meals to the cafeteria.

[0507] Main system components

[0508] Login method

[0509] To access the system, users log in by entering their username and password, which is then authenticated by the server. If authentication is successful, the user is given access to their personal dashboard.

[0510] Data Entry Method

[0511] An interface is provided for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to the server.

[0512] AI analysis means

[0513] The server analyzes the received data and inputs it into an AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete data and proposes the optimal menu.

[0514] Menu display method

[0515] The device displays the generated menu to the user, and the user's dashboard displays the detailed menu contents, allowing the user to check them.

[0516] Check and corrective measures

[0517] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu.

[0518] Collaboration methods

[0519] The terminal sends the final approved menu to the server, which then links it to the cafeteria system. The menu is provided to the cafeteria as an individual nutrition plan for each player.

[0520] Providing means

[0521] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[0522] Specific examples

[0523] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[0524] Breakfast: Oatmeal, chicken breast, banana

[0525] Lunch: Brown rice, grilled salmon, salad

[0526] Dinner: Chicken breast steak, vegetable soup, protein shake

[0527] This menu is displayed on the user's dashboard, where the user can review, modify, and finally approve it. The approved menu is linked to the cafeteria system via the server, and the cafeteria prepares and serves the food based on it.

[0528] The system of the present invention can efficiently meet the individual nutritional needs of athletes, contributing to improved performance and health maintenance.

[0529] The processing flow will be explained below.

[0530] Step 1:

[0531] The user logs in to the terminal, enters the username and password, and clicks the "Submit" button. The terminal sends the entered authentication information to the server.

[0532] Step 2:

[0533] The server receives the authentication information and checks it against the user information in its database. If it matches, authentication is successful and a login success message is returned to the user. If authentication fails, an error message is returned.

[0534] Step 3:

[0535] After successful authentication, the user accesses the dashboard and enters physical data such as weight, body fat, muscle mass, areas to train, and target weight. Once the input is complete, the user clicks the "Submit" button.

[0536] Step 4:

[0537] The terminal encodes the input data in JSON format or similar and sends it to the server using an HTTP POST request.

[0538] Step 5:

[0539] The server parses the received data, converts it into the appropriate format, and then feeds the data into the AI ​​model, starting the analysis process.

[0540] Step 6:

[0541] The AI ​​model on the server analyzes the player's data and generates a menu that takes into account the optimal nutritional balance. The generated menu is then converted into JSON format.

[0542] Step 7:

[0543] The server sends the generated menu in JSON format to the terminal, which returns the menu as an HTTP response.

[0544] Step 8:

[0545] The device analyzes the menu received and displays it on the user interface. An individual menu for each player is displayed on the dashboard.

[0546] Step 9:

[0547] The user checks the displayed menu and makes any necessary corrections. Once the corrections are complete, the user clicks the "Approve" button.

[0548] Step 10:

[0549] The terminal encodes the final approved menu, reflecting the user's modifications, in JSON format again and sends it to the server.

[0550] Step 11:

[0551] The server receives the final approved menu and connects it to the cafeteria system. Data is also sent in JSON format when connecting.

[0552] Step 12:

[0553] The cafeteria will prepare individual nutritional meals for each player based on the coordinated menu, and serve the cooked meals to the players.

[0554] Example 1

[0555] 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."

[0556] Nutritional management for athletes is extremely important for improving performance and maintaining health. However, planning daily menus that meet individual nutritional needs and efficiently delivering those meals is difficult. This difficulty is particularly exacerbated in environments without the knowledge of specialized nutritionists. Furthermore, it is necessary to quickly create optimal nutrition plans tailored to each athlete's different goals and physical constitutions. To solve this problem, a system is needed that can automatically generate personalized menus based on athletes' physical data and provide them in conjunction with the cafeteria system.

[0557] 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.

[0558] In this invention, the server includes a login means for users to access and log in to the system, a data input means for inputting the player's physical data, and an analysis means for analyzing the data received from the data input means and generating a menu that takes nutritional balance into consideration using a generative AI model. This allows the server to efficiently generate an optimal menu based on the player's individual nutritional needs, display the menu, and link it to the cafeteria system after modification and approval. This allows players to receive nutritional meals optimized for their goals while receiving professional nutritional management.

[0559] The "login means" is a function that allows a user to access the system, enter a user name and password for authentication, and then access a dedicated dashboard.

[0560] The "data input means" is an interface that allows the user to input physical data such as the player's weight, body fat, muscle mass, target weight, and body parts to be trained, and transmit this data from the terminal to the server.

[0561] The "analysis means" is a function that analyzes the physical data received from the data input means, and in particular includes generating a menu that takes nutritional balance into consideration using a generative AI model.

[0562] The "display means" is a function that displays the generated menu on the user's dashboard, allowing the user to check its contents.

[0563] The "confirmation and correction means" is a function that allows the user to confirm the contents of the displayed menu, correct them if necessary, and finally approve them.

[0564] The "linking means" is a function for sending the final approved menu to the cafeteria system and linking it.

[0565] The "means of provision" is a function for preparing nutritious food based on a menu linked to the cafeteria system and providing it to athletes.

[0566] The "generative AI model" is an artificial intelligence model that generates optimal menus based on athletes' physical data, and mimics the knowledge of professional nutritionists.

[0567] A "prompt" is a sentence containing specific data or a question that is input into a generative AI model, providing instructions for the model to generate appropriate output.

[0568] The present invention relates to a system for efficiently managing the individual nutritional needs of athletes. This system automatically generates appropriate menus based on the athlete's current physical data and goals, and provides individually optimized nutritional meals in cooperation with a cafeteria system. Specific embodiments for implementing the present invention are described below.

[0569] Main configuration

[0570] 1. Login method

[0571] A user logs in to access the system. They enter their username and password, and the server authenticates them. If authentication is successful, a dashboard dedicated to the user is displayed on the terminal. This login function allows users to securely access their own data.

[0572] 2. Data entry method

[0573] An interface is provided for users to input the player's physical data. Specifically, information such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight is input. This information is sent to the server via the terminal. For example, if a player weighs 75 kg, has 15% body fat, and is aiming to increase muscle mass and strengthen his legs, he would enter that data.

[0574] 3. Analysis method

[0575] The server analyzes the received data and inputs it into a generative AI model. The AI ​​model then uses the athlete's data to mimic the knowledge of professional nutritionists and proposes optimal menus. This analysis is performed quickly using a cloud service. For example, the following data could be input into the AI ​​model as a prompt: "Weight: 75kg," "Body fat: 15%," "Goal: Increase muscle mass and strengthen legs."

[0576] 4. Menu Creation

[0577] The server uses an AI model to automatically generate a menu that takes into account the optimal nutritional balance for the athlete. The generated menu is encoded in JSON format and sent to the device. For example, the following menu may be generated:

[0578] Breakfast: Oatmeal, chicken breast, banana

[0579] Lunch: Brown rice, grilled salmon, salad

[0580] Dinner: Chicken breast steak, vegetable soup, protein shake

[0581] 5. Display of menu contents

[0582] The device displays the generated menu to the user. The user's dashboard displays the detailed menu, allowing the user to review it. This feature allows the user to visualize their daily meal plan.

[0583] 6. Verification and correction methods

[0584] The user can check the displayed menu and make any necessary changes, for example, if they do not want to use a particular ingredient or want to adjust the calorie content. These changes are made via the device and are reflected in the dashboard in real time.

[0585] 7. Final approval of the menu

[0586] The user finally approves the revised menu. The approved menu is sent from the device to the server. This function allows the user to determine the optimal meal plan.

[0587] 8. Collaboration with the cafeteria system

[0588] The server then connects the final approved menu to the cafeteria system. Specifically, the menu is sent to the cafeteria system as an individual nutrition plan for each player. This connection enables the cafeteria to prepare the optimal meal for each player.

[0589] 9. Providing nutritious meals

[0590] The cafeteria prepares and serves nutritious meals based on the coordinated menu to the athletes. The meals are optimized for each athlete's individual goals, allowing them to efficiently improve their performance and maintain their health.

[0591] Prompt Sentence Examples

[0592] Based on the athlete's physical data (weight: 75kg, body fat: 15%, goal: increase muscle mass and strengthen legs), please propose a menu for each meal. Please include specific menus for breakfast, lunch, and dinner.

[0593] This system allows athletes to efficiently manage their individual nutritional needs, helping them improve their performance and maintain their health.

[0594] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0595] Step 1:

[0596] A user accesses the system and logs in. Specifically, they enter their username and password and send them to the server. The server receives them and authenticates them by checking the database. The input data is the username and password, and the output is the authentication result. If authentication is successful, a dashboard dedicated to the user is displayed on the terminal.

[0597] Step 2:

[0598] The user inputs the athlete's physical data. Input items include weight, body fat, muscle mass, body parts to train, and target weight. The device sends this data to the server. The input data is physical data, and the output is data sent to the server for analysis. For example, "Weight: 75 kg," "Body fat: 15%," and "Goal: Increase muscle mass and strengthen legs" are input.

[0599] Step 3:

[0600] The server analyzes the received physical data. Specifically, the data is input into a generative AI model to generate a menu that takes into account optimal nutritional balance. The input data is the user's physical data, and the output is the menu generated by the AI ​​model. For example, the prompt sentences "Weight: 75 kg," "Body fat: 15%," and "Goal: Increase muscle mass and strengthen legs" are input.

[0601] Step 4:

[0602] The server encodes the generated menu in JSON format and sends it to the terminal. The input data is the generated menu, and the output is the encoded JSON data. For example, JSON data containing specific menus for breakfast, lunch, and dinner is generated.

[0603] Step 5:

[0604] The device displays the generated menu to the user. The displayed data is a detailed menu encoded in JSON format. The user can check the contents. For example, specific menu items such as oatmeal, chicken breast, and bananas are displayed on the dashboard.

[0605] Step 6:

[0606] The user checks the displayed menu and makes any necessary changes. Once the changes are complete, the menu is finally approved through the confirmation and correction process. The input data is the revised menu, and the output is the final approved menu. For example, a change can be made to change the salmon for lunch to pork.

[0607] Step 7:

[0608] The terminal sends the final approved menu to the server. The input data is the final approved menu, and the output is the data sent to the server. This data is then linked to the cafeteria system.

[0609] Step 8:

[0610] The server then sends the final approved menu to the cafeteria system. The input data is the final approved menu content, and the output is the data sent to the cafeteria system. For example, an individual nutrition plan for each player is sent to the cafeteria system.

[0611] Step 9:

[0612] The cafeteria prepares and serves nutritious meals to athletes based on the linked menu. The input data is the nutrition plan sent to the cafeteria system, and the output is the prepared nutritious meals. This allows athletes to receive meals optimized for their goals.

[0613] (Application example 1)

[0614] 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."

[0615] Conventional nutrition management systems for athletes have a complicated process for providing athletes with individually optimized nutritional meals, and are often limited to specific facilities. This makes it difficult for athletes to quickly receive optimal nutritional meals no matter where they are. In addition, the process of linking data and modifying menus is complicated, making it difficult to carry out efficiently.

[0616] 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.

[0617] In this invention, the server includes a login means, a data input means for inputting the athlete's weight, body fat, muscle mass, body parts to be trained, and target weight, an AI analysis means for analyzing the data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration, a menu display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu, a linking means for linking the finally approved menu to the cafeteria and delivery service, and a provision means for cooking and providing nutritious meals based on the linked menu. This enables athletes to quickly receive optimized nutritious meals wherever they are.

[0618] A "login means" is a means for performing the authentication required for a user to access the system.

[0619] The "data input means" is a means for inputting physical data such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight.

[0620] The "AI analysis means" is a means for generating a menu that takes into consideration nutritional balance using an AI model based on the physical data received from the data input means.

[0621] The "menu display means" is a means for visually presenting the generated menu to the user.

[0622] The "Means for Confirmation and Correction" is a means by which the user can confirm the displayed menu and make corrections and final approval as necessary.

[0623] The "linking means" is a means for transmitting the final approved menu to the cafeteria and delivery service system and linking them together.

[0624] The "providing means" is a means for preparing nutritious dishes based on the menu provided by the linking means and providing them to the players.

[0625] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for structuring data and representing it in a text-based format.

[0626] A "terminal" is a computer or mobile device that a user uses to operate the system.

[0627] A "server" is a computer system for storing data, processing data, and providing various system functions.

[0628] "Delivery Service" means a service for delivering nutritious meals prepared in a cafeteria or cooking facility to a location designated by the player.

[0629] This invention is a system for efficiently managing the nutritional needs of athletes. This system automatically generates optimal menus based on the athletes' physical data and goals, and then links these to cafeterias and delivery services to provide individually optimized nutritional meals. The main components of the system and their specific implementation methods are described below.

[0630] Login method

[0631] This is an authentication method for users to access the system. Users enter their username and password, and the server performs authentication. If authentication is successful, users can access their own dashboard.

[0632] Data Entry Method

[0633] It provides an interface for users to input physical data such as the player's weight, body fat, muscle mass, areas to train, target weight, etc. The input information is sent from the user's terminal to the server.

[0634] AI analysis means

[0635] The server analyzes the physical data received from the data input means and inputs it into an AI model. The AI ​​model then generates optimal menus based on the athlete's data, mimicking the knowledge of professional nutritionists. This AI analysis is implemented using the programming language Python and the machine learning libraries TensorFlow and PyTorch.

[0636] Menu display method

[0637] The generated menu is visually displayed on the user's device, allowing the user to check the menu details.

[0638] Check and corrective measures

[0639] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu. This procedure is designed to be intuitively operated on the dashboard.

[0640] Collaboration methods

[0641] The server then sends the final approved menu to the restaurant and delivery service system. The menu is encoded in JSON format and sent to the restaurant and delivery service. This communication is performed via the HTTP protocol.

[0642] Providing means

[0643] The cafeteria and delivery service will prepare and serve nutritious meals based on the coordinated menu, ensuring that players receive individually optimized meals.

[0644] Specific examples

[0645] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[0646] Breakfast: Oatmeal, chicken breast, banana

[0647] Lunch: Brown rice, grilled salmon, salad

[0648] Dinner: Chicken breast steak, vegetable soup, protein shake

[0649] This menu is displayed on the user's dashboard, where the user can review, modify, and approve it, and the menu data is then sent to the delivery service, which then delivers meals based on this menu to the athletes.

[0650] Prompt Sentence Examples

[0651] The prompt statement is entered as follows:

[0652] The athlete weighs 75kg, has 15% body fat, and is looking to increase muscle mass and strengthen his legs. Can you suggest optimal meals for breakfast, lunch, and dinner?

[0653] In this way, the system can efficiently meet the individual nutritional needs of athletes, contributing to improved performance and health maintenance.

[0654] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0655] Step 1:

[0656] The terminal obtains the user's login information. The user enters the username and password and sends them to the server. The server authenticates the received login information and, if successful, allows access to the user's dedicated dashboard. The input is the username and password, and the output is the authentication result.

[0657] Step 2:

[0658] The terminal obtains the player's physical data from the user. The user inputs the player's weight, body fat, muscle mass, body parts to train, target weight, etc., and sends this information to the server. The server stores the received information in a database. The input is the physical data, and the output is a message indicating that the data has been successfully saved to the database.

