system

Abstract

Provide a system. The system includes: a means for calculating the gap between the user's current condition and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; A means for receiving and analyzing dietary information and exercise information input by a user on a daily basis; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the generated exercise and meal plan to the user; means for receiving the results of the exercise and diet performed by the user again and evaluating the progress; a means of adjusting the next day's exercise and meal plan based on the assessment results; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional diet and training programs often have a one-size-fits-all approach, making it difficult to customize them to reflect the physical constitution and lifestyle of each individual user. Furthermore, few systems provide appropriate feedback based on the user's progress, making continuous and efficient weight and body fat management difficult. Furthermore, there has been a lack of systems that adjust future plans based on daily behavioral data and provide appropriate advice to help users achieve their goals without straining themselves. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: A means is provided to calculate the gap between the user's current state and the achievement of their goal based on the user's weight, body fat percentage, target weight, and target body fat percentage. Also, a means is provided to receive and analyze the dietary and exercise information input by the user daily. A system is provided that includes a means to generate an optimal exercise and meal plan for the user based on the results of this analysis. Furthermore, a means is provided to provide the generated exercise and meal plan to the user, receive the results again, and evaluate the user's progress. The system also includes a means to adjust the next day's exercise and meal plan based on the evaluation results. In this way, customized feedback and advice is provided to each individual user, enabling continuous and efficient weight and body fat management.

[0006] A "user" is an individual who uses the system to manage their weight and body fat percentage.

[0007] "Weight" is a value indicating the weight of the user's entire body.

[0008] "Body fat percentage" is a value that indicates the percentage of fat in the total weight of the user's body.

[0009] "Target weight" is the weight value that the user sets and wants to achieve.

[0010] "Target body fat percentage" is a value that indicates the percentage of body fat that the user wants to achieve.

[0011] A "database" is an information system for storing and managing information entered by users.

[0012] "Analysis" is the process of evaluating a user's condition and tendencies based on input data.

[0013] A "plan" is specific instructions regarding exercise and diet that are presented to the user.

[0014] An "exercise task" is a plan that includes the type, duration, intensity, etc. of exercise that a user should perform.

[0015] A "meal task" is a plan that includes the meal contents and nutritional balance that the user should take.

[0016] "Feedback" refers to evaluation and advice information provided based on the user's execution results.

[0017] An "algorithm" is a set of procedures or rules for calculating and generating optimal exercise and meal plans for a user.

[0018] "Receiving" is the process by which a terminal or server acquires data.

[0019] "Evaluation" is the process of judging progress and effectiveness based on user input and execution results.

[0020] "Customization" means adjusting the behavior of a system according to the individual state and needs of the user. [Brief explanation of the drawings]

[0021] [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 illustrating 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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention is a system that proposes optimal exercise and dietary methods based on dietary and exercise information to help a user achieve their target weight and body fat percentage. An embodiment of this system will be described in detail below.

[0043] System Overview

[0044] This system calculates and analyzes the gap between the user's current condition and the achievement of their goal based on the weight and body fat percentage data entered by the user. Based on the analysis results, it generates an optimal exercise and meal plan and provides it to the user. It also receives the results of the user's exercise and meal, evaluates their progress, and adjusts the plan for the next day.

[0045] This system consists of three main components: a "server," a "terminal," and a "user."

[0046] Server Roles

[0047] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[0048] 1. Data Reception

[0049] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the terminal and stores the data in a database.

[0050] 2. Data Analysis

[0051] The server calculates the user's current status and the gap between them and achieving their goals based on the received data, and also evaluates the user's progress based on the user's past data, generating an optimal exercise and meal plan.

[0052] 3. Plan Generation

[0053] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, and the meal plan includes a menu that takes nutritional balance into consideration.

[0054] 4. Feedback Generation

[0055] It evaluates the results of the user's exercise and diet and generates feedback that is reflected in the next day's plan, including advice on how to improve the user's progress.

[0056] Device Role

[0057] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[0058] 1. Data Entry

[0059] The user inputs their weight, body fat percentage, dietary habits, and exercise habits into the device, which then stores this data in a local database and prepares it for transmission to the server.

[0060] 2. Data Transmission

[0061] The terminal transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[0062] 3. Plan presentation

[0063] The device displays the exercise and meal plans received from the server in a user-friendly format, allowing the user to carry out their daily tasks.

[0064] User Roles

[0065] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[0066] 1. Initial Setup

[0067] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[0068] 2. Daily entries

[0069] The user inputs daily diet and exercise data into the device, including the type, duration, and intensity of exercise, as well as the type, calories, and nutritional balance of the food.

[0070] 3. Task execution

[0071] The user performs daily tasks according to the exercise and meal plan presented by the server.

[0072] 4. Enter results

[0073] Users input their exercise and diet results into the device, and the system generates feedback to improve their plan for the next day.

[0074] Specific examples

[0075] For example, a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, they may have eaten oatmeal for breakfast and run for 30 minutes. This data is sent from the device to the server and analyzed.

[0076] The server generates an optimal exercise and meal plan for the next day based on the user's data. For example, the server suggests a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[0077] The user follows the plan, performs the tasks for the day, and then enters the results back into the device. The server analyzes these results and further optimizes the plan for the next day. By continuing this cycle, the user can achieve their goals without difficulty.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The user launches the app and enters basic information (current weight, body fat percentage, target weight, target body fat percentage) into the device on the new registration screen.

[0081] Step 2:

[0082] The device stores the entered basic information in a local database and generates JSON format data to send to the server.

[0083] Step 3:

[0084] The terminal sends the generated JSON format data to the server.

[0085] Step 4:

[0086] The server stores the basic data of the user received from the terminal in a database and registers it as the initial state.

[0087] Step 5:

[0088] Users input their daily meal details (breakfast, lunch, dinner, snacks) and exercise details (time, type, intensity) into the terminal.

[0089] Step 6:

[0090] The device stores daily diet and exercise information in a local database and generates JSON format data to send to the server.

[0091] Step 7:

[0092] The terminal sends the generated JSON format data to the server.

[0093] Step 8:

[0094] Based on the received data, the server calculates the calorie intake and expenditure for each day and analyzes the user's condition and the gap between them and achieving their goal.

[0095] Step 9:

[0096] Based on the analysis results, the server generates an optimal exercise and meal plan for the user, determining, for example, the duration, intensity, and type of exercise, as well as a menu that takes into account nutritional balance.

[0097] Step 10:

[0098] The server sends the generated exercise and meal plan to the device in JSON format.

[0099] Step 11:

[0100] The terminal interprets the JSON formatted plan data received from the server and displays it in a user-friendly format.

[0101] Step 12:

[0102] The user follows the presented plan and performs the exercise and diet tasks.

[0103] Step 13:

[0104] The user inputs the results of the exercise and diet he or she performed into the terminal, such as the type and duration of the exercise performed, and the details of the diet he or she ate.

[0105] Step 14:

[0106] The terminal stores the execution results in a local database and generates JSON format data to send to the server.

[0107] Step 15:

[0108] The terminal sends the generated JSON format data to the server.

[0109] Step 16:

[0110] The server receives and analyzes the execution result data to assess the user's progress, including progress towards achieving the goal and recommendations.

[0111] Step 17:

[0112] The server adjusts the next day's exercise and meal plan based on the evaluation results and generates feedback.

[0113] Step 18:

[0114] The server sends the generated feedback in JSON format to the device.

[0115] Step 19:

[0116] The terminal displays the feedback received from the server to the user.

[0117] Step 20:

[0118] The user checks the feedback and starts taking action according to the plan for the next day.

[0119] Example 1

[0120] 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."

[0121] Current fitness management systems often lack consistency and accuracy when providing optimal exercise and meal plans for specific user goals, such as weight and body fat percentage. They also lack the ability to analyze users' progress data in real time and incorporate it into the next day's plan. This leaves users with a lack of efficient support to achieve their goals.

[0122] 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.

[0123] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing the user's daily input dietary and exercise information; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the user with the generated exercise and meal plan; means for receiving the user's exercise and meal results again and evaluating progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for storing dietary and exercise information, calorie information, and nutritional balance in a database; means for analyzing the data and generating optimal feedback; and means for using a generative artificial intelligence model to generate meal and exercise plans. This enables highly accurate and efficient support for the user's health management.

[0124] "Weight" is a numerical value that indicates the total weight of the human body.

[0125] "Body fat percentage" is a numerical value that indicates the percentage of fat in the human body.

[0126] "Target weight" is a numerical value indicating the weight that the user wishes to achieve.

[0127] "Target body fat percentage" is a numerical value that indicates the percentage of body fat that the user wishes to achieve.

[0128] A "database" is a system for efficiently storing, managing, and retrieving digital information.

[0129] "Analysis" is the process of processing input data to derive useful information or conclusions.

[0130] "Dietary information" refers to information such as the content, amount, calories, and nutrients of the food consumed.

[0131] "Exercise information" refers to information such as the type, intensity, duration, and number of times of exercise performed.

[0132] "Feedback" is the evaluation and guidance for improvement that the system provides to the user based on past behavior.

[0133] A "generative artificial intelligence model" is an artificial intelligence technology that has the ability to learn from large amounts of data and generate optimal exercise and meal plans.

[0134] "Analysis results" refers to information and conclusions obtained through data analysis.

[0135] An "exercise plan" is a plan that includes a specific combination of exercises, number of repetitions, duration, and intensity that are suggested based on the goals that the user wants to achieve.

[0136] A "meal plan" is a nutritionally balanced meal plan suggested based on the goals the user wants to achieve.

[0137] The present invention is a system that proposes optimal exercise and dietary methods based on dietary and exercise information to help a user achieve their target weight and body fat percentage. An embodiment of this system will be described in detail below.

[0138] System Overview

[0139] This system has the function of calculating and analyzing the gap between the user's current condition and the achievement of their goal based on the weight and body fat percentage data entered by the user. Based on the analysis results, it generates an optimal exercise and meal plan and provides it to the user. It also receives the results of the user's exercise and meal, evaluates progress, and adjusts the plan for the next day. This system consists of three main components: a "server," a "terminal," and a "user."

[0140] Server Roles

[0141] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[0142] 1. Data Reception

[0143] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the device and stores it in a database. Data communication uses the HTTP protocol and REST API.

[0144] 2. Data Analysis

[0145] Based on the received data, the server calculates the gap between the user's current state and the achievement of the goal using Python libraries such as NumPy and Pandas. It also evaluates the user's progress based on their past data and manages it in an SQL database. Google (registered trademark) Cloud Platform and Amazon Web Services are often used as cloud services.

[0146] 3. Plan Generation

[0147] Based on the analysis results, the server uses AI models (such as TENSORFLOW (registered trademark) and PyTorch) to generate optimal exercise and meal plans. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration.

[0148] 4. Feedback Generation

[0149] The server generates feedback based on the user's exercise and dietary habits to be reflected in the next day's plan, including advice on how to improve the user's progress.

[0150] Device Role

[0151] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[0152] 1. Data Entry

[0153] The user inputs their weight, body fat percentage, dietary habits, and exercise habits into the device. These data are stored in a local database (e.g., SQLite) and prepared for transmission to the server.

[0154] 2. Data Transmission

[0155] The device sends the entered data to the server using an HTTP request, and displays the plan and feedback received from the server to the user.

[0156] 3. Plan presentation

[0157] The device receives the exercise and meal plans from the server and displays them in an easy-to-understand format. The app is developed using Java or Kotlin for Android and Swift for iOS.

[0158] User Roles

[0159] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[0160] 1. Initial Setup

[0161] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[0162] 2. Daily entries

[0163] Users enter their daily diet and exercise data into the device, including the type, calories, and nutritional balance of their meals, as well as the type, duration, and intensity of their exercise.

[0164] 3. Task execution

[0165] The user performs daily tasks according to the exercise and meal plan presented by the server.

[0166] 4. Enter results

[0167] The results of the exercises and meals performed are entered into the device and sent to the server, which then generates feedback to improve the plan for the next day.

[0168] Specific examples

[0169] For example, a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, they may have eaten oatmeal for breakfast and run for 30 minutes. This data is sent from the device to the server and analyzed.

[0170] The server generates an optimal exercise and meal plan for the next day based on the user's data. For example, the server suggests a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[0171] The user follows the plan, performs the tasks for the day, and then enters the results back into the device. The server analyzes these results and further optimizes the plan for the next day. By continuing this cycle, the user can achieve their goals without difficulty.

[0172] Example prompts for generative AI models

[0173] Prompt: "If the user weighs 70 kg and has 25% body fat, and sets a goal of 65 kg and 20% body fat, generate the optimal exercise and meal plan. Also consider the past week's exercise and meal data."

[0174] Example expected output:

[0175] "Based on the user's current data, it will suggest an exercise plan that includes 30 minutes of running and 20 squats, as well as a low-fat, high-protein meal plan."

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] System program processing flow

[0178] Step 1:

[0179] User data initial settings

[0180] 1. Specific explanation

[0181] The user starts the application on the terminal and inputs their current weight, body fat percentage, target weight, and target body fat percentage, which allows the system to recognize the user's goals.

[0182] 2. Input / Output

[0183] Input: Weight, body fat percentage, target weight, and target body fat percentage entered by the user on the device

[0184] Data processing: The entered data is collected using the terminal's form interface and stored in a local database.

[0185] Output: User's physical data stored in a local database

[0186] 3. Specific actions

[0187] When the user enters various data into the designated fields and presses the "Save" button, the data is saved in the local database.

[0188] Step 2:

[0189] Data transmission

[0190] 1. Specific explanation

[0191] The device sends the stored data to the server using an HTTP request, and through this process the server receives the user's initial setting data.

[0192] 2. Input / Output

[0193] Input: User's physical data stored in a local database

[0194] Data processing: The data is encoded into JSON format and sent to the server via an HTTP POST request.

[0195] Output: User preference data sent to the server

[0196] 3. Specific actions

[0197] The device sends data to the specified API endpoint via a POST request, which the server receives and stores in the database.

[0198] Step 3:

[0199] Data reception and storage

[0200] 1. Specific explanation

[0201] The server receives the HTTP POST request sent from the terminal and stores it in a database.

[0202] 2. Input / Output

[0203] Input: User preference data sent in the HTTP POST request

[0204] Data processing: Parse the received JSON data and store it in the corresponding column in the database.

[0205] Output: User preference data stored in the database

[0206] 3. Specific actions

[0207] The server's web framework (e.g., Flask or Django) receives the request and executes the logic to save it to the database.

[0208] Step 4:

[0209] Data analysis

[0210] 1. Specific explanation

[0211] Based on the received data, the server calculates the user's current state and the gap to achieving the goal.

[0212] 2. Input / Output

[0213] Input: User preference data stored in the database

[0214] Data processing: Using Python's NumPy and Pandas, calculate the gap (for example, 5 kg between weights of 70 kg and 65 kg).

[0215] Output: Data showing the gap between the user's current situation and their goal

[0216] 3. Specific actions

[0217] The server calls NumPy and Pandas to process the data, calculate the gap information, and store it in the database.

[0218] Step 5:

[0219] Plan Generation

[0220] 1. Specific explanation

[0221] Based on the analysis results, the server uses an AI model (such as TensorFlow or PyTorch) to generate an optimal exercise and meal plan.

[0222] 2. Input / Output

[0223] Input: Current status and goal gap data, past exercise and diet data

[0224] Data processing: Using AI models, we generate optimal exercise and diet plans, such as 30 minutes of running, 20 squats, and a low-fat, high-protein diet.

[0225] Output: Generated exercise and meal plan

[0226] 3. Specific actions

[0227] The AI ​​model takes user data as input and generates a plan, which is then converted to JSON format and saved.

[0228] Step 6:

[0229] Send Plan

[0230] 1. Specific explanation

[0231] The server transmits the generated exercise and meal plan to the terminal.

[0232] 2. Input / Output

[0233] Input: Generated exercise and meal plan

[0234] Data processing: Encode the plan data into JSON format and send it to the end user as an HTTP response.

[0235] Output: Exercise and meal plan sent to device

[0236] 3. Specific actions

[0237] The server sends the plan data to the device through the specified API endpoint.

[0238] Step 7:

[0239] Show Plan

[0240] 1. Specific explanation

[0241] The terminal analyzes the received plan data and displays it on the user interface.

