System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting exercise contents of a user; means for inputting meal contents of the user; means for transmitting the exercise contents and the meal contents to a server; generation and AI means for analyzing the exercise contents and the meal contents; means for generating an optimal training menu and meal advice based on an analysis result; and means for monitoring a progressing state of the user and adjusting a training plan as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional gym training methods and diet support systems face the following challenges. First, hiring a dedicated trainer is expensive, placing a burden on many users. Second, users may not be able to train effectively because they do not know what type of exercise is suitable for their individual goals (weight loss or muscle building). Furthermore, if the results stagnate even after continuing to exercise for a certain period of time, they may not be able to understand the cause and give up. Finally, if a beginner who has never exercised before suddenly starts exercising, they may overtrain and develop physical problems such as runner's knee. To solve these challenges, a system is needed that provides optimal training menus and dietary advice based on each user's exercise and dietary data. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following system. The system includes a means for inputting a user's exercise content, a means for inputting the user's dietary content, and a means for transmitting the exercise content and dietary content to a server. The server further includes a generation AI means for analyzing the exercise content and dietary content, and the generation AI has a means for generating an optimal training menu and dietary advice based on the analysis results. The generated training menu and dietary advice are provided to the user's device. The system also includes a means for monitoring the user's progress and adjusting the training plan as necessary. This allows the user to receive training and dietary management optimized for their own goals, maximizing the effectiveness of their training.

[0006] "User" refers to an individual who uses this system to train or diet.

[0007] "Exercise content" refers to information regarding the type, duration, intensity, etc. of exercise performed by the user.

[0008] "Meal content" refers to information about the menu, portion size, nutrients, etc. of the meal consumed by the user.

[0009] "Server" refers to the central processing unit that receives, stores, and analyzes exercise and dietary information sent by users.

[0010] "Generative AI" refers to artificial intelligence that analyzes exercise and dietary details within the server and generates optimal training menus and dietary advice.

[0011] "Training menu" refers to an exercise plan generated by the generative AI and provided to the user.

[0012] "Dietary Advice" refers to dietary instructions or recommendations generated by Generative AI and provided to a User.

[0013] "Analysis" refers to the process in which the generating AI analyzes the exercise and dietary details and evaluates them based on the user's goals.

[0014] "Progress" refers to the degree to which a user has achieved their goals or results through training and dietary management.

[0015] "Monitoring" refers to the process by which the server periodically checks the user's progress and adjusts the training plan as needed. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention is a system that uses AI to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. The detailed operation of this system is as follows, and will be explained using specific embodiments.

[0038] 1. Data Collection

[0039] Input of user's exercise details

[0040] User: After finishing training, launch the dedicated application and enter the details of the exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[0041] Terminal: The input exercise data is temporarily stored in local storage and sent to a server via the Internet.

[0042] Input of user's meal details

[0043] User: At the end of the day, the user enters the details of their meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application.

[0044] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[0045] 2. Data analysis and training menu generation

[0046] Data collection and organization

[0047] Server: Classifies the received exercise and dietary data for each user and stores them in a database. This also manages the user's past training and dietary history.

[0048] Analysis by generative AI

[0049] Server: Passes the saved data to the generation AI and performs the following processes.

[0050] Calculating energy consumption: Calculate the user's calorie consumption from exercise data. For example, it is estimated that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0051] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[0052] Evaluation based on the user's goals: For example, if the goal is to lose weight, check whether the calorie balance is negative, and if the goal is to build muscle, evaluate whether the protein intake is sufficient.

[0053] Training menu generation

[0054] Server: Based on the analysis results of the generation AI, the following training menu and dietary advice is generated.

[0055] Exercise suggestions: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, the system suggests "20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body)."

[0056] Dietary advice: Advise users to adjust their diet. For example, if a user is lacking in protein, suggest adding a protein shake to their breakfast.

[0057] 3. Providing and monitoring results

[0058] Providing results

[0059] Server: Sends the generated training menu and dietary advice to the user's terminal.

[0060] Device: Displays the received training menu and dietary advice on the application interface.

[0061] Progress monitoring

[0062] Server: Periodically collects new exercise and dietary data and reanalyzes it with Generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[0063] For example, if exercise effectiveness stagnates or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[0064] Specific examples

[0065] User A enters the following information into the app:

[0066] Exercise: 30 minutes of running, 20 minutes of weight training

[0067] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[0068] Data analysis example

[0069] Server: From the exercise data, the energy expenditure is calculated to be 500 kcal, and from the diet data, the calorie intake is calculated to be 1500 kcal. It is also determined that the person is slightly deficient in protein.

[0070] Generative AI: For User A, who wants to increase muscle strength, the AI ​​suggests "20 minutes of running and 30 minutes of weight training (especially strengthening the upper body)" as exercise for the next day, and provides advice on adding a protein shake to meals.

[0071] In this way, the system provides training menus and dietary advice optimized to the user's needs, maximizing the effectiveness of training and supporting a healthy lifestyle.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] User: After training, launch the dedicated app and enter the exercise details (type of exercise, time, intensity).

[0075] Step 2:

[0076] Device: The entered exercise data is temporarily stored in local storage and sent to a server via the Internet.

[0077] Step 3:

[0078] User: At the end of the day, the user uses a dedicated app to enter the details of the day's meals (e.g., details of breakfast, lunch, dinner, and snacks).

[0079] Step 4:

[0080] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[0081] Step 5:

[0082] Server: Stores the received exercise and dietary data in a database and organizes and classifies it for each user.

[0083] Step 6:

[0084] Server: Passes the user's exercise and dietary data from the database to the generation AI.

[0085] Step 7:

[0086] Generative AI: Analyzes exercise data and calculates energy consumption. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0087] Step 8:

[0088] Generative AI: Analyzes dietary data and evaluates the balance of ingested nutrients (protein, carbohydrates, lipids, vitamins, and minerals).

[0089] Step 9:

[0090] Generative AI: Based on the user's goals (e.g., dieting, muscle building), it compares energy expenditure and nutrient intake assessments.

[0091] Step 10:

[0092] Generative AI: Generates optimal training and dietary advice based on the user's goals. For example, a user aiming to improve muscle strength would be advised to run for 20 minutes, do weight training for 30 minutes (especially for upper body strengthening), and add protein shakes to their meals.

[0093] Step 11:

[0094] Server: Sends the generated training menu and dietary advice to the user's terminal.

[0095] Step 12:

[0096] Device: Displays the received training menu and dietary advice on the user app interface.

[0097] Step 13:

[0098] Server: Periodically collects new exercise and diet data and repeats steps 6 through 11 above to monitor the user's progress and adjust the training plan as needed.

[0099] In this way, users can maximize the effectiveness of their training by always receiving training menus and dietary advice optimized for their goals.

[0100] Example 1

[0101] 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."

[0102] In today's world, individualized exercise and dietary management is important for preventing lifestyle-related diseases and promoting health. However, it is difficult for individual users to create appropriate training menus and meal plans on their own. Furthermore, existing systems require cumbersome data entry and analysis, making it difficult to maintain user motivation. There is a need for an efficient system that can solve these issues and provide users with optimal training menus and dietary advice.

[0103] 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.

[0104] In this invention, the server includes means for inputting the user's exercise details, means for inputting the user's meal details, means for transmitting the exercise details and meal details to an information processing device, generation AI means for analyzing the exercise details and meal details, means for generating an optimal training menu and meal advice based on the analysis results, means for providing the generated training menu and meal advice to the user's display device, and means for monitoring the user's progress and adjusting the training plan as necessary. This frees users from the hassle of complicated data entry and analysis work, and enables them to easily receive training and meal plans optimized for their individual needs.

[0105] "User" refers to an individual who uses the system to input exercise and dietary information and receives training menus and dietary advice.

[0106] "Means for inputting exercise details" refers to an interface for recording the type and time of exercise performed by the user and inputting the data.

[0107] "Means for inputting meal contents" refers to an interface that allows the user to record the contents and amounts of food consumed and input them as data.

[0108] "Information processing device" refers to a computer system for receiving, storing, and analyzing user input data.

[0109] "Generative AI means" refers to artificial intelligence technology that analyzes exercise and dietary content based on collected data and generates optimal training menus and dietary advice.

[0110] A "training menu" refers to a specific exercise plan proposed based on the user's training goals and exercise history.

[0111] "Dietary advice" refers to specific dietary guidance suggested based on a user's health goals and dietary history.

[0112] The term "display device" refers to a display or application interface for displaying the generated training menu and dietary advice to the user.

[0113] "Means for monitoring progress" refers to the ability to track a user's daily exercise and food intake, and adjust training plans and dietary advice as needed based on the results.

[0114] "Energy expenditure" refers to the amount of calories a user expends through exercise.

[0115] "Nutrients" refers to nutritional components such as proteins, carbohydrates, lipids, vitamins, and minerals ingested through food.

[0116] "Storage device" refers to a data storage system that stores a user's exercise history and diet history.

[0117] "Database" refers to a data management system for systematically storing and managing individual data of users.

[0118] This invention is a system that utilizes generative AI to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. This system functions in cooperation with the server, terminal, and user at each processing step. Specifically, it is implemented as follows.

[0119] 1. Data Collection

[0120] (User exercise input)

[0121] After completing their training, the user launches a dedicated application and inputs the details of their exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[0122] The device temporarily stores the input exercise data in local storage and transmits it to a server via the Internet.

[0123] (User's meal information)

[0124] At the end of the day, users enter their meal plans (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application.

[0125] The terminal temporarily stores the input meal data in local storage and transmits it to a server via the Internet.

[0126] 2. Data analysis and training menu generation

[0127] (Data collection and organization)

[0128] The server categorizes the received exercise and dietary data for each user and stores them in a database, which also manages the user's past training history and dietary details.

[0129] (Analysis by generative AI)

[0130] The server passes the saved data to the generation AI, which performs the following analysis:

[0131] Calculating energy consumption: Calculate the user's calorie consumption from exercise data. For example, it is estimated that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0132] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[0133] Evaluation based on the user's goals: For example, if the goal is to lose weight, check whether the calorie balance is negative, and if the goal is to build muscle, evaluate whether the protein intake is sufficient.

[0134] (Generating training menus)

[0135] Based on the analysis results of the generation AI, the server generates the following training menu and dietary advice.

[0136] Exercise suggestions: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, the system suggests "20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body)."

[0137] Dietary advice: Advise users to adjust their diet. For example, if a user is lacking in protein, suggest adding a protein shake to their breakfast.

[0138] 3. Providing and monitoring results

[0139] (Providing results)

[0140] The server transmits the generated training menu and dietary advice to the user terminal.

[0141] The device displays the received training menu and dietary advice on the application interface.

[0142] (Progress monitoring)

[0143] The server periodically collects new exercise and dietary data and reanalyzes it with the Generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[0144] Example: For example, if exercise results stagnate or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[0145] Specific examples

[0146] User A's input

[0147] Exercise: 30 minutes of running, 20 minutes of weight training

[0148] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[0149] (Example of data analysis)

[0150] The server calculates from the exercise data that the energy expenditure is 500 kcal, and from the diet data, it analyzes that the calorie intake is 1500 kcal. It also finds that the person is slightly deficient in protein.

[0151] The AI ​​generator suggests to User A, who wants to increase his muscle strength, that he do 20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body) for the next day's exercise, and advises him to add a protein shake to his breakfast.

[0152] By using this system, users can easily obtain individually optimized training menus and dietary advice, making health management more efficient.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1: Data entry

[0155] The user starts a dedicated application and inputs the exercise details (e.g., 30 minutes of running, 20 minutes of weight training). The input details include the type of exercise, exercise time, exercise intensity, etc.

[0156] The device temporarily stores the input exercise data in local storage. The input data is saved in JSON format or similar.

[0157] Input: User's exercise details

[0158] Output: Exercise data saved in local storage

[0159] Specific behavior:

[0160] Tap the "Enter exercise details" button on the app's home screen.

[0161] Enter information such as "30 minutes of running" and "20 minutes of weight training" into the input form and press the "Save" button.

[0162] Step 2: Send data

[0163] The device transmits the stored exercise data, including the user's identification information and a timestamp, to a server via the Internet.

[0164] The server stores the received data in a database.

[0165] Input: Exercise data stored in local storage

[0166] Output: Exercise data stored in the server database

[0167] Specific behavior:

[0168] Tap the "Send" button to send the saved exercise data.

[0169] The data is encrypted using a security protocol and sent to the server.

[0170] Step 3: Enter your meal data

[0171] At the end of the day, the user enters the details of their meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application, including the type and amount of each meal.

[0172] The device temporarily stores the entered meal data in local storage.

[0173] Input: User's diet

[0174] Output: Meal data saved in local storage

[0175] Specific behavior:

[0176] Tap the "Enter meal details" button in the app.

[0177] Fill in the input form with information such as "Breakfast: Oatmeal and banana," "Lunch: Chicken breast salad," and "Dinner: Grilled fish and vegetable soup," and press the "Save" button.

[0178] Step 4: Send your meal data

[0179] The device transmits the stored meal data to a server via the Internet, including the user's identification information and a timestamp.

[0180] The server stores the received data in a database.

[0181] Input: Meal data stored in local storage

[0182] Output: Meal data stored in the server database

[0183] Specific behavior:

[0184] Tap the "Send" button to send the saved meal data.

[0185] The data is encrypted using a security protocol and sent to the server.

[0186] Step 5: Data analysis

[0187] The server passes the saved exercise and dietary data to the generation AI for analysis.

[0188] Calculates energy expenditure: Calculates calorie expenditure from exercise data. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0189] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[0190] Evaluation based on user goals: Evaluate whether calorie balance and protein intake are in line with the user's goals.

[0191] Input: Exercise and diet data stored in the server database

[0192] Output: Analysis results by generative AI

[0193] Specific behavior:

[0194] The user's exercise and dietary data is input as a prompt into the generative AI model.

[0195] The generating AI outputs the calculation results and returns the analysis result: "Energy consumption: 500 kcal, calorie intake: 1500 kcal, protein deficiency."

[0196] Step 6: Generate training menu and dietary advice

[0197] Based on the analysis results of the generation AI, the server generates optimal training menus and dietary advice for each individual user.

[0198] Suggested exercises: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, it suggests "20 minutes of running, 30 minutes of weight training (especially for strengthening the upper body)."

[0199] Dietary advice: Suggestions to adjust dietary content, e.g., adding a protein shake to breakfast if the user is lacking in protein.

[0200] Input: Analysis results by generative AI

[0201] Output: Generated training menu and dietary advice

[0202] Specific behavior:

[0203] The prompt text is input to the generation AI as follows: "Analysis results for user A: energy consumption 500 kcal, calorie intake 1500 kcal, protein deficiency. Goal is to increase muscle strength."

[0204] The generated AI returns the following suggestion: "Next day: 20 minutes of running, 30 minutes of weight training (upper body). Breakfast: Add a protein shake."

[0205] Step 7: Delivering results

[0206] The server transmits the generated training menu and dietary advice to the user's terminal.

[0207] The device displays the received training menu and dietary advice on the application interface.

[0208] Input: Generated training menu and dietary advice

[0209] Output: Training menu and dietary advice displayed on the user's device

[0210] Specific behavior:

[0211] When users open the app, a pop-up notification appears with new training and dietary advice.

[0212] It will be displayed in list format on the training menu details page.

[0213] Step 8: Monitor progress and reanalyze

[0214] The server periodically collects new exercise and dietary data and reanalyzes it with the generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[0215] For example, if exercise effectiveness stagnates or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[0216] Input: New exercise and diet data collected periodically

[0217] Output: Tailored training plan or dietary advice

[0218] Specific behavior:

[0219] At the end of each week, users tap the "Weekly Review" button in the app to send their latest exercise and diet data to the server.

[0220] The server analyzes the data, generates a new training plan and any necessary dietary adjustments, and notifies the user.

[0221] In this way, the entire system works together to effectively support the user's training and dietary habits and maintain a healthy lifestyle.

[0222] (Application example 1)

[0223] 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."

[0224] Traditionally, there have been significant challenges in managing the health of factory workers. In particular, lack of exercise and inappropriate diets have led to reduced productivity and health problems. In addition, there is the issue that optimizing health management for each worker requires a great deal of effort and time. Therefore, there is a need to effectively manage health and improve productivity based on exercise and diet data.

[0225] 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.

[0226] In this invention, the server includes a means for inputting exercise and dietary data of factory workers and recommending optimal training and rest methods and meals to maintain employee health and improve productivity, a means for calculating energy expenditure from the user's exercise content, and a means for evaluating nutrients ingested from the user's diet content, thereby enabling optimal health management for each worker.

[0227] Key Word Definitions

[0228] "Exercise details" refers to details such as the type, duration, and intensity of the exercise performed by the user.

[0229] "Meal details" refers to details such as the type, amount, and nutritional components of the food consumed by the user.

[0230] "Server" means a central system for collecting, storing, and analyzing data over the Internet.

[0231] "Generative AI means" is an artificial intelligence technology that analyzes data collected from users and generates optimal training menus and dietary advice.

[0232] A "training menu" is a plan that specifically indicates the exercise content that the user will perform.

[0233] "Dietary advice" refers to the content and method of meals recommended based on the user's health condition and goals.

[0234] A "user's terminal" is an electronic device used by a user, such as a smartphone or tablet.

[0235] "Progress" refers to the degree of achievement or progress toward health or exercise goals set by the user.

[0236] "Monitoring" refers to the continuous collection and analysis of the user's exercise and dietary data to monitor the situation.

[0237] "Factory workers" refers to employees who work in factories, and are the subjects for evaluating their health status and productivity.

[0238] "Energy expenditure" refers to the amount of calories burned by the user through exercise.

[0239] "Nutrient assessment" refers to assessing the nutritional value of a meal consumed by a user.

[0240] "History" is a record of exercise and dietary data entered by the user in the past.

[0241] A "database" is a system that stores collected data and manages it so that it can be easily searched and analyzed.

[0242] "Customization" means providing optimal training menus and dietary advice based on the user's individual needs and circumstances.

[0243] MODE FOR CARRYING OUT THE INVENTION

[0244] This invention is a system that utilizes AI to provide optimal training menus and dietary advice for each worker based on the user's exercise and dietary habits. Specific embodiments of this system are described below.

[0245] Data collection

[0246] Input of user's exercise details

[0247] After completing their training, users launch a dedicated application and input their exercise details (e.g., 30 minutes of walking, 20 minutes of weight training). The user's device temporarily stores the input exercise data in local storage and transmits it to a server via the Internet.

[0248] Input of user's meal details

[0249] At the end of the day, the user enters the details of their meals (e.g., salad and chicken for lunch, grilled fish and vegetable soup for dinner) into a dedicated application. The user's device temporarily stores the entered meal data in local storage and transmits it to a server via the Internet.

[0250] Data analysis and training menu generation

[0251] Data collection and organization

[0252] The server categorizes the received exercise and dietary data for each user and stores them in a database, which also manages the user's past training and dietary history.

[0253] Analysis by generative AI

[0254] The server passes the saved data to the generation AI, which performs the following processes: Calculates the user's calorie consumption from the exercise data (for example, it is estimated that walking for 30 minutes burns about 150 kcal, and weight training for 20 minutes burns about 200 kcal), and evaluates the nutrients ingested from the diet data (protein, carbohydrates, lipids, vitamins, minerals, etc.).

[0255] It also evaluates the user's goals. For example, if the goal is to lose weight, it checks whether the calorie balance is negative, and if the goal is to build muscle, it evaluates whether the protein intake is sufficient.

[0256] Training menu generation

[0257] Based on the analysis results of the generative AI, the server will suggest exercise regimens and rest methods for the next day. It will also provide advice on dietary habits, helping users maintain their health and improve their work efficiency.

[0258] Specific examples

[0259] As a specific example of usage, user A enters the following information into the app: exercise details are "30 minutes of walking, 20 minutes of weight training," and meal details are "salad and chicken for lunch, grilled fish and vegetable soup for dinner." The server analyzes this data and generates the following advice: "Today's calorie expenditure is 350 kcal, but calorie intake is 700 kcal. As an immediate goal, we recommend that you either increase your exercise volume tomorrow or reduce the calories in your diet."

[0260] Hardware and software used

[0261] User device: smartphone, tablet, or PC

[0262] Server: Cloud-based database and AI models

[0263] Generation AI: Python, scikit-learn, RandomForestRegressor

[0264] Generative AI model prompt

[0265] The generative AI model is given a prompt like this:

[0266] "User's exercise data: Please suggest optimal training and dietary advice based on User A's exercise and dietary data."

[0267] As described above, the present invention is a system that optimizes health management for each worker and improves factory productivity.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Program processing flow

[0270] Step 1:

[0271] The user launches a dedicated application and inputs their exercise and meal details. Examples of inputs include "30 minutes of walking, 20 minutes of weight training" or "Salad and chicken for lunch, grilled fish and vegetable soup for dinner." This allows the user's exercise and meal data to be collected.

[0272] Step 2:

[0273] The user's device temporarily stores the input exercise and dietary data in local storage, then transmits this data to a server via the Internet. The input data includes the type of exercise, duration, dietary content, and calorie intake.

[0274] Step 3:

[0275] The server classifies the received exercise and dietary data for each user and stores them in a database. This allows past training and dietary history to be managed. Data stored in the database includes date and time, exercise content, and dietary content.

[0276] Step 4:

[0277] The server then passes the saved data to the AI ​​generator. During this process, the user's energy expenditure is calculated from their exercise data, and calorie and nutrient intake is assessed from their dietary data. For example, it is estimated that 30 minutes of walking will burn approximately 150 kcal, and 20 minutes of weight training will burn approximately 200 kcal.

[0278] Step 5:

[0279] Based on the analysis results, the AI ​​generates training menus and dietary advice tailored to the user's goals (such as building muscle or losing weight). Specifically, this includes suggestions for the next day's exercise menu and appropriate meals. Different advice is provided for each user depending on the analysis results.