[0659] Step 3:

[0660] The server inputs the stored physical data into an AI model for analysis. The AI ​​model generates an optimal menu based on multiple nutritional balance data sets. As a result of the analysis, menus for each meal (breakfast, lunch, and dinner) are suggested. The input is physical data, and the output is a menu.

[0661] Step 4:

[0662] The terminal displays the menu returned from the server on the user dashboard. The user can check the menu in detail. The input is the menu, and the output is the display on the user dashboard.

[0663] Step 5:

[0664] The user checks the displayed menu and makes any necessary corrections. After the corrections are complete, the user finally approves the menu and sends it from the terminal to the server. The input is the revised menu, and the output is the approved menu.

[0665] Step 6:

[0666] The server encodes the approved menu in JSON format and sends it to the cafeteria and delivery service system, which then begins preparing the nutritional meals for the athletes. The input is the approved menu, and the output is the encoded data and a message that transmission was successful.

[0667] Step 7:

[0668] The cafeteria and delivery service prepares and serves nutritious meals to athletes based on the menu sent from the server. Athletes can receive their meals at the cafeteria or receive the nutritious meals at a designated location such as their home via a delivery service. The input is the menu, and the output is the prepared nutritious meals.

[0669] 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.

[0670] This invention relates to a system for efficient nutritional management of athletes. This system uses an AI model and an emotion engine to automatically generate appropriate menus based on the athlete's current physical data and goals, and then connects them to the cafeteria to provide individually optimized nutritional meals.

[0671] Main system components

[0672] Login method

[0673] To access the system, the user logs in by entering their username and password and clicking the "Submit" button. The terminal then sends the entered authentication information to the server.

[0674] Data Entry Method

[0675] An interface is provided for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to the server.

[0676] AI analysis means

[0677] The server analyzes the received data and inputs it into an AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete data and proposes the optimal menu.

[0678] Menu display method

[0679] The device displays the generated menu to the user, and the user's dashboard displays the detailed menu contents, allowing the user to check them.

[0680] Check and corrective measures

[0681] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu.

[0682] Collaboration methods

[0683] The terminal sends the final approved menu to the server, which then links it to the cafeteria system. The menu is provided to the cafeteria as an individual nutrition plan for each player.

[0684] Providing means

[0685] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[0686] Introducing the Emotion Engine

[0687] emotion recognition means

[0688] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice, detecting emotions from the user's facial expressions and voice as they check the menu.

[0689] Emotion analysis means

[0690] The server receives the emotional data sent from the emotion engine and analyzes whether the user is satisfied or dissatisfied. This emotional data is fed back to the AI ​​analysis means.

[0691] Feedback Adjustment Means

[0692] The server uses the emotional data to generate a new menu using the AI ​​model if necessary, allowing it to re-propose a more optimal menu that reflects the user's emotional state.

[0693] Specific examples

[0694] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[0695] Breakfast: Oatmeal, chicken breast, banana

[0696] Lunch: Brown rice, grilled salmon, salad

[0697] Dinner: Chicken breast steak, vegetable soup, protein shake

[0698] This menu is displayed on the user's dashboard, and when the user confirms it, the emotion engine analyzes the user's facial expressions and voice. For example, if the user is dissatisfied, the emotion data is sent to the server, and the AI ​​model regenerates the menu. The regenerated menu takes the user's emotions into account, resulting in a more satisfying menu.

[0699] The system of the present invention realizes efficient nutritional management that takes into account the individual nutritional needs of athletes as well as their emotional state, contributing to improved performance and health maintenance.

[0700] The processing flow will be explained below.

[0701] Step 1:

[0702] The user logs in to the terminal, enters the username and password, and clicks the "Submit" button. The terminal sends the entered authentication information to the server.

[0703] Step 2:

[0704] The server receives the authentication information and checks it against the user information in its database. If it matches, authentication is successful and a login success message is returned to the user. If authentication fails, an error message is returned.

[0705] Step 3:

[0706] After successful authentication, the user accesses the dashboard and enters physical data such as weight, body fat, muscle mass, areas to train, and target weight. Once the input is complete, the user clicks the "Submit" button.

[0707] Step 4:

[0708] The terminal encodes the input data in JSON format or similar and sends it to the server using an HTTP POST request.

[0709] Step 5:

[0710] The server parses the received data, converts it into the appropriate format, and then feeds the data into the AI ​​model, starting the analysis process.

[0711] Step 6:

[0712] The AI ​​model on the server analyzes the player's data and generates a menu that takes into account the optimal nutritional balance. The generated menu is then converted into JSON format.

[0713] Step 7:

[0714] The server sends the generated menu in JSON format to the terminal, which returns the menu as an HTTP response.

[0715] Step 8:

[0716] The device analyzes the menu received and displays it on the user interface. An individual menu for each player is displayed on the dashboard.

[0717] Step 9:

[0718] The user checks the displayed menu. The emotion engine analyzes the user's facial expressions and voice via the device's camera and microphone. The analysis results are sent to the server.

[0719] Step 10:

[0720] The server receives the sentiment analysis results and determines whether the user is satisfied or dissatisfied. If dissatisfaction is detected, it sends feedback to the AI ​​model and instructs it to regenerate the menu. If the user is satisfied, it proceeds to the next step.

[0721] Step 11:

[0722] The server sends the regenerated menu to the terminal, which displays it again to the user, and the process is repeated until the user is satisfied.

[0723] Step 12:

[0724] The user checks the menu for the final time and makes any necessary corrections. After making any corrections, the user clicks the "Approve" button.

[0725] Step 13:

[0726] The terminal encodes the final approved menu again in JSON format and sends it to the server.

[0727] Step 14:

[0728] The server receives the final approved menu and connects it to the cafeteria system. Data is also sent in JSON format when connecting.

[0729] Step 15:

[0730] The cafeteria will prepare individual nutritional meals for each player based on the coordinated menu, and serve the cooked meals to the players.

[0731] Example 2

[0732] 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."

[0733] For modern athletes, efficient individual nutritional management is crucial for improving performance and maintaining health. However, traditional methods require nutritionists to manually create and individually manage menus for each athlete, which requires significant effort and time. It is also difficult for athletes to reflect their satisfaction with the proposed menu or their emotional state in real time, which can result in menus that are not optimal for the athlete. Furthermore, the lack of a system for smoothly changing menus makes it difficult to quickly incorporate athletes' feedback.

[0734] 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 login means, a data input means for inputting the athlete's weight, body fat, muscle mass, body part to be trained, and target weight, an AI analysis means for analyzing the data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration, a menu display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu, a linking means for linking the finally approved menu to the cafeteria system, a serving means for cooking and serving nutritious meals based on the linked menu, an emotion recognition means for analyzing the user's facial expressions and voice to recognize emotions, an emotion analysis means for analyzing the emotion data and evaluating satisfaction, and a feedback adjustment means for reanalyzing the emotion data as feedback and regenerating a menu as necessary. This enables efficient and optimal nutritional management that takes into account not only the individual nutritional needs of athletes but also their emotional state.

[0735] A "login means" is a means by which a user accesses the system and receives authentication by entering authentication information.

[0736] The "data input means" is an interface that allows the user to input physical data such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight.

[0737] The "AI analysis means" is a means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration.

[0738] The "menu display means" is a means for visually displaying the generated menu to the user.

[0739] The "Means for Confirmation and Correction" is a means by which the user can confirm the displayed menu, correct it if necessary, and finally approve it.

[0740] The "linking means" is a means for linking the final approved menu to the cafeteria system.

[0741] The "providing means" is a means for cooking based on the linked menu and providing nutritious food.

[0742] The "emotion recognition means" is a means for analyzing the user's facial expressions and voice and recognizing their emotions.

[0743] The "emotion analysis means" is a means for analyzing the emotion data obtained from the emotion recognition means and evaluating the user's satisfaction level.

[0744] The "feedback adjustment means" is a means for re-analyzing the emotion data and regenerating the menu as necessary.

[0745] This invention relates to a system for efficient nutritional management of athletes. This system uses an AI model and an emotion engine to automatically generate appropriate menus based on the athlete's current physical data and goals, and then connects them to the cafeteria to provide individually optimized nutritional meals.

[0746] The system includes the following main means:

[0747] Login method

[0748] To access the system, the user logs in by entering a username and password and clicking the "Submit" button. The terminal sends the entered authentication information to the server, which then verifies the authentication information.

[0749] Data Entry Method

[0750] It provides an interface for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to a server, which then stores it in a database.

[0751] AI analysis means

[0752] The server analyzes the received data and inputs it into a generative AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete's data and proposes the optimal menu. Specifically, the deep learning model is run on Python.

[0753] Menu display method

[0754] The device displays the generated menu to the user. The user's dashboard displays the detailed menu contents in an HTML table format.

[0755] Check and corrective measures

[0756] The user can check the displayed menu and make any necessary changes. Once the changes are complete, the user finally approves the menu. The device sends the changes to the server, which stores them in the database.

[0757] Collaboration methods

[0758] This is the means by which the final approved menu is linked to the cafeteria system. The terminal sends the final approved menu to the server, and the server links the menu to the cafeteria system. This includes sending data via REST API.

[0759] Providing means

[0760] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[0761] emotion recognition means

[0762] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice. When the user is reviewing a menu, emotions are detected from their facial expressions and voice. The system is implemented using the OpenVINO toolkit.

[0763] Emotion analysis means

[0764] The server receives the emotional data sent from the emotion engine and analyzes whether the user is satisfied or dissatisfied. This emotional data is fed back to the AI ​​analysis means.

[0765] Feedback Adjustment Means

[0766] The server generates a menu using the generative AI model again if necessary based on the emotional data, allowing it to re-propose a more optimal menu that reflects the user's emotional state.

[0767] Specific examples

[0768] For example, consider an athlete who weighs 75kg, has 15% body fat, and wants to increase muscle mass and strengthen their legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts they want to train. The server receives the data, and the generative AI model proposes the following menu:

[0769] Breakfast: Oatmeal, chicken breast, banana

[0770] Lunch: Brown rice, grilled salmon, salad

[0771] Dinner: Chicken breast steak, vegetable soup, protein shake

[0772] Prompt Sentence Examples

[0773] "Example: Please suggest three meal plans that are ideal for a 75kg athlete with 15% body fat who wants to gain muscle mass and strengthen his legs."

[0774] When this prompt is input into the generative AI model, an appropriate menu is returned. In this way, the system realizes efficient nutritional management that takes into account the individual nutritional needs of athletes as well as their emotional state.

[0775] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0776] Step 1: Log in

[0777] Specific actions

[0778] 1. The user accesses the login screen and enters their username and password.

[0779] 2. The user clicks the "Submit" button.

[0780] 3. The device sends the entered authentication information (user name and password) to the server.

[0781] 4. The server validates the credentials

[0782] Input: Username, Password

[0783] Data manipulation: Compare with authentication information stored in the database

[0784] Output: Authentication result (success or failure)

[0785] 5. If authentication is successful, the server starts a session and returns a login success message to the terminal, otherwise it returns an error message.

[0786] Step 2: Enter your physical data

[0787] Specific actions

[0788] 1. The user accesses the physical data entry screen.

[0789] 2. The user enters information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight.

[0790] 3. The user clicks the "Submit" button.

[0791] 4. The device sends the entered physical data to the server.

[0792] 5. The server stores the received body data in a database.

[0793] Input: weight, body fat, muscle mass, area to train, target weight

[0794] Data processing: format check, range check

[0795] Output: Message that data was successfully saved to the database

[0796] Step 3: Menu generation by AI

[0797] Specific actions

[0798] 1. The server acquires the player's physical data.

[0799] Input: Player's physical data

[0800] Data processing: Reading player data from the database

[0801] Output: Player data

[0802] 2. The server inputs the body data into the generative AI model.

[0803] Input: Player data

[0804] Data Computation: Analysis with Deep Learning Models

[0805] Output: Optimal menu

[0806] 3. The AI ​​model mimics the knowledge of professional nutritionists and generates optimal meal plans.

[0807] 4. The server saves the generated menu in the database.

[0808] Input: Menu

[0809] Data processing: Save the menu in the database

[0810] Output: Data saved successfully message

[0811] Step 4: Display the menu

[0812] Specific actions

[0813] 1. The user accesses the dashboard screen.

[0814] 2. The device retrieves the latest menu from the server.

[0815] Input: User ID

[0816] Data calculation: Get the latest menu from the database

[0817] Output: Menu

[0818] 3. The device displays the menu to the user.

[0819] Data processing: Convert to HTML format

[0820] Output: Display the menu on the dashboard screen

[0821] Step 5: Check and edit the menu

[0822] Specific actions

[0823] 1. The user checks the displayed menu.

[0824] 2. If corrections are required, the user enters the corrections.

[0825] 3. The user clicks the "Done" button.

[0826] 4. The device sends the modifications to the server.

[0827] 5. The server updates the changes and saves them in the database

[0828] Input: Correction details

[0829] Data processing: Update the menu in the database

[0830] Output: Data update successful message

[0831] Step 6: Menu Integration

[0832] Specific actions

[0833] 1. The user gives final approval to the menu that has been confirmed and modified.

[0834] 2. The terminal sends the final approved menu to the server.

[0835] 3. The server connects the menu to the cafeteria system

[0836] Input: Final approved menu

[0837] Data Calculation: Send data via REST API

[0838] Output: Integration success message

[0839] Step 7: Serve the food

[0840] Specific actions

[0841] 1. The restaurant receives the linked menu from the server

[0842] Input: Menu

[0843] Data processing: Converting menus into a cooking format

[0844] Output: Cooking instructions

[0845] 2. The cafeteria will prepare nutritious meals based on the menu.

[0846] 3. The cafeteria will provide prepared meals to the athletes.

[0847] Step 8: Implementing the Emotion Engine

[0848] Specific actions

[0849] 1. When a user checks the menu, the device analyzes the user's facial expressions and voice.

[0850] Input: facial expression data, voice data

[0851] Data Computing: Applying Emotion Recognition Algorithms (Using the OpenVINO Toolkit)

[0852] Output: Emotion data

[0853] 2. The device sends the emotion data to the server.

[0854] 3. The server analyzes the emotional data and evaluates the user's satisfaction.

[0855] Input: Emotion data

[0856] Data Computing: Applying Sentiment Analysis Algorithms

[0857] Output: Satisfaction rating data

[0858] 4. If necessary, the server will feed back the emotion data and generate a new menu using the AI ​​model.

[0859] Input: Satisfaction rating data

[0860] Data calculation: Executing regenerative AI models

[0861] Output: Regenerated menu

[0862] Thus, by carrying out these processing steps, optimal nutritional management can be achieved, taking into account the individual nutritional needs and emotional state of the athlete.

[0863] (Application example 2)

[0864] 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."

[0865] For athletes, proper nutritional management is important for improving performance and maintaining health. However, it is difficult to create effective menus based on each athlete's individual nutritional needs and goals, while also incorporating emotional feedback. Similarly, factory robots require the automatic generation and management of optimal maintenance schedules and energy supply plans based on their operating status and performance goals. Using conventional methods, it is difficult to efficiently manage such complex tasks, requiring a great deal of time and effort.