[0242] 2. Input / Output

[0243] Input: Exercise and meal plan received in HTTP response

[0244] Data processing: Parse the JSON data and convert it into a format that can be displayed in the graphical user interface (GUI).

[0245] Output: Plan information presented visually to the user

[0246] 3. Specific actions

[0247] The device analyzes the received data and visually displays exercise and meal plans on the app's dashboard screen.

[0248] Step 8:

[0249] Daily data entry

[0250] 1. Specific explanation

[0251] The user inputs the results of their daily exercise and diet into the terminal.

[0252] 2. Input / Output

[0253] Input: Exercise and diet information entered by the user into the device (e.g., calories burned, dietary details)

[0254] Data processing: The entered data is stored in the device's local database.

[0255] Output: Daily exercise and diet information stored in a local database

[0256] 3. Specific actions

[0257] The user enters the results of their exercise and diet into the form on the device app and presses the "Save" button to save the data.

[0258] Step 9:

[0259] Sending result data

[0260] 1. Specific explanation

[0261] The terminal sends the input result data to the server via an HTTP POST request.

[0262] 2. Input / Output

[0263] Input: Daily exercise and diet information stored in a local database

[0264] Data processing: The data is again encoded into JSON format and sent to the server via an HTTP POST request.

[0265] Output: Daily results data sent to the server

[0266] 3. Specific actions

[0267] The device sends the result data to the specified API endpoint via a POST request, which the server receives and analyzes.

[0268] Step 10:

[0269] Feedback Generation

[0270] 1. Specific explanation

[0271] The server analyzes the received results data, evaluates the user's progress, and generates feedback to optimize the next day's plan.

[0272] 2. Input / Output

[0273] Input: Daily results data sent to the server

[0274] Data processing: Analyze and process the results data to evaluate the user's progress and generate feedback to optimize exercise and meal plans for the next day.

[0275] Output: Generated feedback and optimal plan for the next day

[0276] 3. Specific actions

[0277] The server performs the analysis, generates feedback, stores it in a database, and updates the plan for the next day. The updated data is then sent back to the device.

[0278] (Application example 1)

[0279] 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."

[0280] Traditional health management systems not only provide users with optimal exercise and meal plans, but also provide feedback based on the results of each day and adjust the plan for the next day. However, these systems lack the functionality to allow users to easily purchase healthy products and fitness gear, which means users have to go through the trouble of searching for and purchasing these products separately.

[0281] 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.

[0282] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the weight, body fat percentage, target weight, and target body fat percentage input by the user; means for receiving and analyzing the dietary and exercise information input by the user daily; means for generating an optimal exercise and meal plan for the user based on the analysis results; and means for suggesting related health products and fitness goods based on the generated exercise and meal plan and allowing the user to purchase them. This allows the user to easily purchase the optimal health products and fitness goods and manage their overall health in a unified manner.

[0283] "Body weight" is an indicator of the total weight of the body.

[0284] "Body fat percentage" is an index showing the proportion of body fat to total body weight.

[0285] "Target weight" is the weight value that the user sets and wants to achieve.

[0286] "Target body fat percentage" is the body fat percentage value that the user sets and wants to achieve.

[0287] A "gap" refers to the difference or difference between the current state and the goal.

[0288] "Dietary information" is data indicating the types of food and drink that a user consumes daily.

[0289] "Exercise information" is data that indicates information such as the type, intensity, and duration of exercise performed by the user each day.

[0290] The "analysis results" are results calculated based on the data entered by the user, showing the difference between the current state and the progress towards achieving the goal.

[0291] An "exercise plan" is a plan that includes specific exercise content, intensity, time, etc. that is proposed to the user.

[0292] A "meal plan" is a plan that takes into consideration specific meal contents and nutritional balance proposed to a user.

[0293] "Evaluation results" refers to feedback generated based on the user's progress.

[0294] "Health products" refer to items that are considered useful for managing or improving the user's health.

[0295] "Fitness goods" refers to equipment and devices used for exercise and training.

[0296] "User" refers to an individual who uses this system to manage their own health.

[0297] "Server" refers to a central system for analyzing data received from users, generating results, and transmitting required information.

[0298] "Terminal" refers to a device through which a user can enter data and view received information.

[0299]

[0300] This system provides a means for users to effectively manage their health. Specific embodiments are described below.

[0301] Server Roles

[0302] The server is responsible for calculating the gap between the user's current condition and the goal based on the user's weight, body fat percentage, target weight, and target body fat percentage. It also receives and analyzes the dietary and exercise information entered daily by the user. Based on the analysis results, it generates an optimal exercise and meal plan for the user. Furthermore, based on the generated exercise and meal plan, it recommends related health and fitness products and provides a means for the user to purchase them. For this, a Python (registered trademark)-based web server (e.g., Flask or Django) is used. Databases such as MySQL (registered trademark) and PostgreSQL are used for data management. Data analysis is also performed using numerical calculation libraries such as NumPy and Pandas.

[0303] Device Role

[0304] The terminal provides an interface for users to input data and communicate with the server. This terminal is a mobile device such as a smartphone. Smartphone apps are developed using Swift for iOS and Kotlin / Java for Android. Users input their weight, body fat percentage, dietary details, and exercise details through the terminal. The device also receives and displays exercise and meal plans sent from the server, as well as suggestions for related health and fitness products.

[0305] User Roles

[0306] As an initial setting, the user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the device. This allows the system to understand the user's goals and begin analysis. The user then inputs their daily diet and exercise data into the device. They then perform daily tasks according to the exercise and diet plan provided, and enter the results back into the device. The input results are then sent to the server, where evaluation and feedback are provided.

[0307] Specific examples

[0308] For example, suppose a user inputs their current weight of 70 kg and body fat percentage of 25%, and sets their target weight of 65 kg and target body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, suppose they ate oatmeal for breakfast and ran for 30 minutes. This data is sent from the device to the server and analyzed.

[0309] The server generates the optimal exercise and meal plan for the next day based on the user's data. For example, the user might be recommended a 30-minute run, 20 squats, and a low-fat, high-protein diet. Healthy products like protein powder and running shoes are also recommended, and can be easily purchased within the app. An example of a prompt to be input into the generative AI model is as follows:

[0310] A user inputs their current weight of 70kg and body fat percentage of 25%, and sets their goal weight of 65kg and body fat percentage of 20%. Generate exercise and meal suggestions. For example, the next day's plan is to run 30 minutes, do 20 squats, and eat chicken breast and salad.

[0311] This allows users to manage their health in a centralized manner and purchase optimal health products, enabling more effective improvements to their health.

[0312] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0313] Step 1:

[0314] The user enters input data into the terminal.

[0315] Users input their current weight, body fat percentage, target weight, and target body fat percentage into a smartphone app, which temporarily stores the input data in a local database on the device.

[0316] Step 2:

[0317] The terminal sends the input data to the server.

[0318] The terminal sends the input data, including weight, body fat percentage, target weight, and target body fat percentage, to the server, which receives it and stores it in a database.

[0319] Step 3:

[0320] The server performs the analysis and calculates the gap.

[0321] The server calculates the gap between the user's current state and the goal based on the received data. For this calculation, it uses a numerical calculation library (NumPy or Pandas). For example, it calculates the change from a weight of 70 kg and a body fat percentage of 25% to a weight of 65 kg and a body fat percentage of 20%.

[0322] Step 4:

[0323] The server generates an optimal exercise and diet plan.

[0324] The server generates an optimal exercise and meal plan for the user based on the calculated gap. The exercise plan includes the type, intensity, and duration of exercise, and the meal plan includes nutritional balance. The generated plan is saved in a database.

[0325] Step 5:

[0326] The server will suggest related health and fitness products.

[0327] The server uses a generative AI model to suggest relevant health and fitness products based on the user's health status and goals, such as protein powder or running shoes, and these suggestions are also stored in a database.

[0328] Step 6:

[0329] The terminal displays the generated plans and proposals to the user.

[0330] The device displays the exercise and meal plans sent from the server, as well as suggestions for health and fitness products, to the user, who can then check this information through the app.

[0331] Step 7:

[0332] The user performs daily tasks and inputs the results into the terminal.

[0333] The user performs daily tasks according to the exercise and meal plan provided by the server. As a result, the user inputs the details of the meals and exercise into the device again. For example, if the user ate oatmeal for breakfast and ran for 30 minutes, the user inputs the details.

[0334] Step 8:

[0335] The terminal sends the input data to the server, which evaluates it.

[0336] The device sends the user's input results (meal and exercise details) to the server, which receives them and evaluates the progress. The evaluation includes comparing with past data and calculating the progress rate.

[0337] Step 9:

[0338] The server adjusts the plan for the next day and generates a new plan.

[0339] The server then adjusts the next day's exercise and meal plan based on the evaluation results. For example, if progress is good, it may extend the exercise time and increase the amount of protein in meals. The new plan is then saved in a database.

[0340] Step 10:

[0341] The terminal displays the new plan to the user and the cycle continues.

[0342] The device displays the new exercise and meal plan sent from the server to the user, helping the user to continue with their daily tasks and maintain a cycle toward achieving their goals.

[0343] 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.

[0344] This system proposes optimal exercise and dietary methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. It also combines an emotion engine that recognizes the user's emotions and adjusts the plan accordingly, thereby maintaining and improving motivation.

[0345] System Overview

[0346] This system consists of three main components: a server, a terminal, and a user, along with an emotion engine that connects them. Based on weight and body fat percentage data entered by the user, the server calculates the gap between the user's current state and their goals, and then analyzes the data to generate an optimal exercise and meal plan. The system also recognizes the user's emotions, adjusting the plan and providing feedback based on those emotions.

[0347] Server Roles

[0348] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[0349] 1. Data Reception

[0350] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the terminal and stores the data in a database.

[0351] 2. Data Analysis

[0352] The server calculates the gap between the user's current state and the goal achievement based on the received data, and also analyzes the user's emotional data to evaluate the progress of each individual user.

[0353] 3. Plan Generation

[0354] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. It also makes adjustments based on emotional data to maintain and improve motivation.

[0355] 4. Feedback Generation

[0356] The server evaluates the results of the user's exercise and diet and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to provide messages of encouragement and advice based on the user's emotions.

[0357] Device Role

[0358] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[0359] 1. Data Entry

[0360] The user inputs data about weight, body fat percentage, dietary habits, exercise habits, and emotions into the device, which stores the data in a local database and prepares it for transmission to the server.

[0361] 2. Data Transmission

[0362] The terminal transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[0363] 3. Plan presentation

[0364] The terminal displays the exercise and meal plan received from the server in a user-friendly format.

[0365] User Roles

[0366] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[0367] 1. Initial Setup

[0368] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[0369] 2. Daily entries

[0370] The user inputs daily diet and exercise data and emotional state into the device, including, for example, the contents of meals, calories, nutritional balance, type of exercise, time, intensity, and mood of the day.

[0371] 3. Task execution

[0372] The user performs daily tasks according to the exercise and meal plan presented by the server.

[0373] 4. Enter results

[0374] Users input their exercise routines, dietary habits, and daily emotional state into the device, and the system generates feedback to improve their plan for the next day.

[0375] The role of the emotional engine

[0376] The emotion engine recognizes and analyzes the user's emotional state from input data. Specifically, it includes the following functions:

[0377] 1. Emotion recognition

[0378] It analyzes the emotional data entered by the user (e.g., mood, stress level, motivation, etc.) to understand their emotional state for the day.

[0379] 2. Feedback adjustment

[0380] The emotion engine generates feedback according to the user's state. For example, if the user's motivation is low, it provides encouraging messages and appropriate advice.

[0381] 3. Plan adjustment

[0382] It adjusts exercise and meal plans based on the user's emotional state, for example suggesting light exercise or recommending nutritious meals if the user is feeling stressed.

[0383] Specific examples

[0384] For example, suppose a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. During initial setup, this data is entered into the device and registered in the system. The user then enters their daily diet and exercise data into the device. For example, they may eat oatmeal for breakfast, run for 30 minutes, and enter their mood for the day as "normal."

[0385] The server analyzes this data and generates an optimal exercise and meal plan for the next day. For example, the server might suggest a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is then sent to the device and presented to the user.

[0386] The user follows the presented plan, performs the day's tasks, and enters the results into the device, such as the type and time of exercise performed, food intake, and daily emotional state. The server analyzes these results and optimizes the plan for the next day. The emotion engine also recognizes when the user's motivation is low on a particular day and provides encouraging messages to restore motivation.

[0387] In this way, the system of the present invention not only supports the user in managing their weight and body fat percentage, but also recognizes their emotional state and provides appropriate feedback to help them maintain their motivation and achieve their goals.

[0388] The processing flow will be explained below.

[0389] Step 1:

[0390] The user launches the app and enters basic information (current weight, body fat percentage, target weight, target body fat percentage) into the device on the new registration screen.

[0391] Step 2:

[0392] The device stores the entered basic information in a local database and generates JSON format data to send to the server.

[0393] Step 3:

[0394] The terminal sends the generated JSON format data to the server.

[0395] Step 4:

[0396] The server stores the basic data of the user received from the terminal in a database and registers it as the initial state.

[0397] Step 5:

[0398] Users input their daily meal details (breakfast, lunch, dinner, snacks), exercise details (time, type, intensity), and emotional data (mood, stress level, motivation) into the device.

[0399] Step 6:

[0400] The device stores daily diet, exercise, and emotional information in a local database and generates JSON format data to send to the server.

[0401] Step 7:

[0402] The terminal sends the generated JSON format data to the server.

[0403] Step 8:

[0404] The server calculates the calorie intake and expenditure for each day based on the received data and analyzes the gap between the user's condition and the achievement of their goal. The emotion engine also analyzes the received emotion data to grasp the user's emotional state for that day.

[0405] Step 9:

[0406] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. The plan can also be adjusted based on emotional data.

[0407] Step 10:

[0408] The server sends the generated exercise and meal plan to the device in JSON format.

[0409] Step 11:

[0410] The terminal interprets the JSON formatted plan data received from the server and displays it in a user-friendly format.

[0411] Step 12:

[0412] The user follows the presented plan and performs the exercise and diet tasks.

[0413] Step 13:

[0414] The user inputs the exercise and meals they have performed, as well as their daily emotional state, into the terminal, such as the type and duration of exercise they performed, the food they ate, and their mood that day.

[0415] Step 14:

[0416] The terminal stores the execution results in a local database and generates JSON format data to send to the server.

[0417] Step 15:

[0418] The terminal sends the generated JSON format data to the server.

[0419] Step 16:

[0420] The server receives the execution result data, analyzes it, and evaluates the user's progress. The emotion engine reevaluates the user's emotional state based on the emotion data.

[0421] Step 17:

[0422] The server adjusts the next day's exercise and meal plan based on the evaluation results, using an emotion engine to generate encouraging and advice messages according to the user's emotions.

[0423] Step 18:

[0424] The server sends the generated plan for the next day and feedback to the terminal in JSON format.

[0425] Step 19:

[0426] The terminal displays the next day's plan and feedback received from the server to the user.

[0427] Step 20:

[0428] The user checks the feedback and starts taking action according to the plan for the next day.

[0429] Example 2

[0430] 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."

[0431] Conventional weight and body fat management systems provide analysis based on the user's weight and body fat percentage data, as well as exercise and meal plans, but lack feedback or plan adjustments that take into account the user's emotional state, making it difficult to maintain motivation or provide continuous support for achieving goals.In addition, daily data input and feedback of analysis results are often not done consistently across the entire system, making it difficult to provide and implement effective plans.

[0432] 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.

[0433] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing dietary and exercise information input daily by the user; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the generated exercise and meal plan to the user; means for receiving the results of the user's exercise and meal plans and evaluating the progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for receiving and analyzing the user's emotional data; and means for adjusting the plan to provide feedback based on the emotional analysis results. This makes it possible to provide a comprehensive exercise and meal plan that also takes the user's emotional state into consideration, thereby maintaining and improving motivation and realizing an effective system for supporting goal achievement.

[0434] "User" refers to an individual who uses this system to manage their weight and body fat percentage.

[0435] "Server" refers to the computer system that receives data sent by users and performs analysis and plan generation.

[0436] "Terminal" refers to a device (such as a smartphone or PC) that provides an interface for users to input data and communicate with a server.

[0437] "Weight" represents data indicating the current weight of the user.