[0280] Step 6:

[0281] The generated training menu and dietary advice are sent from the server to the user's device. The user's device receives it and displays it on the interface of a dedicated application. For example, an exercise menu might be displayed as "20 minutes of running, 30 minutes of weight training (especially for upper body strengthening)."

[0282] Step 7:

[0283] The user implements the training menu and dietary advice and then enters the results back into the application, which then collects progress data. The user's progress data is sent to a server and periodically reanalyzed by the generating AI.

[0284] Step 8:

[0285] The server reanalyzes the progress data using AI and adjusts the training plan and dietary advice as needed, thereby maximizing the effectiveness of the user's training and optimizing health management.

[0286] The above is the specific processing flow of the system program of the application example. This system aims to maintain the user's health and improve work efficiency.

[0287] 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.

[0288] This invention is a system that uses generative AI and an emotion engine to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. The detailed operation of this system is described below.

[0289] 1. Data Collection

[0290] Input of user's exercise details

[0291] User: After training, launch the dedicated application and enter the details of the exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[0292] Terminal: The input exercise data is temporarily stored in local storage and sent to a server via the Internet.

[0293] Input of user's meal details

[0294] User: At the end of the day, the user uses a dedicated application to input the details of the day's meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner).

[0295] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[0296] 2. Emotional Recognition

[0297] Input of user emotion data

[0298] User: Uses the application to add emotional data to the format when entering training or dietary information (e.g., taking a photo of an expression, inputting voice data, or tagging text with emotions).

[0299] Terminal: The input emotion data is temporarily stored in local storage and sent to a server via the Internet.

[0300] Emotional Data Analysis

[0301] Server: Passes the received emotion data to the emotion engine and analyzes the user's emotional state. The emotion engine uses facial expression recognition, voice analysis, and character analysis to classify and evaluate the user's emotions (e.g., joy, sadness, anger, stress).

[0302] 3. Data analysis and training menu generation

[0303] Data collection and organization

[0304] Server: In addition to exercise and dietary data, emotional data is classified and organized for each user and stored in a database.

[0305] Analysis by generative AI

[0306] Server: Passes the user's exercise data, dietary data, and emotional data from the database to the generation AI, and performs the following processes.

[0307] Calculating energy expenditure: Calculating the user's calorie expenditure from exercise data.

[0308] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[0309] Emotional data evaluation: Evaluate the user's emotional state from emotional data (e.g., recommend light exercise if stress levels are high, or hard training if positive emotions predominate).

[0310] Training menu generation

[0311] Server: Based on the analysis results of the generation AI, the following training menu and dietary advice is generated.

[0312] Exercise suggestions: The system suggests exercise menus based on the user's goals and emotional state. For example, a user aiming to improve muscle strength would be suggested to do 20 minutes of running and 30 minutes of weight training (especially for upper body strengthening), while a user experiencing high stress would be suggested to do 30 minutes of yoga.

[0313] Dietary advice: Advise users to adjust their diet taking into account their emotional state and nutritional balance. For example, recommend chamomile tea to relieve stress.

[0314] 4. Providing and monitoring results

[0315] Providing results

[0316] Server: Sends the generated training menu and dietary advice to the user's terminal along with advice on emotional state.

[0317] Terminal: Displays the received training menu, dietary advice, and emotional advice on the application interface.

[0318] Progress monitoring

[0319] Server: Periodically collects new exercise, diet, and emotional data and reanalyzes it with generative AI, allowing it to monitor the user's progress in real time and adjust the training plan as needed.

[0320] For example, if exercise effectiveness stagnates or emotional data indicates high stress levels, the generative AI will generate new training menus and dietary advice and immediately provide them to the user.

[0321] Specific examples

[0322] User B enters the following information into the app:

[0323] Exercise: 30 minutes of running, 20 minutes of weight training

[0324] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[0325] Emotional data: Report stressful experiences during training via voice input

[0326] Data analysis example

[0327] Server: From the exercise data, the energy expenditure is calculated to be 500 kcal, and from the dietary data, the calorie intake is calculated to be 1500 kcal. Furthermore, from the emotion data, it is determined that User B's stress level is high.

[0328] Generative AI: Because stress relief is necessary, the system suggests "30 minutes of yoga" as exercise for the next day and "chamomile tea, which has a relaxing effect" as food.

[0329] In this way, the system supports both training and mental care by providing training menus and dietary advice optimized for the user's needs and emotional state.

[0330] The processing flow will be explained below.

[0331] Step 1:

[0332] User: After training, launch the dedicated app and enter the exercise details (type of exercise, time, intensity).

[0333] Step 2:

[0334] Device: The entered exercise data is temporarily stored in local storage and sent to a server via the Internet.

[0335] Step 3:

[0336] User: At the end of the day, the user uses a dedicated app to enter the details of the day's meals (e.g., details of breakfast, lunch, dinner, and snacks).

[0337] Step 4:

[0338] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[0339] Step 5:

[0340] Users: Enter emotional data when entering workout or diet details within the same app, for example by taking a photo of their face, reporting their emotions via voice, or tagging text with emotional tags.

[0341] Step 6:

[0342] Terminal: The input emotion data is temporarily stored in local storage and sent to a server via the Internet.

[0343] Step 7:

[0344] Server: Organizes the received exercise data, dietary data, and emotional data for each user and stores them in a database.

[0345] Step 8:

[0346] Server: Passes the stored data to the generative AI and emotion engine, which analyzes the user's emotional state using facial, voice, and text analysis.

[0347] Step 9:

[0348] Generative AI: Analyzes exercise data and calculates energy consumption. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0349] Step 10:

[0350] Generative AI: Analyzes dietary data and evaluates the balance of ingested nutrients (protein, carbohydrates, lipids, vitamins, minerals, etc.).

[0351] Step 11:

[0352] Emotion engine: Analyzes emotional data to assess the user's emotional state. For example, it can determine "stress," "joy," or "sadness" from facial photos and voice analysis.

[0353] Step 12:

[0354] Generative AI: Based on the evaluation results of the emotion engine, the system comprehensively compares exercise and dietary data to generate optimal training menus and dietary advice based on the user's goals. For example, a user experiencing high stress may be recommended to do 30 minutes of yoga and add chamomile tea to their diet.

[0355] Step 13:

[0356] Server: Sends the generated training menu, dietary advice, and emotional state feedback to the user terminal.

[0357] Step 14:

[0358] Device: Displays the received training menu, dietary advice, and emotional feedback on the app interface.

[0359] Step 15:

[0360] Server: Periodically collects new exercise, dietary, and emotional data and reanalyzes it with the generative AI and emotion engine, allowing it to monitor the user's progress in real time and update training plans and dietary advice as needed.

[0361] In this way, users can always receive training menus and dietary advice optimized for their goals and emotional state.

[0362] Example 2

[0363] 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."

[0364] Many people today are being asked to review their exercise and dietary habits to stay healthy, but providing training menus and meal plans that fit each individual's lifestyle and emotional state is extremely difficult. While conventional systems take exercise and dietary data into account, they are unable to provide advice that reflects the user's emotional state, making it difficult to achieve effective health management. Furthermore, they are also inadequate at monitoring users' progress in real time and adjusting plans as needed.

[0365] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0366] In this invention, the server includes means for inputting the user's exercise details, means for inputting the user's diet details, means for inputting the user's emotional data, means for transmitting the exercise details, diet details, and emotional data to the server, generation AI means for analyzing the exercise details, diet details, and emotional data, means for generating an optimal training menu and dietary advice based on the analysis results, means for providing the generated training menu, dietary advice, and emotional state advice to the user's terminal, and means for monitoring the user's progress and adjusting the training plan as necessary. This makes it possible to achieve optimal health management tailored to individual needs by comprehensively considering the user's exercise details, dietary details, and emotional state.

[0367] "Means for inputting user's exercise details" refers to an interface for inputting details such as the type, duration, and intensity of the exercise performed by the user.

[0368] "Means for inputting the user's dietary details" refers to an interface for inputting details such as the type, amount, and nutrients of the food consumed by the user.

[0369] "Means for inputting user emotional data" refers to an interface for inputting the user's emotional state, such as attaching emotion tags to a photograph of the user's facial expression, voice input, or text input.

[0370] The "means for transmitting the exercise details, meal details, and emotion data to the server" refers to a communication means for transmitting the exercise details, meal details, and emotion data from the terminal to the server via the Internet.

[0371] "Generative AI means for analyzing the exercise content, dietary content and emotional data" refers to artificial intelligence technology for analyzing exercise content, dietary content and emotional data and generating optimal training menus and dietary advice for users.

[0372] "Means for generating optimal training menus and dietary advice based on analysis results" refers to means for generating training menus and dietary advice according to the user's goals and condition based on data analyzed by the generation AI.

[0373] "Means for providing the generated training menu, dietary advice, and emotional state advice to the user's terminal" refers to an interface for transmitting the training menu, dietary advice, and emotional advice generated by the server to the user's terminal and displaying them.

[0374] "Means for monitoring the user's progress and adjusting the training plan as needed" refers to means for periodically collecting new exercise, dietary and emotional data, monitoring the user's progress based on that data, and updating training and dietary advice as needed.

[0375] "Means for calculating energy expenditure" refers to algorithms or functions for calculating calories burned based on the activity.

[0376] "Means for assessing nutrient intake" refers to algorithms or databases for assessing the types and amounts of nutrients ingested based on dietary content.

[0377] "Means for assessing emotional state" refers to algorithms and artificial intelligence technologies for analyzing and assessing the user's emotional state based on the acquired emotional data.

[0378] "Means for storing in a database" refers to a relational or non-relational database for structuring and storing the user's exercise, diet, and emotional data for long-term storage.

[0379] "Means for customizing training menus and dietary advice" refers to generative AI technology that uses stored past data to individually create optimal training menus and dietary advice for each user.

[0380] The present invention is a system that provides optimal training menus and dietary advice based on a user's exercise and dietary habits by utilizing a generative AI and an emotion engine. The following describes in detail an embodiment of the present invention.

[0381] Data collection

[0382] Users use a dedicated application to input their exercise and dietary information. This application runs on devices such as smartphones and tablets, and uses keyboard input, voice input, and a camera as interfaces. For example, after training, a user might input their exercise information, such as 30 minutes of running and 20 minutes of weight training, and at the end of the day, they might input their diet information, such as oatmeal and a banana for breakfast, chicken breast salad for lunch, and grilled fish and vegetable soup for dinner. This data is temporarily stored in the device's local storage and then sent to a server via the Internet.

[0383] Emotion recognition

[0384] When users enter their workout or diet details, they also add emotional data. This emotional data is acquired by taking photos of their facial expressions, inputting voice, or tagging text with emotions. The device temporarily stores this data in local storage and then transmits it to a server via the Internet. The server then passes the received emotional data to an emotion engine, which analyzes the user's emotional state. For example, facial recognition is performed using Amazon Rekognition, and voice analysis is performed using Google Cloud Speech-to-Text.

[0385] Data analysis and training menu generation

[0386] The server stores the user's exercise data, diet data, and emotional data in a centralized database. This database can be a relational database (e.g., MySQL) or a non-relational database (e.g., MongoDB). Next, a generative AI (e.g., OpenAI GPT-3) analyzes this data and performs the following processes:

[0387] Energy expenditure calculation: Calculate calorie expenditure based on exercise data.

[0388] Assessment of nutritional balance: Evaluate nutrients ingested based on dietary data.

[0389] Emotional Data Evaluation: Evaluate the user's emotional state based on the emotional data.

[0390] Based on the analysis results, the generative AI generates optimal training menus and dietary advice. For example, for a user with high stress levels, it might recommend 30 minutes of yoga and suggest chamomile tea as a meal.

[0391] Delivering and monitoring results

[0392] The generated training menu and dietary advice are sent from the server to the user's device. The application on the device displays this advice on its interface. The server also periodically collects new data and reanalyzes it using the generating AI to monitor the user's progress and adjust the training plan as needed. For example, if exercise results stagnate or emotional data indicates high stress levels, the server will instantly generate a new training menu and dietary advice and provide it to the user.

[0393] Specific examples

[0394] If User B enters the following information into the app:

[0395] Exercise: 30 minutes of running, 20 minutes of weight training

[0396] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[0397] Emotional data: Report "I felt stressed" during training by voice input

[0398] The server calculates energy expenditure from exercise data to be 500 kcal, and analyzes calorie intake from dietary data to be 1500 kcal. Emotional data reveals that User B has a high stress level, and the AI ​​recommends "30 minutes of yoga" as exercise for the next day and "chamomile tea, which has a relaxing effect" as food.

[0399] Prompt Sentence Examples

[0400] "Analyze the user's exercise data, dietary data, and emotional data to generate optimal training menus and dietary advice."

[0401] Using these prompts, the generative AI can suggest optimal training menus and dietary advice to users based on the input data.

[0402] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0403] Step 1:

[0404] The user starts the dedicated application and inputs the details of the exercise.

[0405] Input: Exercise data provided by the user (e.g., 30 minutes of running, 20 minutes of weight training).

[0406] How it works: The user fills in the application's form with details such as the type of exercise, duration, and intensity. If keyed in or voice-activated, the exercise data is captured accordingly.

[0407] Output: The exercise data is saved in the device's local storage.

[0408] Step 2:

[0409] At the end of the day, the user inputs the details of their meals using a dedicated application.

[0410] Input: Dietary data provided by the user (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner).

[0411] How it works: The user fills out a form in the application to enter details about the food, such as type, amount, and nutrients.

[0412] Output: Meal data is saved to the device's local storage.

[0413] Step 3:

[0414] The user inputs emotion data.

[0415] Input: User emotion data (e.g., facial photos, voice input, emotion tagging of text input).

[0416] How it works: When entering workout or diet information, users select the option to add emotional data, which includes capturing facial expressions with a camera and recording audio.

[0417] Output: Emotion data is saved in the device's local storage.

[0418] Step 4:

[0419] The terminal transmits the collected exercise data, diet data, and emotion data to a server.

[0420] Input: Exercise data, dietary data, and emotional data stored on the device.

[0421] How it works: The device sends data to a server over the internet using a secure communication protocol such as HTTPS.

[0422] Output: Exercise data, diet data and emotion data are sent to the server.

[0423] Step 5:

[0424] The server analyzes the received data.

[0425] Input: Exercise data, diet data and emotion data sent to the server.

[0426] How it works: The server stores this data in a database and passes it to the generation AI, which calculates energy consumption, evaluates nutritional balance, and analyzes emotional data.

[0427] Output: The results of the generative AI analysis, specifically energy expenditure (e.g., 500 kcal), nutritional assessment, and emotional state (e.g., high stress level).

[0428] Step 6:

[0429] Based on the analysis results, the generative AI generates optimal training menus and dietary advice.

[0430] Input: Analysis results by the generative AI (e.g., energy consumption, nutritional balance, emotional state).

[0431] How it works: The generative AI generates optimal training and dietary advice based on the user's goals and condition. For example, for a user with high stress levels, it might suggest 30 minutes of yoga and recommend chamomile tea to reduce stress.

[0432] Output: Generated training menu and dietary advice.

[0433] Step 7:

[0434] The server transmits the generated training menu and dietary advice to the user's terminal.

[0435] Input: Generated training menu and dietary advice.

[0436] How it works: The server sends this data to the device. The communication is secure and fast.

[0437] Output: Training menu and dietary advice are sent to the device.

[0438] Step 8:

[0439] The device displays the received training menu and dietary advice.

[0440] Input: Training menu and dietary advice sent to the device.

[0441] How it works: An application on the device displays this information to the user in an easy-to-understand manner, using text and graphics as the user interface.

[0442] Output: The user will be able to see the training menu and dietary advice.

[0443] Step 9:

[0444] The server periodically collects new data and reanalyzes it using the generating AI.

[0445] Input: New exercise data, diet data, and emotion data.

[0446] How it works: The server periodically collects data and asks the generator AI to reanalyze it, allowing it to monitor the user's progress and adjust the plan as needed.

[0447] Output: Updated training menu and dietary advice.

[0448] (Application example 2)

[0449] 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."

[0450] Conventional training and meal planning systems only provide advice based on the user's exercise and dietary habits, but do not take into account the user's emotional state or psychological factors. This can lead to inappropriate training and dietary advice that ignores the user's stress and emotional fluctuations. Another problem is that the systems do not recommend appropriate products, which does not contribute to improving the user's wellness.

[0451] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting emotion data in addition to the user's exercise details and dietary details, a generation AI means for analyzing the input exercise details, dietary details, and emotion data, a means for recommending optimal training menus, dietary advice, and products based on the analysis results, and a means for monitoring the user's progress and adjusting the training plan as necessary. This makes it possible to provide a comprehensive training menu and dietary advice that takes into account both the user's physical and mental conditions.

[0452] "User's exercise content" refers to specific information such as the type, duration, and intensity of physical exercise performed by the user.

[0453] "User's dietary details" refers to specific information such as the type, amount, and time of food intake by the user.

[0454] "Emotion data" is data that indicates the psychological state of the user, such as stress, happiness, or anxiety, and is collected through facial expression photographs, voice input, and text input.

[0455] The "generative AI means" is an artificial intelligence technology that analyzes exercise, dietary and emotional data obtained from users and generates training menus, dietary advice and product recommendations based on the data.

[0456] A "training menu" is a specific exercise plan tailored to the user's fitness goals and physical condition.

[0457] "Dietary advice" refers to specific instructions or suggestions for recommending an appropriate diet based on the user's nutritional balance and health condition.

[0458] "Product recommendation" refers to suggesting suitable products such as fitness equipment and supplements based on a user's exercise, diet, and emotional data.

[0459] "Progress monitoring means" refers to technology that tracks a user's exercise data, dietary data, and emotional data over a period of time and evaluates the user's progress toward their goals.

[0460] The "means for adjusting the training plan" refers to technology that changes and optimizes the training menu and dietary advice according to the user's progress and fluctuations in emotional data.

[0461] As an embodiment of the present invention, a specific system configuration and processing method will be described.

[0462] System configuration

[0463] This system collects and analyzes the user's exercise, diet, and emotional data to generate optimal training menus, dietary advice, and product recommendations. Each component is described in detail below.

[0464] 1. User Device

[0465] The user terminal is a smartphone, tablet, or computer, and allows the user to input exercise, diet, and emotional data. This terminal also has the ability to communicate with a server via the internet and transmit the collected data.

[0466] 2. Server

[0467] The server receives the data sent from the user and performs the following processing.

[0468] Data collection: Receives user exercise data, diet data, and emotion data and temporarily stores them in local storage.

[0469] Data analysis: The received data is passed to the generation AI and emotion engine for analysis.

[0470] Training menu generation: Based on the analysis results, an optimal training menu and dietary advice is generated.

[0471] Product Recommendations: Recommend products such as fitness equipment and supplements.

[0472] Progress monitoring: We periodically reanalyze your data and adjust your training plan as needed.

[0473] Technology and software used

[0474] Hardware: smartphones, tablets, computers, servers

[0475] software:

[0476] Python: Programming Language

[0477] Keras: A deep learning library

[0478] Scikit-learn: a data preprocessing library

[0479] Flask / Django: Web Frameworks

[0480] Emotion Model: Emotion Analysis Engine

[0481] Generative AI model: Generative AI for analyzing exercise, diet, and emotion data

[0482] Specific examples

[0483] The specific flow of data collection and analysis is explained below.

[0484] Data collection

[0485] Using a smartphone, the user inputs their morning exercise schedule as "30 minutes of running, 20 minutes of weight training," and their evening meal schedule as "oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner." Furthermore, they report emotional data by voice input, such as "feeling stressed during training."

[0486] Data analysis

[0487] The server passes the received exercise data, dietary data, and emotion data to the generation AI and emotion engine. The generation AI calculates energy expenditure from the exercise details and analyzes calorie intake from the diet details. The emotion engine evaluates the user's stress level from the voice data.

[0488] Generate training menus and dietary advice

[0489] Based on the analysis results, the generating AI will suggest "30 minutes of yoga" as a training menu to relieve the user's stress, and will recommend "chamomile tea, which has a relaxing effect" as dietary advice.

[0490] product recommendation

[0491] In addition, the generative AI will recommend related products such as "yoga mats" and "relaxing herbal tea."

[0492] Prompt Sentence Examples

[0493] Below are some example prompts to give to the generative AI model:

[0494] Please generate a training menu and dietary advice suitable for the user based on the following exercise data, dietary data, and emotional data.

[0495] Exercise data: 30 minutes of running, 20 minutes of weight training

[0496] Dietary information: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[0497] Emotional data: Feeling stressed during training

[0498] Output format:

[0499] 1. Training Menu

[0500] 2. Dietary advice

[0501] 3. Recommended product list

[0502] In this way, the present invention can provide optimal health management based on a comprehensive assessment of a user's exercise, diet, and emotional state.

[0503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0504] Step 1:

[0505] The user inputs the details of their exercise. Specifically, the user launches a dedicated application on a smartphone or tablet and inputs exercise data such as "30 minutes of running, 20 minutes of weight training." This input data is temporarily stored in local storage.

[0506] Step 2:

[0507] The user enters their meal plan. At the end of the day, the user enters what they had for breakfast, lunch, and dinner into the application. For example, they might enter "oatmeal and banana for breakfast, chicken breast salad for lunch, and grilled fish and vegetable soup for dinner." This data is also stored in local storage.

[0508] Step 3:

[0509] The user inputs emotional data. When the user inputs exercise and meal details, they can report emotional data such as "I felt stressed during training" using facial photos, voice input, or text input. This emotional data is stored in local storage.

[0510] Step 4:

[0511] The device sends exercise data, diet data, and emotion data to the server. The device then sends this data to the server via the Internet. At this time, the data sent also includes the user ID.

[0512] Step 5:

[0513] The server passes the received data to the generation AI means and emotion engine. The server compiles the received exercise data, diet data, and emotion data and provides it to the generation AI and emotion engine.

[0514] Step 6:

[0515] The server analyzes the data. The generation AI calculates energy consumption from exercise data and evaluates nutrient intake from dietary data. The emotion engine evaluates the user's emotional state from voice and facial expression data. Specifically, the analysis results include "500 kcal consumed" from exercise data, "1500 kcal intake" from dietary data, and "high stress level" from emotion data.