[0866] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0867] In this invention, the server includes a login means, a data input means, an AI analysis means, a display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu and schedule, a linking means for linking the final approved menu and plan, and a supply means. This enables the automation of nutritional management, energy supply, and maintenance optimized to the individual needs of athletes and factory robots.

[0868] A "login means" is an authentication means for a user to access the system.

[0869] The "data input means" is an interface that allows system users to input data such as the athlete's weight, body fat, muscle mass, areas to be trained, and target weight.

[0870] The "AI analysis means" is an artificial intelligence engine that analyzes data received from the data input means and generates optimal menus and maintenance plans.

[0871] The "menu display means" is a means for displaying the generated menu to the user.

[0872] "Means for confirmation and correction" refers to the means by which users can check the menu and schedule, and correct or approve them as necessary.

[0873] The "linking means" is a means for linking the final approved menu and maintenance plan to the related cooking equipment and automated equipment.

[0874] "Supply means" refers to a means for providing nutritious meals based on a linked menu, and for carrying out energy supply and maintenance based on a linked schedule and plan.

[0875] The embodiments of the present invention will be described in detail below.

[0876] System Configuration

[0877] 1. Login method

[0878] The server provides authentication functionality for users to access the system. Users enter their username and password and click the "Submit" button to send the authentication information to the server.

[0879] 2. Data entry method

[0880] The server provides an interface for inputting physical data such as the athlete's weight, body fat, muscle mass, areas to be trained, and target weight. This data is sent from the terminal to the server.

[0881] 3. AI analysis means

[0882] The server analyzes the data received from the data input means and generates an optimal menu using an AI model. It is desirable to use a machine learning library such as TensorFlow as the AI ​​model used for analysis. Here, the server proposes an optimal menu based on the player's data, imitating the knowledge of a nutritionist.

[0883] 4. Menu display method

[0884] The server provides a means to display the generated menu to the user. This menu display is performed through a web dashboard or a smartphone application. The user can check the detailed menu contents.

[0885] 5. Verification and correction methods

[0886] The server provides an interface through which the user can review the displayed menu and make any necessary modifications, and through which the user can finally approve the menu.

[0887] 6. Collaboration Methods

[0888] The server provides a means for synchronizing the final approved menu with the food preparation facility, which then provides the nutrition plan individually optimized for each athlete.

[0889] 7. Means of supply

[0890] The kitchen facilities will prepare and serve nutritious meals to athletes based on coordinated menus.

[0891] Factory robot applications

[0892] 1. Data entry method

[0893] The server provides an interface for inputting data such as factory robot operating hours, energy consumption, and performance targets, which are then sent from the terminal to the server.

[0894] 2. AI analysis means

[0895] The server analyzes the input data and generates optimal maintenance schedules and energy supply plans using an AI model. It is desirable to use a machine learning library such as TensorFlow as the AI ​​model used for analysis.

[0896] 3. Plan display method

[0897] The server provides a means to display the generated maintenance schedule and energy supply plan to the user. This plan display is performed via a web dashboard or a smartphone application, and the user can check the detailed plan contents.

[0898] 4. Verification and correction methods

[0899] The server provides an interface through which the user can review the displayed schedule and plan, modify it if necessary, and finally approve the plan.

[0900] 5. Collaboration Methods

[0901] The server provides a means to link the final approved maintenance schedule and energy supply plan to the automated equipment, and the linked plan is then provided to the robots.

[0902] 6. Means of supply

[0903] Automated equipment performs energy supply and maintenance based on coordinated schedules and plans.

[0904] Specific examples

[0905] For example, if factory robot A operates for eight hours every day and shows signs of a decline in performance, the user can input the data into the system. The AI ​​engine uses that data to generate an optimal maintenance schedule and energy supply plan. For example, it may suggest performing maintenance twice a week and increasing energy supply by 20%. If the user is satisfied with the suggestion, the emotion recognition engine will apply the plan as is and link it to factory robot A.

[0906] Prompt Sentence Examples

[0907] Factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Please propose an optimal maintenance schedule and energy supply plan based on the most recent performance report.

[0908] The above is a description of specific embodiments of the present invention.

[0909] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0910] Step 1:

[0911] Implementing login methods

[0912] (Subject: Terminal) The user accesses the system login screen on the terminal.

[0913] (Input) Username and password.

[0914] (Process) Enter your username and password and click the "Send" button. The device will send the authentication information to the server.

[0915] (Output) The authentication information is sent to the server and once the user is authenticated the dashboard is displayed.

[0916] Step 2:

[0917] Implementing data entry methods

[0918] (Subject: User) The user enters the necessary data for the player or factory robot into the system.

[0919] (Input) Athlete's weight, body fat, muscle mass, body part to be trained, target weight, or factory robot operating hours, energy consumption, and performance goals.

[0920] (Processing) Enter these values ​​through the data input interface and click the send button. The terminal will send these data to the server.

[0921] (Output) The input data is saved on the server.

[0922] Step 3:

[0923] Implementing AI analysis methods

[0924] (Subject: Server) The server analyzes the received data and generates an optimal menu or maintenance plan.

[0925] (Input) Data sent from the data input means.

[0926] (Processing) AI models (e.g., TensorFlow) are used to generate optimal menus for athletes, and optimal maintenance schedules and energy supply plans for factory robots.

[0927] (Output) The generated menu or maintenance plan.

[0928] Step 4:

[0929] Implementing menu display means

[0930] (Subject: Server) The server displays the generated menu or maintenance plan to the user.

[0931] (Input) The generated menu or maintenance plan.

[0932] The (processed) data is displayed to the user via a web dashboard or smartphone application.

[0933] (Output) The menu or maintenance plan is displayed on the user's screen.

[0934] Step 5:

[0935] Implementing verification and correction measures

[0936] (Subject: User) The user checks the displayed menu or maintenance plan and modifies it if necessary.

[0937] (Input) The displayed menu or maintenance plan.

[0938] (Processing) The user checks the points that need to be corrected, makes the corrections, and clicks the Confirm button when the corrections are complete.

[0939] (Output) The modified data is sent to the server.

[0940] Step 6:

[0941] Implementation of collaboration measures

[0942] (Subject: Server) The server communicates the final approved menu or maintenance plan to the cooking equipment or automation equipment.

[0943] (Input) Final approved menu or maintenance plan.

[0944] (Processing) Sending the final approved data to cooking equipment and automation equipment.

[0945] (Output) Data is sent and reflected in cooking equipment and automated equipment.

[0946] Step 7:

[0947] Implementation of supply measures

[0948] (Subject: cooking equipment or automated equipment) Carry out actual actions based on a coordinated plan.

[0949] (Input) The linked menu or maintenance plan.

[0950] (Processing) The cooking equipment prepares and serves nutritious meals based on the menu. The automated equipment supplies energy and performs maintenance based on the maintenance plan.

[0951] (Output) Optimized nutritional meals are provided to athletes, or optimal energy supply and maintenance is provided to factory robots.

[0952] This enables the system to efficiently manage the nutritional intake of athletes and the maintenance of factory robots.

[0953] Hardware and software used:

[0954] Devices: Smartphones, tablets, PCs.

[0955] Server: Data analysis and AI model execution (e.g. TensorFlow, MySQL, MongoDB).

[0956] Sensors: Sensors that monitor the operating status of factory robots (e.g. temperature sensors, energy consumption sensors).

[0957] Emotion recognition engine: An engine that analyzes user emotions (e.g., Microsoft Azure's Emotion Recognition API).

[0958] Examples of concrete examples and prompts

[0959] As a concrete example, suppose that factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Based on the robot's most recent performance report, the following prompts will be used to propose an optimal maintenance schedule and energy supply plan:

[0960] Factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Please propose an optimal maintenance schedule and energy supply plan based on the most recent performance report.

[0961] The above is the specific processing flow of the system that realizes the application example.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] [Third embodiment]

[0966] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0967] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0968] 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).

[0969] 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.

[0970] 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.

[0971] 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).

[0972] 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.

[0973] 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.

[0974] 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.

[0975] 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.

[0976] 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.

[0977] 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."

[0978] This invention relates to a system for efficiently managing the nutritional intake of athletes. This system automatically generates appropriate menus based on the athletes' current physical data and goals, and then provides individually optimized nutritional meals to the cafeteria.

[0979] Main system components

[0980] Login method

[0981] To access the system, users log in by entering their username and password, which is then authenticated by the server. If authentication is successful, the user is given access to their personal dashboard.

[0982] Data Entry Method

[0983] An interface is provided for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to the server.

[0984] AI analysis means

[0985] The server analyzes the received data and inputs it into an AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete data and proposes the optimal menu.

[0986] Menu display method

[0987] The device displays the generated menu to the user, and the user's dashboard displays the detailed menu contents, allowing the user to check them.

[0988] Check and corrective measures

[0989] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu.

[0990] Collaboration methods

[0991] The terminal sends the final approved menu to the server, which then links it to the cafeteria system. The menu is provided to the cafeteria as an individual nutrition plan for each player.

[0992] Providing means

[0993] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[0994] Specific examples

[0995] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[0996] Breakfast: Oatmeal, chicken breast, banana

[0997] Lunch: Brown rice, grilled salmon, salad

[0998] Dinner: Chicken breast steak, vegetable soup, protein shake

[0999] This menu is displayed on the user's dashboard, where the user can review, modify, and finally approve it. The approved menu is linked to the cafeteria system via the server, and the cafeteria prepares and serves the food based on it.

[1000] The system of the present invention can efficiently meet the individual nutritional needs of athletes, contributing to improved performance and health maintenance.

[1001] The processing flow will be explained below.

[1002] Step 1:

[1003] The user logs in to the terminal, enters the username and password, and clicks the "Submit" button. The terminal sends the entered authentication information to the server.

[1004] Step 2:

[1005] The server receives the authentication information and checks it against the user information in its database. If it matches, authentication is successful and a login success message is returned to the user. If authentication fails, an error message is returned.

[1006] Step 3:

[1007] After successful authentication, the user accesses the dashboard and enters physical data such as weight, body fat, muscle mass, areas to train, and target weight. Once the input is complete, the user clicks the "Submit" button.

[1008] Step 4:

[1009] The terminal encodes the input data in JSON format or similar and sends it to the server using an HTTP POST request.

[1010] Step 5:

[1011] The server parses the received data, converts it into the appropriate format, and then feeds the data into the AI ​​model, starting the analysis process.

[1012] Step 6:

[1013] The AI ​​model on the server analyzes the player's data and generates a menu that takes into account the optimal nutritional balance. The generated menu is then converted into JSON format.

[1014] Step 7:

[1015] The server sends the generated menu in JSON format to the terminal, which returns the menu as an HTTP response.

[1016] Step 8:

[1017] The device analyzes the menu received and displays it on the user interface. An individual menu for each player is displayed on the dashboard.

[1018] Step 9:

[1019] The user checks the displayed menu and makes any necessary corrections. Once the corrections are complete, the user clicks the "Approve" button.

[1020] Step 10:

[1021] The terminal encodes the final approved menu, reflecting the user's modifications, in JSON format again and sends it to the server.

[1022] Step 11:

[1023] The server receives the final approved menu and connects it to the cafeteria system. Data is also sent in JSON format when connecting.

[1024] Step 12:

[1025] The cafeteria will prepare individual nutritional meals for each player based on the coordinated menu, and serve the cooked meals to the players.

[1026] Example 1

[1027] 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."

[1028] Nutritional management for athletes is extremely important for improving performance and maintaining health. However, planning daily menus that meet individual nutritional needs and efficiently delivering those meals is difficult. This difficulty is particularly exacerbated in environments without the knowledge of specialized nutritionists. Furthermore, it is necessary to quickly create optimal nutrition plans tailored to each athlete's different goals and physical constitutions. To solve this problem, a system is needed that can automatically generate personalized menus based on athletes' physical data and provide them in conjunction with the cafeteria system.

[1029] 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.

[1030] In this invention, the server includes a login means for users to access and log in to the system, a data input means for inputting the player's physical data, and an analysis means for analyzing the data received from the data input means and generating a menu that takes nutritional balance into consideration using a generative AI model. This allows the server to efficiently generate an optimal menu based on the player's individual nutritional needs, display the menu, and link it to the cafeteria system after modification and approval. This allows players to receive nutritional meals optimized for their goals while receiving professional nutritional management.

[1031] The "login means" is a function that allows a user to access the system, enter a user name and password for authentication, and then access a dedicated dashboard.

[1032] The "data input means" is an interface that allows the user to input physical data such as the player's weight, body fat, muscle mass, target weight, and body parts to be trained, and transmit this data from the terminal to the server.

[1033] The "analysis means" is a function that analyzes the physical data received from the data input means, and in particular includes generating a menu that takes nutritional balance into consideration using a generative AI model.

[1034] The "display means" is a function that displays the generated menu on the user's dashboard, allowing the user to check its contents.

[1035] The "confirmation and correction means" is a function that allows the user to confirm the contents of the displayed menu, correct them if necessary, and finally approve them.

[1036] The "linking means" is a function for sending the final approved menu to the cafeteria system and linking it.

[1037] The "means of provision" is a function for preparing nutritious food based on a menu linked to the cafeteria system and providing it to athletes.

[1038] The "generative AI model" is an artificial intelligence model that generates optimal menus based on athletes' physical data, and mimics the knowledge of professional nutritionists.

[1039] A "prompt" is a sentence containing specific data or a question that is input into a generative AI model, providing instructions for the model to generate appropriate output.

[1040] The present invention relates to a system for efficiently managing the individual nutritional needs of athletes. This system automatically generates appropriate menus based on the athlete's current physical data and goals, and provides individually optimized nutritional meals in cooperation with a cafeteria system. Specific embodiments for implementing the present invention are described below.

[1041] Main configuration

[1042] 1. Login method

[1043] A user logs in to access the system. They enter their username and password, and the server authenticates them. If authentication is successful, a dashboard dedicated to the user is displayed on the terminal. This login function allows users to securely access their own data.

[1044] 2. Data entry method

[1045] An interface is provided for users to input the player's physical data. Specifically, information such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight is input. This information is sent to the server via the terminal. For example, if a player weighs 75 kg, has 15% body fat, and is aiming to increase muscle mass and strengthen his legs, he would enter that data.

[1046] 3. Analysis method

[1047] The server analyzes the received data and inputs it into a generative AI model. The AI ​​model then uses the athlete's data to mimic the knowledge of professional nutritionists and proposes optimal menus. This analysis is performed quickly using a cloud service. For example, the following data could be input into the AI ​​model as a prompt: "Weight: 75kg," "Body fat: 15%," "Goal: Increase muscle mass and strengthen legs."

[1048] 4. Menu Creation

[1049] The server uses an AI model to automatically generate a menu that takes into account the optimal nutritional balance for the athlete. The generated menu is encoded in JSON format and sent to the device. For example, the following menu may be generated:

[1050] Breakfast: Oatmeal, chicken breast, banana

[1051] Lunch: Brown rice, grilled salmon, salad

[1052] Dinner: Chicken breast steak, vegetable soup, protein shake

[1053] 5. Display of menu contents

[1054] The device displays the generated menu to the user. The user's dashboard displays the detailed menu, allowing the user to review it. This feature allows the user to visualize their daily meal plan.