[0438] "Body fat percentage" represents data indicating the percentage of fat in the user's body.

[0439] "Target weight" refers to data indicating the weight that the user wishes to achieve.

[0440] "Target body fat percentage" represents data indicating the body fat percentage that the user wishes to achieve.

[0441] The "gap" represents data that indicates the difference between the user's current state and the progress toward achieving the goal.

[0442] "Dietary information" refers to data that indicates the contents of the foods and drinks that a user consumes daily.

[0443] "Exercise information" refers to data indicating the type, duration, and intensity of exercise that the user performs daily.

[0444] "Analysis" refers to the process by which the server processes the data it receives and evaluates the user's status and progress.

[0445] "Exercise plan" refers to a plan generated by the server that includes the duration, intensity, and type of exercise optimal for the user.

[0446] "Meal plan" refers to a meal menu generated by the server that takes into consideration the optimal nutritional balance for the user.

[0447] "Progress" refers to data indicating the degree of achievement evaluated based on the results of the exercise and diet performed by the user.

[0448] "Emotion data" refers to data entered by the user that indicates their own mood, stress level, and motivation state.

[0449] "Emotion engine" refers to technology that analyzes a user's emotional data and provides appropriate feedback and plan adjustments.

[0450] "Feedback" refers to information generated by the server based on analysis, including evaluations and advice regarding the user's actions.

[0451] This invention is a system that proposes optimal exercise and diet methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. This system includes three main components: a "server," a "terminal," and a "user," as well as an emotion engine that links them.

[0452] Server Roles

[0453] The server plays a central role in receiving and analyzing the data entered by the user. The server processes the data using the following techniques:

[0454] Data reception and storage: The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the device and stores it in a database (e.g., MySQL).

[0455] Data analysis: The server uses data analysis tools such as Python and R to calculate the user's current state and the gap between the goal and the goal based on the received data. It also analyzes emotional data to evaluate the progress of each individual user.

[0456] Plan generation: Based on the analysis results, the server generates an optimal exercise and meal plan for the user. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. Adjustments are also made based on emotional data to maintain and improve motivation.

[0457] Feedback generation: The server evaluates the results of the user's exercise and diet and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to provide messages of encouragement and advice based on the user's emotions.

[0458] Device Role

[0459] The terminal provides an interface for the user to input data and communicate with the server. The specific operation is as follows.

[0460] Data input: The user inputs data about weight, body fat percentage, diet, exercise, and emotions into the device. The device stores this data in a local database (e.g., SQLite) and prepares it for transmission to the server.

[0461] Data transmission: The device transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[0462] Plan presentation: The device displays the exercise and meal plan received from the server in a user-friendly format. Front-end frameworks such as React and Vue.js are used for the UI.

[0463] User Roles

[0464] Users use the system to manage their own weight and body fat percentage. They utilize the system by performing the following actions:

[0465] Initial setting: The user inputs the current weight, body fat percentage, target weight, and target body fat percentage into the terminal.

[0466] Daily input: The user inputs their daily diet and exercise data, as well as their emotional state, into the device. For example, they may have oatmeal for breakfast, go for a 30-minute run, and enter their mood for the day as "normal."

[0467] Task execution: The user performs daily tasks according to the exercise and meal plan presented by the server.

[0468] Result input: Enter the results of your exercise, diet, and daily emotional state into your device.

[0469] The role of the emotional engine

[0470] The emotion engine recognizes and analyzes the emotional state of the user from input data. This technology has the following functions:

[0471] Emotion recognition: Analyzes the emotional data entered by the user (e.g., mood, stress level, motivation, etc.) to understand their emotional state for the day.

[0472] Feedback adjustment: The emotion engine generates feedback according to the user's state. For example, if the user's motivation is low, it provides encouraging messages or appropriate advice.

[0473] Plan Adjustment: Adjust your exercise and meal plan based on your emotional state. For example, if you're feeling stressed, it might suggest light exercise or recommend a nutritious meal.

[0474] Specific examples

[0475] If a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%, they enter this data into the device and register it in the system. They also enter their daily diet and exercise data into the device. For example, they might eat oatmeal for breakfast, run for 30 minutes, and enter their mood for the day as "normal."

[0476] The server analyzes this data and generates the optimal exercise and meal plan for the next day. The user is suggested to run for 30 minutes, do 20 squats, and eat a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[0477] The user follows the presented plan, performs the day's tasks, and enters the results into the device, such as the type and time of exercise performed, food intake, and daily emotional state. The server analyzes these results and optimizes the plan for the next day. The emotion engine also recognizes when the user's motivation is declining and provides encouraging messages to restore motivation.

[0478] Prompt Sentence Examples

[0479] "I want to lose weight, what kind of exercise is effective?"

[0480] "Suggest a meal plan for today."

[0481] "What message would you give me to encourage me if I lose motivation?"

[0482] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0483] Step 1: Enter initial setup data

[0484] Input: The user enters their current weight, body fat percentage, target weight, and target body fat percentage into a dedicated mobile app or a web interface on their PC.

[0485] Operation: The device receives this data, formats it into a data format such as JSON, and temporarily stores it in a local database (such as SQLite).

[0486] Output: The formatted JSON data is saved in the terminal.

[0487] Step 2: Receiving and storing data

[0488] Input: User's weight, body fat percentage, target weight, and target body fat percentage data sent from the device.

[0489] How it works: The device sends the formatted data to the server as a POST request. The server receives this data and stores it in a database (e.g., MySQL).

[0490] Output: User's weight, body fat percentage, target weight, and target body fat percentage data recorded in the database.

[0491] Step 3: Analyze the data

[0492] Input: The user's current weight and body fat percentage, target weight and target body fat percentage data stored on the server.

[0493] How it works: The server analyzes this data using data analysis tools such as Python and R. It calculates the gap between the user's current state (e.g., weight 70 kg, body fat percentage 25%) and their goal (e.g., weight 65 kg, body fat percentage 20%).

[0494] Output: Calculated gap (e.g. weight gap 5kg, body fat gap 5%).

[0495] Step 4: Create an exercise and meal plan

[0496] Input: Analysis result gap data and user emotion data.

[0497] How it works: The server generates an optimal exercise and meal plan based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise (e.g., 30 minutes of running, 20 squats), and the meal plan includes nutritionally balanced menus. The plan is also personalized using machine learning libraries such as Scikit-learn.

[0498] Output: Generated exercise and meal plans.

[0499] Step 5: Provide feedback

[0500] Input: User exercise and diet results, emotional data.

[0501] How it works: The server evaluates the results and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to generate messages of encouragement and advice based on the user's emotions. For example, if the user enters "low motivation," an encouraging message will be generated.

[0502] Output: Generated feedback and adjusted next day plan.

[0503] Step 6: Daily data entry and submission

[0504] Input: Daily user diet, exercise, and emotional state.

[0505] How it works: Users enter these daily data into a terminal, which formats the data and prepares it for transmission to the server.

[0506] Output: Daily data stored on the device and sent to the server.

[0507] Step 7: Daily data analysis and feedback updates

[0508] Input: User's daily data, emotional state data.

[0509] How it works: The server receives and analyzes this data. Based on the received diet, exercise, and emotional data, it updates and optimizes the next day's exercise and meal plan. It also uses an emotion engine to generate appropriate feedback and messages based on the user's emotional state.

[0510] Output: Updated exercise and meal plans, encouraging and advice feedback.

[0511] (Application example 2)

[0512] 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."

[0513] The present invention aims to provide a system that not only provides diet and exercise plans to help users achieve their target weight and body fat percentage, but also more effectively supports health management by suggesting healthy product selections and meal menus in physical stores. Another objective of the present invention is to provide a system that maintains and improves the user's motivation by taking into account the user's emotional state.

[0514] 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: means for calculating the gap between the user's current state and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing dietary and exercise information input daily by the user; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the generated exercise and meal plan to the user; means for receiving the results of the user's exercise and meal plans and evaluating the progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for providing a real-time health rating for a product when the user scans it in a store; means for providing a health rating for a menu item when the user selects it at a restaurant in the store; and means for recognizing the user's emotional state and providing feedback based on the selected menu item. This enables effective health management of the user even in the store and maintains motivation according to the user's emotional state.

[0515] "Weight" refers to the user's current body weight.

[0516] "Body fat percentage" is a percentage that indicates the proportion of fat in relation to the user's total body weight.

[0517] "Goal weight" refers to the final weight that a user wishes to achieve.

[0518] "Target body fat percentage" refers to the final body fat percentage that the user wishes to achieve.

[0519] The "gap" refers to the difference between the user's current state and the achievement of their goal.

[0520] "Dietary information" is information about the contents, calories, and nutrients of the meals that the user takes in every day.

[0521] "Exercise information" is information relating to the type, duration, intensity, etc. of exercise that the user performs daily.

[0522] The "analysis results" are the results of analyzing the data entered by the user, and indicate the gap between the current state and the achievement of the goal.

[0523] An "exercise plan" is a plan of the type, intensity, and duration of exercise suggested to help the user achieve their goals.

[0524] A "meal plan" is a plan of suggested meal content, calories, and nutritional balance to help the user achieve their goals.

[0525] "Progress" refers to the progress a user makes toward achieving a goal.

[0526] "Health rating" refers to the calorie and nutritional balance rating of the product scanned by the user or the meal menu selected by the user.

[0527] "Emotional state" is information about the user's daily mood, stress level, motivation, etc.

[0528] "Feedback" refers to messages of advice or encouragement provided to the user.

[0529] "Real-time" refers to processing and information being provided immediately without delay.

[0530] A "brick and mortar store" refers to a physical location where consumers visit in person to purchase goods or use services.

[0531] The present invention is a system that suggests optimal exercise and dietary methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts the plan accordingly, motivation is maintained and improved. Specific embodiments of the present invention are described below.

[0532] System Configuration

[0533] This system consists of three main components: a server, a terminal, and a user, and includes an emotion engine that connects these components. Its main roles are as follows:

[0534] server

[0535] The server has the following functions:

[0536] 1. Data reception: The server receives the weight, body fat percentage, target weight, and target body fat percentage entered by the user and stores them in a database.

[0537] 2. Data analysis: The server calculates the gap between the user's current status and their goal based on the received data, and also analyzes the user's daily diet and exercise information.

[0538] 3. Plan Generation: The server generates an optimal exercise and meal plan based on the analysis results. Furthermore, the emotion engine adjusts the plan to take into account the user's emotional state.

[0539] 4. Feedback generation: The server evaluates the results of the exercise and diet performed by the user and generates feedback to be reflected in the plan for the next day.

[0540] Terminal

[0541] The terminal has the following functions:

[0542] 1. Data input: Provides an interface for users to input their weight, body fat percentage, dietary habits, exercise habits, and emotional state. Prepares the input data to be sent to the server.

[0543] 2. Data transmission: The device transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[0544] 3. Plan presentation: The device displays the exercise and meal plan received from the server in an easy-to-understand manner to the user.

[0545] User

[0546] Users use the system to manage their own weight and body fat percentage.

[0547] 1. Initial setup: The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal.

[0548] 2. Daily input: The user inputs their daily diet, exercise, and emotional state into the device.

[0549] 3. Task execution: The user follows the exercise and meal plan provided by the server.

[0550] 4. Enter results: Enter the results of your exercise and meals into the device.

[0551] Emotion Engine

[0552] The emotion engine recognizes and analyzes the user's emotional state.

[0553] 1. Emotion recognition: Analyzes the emotional data entered by the user to understand their emotional state for the day.

[0554] 2. Feedback adjustment: The emotion engine generates feedback according to the user's state.

[0555] 3. Plan Adjustment: Adjust exercise and meal plans based on the user's emotional state.

[0556] Hardware and software used

[0557] Hardware: Smartphones, smart glasses

[0558] Software: Python, Flask, SQLite

[0559] Library: emotion_engine

[0560] Specific examples

[0561] For example, suppose a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user inputs initial data such as weight, body fat percentage, target weight, and target body fat percentage into the terminal and registers it in the system. Next, the user inputs their daily diet and exercise information.

[0562] When users visit a physical store and scan an item, they are given a real-time health rating for that item, when they select a meal at an in-store restaurant, they are given a health rating for that menu item, and they can input their emotional state and receive real-time feedback based on that.

[0563] Prompt Sentence Examples

[0564] "I'm very tired today"

[0565] "I'm stressed out at work"

[0566] "Exercise feels fun"

[0567] An example prompt is:

[0568] "Generate appropriate feedback based on user input emotion. Input: {emotion_input}. Feedback: "

[0569] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0570] Step 1:

[0571] The user inputs their weight, body fat percentage, target weight, target body fat percentage, dietary information, exercise information, and emotional information into the device. The device stores this data in a local database and prepares it for transmission to the server. The user inputs the data directly by operating the device, and the input data is converted into XML or JSON format. When the data is ready to be sent to the server, the send command is executed.

[0572] Step 2:

[0573] The terminal sends the data entered by the user to the server. Specifically, it sends the data stored in the terminal to the server using the HTTP protocol. The server processes the received data using a reception module and stores it in the necessary database. The input here is the user's input data, and the output is the result stored in the server's database. After transmission is complete, the terminal waits for a response from the server.

[0574] Step 3:

[0575] The server calculates the gap between the user's current status and their goal based on the received data. To do this, it uses an algorithm that matches the user's weight, body fat percentage, target weight, and target body fat percentage. Specifically, it calculates the difference between the current and target values ​​for weight and body fat percentage, and lists the items the user needs to achieve. The input is the user's weight, body fat percentage, target weight, and target body fat percentage, and the output is the gap calculation result.

[0576] Step 4:

[0577] The server analyzes daily input data (diet, exercise, and emotional information) and generates an optimal exercise and meal plan. Here, the received data is filtered, compared with a health database, and then an algorithm is applied to suggest an appropriate exercise and meal plan. The input is the user's diet, exercise, and emotional information, and the output is an exercise and meal plan. The generated plan is sent to the user.

[0578] Step 5:

[0579] The device displays the exercise and meal plan received from the server to the user. The input here is the plan data sent from the server, and the output is the user's plan information displayed on the device's display. The device notifies the user whenever the plan is updated.

[0580] Step 6:

[0581] The user inputs the results of their exercise and diet into the terminal. Here, the user inputs the duration and type of exercise they performed, the type of meal they ate, their emotional state, etc. The input data is prepared for transmission to the server again. The input is the user's exercise results, diet results, and emotional state data, and the output is the data prepared for transmission to the server.

[0582] Step 7:

[0583] The device sends the results of the user's exercise and diet to the server. Specifically, the device sends the previously entered data to the server as an HTTP request, and the server receives the data and registers it in a database. The input is the user's exercise results, diet results, and emotional state data, and the output is the registration results in the server's database.

[0584] Step 8:

[0585] The server evaluates the user's progress based on the received data and adjusts the exercise and meal plan for the next day. This evaluation process uses analytical algorithms to generate an optimal plan based on the user's progress. The input is the user's progress data, and the output is the adjusted plan for the next day.

[0586] Step 9:

[0587] The server sends the generated new plan to the terminal, which then presents it to the user. The input is the new exercise and meal plan, and the output is the user's new plan information displayed on the terminal.

[0588] Step 10:

[0589] When a user visits a physical store and scans a product or selects an item from the restaurant menu, the terminal sends the product barcode or menu information to the server. The server receives this data and performs a real-time health assessment. The input is the product or menu information, and the output is the health assessment result. The health assessment result is immediately returned to the terminal and displayed to the user.

[0590] Step 11:

[0591] When the user's emotional state is input, the server uses an emotion engine to perform emotion analysis and generate feedback based on the results. The input is the user's emotional data, and the output is a feedback message. The generated feedback is sent to the terminal and presented to the user.

[0592] Example prompt sentences

[0593] For example, it generates appropriate feedback based on emotional input such as "I feel very tired today," "I'm stressed at work," or "I enjoy exercising." Examples of prompt sentences are as follows:

[0594] "Generate appropriate feedback based on user input emotion. Input: {emotion_input}. Feedback: "

[0595] 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.

[0596] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] 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.

[0598] [Second embodiment]

[0599] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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."

[0611] The present invention is a system that proposes optimal exercise and dietary methods based on dietary and exercise information to help a user achieve their target weight and body fat percentage. An embodiment of this system will be described in detail below.