[0516] Step 7:

[0517] The server generates optimal training menus and dietary advice based on the generated results. For example, if stress relief is needed, the server suggests "30 minutes of yoga" as a training menu and "chamomile tea, which has a relaxing effect" as a meal.

[0518] Step 8:

[0519] The server transmits the generated training menu and dietary advice to the user's terminal. The server transmits the generated training menu and dietary advice together with the analysis results to the user's terminal.

[0520] Step 9:

[0521] The terminal provides the received training menu, dietary advice, and product recommendations to the user, who can then view the information through the terminal's application interface.

[0522] Step 10:

[0523] The server periodically collects new user data and reanalyzes it using the AI ​​generator. The AI ​​monitors the user's progress and updates and optimizes training plans and dietary advice as needed. For example, if exercise results stagnate, a new training menu is generated and immediately provided to the user.

[0524] 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.

[0525] 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.

[0526] 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.

[0527] [Second embodiment]

[0528] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0529] 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.

[0530] 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).

[0531] 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.

[0532] 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.

[0533] 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).

[0534] 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.

[0535] 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.

[0536] 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.

[0537] 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.

[0538] 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.

[0539] 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."

[0540] This invention is a system that uses AI to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. The detailed operation of this system is as follows, and will be explained using specific embodiments.

[0541] 1. Data Collection

[0542] Input of user's exercise details

[0543] User: After finishing training, launch the dedicated application and enter the details of the exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[0544] Terminal: The input exercise data is temporarily stored in local storage and sent to a server via the Internet.

[0545] Input of user's meal details

[0546] User: At the end of the day, the user enters the details of their meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application.

[0547] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[0548] 2. Data analysis and training menu generation

[0549] Data collection and organization

[0550] Server: Classifies the received exercise and dietary data for each user and stores them in a database. This also manages the user's past training and dietary history.

[0551] Analysis by generative AI

[0552] Server: Passes the saved data to the generation AI and performs the following processes.

[0553] Calculating energy consumption: Calculate the user's calorie consumption from exercise data. For example, it is estimated that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0554] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[0555] Evaluation based on the user's goals: For example, if the goal is to lose weight, check whether the calorie balance is negative, and if the goal is to build muscle, evaluate whether the protein intake is sufficient.

[0556] Training menu generation

[0557] Server: Based on the analysis results of the generation AI, the following training menu and dietary advice is generated.

[0558] Exercise suggestions: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, the system suggests "20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body)."

[0559] Dietary advice: Advise users to adjust their diet. For example, if a user is lacking in protein, suggest adding a protein shake to their breakfast.

[0560] 3. Providing and monitoring results

[0561] Providing results

[0562] Server: Sends the generated training menu and dietary advice to the user's terminal.

[0563] Device: Displays the received training menu and dietary advice on the application interface.

[0564] Progress monitoring

[0565] Server: Periodically collects new exercise and dietary data and reanalyzes it with Generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[0566] For example, if exercise effectiveness stagnates or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[0567] Specific examples

[0568] User A enters the following information into the app:

[0569] Exercise: 30 minutes of running, 20 minutes of weight training

[0570] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[0571] Data analysis example

[0572] Server: From the exercise data, the energy expenditure is calculated to be 500 kcal, and from the diet data, the calorie intake is calculated to be 1500 kcal. It is also determined that the person is slightly deficient in protein.

[0573] Generative AI: For User A, who wants to increase muscle strength, the AI ​​suggests "20 minutes of running and 30 minutes of weight training (especially strengthening the upper body)" as exercise for the next day, and provides advice on adding a protein shake to meals.

[0574] In this way, the system provides training menus and dietary advice optimized to the user's needs, maximizing the effectiveness of training and supporting a healthy lifestyle.

[0575] The processing flow will be explained below.

[0576] Step 1:

[0577] User: After training, launch the dedicated app and enter the exercise details (type of exercise, time, intensity).

[0578] Step 2:

[0579] Device: The entered exercise data is temporarily stored in local storage and sent to a server via the Internet.

[0580] Step 3:

[0581] User: At the end of the day, the user uses a dedicated app to enter the details of the day's meals (e.g., details of breakfast, lunch, dinner, and snacks).

[0582] Step 4:

[0583] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[0584] Step 5:

[0585] Server: Stores the received exercise and dietary data in a database and organizes and classifies it for each user.

[0586] Step 6:

[0587] Server: Passes the user's exercise and dietary data from the database to the generation AI.

[0588] Step 7:

[0589] Generative AI: Analyzes exercise data and calculates energy consumption. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0590] Step 8:

[0591] Generative AI: Analyzes dietary data and evaluates the balance of ingested nutrients (protein, carbohydrates, lipids, vitamins, and minerals).

[0592] Step 9:

[0593] Generative AI: Based on the user's goals (e.g., dieting, muscle building), it compares energy expenditure and nutrient intake assessments.

[0594] Step 10:

[0595] Generative AI: Generates optimal training and dietary advice based on the user's goals. For example, a user aiming to improve muscle strength would be advised to run for 20 minutes, do weight training for 30 minutes (especially for upper body strengthening), and add protein shakes to their meals.

[0596] Step 11:

[0597] Server: Sends the generated training menu and dietary advice to the user's terminal.

[0598] Step 12:

[0599] Device: Displays the received training menu and dietary advice on the user app interface.

[0600] Step 13:

[0601] Server: Periodically collects new exercise and diet data and repeats steps 6 through 11 above to monitor the user's progress and adjust the training plan as needed.

[0602] In this way, users can maximize the effectiveness of their training by always receiving training menus and dietary advice optimized for their goals.

[0603] Example 1

[0604] 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."

[0605] In today's world, individualized exercise and dietary management is important for preventing lifestyle-related diseases and promoting health. However, it is difficult for individual users to create appropriate training menus and meal plans on their own. Furthermore, existing systems require cumbersome data entry and analysis, making it difficult to maintain user motivation. There is a need for an efficient system that can solve these issues and provide users with optimal training menus and dietary advice.

[0606] 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.

[0607] In this invention, the server includes means for inputting the user's exercise details, means for inputting the user's meal details, means for transmitting the exercise details and meal details to an information processing device, generation AI means for analyzing the exercise details and meal details, means for generating an optimal training menu and meal advice based on the analysis results, means for providing the generated training menu and meal advice to the user's display device, and means for monitoring the user's progress and adjusting the training plan as necessary. This frees users from the hassle of complicated data entry and analysis work, and enables them to easily receive training and meal plans optimized for their individual needs.

[0608] "User" refers to an individual who uses the system to input exercise and dietary information and receives training menus and dietary advice.

[0609] "Means for inputting exercise details" refers to an interface for recording the type and time of exercise performed by the user and inputting the data.

[0610] "Means for inputting meal contents" refers to an interface that allows the user to record the contents and amounts of food consumed and input them as data.

[0611] "Information processing device" refers to a computer system for receiving, storing, and analyzing user input data.

[0612] "Generative AI means" refers to artificial intelligence technology that analyzes exercise and dietary content based on collected data and generates optimal training menus and dietary advice.

[0613] A "training menu" refers to a specific exercise plan proposed based on the user's training goals and exercise history.

[0614] "Dietary advice" refers to specific dietary guidance suggested based on a user's health goals and dietary history.

[0615] The term "display device" refers to a display or application interface for displaying the generated training menu and dietary advice to the user.

[0616] "Means for monitoring progress" refers to the ability to track a user's daily exercise and food intake, and adjust training plans and dietary advice as needed based on the results.

[0617] "Energy expenditure" refers to the amount of calories a user expends through exercise.

[0618] "Nutrients" refers to nutritional components such as proteins, carbohydrates, lipids, vitamins, and minerals ingested through food.

[0619] "Storage device" refers to a data storage system that stores a user's exercise history and diet history.

[0620] "Database" refers to a data management system for systematically storing and managing individual data of users.

[0621] This invention is a system that utilizes generative AI to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. This system functions in cooperation with the server, terminal, and user at each processing step. Specifically, it is implemented as follows.

[0622] 1. Data Collection

[0623] (User exercise input)

[0624] After completing their training, the user launches a dedicated application and inputs the details of their exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[0625] The device temporarily stores the input exercise data in local storage and transmits it to a server via the Internet.

[0626] (User's meal information)

[0627] At the end of the day, users enter their meal plans (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application.

[0628] The terminal temporarily stores the input meal data in local storage and transmits it to a server via the Internet.

[0629] 2. Data analysis and training menu generation

[0630] (Data collection and organization)

[0631] The server categorizes the received exercise and dietary data for each user and stores them in a database, which also manages the user's past training history and dietary details.

[0632] (Analysis by generative AI)

[0633] The server passes the saved data to the generation AI, which performs the following analysis:

[0634] Calculating energy consumption: Calculate the user's calorie consumption from exercise data. For example, it is estimated that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0635] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[0636] Evaluation based on the user's goals: For example, if the goal is to lose weight, check whether the calorie balance is negative, and if the goal is to build muscle, evaluate whether the protein intake is sufficient.

[0637] (Generating training menus)

[0638] Based on the analysis results of the generation AI, the server generates the following training menu and dietary advice.

[0639] Exercise suggestions: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, the system suggests "20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body)."

[0640] Dietary advice: Advise users to adjust their diet. For example, if a user is lacking in protein, suggest adding a protein shake to their breakfast.

[0641] 3. Providing and monitoring results

[0642] (Providing results)

[0643] The server transmits the generated training menu and dietary advice to the user terminal.

[0644] The device displays the received training menu and dietary advice on the application interface.

[0645] (Progress monitoring)

[0646] The server periodically collects new exercise and dietary data and reanalyzes it with the Generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[0647] Example: For example, if exercise results stagnate or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[0648] Specific examples

[0649] User A's input

[0650] Exercise: 30 minutes of running, 20 minutes of weight training

[0651] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[0652] (Example of data analysis)

[0653] The server calculates from the exercise data that the energy expenditure is 500 kcal, and from the diet data, it analyzes that the calorie intake is 1500 kcal. It also finds that the person is slightly deficient in protein.

[0654] The AI ​​generator suggests to User A, who wants to increase his muscle strength, that he do 20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body) for the next day's exercise, and advises him to add a protein shake to his breakfast.

[0655] By using this system, users can easily obtain individually optimized training menus and dietary advice, making health management more efficient.

[0656] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0657] Step 1: Data entry

[0658] The user starts a dedicated application and inputs the exercise details (e.g., 30 minutes of running, 20 minutes of weight training). The input details include the type of exercise, exercise time, exercise intensity, etc.

[0659] The device temporarily stores the input exercise data in local storage. The input data is saved in JSON format or similar.

[0660] Input: User's exercise details

[0661] Output: Exercise data saved in local storage

[0662] Specific behavior:

[0663] Tap the "Enter exercise details" button on the app's home screen.

[0664] Enter information such as "30 minutes of running" and "20 minutes of weight training" into the input form and press the "Save" button.

[0665] Step 2: Send data

[0666] The device transmits the stored exercise data, including the user's identification information and a timestamp, to a server via the Internet.

[0667] The server stores the received data in a database.

[0668] Input: Exercise data stored in local storage

[0669] Output: Exercise data stored in the server database

[0670] Specific behavior:

[0671] Tap the "Send" button to send the saved exercise data.

[0672] The data is encrypted using a security protocol and sent to the server.

[0673] Step 3: Enter your meal data

[0674] At the end of the day, the user enters the details of their meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application, including the type and amount of each meal.

[0675] The device temporarily stores the entered meal data in local storage.

[0676] Input: User's diet

[0677] Output: Meal data saved in local storage

[0678] Specific behavior:

[0679] Tap the "Enter meal details" button in the app.

[0680] Fill in the input form with information such as "Breakfast: Oatmeal and banana," "Lunch: Chicken breast salad," and "Dinner: Grilled fish and vegetable soup," and press the "Save" button.

[0681] Step 4: Send your meal data

[0682] The device transmits the stored meal data to a server via the Internet, including the user's identification information and a timestamp.

[0683] The server stores the received data in a database.

[0684] Input: Meal data stored in local storage

[0685] Output: Meal data stored in the server database

[0686] Specific behavior:

[0687] Tap the "Send" button to send the saved meal data.

[0688] The data is encrypted using a security protocol and sent to the server.

[0689] Step 5: Data analysis

[0690] The server passes the saved exercise and dietary data to the generation AI for analysis.

[0691] Calculates energy expenditure: Calculates calorie expenditure from exercise data. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0692] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[0693] Evaluation based on user goals: Evaluate whether calorie balance and protein intake are in line with the user's goals.

[0694] Input: Exercise and diet data stored in the server database

[0695] Output: Analysis results by generative AI

[0696] Specific behavior:

[0697] The user's exercise and dietary data is input as a prompt into the generative AI model.

[0698] The generating AI outputs the calculation results and returns the analysis result: "Energy consumption: 500 kcal, calorie intake: 1500 kcal, protein deficiency."

[0699] Step 6: Generate training menu and dietary advice

[0700] Based on the analysis results of the generation AI, the server generates optimal training menus and dietary advice for each individual user.

[0701] Suggested exercises: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, it suggests "20 minutes of running, 30 minutes of weight training (especially for strengthening the upper body)."

[0702] Dietary advice: Suggestions to adjust dietary content, e.g., adding a protein shake to breakfast if the user is lacking in protein.

[0703] Input: Analysis results by generative AI

[0704] Output: Generated training menu and dietary advice

[0705] Specific behavior:

[0706] The prompt text is input to the generation AI as follows: "Analysis results for user A: energy consumption 500 kcal, calorie intake 1500 kcal, protein deficiency. Goal is to increase muscle strength."

[0707] The generated AI returns the following suggestion: "Next day: 20 minutes of running, 30 minutes of weight training (upper body). Breakfast: Add a protein shake."

[0708] Step 7: Delivering results

[0709] The server transmits the generated training menu and dietary advice to the user's terminal.

[0710] The device displays the received training menu and dietary advice on the application interface.

[0711] Input: Generated training menu and dietary advice

[0712] Output: Training menu and dietary advice displayed on the user's device

[0713] Specific behavior:

[0714] When users open the app, a pop-up notification appears with new training and dietary advice.

[0715] It will be displayed in list format on the training menu details page.

[0716] Step 8: Monitor progress and reanalyze

[0717] The server periodically collects new exercise and dietary data and reanalyzes it with the generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[0718] For example, if exercise effectiveness stagnates or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[0719] Input: New exercise and diet data collected periodically

[0720] Output: Tailored training plan or dietary advice

[0721] Specific behavior:

[0722] At the end of each week, users tap the "Weekly Review" button in the app to send their latest exercise and diet data to the server.

[0723] The server analyzes the data, generates a new training plan and any necessary dietary adjustments, and notifies the user.

[0724] In this way, the entire system works together to effectively support the user's training and dietary habits and maintain a healthy lifestyle.

[0725] (Application example 1)

[0726] 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."

[0727] Traditionally, there have been significant challenges in managing the health of factory workers. In particular, lack of exercise and inappropriate diets have led to reduced productivity and health problems. In addition, there is the issue that optimizing health management for each worker requires a great deal of effort and time. Therefore, there is a need to effectively manage health and improve productivity based on exercise and diet data.

[0728] 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.

[0729] In this invention, the server includes a means for inputting exercise and dietary data of factory workers and recommending optimal training and rest methods and meals to maintain employee health and improve productivity, a means for calculating energy expenditure from the user's exercise content, and a means for evaluating nutrients ingested from the user's diet content, thereby enabling optimal health management for each worker.

[0730] Key Word Definitions

[0731] "Exercise details" refers to details such as the type, duration, and intensity of the exercise performed by the user.

[0732] "Meal details" refers to details such as the type, amount, and nutritional components of the food consumed by the user.

[0733] "Server" means a central system for collecting, storing, and analyzing data over the Internet.

[0734] "Generative AI means" is an artificial intelligence technology that analyzes data collected from users and generates optimal training menus and dietary advice.

[0735] A "training menu" is a plan that specifically indicates the exercise content that the user will perform.

[0736] "Dietary advice" refers to the content and method of meals recommended based on the user's health condition and goals.

[0737] A "user's terminal" is an electronic device used by a user, such as a smartphone or tablet.

[0738] "Progress" refers to the degree of achievement or progress toward health or exercise goals set by the user.

[0739] "Monitoring" refers to the continuous collection and analysis of the user's exercise and dietary data to monitor the situation.

[0740] "Factory workers" refers to employees who work in factories, and are the subjects for evaluating their health status and productivity.

[0741] "Energy expenditure" refers to the amount of calories burned by the user through exercise.

[0742] "Nutrient assessment" refers to assessing the nutritional value of a meal consumed by a user.

[0743] "History" is a record of exercise and dietary data entered by the user in the past.

[0744] A "database" is a system that stores collected data and manages it so that it can be easily searched and analyzed.

[0745] "Customization" means providing optimal training menus and dietary advice based on the user's individual needs and circumstances.

[0746] MODE FOR CARRYING OUT THE INVENTION

[0747] This invention is a system that utilizes AI to provide optimal training menus and dietary advice for each worker based on the user's exercise and dietary habits. Specific embodiments of this system are described below.

[0748] Data collection

[0749] Input of user's exercise details

[0750] After completing their training, users launch a dedicated application and input their exercise details (e.g., 30 minutes of walking, 20 minutes of weight training). The user's device temporarily stores the input exercise data in local storage and transmits it to a server via the Internet.

[0751] Input of user's meal details

[0752] At the end of the day, the user enters the details of their meals (e.g., salad and chicken for lunch, grilled fish and vegetable soup for dinner) into a dedicated application. The user's device temporarily stores the entered meal data in local storage and transmits it to a server via the Internet.

[0753] Data analysis and training menu generation

[0754] Data collection and organization

[0755] The server categorizes the received exercise and dietary data for each user and stores them in a database, which also manages the user's past training and dietary history.

[0756] Analysis by generative AI

[0757] The server passes the saved data to the generation AI, which performs the following processes: Calculates the user's calorie consumption from the exercise data (for example, it is estimated that walking for 30 minutes burns about 150 kcal, and weight training for 20 minutes burns about 200 kcal), and evaluates the nutrients ingested from the diet data (protein, carbohydrates, lipids, vitamins, minerals, etc.).

[0758] It also evaluates the user's goals. For example, if the goal is to lose weight, it checks whether the calorie balance is negative, and if the goal is to build muscle, it evaluates whether the protein intake is sufficient.

[0759] Training menu generation

[0760] Based on the analysis results of the generative AI, the server will suggest exercise regimens and rest methods for the next day. It will also provide advice on dietary habits, helping users maintain their health and improve their work efficiency.

[0761] Specific examples

[0762] As a specific example of usage, user A enters the following information into the app: exercise details are "30 minutes of walking, 20 minutes of weight training," and meal details are "salad and chicken for lunch, grilled fish and vegetable soup for dinner." The server analyzes this data and generates the following advice: "Today's calorie expenditure is 350 kcal, but calorie intake is 700 kcal. As an immediate goal, we recommend that you either increase your exercise volume tomorrow or reduce the calories in your diet."

[0763] Hardware and software used

[0764] User device: smartphone, tablet, or PC

[0765] Server: Cloud-based database and AI models

[0766] Generation AI: Python, scikit-learn, RandomForestRegressor

[0767] Generative AI model prompt

[0768] The generative AI model is given a prompt like this:

[0769] "User's exercise data: Please suggest optimal training and dietary advice based on User A's exercise and dietary data."

[0770] As described above, the present invention is a system that optimizes health management for each worker and improves factory productivity.

[0771] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0772] Program processing flow

[0773] Step 1:

[0774] The user launches a dedicated application and inputs their exercise and meal details. Examples of inputs include "30 minutes of walking, 20 minutes of weight training" or "Salad and chicken for lunch, grilled fish and vegetable soup for dinner." This allows the user's exercise and meal data to be collected.

[0775] Step 2:

[0776] The user's device temporarily stores the input exercise and dietary data in local storage, then transmits this data to a server via the Internet. The input data includes the type of exercise, duration, dietary content, and calorie intake.

[0777] Step 3:

[0778] The server classifies the received exercise and dietary data for each user and stores them in a database. This allows past training and dietary history to be managed. Data stored in the database includes date and time, exercise content, and dietary content.

[0779] Step 4:

[0780] The server then passes the saved data to the AI ​​generator. During this process, the user's energy expenditure is calculated from their exercise data, and calorie and nutrient intake is assessed from their dietary data. For example, it is estimated that 30 minutes of walking will burn approximately 150 kcal, and 20 minutes of weight training will burn approximately 200 kcal.

[0781] Step 5:

[0782] Based on the analysis results, the AI ​​generates training menus and dietary advice tailored to the user's goals (such as building muscle or losing weight). Specifically, this includes suggestions for the next day's exercise menu and appropriate meals. Different advice is provided for each user depending on the analysis results.

[0783] Step 6:

[0784] The generated training menu and dietary advice are sent from the server to the user's device. The user's device receives it and displays it on the interface of a dedicated application. For example, an exercise menu might be displayed as "20 minutes of running, 30 minutes of weight training (especially for upper body strengthening)."

[0785] Step 7:

[0786] The user implements the training menu and dietary advice and then enters the results back into the application, which then collects progress data. The user's progress data is sent to a server and periodically reanalyzed by the generating AI.

[0787] Step 8:

[0788] The server reanalyzes the progress data using AI and adjusts the training plan and dietary advice as needed, thereby maximizing the effectiveness of the user's training and optimizing health management.

[0789] The above is the specific processing flow of the system program of the application example. This system aims to maintain the user's health and improve work efficiency.

[0790] 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.

[0791] This invention is a system that uses generative AI and an emotion engine to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. The detailed operation of this system is described below.

[0792] 1. Data Collection

[0793] Input of user's exercise details

[0794] User: After training, launch the dedicated application and enter the details of the exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[0795] Terminal: The input exercise data is temporarily stored in local storage and sent to a server via the Internet.