[1055] 6. Verification and correction methods

[1056] The user can check the displayed menu and make any necessary changes, for example, if they do not want to use a particular ingredient or want to adjust the calorie content. These changes are made via the device and are reflected in the dashboard in real time.

[1057] 7. Final approval of the menu

[1058] The user finally approves the revised menu. The approved menu is sent from the device to the server. This function allows the user to determine the optimal meal plan.

[1059] 8. Collaboration with the cafeteria system

[1060] The server then connects the final approved menu to the cafeteria system. Specifically, the menu is sent to the cafeteria system as an individual nutrition plan for each player. This connection enables the cafeteria to prepare the optimal meal for each player.

[1061] 9. Providing nutritious meals

[1062] The cafeteria prepares and serves nutritious meals based on the coordinated menu to the athletes. The meals are optimized for each athlete's individual goals, allowing them to efficiently improve their performance and maintain their health.

[1063] Prompt Sentence Examples

[1064] Based on the athlete's physical data (weight: 75kg, body fat: 15%, goal: increase muscle mass and strengthen legs), please propose a menu for each meal. Please include specific menus for breakfast, lunch, and dinner.

[1065] This system allows athletes to efficiently manage their individual nutritional needs, helping them improve their performance and maintain their health.

[1066] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1067] Step 1:

[1068] A user accesses the system and logs in. Specifically, they enter their username and password and send them to the server. The server receives them and authenticates them by checking the database. The input data is the username and password, and the output is the authentication result. If authentication is successful, a dashboard dedicated to the user is displayed on the terminal.

[1069] Step 2:

[1070] The user inputs the athlete's physical data. Input items include weight, body fat, muscle mass, body parts to train, and target weight. The device sends this data to the server. The input data is physical data, and the output is data sent to the server for analysis. For example, "Weight: 75 kg," "Body fat: 15%," and "Goal: Increase muscle mass and strengthen legs" are input.

[1071] Step 3:

[1072] The server analyzes the received physical data. Specifically, the data is input into a generative AI model to generate a menu that takes into account optimal nutritional balance. The input data is the user's physical data, and the output is the menu generated by the AI ​​model. For example, the prompt sentences "Weight: 75 kg," "Body fat: 15%," and "Goal: Increase muscle mass and strengthen legs" are input.

[1073] Step 4:

[1074] The server encodes the generated menu in JSON format and sends it to the terminal. The input data is the generated menu, and the output is the encoded JSON data. For example, JSON data containing specific menus for breakfast, lunch, and dinner is generated.

[1075] Step 5:

[1076] The device displays the generated menu to the user. The displayed data is a detailed menu encoded in JSON format. The user can check the contents. For example, specific menu items such as oatmeal, chicken breast, and bananas are displayed on the dashboard.

[1077] Step 6:

[1078] The user checks the displayed menu and makes any necessary changes. Once the changes are complete, the menu is finally approved through the confirmation and correction process. The input data is the revised menu, and the output is the final approved menu. For example, a change can be made to change the salmon for lunch to pork.

[1079] Step 7:

[1080] The terminal sends the final approved menu to the server. The input data is the final approved menu, and the output is the data sent to the server. This data is then linked to the cafeteria system.

[1081] Step 8:

[1082] The server then sends the final approved menu to the cafeteria system. The input data is the final approved menu content, and the output is the data sent to the cafeteria system. For example, an individual nutrition plan for each player is sent to the cafeteria system.

[1083] Step 9:

[1084] The cafeteria prepares and serves nutritious meals to athletes based on the linked menu. The input data is the nutrition plan sent to the cafeteria system, and the output is the prepared nutritious meals. This allows athletes to receive meals optimized for their goals.

[1085] (Application example 1)

[1086] 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."

[1087] Conventional nutrition management systems for athletes have a complicated process for providing athletes with individually optimized nutritional meals, and are often limited to specific facilities. This makes it difficult for athletes to quickly receive optimal nutritional meals no matter where they are. In addition, the process of linking data and modifying menus is complicated, making it difficult to carry out efficiently.

[1088] 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.

[1089] In this invention, the server includes a login means, a data input means for inputting the athlete's weight, body fat, muscle mass, body parts to be trained, and target weight, an AI analysis means for analyzing the data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration, a menu display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu, a linking means for linking the finally approved menu to the cafeteria and delivery service, and a provision means for cooking and providing nutritious meals based on the linked menu. This enables athletes to quickly receive optimized nutritious meals wherever they are.

[1090] A "login means" is a means for performing the authentication required for a user to access the system.

[1091] The "data input means" is a means for inputting physical data such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight.

[1092] The "AI analysis means" is a means for generating a menu that takes into consideration nutritional balance using an AI model based on the physical data received from the data input means.

[1093] The "menu display means" is a means for visually presenting the generated menu to the user.

[1094] The "Means for Confirmation and Correction" is a means by which the user can confirm the displayed menu and make corrections and final approval as necessary.

[1095] The "linking means" is a means for transmitting the final approved menu to the cafeteria and delivery service system and linking them together.

[1096] The "providing means" is a means for preparing nutritious dishes based on the menu provided by the linking means and providing them to the players.

[1097] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for structuring data and representing it in a text-based format.

[1098] A "terminal" is a computer or mobile device that a user uses to operate the system.

[1099] A "server" is a computer system for storing data, processing data, and providing various system functions.

[1100] "Delivery Service" means a service for delivering nutritious meals prepared in a cafeteria or cooking facility to a location designated by the player.

[1101] This invention is a system for efficiently managing the nutritional needs of athletes. This system automatically generates optimal menus based on the athletes' physical data and goals, and then links these to cafeterias and delivery services to provide individually optimized nutritional meals. The main components of the system and their specific implementation methods are described below.

[1102] Login method

[1103] This is an authentication method for users to access the system. Users enter their username and password, and the server performs authentication. If authentication is successful, users can access their own dashboard.

[1104] Data Entry Method

[1105] It provides an interface for users to input physical data such as the player's weight, body fat, muscle mass, areas to train, target weight, etc. The input information is sent from the user's terminal to the server.

[1106] AI analysis means

[1107] The server analyzes the physical data received from the data input means and inputs it into an AI model. The AI ​​model then generates optimal menus based on the athlete's data, mimicking the knowledge of professional nutritionists. This AI analysis is implemented using the programming language Python and the machine learning libraries TensorFlow and PyTorch.

[1108] Menu display method

[1109] The generated menu is visually displayed on the user's device, allowing the user to check the menu details.

[1110] Check and corrective measures

[1111] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu. This procedure is designed to be intuitively operated on the dashboard.

[1112] Collaboration methods

[1113] The server then sends the final approved menu to the restaurant and delivery service system. The menu is encoded in JSON format and sent to the restaurant and delivery service. This communication is performed via the HTTP protocol.

[1114] Providing means

[1115] The cafeteria and delivery service will prepare and serve nutritious meals based on the coordinated menu, ensuring that players receive individually optimized meals.

[1116] Specific examples

[1117] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[1118] Breakfast: Oatmeal, chicken breast, banana

[1119] Lunch: Brown rice, grilled salmon, salad

[1120] Dinner: Chicken breast steak, vegetable soup, protein shake

[1121] This menu is displayed on the user's dashboard, where the user can review, modify, and approve it, and the menu data is then sent to the delivery service, which then delivers meals based on this menu to the athletes.

[1122] Prompt Sentence Examples

[1123] The prompt statement is entered as follows:

[1124] The athlete weighs 75kg, has 15% body fat, and is looking to increase muscle mass and strengthen his legs. Can you suggest optimal meals for breakfast, lunch, and dinner?

[1125] In this way, the system can efficiently meet the individual nutritional needs of athletes, contributing to improved performance and health maintenance.

[1126] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1127] Step 1:

[1128] The terminal obtains the user's login information. The user enters the username and password and sends them to the server. The server authenticates the received login information and, if successful, allows access to the user's dedicated dashboard. The input is the username and password, and the output is the authentication result.

[1129] Step 2:

[1130] The terminal obtains the player's physical data from the user. The user inputs the player's weight, body fat, muscle mass, body parts to train, target weight, etc., and sends this information to the server. The server stores the received information in a database. The input is the physical data, and the output is a message indicating that the data has been successfully saved to the database.

[1131] Step 3:

[1132] The server inputs the stored physical data into an AI model for analysis. The AI ​​model generates an optimal menu based on multiple nutritional balance data sets. As a result of the analysis, menus for each meal (breakfast, lunch, and dinner) are suggested. The input is physical data, and the output is a menu.

[1133] Step 4:

[1134] The terminal displays the menu returned from the server on the user dashboard. The user can check the menu in detail. The input is the menu, and the output is the display on the user dashboard.

[1135] Step 5:

[1136] The user checks the displayed menu and makes any necessary corrections. After the corrections are complete, the user finally approves the menu and sends it from the terminal to the server. The input is the revised menu, and the output is the approved menu.

[1137] Step 6:

[1138] The server encodes the approved menu in JSON format and sends it to the cafeteria and delivery service system, which then begins preparing the nutritional meals for the athletes. The input is the approved menu, and the output is the encoded data and a message that transmission was successful.

[1139] Step 7:

[1140] The cafeteria and delivery service prepares and serves nutritious meals to athletes based on the menu sent from the server. Athletes can receive their meals at the cafeteria or receive the nutritious meals at a designated location such as their home via a delivery service. The input is the menu, and the output is the prepared nutritious meals.

[1141] 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.

[1142] This invention relates to a system for efficient nutritional management of athletes. This system uses an AI model and an emotion engine to automatically generate appropriate menus based on the athlete's current physical data and goals, and then connects them to the cafeteria to provide individually optimized nutritional meals.

[1143] Main system components

[1144] Login method

[1145] To access the system, the user logs in by entering their username and password and clicking the "Submit" button. The terminal then sends the entered authentication information to the server.

[1146] Data Entry Method

[1147] An interface is provided for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to the server.

[1148] AI analysis means

[1149] The server analyzes the received data and inputs it into an AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete data and proposes the optimal menu.

[1150] Menu display method

[1151] The device displays the generated menu to the user, and the user's dashboard displays the detailed menu contents, allowing the user to check them.

[1152] Check and corrective measures

[1153] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu.

[1154] Collaboration methods

[1155] The terminal sends the final approved menu to the server, which then links it to the cafeteria system. The menu is provided to the cafeteria as an individual nutrition plan for each player.

[1156] Providing means

[1157] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[1158] Introducing the Emotion Engine

[1159] emotion recognition means

[1160] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice, detecting emotions from the user's facial expressions and voice as they check the menu.

[1161] Emotion analysis means

[1162] The server receives the emotional data sent from the emotion engine and analyzes whether the user is satisfied or dissatisfied. This emotional data is fed back to the AI ​​analysis means.

[1163] Feedback Adjustment Means

[1164] The server uses the emotional data to generate a new menu using the AI ​​model if necessary, allowing it to re-propose a more optimal menu that reflects the user's emotional state.

[1165] Specific examples

[1166] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[1167] Breakfast: Oatmeal, chicken breast, banana

[1168] Lunch: Brown rice, grilled salmon, salad

[1169] Dinner: Chicken breast steak, vegetable soup, protein shake

[1170] This menu is displayed on the user's dashboard, and when the user confirms it, the emotion engine analyzes the user's facial expressions and voice. For example, if the user is dissatisfied, the emotion data is sent to the server, and the AI ​​model regenerates the menu. The regenerated menu takes the user's emotions into account, resulting in a more satisfying menu.

[1171] The system of the present invention realizes efficient nutritional management that takes into account the individual nutritional needs of athletes as well as their emotional state, contributing to improved performance and health maintenance.

[1172] The processing flow will be explained below.

[1173] Step 1:

[1174] The user logs in to the terminal, enters the username and password, and clicks the "Submit" button. The terminal sends the entered authentication information to the server.

[1175] Step 2:

[1176] The server receives the authentication information and checks it against the user information in its database. If it matches, authentication is successful and a login success message is returned to the user. If authentication fails, an error message is returned.

[1177] Step 3:

[1178] After successful authentication, the user accesses the dashboard and enters physical data such as weight, body fat, muscle mass, areas to train, and target weight. Once the input is complete, the user clicks the "Submit" button.

[1179] Step 4:

[1180] The terminal encodes the input data in JSON format or similar and sends it to the server using an HTTP POST request.

[1181] Step 5:

[1182] The server parses the received data, converts it into the appropriate format, and then feeds the data into the AI ​​model, starting the analysis process.

[1183] Step 6:

[1184] The AI ​​model on the server analyzes the player's data and generates a menu that takes into account the optimal nutritional balance. The generated menu is then converted into JSON format.

[1185] Step 7:

[1186] The server sends the generated menu in JSON format to the terminal, which returns the menu as an HTTP response.

[1187] Step 8:

[1188] The device analyzes the menu received and displays it on the user interface. An individual menu for each player is displayed on the dashboard.

[1189] Step 9:

[1190] The user checks the displayed menu. The emotion engine analyzes the user's facial expressions and voice via the device's camera and microphone. The analysis results are sent to the server.

[1191] Step 10:

[1192] The server receives the sentiment analysis results and determines whether the user is satisfied or dissatisfied. If dissatisfaction is detected, it sends feedback to the AI ​​model and instructs it to regenerate the menu. If the user is satisfied, it proceeds to the next step.

[1193] Step 11:

[1194] The server sends the regenerated menu to the terminal, which displays it again to the user, and the process is repeated until the user is satisfied.

[1195] Step 12:

[1196] The user checks the menu for the final time and makes any necessary corrections. After making any corrections, the user clicks the "Approve" button.

[1197] Step 13:

[1198] The terminal encodes the final approved menu again in JSON format and sends it to the server.

[1199] Step 14:

[1200] The server receives the final approved menu and connects it to the cafeteria system. Data is also sent in JSON format when connecting.

[1201] Step 15:

[1202] The cafeteria will prepare individual nutritional meals for each player based on the coordinated menu, and serve the cooked meals to the players.

[1203] Example 2

[1204] 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."

[1205] For modern athletes, efficient individual nutritional management is crucial for improving performance and maintaining health. However, traditional methods require nutritionists to manually create and individually manage menus for each athlete, which requires significant effort and time. It is also difficult for athletes to reflect their satisfaction with the proposed menu or their emotional state in real time, which can result in menus that are not optimal for the athlete. Furthermore, the lack of a system for smoothly changing menus makes it difficult to quickly incorporate athletes' feedback.

[1206] 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 login means, a data input means for inputting the athlete's weight, body fat, muscle mass, body part to be trained, and target weight, an AI analysis means for analyzing the data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration, a menu display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu, a linking means for linking the finally approved menu to the cafeteria system, a serving means for cooking and serving nutritious meals based on the linked menu, an emotion recognition means for analyzing the user's facial expressions and voice to recognize emotions, an emotion analysis means for analyzing the emotion data and evaluating satisfaction, and a feedback adjustment means for reanalyzing the emotion data as feedback and regenerating a menu as necessary. This enables efficient and optimal nutritional management that takes into account not only the individual nutritional needs of athletes but also their emotional state.

[1207] A "login means" is a means by which a user accesses the system and receives authentication by entering authentication information.

[1208] The "data input means" is an interface that allows the user to input physical data such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight.

[1209] The "AI analysis means" is a means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration.