[0612] System Overview

[0613] This system calculates and analyzes the gap between the user's current condition and the achievement of their goal based on the weight and body fat percentage data entered by the user. Based on the analysis results, it generates an optimal exercise and meal plan and provides it to the user. It also receives the results of the user's exercise and meal, evaluates their progress, and adjusts the plan for the next day.

[0614] This system consists of three main components: a "server," a "terminal," and a "user."

[0615] Server Roles

[0616] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[0617] 1. Data Reception

[0618] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the terminal and stores the data in a database.

[0619] 2. Data Analysis

[0620] The server calculates the user's current status and the gap between them and achieving their goals based on the received data, and also evaluates the user's progress based on the user's past data, generating an optimal exercise and meal plan.

[0621] 3. Plan Generation

[0622] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, and the meal plan includes a menu that takes nutritional balance into consideration.

[0623] 4. Feedback Generation

[0624] It evaluates the results of the user's exercise and diet and generates feedback that is reflected in the next day's plan, including advice on how to improve the user's progress.

[0625] Device Role

[0626] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[0627] 1. Data Entry

[0628] The user inputs their weight, body fat percentage, dietary habits, and exercise habits into the device, which then stores this data in a local database and prepares it for transmission to the server.

[0629] 2. Data Transmission

[0630] The terminal transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[0631] 3. Plan presentation

[0632] The device displays the exercise and meal plans received from the server in a user-friendly format, allowing the user to carry out their daily tasks.

[0633] User Roles

[0634] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[0635] 1. Initial Setup

[0636] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[0637] 2. Daily entries

[0638] The user inputs daily diet and exercise data into the device, including the type, duration, and intensity of exercise, as well as the type, calories, and nutritional balance of the food.

[0639] 3. Task execution

[0640] The user performs daily tasks according to the exercise and meal plan presented by the server.

[0641] 4. Enter results

[0642] Users input their exercise and diet results into the device, and the system generates feedback to improve their plan for the next day.

[0643] Specific examples

[0644] For example, a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, they may have eaten oatmeal for breakfast and run for 30 minutes. This data is sent from the device to the server and analyzed.

[0645] The server generates an optimal exercise and meal plan for the next day based on the user's data. For example, the server suggests a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[0646] The user follows the plan, performs the tasks for the day, and then enters the results back into the device. The server analyzes these results and further optimizes the plan for the next day. By continuing this cycle, the user can achieve their goals without difficulty.

[0647] The processing flow will be explained below.

[0648] Step 1:

[0649] The user launches the app and enters basic information (current weight, body fat percentage, target weight, target body fat percentage) into the device on the new registration screen.

[0650] Step 2:

[0651] The device stores the entered basic information in a local database and generates JSON format data to send to the server.

[0652] Step 3:

[0653] The terminal sends the generated JSON format data to the server.

[0654] Step 4:

[0655] The server stores the basic data of the user received from the terminal in a database and registers it as the initial state.

[0656] Step 5:

[0657] Users input their daily meal details (breakfast, lunch, dinner, snacks) and exercise details (time, type, intensity) into the terminal.

[0658] Step 6:

[0659] The device stores daily diet and exercise information in a local database and generates JSON format data to send to the server.

[0660] Step 7:

[0661] The terminal sends the generated JSON format data to the server.

[0662] Step 8:

[0663] Based on the received data, the server calculates the calorie intake and expenditure for each day and analyzes the user's condition and the gap between them and achieving their goal.

[0664] Step 9:

[0665] Based on the analysis results, the server generates an optimal exercise and meal plan for the user, determining, for example, the duration, intensity, and type of exercise, as well as a menu that takes into account nutritional balance.

[0666] Step 10:

[0667] The server sends the generated exercise and meal plan to the device in JSON format.

[0668] Step 11:

[0669] The terminal interprets the JSON formatted plan data received from the server and displays it in a user-friendly format.

[0670] Step 12:

[0671] The user follows the presented plan and performs the exercise and diet tasks.

[0672] Step 13:

[0673] The user inputs the results of the exercise and diet he or she performed into the terminal, such as the type and duration of the exercise performed, and the details of the diet he or she ate.

[0674] Step 14:

[0675] The terminal stores the execution results in a local database and generates JSON format data to send to the server.

[0676] Step 15:

[0677] The terminal sends the generated JSON format data to the server.

[0678] Step 16:

[0679] The server receives and analyzes the execution result data to assess the user's progress, including progress towards achieving the goal and recommendations.

[0680] Step 17:

[0681] The server adjusts the next day's exercise and meal plan based on the evaluation results and generates feedback.

[0682] Step 18:

[0683] The server sends the generated feedback in JSON format to the device.

[0684] Step 19:

[0685] The terminal displays the feedback received from the server to the user.

[0686] Step 20:

[0687] The user checks the feedback and starts taking action according to the plan for the next day.

[0688] Example 1

[0689] 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."

[0690] Current fitness management systems often lack consistency and accuracy when providing optimal exercise and meal plans for specific user goals, such as weight and body fat percentage. They also lack the ability to analyze users' progress data in real time and incorporate it into the next day's plan. This leaves users with a lack of efficient support to achieve their goals.

[0691] 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.

[0692] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing the user's daily input dietary and exercise information; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the user with the generated exercise and meal plan; means for receiving the user's exercise and meal results again and evaluating progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for storing dietary and exercise information, calorie information, and nutritional balance in a database; means for analyzing the data and generating optimal feedback; and means for using a generative artificial intelligence model to generate meal and exercise plans. This enables highly accurate and efficient support for the user's health management.

[0693] "Weight" is a numerical value that indicates the total weight of the human body.

[0694] "Body fat percentage" is a numerical value that indicates the percentage of fat in the human body.

[0695] "Target weight" is a numerical value indicating the weight that the user wishes to achieve.

[0696] "Target body fat percentage" is a numerical value that indicates the percentage of body fat that the user wishes to achieve.

[0697] A "database" is a system for efficiently storing, managing, and retrieving digital information.

[0698] "Analysis" is the process of processing input data to derive useful information or conclusions.

[0699] "Dietary information" refers to information such as the content, amount, calories, and nutrients of the food consumed.

[0700] "Exercise information" refers to information such as the type, intensity, duration, and number of times of exercise performed.

[0701] "Feedback" is the evaluation and guidance for improvement that the system provides to the user based on past behavior.

[0702] A "generative artificial intelligence model" is an artificial intelligence technology that has the ability to learn from large amounts of data and generate optimal exercise and meal plans.

[0703] "Analysis results" refers to information and conclusions obtained through data analysis.

[0704] An "exercise plan" is a plan that includes a specific combination of exercises, number of repetitions, duration, and intensity that are suggested based on the goals that the user wants to achieve.

[0705] A "meal plan" is a nutritionally balanced meal plan suggested based on the goals the user wants to achieve.

[0706] The present invention is a system that proposes optimal exercise and dietary methods based on dietary and exercise information to help a user achieve their target weight and body fat percentage. An embodiment of this system will be described in detail below.

[0707] System Overview

[0708] This system has the function of calculating and analyzing the gap between the user's current condition and the achievement of their goal based on the weight and body fat percentage data entered by the user. Based on the analysis results, it generates an optimal exercise and meal plan and provides it to the user. It also receives the results of the user's exercise and meal, evaluates progress, and adjusts the plan for the next day. This system consists of three main components: a "server," a "terminal," and a "user."

[0709] Server Roles

[0710] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[0711] 1. Data Reception

[0712] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the device and stores it in a database. Data communication uses the HTTP protocol and REST API.

[0713] 2. Data Analysis

[0714] Based on the received data, the server calculates the gap between the user's current state and the achievement of the goal using Python libraries such as NumPy and Pandas. It also evaluates the user's progress based on their past data and manages it in an SQL database. Google Cloud Platform and Amazon Web Services are often used as cloud services.

[0715] 3. Plan Generation

[0716] Based on the analysis results, the server uses AI models (such as TensorFlow and PyTorch) to generate optimal exercise and meal plans. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration.

[0717] 4. Feedback Generation

[0718] The server generates feedback based on the user's exercise and dietary habits to be reflected in the next day's plan, including advice on how to improve the user's progress.

[0719] Device Role

[0720] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[0721] 1. Data Entry

[0722] The user inputs their weight, body fat percentage, dietary habits, and exercise habits into the device. These data are stored in a local database (e.g., SQLite) and prepared for transmission to the server.

[0723] 2. Data Transmission

[0724] The device sends the entered data to the server using an HTTP request, and displays the plan and feedback received from the server to the user.

[0725] 3. Plan presentation

[0726] The device receives the exercise and meal plans from the server and displays them in an easy-to-understand format. The app is developed using Java or Kotlin for Android and Swift for iOS.

[0727] User Roles

[0728] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[0729] 1. Initial Setup

[0730] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[0731] 2. Daily entries

[0732] Users enter their daily diet and exercise data into the device, including the type, calories, and nutritional balance of their meals, as well as the type, duration, and intensity of their exercise.

[0733] 3. Task execution

[0734] The user performs daily tasks according to the exercise and meal plan presented by the server.

[0735] 4. Enter results

[0736] The results of the exercises and meals performed are entered into the device and sent to the server, which then generates feedback to improve the plan for the next day.

[0737] Specific examples

[0738] For example, a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, they may have eaten oatmeal for breakfast and run for 30 minutes. This data is sent from the device to the server and analyzed.

[0739] The server generates an optimal exercise and meal plan for the next day based on the user's data. For example, the server suggests a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[0740] The user follows the plan, performs the tasks for the day, and then enters the results back into the device. The server analyzes these results and further optimizes the plan for the next day. By continuing this cycle, the user can achieve their goals without difficulty.

[0741] Example prompts for generative AI models

[0742] Prompt: "If the user weighs 70 kg and has 25% body fat, and sets a goal of 65 kg and 20% body fat, generate the optimal exercise and meal plan. Also consider the past week's exercise and meal data."

[0743] Example expected output:

[0744] "Based on the user's current data, it will suggest an exercise plan that includes 30 minutes of running and 20 squats, as well as a low-fat, high-protein meal plan."

[0745] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0746] System program processing flow

[0747] Step 1:

[0748] User data initial settings

[0749] 1. Specific explanation

[0750] The user starts the application on the terminal and inputs their current weight, body fat percentage, target weight, and target body fat percentage, which allows the system to recognize the user's goals.

[0751] 2. Input / Output

[0752] Input: Weight, body fat percentage, target weight, and target body fat percentage entered by the user on the device

[0753] Data processing: The entered data is collected using the terminal's form interface and stored in a local database.

[0754] Output: User's physical data stored in a local database

[0755] 3. Specific actions

[0756] When the user enters various data into the designated fields and presses the "Save" button, the data is saved in the local database.

[0757] Step 2:

[0758] Data transmission

[0759] 1. Specific explanation

[0760] The device sends the stored data to the server using an HTTP request, and through this process the server receives the user's initial setting data.

[0761] 2. Input / Output

[0762] Input: User's physical data stored in a local database

[0763] Data processing: The data is encoded into JSON format and sent to the server via an HTTP POST request.

[0764] Output: User preference data sent to the server

[0765] 3. Specific actions

[0766] The device sends data to the specified API endpoint via a POST request, which the server receives and stores in the database.

[0767] Step 3:

[0768] Data reception and storage

[0769] 1. Specific explanation

[0770] The server receives the HTTP POST request sent from the terminal and stores it in a database.

[0771] 2. Input / Output

[0772] Input: User preference data sent in the HTTP POST request

[0773] Data processing: Parse the received JSON data and store it in the corresponding column in the database.

[0774] Output: User preference data stored in the database

[0775] 3. Specific actions

[0776] The server's web framework (e.g., Flask or Django) receives the request and executes the logic to save it to the database.

[0777] Step 4:

[0778] Data analysis

[0779] 1. Specific explanation

[0780] Based on the received data, the server calculates the user's current state and the gap to achieving the goal.

[0781] 2. Input / Output

[0782] Input: User preference data stored in the database

[0783] Data processing: Using Python's NumPy and Pandas, calculate the gap (for example, 5 kg between weights of 70 kg and 65 kg).

[0784] Output: Data showing the gap between the user's current situation and their goal

[0785] 3. Specific actions

[0786] The server calls NumPy and Pandas to process the data, calculate the gap information, and store it in the database.

[0787] Step 5:

[0788] Plan Generation

[0789] 1. Specific explanation

[0790] Based on the analysis results, the server uses an AI model (such as TensorFlow or PyTorch) to generate an optimal exercise and meal plan.

[0791] 2. Input / Output

[0792] Input: Current status and goal gap data, past exercise and diet data

[0793] Data processing: Using AI models, we generate optimal exercise and diet plans, such as 30 minutes of running, 20 squats, and a low-fat, high-protein diet.

[0794] Output: Generated exercise and meal plan

[0795] 3. Specific actions

[0796] The AI ​​model takes user data as input and generates a plan, which is then converted to JSON format and saved.

[0797] Step 6:

[0798] Send Plan

[0799] 1. Specific explanation

[0800] The server transmits the generated exercise and meal plan to the terminal.

[0801] 2. Input / Output

[0802] Input: Generated exercise and meal plan

[0803] Data processing: Encode the plan data into JSON format and send it to the end user as an HTTP response.

[0804] Output: Exercise and meal plan sent to device

[0805] 3. Specific actions

[0806] The server sends the plan data to the device through the specified API endpoint.

[0807] Step 7:

[0808] Show Plan

[0809] 1. Specific explanation

[0810] The terminal analyzes the received plan data and displays it on the user interface.

[0811] 2. Input / Output

[0812] Input: Exercise and meal plan received in HTTP response

[0813] Data processing: Parse the JSON data and convert it into a format that can be displayed in the graphical user interface (GUI).

[0814] Output: Plan information presented visually to the user

[0815] 3. Specific actions

[0816] The device analyzes the received data and visually displays exercise and meal plans on the app's dashboard screen.

[0817] Step 8:

[0818] Daily data entry

[0819] 1. Specific explanation

[0820] The user inputs the results of their daily exercise and diet into the terminal.

[0821] 2. Input / Output

[0822] Input: Exercise and diet information entered by the user into the device (e.g., calories burned, dietary details)

[0823] Data processing: The entered data is stored in the device's local database.

[0824] Output: Daily exercise and diet information stored in a local database

[0825] 3. Specific actions

[0826] The user enters the results of their exercise and diet into the form on the device app and presses the "Save" button to save the data.

[0827] Step 9:

[0828] Sending result data

[0829] 1. Specific explanation

[0830] The terminal sends the input result data to the server via an HTTP POST request.

[0831] 2. Input / Output

[0832] Input: Daily exercise and diet information stored in a local database

[0833] Data processing: The data is again encoded into JSON format and sent to the server via an HTTP POST request.

[0834] Output: Daily results data sent to the server

[0835] 3. Specific actions

[0836] The device sends the result data to the specified API endpoint via a POST request, which the server receives and analyzes.

[0837] Step 10:

[0838] Feedback Generation

[0839] 1. Specific explanation

[0840] The server analyzes the received results data, evaluates the user's progress, and generates feedback to optimize the next day's plan.

[0841] 2. Input / Output

[0842] Input: Daily results data sent to the server

[0843] Data processing: Analyze and process the results data to evaluate the user's progress and generate feedback to optimize exercise and meal plans for the next day.

[0844] Output: Generated feedback and optimal plan for the next day

[0845] 3. Specific actions

[0846] The server performs the analysis, generates feedback, stores it in a database, and updates the plan for the next day. The updated data is then sent back to the device.

[0847] (Application example 1)

[0848] 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."

[0849] Traditional health management systems not only provide users with optimal exercise and meal plans, but also provide feedback based on the results of each day and adjust the plan for the next day. However, these systems lack the functionality to allow users to easily purchase healthy products and fitness gear, which means users have to go through the trouble of searching for and purchasing these products separately.

[0850] 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.

[0851] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the weight, body fat percentage, target weight, and target body fat percentage input by the user; means for receiving and analyzing the dietary and exercise information input by the user daily; means for generating an optimal exercise and meal plan for the user based on the analysis results; and means for suggesting related health products and fitness goods based on the generated exercise and meal plan and allowing the user to purchase them. This allows the user to easily purchase the optimal health products and fitness goods and manage their overall health in a unified manner.