[0796] Input of user's meal details

[0797] User: At the end of the day, the user uses a dedicated application to input the details of the day's meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner).

[0798] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[0799] 2. Emotional Recognition

[0800] Input of user emotion data

[0801] User: Uses the application to add emotional data to the format when entering training or dietary information (e.g., taking a photo of an expression, inputting voice data, or tagging text with emotions).

[0802] Terminal: The input emotion data is temporarily stored in local storage and sent to a server via the Internet.

[0803] Emotional Data Analysis

[0804] Server: Passes the received emotion data to the emotion engine and analyzes the user's emotional state. The emotion engine uses facial expression recognition, voice analysis, and character analysis to classify and evaluate the user's emotions (e.g., joy, sadness, anger, stress).

[0805] 3. Data analysis and training menu generation

[0806] Data collection and organization

[0807] Server: In addition to exercise and dietary data, emotional data is classified and organized for each user and stored in a database.

[0808] Analysis by generative AI

[0809] Server: Passes the user's exercise data, dietary data, and emotional data from the database to the generation AI, and performs the following processes.

[0810] Calculating energy expenditure: Calculating the user's calorie expenditure from exercise data.

[0811] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[0812] Emotional data evaluation: Evaluate the user's emotional state from emotional data (e.g., recommend light exercise if stress levels are high, or hard training if positive emotions predominate).

[0813] Training menu generation

[0814] Server: Based on the analysis results of the generation AI, the following training menu and dietary advice is generated.

[0815] Exercise suggestions: The system suggests exercise menus based on the user's goals and emotional state. For example, a user aiming to improve muscle strength would be suggested to do 20 minutes of running and 30 minutes of weight training (especially for upper body strengthening), while a user experiencing high stress would be suggested to do 30 minutes of yoga.

[0816] Dietary advice: Advise users to adjust their diet taking into account their emotional state and nutritional balance. For example, recommend chamomile tea to relieve stress.

[0817] 4. Providing and monitoring results

[0818] Providing results

[0819] Server: Sends the generated training menu and dietary advice to the user's terminal along with advice on emotional state.

[0820] Terminal: Displays the received training menu, dietary advice, and emotional advice on the application interface.

[0821] Progress monitoring

[0822] Server: Periodically collects new exercise, diet, and emotional data and reanalyzes it with generative AI, allowing it to monitor the user's progress in real time and adjust the training plan as needed.

[0823] For example, if exercise effectiveness stagnates or emotional data indicates high stress levels, the generative AI will generate new training menus and dietary advice and immediately provide them to the user.

[0824] Specific examples

[0825] User B enters the following information into the app:

[0826] Exercise: 30 minutes of running, 20 minutes of weight training

[0827] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[0828] Emotional data: Report stressful experiences during training via voice input

[0829] Data analysis example

[0830] Server: From the exercise data, the energy expenditure is calculated to be 500 kcal, and from the dietary data, the calorie intake is calculated to be 1500 kcal. Furthermore, from the emotion data, it is determined that User B's stress level is high.

[0831] Generative AI: Because stress relief is necessary, the system suggests "30 minutes of yoga" as exercise for the next day and "chamomile tea, which has a relaxing effect" as food.

[0832] In this way, the system supports both training and mental care by providing training menus and dietary advice optimized for the user's needs and emotional state.

[0833] The processing flow will be explained below.

[0834] Step 1:

[0835] User: After training, launch the dedicated app and enter the exercise details (type of exercise, time, intensity).

[0836] Step 2:

[0837] Device: The entered exercise data is temporarily stored in local storage and sent to a server via the Internet.

[0838] Step 3:

[0839] User: At the end of the day, the user uses a dedicated app to enter the details of the day's meals (e.g., details of breakfast, lunch, dinner, and snacks).

[0840] Step 4:

[0841] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[0842] Step 5:

[0843] Users: Enter emotional data when entering workout or diet details within the same app, for example by taking a photo of their face, reporting their emotions via voice, or tagging text with emotional tags.

[0844] Step 6:

[0845] Terminal: The input emotion data is temporarily stored in local storage and sent to a server via the Internet.

[0846] Step 7:

[0847] Server: Organizes the received exercise data, dietary data, and emotional data for each user and stores them in a database.

[0848] Step 8:

[0849] Server: Passes the stored data to the generative AI and emotion engine, which analyzes the user's emotional state using facial, voice, and text analysis.

[0850] Step 9:

[0851] Generative AI: Analyzes exercise data and calculates energy consumption. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[0852] Step 10:

[0853] Generative AI: Analyzes dietary data and evaluates the balance of ingested nutrients (protein, carbohydrates, lipids, vitamins, minerals, etc.).

[0854] Step 11:

[0855] Emotion engine: Analyzes emotional data to assess the user's emotional state. For example, it can determine "stress," "joy," or "sadness" from facial photos and voice analysis.

[0856] Step 12:

[0857] Generative AI: Based on the evaluation results of the emotion engine, the system comprehensively compares exercise and dietary data to generate optimal training menus and dietary advice based on the user's goals. For example, a user experiencing high stress may be recommended to do 30 minutes of yoga and add chamomile tea to their diet.

[0858] Step 13:

[0859] Server: Sends the generated training menu, dietary advice, and emotional state feedback to the user terminal.

[0860] Step 14:

[0861] Device: Displays the received training menu, dietary advice, and emotional feedback on the app interface.

[0862] Step 15:

[0863] Server: Periodically collects new exercise, dietary, and emotional data and reanalyzes it with the generative AI and emotion engine, allowing it to monitor the user's progress in real time and update training plans and dietary advice as needed.

[0864] In this way, users can always receive training menus and dietary advice optimized for their goals and emotional state.

[0865] Example 2

[0866] 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."

[0867] Many people today are being asked to review their exercise and dietary habits to stay healthy, but providing training menus and meal plans that fit each individual's lifestyle and emotional state is extremely difficult. While conventional systems take exercise and dietary data into account, they are unable to provide advice that reflects the user's emotional state, making it difficult to achieve effective health management. Furthermore, they are also inadequate at monitoring users' progress in real time and adjusting plans as needed.

[0868] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0869] In this invention, the server includes means for inputting the user's exercise details, means for inputting the user's diet details, means for inputting the user's emotional data, means for transmitting the exercise details, diet details, and emotional data to the server, generation AI means for analyzing the exercise details, diet details, and emotional data, means for generating an optimal training menu and dietary advice based on the analysis results, means for providing the generated training menu, dietary advice, and emotional state advice to the user's terminal, and means for monitoring the user's progress and adjusting the training plan as necessary. This makes it possible to achieve optimal health management tailored to individual needs by comprehensively considering the user's exercise details, dietary details, and emotional state.

[0870] "Means for inputting user's exercise details" refers to an interface for inputting details such as the type, duration, and intensity of the exercise performed by the user.

[0871] "Means for inputting the user's dietary details" refers to an interface for inputting details such as the type, amount, and nutrients of the food consumed by the user.

[0872] "Means for inputting user emotional data" refers to an interface for inputting the user's emotional state, such as attaching emotion tags to a photograph of the user's facial expression, voice input, or text input.

[0873] The "means for transmitting the exercise details, meal details, and emotion data to the server" refers to a communication means for transmitting the exercise details, meal details, and emotion data from the terminal to the server via the Internet.

[0874] "Generative AI means for analyzing the exercise content, dietary content and emotional data" refers to artificial intelligence technology for analyzing exercise content, dietary content and emotional data and generating optimal training menus and dietary advice for users.

[0875] "Means for generating optimal training menus and dietary advice based on analysis results" refers to means for generating training menus and dietary advice according to the user's goals and condition based on data analyzed by the generation AI.

[0876] "Means for providing the generated training menu, dietary advice, and emotional state advice to the user's terminal" refers to an interface for transmitting the training menu, dietary advice, and emotional advice generated by the server to the user's terminal and displaying them.

[0877] "Means for monitoring the user's progress and adjusting the training plan as needed" refers to means for periodically collecting new exercise, dietary and emotional data, monitoring the user's progress based on that data, and updating training and dietary advice as needed.

[0878] "Means for calculating energy expenditure" refers to algorithms or functions for calculating calories burned based on the activity.

[0879] "Means for assessing nutrient intake" refers to algorithms or databases for assessing the types and amounts of nutrients ingested based on dietary content.

[0880] "Means for assessing emotional state" refers to algorithms and artificial intelligence technologies for analyzing and assessing the user's emotional state based on the acquired emotional data.

[0881] "Means for storing in a database" refers to a relational or non-relational database for structuring and storing the user's exercise, diet, and emotional data for long-term storage.

[0882] "Means for customizing training menus and dietary advice" refers to generative AI technology that uses stored past data to individually create optimal training menus and dietary advice for each user.

[0883] The present invention is a system that provides optimal training menus and dietary advice based on a user's exercise and dietary habits by utilizing a generative AI and an emotion engine. The following describes in detail an embodiment of the present invention.

[0884] Data collection

[0885] Users use a dedicated application to input their exercise and dietary information. This application runs on devices such as smartphones and tablets, and uses keyboard input, voice input, and a camera as interfaces. For example, after training, a user might input their exercise information, such as 30 minutes of running and 20 minutes of weight training, and at the end of the day, they might input their diet information, such as oatmeal and a banana for breakfast, chicken breast salad for lunch, and grilled fish and vegetable soup for dinner. This data is temporarily stored in the device's local storage and then sent to a server via the Internet.

[0886] Emotion recognition

[0887] When users enter their workout or diet details, they also add emotional data. This emotional data is acquired by taking photos of their facial expressions, inputting voice, or tagging text with emotions. The device temporarily stores this data in local storage and then transmits it to a server via the Internet. The server then passes the received emotional data to an emotion engine, which analyzes the user's emotional state. For example, facial recognition is performed using Amazon Rekognition, and voice analysis is performed using Google Cloud Speech-to-Text.

[0888] Data analysis and training menu generation

[0889] The server stores the user's exercise data, diet data, and emotional data in a centralized database. This database can be a relational database (e.g., MySQL) or a non-relational database (e.g., MongoDB). Next, a generative AI (e.g., OpenAI GPT-3) analyzes this data and performs the following processes:

[0890] Energy expenditure calculation: Calculate calorie expenditure based on exercise data.

[0891] Assessment of nutritional balance: Evaluate nutrients ingested based on dietary data.

[0892] Emotional Data Evaluation: Evaluate the user's emotional state based on the emotional data.

[0893] Based on the analysis results, the generative AI generates optimal training menus and dietary advice. For example, for a user with high stress levels, it might recommend 30 minutes of yoga and suggest chamomile tea as a meal.

[0894] Delivering and monitoring results

[0895] The generated training menu and dietary advice are sent from the server to the user's device. The application on the device displays this advice on its interface. The server also periodically collects new data and reanalyzes it using the generating AI to monitor the user's progress and adjust the training plan as needed. For example, if exercise results stagnate or emotional data indicates high stress levels, the server will instantly generate a new training menu and dietary advice and provide it to the user.

[0896] Specific examples

[0897] If User B enters the following information into the app:

[0898] Exercise: 30 minutes of running, 20 minutes of weight training

[0899] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[0900] Emotional data: Report "I felt stressed" during training by voice input

[0901] The server calculates energy expenditure from exercise data to be 500 kcal, and analyzes calorie intake from dietary data to be 1500 kcal. Emotional data reveals that User B has a high stress level, and the AI ​​recommends "30 minutes of yoga" as exercise for the next day and "chamomile tea, which has a relaxing effect" as food.

[0902] Prompt Sentence Examples

[0903] "Analyze the user's exercise data, dietary data, and emotional data to generate optimal training menus and dietary advice."

[0904] Using these prompts, the generative AI can suggest optimal training menus and dietary advice to users based on the input data.

[0905] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0906] Step 1:

[0907] The user starts the dedicated application and inputs the details of the exercise.

[0908] Input: Exercise data provided by the user (e.g., 30 minutes of running, 20 minutes of weight training).

[0909] How it works: The user fills in the application's form with details such as the type of exercise, duration, and intensity. If keyed in or voice-activated, the exercise data is captured accordingly.

[0910] Output: The exercise data is saved in the device's local storage.

[0911] Step 2:

[0912] At the end of the day, the user inputs the details of their meals using a dedicated application.

[0913] Input: Dietary data provided by the user (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner).

[0914] How it works: The user fills out a form in the application to enter details about the food, such as type, amount, and nutrients.

[0915] Output: Meal data is saved to the device's local storage.

[0916] Step 3:

[0917] The user inputs emotion data.

[0918] Input: User emotion data (e.g., facial photos, voice input, emotion tagging of text input).

[0919] How it works: When entering workout or diet information, users select the option to add emotional data, which includes capturing facial expressions with a camera and recording audio.

[0920] Output: Emotion data is saved in the device's local storage.

[0921] Step 4:

[0922] The terminal transmits the collected exercise data, diet data, and emotion data to a server.

[0923] Input: Exercise data, dietary data, and emotional data stored on the device.

[0924] How it works: The device sends data to a server over the internet using a secure communication protocol such as HTTPS.

[0925] Output: Exercise data, diet data and emotion data are sent to the server.

[0926] Step 5:

[0927] The server analyzes the received data.

[0928] Input: Exercise data, diet data and emotion data sent to the server.

[0929] How it works: The server stores this data in a database and passes it to the generation AI, which calculates energy consumption, evaluates nutritional balance, and analyzes emotional data.

[0930] Output: The results of the generative AI analysis, specifically energy expenditure (e.g., 500 kcal), nutritional assessment, and emotional state (e.g., high stress level).

[0931] Step 6:

[0932] Based on the analysis results, the generative AI generates optimal training menus and dietary advice.

[0933] Input: Analysis results by the generative AI (e.g., energy consumption, nutritional balance, emotional state).

[0934] How it works: The generative AI generates optimal training and dietary advice based on the user's goals and condition. For example, for a user with high stress levels, it might suggest 30 minutes of yoga and recommend chamomile tea to reduce stress.

[0935] Output: Generated training menu and dietary advice.

[0936] Step 7:

[0937] The server transmits the generated training menu and dietary advice to the user's terminal.

[0938] Input: Generated training menu and dietary advice.

[0939] How it works: The server sends this data to the device. The communication is secure and fast.

[0940] Output: Training menu and dietary advice are sent to the device.

[0941] Step 8:

[0942] The device displays the received training menu and dietary advice.

[0943] Input: Training menu and dietary advice sent to the device.

[0944] How it works: An application on the device displays this information to the user in an easy-to-understand manner, using text and graphics as the user interface.

[0945] Output: The user will be able to see the training menu and dietary advice.

[0946] Step 9:

[0947] The server periodically collects new data and reanalyzes it using the generating AI.

[0948] Input: New exercise data, diet data, and emotion data.

[0949] How it works: The server periodically collects data and asks the generator AI to reanalyze it, allowing it to monitor the user's progress and adjust the plan as needed.

[0950] Output: Updated training menu and dietary advice.

[0951] (Application example 2)

[0952] 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."

[0953] Conventional training and meal planning systems only provide advice based on the user's exercise and dietary habits, but do not take into account the user's emotional state or psychological factors. This can lead to inappropriate training and dietary advice that ignores the user's stress and emotional fluctuations. Another problem is that the systems do not recommend appropriate products, which does not contribute to improving the user's wellness.

[0954] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting emotion data in addition to the user's exercise details and dietary details, a generation AI means for analyzing the input exercise details, dietary details, and emotion data, a means for recommending optimal training menus, dietary advice, and products based on the analysis results, and a means for monitoring the user's progress and adjusting the training plan as necessary. This makes it possible to provide a comprehensive training menu and dietary advice that takes into account both the user's physical and mental conditions.

[0955] "User's exercise content" refers to specific information such as the type, duration, and intensity of physical exercise performed by the user.

[0956] "User's dietary details" refers to specific information such as the type, amount, and time of food intake by the user.

[0957] "Emotion data" is data that indicates the psychological state of the user, such as stress, happiness, or anxiety, and is collected through facial expression photographs, voice input, and text input.

[0958] The "generative AI means" is an artificial intelligence technology that analyzes exercise, dietary and emotional data obtained from users and generates training menus, dietary advice and product recommendations based on the data.

[0959] A "training menu" is a specific exercise plan tailored to the user's fitness goals and physical condition.

[0960] "Dietary advice" refers to specific instructions or suggestions for recommending an appropriate diet based on the user's nutritional balance and health condition.

[0961] "Product recommendation" refers to suggesting suitable products such as fitness equipment and supplements based on a user's exercise, diet, and emotional data.

[0962] "Progress monitoring means" refers to technology that tracks a user's exercise data, dietary data, and emotional data over a period of time and evaluates the user's progress toward their goals.

[0963] The "means for adjusting the training plan" refers to technology that changes and optimizes the training menu and dietary advice according to the user's progress and fluctuations in emotional data.

[0964] As an embodiment of the present invention, a specific system configuration and processing method will be described.

[0965] System configuration

[0966] This system collects and analyzes the user's exercise, diet, and emotional data to generate optimal training menus, dietary advice, and product recommendations. Each component is described in detail below.

[0967] 1. User Device

[0968] The user terminal is a smartphone, tablet, or computer, and allows the user to input exercise, diet, and emotional data. This terminal also has the ability to communicate with a server via the internet and transmit the collected data.

[0969] 2. Server

[0970] The server receives the data sent from the user and performs the following processing.

[0971] Data collection: Receives user exercise data, diet data, and emotion data and temporarily stores them in local storage.

[0972] Data analysis: The received data is passed to the generation AI and emotion engine for analysis.

[0973] Training menu generation: Based on the analysis results, an optimal training menu and dietary advice is generated.

[0974] Product Recommendations: Recommend products such as fitness equipment and supplements.

[0975] Progress monitoring: We periodically reanalyze your data and adjust your training plan as needed.

[0976] Technology and software used

[0977] Hardware: smartphones, tablets, computers, servers

[0978] software:

[0979] Python: Programming Language

[0980] Keras: A deep learning library

[0981] Scikit-learn: a data preprocessing library

[0982] Flask / Django: Web Frameworks

[0983] Emotion Model: Emotion Analysis Engine

[0984] Generative AI model: Generative AI for analyzing exercise, diet, and emotion data

[0985] Specific examples

[0986] The specific flow of data collection and analysis is explained below.

[0987] Data collection

[0988] Using a smartphone, the user inputs their morning exercise schedule as "30 minutes of running, 20 minutes of weight training," and their evening meal schedule as "oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner." Furthermore, they report emotional data by voice input, such as "feeling stressed during training."

[0989] Data analysis

[0990] The server passes the received exercise data, dietary data, and emotion data to the generation AI and emotion engine. The generation AI calculates energy expenditure from the exercise details and analyzes calorie intake from the diet details. The emotion engine evaluates the user's stress level from the voice data.

[0991] Generate training menus and dietary advice

[0992] Based on the analysis results, the generating AI will suggest "30 minutes of yoga" as a training menu to relieve the user's stress, and will recommend "chamomile tea, which has a relaxing effect" as dietary advice.

[0993] product recommendation

[0994] In addition, the generative AI will recommend related products such as "yoga mats" and "relaxing herbal tea."

[0995] Prompt Sentence Examples

[0996] Below are some example prompts to give to the generative AI model:

[0997] Please generate a training menu and dietary advice suitable for the user based on the following exercise data, dietary data, and emotional data.

[0998] Exercise data: 30 minutes of running, 20 minutes of weight training

[0999] Dietary information: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1000] Emotional data: Feeling stressed during training

[1001] Output format:

[1002] 1. Training Menu

[1003] 2. Dietary advice

[1004] 3. Recommended product list

[1005] In this way, the present invention can provide optimal health management based on a comprehensive assessment of a user's exercise, diet, and emotional state.

[1006] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1007] Step 1:

[1008] The user inputs the details of their exercise. Specifically, the user launches a dedicated application on a smartphone or tablet and inputs exercise data such as "30 minutes of running, 20 minutes of weight training." This input data is temporarily stored in local storage.

[1009] Step 2:

[1010] The user enters their meal plan. At the end of the day, the user enters what they had for breakfast, lunch, and dinner into the application. For example, they might enter "oatmeal and banana for breakfast, chicken breast salad for lunch, and grilled fish and vegetable soup for dinner." This data is also stored in local storage.

[1011] Step 3:

[1012] The user inputs emotional data. When the user inputs exercise and meal details, they can report emotional data such as "I felt stressed during training" using facial photos, voice input, or text input. This emotional data is stored in local storage.

[1013] Step 4:

[1014] The device sends exercise data, diet data, and emotion data to the server. The device then sends this data to the server via the Internet. At this time, the data sent also includes the user ID.

[1015] Step 5:

[1016] The server passes the received data to the generation AI means and emotion engine. The server compiles the received exercise data, diet data, and emotion data and provides it to the generation AI and emotion engine.

[1017] Step 6:

[1018] The server analyzes the data. The generation AI calculates energy consumption from exercise data and evaluates nutrient intake from dietary data. The emotion engine evaluates the user's emotional state from voice and facial expression data. Specifically, the analysis results include "500 kcal consumed" from exercise data, "1500 kcal intake" from dietary data, and "high stress level" from emotion data.

[1019] Step 7:

[1020] The server generates optimal training menus and dietary advice based on the generated results. For example, if stress relief is needed, the server suggests "30 minutes of yoga" as a training menu and "chamomile tea, which has a relaxing effect" as a meal.

[1021] Step 8:

[1022] The server transmits the generated training menu and dietary advice to the user's terminal. The server transmits the generated training menu and dietary advice together with the analysis results to the user's terminal.

[1023] Step 9:

[1024] The terminal provides the received training menu, dietary advice, and product recommendations to the user, who can then view the information through the terminal's application interface.

[1025] Step 10:

[1026] The server periodically collects new user data and reanalyzes it using the AI ​​generator. The AI ​​monitors the user's progress and updates and optimizes training plans and dietary advice as needed. For example, if exercise results stagnate, a new training menu is generated and immediately provided to the user.

[1027] 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.