[1210] The "menu display means" is a means for visually displaying the generated menu to the user.

[1211] The "Means for Confirmation and Correction" is a means by which the user can confirm the displayed menu, correct it if necessary, and finally approve it.

[1212] The "linking means" is a means for linking the final approved menu to the cafeteria system.

[1213] The "providing means" is a means for cooking based on the linked menu and providing nutritious food.

[1214] The "emotion recognition means" is a means for analyzing the user's facial expressions and voice and recognizing their emotions.

[1215] The "emotion analysis means" is a means for analyzing the emotion data obtained from the emotion recognition means and evaluating the user's satisfaction level.

[1216] The "feedback adjustment means" is a means for re-analyzing the emotion data and regenerating the menu as necessary.

[1217] This invention relates to a system for efficient nutritional management of athletes. This system uses an AI model and an emotion engine to automatically generate appropriate menus based on the athlete's current physical data and goals, and then connects them to the cafeteria to provide individually optimized nutritional meals.

[1218] The system includes the following main means:

[1219] Login method

[1220] To access the system, the user logs in by entering a username and password and clicking the "Submit" button. The terminal sends the entered authentication information to the server, which then verifies the authentication information.

[1221] Data Entry Method

[1222] It provides an interface for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to a server, which then stores it in a database.

[1223] AI analysis means

[1224] The server analyzes the received data and inputs it into a generative AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete's data and proposes the optimal menu. Specifically, the deep learning model is run on Python.

[1225] Menu display method

[1226] The device displays the generated menu to the user. The user's dashboard displays the detailed menu contents in an HTML table format.

[1227] Check and corrective measures

[1228] The user can check the displayed menu and make any necessary changes. Once the changes are complete, the user finally approves the menu. The device sends the changes to the server, which stores them in the database.

[1229] Collaboration methods

[1230] This is the means by which the final approved menu is linked to the cafeteria system. The terminal sends the final approved menu to the server, and the server links the menu to the cafeteria system. This includes sending data via REST API.

[1231] Providing means

[1232] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[1233] emotion recognition means

[1234] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice. When the user is reviewing a menu, emotions are detected from their facial expressions and voice. The system is implemented using the OpenVINO toolkit.

[1235] Emotion analysis means

[1236] The server receives the emotional data sent from the emotion engine and analyzes whether the user is satisfied or dissatisfied. This emotional data is fed back to the AI ​​analysis means.

[1237] Feedback Adjustment Means

[1238] The server generates a menu using the generative AI model again if necessary based on the emotional data, allowing it to re-propose a more optimal menu that reflects the user's emotional state.

[1239] Specific examples

[1240] For example, consider an athlete who weighs 75kg, has 15% body fat, and wants to increase muscle mass and strengthen their legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts they want to train. The server receives the data, and the generative AI model proposes the following menu:

[1241] Breakfast: Oatmeal, chicken breast, banana

[1242] Lunch: Brown rice, grilled salmon, salad

[1243] Dinner: Chicken breast steak, vegetable soup, protein shake

[1244] Prompt Sentence Examples

[1245] "Example: Please suggest three meal plans that are ideal for a 75kg athlete with 15% body fat who wants to gain muscle mass and strengthen his legs."

[1246] When this prompt is input into the generative AI model, an appropriate menu is returned. In this way, the system realizes efficient nutritional management that takes into account the individual nutritional needs of athletes as well as their emotional state.

[1247] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1248] Step 1: Log in

[1249] Specific actions

[1250] 1. The user accesses the login screen and enters their username and password.

[1251] 2. The user clicks the "Submit" button.

[1252] 3. The device sends the entered authentication information (user name and password) to the server.

[1253] 4. The server validates the credentials

[1254] Input: Username, Password

[1255] Data manipulation: Compare with authentication information stored in the database

[1256] Output: Authentication result (success or failure)

[1257] 5. If authentication is successful, the server starts a session and returns a login success message to the terminal, otherwise it returns an error message.

[1258] Step 2: Enter your physical data

[1259] Specific actions

[1260] 1. The user accesses the physical data entry screen.

[1261] 2. The user enters information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight.

[1262] 3. The user clicks the "Submit" button.

[1263] 4. The device sends the entered physical data to the server.

[1264] 5. The server stores the received body data in a database.

[1265] Input: weight, body fat, muscle mass, area to train, target weight

[1266] Data processing: format check, range check

[1267] Output: Message that data was successfully saved to the database

[1268] Step 3: Menu generation by AI

[1269] Specific actions

[1270] 1. The server acquires the player's physical data.

[1271] Input: Player's physical data

[1272] Data processing: Reading player data from the database

[1273] Output: Player data

[1274] 2. The server inputs the body data into the generative AI model.

[1275] Input: Player data

[1276] Data Computation: Analysis with Deep Learning Models

[1277] Output: Optimal menu

[1278] 3. The AI ​​model mimics the knowledge of professional nutritionists and generates optimal meal plans.

[1279] 4. The server saves the generated menu in the database.

[1280] Input: Menu

[1281] Data processing: Save the menu in the database

[1282] Output: Data saved successfully message

[1283] Step 4: Display the menu

[1284] Specific actions

[1285] 1. The user accesses the dashboard screen.

[1286] 2. The device retrieves the latest menu from the server.

[1287] Input: User ID

[1288] Data calculation: Get the latest menu from the database

[1289] Output: Menu

[1290] 3. The device displays the menu to the user.

[1291] Data processing: Convert to HTML format

[1292] Output: Display the menu on the dashboard screen

[1293] Step 5: Check and edit the menu

[1294] Specific actions

[1295] 1. The user checks the displayed menu.

[1296] 2. If corrections are required, the user enters the corrections.

[1297] 3. The user clicks the "Done" button.

[1298] 4. The device sends the modifications to the server.

[1299] 5. The server updates the changes and saves them in the database

[1300] Input: Correction details

[1301] Data processing: Update the menu in the database

[1302] Output: Data update successful message

[1303] Step 6: Menu Integration

[1304] Specific actions

[1305] 1. The user gives final approval to the menu that has been confirmed and modified.

[1306] 2. The terminal sends the final approved menu to the server.

[1307] 3. The server connects the menu to the cafeteria system

[1308] Input: Final approved menu

[1309] Data Calculation: Send data via REST API

[1310] Output: Integration success message

[1311] Step 7: Serve the food

[1312] Specific actions

[1313] 1. The restaurant receives the linked menu from the server

[1314] Input: Menu

[1315] Data processing: Converting menus into a cooking format

[1316] Output: Cooking instructions

[1317] 2. The cafeteria will prepare nutritious meals based on the menu.

[1318] 3. The cafeteria will provide prepared meals to the athletes.

[1319] Step 8: Implementing the Emotion Engine

[1320] Specific actions

[1321] 1. When a user checks the menu, the device analyzes the user's facial expressions and voice.

[1322] Input: facial expression data, voice data

[1323] Data Computing: Applying Emotion Recognition Algorithms (Using the OpenVINO Toolkit)

[1324] Output: Emotion data

[1325] 2. The device sends the emotion data to the server.

[1326] 3. The server analyzes the emotional data and evaluates the user's satisfaction.

[1327] Input: Emotion data

[1328] Data Computing: Applying Sentiment Analysis Algorithms

[1329] Output: Satisfaction rating data

[1330] 4. If necessary, the server will feed back the emotion data and generate a new menu using the AI ​​model.

[1331] Input: Satisfaction rating data

[1332] Data calculation: Executing regenerative AI models

[1333] Output: Regenerated menu

[1334] Thus, by carrying out these processing steps, optimal nutritional management can be achieved, taking into account the individual nutritional needs and emotional state of the athlete.

[1335] (Application example 2)

[1336] 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."

[1337] For athletes, proper nutritional management is important for improving performance and maintaining health. However, it is difficult to create effective menus based on each athlete's individual nutritional needs and goals, while also incorporating emotional feedback. Similarly, factory robots require the automatic generation and management of optimal maintenance schedules and energy supply plans based on their operating status and performance goals. Using conventional methods, it is difficult to efficiently manage such complex tasks, requiring a great deal of time and effort.

[1338] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1339] In this invention, the server includes a login means, a data input means, an AI analysis means, a display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu and schedule, a linking means for linking the final approved menu and plan, and a supply means. This enables the automation of nutritional management, energy supply, and maintenance optimized to the individual needs of athletes and factory robots.

[1340] A "login means" is an authentication means for a user to access the system.

[1341] The "data input means" is an interface that allows system users to input data such as the athlete's weight, body fat, muscle mass, areas to be trained, and target weight.

[1342] The "AI analysis means" is an artificial intelligence engine that analyzes data received from the data input means and generates optimal menus and maintenance plans.

[1343] The "menu display means" is a means for displaying the generated menu to the user.

[1344] "Means for confirmation and correction" refers to the means by which users can check the menu and schedule, and correct or approve them as necessary.

[1345] The "linking means" is a means for linking the final approved menu and maintenance plan to the related cooking equipment and automated equipment.

[1346] "Supply means" refers to a means for providing nutritious meals based on a linked menu, and for carrying out energy supply and maintenance based on a linked schedule and plan.

[1347] The embodiments of the present invention will be described in detail below.

[1348] System Configuration

[1349] 1. Login method

[1350] The server provides authentication functionality for users to access the system. Users enter their username and password and click the "Submit" button to send the authentication information to the server.

[1351] 2. Data entry method

[1352] The server provides an interface for inputting physical data such as the athlete's weight, body fat, muscle mass, areas to be trained, and target weight. This data is sent from the terminal to the server.

[1353] 3. AI analysis means

[1354] The server analyzes the data received from the data input means and generates an optimal menu using an AI model. It is desirable to use a machine learning library such as TensorFlow as the AI ​​model used for analysis. Here, the server proposes an optimal menu based on the player's data, imitating the knowledge of a nutritionist.

[1355] 4. Menu display method

[1356] The server provides a means to display the generated menu to the user. This menu display is performed through a web dashboard or a smartphone application. The user can check the detailed menu contents.

[1357] 5. Verification and correction methods

[1358] The server provides an interface through which the user can review the displayed menu and make any necessary modifications, and through which the user can finally approve the menu.

[1359] 6. Collaboration Methods

[1360] The server provides a means for synchronizing the final approved menu with the food preparation facility, which then provides the nutrition plan individually optimized for each athlete.

[1361] 7. Means of supply

[1362] The kitchen facilities will prepare and serve nutritious meals to athletes based on coordinated menus.

[1363] Factory robot applications

[1364] 1. Data entry method

[1365] The server provides an interface for inputting data such as factory robot operating hours, energy consumption, and performance targets, which are then sent from the terminal to the server.

[1366] 2. AI analysis means

[1367] The server analyzes the input data and generates optimal maintenance schedules and energy supply plans using an AI model. It is desirable to use a machine learning library such as TensorFlow as the AI ​​model used for analysis.

[1368] 3. Plan display method

[1369] The server provides a means to display the generated maintenance schedule and energy supply plan to the user. This plan display is performed via a web dashboard or a smartphone application, and the user can check the detailed plan contents.

[1370] 4. Verification and correction methods

[1371] The server provides an interface through which the user can review the displayed schedule and plan, modify it if necessary, and finally approve the plan.

[1372] 5. Collaboration Methods

[1373] The server provides a means to link the final approved maintenance schedule and energy supply plan to the automated equipment, and the linked plan is then provided to the robots.

[1374] 6. Means of supply

[1375] Automated equipment performs energy supply and maintenance based on coordinated schedules and plans.

[1376] Specific examples

[1377] For example, if factory robot A operates for eight hours every day and shows signs of a decline in performance, the user can input the data into the system. The AI ​​engine uses that data to generate an optimal maintenance schedule and energy supply plan. For example, it may suggest performing maintenance twice a week and increasing energy supply by 20%. If the user is satisfied with the suggestion, the emotion recognition engine will apply the plan as is and link it to factory robot A.

[1378] Prompt Sentence Examples

[1379] Factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Please propose an optimal maintenance schedule and energy supply plan based on the most recent performance report.

[1380] The above is a description of specific embodiments of the present invention.

[1381] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1382] Step 1:

[1383] Implementing login methods

[1384] (Subject: Terminal) The user accesses the system login screen on the terminal.

[1385] (Input) Username and password.

[1386] (Process) Enter your username and password and click the "Send" button. The device will send the authentication information to the server.

[1387] (Output) The authentication information is sent to the server and once the user is authenticated the dashboard is displayed.

[1388] Step 2:

[1389] Implementing data entry methods

[1390] (Subject: User) The user enters the necessary data for the player or factory robot into the system.

[1391] (Input) Athlete's weight, body fat, muscle mass, body part to be trained, target weight, or factory robot operating hours, energy consumption, and performance goals.

[1392] (Processing) Enter these values ​​through the data input interface and click the send button. The terminal will send these data to the server.

[1393] (Output) The input data is saved on the server.

[1394] Step 3:

[1395] Implementing AI analysis methods

[1396] (Subject: Server) The server analyzes the received data and generates an optimal menu or maintenance plan.

[1397] (Input) Data sent from the data input means.

[1398] (Processing) AI models (e.g., TensorFlow) are used to generate optimal menus for athletes, and optimal maintenance schedules and energy supply plans for factory robots.

[1399] (Output) The generated menu or maintenance plan.

[1400] Step 4:

[1401] Implementing menu display means

[1402] (Subject: Server) The server displays the generated menu or maintenance plan to the user.

[1403] (Input) The generated menu or maintenance plan.

[1404] The (processed) data is displayed to the user via a web dashboard or smartphone application.

[1405] (Output) The menu or maintenance plan is displayed on the user's screen.

[1406] Step 5:

[1407] Implementing verification and correction measures

[1408] (Subject: User) The user checks the displayed menu or maintenance plan and modifies it if necessary.

[1409] (Input) The displayed menu or maintenance plan.

[1410] (Processing) The user checks the points that need to be corrected, makes the corrections, and clicks the Confirm button when the corrections are complete.

[1411] (Output) The modified data is sent to the server.

[1412] Step 6:

[1413] Implementation of collaboration measures

[1414] (Subject: Server) The server communicates the final approved menu or maintenance plan to the cooking equipment or automation equipment.

[1415] (Input) Final approved menu or maintenance plan.

[1416] (Processing) Sending the final approved data to cooking equipment and automation equipment.

[1417] (Output) Data is sent and reflected in cooking equipment and automated equipment.

[1418] Step 7:

[1419] Implementation of supply measures

[1420] (Subject: cooking equipment or automated equipment) Carry out actual actions based on a coordinated plan.

[1421] (Input) The linked menu or maintenance plan.

[1422] (Processing) The cooking equipment prepares and serves nutritious meals based on the menu. The automated equipment supplies energy and performs maintenance based on the maintenance plan.

[1423] (Output) Optimized nutritional meals are provided to athletes, or optimal energy supply and maintenance is provided to factory robots.

[1424] This enables the system to efficiently manage the nutritional intake of athletes and the maintenance of factory robots.

[1425] Hardware and software used:

[1426] Devices: Smartphones, tablets, PCs.

[1427] Server: Data analysis and AI model execution (e.g. TensorFlow, MySQL, MongoDB).

[1428] Sensors: Sensors that monitor the operating status of factory robots (e.g. temperature sensors, energy consumption sensors).