[0852] "Body weight" is an indicator of the total weight of the body.

[0853] "Body fat percentage" is an index showing the proportion of body fat to total body weight.

[0854] "Target weight" is the weight value that the user sets and wants to achieve.

[0855] "Target body fat percentage" is the body fat percentage value that the user sets and wants to achieve.

[0856] A "gap" refers to the difference or difference between the current state and the goal.

[0857] "Dietary information" is data indicating the types of food and drink that a user consumes daily.

[0858] "Exercise information" is data that indicates information such as the type, intensity, and duration of exercise performed by the user each day.

[0859] The "analysis results" are results calculated based on the data entered by the user, showing the difference between the current state and the progress towards achieving the goal.

[0860] An "exercise plan" is a plan that includes specific exercise content, intensity, time, etc. that is proposed to the user.

[0861] A "meal plan" is a plan that takes into consideration specific meal contents and nutritional balance proposed to a user.

[0862] "Evaluation results" refers to feedback generated based on the user's progress.

[0863] "Health products" refer to items that are considered useful for managing or improving the user's health.

[0864] "Fitness goods" refers to equipment and devices used for exercise and training.

[0865] "User" refers to an individual who uses this system to manage their own health.

[0866] "Server" refers to a central system for analyzing data received from users, generating results, and transmitting required information.

[0867] "Terminal" refers to a device through which a user can enter data and view received information.

[0868]

[0869] This system provides a means for users to effectively manage their health. Specific embodiments are described below.

[0870] Server Roles

[0871] The server is responsible for calculating the gap between the user's current condition and the achievement of their goal based on the user's weight, body fat percentage, target weight, and target body fat percentage. It also receives and analyzes the dietary and exercise information entered daily by the user. Based on the analysis results, it generates an optimal exercise and meal plan for the user. Furthermore, based on the generated exercise and meal plan, it suggests related health and fitness products and provides a means for the user to purchase them. This is done using a Python-based web server (such as Flask or Django). Databases such as MySQL or PostgreSQL are used to manage the data. Furthermore, data analysis is performed using numerical calculation libraries such as NumPy and Pandas.

[0872] Device Role

[0873] The terminal provides an interface for users to input data and communicate with the server. This terminal is a mobile device such as a smartphone. Smartphone apps are developed using Swift for iOS and Kotlin / Java for Android. Users input their weight, body fat percentage, dietary details, and exercise details through the terminal. The device also receives and displays exercise and meal plans sent from the server, as well as suggestions for related health and fitness products.

[0874] User Roles

[0875] As an initial setting, the user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the device. This allows the system to understand the user's goals and begin analysis. The user then inputs their daily diet and exercise data into the device. They then perform daily tasks according to the exercise and diet plan provided, and enter the results back into the device. The input results are then sent to the server, where evaluation and feedback are provided.

[0876] Specific examples

[0877] For example, suppose a user inputs their current weight of 70 kg and body fat percentage of 25%, and sets their target weight of 65 kg and target body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, suppose they ate oatmeal for breakfast and ran for 30 minutes. This data is sent from the device to the server and analyzed.

[0878] The server generates the optimal exercise and meal plan for the next day based on the user's data. For example, the user might be recommended a 30-minute run, 20 squats, and a low-fat, high-protein diet. Healthy products like protein powder and running shoes are also recommended, and can be easily purchased within the app. An example of a prompt to be input into the generative AI model is as follows:

[0879] A user inputs their current weight of 70kg and body fat percentage of 25%, and sets their goal weight of 65kg and body fat percentage of 20%. Generate exercise and meal suggestions. For example, the next day's plan is to run 30 minutes, do 20 squats, and eat chicken breast and salad.

[0880] This allows users to manage their health in a centralized manner and purchase optimal health products, enabling more effective improvements to their health.

[0881] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0882] Step 1:

[0883] The user enters input data into the terminal.

[0884] Users input their current weight, body fat percentage, target weight, and target body fat percentage into a smartphone app, which temporarily stores the input data in a local database on the device.

[0885] Step 2:

[0886] The terminal sends the input data to the server.

[0887] The terminal sends the input data, including weight, body fat percentage, target weight, and target body fat percentage, to the server, which receives it and stores it in a database.

[0888] Step 3:

[0889] The server performs the analysis and calculates the gap.

[0890] The server calculates the gap between the user's current state and the goal based on the received data. For this calculation, it uses a numerical calculation library (NumPy or Pandas). For example, it calculates the change from a weight of 70 kg and a body fat percentage of 25% to a weight of 65 kg and a body fat percentage of 20%.

[0891] Step 4:

[0892] The server generates an optimal exercise and diet plan.

[0893] The server generates an optimal exercise and meal plan for the user based on the calculated gap. The exercise plan includes the type, intensity, and duration of exercise, and the meal plan includes nutritional balance. The generated plan is saved in a database.

[0894] Step 5:

[0895] The server will suggest related health and fitness products.

[0896] The server uses a generative AI model to suggest relevant health and fitness products based on the user's health status and goals, such as protein powder or running shoes, and these suggestions are also stored in a database.

[0897] Step 6:

[0898] The terminal displays the generated plans and proposals to the user.

[0899] The device displays the exercise and meal plans sent from the server, as well as suggestions for health and fitness products, to the user, who can then check this information through the app.

[0900] Step 7:

[0901] The user performs daily tasks and inputs the results into the terminal.

[0902] The user performs daily tasks according to the exercise and meal plan provided by the server. As a result, the user inputs the details of the meals and exercise into the device again. For example, if the user ate oatmeal for breakfast and ran for 30 minutes, the user inputs the details.

[0903] Step 8:

[0904] The terminal sends the input data to the server, which evaluates it.

[0905] The device sends the user's input results (meal and exercise details) to the server, which receives them and evaluates the progress. The evaluation includes comparing with past data and calculating the progress rate.

[0906] Step 9:

[0907] The server adjusts the plan for the next day and generates a new plan.

[0908] The server then adjusts the next day's exercise and meal plan based on the evaluation results. For example, if progress is good, it may extend the exercise time and increase the amount of protein in meals. The new plan is then saved in a database.

[0909] Step 10:

[0910] The terminal displays the new plan to the user and the cycle continues.

[0911] The device displays the new exercise and meal plan sent from the server to the user, helping the user to continue with their daily tasks and maintain a cycle toward achieving their goals.

[0912] 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.

[0913] This system proposes optimal exercise and dietary methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. It also combines an emotion engine that recognizes the user's emotions and adjusts the plan accordingly, thereby maintaining and improving motivation.

[0914] System Overview

[0915] This system consists of three main components: a server, a terminal, and a user, along with an emotion engine that connects them. Based on weight and body fat percentage data entered by the user, the server calculates the gap between the user's current state and their goals, and then analyzes the data to generate an optimal exercise and meal plan. The system also recognizes the user's emotions, adjusting the plan and providing feedback based on those emotions.

[0916] Server Roles

[0917] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[0918] 1. Data Reception

[0919] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the terminal and stores the data in a database.

[0920] 2. Data Analysis

[0921] The server calculates the gap between the user's current state and the goal achievement based on the received data, and also analyzes the user's emotional data to evaluate the progress of each individual user.

[0922] 3. Plan Generation

[0923] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. It also makes adjustments based on emotional data to maintain and improve motivation.

[0924] 4. Feedback Generation

[0925] The server evaluates the results of the user's exercise and diet and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to provide messages of encouragement and advice based on the user's emotions.

[0926] Device Role

[0927] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[0928] 1. Data Entry

[0929] The user inputs data about weight, body fat percentage, dietary habits, exercise habits, and emotions into the device, which stores the data in a local database and prepares it for transmission to the server.

[0930] 2. Data Transmission

[0931] The terminal transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[0932] 3. Plan presentation

[0933] The terminal displays the exercise and meal plan received from the server in a user-friendly format.

[0934] User Roles

[0935] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[0936] 1. Initial Setup

[0937] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[0938] 2. Daily entries

[0939] The user inputs daily diet and exercise data and emotional state into the device, including, for example, the contents of meals, calories, nutritional balance, type of exercise, time, intensity, and mood of the day.

[0940] 3. Task execution

[0941] The user performs daily tasks according to the exercise and meal plan presented by the server.

[0942] 4. Enter results

[0943] Users input their exercise routines, dietary habits, and daily emotional state into the device, and the system generates feedback to improve their plan for the next day.

[0944] The role of the emotional engine

[0945] The emotion engine recognizes and analyzes the user's emotional state from input data. Specifically, it includes the following functions:

[0946] 1. Emotion recognition

[0947] It analyzes the emotional data entered by the user (e.g., mood, stress level, motivation, etc.) to understand their emotional state for the day.

[0948] 2. Feedback adjustment

[0949] The emotion engine generates feedback according to the user's state. For example, if the user's motivation is low, it provides encouraging messages and appropriate advice.

[0950] 3. Plan adjustment

[0951] It adjusts exercise and meal plans based on the user's emotional state, for example suggesting light exercise or recommending nutritious meals if the user is feeling stressed.

[0952] Specific examples

[0953] For example, suppose a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. During initial setup, this data is entered into the device and registered in the system. The user then enters their daily diet and exercise data into the device. For example, they may eat oatmeal for breakfast, run for 30 minutes, and enter their mood for the day as "normal."

[0954] The server analyzes this data and generates an optimal exercise and meal plan for the next day. For example, the server might suggest a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is then sent to the device and presented to the user.

[0955] The user follows the presented plan, performs the day's tasks, and enters the results into the device, such as the type and time of exercise performed, food intake, and daily emotional state. The server analyzes these results and optimizes the plan for the next day. The emotion engine also recognizes when the user's motivation is low on a particular day and provides encouraging messages to restore motivation.

[0956] In this way, the system of the present invention not only supports the user in managing their weight and body fat percentage, but also recognizes their emotional state and provides appropriate feedback to help them maintain their motivation and achieve their goals.

[0957] The processing flow will be explained below.

[0958] Step 1:

[0959] The user launches the app and enters basic information (current weight, body fat percentage, target weight, target body fat percentage) into the device on the new registration screen.

[0960] Step 2:

[0961] The device stores the entered basic information in a local database and generates JSON format data to send to the server.

[0962] Step 3:

[0963] The terminal sends the generated JSON format data to the server.

[0964] Step 4:

[0965] The server stores the basic data of the user received from the terminal in a database and registers it as the initial state.

[0966] Step 5:

[0967] Users input their daily meal details (breakfast, lunch, dinner, snacks), exercise details (time, type, intensity), and emotional data (mood, stress level, motivation) into the device.

[0968] Step 6:

[0969] The device stores daily diet, exercise, and emotional information in a local database and generates JSON format data to send to the server.

[0970] Step 7:

[0971] The terminal sends the generated JSON format data to the server.

[0972] Step 8:

[0973] The server calculates the calorie intake and expenditure for each day based on the received data and analyzes the gap between the user's condition and the achievement of their goal. The emotion engine also analyzes the received emotion data to grasp the user's emotional state for that day.

[0974] Step 9:

[0975] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. The plan can also be adjusted based on emotional data.

[0976] Step 10:

[0977] The server sends the generated exercise and meal plan to the device in JSON format.

[0978] Step 11:

[0979] The terminal interprets the JSON formatted plan data received from the server and displays it in a user-friendly format.

[0980] Step 12:

[0981] The user follows the presented plan and performs the exercise and diet tasks.

[0982] Step 13:

[0983] The user inputs the exercise and meals they have performed, as well as their daily emotional state, into the terminal, such as the type and duration of exercise they performed, the food they ate, and their mood that day.

[0984] Step 14:

[0985] The terminal stores the execution results in a local database and generates JSON format data to send to the server.

[0986] Step 15:

[0987] The terminal sends the generated JSON format data to the server.

[0988] Step 16:

[0989] The server receives the execution result data, analyzes it, and evaluates the user's progress. The emotion engine reevaluates the user's emotional state based on the emotion data.

[0990] Step 17:

[0991] The server adjusts the next day's exercise and meal plan based on the evaluation results, using an emotion engine to generate encouraging and advice messages according to the user's emotions.

[0992] Step 18:

[0993] The server sends the generated plan for the next day and feedback to the terminal in JSON format.

[0994] Step 19:

[0995] The terminal displays the next day's plan and feedback received from the server to the user.

[0996] Step 20:

[0997] The user checks the feedback and starts taking action according to the plan for the next day.

[0998] Example 2

[0999] 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."

[1000] Conventional weight and body fat management systems provide analysis based on the user's weight and body fat percentage data, as well as exercise and meal plans, but lack feedback or plan adjustments that take into account the user's emotional state, making it difficult to maintain motivation or provide continuous support for achieving goals.In addition, daily data input and feedback of analysis results are often not done consistently across the entire system, making it difficult to provide and implement effective plans.

[1001] 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.

[1002] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing dietary and exercise information input daily by the user; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the generated exercise and meal plan to the user; means for receiving the results of the user's exercise and meal plans and evaluating the progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for receiving and analyzing the user's emotional data; and means for adjusting the plan to provide feedback based on the emotional analysis results. This makes it possible to provide a comprehensive exercise and meal plan that also takes the user's emotional state into consideration, thereby maintaining and improving motivation and realizing an effective system for supporting goal achievement.

[1003] "User" refers to an individual who uses this system to manage their weight and body fat percentage.

[1004] "Server" refers to the computer system that receives data sent by users and performs analysis and plan generation.

[1005] "Terminal" refers to a device (such as a smartphone or PC) that provides an interface for users to input data and communicate with a server.

[1006] "Weight" represents data indicating the current weight of the user.

[1007] "Body fat percentage" represents data indicating the percentage of fat in the user's body.

[1008] "Target weight" refers to data indicating the weight that the user wishes to achieve.

[1009] "Target body fat percentage" represents data indicating the body fat percentage that the user wishes to achieve.

[1010] The "gap" represents data that indicates the difference between the user's current state and the progress toward achieving the goal.

[1011] "Dietary information" refers to data that indicates the contents of the foods and drinks that a user consumes daily.

[1012] "Exercise information" refers to data indicating the type, duration, and intensity of exercise that the user performs daily.

[1013] "Analysis" refers to the process by which the server processes the data it receives and evaluates the user's status and progress.

[1014] "Exercise plan" refers to a plan generated by the server that includes the duration, intensity, and type of exercise optimal for the user.

[1015] "Meal plan" refers to a meal menu generated by the server that takes into consideration the optimal nutritional balance for the user.

[1016] "Progress" refers to data indicating the degree of achievement evaluated based on the results of the exercise and diet performed by the user.

[1017] "Emotion data" refers to data entered by the user that indicates their own mood, stress level, and motivation state.

[1018] "Emotion engine" refers to technology that analyzes a user's emotional data and provides appropriate feedback and plan adjustments.

[1019] "Feedback" refers to information generated by the server based on analysis, including evaluations and advice regarding the user's actions.

[1020] This invention is a system that proposes optimal exercise and diet methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. This system includes three main components: a "server," a "terminal," and a "user," as well as an emotion engine that links them.

[1021] Server Roles

[1022] The server plays a central role in receiving and analyzing the data entered by the user. The server processes the data using the following techniques:

[1023] Data reception and storage: The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the device and stores it in a database (e.g., MySQL).

[1024] Data analysis: The server uses data analysis tools such as Python and R to calculate the user's current state and the gap between the goal and the goal based on the received data. It also analyzes emotional data to evaluate the progress of each individual user.

[1025] Plan generation: Based on the analysis results, the server generates an optimal exercise and meal plan for the user. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. Adjustments are also made based on emotional data to maintain and improve motivation.

[1026] Feedback generation: The server evaluates the results of the user's exercise and diet and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to provide messages of encouragement and advice based on the user's emotions.

[1027] Device Role

[1028] The terminal provides an interface for the user to input data and communicate with the server. The specific operation is as follows.

[1029] Data input: The user inputs data about weight, body fat percentage, diet, exercise, and emotions into the device. The device stores this data in a local database (e.g., SQLite) and prepares it for transmission to the server.

[1030] Data transmission: The device transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[1031] Plan presentation: The device displays the exercise and meal plan received from the server in a user-friendly format. Front-end frameworks such as React and Vue.js are used for the UI.