[1028] 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.

[1029] 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.

[1030] [Third embodiment]

[1031] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1032] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1033] 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).

[1034] 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.

[1035] 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.

[1036] 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).

[1037] 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.

[1038] 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.

[1039] 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.

[1040] 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.

[1041] 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.

[1042] 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."

[1043] This invention is a system that uses AI to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. The detailed operation of this system is as follows, and will be explained using specific embodiments.

[1044] 1. Data Collection

[1045] Input of user's exercise details

[1046] User: After finishing training, launch the dedicated application and enter the details of the exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[1047] Terminal: The input exercise data is temporarily stored in local storage and sent to a server via the Internet.

[1048] Input of user's meal details

[1049] User: At the end of the day, the user enters the details of their meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application.

[1050] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[1051] 2. Data analysis and training menu generation

[1052] Data collection and organization

[1053] Server: Classifies the received exercise and dietary data for each user and stores them in a database. This also manages the user's past training and dietary history.

[1054] Analysis by generative AI

[1055] Server: Passes the saved data to the generation AI and performs the following processes.

[1056] Calculating energy consumption: Calculate the user's calorie consumption from exercise data. For example, it is estimated that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1057] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[1058] Evaluation based on the user's goals: For example, if the goal is to lose weight, check whether the calorie balance is negative, and if the goal is to build muscle, evaluate whether the protein intake is sufficient.

[1059] Training menu generation

[1060] Server: Based on the analysis results of the generation AI, the following training menu and dietary advice is generated.

[1061] Exercise suggestions: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, the system suggests "20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body)."

[1062] Dietary advice: Advise users to adjust their diet. For example, if a user is lacking in protein, suggest adding a protein shake to their breakfast.

[1063] 3. Providing and monitoring results

[1064] Providing results

[1065] Server: Sends the generated training menu and dietary advice to the user's terminal.

[1066] Device: Displays the received training menu and dietary advice on the application interface.

[1067] Progress monitoring

[1068] Server: Periodically collects new exercise and dietary data and reanalyzes it with Generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[1069] For example, if exercise effectiveness stagnates or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[1070] Specific examples

[1071] User A enters the following information into the app:

[1072] Exercise: 30 minutes of running, 20 minutes of weight training

[1073] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1074] Data analysis example

[1075] Server: From the exercise data, the energy expenditure is calculated to be 500 kcal, and from the diet data, the calorie intake is calculated to be 1500 kcal. It is also determined that the person is slightly deficient in protein.

[1076] Generative AI: For User A, who wants to increase muscle strength, the AI ​​suggests "20 minutes of running and 30 minutes of weight training (especially strengthening the upper body)" as exercise for the next day, and provides advice on adding a protein shake to meals.

[1077] In this way, the system provides training menus and dietary advice optimized to the user's needs, maximizing the effectiveness of training and supporting a healthy lifestyle.

[1078] The processing flow will be explained below.

[1079] Step 1:

[1080] User: After training, launch the dedicated app and enter the exercise details (type of exercise, time, intensity).

[1081] Step 2:

[1082] Device: The entered exercise data is temporarily stored in local storage and sent to a server via the Internet.

[1083] Step 3:

[1084] User: At the end of the day, the user uses a dedicated app to enter the details of the day's meals (e.g., details of breakfast, lunch, dinner, and snacks).

[1085] Step 4:

[1086] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[1087] Step 5:

[1088] Server: Stores the received exercise and dietary data in a database and organizes and classifies it for each user.

[1089] Step 6:

[1090] Server: Passes the user's exercise and dietary data from the database to the generation AI.

[1091] Step 7:

[1092] Generative AI: Analyzes exercise data and calculates energy consumption. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1093] Step 8:

[1094] Generative AI: Analyzes dietary data and evaluates the balance of ingested nutrients (protein, carbohydrates, lipids, vitamins, and minerals).

[1095] Step 9:

[1096] Generative AI: Based on the user's goals (e.g., dieting, muscle building), it compares energy expenditure and nutrient intake assessments.

[1097] Step 10:

[1098] Generative AI: Generates optimal training and dietary advice based on the user's goals. For example, a user aiming to improve muscle strength would be advised to run for 20 minutes, do weight training for 30 minutes (especially for upper body strengthening), and add protein shakes to their meals.

[1099] Step 11:

[1100] Server: Sends the generated training menu and dietary advice to the user's terminal.

[1101] Step 12:

[1102] Device: Displays the received training menu and dietary advice on the user app interface.

[1103] Step 13:

[1104] Server: Periodically collects new exercise and diet data and repeats steps 6 through 11 above to monitor the user's progress and adjust the training plan as needed.

[1105] In this way, users can maximize the effectiveness of their training by always receiving training menus and dietary advice optimized for their goals.

[1106] Example 1

[1107] 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."

[1108] In today's world, individualized exercise and dietary management is important for preventing lifestyle-related diseases and promoting health. However, it is difficult for individual users to create appropriate training menus and meal plans on their own. Furthermore, existing systems require cumbersome data entry and analysis, making it difficult to maintain user motivation. There is a need for an efficient system that can solve these issues and provide users with optimal training menus and dietary advice.

[1109] 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.

[1110] In this invention, the server includes means for inputting the user's exercise details, means for inputting the user's meal details, means for transmitting the exercise details and meal details to an information processing device, generation AI means for analyzing the exercise details and meal details, means for generating an optimal training menu and meal advice based on the analysis results, means for providing the generated training menu and meal advice to the user's display device, and means for monitoring the user's progress and adjusting the training plan as necessary. This frees users from the hassle of complicated data entry and analysis work, and enables them to easily receive training and meal plans optimized for their individual needs.

[1111] "User" refers to an individual who uses the system to input exercise and dietary information and receives training menus and dietary advice.

[1112] "Means for inputting exercise details" refers to an interface for recording the type and time of exercise performed by the user and inputting the data.

[1113] "Means for inputting meal contents" refers to an interface that allows the user to record the contents and amounts of food consumed and input them as data.

[1114] "Information processing device" refers to a computer system for receiving, storing, and analyzing user input data.

[1115] "Generative AI means" refers to artificial intelligence technology that analyzes exercise and dietary content based on collected data and generates optimal training menus and dietary advice.

[1116] A "training menu" refers to a specific exercise plan proposed based on the user's training goals and exercise history.

[1117] "Dietary advice" refers to specific dietary guidance suggested based on a user's health goals and dietary history.

[1118] The term "display device" refers to a display or application interface for displaying the generated training menu and dietary advice to the user.

[1119] "Means for monitoring progress" refers to the ability to track a user's daily exercise and food intake, and adjust training plans and dietary advice as needed based on the results.

[1120] "Energy expenditure" refers to the amount of calories a user expends through exercise.

[1121] "Nutrients" refers to nutritional components such as proteins, carbohydrates, lipids, vitamins, and minerals ingested through food.

[1122] "Storage device" refers to a data storage system that stores a user's exercise history and diet history.

[1123] "Database" refers to a data management system for systematically storing and managing individual data of users.

[1124] This invention is a system that utilizes generative AI to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. This system functions in cooperation with the server, terminal, and user at each processing step. Specifically, it is implemented as follows.

[1125] 1. Data Collection

[1126] (User exercise input)

[1127] After completing their training, the user launches a dedicated application and inputs the details of their exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[1128] The device temporarily stores the input exercise data in local storage and transmits it to a server via the Internet.

[1129] (User's meal information)

[1130] At the end of the day, users enter their meal plans (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application.

[1131] The terminal temporarily stores the input meal data in local storage and transmits it to a server via the Internet.

[1132] 2. Data analysis and training menu generation

[1133] (Data collection and organization)

[1134] The server categorizes the received exercise and dietary data for each user and stores them in a database, which also manages the user's past training history and dietary details.

[1135] (Analysis by generative AI)

[1136] The server passes the saved data to the generation AI, which performs the following analysis:

[1137] Calculating energy consumption: Calculate the user's calorie consumption from exercise data. For example, it is estimated that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1138] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[1139] Evaluation based on the user's goals: For example, if the goal is to lose weight, check whether the calorie balance is negative, and if the goal is to build muscle, evaluate whether the protein intake is sufficient.

[1140] (Generating training menus)

[1141] Based on the analysis results of the generation AI, the server generates the following training menu and dietary advice.

[1142] Exercise suggestions: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, the system suggests "20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body)."

[1143] Dietary advice: Advise users to adjust their diet. For example, if a user is lacking in protein, suggest adding a protein shake to their breakfast.

[1144] 3. Providing and monitoring results

[1145] (Providing results)

[1146] The server transmits the generated training menu and dietary advice to the user terminal.

[1147] The device displays the received training menu and dietary advice on the application interface.

[1148] (Progress monitoring)

[1149] The server periodically collects new exercise and dietary data and reanalyzes it with the Generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[1150] Example: For example, if exercise results stagnate or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[1151] Specific examples

[1152] User A's input

[1153] Exercise: 30 minutes of running, 20 minutes of weight training

[1154] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1155] (Example of data analysis)

[1156] The server calculates from the exercise data that the energy expenditure is 500 kcal, and from the diet data, it analyzes that the calorie intake is 1500 kcal. It also finds that the person is slightly deficient in protein.

[1157] The AI ​​generator suggests to User A, who wants to increase his muscle strength, that he do 20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body) for the next day's exercise, and advises him to add a protein shake to his breakfast.

[1158] By using this system, users can easily obtain individually optimized training menus and dietary advice, making health management more efficient.

[1159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1160] Step 1: Data entry

[1161] The user starts a dedicated application and inputs the exercise details (e.g., 30 minutes of running, 20 minutes of weight training). The input details include the type of exercise, exercise time, exercise intensity, etc.

[1162] The device temporarily stores the input exercise data in local storage. The input data is saved in JSON format or similar.

[1163] Input: User's exercise details

[1164] Output: Exercise data saved in local storage

[1165] Specific behavior:

[1166] Tap the "Enter exercise details" button on the app's home screen.

[1167] Enter information such as "30 minutes of running" and "20 minutes of weight training" into the input form and press the "Save" button.

[1168] Step 2: Send data

[1169] The device transmits the stored exercise data, including the user's identification information and a timestamp, to a server via the Internet.

[1170] The server stores the received data in a database.

[1171] Input: Exercise data stored in local storage

[1172] Output: Exercise data stored in the server database

[1173] Specific behavior:

[1174] Tap the "Send" button to send the saved exercise data.

[1175] The data is encrypted using a security protocol and sent to the server.

[1176] Step 3: Enter your meal data

[1177] At the end of the day, the user enters the details of their meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application, including the type and amount of each meal.

[1178] The device temporarily stores the entered meal data in local storage.

[1179] Input: User's diet

[1180] Output: Meal data saved in local storage

[1181] Specific behavior:

[1182] Tap the "Enter meal details" button in the app.

[1183] Fill in the input form with information such as "Breakfast: Oatmeal and banana," "Lunch: Chicken breast salad," and "Dinner: Grilled fish and vegetable soup," and press the "Save" button.

[1184] Step 4: Send your meal data

[1185] The device transmits the stored meal data to a server via the Internet, including the user's identification information and a timestamp.

[1186] The server stores the received data in a database.

[1187] Input: Meal data stored in local storage

[1188] Output: Meal data stored in the server database

[1189] Specific behavior:

[1190] Tap the "Send" button to send the saved meal data.

[1191] The data is encrypted using a security protocol and sent to the server.

[1192] Step 5: Data analysis

[1193] The server passes the saved exercise and dietary data to the generation AI for analysis.

[1194] Calculates energy expenditure: Calculates calorie expenditure from exercise data. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1195] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[1196] Evaluation based on user goals: Evaluate whether calorie balance and protein intake are in line with the user's goals.

[1197] Input: Exercise and diet data stored in the server database

[1198] Output: Analysis results by generative AI

[1199] Specific behavior:

[1200] The user's exercise and dietary data is input as a prompt into the generative AI model.

[1201] The generating AI outputs the calculation results and returns the analysis result: "Energy consumption: 500 kcal, calorie intake: 1500 kcal, protein deficiency."

[1202] Step 6: Generate training menu and dietary advice

[1203] Based on the analysis results of the generation AI, the server generates optimal training menus and dietary advice for each individual user.

[1204] Suggested exercises: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, it suggests "20 minutes of running, 30 minutes of weight training (especially for strengthening the upper body)."

[1205] Dietary advice: Suggestions to adjust dietary content, e.g., adding a protein shake to breakfast if the user is lacking in protein.

[1206] Input: Analysis results by generative AI

[1207] Output: Generated training menu and dietary advice

[1208] Specific behavior:

[1209] The prompt text is input to the generation AI as follows: "Analysis results for user A: energy consumption 500 kcal, calorie intake 1500 kcal, protein deficiency. Goal is to increase muscle strength."

[1210] The generated AI returns the following suggestion: "Next day: 20 minutes of running, 30 minutes of weight training (upper body). Breakfast: Add a protein shake."

[1211] Step 7: Delivering results

[1212] The server transmits the generated training menu and dietary advice to the user's terminal.

[1213] The device displays the received training menu and dietary advice on the application interface.

[1214] Input: Generated training menu and dietary advice

[1215] Output: Training menu and dietary advice displayed on the user's device

[1216] Specific behavior:

[1217] When users open the app, a pop-up notification appears with new training and dietary advice.

[1218] It will be displayed in list format on the training menu details page.

[1219] Step 8: Monitor progress and reanalyze

[1220] The server periodically collects new exercise and dietary data and reanalyzes it with the generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[1221] For example, if exercise effectiveness stagnates or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[1222] Input: New exercise and diet data collected periodically

[1223] Output: Tailored training plan or dietary advice

[1224] Specific behavior:

[1225] At the end of each week, users tap the "Weekly Review" button in the app to send their latest exercise and diet data to the server.

[1226] The server analyzes the data, generates a new training plan and any necessary dietary adjustments, and notifies the user.

[1227] In this way, the entire system works together to effectively support the user's training and dietary habits and maintain a healthy lifestyle.

[1228] (Application example 1)

[1229] 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."

[1230] Traditionally, there have been significant challenges in managing the health of factory workers. In particular, lack of exercise and inappropriate diets have led to reduced productivity and health problems. In addition, there is the issue that optimizing health management for each worker requires a great deal of effort and time. Therefore, there is a need to effectively manage health and improve productivity based on exercise and diet data.

[1231] 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.

[1232] In this invention, the server includes a means for inputting exercise and dietary data of factory workers and recommending optimal training and rest methods and meals to maintain employee health and improve productivity, a means for calculating energy expenditure from the user's exercise content, and a means for evaluating nutrients ingested from the user's diet content, thereby enabling optimal health management for each worker.

[1233] Key Word Definitions

[1234] "Exercise details" refers to details such as the type, duration, and intensity of the exercise performed by the user.

[1235] "Meal details" refers to details such as the type, amount, and nutritional components of the food consumed by the user.

[1236] "Server" means a central system for collecting, storing, and analyzing data over the Internet.

[1237] "Generative AI means" is an artificial intelligence technology that analyzes data collected from users and generates optimal training menus and dietary advice.

[1238] A "training menu" is a plan that specifically indicates the exercise content that the user will perform.

[1239] "Dietary advice" refers to the content and method of meals recommended based on the user's health condition and goals.

[1240] A "user's terminal" is an electronic device used by a user, such as a smartphone or tablet.

[1241] "Progress" refers to the degree of achievement or progress toward health or exercise goals set by the user.

[1242] "Monitoring" refers to the continuous collection and analysis of the user's exercise and dietary data to monitor the situation.

[1243] "Factory workers" refers to employees who work in factories, and are the subjects for evaluating their health status and productivity.

[1244] "Energy expenditure" refers to the amount of calories burned by the user through exercise.

[1245] "Nutrient assessment" refers to assessing the nutritional value of a meal consumed by a user.

[1246] "History" is a record of exercise and dietary data entered by the user in the past.

[1247] A "database" is a system that stores collected data and manages it so that it can be easily searched and analyzed.

[1248] "Customization" means providing optimal training menus and dietary advice based on the user's individual needs and circumstances.

[1249] MODE FOR CARRYING OUT THE INVENTION

[1250] This invention is a system that utilizes AI to provide optimal training menus and dietary advice for each worker based on the user's exercise and dietary habits. Specific embodiments of this system are described below.

[1251] Data collection

[1252] Input of user's exercise details

[1253] After completing their training, users launch a dedicated application and input their exercise details (e.g., 30 minutes of walking, 20 minutes of weight training). The user's device temporarily stores the input exercise data in local storage and transmits it to a server via the Internet.

[1254] Input of user's meal details

[1255] At the end of the day, the user enters the details of their meals (e.g., salad and chicken for lunch, grilled fish and vegetable soup for dinner) into a dedicated application. The user's device temporarily stores the entered meal data in local storage and transmits it to a server via the Internet.

[1256] Data analysis and training menu generation

[1257] Data collection and organization

[1258] The server categorizes the received exercise and dietary data for each user and stores them in a database, which also manages the user's past training and dietary history.

[1259] Analysis by generative AI

[1260] The server passes the saved data to the generation AI, which performs the following processes: Calculates the user's calorie consumption from the exercise data (for example, it is estimated that walking for 30 minutes burns about 150 kcal, and weight training for 20 minutes burns about 200 kcal), and evaluates the nutrients ingested from the diet data (protein, carbohydrates, lipids, vitamins, minerals, etc.).

[1261] It also evaluates the user's goals. For example, if the goal is to lose weight, it checks whether the calorie balance is negative, and if the goal is to build muscle, it evaluates whether the protein intake is sufficient.

[1262] Training menu generation

[1263] Based on the analysis results of the generative AI, the server will suggest exercise regimens and rest methods for the next day. It will also provide advice on dietary habits, helping users maintain their health and improve their work efficiency.

[1264] Specific examples

[1265] As a specific example of usage, user A enters the following information into the app: exercise details are "30 minutes of walking, 20 minutes of weight training," and meal details are "salad and chicken for lunch, grilled fish and vegetable soup for dinner." The server analyzes this data and generates the following advice: "Today's calorie expenditure is 350 kcal, but calorie intake is 700 kcal. As an immediate goal, we recommend that you either increase your exercise volume tomorrow or reduce the calories in your diet."

[1266] Hardware and software used

[1267] User device: smartphone, tablet, or PC

[1268] Server: Cloud-based database and AI models

[1269] Generation AI: Python, scikit-learn, RandomForestRegressor

[1270] Generative AI model prompt

[1271] The generative AI model is given a prompt like this:

[1272] "User's exercise data: Please suggest optimal training and dietary advice based on User A's exercise and dietary data."

[1273] As described above, the present invention is a system that optimizes health management for each worker and improves factory productivity.

[1274] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1275] Program processing flow

[1276] Step 1:

[1277] The user launches a dedicated application and inputs their exercise and meal details. Examples of inputs include "30 minutes of walking, 20 minutes of weight training" or "Salad and chicken for lunch, grilled fish and vegetable soup for dinner." This allows the user's exercise and meal data to be collected.

[1278] Step 2:

[1279] The user's device temporarily stores the input exercise and dietary data in local storage, then transmits this data to a server via the Internet. The input data includes the type of exercise, duration, dietary content, and calorie intake.

[1280] Step 3:

[1281] The server classifies the received exercise and dietary data for each user and stores them in a database. This allows past training and dietary history to be managed. Data stored in the database includes date and time, exercise content, and dietary content.

[1282] Step 4:

[1283] The server then passes the saved data to the AI ​​generator. During this process, the user's energy expenditure is calculated from their exercise data, and calorie and nutrient intake is assessed from their dietary data. For example, it is estimated that 30 minutes of walking will burn approximately 150 kcal, and 20 minutes of weight training will burn approximately 200 kcal.

[1284] Step 5:

[1285] Based on the analysis results, the AI ​​generates training menus and dietary advice tailored to the user's goals (such as building muscle or losing weight). Specifically, this includes suggestions for the next day's exercise menu and appropriate meals. Different advice is provided for each user depending on the analysis results.

[1286] Step 6:

[1287] The generated training menu and dietary advice are sent from the server to the user's device. The user's device receives it and displays it on the interface of a dedicated application. For example, an exercise menu might be displayed as "20 minutes of running, 30 minutes of weight training (especially for upper body strengthening)."

[1288] Step 7:

[1289] The user implements the training menu and dietary advice and then enters the results back into the application, which then collects progress data. The user's progress data is sent to a server and periodically reanalyzed by the generating AI.

[1290] Step 8:

[1291] The server reanalyzes the progress data using AI and adjusts the training plan and dietary advice as needed, thereby maximizing the effectiveness of the user's training and optimizing health management.

[1292] The above is the specific processing flow of the system program of the application example. This system aims to maintain the user's health and improve work efficiency.

[1293] 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.

[1294] This invention is a system that uses generative AI and an emotion engine to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. The detailed operation of this system is described below.

[1295] 1. Data Collection

[1296] Input of user's exercise details

[1297] User: After training, launch the dedicated application and enter the details of the exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[1298] Terminal: The input exercise data is temporarily stored in local storage and sent to a server via the Internet.

[1299] Input of user's meal details

[1300] User: At the end of the day, the user uses a dedicated application to input the details of the day's meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner).

[1301] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[1302] 2. Emotional Recognition

[1303] Input of user emotion data

[1304] User: Uses the application to add emotional data to the format when entering training or dietary information (e.g., taking a photo of an expression, inputting voice data, or tagging text with emotions).

[1305] Terminal: The input emotion data is temporarily stored in local storage and sent to a server via the Internet.

[1306] Emotional Data Analysis

[1307] Server: Passes the received emotion data to the emotion engine and analyzes the user's emotional state. The emotion engine uses facial expression recognition, voice analysis, and character analysis to classify and evaluate the user's emotions (e.g., joy, sadness, anger, stress).

[1308] 3. Data analysis and training menu generation

[1309] Data collection and organization

[1310] Server: In addition to exercise and dietary data, emotional data is classified and organized for each user and stored in a database.