[1429] Emotion recognition engine: An engine that analyzes user emotions (e.g., Microsoft Azure's Emotion Recognition API).

[1430] Examples of concrete examples and prompts

[1431] As a concrete example, suppose that factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Based on the robot's most recent performance report, the following prompts will be used to propose an optimal maintenance schedule and energy supply plan:

[1432] Factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Please propose an optimal maintenance schedule and energy supply plan based on the most recent performance report.

[1433] The above is the specific processing flow of the system that realizes the application example.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] [Fourth embodiment]

[1438] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1439] 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.

[1440] 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).

[1441] 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.

[1442] 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.

[1443] 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).

[1444] 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.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] 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.

[1449] 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.

[1450] 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."

[1451] This invention relates to a system for efficiently managing the nutritional intake of athletes. This system automatically generates appropriate menus based on the athletes' current physical data and goals, and then provides individually optimized nutritional meals to the cafeteria.

[1452] Main system components

[1453] Login method

[1454] To access the system, users log in by entering their username and password, which is then authenticated by the server. If authentication is successful, the user is given access to their personal dashboard.

[1455] Data Entry Method

[1456] An interface is provided for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to the server.

[1457] AI analysis means

[1458] The server analyzes the received data and inputs it into an AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete data and proposes the optimal menu.

[1459] Menu display method

[1460] The device displays the generated menu to the user, and the user's dashboard displays the detailed menu contents, allowing the user to check them.

[1461] Check and corrective measures

[1462] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu.

[1463] Collaboration methods

[1464] The terminal sends the final approved menu to the server, which then links it to the cafeteria system. The menu is provided to the cafeteria as an individual nutrition plan for each player.

[1465] Providing means

[1466] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[1467] Specific examples

[1468] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[1469] Breakfast: Oatmeal, chicken breast, banana

[1470] Lunch: Brown rice, grilled salmon, salad

[1471] Dinner: Chicken breast steak, vegetable soup, protein shake

[1472] This menu is displayed on the user's dashboard, where the user can review, modify, and finally approve it. The approved menu is linked to the cafeteria system via the server, and the cafeteria prepares and serves the food based on it.

[1473] The system of the present invention can efficiently meet the individual nutritional needs of athletes, contributing to improved performance and health maintenance.

[1474] The processing flow will be explained below.

[1475] Step 1:

[1476] The user logs in to the terminal, enters the username and password, and clicks the "Submit" button. The terminal sends the entered authentication information to the server.

[1477] Step 2:

[1478] The server receives the authentication information and checks it against the user information in its database. If it matches, authentication is successful and a login success message is returned to the user. If authentication fails, an error message is returned.

[1479] Step 3:

[1480] After successful authentication, the user accesses the dashboard and enters physical data such as weight, body fat, muscle mass, areas to train, and target weight. Once the input is complete, the user clicks the "Submit" button.

[1481] Step 4:

[1482] The terminal encodes the input data in JSON format or similar and sends it to the server using an HTTP POST request.

[1483] Step 5:

[1484] The server parses the received data, converts it into the appropriate format, and then feeds the data into the AI ​​model, starting the analysis process.

[1485] Step 6:

[1486] The AI ​​model on the server analyzes the player's data and generates a menu that takes into account the optimal nutritional balance. The generated menu is then converted into JSON format.

[1487] Step 7:

[1488] The server sends the generated menu in JSON format to the terminal, which returns the menu as an HTTP response.

[1489] Step 8:

[1490] The device analyzes the menu received and displays it on the user interface. An individual menu for each player is displayed on the dashboard.

[1491] Step 9:

[1492] The user checks the displayed menu and makes any necessary corrections. Once the corrections are complete, the user clicks the "Approve" button.

[1493] Step 10:

[1494] The terminal encodes the final approved menu, reflecting the user's modifications, in JSON format again and sends it to the server.

[1495] Step 11:

[1496] The server receives the final approved menu and connects it to the cafeteria system. Data is also sent in JSON format when connecting.

[1497] Step 12:

[1498] The cafeteria will prepare individual nutritional meals for each player based on the coordinated menu, and serve the cooked meals to the players.

[1499] Example 1

[1500] 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."

[1501] Nutritional management for athletes is extremely important for improving performance and maintaining health. However, planning daily menus that meet individual nutritional needs and efficiently delivering those meals is difficult. This difficulty is particularly exacerbated in environments without the knowledge of specialized nutritionists. Furthermore, it is necessary to quickly create optimal nutrition plans tailored to each athlete's different goals and physical constitutions. To solve this problem, a system is needed that can automatically generate personalized menus based on athletes' physical data and provide them in conjunction with the cafeteria system.

[1502] 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.

[1503] In this invention, the server includes a login means for users to access and log in to the system, a data input means for inputting the player's physical data, and an analysis means for analyzing the data received from the data input means and generating a menu that takes nutritional balance into consideration using a generative AI model. This allows the server to efficiently generate an optimal menu based on the player's individual nutritional needs, display the menu, and link it to the cafeteria system after modification and approval. This allows players to receive nutritional meals optimized for their goals while receiving professional nutritional management.

[1504] The "login means" is a function that allows a user to access the system, enter a user name and password for authentication, and then access a dedicated dashboard.

[1505] The "data input means" is an interface that allows the user to input physical data such as the player's weight, body fat, muscle mass, target weight, and body parts to be trained, and transmit this data from the terminal to the server.

[1506] The "analysis means" is a function that analyzes the physical data received from the data input means, and in particular includes generating a menu that takes nutritional balance into consideration using a generative AI model.

[1507] The "display means" is a function that displays the generated menu on the user's dashboard, allowing the user to check its contents.

[1508] The "confirmation and correction means" is a function that allows the user to confirm the contents of the displayed menu, correct them if necessary, and finally approve them.

[1509] The "linking means" is a function for sending the final approved menu to the cafeteria system and linking it.

[1510] The "means of provision" is a function for preparing nutritious food based on a menu linked to the cafeteria system and providing it to athletes.

[1511] The "generative AI model" is an artificial intelligence model that generates optimal menus based on athletes' physical data, and mimics the knowledge of professional nutritionists.

[1512] A "prompt" is a sentence containing specific data or a question that is input into a generative AI model, providing instructions for the model to generate appropriate output.

[1513] The present invention relates to a system for efficiently managing the individual nutritional needs of athletes. This system automatically generates appropriate menus based on the athlete's current physical data and goals, and provides individually optimized nutritional meals in cooperation with a cafeteria system. Specific embodiments for implementing the present invention are described below.

[1514] Main configuration

[1515] 1. Login method

[1516] A user logs in to access the system. They enter their username and password, and the server authenticates them. If authentication is successful, a dashboard dedicated to the user is displayed on the terminal. This login function allows users to securely access their own data.

[1517] 2. Data entry method

[1518] An interface is provided for users to input the player's physical data. Specifically, information such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight is input. This information is sent to the server via the terminal. For example, if a player weighs 75 kg, has 15% body fat, and is aiming to increase muscle mass and strengthen his legs, he would enter that data.

[1519] 3. Analysis method

[1520] The server analyzes the received data and inputs it into a generative AI model. The AI ​​model then uses the athlete's data to mimic the knowledge of professional nutritionists and proposes optimal menus. This analysis is performed quickly using a cloud service. For example, the following data could be input into the AI ​​model as a prompt: "Weight: 75kg," "Body fat: 15%," "Goal: Increase muscle mass and strengthen legs."

[1521] 4. Menu Creation

[1522] The server uses an AI model to automatically generate a menu that takes into account the optimal nutritional balance for the athlete. The generated menu is encoded in JSON format and sent to the device. For example, the following menu may be generated:

[1523] Breakfast: Oatmeal, chicken breast, banana

[1524] Lunch: Brown rice, grilled salmon, salad

[1525] Dinner: Chicken breast steak, vegetable soup, protein shake

[1526] 5. Display of menu contents

[1527] The device displays the generated menu to the user. The user's dashboard displays the detailed menu, allowing the user to review it. This feature allows the user to visualize their daily meal plan.

[1528] 6. Verification and correction methods

[1529] The user can check the displayed menu and make any necessary changes, for example, if they do not want to use a particular ingredient or want to adjust the calorie content. These changes are made via the device and are reflected in the dashboard in real time.

[1530] 7. Final approval of the menu

[1531] The user finally approves the revised menu. The approved menu is sent from the device to the server. This function allows the user to determine the optimal meal plan.

[1532] 8. Collaboration with the cafeteria system

[1533] The server then connects the final approved menu to the cafeteria system. Specifically, the menu is sent to the cafeteria system as an individual nutrition plan for each player. This connection enables the cafeteria to prepare the optimal meal for each player.

[1534] 9. Providing nutritious meals

[1535] The cafeteria prepares and serves nutritious meals based on the coordinated menu to the athletes. The meals are optimized for each athlete's individual goals, allowing them to efficiently improve their performance and maintain their health.

[1536] Prompt Sentence Examples

[1537] Based on the athlete's physical data (weight: 75kg, body fat: 15%, goal: increase muscle mass and strengthen legs), please propose a menu for each meal. Please include specific menus for breakfast, lunch, and dinner.

[1538] This system allows athletes to efficiently manage their individual nutritional needs, helping them improve their performance and maintain their health.

[1539] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1540] Step 1:

[1541] A user accesses the system and logs in. Specifically, they enter their username and password and send them to the server. The server receives them and authenticates them by checking the database. The input data is the username and password, and the output is the authentication result. If authentication is successful, a dashboard dedicated to the user is displayed on the terminal.

[1542] Step 2:

[1543] The user inputs the athlete's physical data. Input items include weight, body fat, muscle mass, body parts to train, and target weight. The device sends this data to the server. The input data is physical data, and the output is data sent to the server for analysis. For example, "Weight: 75 kg," "Body fat: 15%," and "Goal: Increase muscle mass and strengthen legs" are input.

[1544] Step 3:

[1545] The server analyzes the received physical data. Specifically, the data is input into a generative AI model to generate a menu that takes into account optimal nutritional balance. The input data is the user's physical data, and the output is the menu generated by the AI ​​model. For example, the prompt sentences "Weight: 75 kg," "Body fat: 15%," and "Goal: Increase muscle mass and strengthen legs" are input.

[1546] Step 4:

[1547] The server encodes the generated menu in JSON format and sends it to the terminal. The input data is the generated menu, and the output is the encoded JSON data. For example, JSON data containing specific menus for breakfast, lunch, and dinner is generated.

[1548] Step 5:

[1549] The device displays the generated menu to the user. The displayed data is a detailed menu encoded in JSON format. The user can check the contents. For example, specific menu items such as oatmeal, chicken breast, and bananas are displayed on the dashboard.

[1550] Step 6:

[1551] The user checks the displayed menu and makes any necessary changes. Once the changes are complete, the menu is finally approved through the confirmation and correction process. The input data is the revised menu, and the output is the final approved menu. For example, a change can be made to change the salmon for lunch to pork.

[1552] Step 7:

[1553] The terminal sends the final approved menu to the server. The input data is the final approved menu, and the output is the data sent to the server. This data is then linked to the cafeteria system.

[1554] Step 8:

[1555] The server then sends the final approved menu to the cafeteria system. The input data is the final approved menu content, and the output is the data sent to the cafeteria system. For example, an individual nutrition plan for each player is sent to the cafeteria system.

[1556] Step 9:

[1557] The cafeteria prepares and serves nutritious meals to athletes based on the linked menu. The input data is the nutrition plan sent to the cafeteria system, and the output is the prepared nutritious meals. This allows athletes to receive meals optimized for their goals.

[1558] (Application example 1)

[1559] 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."

[1560] Conventional nutrition management systems for athletes have a complicated process for providing athletes with individually optimized nutritional meals, and are often limited to specific facilities. This makes it difficult for athletes to quickly receive optimal nutritional meals no matter where they are. In addition, the process of linking data and modifying menus is complicated, making it difficult to carry out efficiently.

[1561] 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.

[1562] In this invention, the server includes a login means, a data input means for inputting the athlete's weight, body fat, muscle mass, body parts to be trained, and target weight, an AI analysis means for analyzing the data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration, a menu display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu, a linking means for linking the finally approved menu to the cafeteria and delivery service, and a provision means for cooking and providing nutritious meals based on the linked menu. This enables athletes to quickly receive optimized nutritious meals wherever they are.

[1563] A "login means" is a means for performing the authentication required for a user to access the system.

[1564] The "data input means" is a means for inputting physical data such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight.

[1565] The "AI analysis means" is a means for generating a menu that takes into consideration nutritional balance using an AI model based on the physical data received from the data input means.

[1566] The "menu display means" is a means for visually presenting the generated menu to the user.

[1567] The "Means for Confirmation and Correction" is a means by which the user can confirm the displayed menu and make corrections and final approval as necessary.

[1568] The "linking means" is a means for transmitting the final approved menu to the cafeteria and delivery service system and linking them together.

[1569] The "providing means" is a means for preparing nutritious dishes based on the menu provided by the linking means and providing them to the players.

[1570] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for structuring data and representing it in a text-based format.

[1571] A "terminal" is a computer or mobile device that a user uses to operate the system.

[1572] A "server" is a computer system for storing data, processing data, and providing various system functions.

[1573] "Delivery Service" means a service for delivering nutritious meals prepared in a cafeteria or cooking facility to a location designated by the player.

[1574] This invention is a system for efficiently managing the nutritional needs of athletes. This system automatically generates optimal menus based on the athletes' physical data and goals, and then links these to cafeterias and delivery services to provide individually optimized nutritional meals. The main components of the system and their specific implementation methods are described below.

[1575] Login method

[1576] This is an authentication method for users to access the system. Users enter their username and password, and the server performs authentication. If authentication is successful, users can access their own dashboard.

[1577] Data Entry Method

[1578] It provides an interface for users to input physical data such as the player's weight, body fat, muscle mass, areas to train, target weight, etc. The input information is sent from the user's terminal to the server.

[1579] AI analysis means

[1580] The server analyzes the physical data received from the data input means and inputs it into an AI model. The AI ​​model then generates optimal menus based on the athlete's data, mimicking the knowledge of professional nutritionists. This AI analysis is implemented using the programming language Python and the machine learning libraries TensorFlow and PyTorch.

[1581] Menu display method

[1582] The generated menu is visually displayed on the user's device, allowing the user to check the menu details.

[1583] Check and corrective measures

[1584] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu. This procedure is designed to be intuitively operated on the dashboard.

[1585] Collaboration methods

[1586] The server then sends the final approved menu to the restaurant and delivery service system. The menu is encoded in JSON format and sent to the restaurant and delivery service. This communication is performed via the HTTP protocol.

[1587] Providing means

[1588] The cafeteria and delivery service will prepare and serve nutritious meals based on the coordinated menu, ensuring that players receive individually optimized meals.

[1589] Specific examples

[1590] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[1591] Breakfast: Oatmeal, chicken breast, banana

[1592] Lunch: Brown rice, grilled salmon, salad

[1593] Dinner: Chicken breast steak, vegetable soup, protein shake

[1594] This menu is displayed on the user's dashboard, where the user can review, modify, and approve it, and the menu data is then sent to the delivery service, which then delivers meals based on this menu to the athletes.