[1032] User Roles

[1033] Users use the system to manage their own weight and body fat percentage. They utilize the system by performing the following actions:

[1034] Initial setting: The user inputs the current weight, body fat percentage, target weight, and target body fat percentage into the terminal.

[1035] Daily input: The user inputs their daily diet and exercise data, as well as their emotional state, into the device. For example, they may have oatmeal for breakfast, go for a 30-minute run, and enter their mood for the day as "normal."

[1036] Task execution: The user performs daily tasks according to the exercise and meal plan presented by the server.

[1037] Result input: Enter the results of your exercise, diet, and daily emotional state into your device.

[1038] The role of the emotional engine

[1039] The emotion engine recognizes and analyzes the emotional state of the user from input data. This technology has the following functions:

[1040] Emotion recognition: Analyzes the emotional data entered by the user (e.g., mood, stress level, motivation, etc.) to understand their emotional state for the day.

[1041] Feedback adjustment: The emotion engine generates feedback according to the user's state. For example, if the user's motivation is low, it provides encouraging messages or appropriate advice.

[1042] Plan Adjustment: Adjust your exercise and meal plan based on your emotional state. For example, if you're feeling stressed, it might suggest light exercise or recommend a nutritious meal.

[1043] Specific examples

[1044] If a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%, they enter this data into the device and register it in the system. They also enter their daily diet and exercise data into the device. For example, they might eat oatmeal for breakfast, run for 30 minutes, and enter their mood for the day as "normal."

[1045] The server analyzes this data and generates the optimal exercise and meal plan for the next day. The user is suggested to run for 30 minutes, do 20 squats, and eat a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[1046] The user follows the presented plan, performs the day's tasks, and enters the results into the device, such as the type and time of exercise performed, food intake, and daily emotional state. The server analyzes these results and optimizes the plan for the next day. The emotion engine also recognizes when the user's motivation is declining and provides encouraging messages to restore motivation.

[1047] Prompt Sentence Examples

[1048] "I want to lose weight, what kind of exercise is effective?"

[1049] "Suggest a meal plan for today."

[1050] "What message would you give me to encourage me if I lose motivation?"

[1051] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1052] Step 1: Enter initial setup data

[1053] Input: The user enters their current weight, body fat percentage, target weight, and target body fat percentage into a dedicated mobile app or a web interface on their PC.

[1054] Operation: The device receives this data, formats it into a data format such as JSON, and temporarily stores it in a local database (such as SQLite).

[1055] Output: The formatted JSON data is saved in the terminal.

[1056] Step 2: Receiving and storing data

[1057] Input: User's weight, body fat percentage, target weight, and target body fat percentage data sent from the device.

[1058] How it works: The device sends the formatted data to the server as a POST request. The server receives this data and stores it in a database (e.g., MySQL).

[1059] Output: User's weight, body fat percentage, target weight, and target body fat percentage data recorded in the database.

[1060] Step 3: Analyze the data

[1061] Input: The user's current weight and body fat percentage, target weight and target body fat percentage data stored on the server.

[1062] How it works: The server analyzes this data using data analysis tools such as Python and R. It calculates the gap between the user's current state (e.g., weight 70 kg, body fat percentage 25%) and their goal (e.g., weight 65 kg, body fat percentage 20%).

[1063] Output: Calculated gap (e.g. weight gap 5kg, body fat gap 5%).

[1064] Step 4: Create an exercise and meal plan

[1065] Input: Analysis result gap data and user emotion data.

[1066] How it works: The server generates an optimal exercise and meal plan based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise (e.g., 30 minutes of running, 20 squats), and the meal plan includes nutritionally balanced menus. The plan is also personalized using machine learning libraries such as Scikit-learn.

[1067] Output: Generated exercise and meal plans.

[1068] Step 5: Provide feedback

[1069] Input: User exercise and diet results, emotional data.

[1070] How it works: The server evaluates the results and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to generate messages of encouragement and advice based on the user's emotions. For example, if the user enters "low motivation," an encouraging message will be generated.

[1071] Output: Generated feedback and adjusted next day plan.

[1072] Step 6: Daily data entry and submission

[1073] Input: Daily user diet, exercise, and emotional state.

[1074] How it works: Users enter these daily data into a terminal, which formats the data and prepares it for transmission to the server.

[1075] Output: Daily data stored on the device and sent to the server.

[1076] Step 7: Daily data analysis and feedback updates

[1077] Input: User's daily data, emotional state data.

[1078] How it works: The server receives and analyzes this data. Based on the received diet, exercise, and emotional data, it updates and optimizes the next day's exercise and meal plan. It also uses an emotion engine to generate appropriate feedback and messages based on the user's emotional state.

[1079] Output: Updated exercise and meal plans, encouraging and advice feedback.

[1080] (Application example 2)

[1081] 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."

[1082] The present invention aims to provide a system that not only provides diet and exercise plans to help users achieve their target weight and body fat percentage, but also more effectively supports health management by suggesting healthy product selections and meal menus in physical stores. Another objective of the present invention is to provide a system that maintains and improves the user's motivation by taking into account the user's emotional state.

[1083] 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: means for calculating the gap between the user's current state and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing dietary and exercise information input daily by the user; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the generated exercise and meal plan to the user; means for receiving the results of the user's exercise and meal plans and evaluating the progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for providing a real-time health rating for a product when the user scans it in a store; means for providing a health rating for a menu item when the user selects it at a restaurant in the store; and means for recognizing the user's emotional state and providing feedback based on the selected menu item. This enables effective health management of the user even in the store and maintains motivation according to the user's emotional state.

[1084] "Weight" refers to the user's current body weight.

[1085] "Body fat percentage" is a percentage that indicates the proportion of fat in relation to the user's total body weight.

[1086] "Goal weight" refers to the final weight that a user wishes to achieve.

[1087] "Target body fat percentage" refers to the final body fat percentage that the user wishes to achieve.

[1088] The "gap" refers to the difference between the user's current state and the achievement of their goal.

[1089] "Dietary information" is information about the contents, calories, and nutrients of the meals that the user takes in every day.

[1090] "Exercise information" is information relating to the type, duration, intensity, etc. of exercise that the user performs daily.

[1091] The "analysis results" are the results of analyzing the data entered by the user, and indicate the gap between the current state and the achievement of the goal.

[1092] An "exercise plan" is a plan of the type, intensity, and duration of exercise suggested to help the user achieve their goals.

[1093] A "meal plan" is a plan of suggested meal content, calories, and nutritional balance to help the user achieve their goals.

[1094] "Progress" refers to the progress a user makes toward achieving a goal.

[1095] "Health rating" refers to the calorie and nutritional balance rating of the product scanned by the user or the meal menu selected by the user.

[1096] "Emotional state" is information about the user's daily mood, stress level, motivation, etc.

[1097] "Feedback" refers to messages of advice or encouragement provided to the user.

[1098] "Real-time" refers to processing and information being provided immediately without delay.

[1099] A "brick and mortar store" refers to a physical location where consumers visit in person to purchase goods or use services.

[1100] The present invention is a system that suggests optimal exercise and dietary methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts the plan accordingly, motivation is maintained and improved. Specific embodiments of the present invention are described below.

[1101] System Configuration

[1102] This system consists of three main components: a server, a terminal, and a user, and includes an emotion engine that connects these components. Its main roles are as follows:

[1103] server

[1104] The server has the following functions:

[1105] 1. Data reception: The server receives the weight, body fat percentage, target weight, and target body fat percentage entered by the user and stores them in a database.

[1106] 2. Data analysis: The server calculates the gap between the user's current status and their goal based on the received data, and also analyzes the user's daily diet and exercise information.

[1107] 3. Plan Generation: The server generates an optimal exercise and meal plan based on the analysis results. Furthermore, the emotion engine adjusts the plan to take into account the user's emotional state.

[1108] 4. Feedback generation: The server evaluates the results of the exercise and diet performed by the user and generates feedback to be reflected in the plan for the next day.

[1109] Terminal

[1110] The terminal has the following functions:

[1111] 1. Data input: Provides an interface for users to input their weight, body fat percentage, dietary habits, exercise habits, and emotional state. Prepares the input data to be sent to the server.

[1112] 2. Data transmission: The device transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[1113] 3. Plan presentation: The device displays the exercise and meal plan received from the server in an easy-to-understand manner to the user.

[1114] User

[1115] Users use the system to manage their own weight and body fat percentage.

[1116] 1. Initial setup: The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal.

[1117] 2. Daily input: The user inputs their daily diet, exercise, and emotional state into the device.

[1118] 3. Task execution: The user follows the exercise and meal plan provided by the server.

[1119] 4. Enter results: Enter the results of your exercise and meals into the device.

[1120] Emotion Engine

[1121] The emotion engine recognizes and analyzes the user's emotional state.

[1122] 1. Emotion recognition: Analyzes the emotional data entered by the user to understand their emotional state for the day.

[1123] 2. Feedback adjustment: The emotion engine generates feedback according to the user's state.

[1124] 3. Plan Adjustment: Adjust exercise and meal plans based on the user's emotional state.

[1125] Hardware and software used

[1126] Hardware: Smartphones, smart glasses

[1127] Software: Python, Flask, SQLite

[1128] Library: emotion_engine

[1129] Specific examples

[1130] For example, suppose a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user inputs initial data such as weight, body fat percentage, target weight, and target body fat percentage into the terminal and registers it in the system. Next, the user inputs their daily diet and exercise information.

[1131] When users visit a physical store and scan an item, they are given a real-time health rating for that item, when they select a meal at an in-store restaurant, they are given a health rating for that menu item, and they can input their emotional state and receive real-time feedback based on that.

[1132] Prompt Sentence Examples

[1133] "I'm very tired today"

[1134] "I'm stressed out at work"

[1135] "Exercise feels fun"

[1136] An example prompt is:

[1137] "Generate appropriate feedback based on user input emotion. Input: {emotion_input}. Feedback: "

[1138] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1139] Step 1:

[1140] The user inputs their weight, body fat percentage, target weight, target body fat percentage, dietary information, exercise information, and emotional information into the device. The device stores this data in a local database and prepares it for transmission to the server. The user inputs the data directly by operating the device, and the input data is converted into XML or JSON format. When the data is ready to be sent to the server, the send command is executed.

[1141] Step 2:

[1142] The terminal sends the data entered by the user to the server. Specifically, it sends the data stored in the terminal to the server using the HTTP protocol. The server processes the received data using a reception module and stores it in the necessary database. The input here is the user's input data, and the output is the result stored in the server's database. After transmission is complete, the terminal waits for a response from the server.

[1143] Step 3:

[1144] The server calculates the gap between the user's current status and their goal based on the received data. To do this, it uses an algorithm that matches the user's weight, body fat percentage, target weight, and target body fat percentage. Specifically, it calculates the difference between the current and target values ​​for weight and body fat percentage, and lists the items the user needs to achieve. The input is the user's weight, body fat percentage, target weight, and target body fat percentage, and the output is the gap calculation result.

[1145] Step 4:

[1146] The server analyzes daily input data (diet, exercise, and emotional information) and generates an optimal exercise and meal plan. Here, the received data is filtered, compared with a health database, and then an algorithm is applied to suggest an appropriate exercise and meal plan. The input is the user's diet, exercise, and emotional information, and the output is an exercise and meal plan. The generated plan is sent to the user.

[1147] Step 5:

[1148] The device displays the exercise and meal plan received from the server to the user. The input here is the plan data sent from the server, and the output is the user's plan information displayed on the device's display. The device notifies the user whenever the plan is updated.

[1149] Step 6:

[1150] The user inputs the results of their exercise and diet into the terminal. Here, the user inputs the duration and type of exercise they performed, the type of meal they ate, their emotional state, etc. The input data is prepared for transmission to the server again. The input is the user's exercise results, diet results, and emotional state data, and the output is the data prepared for transmission to the server.

[1151] Step 7:

[1152] The device sends the results of the user's exercise and diet to the server. Specifically, the device sends the previously entered data to the server as an HTTP request, and the server receives the data and registers it in a database. The input is the user's exercise results, diet results, and emotional state data, and the output is the registration results in the server's database.

[1153] Step 8:

[1154] The server evaluates the user's progress based on the received data and adjusts the exercise and meal plan for the next day. This evaluation process uses analytical algorithms to generate an optimal plan based on the user's progress. The input is the user's progress data, and the output is the adjusted plan for the next day.

[1155] Step 9:

[1156] The server sends the generated new plan to the terminal, which then presents it to the user. The input is the new exercise and meal plan, and the output is the user's new plan information displayed on the terminal.

[1157] Step 10:

[1158] When a user visits a physical store and scans a product or selects an item from the restaurant menu, the terminal sends the product barcode or menu information to the server. The server receives this data and performs a real-time health assessment. The input is the product or menu information, and the output is the health assessment result. The health assessment result is immediately returned to the terminal and displayed to the user.

[1159] Step 11:

[1160] When the user's emotional state is input, the server uses an emotion engine to perform emotion analysis and generate feedback based on the results. The input is the user's emotional data, and the output is a feedback message. The generated feedback is sent to the terminal and presented to the user.

[1161] Example prompt sentences

[1162] For example, it generates appropriate feedback based on emotional input such as "I feel very tired today," "I'm stressed at work," or "I enjoy exercising." Examples of prompt sentences are as follows:

[1163] "Generate appropriate feedback based on user input emotion. Input: {emotion_input}. Feedback: "

[1164] 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.

[1165] 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.

[1166] 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.

[1167] [Third embodiment]

[1168] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1169] 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.

[1170] 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).

[1171] 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.

[1172] 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.

[1173] 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).

[1174] 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.

[1175] 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.

[1176] 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.

[1177] 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.

[1178] 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.

[1179] 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."

[1180] The present invention is a system that proposes optimal exercise and dietary methods based on dietary and exercise information to help a user achieve their target weight and body fat percentage. An embodiment of this system will be described in detail below.

[1181] System Overview

[1182] This system calculates and analyzes the gap between the user's current condition and the achievement of their goal based on the weight and body fat percentage data entered by the user. Based on the analysis results, it generates an optimal exercise and meal plan and provides it to the user. It also receives the results of the user's exercise and meal, evaluates their progress, and adjusts the plan for the next day.

[1183] This system consists of three main components: a "server," a "terminal," and a "user."

[1184] Server Roles

[1185] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[1186] 1. Data Reception

[1187] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the terminal and stores the data in a database.

[1188] 2. Data Analysis

[1189] The server calculates the user's current status and the gap between them and achieving their goals based on the received data, and also evaluates the user's progress based on the user's past data, generating an optimal exercise and meal plan.

[1190] 3. Plan Generation

[1191] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, and the meal plan includes a menu that takes nutritional balance into consideration.

[1192] 4. Feedback Generation

[1193] It evaluates the results of the user's exercise and diet and generates feedback that is reflected in the next day's plan, including advice on how to improve the user's progress.

[1194] Device Role

[1195] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[1196] 1. Data Entry

[1197] The user inputs their weight, body fat percentage, dietary habits, and exercise habits into the device, which then stores this data in a local database and prepares it for transmission to the server.

[1198] 2. Data Transmission

[1199] The terminal transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[1200] 3. Plan presentation

[1201] The device displays the exercise and meal plans received from the server in a user-friendly format, allowing the user to carry out their daily tasks.

[1202] User Roles

[1203] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[1204] 1. Initial Setup

[1205] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[1206] 2. Daily entries

[1207] The user inputs daily diet and exercise data into the device, including the type, duration, and intensity of exercise, as well as the type, calories, and nutritional balance of the food.

[1208] 3. Task execution

[1209] The user performs daily tasks according to the exercise and meal plan presented by the server.

[1210] 4. Enter results

[1211] Users input their exercise and diet results into the device, and the system generates feedback to improve their plan for the next day.

[1212] Specific examples

[1213] For example, a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, they may have eaten oatmeal for breakfast and run for 30 minutes. This data is sent from the device to the server and analyzed.

[1214] The server generates an optimal exercise and meal plan for the next day based on the user's data. For example, the server suggests a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[1215] The user follows the plan, performs the tasks for the day, and then enters the results back into the device. The server analyzes these results and further optimizes the plan for the next day. By continuing this cycle, the user can achieve their goals without difficulty.

[1216] The processing flow will be explained below.

[1217] Step 1:

[1218] The user launches the app and enters basic information (current weight, body fat percentage, target weight, target body fat percentage) into the device on the new registration screen.