[1311] Analysis by generative AI

[1312] Server: Passes the user's exercise data, dietary data, and emotional data from the database to the generation AI, and performs the following processes.

[1313] Calculating energy expenditure: Calculating the user's calorie expenditure from exercise data.

[1314] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[1315] Emotional data evaluation: Evaluate the user's emotional state from emotional data (e.g., recommend light exercise if stress levels are high, or hard training if positive emotions predominate).

[1316] Training menu generation

[1317] Server: Based on the analysis results of the generation AI, the following training menu and dietary advice is generated.

[1318] Exercise suggestions: The system suggests exercise menus based on the user's goals and emotional state. For example, a user aiming to improve muscle strength would be suggested to do 20 minutes of running and 30 minutes of weight training (especially for upper body strengthening), while a user experiencing high stress would be suggested to do 30 minutes of yoga.

[1319] Dietary advice: Advise users to adjust their diet taking into account their emotional state and nutritional balance. For example, recommend chamomile tea to relieve stress.

[1320] 4. Providing and monitoring results

[1321] Providing results

[1322] Server: Sends the generated training menu and dietary advice to the user's terminal along with advice on emotional state.

[1323] Terminal: Displays the received training menu, dietary advice, and emotional advice on the application interface.

[1324] Progress monitoring

[1325] Server: Periodically collects new exercise, diet, and emotional data and reanalyzes it with generative AI, allowing it to monitor the user's progress in real time and adjust the training plan as needed.

[1326] For example, if exercise effectiveness stagnates or emotional data indicates high stress levels, the generative AI will generate new training menus and dietary advice and immediately provide them to the user.

[1327] Specific examples

[1328] User B enters the following information into the app:

[1329] Exercise: 30 minutes of running, 20 minutes of weight training

[1330] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1331] Emotional data: Report stressful experiences during training via voice input

[1332] Data analysis example

[1333] Server: From the exercise data, the energy expenditure is calculated to be 500 kcal, and from the dietary data, the calorie intake is calculated to be 1500 kcal. Furthermore, from the emotion data, it is determined that User B's stress level is high.

[1334] Generative AI: Because stress relief is necessary, the system suggests "30 minutes of yoga" as exercise for the next day and "chamomile tea, which has a relaxing effect" as food.

[1335] In this way, the system supports both training and mental care by providing training menus and dietary advice optimized for the user's needs and emotional state.

[1336] The processing flow will be explained below.

[1337] Step 1:

[1338] User: After training, launch the dedicated app and enter the exercise details (type of exercise, time, intensity).

[1339] Step 2:

[1340] Device: The entered exercise data is temporarily stored in local storage and sent to a server via the Internet.

[1341] Step 3:

[1342] User: At the end of the day, the user uses a dedicated app to enter the details of the day's meals (e.g., details of breakfast, lunch, dinner, and snacks).

[1343] Step 4:

[1344] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[1345] Step 5:

[1346] Users: Enter emotional data when entering workout or diet details within the same app, for example by taking a photo of their face, reporting their emotions via voice, or tagging text with emotional tags.

[1347] Step 6:

[1348] Terminal: The input emotion data is temporarily stored in local storage and sent to a server via the Internet.

[1349] Step 7:

[1350] Server: Organizes the received exercise data, dietary data, and emotional data for each user and stores them in a database.

[1351] Step 8:

[1352] Server: Passes the stored data to the generative AI and emotion engine, which analyzes the user's emotional state using facial, voice, and text analysis.

[1353] Step 9:

[1354] Generative AI: Analyzes exercise data and calculates energy consumption. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1355] Step 10:

[1356] Generative AI: Analyzes dietary data and evaluates the balance of ingested nutrients (protein, carbohydrates, lipids, vitamins, minerals, etc.).

[1357] Step 11:

[1358] Emotion engine: Analyzes emotional data to assess the user's emotional state. For example, it can determine "stress," "joy," or "sadness" from facial photos and voice analysis.

[1359] Step 12:

[1360] Generative AI: Based on the evaluation results of the emotion engine, the system comprehensively compares exercise and dietary data to generate optimal training menus and dietary advice based on the user's goals. For example, a user experiencing high stress may be recommended to do 30 minutes of yoga and add chamomile tea to their diet.

[1361] Step 13:

[1362] Server: Sends the generated training menu, dietary advice, and emotional state feedback to the user terminal.

[1363] Step 14:

[1364] Device: Displays the received training menu, dietary advice, and emotional feedback on the app interface.

[1365] Step 15:

[1366] Server: Periodically collects new exercise, dietary, and emotional data and reanalyzes it with the generative AI and emotion engine, allowing it to monitor the user's progress in real time and update training plans and dietary advice as needed.

[1367] In this way, users can always receive training menus and dietary advice optimized for their goals and emotional state.

[1368] Example 2

[1369] 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."

[1370] Many people today are being asked to review their exercise and dietary habits to stay healthy, but providing training menus and meal plans that fit each individual's lifestyle and emotional state is extremely difficult. While conventional systems take exercise and dietary data into account, they are unable to provide advice that reflects the user's emotional state, making it difficult to achieve effective health management. Furthermore, they are also inadequate at monitoring users' progress in real time and adjusting plans as needed.

[1371] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1372] In this invention, the server includes means for inputting the user's exercise details, means for inputting the user's diet details, means for inputting the user's emotional data, means for transmitting the exercise details, diet details, and emotional data to the server, generation AI means for analyzing the exercise details, diet details, and emotional data, means for generating an optimal training menu and dietary advice based on the analysis results, means for providing the generated training menu, dietary advice, and emotional state advice to the user's terminal, and means for monitoring the user's progress and adjusting the training plan as necessary. This makes it possible to achieve optimal health management tailored to individual needs by comprehensively considering the user's exercise details, dietary details, and emotional state.

[1373] "Means for inputting user's exercise details" refers to an interface for inputting details such as the type, duration, and intensity of the exercise performed by the user.

[1374] "Means for inputting the user's dietary details" refers to an interface for inputting details such as the type, amount, and nutrients of the food consumed by the user.

[1375] "Means for inputting user emotional data" refers to an interface for inputting the user's emotional state, such as attaching emotion tags to a photograph of the user's facial expression, voice input, or text input.

[1376] The "means for transmitting the exercise details, meal details, and emotion data to the server" refers to a communication means for transmitting the exercise details, meal details, and emotion data from the terminal to the server via the Internet.

[1377] "Generative AI means for analyzing the exercise content, dietary content and emotional data" refers to artificial intelligence technology for analyzing exercise content, dietary content and emotional data and generating optimal training menus and dietary advice for users.

[1378] "Means for generating optimal training menus and dietary advice based on analysis results" refers to means for generating training menus and dietary advice according to the user's goals and condition based on data analyzed by the generation AI.

[1379] "Means for providing the generated training menu, dietary advice, and emotional state advice to the user's terminal" refers to an interface for transmitting the training menu, dietary advice, and emotional advice generated by the server to the user's terminal and displaying them.

[1380] "Means for monitoring the user's progress and adjusting the training plan as needed" refers to means for periodically collecting new exercise, dietary and emotional data, monitoring the user's progress based on that data, and updating training and dietary advice as needed.

[1381] "Means for calculating energy expenditure" refers to algorithms or functions for calculating calories burned based on the activity.

[1382] "Means for assessing nutrient intake" refers to algorithms or databases for assessing the types and amounts of nutrients ingested based on dietary content.

[1383] "Means for assessing emotional state" refers to algorithms and artificial intelligence technologies for analyzing and assessing the user's emotional state based on the acquired emotional data.

[1384] "Means for storing in a database" refers to a relational or non-relational database for structuring and storing the user's exercise, diet, and emotional data for long-term storage.

[1385] "Means for customizing training menus and dietary advice" refers to generative AI technology that uses stored past data to individually create optimal training menus and dietary advice for each user.

[1386] The present invention is a system that provides optimal training menus and dietary advice based on a user's exercise and dietary habits by utilizing a generative AI and an emotion engine. The following describes in detail an embodiment of the present invention.

[1387] Data collection

[1388] Users use a dedicated application to input their exercise and dietary information. This application runs on devices such as smartphones and tablets, and uses keyboard input, voice input, and a camera as interfaces. For example, after training, a user might input their exercise information, such as 30 minutes of running and 20 minutes of weight training, and at the end of the day, they might input their diet information, such as oatmeal and a banana for breakfast, chicken breast salad for lunch, and grilled fish and vegetable soup for dinner. This data is temporarily stored in the device's local storage and then sent to a server via the Internet.

[1389] Emotion recognition

[1390] When users enter their workout or diet details, they also add emotional data. This emotional data is acquired by taking photos of their facial expressions, inputting voice, or tagging text with emotions. The device temporarily stores this data in local storage and then transmits it to a server via the Internet. The server then passes the received emotional data to an emotion engine, which analyzes the user's emotional state. For example, facial recognition is performed using Amazon Rekognition, and voice analysis is performed using Google Cloud Speech-to-Text.

[1391] Data analysis and training menu generation

[1392] The server stores the user's exercise data, diet data, and emotional data in a centralized database. This database can be a relational database (e.g., MySQL) or a non-relational database (e.g., MongoDB). Next, a generative AI (e.g., OpenAI GPT-3) analyzes this data and performs the following processes:

[1393] Energy expenditure calculation: Calculate calorie expenditure based on exercise data.

[1394] Assessment of nutritional balance: Evaluate nutrients ingested based on dietary data.

[1395] Emotional Data Evaluation: Evaluate the user's emotional state based on the emotional data.

[1396] Based on the analysis results, the generative AI generates optimal training menus and dietary advice. For example, for a user with high stress levels, it might recommend 30 minutes of yoga and suggest chamomile tea as a meal.

[1397] Delivering and monitoring results

[1398] The generated training menu and dietary advice are sent from the server to the user's device. The application on the device displays this advice on its interface. The server also periodically collects new data and reanalyzes it using the generating AI to monitor the user's progress and adjust the training plan as needed. For example, if exercise results stagnate or emotional data indicates high stress levels, the server will instantly generate a new training menu and dietary advice and provide it to the user.

[1399] Specific examples

[1400] If User B enters the following information into the app:

[1401] Exercise: 30 minutes of running, 20 minutes of weight training

[1402] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1403] Emotional data: Report "I felt stressed" during training by voice input

[1404] The server calculates energy expenditure from exercise data to be 500 kcal, and analyzes calorie intake from dietary data to be 1500 kcal. Emotional data reveals that User B has a high stress level, and the AI ​​recommends "30 minutes of yoga" as exercise for the next day and "chamomile tea, which has a relaxing effect" as food.

[1405] Prompt Sentence Examples

[1406] "Analyze the user's exercise data, dietary data, and emotional data to generate optimal training menus and dietary advice."

[1407] Using these prompts, the generative AI can suggest optimal training menus and dietary advice to users based on the input data.

[1408] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1409] Step 1:

[1410] The user starts the dedicated application and inputs the details of the exercise.

[1411] Input: Exercise data provided by the user (e.g., 30 minutes of running, 20 minutes of weight training).

[1412] How it works: The user fills in the application's form with details such as the type of exercise, duration, and intensity. If keyed in or voice-activated, the exercise data is captured accordingly.

[1413] Output: The exercise data is saved in the device's local storage.

[1414] Step 2:

[1415] At the end of the day, the user inputs the details of their meals using a dedicated application.

[1416] Input: Dietary data provided by the user (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner).

[1417] How it works: The user fills out a form in the application to enter details about the food, such as type, amount, and nutrients.

[1418] Output: Meal data is saved to the device's local storage.

[1419] Step 3:

[1420] The user inputs emotion data.

[1421] Input: User emotion data (e.g., facial photos, voice input, emotion tagging of text input).

[1422] How it works: When entering workout or diet information, users select the option to add emotional data, which includes capturing facial expressions with a camera and recording audio.

[1423] Output: Emotion data is saved in the device's local storage.

[1424] Step 4:

[1425] The terminal transmits the collected exercise data, diet data, and emotion data to a server.

[1426] Input: Exercise data, dietary data, and emotional data stored on the device.

[1427] How it works: The device sends data to a server over the internet using a secure communication protocol such as HTTPS.

[1428] Output: Exercise data, diet data and emotion data are sent to the server.

[1429] Step 5:

[1430] The server analyzes the received data.

[1431] Input: Exercise data, diet data and emotion data sent to the server.

[1432] How it works: The server stores this data in a database and passes it to the generation AI, which calculates energy consumption, evaluates nutritional balance, and analyzes emotional data.

[1433] Output: The results of the generative AI analysis, specifically energy expenditure (e.g., 500 kcal), nutritional assessment, and emotional state (e.g., high stress level).

[1434] Step 6:

[1435] Based on the analysis results, the generative AI generates optimal training menus and dietary advice.

[1436] Input: Analysis results by the generative AI (e.g., energy consumption, nutritional balance, emotional state).

[1437] How it works: The generative AI generates optimal training and dietary advice based on the user's goals and condition. For example, for a user with high stress levels, it might suggest 30 minutes of yoga and recommend chamomile tea to reduce stress.

[1438] Output: Generated training menu and dietary advice.

[1439] Step 7:

[1440] The server transmits the generated training menu and dietary advice to the user's terminal.

[1441] Input: Generated training menu and dietary advice.

[1442] How it works: The server sends this data to the device. The communication is secure and fast.

[1443] Output: Training menu and dietary advice are sent to the device.

[1444] Step 8:

[1445] The device displays the received training menu and dietary advice.

[1446] Input: Training menu and dietary advice sent to the device.

[1447] How it works: An application on the device displays this information to the user in an easy-to-understand manner, using text and graphics as the user interface.

[1448] Output: The user will be able to see the training menu and dietary advice.

[1449] Step 9:

[1450] The server periodically collects new data and reanalyzes it using the generating AI.

[1451] Input: New exercise data, diet data, and emotion data.

[1452] How it works: The server periodically collects data and asks the generator AI to reanalyze it, allowing it to monitor the user's progress and adjust the plan as needed.

[1453] Output: Updated training menu and dietary advice.

[1454] (Application example 2)

[1455] 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."

[1456] Conventional training and meal planning systems only provide advice based on the user's exercise and dietary habits, but do not take into account the user's emotional state or psychological factors. This can lead to inappropriate training and dietary advice that ignores the user's stress and emotional fluctuations. Another problem is that the systems do not recommend appropriate products, which does not contribute to improving the user's wellness.

[1457] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting emotion data in addition to the user's exercise details and dietary details, a generation AI means for analyzing the input exercise details, dietary details, and emotion data, a means for recommending optimal training menus, dietary advice, and products based on the analysis results, and a means for monitoring the user's progress and adjusting the training plan as necessary. This makes it possible to provide a comprehensive training menu and dietary advice that takes into account both the user's physical and mental conditions.

[1458] "User's exercise content" refers to specific information such as the type, duration, and intensity of physical exercise performed by the user.

[1459] "User's dietary details" refers to specific information such as the type, amount, and time of food intake by the user.

[1460] "Emotion data" is data that indicates the psychological state of the user, such as stress, happiness, or anxiety, and is collected through facial expression photographs, voice input, and text input.

[1461] The "generative AI means" is an artificial intelligence technology that analyzes exercise, dietary and emotional data obtained from users and generates training menus, dietary advice and product recommendations based on the data.

[1462] A "training menu" is a specific exercise plan tailored to the user's fitness goals and physical condition.

[1463] "Dietary advice" refers to specific instructions or suggestions for recommending an appropriate diet based on the user's nutritional balance and health condition.

[1464] "Product recommendation" refers to suggesting suitable products such as fitness equipment and supplements based on a user's exercise, diet, and emotional data.

[1465] "Progress monitoring means" refers to technology that tracks a user's exercise data, dietary data, and emotional data over a period of time and evaluates the user's progress toward their goals.

[1466] The "means for adjusting the training plan" refers to technology that changes and optimizes the training menu and dietary advice according to the user's progress and fluctuations in emotional data.

[1467] As an embodiment of the present invention, a specific system configuration and processing method will be described.

[1468] System configuration

[1469] This system collects and analyzes the user's exercise, diet, and emotional data to generate optimal training menus, dietary advice, and product recommendations. Each component is described in detail below.

[1470] 1. User Device

[1471] The user terminal is a smartphone, tablet, or computer, and allows the user to input exercise, diet, and emotional data. This terminal also has the ability to communicate with a server via the internet and transmit the collected data.

[1472] 2. Server

[1473] The server receives the data sent from the user and performs the following processing.

[1474] Data collection: Receives user exercise data, diet data, and emotion data and temporarily stores them in local storage.

[1475] Data analysis: The received data is passed to the generation AI and emotion engine for analysis.

[1476] Training menu generation: Based on the analysis results, an optimal training menu and dietary advice is generated.

[1477] Product Recommendations: Recommend products such as fitness equipment and supplements.

[1478] Progress monitoring: We periodically reanalyze your data and adjust your training plan as needed.

[1479] Technology and software used

[1480] Hardware: smartphones, tablets, computers, servers

[1481] software:

[1482] Python: Programming Language

[1483] Keras: A deep learning library

[1484] Scikit-learn: a data preprocessing library

[1485] Flask / Django: Web Frameworks

[1486] Emotion Model: Emotion Analysis Engine

[1487] Generative AI model: Generative AI for analyzing exercise, diet, and emotion data

[1488] Specific examples

[1489] The specific flow of data collection and analysis is explained below.

[1490] Data collection

[1491] Using a smartphone, the user inputs their morning exercise schedule as "30 minutes of running, 20 minutes of weight training," and their evening meal schedule as "oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner." Furthermore, they report emotional data by voice input, such as "feeling stressed during training."

[1492] Data analysis

[1493] The server passes the received exercise data, dietary data, and emotion data to the generation AI and emotion engine. The generation AI calculates energy expenditure from the exercise details and analyzes calorie intake from the diet details. The emotion engine evaluates the user's stress level from the voice data.

[1494] Generate training menus and dietary advice

[1495] Based on the analysis results, the generating AI will suggest "30 minutes of yoga" as a training menu to relieve the user's stress, and will recommend "chamomile tea, which has a relaxing effect" as dietary advice.

[1496] product recommendation

[1497] In addition, the generative AI will recommend related products such as "yoga mats" and "relaxing herbal tea."

[1498] Prompt Sentence Examples

[1499] Below are some example prompts to give to the generative AI model:

[1500] Please generate a training menu and dietary advice suitable for the user based on the following exercise data, dietary data, and emotional data.

[1501] Exercise data: 30 minutes of running, 20 minutes of weight training

[1502] Dietary information: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1503] Emotional data: Feeling stressed during training

[1504] Output format:

[1505] 1. Training Menu

[1506] 2. Dietary advice

[1507] 3. Recommended product list

[1508] In this way, the present invention can provide optimal health management based on a comprehensive assessment of a user's exercise, diet, and emotional state.

[1509] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1510] Step 1:

[1511] The user inputs the details of their exercise. Specifically, the user launches a dedicated application on a smartphone or tablet and inputs exercise data such as "30 minutes of running, 20 minutes of weight training." This input data is temporarily stored in local storage.

[1512] Step 2:

[1513] The user enters their meal plan. At the end of the day, the user enters what they had for breakfast, lunch, and dinner into the application. For example, they might enter "oatmeal and banana for breakfast, chicken breast salad for lunch, and grilled fish and vegetable soup for dinner." This data is also stored in local storage.

[1514] Step 3:

[1515] The user inputs emotional data. When the user inputs exercise and meal details, they can report emotional data such as "I felt stressed during training" using facial photos, voice input, or text input. This emotional data is stored in local storage.

[1516] Step 4:

[1517] The device sends exercise data, diet data, and emotion data to the server. The device then sends this data to the server via the Internet. At this time, the data sent also includes the user ID.

[1518] Step 5:

[1519] The server passes the received data to the generation AI means and emotion engine. The server compiles the received exercise data, diet data, and emotion data and provides it to the generation AI and emotion engine.

[1520] Step 6:

[1521] The server analyzes the data. The generation AI calculates energy consumption from exercise data and evaluates nutrient intake from dietary data. The emotion engine evaluates the user's emotional state from voice and facial expression data. Specifically, the analysis results include "500 kcal consumed" from exercise data, "1500 kcal intake" from dietary data, and "high stress level" from emotion data.

[1522] Step 7:

[1523] The server generates optimal training menus and dietary advice based on the generated results. For example, if stress relief is needed, the server suggests "30 minutes of yoga" as a training menu and "chamomile tea, which has a relaxing effect" as a meal.

[1524] Step 8:

[1525] The server transmits the generated training menu and dietary advice to the user's terminal. The server transmits the generated training menu and dietary advice together with the analysis results to the user's terminal.

[1526] Step 9:

[1527] The terminal provides the received training menu, dietary advice, and product recommendations to the user, who can then view the information through the terminal's application interface.

[1528] Step 10:

[1529] The server periodically collects new user data and reanalyzes it using the AI ​​generator. The AI ​​monitors the user's progress and updates and optimizes training plans and dietary advice as needed. For example, if exercise results stagnate, a new training menu is generated and immediately provided to the user.

[1530] 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.

[1531] 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.

[1532] 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.

[1533] [Fourth embodiment]

[1534] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1535] 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.

[1536] 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).

[1537] 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.

[1538] 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.

[1539] 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).

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] 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.

[1546] 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."

[1547] This invention is a system that uses AI to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. The detailed operation of this system is as follows, and will be explained using specific embodiments.

[1548] 1. Data Collection

[1549] Input of user's exercise details

[1550] User: After finishing training, launch the dedicated application and enter the details of the exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[1551] Terminal: The input exercise data is temporarily stored in local storage and sent to a server via the Internet.

[1552] Input of user's meal details

[1553] User: At the end of the day, the user enters the details of their meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application.

[1554] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[1555] 2. Data analysis and training menu generation

[1556] Data collection and organization

[1557] Server: Classifies the received exercise and dietary data for each user and stores them in a database. This also manages the user's past training and dietary history.

[1558] Analysis by generative AI

[1559] Server: Passes the saved data to the generation AI and performs the following processes.