[1595] Prompt Sentence Examples

[1596] The prompt statement is entered as follows:

[1597] The athlete weighs 75kg, has 15% body fat, and is looking to increase muscle mass and strengthen his legs. Can you suggest optimal meals for breakfast, lunch, and dinner?

[1598] In this way, the system can efficiently meet the individual nutritional needs of athletes, contributing to improved performance and health maintenance.

[1599] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1600] Step 1:

[1601] The terminal obtains the user's login information. The user enters the username and password and sends them to the server. The server authenticates the received login information and, if successful, allows access to the user's dedicated dashboard. The input is the username and password, and the output is the authentication result.

[1602] Step 2:

[1603] The terminal obtains the player's physical data from the user. The user inputs the player's weight, body fat, muscle mass, body parts to train, target weight, etc., and sends this information to the server. The server stores the received information in a database. The input is the physical data, and the output is a message indicating that the data has been successfully saved to the database.

[1604] Step 3:

[1605] The server inputs the stored physical data into an AI model for analysis. The AI ​​model generates an optimal menu based on multiple nutritional balance data sets. As a result of the analysis, menus for each meal (breakfast, lunch, and dinner) are suggested. The input is physical data, and the output is a menu.

[1606] Step 4:

[1607] The terminal displays the menu returned from the server on the user dashboard. The user can check the menu in detail. The input is the menu, and the output is the display on the user dashboard.

[1608] Step 5:

[1609] The user checks the displayed menu and makes any necessary corrections. After the corrections are complete, the user finally approves the menu and sends it from the terminal to the server. The input is the revised menu, and the output is the approved menu.

[1610] Step 6:

[1611] The server encodes the approved menu in JSON format and sends it to the cafeteria and delivery service system, which then begins preparing the nutritional meals for the athletes. The input is the approved menu, and the output is the encoded data and a message that transmission was successful.

[1612] Step 7:

[1613] The cafeteria and delivery service prepares and serves nutritious meals to athletes based on the menu sent from the server. Athletes can receive their meals at the cafeteria or receive the nutritious meals at a designated location such as their home via a delivery service. The input is the menu, and the output is the prepared nutritious meals.

[1614] 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.

[1615] This invention relates to a system for efficient nutritional management of athletes. This system uses an AI model and an emotion engine to automatically generate appropriate menus based on the athlete's current physical data and goals, and then connects them to the cafeteria to provide individually optimized nutritional meals.

[1616] Main system components

[1617] Login method

[1618] To access the system, the user logs in by entering their username and password and clicking the "Submit" button. The terminal then sends the entered authentication information to the server.

[1619] Data Entry Method

[1620] An interface is provided for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to the server.

[1621] AI analysis means

[1622] The server analyzes the received data and inputs it into an AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete data and proposes the optimal menu.

[1623] Menu display method

[1624] The device displays the generated menu to the user, and the user's dashboard displays the detailed menu contents, allowing the user to check them.

[1625] Check and corrective measures

[1626] The user can check the displayed menu and make any necessary corrections. Once the corrections are complete, the user finally approves the menu.

[1627] Collaboration methods

[1628] The terminal sends the final approved menu to the server, which then links it to the cafeteria system. The menu is provided to the cafeteria as an individual nutrition plan for each player.

[1629] Providing means

[1630] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[1631] Introducing the Emotion Engine

[1632] emotion recognition means

[1633] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice, detecting emotions from the user's facial expressions and voice as they check the menu.

[1634] Emotion analysis means

[1635] The server receives the emotional data sent from the emotion engine and analyzes whether the user is satisfied or dissatisfied. This emotional data is fed back to the AI ​​analysis means.

[1636] Feedback Adjustment Means

[1637] The server uses the emotional data to generate a new menu using the AI ​​model if necessary, allowing it to re-propose a more optimal menu that reflects the user's emotional state.

[1638] Specific examples

[1639] For example, consider an athlete who weighs 75 kg, has 15% body fat, and wants to increase muscle mass and strengthen his legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts he wants to train. The server receives the data, and the AI ​​model suggests the following menu:

[1640] Breakfast: Oatmeal, chicken breast, banana

[1641] Lunch: Brown rice, grilled salmon, salad

[1642] Dinner: Chicken breast steak, vegetable soup, protein shake

[1643] This menu is displayed on the user's dashboard, and when the user confirms it, the emotion engine analyzes the user's facial expressions and voice. For example, if the user is dissatisfied, the emotion data is sent to the server, and the AI ​​model regenerates the menu. The regenerated menu takes the user's emotions into account, resulting in a more satisfying menu.

[1644] The system of the present invention realizes efficient nutritional management that takes into account the individual nutritional needs of athletes as well as their emotional state, contributing to improved performance and health maintenance.

[1645] The processing flow will be explained below.

[1646] Step 1:

[1647] The user logs in to the terminal, enters the username and password, and clicks the "Submit" button. The terminal sends the entered authentication information to the server.

[1648] Step 2:

[1649] The server receives the authentication information and checks it against the user information in its database. If it matches, authentication is successful and a login success message is returned to the user. If authentication fails, an error message is returned.

[1650] Step 3:

[1651] After successful authentication, the user accesses the dashboard and enters physical data such as weight, body fat, muscle mass, areas to train, and target weight. Once the input is complete, the user clicks the "Submit" button.

[1652] Step 4:

[1653] The terminal encodes the input data in JSON format or similar and sends it to the server using an HTTP POST request.

[1654] Step 5:

[1655] The server parses the received data, converts it into the appropriate format, and then feeds the data into the AI ​​model, starting the analysis process.

[1656] Step 6:

[1657] The AI ​​model on the server analyzes the player's data and generates a menu that takes into account the optimal nutritional balance. The generated menu is then converted into JSON format.

[1658] Step 7:

[1659] The server sends the generated menu in JSON format to the terminal, which returns the menu as an HTTP response.

[1660] Step 8:

[1661] The device analyzes the menu received and displays it on the user interface. An individual menu for each player is displayed on the dashboard.

[1662] Step 9:

[1663] The user checks the displayed menu. The emotion engine analyzes the user's facial expressions and voice via the device's camera and microphone. The analysis results are sent to the server.

[1664] Step 10:

[1665] The server receives the sentiment analysis results and determines whether the user is satisfied or dissatisfied. If dissatisfaction is detected, it sends feedback to the AI ​​model and instructs it to regenerate the menu. If the user is satisfied, it proceeds to the next step.

[1666] Step 11:

[1667] The server sends the regenerated menu to the terminal, which displays it again to the user, and the process is repeated until the user is satisfied.

[1668] Step 12:

[1669] The user checks the menu for the final time and makes any necessary corrections. After making any corrections, the user clicks the "Approve" button.

[1670] Step 13:

[1671] The terminal encodes the final approved menu again in JSON format and sends it to the server.

[1672] Step 14:

[1673] The server receives the final approved menu and connects it to the cafeteria system. Data is also sent in JSON format when connecting.

[1674] Step 15:

[1675] The cafeteria will prepare individual nutritional meals for each player based on the coordinated menu, and serve the cooked meals to the players.

[1676] Example 2

[1677] 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."

[1678] For modern athletes, efficient individual nutritional management is crucial for improving performance and maintaining health. However, traditional methods require nutritionists to manually create and individually manage menus for each athlete, which requires significant effort and time. It is also difficult for athletes to reflect their satisfaction with the proposed menu or their emotional state in real time, which can result in menus that are not optimal for the athlete. Furthermore, the lack of a system for smoothly changing menus makes it difficult to quickly incorporate athletes' feedback.

[1679] 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 login means, a data input means for inputting the athlete's weight, body fat, muscle mass, body part to be trained, and target weight, an AI analysis means for analyzing the data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration, a menu display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu, a linking means for linking the finally approved menu to the cafeteria system, a serving means for cooking and serving nutritious meals based on the linked menu, an emotion recognition means for analyzing the user's facial expressions and voice to recognize emotions, an emotion analysis means for analyzing the emotion data and evaluating satisfaction, and a feedback adjustment means for reanalyzing the emotion data as feedback and regenerating a menu as necessary. This enables efficient and optimal nutritional management that takes into account not only the individual nutritional needs of athletes but also their emotional state.

[1680] A "login means" is a means by which a user accesses the system and receives authentication by entering authentication information.

[1681] The "data input means" is an interface that allows the user to input physical data such as the player's weight, body fat, muscle mass, body parts to be trained, and target weight.

[1682] The "AI analysis means" is a means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration.

[1683] The "menu display means" is a means for visually displaying the generated menu to the user.

[1684] The "Means for Confirmation and Correction" is a means by which the user can confirm the displayed menu, correct it if necessary, and finally approve it.

[1685] The "linking means" is a means for linking the final approved menu to the cafeteria system.

[1686] The "providing means" is a means for cooking based on the linked menu and providing nutritious food.

[1687] The "emotion recognition means" is a means for analyzing the user's facial expressions and voice and recognizing their emotions.

[1688] The "emotion analysis means" is a means for analyzing the emotion data obtained from the emotion recognition means and evaluating the user's satisfaction level.

[1689] The "feedback adjustment means" is a means for re-analyzing the emotion data and regenerating the menu as necessary.

[1690] This invention relates to a system for efficient nutritional management of athletes. This system uses an AI model and an emotion engine to automatically generate appropriate menus based on the athlete's current physical data and goals, and then connects them to the cafeteria to provide individually optimized nutritional meals.

[1691] The system includes the following main means:

[1692] Login method

[1693] To access the system, the user logs in by entering a username and password and clicking the "Submit" button. The terminal sends the entered authentication information to the server, which then verifies the authentication information.

[1694] Data Entry Method

[1695] It provides an interface for users to input physical data of athletes. Here, information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight is input. The input information is sent from the terminal to a server, which then stores it in a database.

[1696] AI analysis means

[1697] The server analyzes the received data and inputs it into a generative AI model to generate a menu that takes into account optimal nutritional balance. The AI ​​model mimics the knowledge of professional nutritionists based on the athlete's data and proposes the optimal menu. Specifically, the deep learning model is run on Python.

[1698] Menu display method

[1699] The device displays the generated menu to the user. The user's dashboard displays the detailed menu contents in an HTML table format.

[1700] Check and corrective measures

[1701] The user can check the displayed menu and make any necessary changes. Once the changes are complete, the user finally approves the menu. The device sends the changes to the server, which stores them in the database.

[1702] Collaboration methods

[1703] This is the means by which the final approved menu is linked to the cafeteria system. The terminal sends the final approved menu to the server, and the server links the menu to the cafeteria system. This includes sending data via REST API.

[1704] Providing means

[1705] The cafeteria will prepare and serve nutritious meals based on the coordinated menu, allowing athletes to receive meals optimized to their individual goals.

[1706] emotion recognition means

[1707] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice. When the user is reviewing a menu, emotions are detected from their facial expressions and voice. The system is implemented using the OpenVINO toolkit.

[1708] Emotion analysis means

[1709] The server receives the emotional data sent from the emotion engine and analyzes whether the user is satisfied or dissatisfied. This emotional data is fed back to the AI ​​analysis means.

[1710] Feedback Adjustment Means

[1711] The server generates a menu using the generative AI model again if necessary based on the emotional data, allowing it to re-propose a more optimal menu that reflects the user's emotional state.

[1712] Specific examples

[1713] For example, consider an athlete who weighs 75kg, has 15% body fat, and wants to increase muscle mass and strengthen their legs. The athlete logs into the system and inputs data such as weight, body fat, and the body parts they want to train. The server receives the data, and the generative AI model proposes the following menu:

[1714] Breakfast: Oatmeal, chicken breast, banana

[1715] Lunch: Brown rice, grilled salmon, salad

[1716] Dinner: Chicken breast steak, vegetable soup, protein shake

[1717] Prompt Sentence Examples

[1718] "Example: Please suggest three meal plans that are ideal for a 75kg athlete with 15% body fat who wants to gain muscle mass and strengthen his legs."

[1719] When this prompt is input into the generative AI model, an appropriate menu is returned. In this way, the system realizes efficient nutritional management that takes into account the individual nutritional needs of athletes as well as their emotional state.

[1720] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1721] Step 1: Log in

[1722] Specific actions

[1723] 1. The user accesses the login screen and enters their username and password.

[1724] 2. The user clicks the "Submit" button.

[1725] 3. The device sends the entered authentication information (user name and password) to the server.

[1726] 4. The server validates the credentials

[1727] Input: Username, Password

[1728] Data manipulation: Compare with authentication information stored in the database

[1729] Output: Authentication result (success or failure)

[1730] 5. If authentication is successful, the server starts a session and returns a login success message to the terminal, otherwise it returns an error message.

[1731] Step 2: Enter your physical data

[1732] Specific actions

[1733] 1. The user accesses the physical data entry screen.

[1734] 2. The user enters information such as the athlete's weight, body fat, muscle mass, areas to train, and target weight.

[1735] 3. The user clicks the "Submit" button.

[1736] 4. The device sends the entered physical data to the server.

[1737] 5. The server stores the received body data in a database.

[1738] Input: weight, body fat, muscle mass, area to train, target weight

[1739] Data processing: format check, range check

[1740] Output: Message that data was successfully saved to the database

[1741] Step 3: Menu generation by AI

[1742] Specific actions

[1743] 1. The server acquires the player's physical data.

[1744] Input: Player's physical data

[1745] Data processing: Reading player data from the database

[1746] Output: Player data

[1747] 2. The server inputs the body data into the generative AI model.

[1748] Input: Player data

[1749] Data Computation: Analysis with Deep Learning Models

[1750] Output: Optimal menu

[1751] 3. The AI ​​model mimics the knowledge of professional nutritionists and generates optimal meal plans.

[1752] 4. The server saves the generated menu in the database.

[1753] Input: Menu

[1754] Data processing: Save the menu in the database

[1755] Output: Data saved successfully message

[1756] Step 4: Display the menu

[1757] Specific actions

[1758] 1. The user accesses the dashboard screen.

[1759] 2. The device retrieves the latest menu from the server.

[1760] Input: User ID

[1761] Data calculation: Get the latest menu from the database

[1762] Output: Menu

[1763] 3. The device displays the menu to the user.

[1764] Data processing: Convert to HTML format

[1765] Output: Display the menu on the dashboard screen

[1766] Step 5: Check and edit the menu

[1767] Specific actions

[1768] 1. The user checks the displayed menu.

[1769] 2. If corrections are required, the user enters the corrections.

[1770] 3. The user clicks the "Done" button.

[1771] 4. The device sends the modifications to the server.

[1772] 5. The server updates the changes and saves them in the database

[1773] Input: Correction details

[1774] Data processing: Update the menu in the database

[1775] Output: Data update successful message

[1776] Step 6: Menu Integration

[1777] Specific actions

[1778] 1. The user gives final approval to the menu that has been confirmed and modified.

[1779] 2. The terminal sends the final approved menu to the server.

[1780] 3. The server connects the menu to the cafeteria system

[1781] Input: Final approved menu

[1782] Data Calculation: Send data via REST API

[1783] Output: Integration success message

[1784] Step 7: Serve the food

[1785] Specific actions

[1786] 1. The restaurant receives the linked menu from the server

[1787] Input: Menu

[1788] Data processing: Converting menus into a cooking format

[1789] Output: Cooking instructions

[1790] 2. The cafeteria will prepare nutritious meals based on the menu.