[1219] Step 2:

[1220] The device stores the entered basic information in a local database and generates JSON format data to send to the server.

[1221] Step 3:

[1222] The terminal sends the generated JSON format data to the server.

[1223] Step 4:

[1224] The server stores the basic data of the user received from the terminal in a database and registers it as the initial state.

[1225] Step 5:

[1226] Users input their daily meal details (breakfast, lunch, dinner, snacks) and exercise details (time, type, intensity) into the terminal.

[1227] Step 6:

[1228] The device stores daily diet and exercise information in a local database and generates JSON format data to send to the server.

[1229] Step 7:

[1230] The terminal sends the generated JSON format data to the server.

[1231] Step 8:

[1232] Based on the received data, the server calculates the calorie intake and expenditure for each day and analyzes the user's condition and the gap between them and achieving their goal.

[1233] Step 9:

[1234] Based on the analysis results, the server generates an optimal exercise and meal plan for the user, determining, for example, the duration, intensity, and type of exercise, as well as a menu that takes into account nutritional balance.

[1235] Step 10:

[1236] The server sends the generated exercise and meal plan to the device in JSON format.

[1237] Step 11:

[1238] The terminal interprets the JSON formatted plan data received from the server and displays it in a user-friendly format.

[1239] Step 12:

[1240] The user follows the presented plan and performs the exercise and diet tasks.

[1241] Step 13:

[1242] The user inputs the results of the exercise and diet he or she performed into the terminal, such as the type and duration of the exercise performed, and the details of the diet he or she ate.

[1243] Step 14:

[1244] The terminal stores the execution results in a local database and generates JSON format data to send to the server.

[1245] Step 15:

[1246] The terminal sends the generated JSON format data to the server.

[1247] Step 16:

[1248] The server receives and analyzes the execution result data to assess the user's progress, including progress towards achieving the goal and recommendations.

[1249] Step 17:

[1250] The server adjusts the next day's exercise and meal plan based on the evaluation results and generates feedback.

[1251] Step 18:

[1252] The server sends the generated feedback in JSON format to the device.

[1253] Step 19:

[1254] The terminal displays the feedback received from the server to the user.

[1255] Step 20:

[1256] The user checks the feedback and starts taking action according to the plan for the next day.

[1257] Example 1

[1258] 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."

[1259] Current fitness management systems often lack consistency and accuracy when providing optimal exercise and meal plans for specific user goals, such as weight and body fat percentage. They also lack the ability to analyze users' progress data in real time and incorporate it into the next day's plan. This leaves users with a lack of efficient support to achieve their goals.

[1260] 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.

[1261] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing the user's daily input dietary and exercise information; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the user with the generated exercise and meal plan; means for receiving the user's exercise and meal results again and evaluating progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for storing dietary and exercise information, calorie information, and nutritional balance in a database; means for analyzing the data and generating optimal feedback; and means for using a generative artificial intelligence model to generate meal and exercise plans. This enables highly accurate and efficient support for the user's health management.

[1262] "Weight" is a numerical value that indicates the total weight of the human body.

[1263] "Body fat percentage" is a numerical value that indicates the percentage of fat in the human body.

[1264] "Target weight" is a numerical value indicating the weight that the user wishes to achieve.

[1265] "Target body fat percentage" is a numerical value that indicates the percentage of body fat that the user wishes to achieve.

[1266] A "database" is a system for efficiently storing, managing, and retrieving digital information.

[1267] "Analysis" is the process of processing input data to derive useful information or conclusions.

[1268] "Dietary information" refers to information such as the content, amount, calories, and nutrients of the food consumed.

[1269] "Exercise information" refers to information such as the type, intensity, duration, and number of times of exercise performed.

[1270] "Feedback" is the evaluation and guidance for improvement that the system provides to the user based on past behavior.

[1271] A "generative artificial intelligence model" is an artificial intelligence technology that has the ability to learn from large amounts of data and generate optimal exercise and meal plans.

[1272] "Analysis results" refers to information and conclusions obtained through data analysis.

[1273] An "exercise plan" is a plan that includes a specific combination of exercises, number of repetitions, duration, and intensity that are suggested based on the goals that the user wants to achieve.

[1274] A "meal plan" is a nutritionally balanced meal plan suggested based on the goals the user wants to achieve.

[1275] The present invention is a system that proposes optimal exercise and dietary methods based on dietary and exercise information to help a user achieve their target weight and body fat percentage. An embodiment of this system will be described in detail below.

[1276] System Overview

[1277] This system has the function of calculating and analyzing the gap between the user's current condition and the achievement of their goal based on the weight and body fat percentage data entered by the user. Based on the analysis results, it generates an optimal exercise and meal plan and provides it to the user. It also receives the results of the user's exercise and meal, evaluates progress, and adjusts the plan for the next day. This system consists of three main components: a "server," a "terminal," and a "user."

[1278] Server Roles

[1279] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[1280] 1. Data Reception

[1281] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the device and stores it in a database. Data communication uses the HTTP protocol and REST API.

[1282] 2. Data Analysis

[1283] Based on the received data, the server calculates the gap between the user's current state and the achievement of the goal using Python libraries such as NumPy and Pandas. It also evaluates the user's progress based on their past data and manages it in an SQL database. Google Cloud Platform and Amazon Web Services are often used as cloud services.

[1284] 3. Plan Generation

[1285] Based on the analysis results, the server uses AI models (such as TensorFlow and PyTorch) to generate optimal exercise and meal plans. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration.

[1286] 4. Feedback Generation

[1287] The server generates feedback based on the user's exercise and dietary habits to be reflected in the next day's plan, including advice on how to improve the user's progress.

[1288] Device Role

[1289] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[1290] 1. Data Entry

[1291] The user inputs their weight, body fat percentage, dietary habits, and exercise habits into the device. These data are stored in a local database (e.g., SQLite) and prepared for transmission to the server.

[1292] 2. Data Transmission

[1293] The device sends the entered data to the server using an HTTP request, and displays the plan and feedback received from the server to the user.

[1294] 3. Plan presentation

[1295] The device receives the exercise and meal plans from the server and displays them in an easy-to-understand format. The app is developed using Java or Kotlin for Android and Swift for iOS.

[1296] User Roles

[1297] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[1298] 1. Initial Setup

[1299] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[1300] 2. Daily entries

[1301] Users enter their daily diet and exercise data into the device, including the type, calories, and nutritional balance of their meals, as well as the type, duration, and intensity of their exercise.

[1302] 3. Task execution

[1303] The user performs daily tasks according to the exercise and meal plan presented by the server.

[1304] 4. Enter results

[1305] The results of the exercises and meals performed are entered into the device and sent to the server, which then generates feedback to improve the plan for the next day.

[1306] Specific examples

[1307] For example, a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, they may have eaten oatmeal for breakfast and run for 30 minutes. This data is sent from the device to the server and analyzed.

[1308] The server generates an optimal exercise and meal plan for the next day based on the user's data. For example, the server suggests a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[1309] The user follows the plan, performs the tasks for the day, and then enters the results back into the device. The server analyzes these results and further optimizes the plan for the next day. By continuing this cycle, the user can achieve their goals without difficulty.

[1310] Example prompts for generative AI models

[1311] Prompt: "If the user weighs 70 kg and has 25% body fat, and sets a goal of 65 kg and 20% body fat, generate the optimal exercise and meal plan. Also consider the past week's exercise and meal data."

[1312] Example expected output:

[1313] "Based on the user's current data, it will suggest an exercise plan that includes 30 minutes of running and 20 squats, as well as a low-fat, high-protein meal plan."

[1314] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1315] System program processing flow

[1316] Step 1:

[1317] User data initial settings

[1318] 1. Specific explanation

[1319] The user starts the application on the terminal and inputs their current weight, body fat percentage, target weight, and target body fat percentage, which allows the system to recognize the user's goals.

[1320] 2. Input / Output

[1321] Input: Weight, body fat percentage, target weight, and target body fat percentage entered by the user on the device

[1322] Data processing: The entered data is collected using the terminal's form interface and stored in a local database.

[1323] Output: User's physical data stored in a local database

[1324] 3. Specific actions

[1325] When the user enters various data into the designated fields and presses the "Save" button, the data is saved in the local database.

[1326] Step 2:

[1327] Data transmission

[1328] 1. Specific explanation

[1329] The device sends the stored data to the server using an HTTP request, and through this process the server receives the user's initial setting data.

[1330] 2. Input / Output

[1331] Input: User's physical data stored in a local database

[1332] Data processing: The data is encoded into JSON format and sent to the server via an HTTP POST request.

[1333] Output: User preference data sent to the server

[1334] 3. Specific actions

[1335] The device sends data to the specified API endpoint via a POST request, which the server receives and stores in the database.

[1336] Step 3:

[1337] Data reception and storage

[1338] 1. Specific explanation

[1339] The server receives the HTTP POST request sent from the terminal and stores it in a database.

[1340] 2. Input / Output

[1341] Input: User preference data sent in the HTTP POST request

[1342] Data processing: Parse the received JSON data and store it in the corresponding column in the database.

[1343] Output: User preference data stored in the database

[1344] 3. Specific actions

[1345] The server's web framework (e.g., Flask or Django) receives the request and executes the logic to save it to the database.

[1346] Step 4:

[1347] Data analysis

[1348] 1. Specific explanation

[1349] Based on the received data, the server calculates the user's current state and the gap to achieving the goal.

[1350] 2. Input / Output

[1351] Input: User preference data stored in the database

[1352] Data processing: Using Python's NumPy and Pandas, calculate the gap (for example, 5 kg between weights of 70 kg and 65 kg).

[1353] Output: Data showing the gap between the user's current situation and their goal

[1354] 3. Specific actions

[1355] The server calls NumPy and Pandas to process the data, calculate the gap information, and store it in the database.

[1356] Step 5:

[1357] Plan Generation

[1358] 1. Specific explanation

[1359] Based on the analysis results, the server uses an AI model (such as TensorFlow or PyTorch) to generate an optimal exercise and meal plan.

[1360] 2. Input / Output

[1361] Input: Current status and goal gap data, past exercise and diet data

[1362] Data processing: Using AI models, we generate optimal exercise and diet plans, such as 30 minutes of running, 20 squats, and a low-fat, high-protein diet.

[1363] Output: Generated exercise and meal plan

[1364] 3. Specific actions

[1365] The AI ​​model takes user data as input and generates a plan, which is then converted to JSON format and saved.

[1366] Step 6:

[1367] Send Plan

[1368] 1. Specific explanation

[1369] The server transmits the generated exercise and meal plan to the terminal.

[1370] 2. Input / Output

[1371] Input: Generated exercise and meal plan

[1372] Data processing: Encode the plan data into JSON format and send it to the end user as an HTTP response.

[1373] Output: Exercise and meal plan sent to device

[1374] 3. Specific actions

[1375] The server sends the plan data to the device through the specified API endpoint.

[1376] Step 7:

[1377] Show Plan

[1378] 1. Specific explanation

[1379] The terminal analyzes the received plan data and displays it on the user interface.

[1380] 2. Input / Output

[1381] Input: Exercise and meal plan received in HTTP response

[1382] Data processing: Parse the JSON data and convert it into a format that can be displayed in the graphical user interface (GUI).

[1383] Output: Plan information presented visually to the user

[1384] 3. Specific actions

[1385] The device analyzes the received data and visually displays exercise and meal plans on the app's dashboard screen.

[1386] Step 8:

[1387] Daily data entry

[1388] 1. Specific explanation

[1389] The user inputs the results of their daily exercise and diet into the terminal.

[1390] 2. Input / Output

[1391] Input: Exercise and diet information entered by the user into the device (e.g., calories burned, dietary details)

[1392] Data processing: The entered data is stored in the device's local database.

[1393] Output: Daily exercise and diet information stored in a local database

[1394] 3. Specific actions

[1395] The user enters the results of their exercise and diet into the form on the device app and presses the "Save" button to save the data.

[1396] Step 9:

[1397] Sending result data

[1398] 1. Specific explanation

[1399] The terminal sends the input result data to the server via an HTTP POST request.

[1400] 2. Input / Output

[1401] Input: Daily exercise and diet information stored in a local database

[1402] Data processing: The data is again encoded into JSON format and sent to the server via an HTTP POST request.

[1403] Output: Daily results data sent to the server

[1404] 3. Specific actions

[1405] The device sends the result data to the specified API endpoint via a POST request, which the server receives and analyzes.

[1406] Step 10:

[1407] Feedback Generation

[1408] 1. Specific explanation

[1409] The server analyzes the received results data, evaluates the user's progress, and generates feedback to optimize the next day's plan.

[1410] 2. Input / Output

[1411] Input: Daily results data sent to the server

[1412] Data processing: Analyze and process the results data to evaluate the user's progress and generate feedback to optimize exercise and meal plans for the next day.

[1413] Output: Generated feedback and optimal plan for the next day

[1414] 3. Specific actions

[1415] The server performs the analysis, generates feedback, stores it in a database, and updates the plan for the next day. The updated data is then sent back to the device.

[1416] (Application example 1)

[1417] 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."

[1418] Traditional health management systems not only provide users with optimal exercise and meal plans, but also provide feedback based on the results of each day and adjust the plan for the next day. However, these systems lack the functionality to allow users to easily purchase healthy products and fitness gear, which means users have to go through the trouble of searching for and purchasing these products separately.

[1419] 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.

[1420] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the weight, body fat percentage, target weight, and target body fat percentage input by the user; means for receiving and analyzing the dietary and exercise information input by the user daily; means for generating an optimal exercise and meal plan for the user based on the analysis results; and means for suggesting related health products and fitness goods based on the generated exercise and meal plan and allowing the user to purchase them. This allows the user to easily purchase the optimal health products and fitness goods and manage their overall health in a unified manner.

[1421] "Body weight" is an indicator of the total weight of the body.

[1422] "Body fat percentage" is an index showing the proportion of body fat to total body weight.

[1423] "Target weight" is the weight value that the user sets and wants to achieve.

[1424] "Target body fat percentage" is the body fat percentage value that the user sets and wants to achieve.

[1425] A "gap" refers to the difference or difference between the current state and the goal.

[1426] "Dietary information" is data indicating the types of food and drink that a user consumes daily.

[1427] "Exercise information" is data that indicates information such as the type, intensity, and duration of exercise performed by the user each day.

[1428] The "analysis results" are results calculated based on the data entered by the user, showing the difference between the current state and the progress towards achieving the goal.

[1429] An "exercise plan" is a plan that includes specific exercise content, intensity, time, etc. that is proposed to the user.

[1430] A "meal plan" is a plan that takes into consideration specific meal contents and nutritional balance proposed to a user.

[1431] "Evaluation results" refers to feedback generated based on the user's progress.

[1432] "Health products" refer to items that are considered useful for managing or improving the user's health.

[1433] "Fitness goods" refers to equipment and devices used for exercise and training.

[1434] "User" refers to an individual who uses this system to manage their own health.

[1435] "Server" refers to a central system for analyzing data received from users, generating results, and transmitting required information.

[1436] "Terminal" refers to a device through which a user can enter data and view received information.

[1437]

[1438] This system provides a means for users to effectively manage their health. Specific embodiments are described below.

[1439] Server Roles

[1440] The server is responsible for calculating the gap between the user's current condition and the achievement of their goal based on the user's weight, body fat percentage, target weight, and target body fat percentage. It also receives and analyzes the dietary and exercise information entered daily by the user. Based on the analysis results, it generates an optimal exercise and meal plan for the user. Furthermore, based on the generated exercise and meal plan, it suggests related health and fitness products and provides a means for the user to purchase them. This is done using a Python-based web server (such as Flask or Django). Databases such as MySQL or PostgreSQL are used to manage the data. Furthermore, data analysis is performed using numerical calculation libraries such as NumPy and Pandas.

[1441] Device Role

[1442] The terminal provides an interface for users to input data and communicate with the server. This terminal is a mobile device such as a smartphone. Smartphone apps are developed using Swift for iOS and Kotlin / Java for Android. Users input their weight, body fat percentage, dietary details, and exercise details through the terminal. The device also receives and displays exercise and meal plans sent from the server, as well as suggestions for related health and fitness products.