[1560] Calculating energy consumption: Calculate the user's calorie consumption from exercise data. For example, it is estimated that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1561] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[1562] Evaluation based on the user's goals: For example, if the goal is to lose weight, check whether the calorie balance is negative, and if the goal is to build muscle, evaluate whether the protein intake is sufficient.

[1563] Training menu generation

[1564] Server: Based on the analysis results of the generation AI, the following training menu and dietary advice is generated.

[1565] Exercise suggestions: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, the system suggests "20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body)."

[1566] Dietary advice: Advise users to adjust their diet. For example, if a user is lacking in protein, suggest adding a protein shake to their breakfast.

[1567] 3. Providing and monitoring results

[1568] Providing results

[1569] Server: Sends the generated training menu and dietary advice to the user's terminal.

[1570] Device: Displays the received training menu and dietary advice on the application interface.

[1571] Progress monitoring

[1572] Server: Periodically collects new exercise and dietary data and reanalyzes it with Generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[1573] For example, if exercise effectiveness stagnates or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[1574] Specific examples

[1575] User A enters the following information into the app:

[1576] Exercise: 30 minutes of running, 20 minutes of weight training

[1577] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1578] Data analysis example

[1579] Server: From the exercise data, the energy expenditure is calculated to be 500 kcal, and from the diet data, the calorie intake is calculated to be 1500 kcal. It is also determined that the person is slightly deficient in protein.

[1580] Generative AI: For User A, who wants to increase muscle strength, the AI ​​suggests "20 minutes of running and 30 minutes of weight training (especially strengthening the upper body)" as exercise for the next day, and provides advice on adding a protein shake to meals.

[1581] In this way, the system provides training menus and dietary advice optimized to the user's needs, maximizing the effectiveness of training and supporting a healthy lifestyle.

[1582] The processing flow will be explained below.

[1583] Step 1:

[1584] User: After training, launch the dedicated app and enter the exercise details (type of exercise, time, intensity).

[1585] Step 2:

[1586] Device: The entered exercise data is temporarily stored in local storage and sent to a server via the Internet.

[1587] Step 3:

[1588] User: At the end of the day, the user uses a dedicated app to enter the details of the day's meals (e.g., details of breakfast, lunch, dinner, and snacks).

[1589] Step 4:

[1590] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[1591] Step 5:

[1592] Server: Stores the received exercise and dietary data in a database and organizes and classifies it for each user.

[1593] Step 6:

[1594] Server: Passes the user's exercise and dietary data from the database to the generation AI.

[1595] Step 7:

[1596] Generative AI: Analyzes exercise data and calculates energy consumption. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1597] Step 8:

[1598] Generative AI: Analyzes dietary data and evaluates the balance of ingested nutrients (protein, carbohydrates, lipids, vitamins, and minerals).

[1599] Step 9:

[1600] Generative AI: Based on the user's goals (e.g., dieting, muscle building), it compares energy expenditure and nutrient intake assessments.

[1601] Step 10:

[1602] Generative AI: Generates optimal training and dietary advice based on the user's goals. For example, a user aiming to improve muscle strength would be advised to run for 20 minutes, do weight training for 30 minutes (especially for upper body strengthening), and add protein shakes to their meals.

[1603] Step 11:

[1604] Server: Sends the generated training menu and dietary advice to the user's terminal.

[1605] Step 12:

[1606] Device: Displays the received training menu and dietary advice on the user app interface.

[1607] Step 13:

[1608] Server: Periodically collects new exercise and diet data and repeats steps 6 through 11 above to monitor the user's progress and adjust the training plan as needed.

[1609] In this way, users can maximize the effectiveness of their training by always receiving training menus and dietary advice optimized for their goals.

[1610] Example 1

[1611] 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."

[1612] In today's world, individualized exercise and dietary management is important for preventing lifestyle-related diseases and promoting health. However, it is difficult for individual users to create appropriate training menus and meal plans on their own. Furthermore, existing systems require cumbersome data entry and analysis, making it difficult to maintain user motivation. There is a need for an efficient system that can solve these issues and provide users with optimal training menus and dietary advice.

[1613] 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.

[1614] In this invention, the server includes means for inputting the user's exercise details, means for inputting the user's meal details, means for transmitting the exercise details and meal details to an information processing device, generation AI means for analyzing the exercise details and meal details, means for generating an optimal training menu and meal advice based on the analysis results, means for providing the generated training menu and meal advice to the user's display device, and means for monitoring the user's progress and adjusting the training plan as necessary. This frees users from the hassle of complicated data entry and analysis work, and enables them to easily receive training and meal plans optimized for their individual needs.

[1615] "User" refers to an individual who uses the system to input exercise and dietary information and receives training menus and dietary advice.

[1616] "Means for inputting exercise details" refers to an interface for recording the type and time of exercise performed by the user and inputting the data.

[1617] "Means for inputting meal contents" refers to an interface that allows the user to record the contents and amounts of food consumed and input them as data.

[1618] "Information processing device" refers to a computer system for receiving, storing, and analyzing user input data.

[1619] "Generative AI means" refers to artificial intelligence technology that analyzes exercise and dietary content based on collected data and generates optimal training menus and dietary advice.

[1620] A "training menu" refers to a specific exercise plan proposed based on the user's training goals and exercise history.

[1621] "Dietary advice" refers to specific dietary guidance suggested based on a user's health goals and dietary history.

[1622] The term "display device" refers to a display or application interface for displaying the generated training menu and dietary advice to the user.

[1623] "Means for monitoring progress" refers to the ability to track a user's daily exercise and food intake, and adjust training plans and dietary advice as needed based on the results.

[1624] "Energy expenditure" refers to the amount of calories a user expends through exercise.

[1625] "Nutrients" refers to nutritional components such as proteins, carbohydrates, lipids, vitamins, and minerals ingested through food.

[1626] "Storage device" refers to a data storage system that stores a user's exercise history and diet history.

[1627] "Database" refers to a data management system for systematically storing and managing individual data of users.

[1628] This invention is a system that utilizes generative AI to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. This system functions in cooperation with the server, terminal, and user at each processing step. Specifically, it is implemented as follows.

[1629] 1. Data Collection

[1630] (User exercise input)

[1631] After completing their training, the user launches a dedicated application and inputs the details of their exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[1632] The device temporarily stores the input exercise data in local storage and transmits it to a server via the Internet.

[1633] (User's meal information)

[1634] At the end of the day, users enter their meal plans (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application.

[1635] The terminal temporarily stores the input meal data in local storage and transmits it to a server via the Internet.

[1636] 2. Data analysis and training menu generation

[1637] (Data collection and organization)

[1638] The server categorizes the received exercise and dietary data for each user and stores them in a database, which also manages the user's past training history and dietary details.

[1639] (Analysis by generative AI)

[1640] The server passes the saved data to the generation AI, which performs the following analysis:

[1641] Calculating energy consumption: Calculate the user's calorie consumption from exercise data. For example, it is estimated that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1642] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[1643] Evaluation based on the user's goals: For example, if the goal is to lose weight, check whether the calorie balance is negative, and if the goal is to build muscle, evaluate whether the protein intake is sufficient.

[1644] (Generating training menus)

[1645] Based on the analysis results of the generation AI, the server generates the following training menu and dietary advice.

[1646] Exercise suggestions: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, the system suggests "20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body)."

[1647] Dietary advice: Advise users to adjust their diet. For example, if a user is lacking in protein, suggest adding a protein shake to their breakfast.

[1648] 3. Providing and monitoring results

[1649] (Providing results)

[1650] The server transmits the generated training menu and dietary advice to the user terminal.

[1651] The device displays the received training menu and dietary advice on the application interface.

[1652] (Progress monitoring)

[1653] The server periodically collects new exercise and dietary data and reanalyzes it with the Generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[1654] Example: For example, if exercise results stagnate or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[1655] Specific examples

[1656] User A's input

[1657] Exercise: 30 minutes of running, 20 minutes of weight training

[1658] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1659] (Example of data analysis)

[1660] The server calculates from the exercise data that the energy expenditure is 500 kcal, and from the diet data, it analyzes that the calorie intake is 1500 kcal. It also finds that the person is slightly deficient in protein.

[1661] The AI ​​generator suggests to User A, who wants to increase his muscle strength, that he do 20 minutes of running and 30 minutes of weight training (especially for strengthening the upper body) for the next day's exercise, and advises him to add a protein shake to his breakfast.

[1662] By using this system, users can easily obtain individually optimized training menus and dietary advice, making health management more efficient.

[1663] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1664] Step 1: Data entry

[1665] The user starts a dedicated application and inputs the exercise details (e.g., 30 minutes of running, 20 minutes of weight training). The input details include the type of exercise, exercise time, exercise intensity, etc.

[1666] The device temporarily stores the input exercise data in local storage. The input data is saved in JSON format or similar.

[1667] Input: User's exercise details

[1668] Output: Exercise data saved in local storage

[1669] Specific behavior:

[1670] Tap the "Enter exercise details" button on the app's home screen.

[1671] Enter information such as "30 minutes of running" and "20 minutes of weight training" into the input form and press the "Save" button.

[1672] Step 2: Send data

[1673] The device transmits the stored exercise data, including the user's identification information and a timestamp, to a server via the Internet.

[1674] The server stores the received data in a database.

[1675] Input: Exercise data stored in local storage

[1676] Output: Exercise data stored in the server database

[1677] Specific behavior:

[1678] Tap the "Send" button to send the saved exercise data.

[1679] The data is encrypted using a security protocol and sent to the server.

[1680] Step 3: Enter your meal data

[1681] At the end of the day, the user enters the details of their meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner) into a dedicated application, including the type and amount of each meal.

[1682] The device temporarily stores the entered meal data in local storage.

[1683] Input: User's diet

[1684] Output: Meal data saved in local storage

[1685] Specific behavior:

[1686] Tap the "Enter meal details" button in the app.

[1687] Fill in the input form with information such as "Breakfast: Oatmeal and banana," "Lunch: Chicken breast salad," and "Dinner: Grilled fish and vegetable soup," and press the "Save" button.

[1688] Step 4: Send your meal data

[1689] The device transmits the stored meal data to a server via the Internet, including the user's identification information and a timestamp.

[1690] The server stores the received data in a database.

[1691] Input: Meal data stored in local storage

[1692] Output: Meal data stored in the server database

[1693] Specific behavior:

[1694] Tap the "Send" button to send the saved meal data.

[1695] The data is encrypted using a security protocol and sent to the server.

[1696] Step 5: Data analysis

[1697] The server passes the saved exercise and dietary data to the generation AI for analysis.

[1698] Calculates energy expenditure: Calculates calorie expenditure from exercise data. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1699] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[1700] Evaluation based on user goals: Evaluate whether calorie balance and protein intake are in line with the user's goals.

[1701] Input: Exercise and diet data stored in the server database

[1702] Output: Analysis results by generative AI

[1703] Specific behavior:

[1704] The user's exercise and dietary data is input as a prompt into the generative AI model.

[1705] The generating AI outputs the calculation results and returns the analysis result: "Energy consumption: 500 kcal, calorie intake: 1500 kcal, protein deficiency."

[1706] Step 6: Generate training menu and dietary advice

[1707] Based on the analysis results of the generation AI, the server generates optimal training menus and dietary advice for each individual user.

[1708] Suggested exercises: Suggests an exercise menu for the next day. For example, for a user who wants to improve their muscle strength, it suggests "20 minutes of running, 30 minutes of weight training (especially for strengthening the upper body)."

[1709] Dietary advice: Suggestions to adjust dietary content, e.g., adding a protein shake to breakfast if the user is lacking in protein.

[1710] Input: Analysis results by generative AI

[1711] Output: Generated training menu and dietary advice

[1712] Specific behavior:

[1713] The prompt text is input to the generation AI as follows: "Analysis results for user A: energy consumption 500 kcal, calorie intake 1500 kcal, protein deficiency. Goal is to increase muscle strength."

[1714] The generated AI returns the following suggestion: "Next day: 20 minutes of running, 30 minutes of weight training (upper body). Breakfast: Add a protein shake."

[1715] Step 7: Delivering results

[1716] The server transmits the generated training menu and dietary advice to the user's terminal.

[1717] The device displays the received training menu and dietary advice on the application interface.

[1718] Input: Generated training menu and dietary advice

[1719] Output: Training menu and dietary advice displayed on the user's device

[1720] Specific behavior:

[1721] When users open the app, a pop-up notification appears with new training and dietary advice.

[1722] It will be displayed in list format on the training menu details page.

[1723] Step 8: Monitor progress and reanalyze

[1724] The server periodically collects new exercise and dietary data and reanalyzes it with the generative AI, allowing the user's progress to be monitored in real time and the training plan to be adjusted as needed.

[1725] For example, if exercise effectiveness stagnates or overtraining is suspected, the generative AI will generate a new training menu and immediately provide it to the user.

[1726] Input: New exercise and diet data collected periodically

[1727] Output: Tailored training plan or dietary advice

[1728] Specific behavior:

[1729] At the end of each week, users tap the "Weekly Review" button in the app to send their latest exercise and diet data to the server.

[1730] The server analyzes the data, generates a new training plan and any necessary dietary adjustments, and notifies the user.

[1731] In this way, the entire system works together to effectively support the user's training and dietary habits and maintain a healthy lifestyle.

[1732] (Application example 1)

[1733] 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."

[1734] Traditionally, there have been significant challenges in managing the health of factory workers. In particular, lack of exercise and inappropriate diets have led to reduced productivity and health problems. In addition, there is the issue that optimizing health management for each worker requires a great deal of effort and time. Therefore, there is a need to effectively manage health and improve productivity based on exercise and diet data.

[1735] 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.

[1736] In this invention, the server includes a means for inputting exercise and dietary data of factory workers and recommending optimal training and rest methods and meals to maintain employee health and improve productivity, a means for calculating energy expenditure from the user's exercise content, and a means for evaluating nutrients ingested from the user's diet content, thereby enabling optimal health management for each worker.

[1737] Key Word Definitions

[1738] "Exercise details" refers to details such as the type, duration, and intensity of the exercise performed by the user.

[1739] "Meal details" refers to details such as the type, amount, and nutritional components of the food consumed by the user.

[1740] "Server" means a central system for collecting, storing, and analyzing data over the Internet.

[1741] "Generative AI means" is an artificial intelligence technology that analyzes data collected from users and generates optimal training menus and dietary advice.

[1742] A "training menu" is a plan that specifically indicates the exercise content that the user will perform.

[1743] "Dietary advice" refers to the content and method of meals recommended based on the user's health condition and goals.

[1744] A "user's terminal" is an electronic device used by a user, such as a smartphone or tablet.

[1745] "Progress" refers to the degree of achievement or progress toward health or exercise goals set by the user.

[1746] "Monitoring" refers to the continuous collection and analysis of the user's exercise and dietary data to monitor the situation.

[1747] "Factory workers" refers to employees who work in factories, and are the subjects for evaluating their health status and productivity.

[1748] "Energy expenditure" refers to the amount of calories burned by the user through exercise.

[1749] "Nutrient assessment" refers to assessing the nutritional value of a meal consumed by a user.

[1750] "History" is a record of exercise and dietary data entered by the user in the past.

[1751] A "database" is a system that stores collected data and manages it so that it can be easily searched and analyzed.

[1752] "Customization" means providing optimal training menus and dietary advice based on the user's individual needs and circumstances.

[1753] MODE FOR CARRYING OUT THE INVENTION

[1754] This invention is a system that utilizes AI to provide optimal training menus and dietary advice for each worker based on the user's exercise and dietary habits. Specific embodiments of this system are described below.

[1755] Data collection

[1756] Input of user's exercise details

[1757] After completing their training, users launch a dedicated application and input their exercise details (e.g., 30 minutes of walking, 20 minutes of weight training). The user's device temporarily stores the input exercise data in local storage and transmits it to a server via the Internet.

[1758] Input of user's meal details

[1759] At the end of the day, the user enters the details of their meals (e.g., salad and chicken for lunch, grilled fish and vegetable soup for dinner) into a dedicated application. The user's device temporarily stores the entered meal data in local storage and transmits it to a server via the Internet.

[1760] Data analysis and training menu generation

[1761] Data collection and organization

[1762] The server categorizes the received exercise and dietary data for each user and stores them in a database, which also manages the user's past training and dietary history.

[1763] Analysis by generative AI

[1764] The server passes the saved data to the generation AI, which performs the following processes: Calculates the user's calorie consumption from the exercise data (for example, it is estimated that walking for 30 minutes burns about 150 kcal, and weight training for 20 minutes burns about 200 kcal), and evaluates the nutrients ingested from the diet data (protein, carbohydrates, lipids, vitamins, minerals, etc.).

[1765] It also evaluates the user's goals. For example, if the goal is to lose weight, it checks whether the calorie balance is negative, and if the goal is to build muscle, it evaluates whether the protein intake is sufficient.

[1766] Training menu generation

[1767] Based on the analysis results of the generative AI, the server will suggest exercise regimens and rest methods for the next day. It will also provide advice on dietary habits, helping users maintain their health and improve their work efficiency.

[1768] Specific examples

[1769] As a specific example of usage, user A enters the following information into the app: exercise details are "30 minutes of walking, 20 minutes of weight training," and meal details are "salad and chicken for lunch, grilled fish and vegetable soup for dinner." The server analyzes this data and generates the following advice: "Today's calorie expenditure is 350 kcal, but calorie intake is 700 kcal. As an immediate goal, we recommend that you either increase your exercise volume tomorrow or reduce the calories in your diet."

[1770] Hardware and software used

[1771] User device: smartphone, tablet, or PC

[1772] Server: Cloud-based database and AI models

[1773] Generation AI: Python, scikit-learn, RandomForestRegressor

[1774] Generative AI model prompt

[1775] The generative AI model is given a prompt like this:

[1776] "User's exercise data: Please suggest optimal training and dietary advice based on User A's exercise and dietary data."

[1777] As described above, the present invention is a system that optimizes health management for each worker and improves factory productivity.

[1778] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1779] Program processing flow

[1780] Step 1:

[1781] The user launches a dedicated application and inputs their exercise and meal details. Examples of inputs include "30 minutes of walking, 20 minutes of weight training" or "Salad and chicken for lunch, grilled fish and vegetable soup for dinner." This allows the user's exercise and meal data to be collected.

[1782] Step 2:

[1783] The user's device temporarily stores the input exercise and dietary data in local storage, then transmits this data to a server via the Internet. The input data includes the type of exercise, duration, dietary content, and calorie intake.

[1784] Step 3:

[1785] The server classifies the received exercise and dietary data for each user and stores them in a database. This allows past training and dietary history to be managed. Data stored in the database includes date and time, exercise content, and dietary content.

[1786] Step 4:

[1787] The server then passes the saved data to the AI ​​generator. During this process, the user's energy expenditure is calculated from their exercise data, and calorie and nutrient intake is assessed from their dietary data. For example, it is estimated that 30 minutes of walking will burn approximately 150 kcal, and 20 minutes of weight training will burn approximately 200 kcal.

[1788] Step 5:

[1789] Based on the analysis results, the AI ​​generates training menus and dietary advice tailored to the user's goals (such as building muscle or losing weight). Specifically, this includes suggestions for the next day's exercise menu and appropriate meals. Different advice is provided for each user depending on the analysis results.

[1790] Step 6:

[1791] The generated training menu and dietary advice are sent from the server to the user's device. The user's device receives it and displays it on the interface of a dedicated application. For example, an exercise menu might be displayed as "20 minutes of running, 30 minutes of weight training (especially for upper body strengthening)."

[1792] Step 7:

[1793] The user implements the training menu and dietary advice and then enters the results back into the application, which then collects progress data. The user's progress data is sent to a server and periodically reanalyzed by the generating AI.

[1794] Step 8:

[1795] The server reanalyzes the progress data using AI and adjusts the training plan and dietary advice as needed, thereby maximizing the effectiveness of the user's training and optimizing health management.

[1796] The above is the specific processing flow of the system program of the application example. This system aims to maintain the user's health and improve work efficiency.

[1797] 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.

[1798] This invention is a system that uses generative AI and an emotion engine to provide optimal training menus and dietary advice based on the user's exercise and dietary habits. The detailed operation of this system is described below.

[1799] 1. Data Collection

[1800] Input of user's exercise details

[1801] User: After training, launch the dedicated application and enter the details of the exercise (e.g., 30 minutes of running, 20 minutes of weight training).

[1802] Terminal: The input exercise data is temporarily stored in local storage and sent to a server via the Internet.

[1803] Input of user's meal details

[1804] User: At the end of the day, the user uses a dedicated application to input the details of the day's meals (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner).

[1805] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[1806] 2. Emotional Recognition

[1807] Input of user emotion data

[1808] User: Uses the application to add emotional data to the format when entering training or dietary information (e.g., taking a photo of an expression, inputting voice data, or tagging text with emotions).

[1809] Terminal: The input emotion data is temporarily stored in local storage and sent to a server via the Internet.

[1810] Emotional Data Analysis

[1811] Server: Passes the received emotion data to the emotion engine and analyzes the user's emotional state. The emotion engine uses facial expression recognition, voice analysis, and character analysis to classify and evaluate the user's emotions (e.g., joy, sadness, anger, stress).

[1812] 3. Data analysis and training menu generation

[1813] Data collection and organization

[1814] Server: In addition to exercise and dietary data, emotional data is classified and organized for each user and stored in a database.

[1815] Analysis by generative AI

[1816] Server: Passes the user's exercise data, dietary data, and emotional data from the database to the generation AI, and performs the following processes.

[1817] Calculating energy expenditure: Calculating the user's calorie expenditure from exercise data.

[1818] Assessment of nutritional balance: Evaluate the nutrients ingested (protein, carbohydrates, lipids, vitamins, minerals, etc.) from dietary data.

[1819] Emotional data evaluation: Evaluate the user's emotional state from emotional data (e.g., recommend light exercise if stress levels are high, or hard training if positive emotions predominate).

[1820] Training menu generation

[1821] Server: Based on the analysis results of the generation AI, the following training menu and dietary advice is generated.