[1791] 3. The cafeteria will provide prepared meals to the athletes.

[1792] Step 8: Implementing the Emotion Engine

[1793] Specific actions

[1794] 1. When a user checks the menu, the device analyzes the user's facial expressions and voice.

[1795] Input: facial expression data, voice data

[1796] Data Computing: Applying Emotion Recognition Algorithms (Using the OpenVINO Toolkit)

[1797] Output: Emotion data

[1798] 2. The device sends the emotion data to the server.

[1799] 3. The server analyzes the emotional data and evaluates the user's satisfaction.

[1800] Input: Emotion data

[1801] Data Computing: Applying Sentiment Analysis Algorithms

[1802] Output: Satisfaction rating data

[1803] 4. If necessary, the server will feed back the emotion data and generate a new menu using the AI ​​model.

[1804] Input: Satisfaction rating data

[1805] Data calculation: Executing regenerative AI models

[1806] Output: Regenerated menu

[1807] Thus, by carrying out these processing steps, optimal nutritional management can be achieved, taking into account the individual nutritional needs and emotional state of the athlete.

[1808] (Application example 2)

[1809] 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."

[1810] For athletes, proper nutritional management is important for improving performance and maintaining health. However, it is difficult to create effective menus based on each athlete's individual nutritional needs and goals, while also incorporating emotional feedback. Similarly, factory robots require the automatic generation and management of optimal maintenance schedules and energy supply plans based on their operating status and performance goals. Using conventional methods, it is difficult to efficiently manage such complex tasks, requiring a great deal of time and effort.

[1811] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1812] In this invention, the server includes a login means, a data input means, an AI analysis means, a display means for displaying the generated menu to the user, a confirmation / modification means for the user to confirm, modify, and approve the menu and schedule, a linking means for linking the final approved menu and plan, and a supply means. This enables the automation of nutritional management, energy supply, and maintenance optimized to the individual needs of athletes and factory robots.

[1813] A "login means" is an authentication means for a user to access the system.

[1814] The "data input means" is an interface that allows system users to input data such as the athlete's weight, body fat, muscle mass, areas to be trained, and target weight.

[1815] The "AI analysis means" is an artificial intelligence engine that analyzes data received from the data input means and generates optimal menus and maintenance plans.

[1816] The "menu display means" is a means for displaying the generated menu to the user.

[1817] "Means for confirmation and correction" refers to the means by which users can check the menu and schedule, and correct or approve them as necessary.

[1818] The "linking means" is a means for linking the final approved menu and maintenance plan to the related cooking equipment and automated equipment.

[1819] "Supply means" refers to a means for providing nutritious meals based on a linked menu, and for carrying out energy supply and maintenance based on a linked schedule and plan.

[1820] The embodiments of the present invention will be described in detail below.

[1821] System Configuration

[1822] 1. Login method

[1823] The server provides authentication functionality for users to access the system. Users enter their username and password and click the "Submit" button to send the authentication information to the server.

[1824] 2. Data entry method

[1825] The server provides an interface for inputting physical data such as the athlete's weight, body fat, muscle mass, areas to be trained, and target weight. This data is sent from the terminal to the server.

[1826] 3. AI analysis means

[1827] The server analyzes the data received from the data input means and generates an optimal menu using an AI model. It is desirable to use a machine learning library such as TensorFlow as the AI ​​model used for analysis. Here, the server proposes an optimal menu based on the player's data, imitating the knowledge of a nutritionist.

[1828] 4. Menu display method

[1829] The server provides a means to display the generated menu to the user. This menu display is performed through a web dashboard or a smartphone application. The user can check the detailed menu contents.

[1830] 5. Verification and correction methods

[1831] The server provides an interface through which the user can review the displayed menu and make any necessary modifications, and through which the user can finally approve the menu.

[1832] 6. Collaboration Methods

[1833] The server provides a means for synchronizing the final approved menu with the food preparation facility, which then provides the nutrition plan individually optimized for each athlete.

[1834] 7. Means of supply

[1835] The kitchen facilities will prepare and serve nutritious meals to athletes based on coordinated menus.

[1836] Factory robot applications

[1837] 1. Data entry method

[1838] The server provides an interface for inputting data such as factory robot operating hours, energy consumption, and performance targets, which are then sent from the terminal to the server.

[1839] 2. AI analysis means

[1840] The server analyzes the input data and generates optimal maintenance schedules and energy supply plans using an AI model. It is desirable to use a machine learning library such as TensorFlow as the AI ​​model used for analysis.

[1841] 3. Plan display method

[1842] The server provides a means to display the generated maintenance schedule and energy supply plan to the user. This plan display is performed via a web dashboard or a smartphone application, and the user can check the detailed plan contents.

[1843] 4. Verification and correction methods

[1844] The server provides an interface through which the user can review the displayed schedule and plan, modify it if necessary, and finally approve the plan.

[1845] 5. Collaboration Methods

[1846] The server provides a means to link the final approved maintenance schedule and energy supply plan to the automated equipment, and the linked plan is then provided to the robots.

[1847] 6. Means of supply

[1848] Automated equipment performs energy supply and maintenance based on coordinated schedules and plans.

[1849] Specific examples

[1850] For example, if factory robot A operates for eight hours every day and shows signs of a decline in performance, the user can input the data into the system. The AI ​​engine uses that data to generate an optimal maintenance schedule and energy supply plan. For example, it may suggest performing maintenance twice a week and increasing energy supply by 20%. If the user is satisfied with the suggestion, the emotion recognition engine will apply the plan as is and link it to factory robot A.

[1851] Prompt Sentence Examples

[1852] Factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Please propose an optimal maintenance schedule and energy supply plan based on the most recent performance report.

[1853] The above is a description of specific embodiments of the present invention.

[1854] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1855] Step 1:

[1856] Implementing login methods

[1857] (Subject: Terminal) The user accesses the system login screen on the terminal.

[1858] (Input) Username and password.

[1859] (Process) Enter your username and password and click the "Send" button. The device will send the authentication information to the server.

[1860] (Output) The authentication information is sent to the server and once the user is authenticated the dashboard is displayed.

[1861] Step 2:

[1862] Implementing data entry methods

[1863] (Subject: User) The user enters the necessary data for the player or factory robot into the system.

[1864] (Input) Athlete's weight, body fat, muscle mass, body part to be trained, target weight, or factory robot operating hours, energy consumption, and performance goals.

[1865] (Processing) Enter these values ​​through the data input interface and click the send button. The terminal will send these data to the server.

[1866] (Output) The input data is saved on the server.

[1867] Step 3:

[1868] Implementing AI analysis methods

[1869] (Subject: Server) The server analyzes the received data and generates an optimal menu or maintenance plan.

[1870] (Input) Data sent from the data input means.

[1871] (Processing) AI models (e.g., TensorFlow) are used to generate optimal menus for athletes, and optimal maintenance schedules and energy supply plans for factory robots.

[1872] (Output) The generated menu or maintenance plan.

[1873] Step 4:

[1874] Implementing menu display means

[1875] (Subject: Server) The server displays the generated menu or maintenance plan to the user.

[1876] (Input) The generated menu or maintenance plan.

[1877] The (processed) data is displayed to the user via a web dashboard or smartphone application.

[1878] (Output) The menu or maintenance plan is displayed on the user's screen.

[1879] Step 5:

[1880] Implementing verification and correction measures

[1881] (Subject: User) The user checks the displayed menu or maintenance plan and modifies it if necessary.

[1882] (Input) The displayed menu or maintenance plan.

[1883] (Processing) The user checks the points that need to be corrected, makes the corrections, and clicks the Confirm button when the corrections are complete.

[1884] (Output) The modified data is sent to the server.

[1885] Step 6:

[1886] Implementation of collaboration measures

[1887] (Subject: Server) The server communicates the final approved menu or maintenance plan to the cooking equipment or automation equipment.

[1888] (Input) Final approved menu or maintenance plan.

[1889] (Processing) Sending the final approved data to cooking equipment and automation equipment.

[1890] (Output) Data is sent and reflected in cooking equipment and automated equipment.

[1891] Step 7:

[1892] Implementation of supply measures

[1893] (Subject: cooking equipment or automated equipment) Carry out actual actions based on a coordinated plan.

[1894] (Input) The linked menu or maintenance plan.

[1895] (Processing) The cooking equipment prepares and serves nutritious meals based on the menu. The automated equipment supplies energy and performs maintenance based on the maintenance plan.

[1896] (Output) Optimized nutritional meals are provided to athletes, or optimal energy supply and maintenance is provided to factory robots.

[1897] This enables the system to efficiently manage the nutritional intake of athletes and the maintenance of factory robots.

[1898] Hardware and software used:

[1899] Devices: Smartphones, tablets, PCs.

[1900] Server: Data analysis and AI model execution (e.g. TensorFlow, MySQL, MongoDB).

[1901] Sensors: Sensors that monitor the operating status of factory robots (e.g. temperature sensors, energy consumption sensors).

[1902] Emotion recognition engine: An engine that analyzes user emotions (e.g., Microsoft Azure's Emotion Recognition API).

[1903] Examples of concrete examples and prompts

[1904] As a concrete example, suppose that factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Based on the robot's most recent performance report, the following prompts will be used to propose an optimal maintenance schedule and energy supply plan:

[1905] Factory robot A operates 8 hours a day and consumes 50% of its energy per operating hour. Please propose an optimal maintenance schedule and energy supply plan based on the most recent performance report.

[1906] The above is the specific processing flow of the system that realizes the application example.

[1907] 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.

[1908] 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.

[1909] 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.

[1910] 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.

[1911] 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.

[1912] 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.

[1913] 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).

[1914] 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.

[1915] 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."

[1916] 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.

[1917] 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).

[1918] 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.

[1919] 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.

[1920] 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.

[1921] 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.

[1922] 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.

[1923] 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.

[1924] 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.

[1925] 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.

[1926] 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.

[1927] 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.

[1928] The following is further disclosed regarding the above embodiment.

[1929] (Claim 1)

[1930] A login method,

[1931] A data input means for inputting the player's weight, body fat, muscle mass, body parts to be trained, and target weight;

[1932] an AI analysis means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration;

[1933] a menu display means for displaying the generated menu to the user;

[1934] A means for users to confirm, modify, and approve the menu;

[1935] A means of communicating the final approved menu to the cafeteria;

[1936] A means for cooking and providing nutritious meals based on the linked menu;

[1937] A system including:

[1938] (Claim 2)

[1939] The system of claim 1, wherein the menu is encoded in JSON format and transmitted and received between the terminal and the server.

[1940] (Claim 3)

[1941] 10. The system of claim 1, further comprising means for providing individually optimized nutritional meals based on data input by a user.

[1942] "Example 1"

[1943] (Claim 1)

[1944] a login means for a user to access and log in to the system;

[1945] a data input means for inputting physical data of players;

[1946] an analysis means for analyzing data received from the data input means and generating a menu that takes nutritional balance into consideration using a generation AI model;

[1947] a display means for displaying the generated menu to the user;

[1948] A confirmation and correction method for users to check, correct, and finally approve the menu;

[1949] A means for linking the final approved menu to food-related systems;

[1950] A means of preparing meals based on linked menus and providing nutritious meals.

[1951] A system including:

[1952] (Claim 2)

[1953] The system of claim 1, wherein the menu is encoded in JSON format and transmitted and received between the terminal and the server.

[1954] (Claim 3)

[1955] 10. The system of claim 1, further comprising means for providing individually optimized nutritional meals based on data input by a user.

[1956] "Application Example 1"

[1957] (Claim 1)

[1958] A login method,

[1959] A data input means for inputting the player's weight, body fat, muscle mass, body parts to be trained, and target weight;

[1960] an AI analysis means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration;

[1961] a menu display means for displaying the generated menu to the user;

[1962] A means for users to confirm, modify, and approve the menu;

[1963] A means of linking the final approved menu to the restaurant and delivery service;

[1964] A means for cooking and providing nutritious meals based on the linked menu;

[1965] A system including:

[1966] (Claim 2)

[1967] The system of claim 1, wherein the menu is encoded in JSON format and transmitted and received between the terminal and the server, and between the delivery service.

[1968] (Claim 3)

[1969] 10. The system of claim 1, further comprising means for providing individually optimized nutritional meals based on data input by a user.

[1970] "Example 2: Combining Emotion Engines"

[1971] (Claim 1)

[1972] A login method,

[1973] A data input means for inputting the player's weight, body fat, muscle mass, body parts to be trained, and target weight;

[1974] an AI analysis means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration;

[1975] a menu display means for displaying the generated menu to a user;

[1976] A means for users to confirm, modify, and approve the menu;

[1977] A means of linking the final approved menu to the cafeteria system;

[1978] a provision means for cooking and providing nutritious meals based on the linked menu;

[1979] emotion recognition means for analyzing a user's facial expression and voice to recognize emotions;

[1980] an emotion analysis means for analyzing emotion data and evaluating satisfaction;

[1981] A feedback adjustment method that reanalyzes emotion data as feedback and regenerates menus as needed.

[1982] A system including:

[1983] (Claim 2)

[1984] The system of claim 1, wherein the menu is encoded in JSON format and transmitted and received between the terminal and the server.

[1985] (Claim 3)

[1986] 10. The system of claim 1, further comprising means for providing individually optimized nutritional meals based on data input by a user.

[1987] "Application example 2 when combining emotion engines"

[1988] (Claim 1)

[1989] A login method,

[1990] A data input means for inputting the player's weight, body fat, muscle mass, body parts to be trained, and target weight;

[1991] an AI analysis means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration;

[1992] a menu display means for displaying the generated menu to the user;

[1993] A means for users to confirm, modify, and approve the menu;

[1994] A means for linking the final approved menu to the cooking equipment;

[1995] a supply means for supplying nutritious food based on the linked menu;

[1996] AI analysis means that automatically generates optimal maintenance schedules and energy supply plans based on input physical data and target performance, and

[1997] a plan display means for displaying the generated schedule and plan to the user;

[1998] A means for users to review, modify, and approve schedules and plans;

[1999] A means of linking the final approved schedule and plan to the automated equipment;

[2000] A system that includes supply means that perform energy supply and maintenance based on coordinated schedules and plans.

[2001] (Claim 2)

[2002] The system of claim 1, wherein the menu is encoded in JSON format and transmitted and received between the terminal and the server.

[2003] (Claim 3)

[2004] 10. The system of claim 1, further comprising means for providing individually optimized nutritional meals, energy supply, and maintenance based on data input by a user. [Explanation of symbols]

[2005] 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 login method, A data input means for inputting the player's weight, body fat, muscle mass, body parts to be trained, and target weight; an AI analysis means for analyzing data received from the data input means using an AI model and generating a menu that takes nutritional balance into consideration; a menu display means for displaying the generated menu to the user; A confirmation and modification means for the user to confirm, modify, and approve the menu; A means of communicating the final approved menu to the cafeteria; A means for cooking and providing nutritious meals based on the linked menu; A system including:

2. The system according to claim 1, wherein the menu is encoded in JSON format and transmitted and received between the terminal and the server.

3. 10. The system of claim 1, further comprising means for providing individually optimized nutritional meals based on data input by a user.

Images