[1822] Exercise suggestions: The system suggests exercise menus based on the user's goals and emotional state. For example, a user aiming to improve muscle strength would be suggested to do 20 minutes of running and 30 minutes of weight training (especially for upper body strengthening), while a user experiencing high stress would be suggested to do 30 minutes of yoga.

[1823] Dietary advice: Advise users to adjust their diet taking into account their emotional state and nutritional balance. For example, recommend chamomile tea to relieve stress.

[1824] 4. Providing and monitoring results

[1825] Providing results

[1826] Server: Sends the generated training menu and dietary advice to the user's terminal along with advice on emotional state.

[1827] Terminal: Displays the received training menu, dietary advice, and emotional advice on the application interface.

[1828] Progress monitoring

[1829] Server: Periodically collects new exercise, diet, and emotional data and reanalyzes it with generative AI, allowing it to monitor the user's progress in real time and adjust the training plan as needed.

[1830] For example, if exercise effectiveness stagnates or emotional data indicates high stress levels, the generative AI will generate new training menus and dietary advice and immediately provide them to the user.

[1831] Specific examples

[1832] User B enters the following information into the app:

[1833] Exercise: 30 minutes of running, 20 minutes of weight training

[1834] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1835] Emotional data: Report stressful experiences during training via voice input

[1836] Data analysis example

[1837] Server: From the exercise data, the energy expenditure is calculated to be 500 kcal, and from the dietary data, the calorie intake is calculated to be 1500 kcal. Furthermore, from the emotion data, it is determined that User B's stress level is high.

[1838] Generative AI: Because stress relief is necessary, the system suggests "30 minutes of yoga" as exercise for the next day and "chamomile tea, which has a relaxing effect" as food.

[1839] In this way, the system supports both training and mental care by providing training menus and dietary advice optimized for the user's needs and emotional state.

[1840] The processing flow will be explained below.

[1841] Step 1:

[1842] User: After training, launch the dedicated app and enter the exercise details (type of exercise, time, intensity).

[1843] Step 2:

[1844] Device: The entered exercise data is temporarily stored in local storage and sent to a server via the Internet.

[1845] Step 3:

[1846] User: At the end of the day, the user uses a dedicated app to enter the details of the day's meals (e.g., details of breakfast, lunch, dinner, and snacks).

[1847] Step 4:

[1848] Terminal: The entered meal data is temporarily stored in local storage and sent to a server via the Internet.

[1849] Step 5:

[1850] Users: Enter emotional data when entering workout or diet details within the same app, for example by taking a photo of their face, reporting their emotions via voice, or tagging text with emotional tags.

[1851] Step 6:

[1852] Terminal: The input emotion data is temporarily stored in local storage and sent to a server via the Internet.

[1853] Step 7:

[1854] Server: Organizes the received exercise data, dietary data, and emotional data for each user and stores them in a database.

[1855] Step 8:

[1856] Server: Passes the stored data to the generative AI and emotion engine, which analyzes the user's emotional state using facial, voice, and text analysis.

[1857] Step 9:

[1858] Generative AI: Analyzes exercise data and calculates energy consumption. For example, it estimates that 30 minutes of running burns about 300 kcal, and 20 minutes of weight training burns about 200 kcal.

[1859] Step 10:

[1860] Generative AI: Analyzes dietary data and evaluates the balance of ingested nutrients (protein, carbohydrates, lipids, vitamins, minerals, etc.).

[1861] Step 11:

[1862] Emotion engine: Analyzes emotional data to assess the user's emotional state. For example, it can determine "stress," "joy," or "sadness" from facial photos and voice analysis.

[1863] Step 12:

[1864] Generative AI: Based on the evaluation results of the emotion engine, the system comprehensively compares exercise and dietary data to generate optimal training menus and dietary advice based on the user's goals. For example, a user experiencing high stress may be recommended to do 30 minutes of yoga and add chamomile tea to their diet.

[1865] Step 13:

[1866] Server: Sends the generated training menu, dietary advice, and emotional state feedback to the user terminal.

[1867] Step 14:

[1868] Device: Displays the received training menu, dietary advice, and emotional feedback on the app interface.

[1869] Step 15:

[1870] Server: Periodically collects new exercise, dietary, and emotional data and reanalyzes it with the generative AI and emotion engine, allowing it to monitor the user's progress in real time and update training plans and dietary advice as needed.

[1871] In this way, users can always receive training menus and dietary advice optimized for their goals and emotional state.

[1872] Example 2

[1873] 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."

[1874] Many people today are being asked to review their exercise and dietary habits to stay healthy, but providing training menus and meal plans that fit each individual's lifestyle and emotional state is extremely difficult. While conventional systems take exercise and dietary data into account, they are unable to provide advice that reflects the user's emotional state, making it difficult to achieve effective health management. Furthermore, they are also inadequate at monitoring users' progress in real time and adjusting plans as needed.

[1875] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1876] In this invention, the server includes means for inputting the user's exercise details, means for inputting the user's diet details, means for inputting the user's emotional data, means for transmitting the exercise details, diet details, and emotional data to the server, generation AI means for analyzing the exercise details, diet details, and emotional data, means for generating an optimal training menu and dietary advice based on the analysis results, means for providing the generated training menu, dietary advice, and emotional state advice to the user's terminal, and means for monitoring the user's progress and adjusting the training plan as necessary. This makes it possible to achieve optimal health management tailored to individual needs by comprehensively considering the user's exercise details, dietary details, and emotional state.

[1877] "Means for inputting user's exercise details" refers to an interface for inputting details such as the type, duration, and intensity of the exercise performed by the user.

[1878] "Means for inputting the user's dietary details" refers to an interface for inputting details such as the type, amount, and nutrients of the food consumed by the user.

[1879] "Means for inputting user emotional data" refers to an interface for inputting the user's emotional state, such as attaching emotion tags to a photograph of the user's facial expression, voice input, or text input.

[1880] The "means for transmitting the exercise details, meal details, and emotion data to the server" refers to a communication means for transmitting the exercise details, meal details, and emotion data from the terminal to the server via the Internet.

[1881] "Generative AI means for analyzing the exercise content, dietary content and emotional data" refers to artificial intelligence technology for analyzing exercise content, dietary content and emotional data and generating optimal training menus and dietary advice for users.

[1882] "Means for generating optimal training menus and dietary advice based on analysis results" refers to means for generating training menus and dietary advice according to the user's goals and condition based on data analyzed by the generation AI.

[1883] "Means for providing the generated training menu, dietary advice, and emotional state advice to the user's terminal" refers to an interface for transmitting the training menu, dietary advice, and emotional advice generated by the server to the user's terminal and displaying them.

[1884] "Means for monitoring the user's progress and adjusting the training plan as needed" refers to means for periodically collecting new exercise, dietary and emotional data, monitoring the user's progress based on that data, and updating training and dietary advice as needed.

[1885] "Means for calculating energy expenditure" refers to algorithms or functions for calculating calories burned based on the activity.

[1886] "Means for assessing nutrient intake" refers to algorithms or databases for assessing the types and amounts of nutrients ingested based on dietary content.

[1887] "Means for assessing emotional state" refers to algorithms and artificial intelligence technologies for analyzing and assessing the user's emotional state based on the acquired emotional data.

[1888] "Means for storing in a database" refers to a relational or non-relational database for structuring and storing the user's exercise, diet, and emotional data for long-term storage.

[1889] "Means for customizing training menus and dietary advice" refers to generative AI technology that uses stored past data to individually create optimal training menus and dietary advice for each user.

[1890] The present invention is a system that provides optimal training menus and dietary advice based on a user's exercise and dietary habits by utilizing a generative AI and an emotion engine. The following describes in detail an embodiment of the present invention.

[1891] Data collection

[1892] Users use a dedicated application to input their exercise and dietary information. This application runs on devices such as smartphones and tablets, and uses keyboard input, voice input, and a camera as interfaces. For example, after training, a user might input their exercise information, such as 30 minutes of running and 20 minutes of weight training, and at the end of the day, they might input their diet information, such as oatmeal and a banana for breakfast, chicken breast salad for lunch, and grilled fish and vegetable soup for dinner. This data is temporarily stored in the device's local storage and then sent to a server via the Internet.

[1893] Emotion recognition

[1894] When users enter their workout or diet details, they also add emotional data. This emotional data is acquired by taking photos of their facial expressions, inputting voice, or tagging text with emotions. The device temporarily stores this data in local storage and then transmits it to a server via the Internet. The server then passes the received emotional data to an emotion engine, which analyzes the user's emotional state. For example, facial recognition is performed using Amazon Rekognition, and voice analysis is performed using Google Cloud Speech-to-Text.

[1895] Data analysis and training menu generation

[1896] The server stores the user's exercise data, diet data, and emotional data in a centralized database. This database can be a relational database (e.g., MySQL) or a non-relational database (e.g., MongoDB). Next, a generative AI (e.g., OpenAI GPT-3) analyzes this data and performs the following processes:

[1897] Energy expenditure calculation: Calculate calorie expenditure based on exercise data.

[1898] Assessment of nutritional balance: Evaluate nutrients ingested based on dietary data.

[1899] Emotional Data Evaluation: Evaluate the user's emotional state based on the emotional data.

[1900] Based on the analysis results, the generative AI generates optimal training menus and dietary advice. For example, for a user with high stress levels, it might recommend 30 minutes of yoga and suggest chamomile tea as a meal.

[1901] Delivering and monitoring results

[1902] The generated training menu and dietary advice are sent from the server to the user's device. The application on the device displays this advice on its interface. The server also periodically collects new data and reanalyzes it using the generating AI to monitor the user's progress and adjust the training plan as needed. For example, if exercise results stagnate or emotional data indicates high stress levels, the server will instantly generate a new training menu and dietary advice and provide it to the user.

[1903] Specific examples

[1904] If User B enters the following information into the app:

[1905] Exercise: 30 minutes of running, 20 minutes of weight training

[1906] Meal: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[1907] Emotional data: Report "I felt stressed" during training by voice input

[1908] The server calculates energy expenditure from exercise data to be 500 kcal, and analyzes calorie intake from dietary data to be 1500 kcal. Emotional data reveals that User B has a high stress level, and the AI ​​recommends "30 minutes of yoga" as exercise for the next day and "chamomile tea, which has a relaxing effect" as food.

[1909] Prompt Sentence Examples

[1910] "Analyze the user's exercise data, dietary data, and emotional data to generate optimal training menus and dietary advice."

[1911] Using these prompts, the generative AI can suggest optimal training menus and dietary advice to users based on the input data.

[1912] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1913] Step 1:

[1914] The user starts the dedicated application and inputs the details of the exercise.

[1915] Input: Exercise data provided by the user (e.g., 30 minutes of running, 20 minutes of weight training).

[1916] How it works: The user fills in the application's form with details such as the type of exercise, duration, and intensity. If keyed in or voice-activated, the exercise data is captured accordingly.

[1917] Output: The exercise data is saved in the device's local storage.

[1918] Step 2:

[1919] At the end of the day, the user inputs the details of their meals using a dedicated application.

[1920] Input: Dietary data provided by the user (e.g., oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner).

[1921] How it works: The user fills out a form in the application to enter details about the food, such as type, amount, and nutrients.

[1922] Output: Meal data is saved to the device's local storage.

[1923] Step 3:

[1924] The user inputs emotion data.

[1925] Input: User emotion data (e.g., facial photos, voice input, emotion tagging of text input).

[1926] How it works: When entering workout or diet information, users select the option to add emotional data, which includes capturing facial expressions with a camera and recording audio.

[1927] Output: Emotion data is saved in the device's local storage.

[1928] Step 4:

[1929] The terminal transmits the collected exercise data, diet data, and emotion data to a server.

[1930] Input: Exercise data, dietary data, and emotional data stored on the device.

[1931] How it works: The device sends data to a server over the internet using a secure communication protocol such as HTTPS.

[1932] Output: Exercise data, diet data and emotion data are sent to the server.

[1933] Step 5:

[1934] The server analyzes the received data.

[1935] Input: Exercise data, diet data and emotion data sent to the server.

[1936] How it works: The server stores this data in a database and passes it to the generation AI, which calculates energy consumption, evaluates nutritional balance, and analyzes emotional data.

[1937] Output: The results of the generative AI analysis, specifically energy expenditure (e.g., 500 kcal), nutritional assessment, and emotional state (e.g., high stress level).

[1938] Step 6:

[1939] Based on the analysis results, the generative AI generates optimal training menus and dietary advice.

[1940] Input: Analysis results by the generative AI (e.g., energy consumption, nutritional balance, emotional state).

[1941] How it works: The generative AI generates optimal training and dietary advice based on the user's goals and condition. For example, for a user with high stress levels, it might suggest 30 minutes of yoga and recommend chamomile tea to reduce stress.

[1942] Output: Generated training menu and dietary advice.

[1943] Step 7:

[1944] The server transmits the generated training menu and dietary advice to the user's terminal.

[1945] Input: Generated training menu and dietary advice.

[1946] How it works: The server sends this data to the device. The communication is secure and fast.

[1947] Output: Training menu and dietary advice are sent to the device.

[1948] Step 8:

[1949] The device displays the received training menu and dietary advice.

[1950] Input: Training menu and dietary advice sent to the device.

[1951] How it works: An application on the device displays this information to the user in an easy-to-understand manner, using text and graphics as the user interface.

[1952] Output: The user will be able to see the training menu and dietary advice.

[1953] Step 9:

[1954] The server periodically collects new data and reanalyzes it using the generating AI.

[1955] Input: New exercise data, diet data, and emotion data.

[1956] How it works: The server periodically collects data and asks the generator AI to reanalyze it, allowing it to monitor the user's progress and adjust the plan as needed.

[1957] Output: Updated training menu and dietary advice.

[1958] (Application example 2)

[1959] 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."

[1960] Conventional training and meal planning systems only provide advice based on the user's exercise and dietary habits, but do not take into account the user's emotional state or psychological factors. This can lead to inappropriate training and dietary advice that ignores the user's stress and emotional fluctuations. Another problem is that the systems do not recommend appropriate products, which does not contribute to improving the user's wellness.

[1961] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting emotion data in addition to the user's exercise details and dietary details, a generation AI means for analyzing the input exercise details, dietary details, and emotion data, a means for recommending optimal training menus, dietary advice, and products based on the analysis results, and a means for monitoring the user's progress and adjusting the training plan as necessary. This makes it possible to provide a comprehensive training menu and dietary advice that takes into account both the user's physical and mental conditions.

[1962] "User's exercise content" refers to specific information such as the type, duration, and intensity of physical exercise performed by the user.

[1963] "User's dietary details" refers to specific information such as the type, amount, and time of food intake by the user.

[1964] "Emotion data" is data that indicates the psychological state of the user, such as stress, happiness, or anxiety, and is collected through facial expression photographs, voice input, and text input.

[1965] The "generative AI means" is an artificial intelligence technology that analyzes exercise, dietary and emotional data obtained from users and generates training menus, dietary advice and product recommendations based on the data.

[1966] A "training menu" is a specific exercise plan tailored to the user's fitness goals and physical condition.

[1967] "Dietary advice" refers to specific instructions or suggestions for recommending an appropriate diet based on the user's nutritional balance and health condition.

[1968] "Product recommendation" refers to suggesting suitable products such as fitness equipment and supplements based on a user's exercise, diet, and emotional data.

[1969] "Progress monitoring means" refers to technology that tracks a user's exercise data, dietary data, and emotional data over a period of time and evaluates the user's progress toward their goals.

[1970] The "means for adjusting the training plan" refers to technology that changes and optimizes the training menu and dietary advice according to the user's progress and fluctuations in emotional data.

[1971] As an embodiment of the present invention, a specific system configuration and processing method will be described.

[1972] System configuration

[1973] This system collects and analyzes the user's exercise, diet, and emotional data to generate optimal training menus, dietary advice, and product recommendations. Each component is described in detail below.

[1974] 1. User Device

[1975] The user terminal is a smartphone, tablet, or computer, and allows the user to input exercise, diet, and emotional data. This terminal also has the ability to communicate with a server via the internet and transmit the collected data.

[1976] 2. Server

[1977] The server receives the data sent from the user and performs the following processing.

[1978] Data collection: Receives user exercise data, diet data, and emotion data and temporarily stores them in local storage.

[1979] Data analysis: The received data is passed to the generation AI and emotion engine for analysis.

[1980] Training menu generation: Based on the analysis results, an optimal training menu and dietary advice is generated.

[1981] Product Recommendations: Recommend products such as fitness equipment and supplements.

[1982] Progress monitoring: We periodically reanalyze your data and adjust your training plan as needed.

[1983] Technology and software used

[1984] Hardware: smartphones, tablets, computers, servers

[1985] software:

[1986] Python: Programming Language

[1987] Keras: A deep learning library

[1988] Scikit-learn: a data preprocessing library

[1989] Flask / Django: Web Frameworks

[1990] Emotion Model: Emotion Analysis Engine

[1991] Generative AI model: Generative AI for analyzing exercise, diet, and emotion data

[1992] Specific examples

[1993] The specific flow of data collection and analysis is explained below.

[1994] Data collection

[1995] Using a smartphone, the user inputs their morning exercise schedule as "30 minutes of running, 20 minutes of weight training," and their evening meal schedule as "oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner." Furthermore, they report emotional data by voice input, such as "feeling stressed during training."

[1996] Data analysis

[1997] The server passes the received exercise data, dietary data, and emotion data to the generation AI and emotion engine. The generation AI calculates energy expenditure from the exercise details and analyzes calorie intake from the diet details. The emotion engine evaluates the user's stress level from the voice data.

[1998] Generate training menus and dietary advice

[1999] Based on the analysis results, the generating AI will suggest "30 minutes of yoga" as a training menu to relieve the user's stress, and will recommend "chamomile tea, which has a relaxing effect" as dietary advice.

[2000] product recommendation

[2001] In addition, the generative AI will recommend related products such as "yoga mats" and "relaxing herbal tea."

[2002] Prompt Sentence Examples

[2003] Below are some example prompts to give to the generative AI model:

[2004] Please generate a training menu and dietary advice suitable for the user based on the following exercise data, dietary data, and emotional data.

[2005] Exercise data: 30 minutes of running, 20 minutes of weight training

[2006] Dietary information: Oatmeal and banana for breakfast, chicken breast salad for lunch, grilled fish and vegetable soup for dinner

[2007] Emotional data: Feeling stressed during training

[2008] Output format:

[2009] 1. Training Menu

[2010] 2. Dietary advice

[2011] 3. Recommended product list

[2012] In this way, the present invention can provide optimal health management based on a comprehensive assessment of a user's exercise, diet, and emotional state.

[2013] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2014] Step 1:

[2015] The user inputs the details of their exercise. Specifically, the user launches a dedicated application on a smartphone or tablet and inputs exercise data such as "30 minutes of running, 20 minutes of weight training." This input data is temporarily stored in local storage.

[2016] Step 2:

[2017] The user enters their meal plan. At the end of the day, the user enters what they had for breakfast, lunch, and dinner into the application. For example, they might enter "oatmeal and banana for breakfast, chicken breast salad for lunch, and grilled fish and vegetable soup for dinner." This data is also stored in local storage.

[2018] Step 3:

[2019] The user inputs emotional data. When the user inputs exercise and meal details, they can report emotional data such as "I felt stressed during training" using facial photos, voice input, or text input. This emotional data is stored in local storage.

[2020] Step 4:

[2021] The device sends exercise data, diet data, and emotion data to the server. The device then sends this data to the server via the Internet. At this time, the data sent also includes the user ID.

[2022] Step 5:

[2023] The server passes the received data to the generation AI means and emotion engine. The server compiles the received exercise data, diet data, and emotion data and provides it to the generation AI and emotion engine.

[2024] Step 6:

[2025] The server analyzes the data. The generation AI calculates energy consumption from exercise data and evaluates nutrient intake from dietary data. The emotion engine evaluates the user's emotional state from voice and facial expression data. Specifically, the analysis results include "500 kcal consumed" from exercise data, "1500 kcal intake" from dietary data, and "high stress level" from emotion data.

[2026] Step 7:

[2027] The server generates optimal training menus and dietary advice based on the generated results. For example, if stress relief is needed, the server suggests "30 minutes of yoga" as a training menu and "chamomile tea, which has a relaxing effect" as a meal.

[2028] Step 8:

[2029] The server transmits the generated training menu and dietary advice to the user's terminal. The server transmits the generated training menu and dietary advice together with the analysis results to the user's terminal.

[2030] Step 9:

[2031] The terminal provides the received training menu, dietary advice, and product recommendations to the user, who can then view the information through the terminal's application interface.

[2032] Step 10:

[2033] The server periodically collects new user data and reanalyzes it using the AI ​​generator. The AI ​​monitors the user's progress and updates and optimizes training plans and dietary advice as needed. For example, if exercise results stagnate, a new training menu is generated and immediately provided to the user.

[2034] 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.

[2035] 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.

[2036] 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.

[2037] 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.

[2038] 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.

[2039] 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.

[2040] 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).

[2041] 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.

[2042] 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."

[2043] 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.

[2044] 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).

[2045] 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.

[2046] 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.

[2047] 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.

[2048] 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.

[2049] 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.

[2050] 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.

[2051] 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.

[2052] 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.

[2053] 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 p...

Claims

1. A means for inputting the user's exercise content; A means for inputting the user's meal contents; means for transmitting the exercise details and meal details to a server; A generating AI means for analyzing the exercise content and diet content; A means for generating optimal training menus and dietary advice based on the analysis results; means for providing the generated training menu and dietary advice to a user's terminal; The system includes means for monitoring the user's progress and adjusting the training plan as necessary.

2. The generating AI means is a means for calculating energy consumption from the user's exercise content; a means for assessing the nutrient intake of a user's diet; 10. The system of claim 1, further comprising means for matching said energy expenditure and nutrient assessment based on a user's goals.

3. The generating AI means stores the user's exercise and diet history in a database; 2. The system according to claim 1, further comprising means for customizing an optimal training menu and dietary advice for each user based on past data.

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