[1443] User Roles

[1444] As an initial setting, the user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the device. This allows the system to understand the user's goals and begin analysis. The user then inputs their daily diet and exercise data into the device. They then perform daily tasks according to the exercise and diet plan provided, and enter the results back into the device. The input results are then sent to the server, where evaluation and feedback are provided.

[1445] Specific examples

[1446] For example, suppose a user inputs their current weight of 70 kg and body fat percentage of 25%, and sets their target weight of 65 kg and target body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, suppose they ate oatmeal for breakfast and ran for 30 minutes. This data is sent from the device to the server and analyzed.

[1447] The server generates the optimal exercise and meal plan for the next day based on the user's data. For example, the user might be recommended a 30-minute run, 20 squats, and a low-fat, high-protein diet. Healthy products like protein powder and running shoes are also recommended, and can be easily purchased within the app. An example of a prompt to be input into the generative AI model is as follows:

[1448] A user inputs their current weight of 70kg and body fat percentage of 25%, and sets their goal weight of 65kg and body fat percentage of 20%. Generate exercise and meal suggestions. For example, the next day's plan is to run 30 minutes, do 20 squats, and eat chicken breast and salad.

[1449] This allows users to manage their health in a centralized manner and purchase optimal health products, enabling more effective improvements to their health.

[1450] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1451] Step 1:

[1452] The user enters input data into the terminal.

[1453] Users input their current weight, body fat percentage, target weight, and target body fat percentage into a smartphone app, which temporarily stores the input data in a local database on the device.

[1454] Step 2:

[1455] The terminal sends the input data to the server.

[1456] The terminal sends the input data, including weight, body fat percentage, target weight, and target body fat percentage, to the server, which receives it and stores it in a database.

[1457] Step 3:

[1458] The server performs the analysis and calculates the gap.

[1459] The server calculates the gap between the user's current state and the goal based on the received data. For this calculation, it uses a numerical calculation library (NumPy or Pandas). For example, it calculates the change from a weight of 70 kg and a body fat percentage of 25% to a weight of 65 kg and a body fat percentage of 20%.

[1460] Step 4:

[1461] The server generates an optimal exercise and diet plan.

[1462] The server generates an optimal exercise and meal plan for the user based on the calculated gap. The exercise plan includes the type, intensity, and duration of exercise, and the meal plan includes nutritional balance. The generated plan is saved in a database.

[1463] Step 5:

[1464] The server will suggest related health and fitness products.

[1465] The server uses a generative AI model to suggest relevant health and fitness products based on the user's health status and goals, such as protein powder or running shoes, and these suggestions are also stored in a database.

[1466] Step 6:

[1467] The terminal displays the generated plans and proposals to the user.

[1468] The device displays the exercise and meal plans sent from the server, as well as suggestions for health and fitness products, to the user, who can then check this information through the app.

[1469] Step 7:

[1470] The user performs daily tasks and inputs the results into the terminal.

[1471] The user performs daily tasks according to the exercise and meal plan provided by the server. As a result, the user inputs the details of the meals and exercise into the device again. For example, if the user ate oatmeal for breakfast and ran for 30 minutes, the user inputs the details.

[1472] Step 8:

[1473] The terminal sends the input data to the server, which evaluates it.

[1474] The device sends the user's input results (meal and exercise details) to the server, which receives them and evaluates the progress. The evaluation includes comparing with past data and calculating the progress rate.

[1475] Step 9:

[1476] The server adjusts the plan for the next day and generates a new plan.

[1477] The server then adjusts the next day's exercise and meal plan based on the evaluation results. For example, if progress is good, it may extend the exercise time and increase the amount of protein in meals. The new plan is then saved in a database.

[1478] Step 10:

[1479] The terminal displays the new plan to the user and the cycle continues.

[1480] The device displays the new exercise and meal plan sent from the server to the user, helping the user to continue with their daily tasks and maintain a cycle toward achieving their goals.

[1481] 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.

[1482] This system proposes optimal exercise and dietary methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. It also combines an emotion engine that recognizes the user's emotions and adjusts the plan accordingly, thereby maintaining and improving motivation.

[1483] System Overview

[1484] This system consists of three main components: a server, a terminal, and a user, along with an emotion engine that connects them. Based on weight and body fat percentage data entered by the user, the server calculates the gap between the user's current state and their goals, and then analyzes the data to generate an optimal exercise and meal plan. The system also recognizes the user's emotions, adjusting the plan and providing feedback based on those emotions.

[1485] Server Roles

[1486] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[1487] 1. Data Reception

[1488] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the terminal and stores the data in a database.

[1489] 2. Data Analysis

[1490] The server calculates the gap between the user's current state and the goal achievement based on the received data, and also analyzes the user's emotional data to evaluate the progress of each individual user.

[1491] 3. Plan Generation

[1492] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. It also makes adjustments based on emotional data to maintain and improve motivation.

[1493] 4. Feedback Generation

[1494] The server evaluates the results of the user's exercise and diet and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to provide messages of encouragement and advice based on the user's emotions.

[1495] Device Role

[1496] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[1497] 1. Data Entry

[1498] The user inputs data about weight, body fat percentage, dietary habits, exercise habits, and emotions into the device, which stores the data in a local database and prepares it for transmission to the server.

[1499] 2. Data Transmission

[1500] The terminal transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[1501] 3. Plan presentation

[1502] The terminal displays the exercise and meal plan received from the server in a user-friendly format.

[1503] User Roles

[1504] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[1505] 1. Initial Setup

[1506] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[1507] 2. Daily entries

[1508] The user inputs daily diet and exercise data and emotional state into the device, including, for example, the contents of meals, calories, nutritional balance, type of exercise, time, intensity, and mood of the day.

[1509] 3. Task execution

[1510] The user performs daily tasks according to the exercise and meal plan presented by the server.

[1511] 4. Enter results

[1512] Users input their exercise routines, dietary habits, and daily emotional state into the device, and the system generates feedback to improve their plan for the next day.

[1513] The role of the emotional engine

[1514] The emotion engine recognizes and analyzes the user's emotional state from input data. Specifically, it includes the following functions:

[1515] 1. Emotion recognition

[1516] It analyzes the emotional data entered by the user (e.g., mood, stress level, motivation, etc.) to understand their emotional state for the day.

[1517] 2. Feedback adjustment

[1518] The emotion engine generates feedback according to the user's state. For example, if the user's motivation is low, it provides encouraging messages and appropriate advice.

[1519] 3. Plan adjustment

[1520] It adjusts exercise and meal plans based on the user's emotional state, for example suggesting light exercise or recommending nutritious meals if the user is feeling stressed.

[1521] Specific examples

[1522] For example, suppose a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. During initial setup, this data is entered into the device and registered in the system. The user then enters their daily diet and exercise data into the device. For example, they may eat oatmeal for breakfast, run for 30 minutes, and enter their mood for the day as "normal."

[1523] The server analyzes this data and generates an optimal exercise and meal plan for the next day. For example, the server might suggest a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is then sent to the device and presented to the user.

[1524] The user follows the presented plan, performs the day's tasks, and enters the results into the device, such as the type and time of exercise performed, food intake, and daily emotional state. The server analyzes these results and optimizes the plan for the next day. The emotion engine also recognizes when the user's motivation is low on a particular day and provides encouraging messages to restore motivation.

[1525] In this way, the system of the present invention not only supports the user in managing their weight and body fat percentage, but also recognizes their emotional state and provides appropriate feedback to help them maintain their motivation and achieve their goals.

[1526] The processing flow will be explained below.

[1527] Step 1:

[1528] The user launches the app and enters basic information (current weight, body fat percentage, target weight, target body fat percentage) into the device on the new registration screen.

[1529] Step 2:

[1530] The device stores the entered basic information in a local database and generates JSON format data to send to the server.

[1531] Step 3:

[1532] The terminal sends the generated JSON format data to the server.

[1533] Step 4:

[1534] The server stores the basic data of the user received from the terminal in a database and registers it as the initial state.

[1535] Step 5:

[1536] Users input their daily meal details (breakfast, lunch, dinner, snacks), exercise details (time, type, intensity), and emotional data (mood, stress level, motivation) into the device.

[1537] Step 6:

[1538] The device stores daily diet, exercise, and emotional information in a local database and generates JSON format data to send to the server.

[1539] Step 7:

[1540] The terminal sends the generated JSON format data to the server.

[1541] Step 8:

[1542] The server calculates the calorie intake and expenditure for each day based on the received data and analyzes the gap between the user's condition and the achievement of their goal. The emotion engine also analyzes the received emotion data to grasp the user's emotional state for that day.

[1543] Step 9:

[1544] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. The plan can also be adjusted based on emotional data.

[1545] Step 10:

[1546] The server sends the generated exercise and meal plan to the device in JSON format.

[1547] Step 11:

[1548] The terminal interprets the JSON formatted plan data received from the server and displays it in a user-friendly format.

[1549] Step 12:

[1550] The user follows the presented plan and performs the exercise and diet tasks.

[1551] Step 13:

[1552] The user inputs the exercise and meals they have performed, as well as their daily emotional state, into the terminal, such as the type and duration of exercise they performed, the food they ate, and their mood that day.

[1553] Step 14:

[1554] The terminal stores the execution results in a local database and generates JSON format data to send to the server.

[1555] Step 15:

[1556] The terminal sends the generated JSON format data to the server.

[1557] Step 16:

[1558] The server receives the execution result data, analyzes it, and evaluates the user's progress. The emotion engine reevaluates the user's emotional state based on the emotion data.

[1559] Step 17:

[1560] The server adjusts the next day's exercise and meal plan based on the evaluation results, using an emotion engine to generate encouraging and advice messages according to the user's emotions.

[1561] Step 18:

[1562] The server sends the generated plan for the next day and feedback to the terminal in JSON format.

[1563] Step 19:

[1564] The terminal displays the next day's plan and feedback received from the server to the user.

[1565] Step 20:

[1566] The user checks the feedback and starts taking action according to the plan for the next day.

[1567] Example 2

[1568] 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."

[1569] Conventional weight and body fat management systems provide analysis based on the user's weight and body fat percentage data, as well as exercise and meal plans, but lack feedback or plan adjustments that take into account the user's emotional state, making it difficult to maintain motivation or provide continuous support for achieving goals.In addition, daily data input and feedback of analysis results are often not done consistently across the entire system, making it difficult to provide and implement effective plans.

[1570] 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.

[1571] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing dietary and exercise information input daily by the user; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the generated exercise and meal plan to the user; means for receiving the results of the user's exercise and meal plans and evaluating the progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for receiving and analyzing the user's emotional data; and means for adjusting the plan to provide feedback based on the emotional analysis results. This makes it possible to provide a comprehensive exercise and meal plan that also takes the user's emotional state into consideration, thereby maintaining and improving motivation and realizing an effective system for supporting goal achievement.

[1572] "User" refers to an individual who uses this system to manage their weight and body fat percentage.

[1573] "Server" refers to the computer system that receives data sent by users and performs analysis and plan generation.

[1574] "Terminal" refers to a device (such as a smartphone or PC) that provides an interface for users to input data and communicate with a server.

[1575] "Weight" represents data indicating the current weight of the user.

[1576] "Body fat percentage" represents data indicating the percentage of fat in the user's body.

[1577] "Target weight" refers to data indicating the weight that the user wishes to achieve.

[1578] "Target body fat percentage" represents data indicating the body fat percentage that the user wishes to achieve.

[1579] The "gap" represents data that indicates the difference between the user's current state and the progress toward achieving the goal.

[1580] "Dietary information" refers to data that indicates the contents of the foods and drinks that a user consumes daily.

[1581] "Exercise information" refers to data indicating the type, duration, and intensity of exercise that the user performs daily.

[1582] "Analysis" refers to the process by which the server processes the data it receives and evaluates the user's status and progress.

[1583] "Exercise plan" refers to a plan generated by the server that includes the duration, intensity, and type of exercise optimal for the user.

[1584] "Meal plan" refers to a meal menu generated by the server that takes into consideration the optimal nutritional balance for the user.

[1585] "Progress" refers to data indicating the degree of achievement evaluated based on the results of the exercise and diet performed by the user.

[1586] "Emotion data" refers to data entered by the user that indicates their own mood, stress level, and motivation state.

[1587] "Emotion engine" refers to technology that analyzes a user's emotional data and provides appropriate feedback and plan adjustments.

[1588] "Feedback" refers to information generated by the server based on analysis, including evaluations and advice regarding the user's actions.

[1589] This invention is a system that proposes optimal exercise and diet methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. This system includes three main components: a "server," a "terminal," and a "user," as well as an emotion engine that links them.

[1590] Server Roles

[1591] The server plays a central role in receiving and analyzing the data entered by the user. The server processes the data using the following techniques:

[1592] Data reception and storage: The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the device and stores it in a database (e.g., MySQL).

[1593] Data analysis: The server uses data analysis tools such as Python and R to calculate the user's current state and the gap between the goal and the goal based on the received data. It also analyzes emotional data to evaluate the progress of each individual user.

[1594] Plan generation: Based on the analysis results, the server generates an optimal exercise and meal plan for the user. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. Adjustments are also made based on emotional data to maintain and improve motivation.

[1595] Feedback generation: The server evaluates the results of the user's exercise and diet and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to provide messages of encouragement and advice based on the user's emotions.

[1596] Device Role

[1597] The terminal provides an interface for the user to input data and communicate with the server. The specific operation is as follows.

[1598] Data input: The user inputs data about weight, body fat percentage, diet, exercise, and emotions into the device. The device stores this data in a local database (e.g., SQLite) and prepares it for transmission to the server.

[1599] Data transmission: The device transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[1600] Plan presentation: The device displays the exercise and meal plan received from the server in a user-friendly format. Front-end frameworks such as React and Vue.js are used for the UI.

[1601] User Roles

[1602] Users use the system to manage their own weight and body fat percentage. They utilize the system by performing the following actions:

[1603] Initial setting: The user inputs the current weight, body fat percentage, target weight, and target body fat percentage into the terminal.

[1604] Daily input: The user inputs their daily diet and exercise data, as well as their emotional state, into the device. For example, they may have oatmeal for breakfast, go for a 30-minute run, and enter their mood for the day as "normal."

[1605] Task execution: The user performs daily tasks according to the exercise and meal plan presented by the server.

[1606] Result input: Enter the results of your exercise, diet, and daily emotional state into your device.

[1607] The role of the emotional engine

[1608] The emotion engine recognizes and analyzes the emotional state of the user from input data. This technology has the following functions:

[1609] Emotion recognition: Analyzes the emotional data entered by the user (e.g., mood, stress level, motivation, etc.) to understand their emotional state for the day.

[1610] Feedback adjustment: The emotion engine generates feedback according to the user's state. For example, if the user's motivation is low, it provides encouraging messages or appropriate advice.

[1611] Plan Adjustment: Adjust your exercise and meal plan based on your emotional state. For example, if you're feeling stressed, it might suggest light exercise or recommend a nutritious meal.

[1612] Specific examples

[1613] If a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%, they enter this data into the device and register it in the system. They also enter their daily diet and exercise data into the device. For example, they might eat oatmeal for breakfast, run for 30 minutes, and enter their mood for the day as "normal."

[1614] The server analyzes this data and generates the optimal exercise and meal plan for the next day. The user is suggested to run for 30 minutes, do 20 squats, and eat a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[1615] The user follows the presented plan, performs the day's tasks, and enters the results into the device, such as the type and time of exercise performed, food intake, and daily emotional state. The server analyzes these results and optimizes the plan for the next day. The emotion engine also recognizes when the user's motivation is declining and provides encouraging messages to restore motivation.

[1616] Prompt Sentence Examples

[1617] "I want to lose weight, what kind of exercise is effective?"

[1618] "Suggest a meal plan for today."

[1619] "What message would you give me to encourage me if I lose motivation?"

[1620] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1621] Step 1: Enter initial setup data

[1622] Input: The user enters their current weight, body fat percentage, target weight, and target body fat percentage into a dedicated mobile app or a web interface on their PC.

[1623] Operation: The device receives this data, formats it into a data format such as JSON, and temporarily stores it in a local database (such as SQLite).

[1624] Output: The formatted JSON data is saved in the terminal.

[1625] Step 2: Receiving and storing data

[1626] Input: User's weight, body fat percentage, target weight, and target body fat percentage data sent from the device.

[1627] How it works: The device sends the formatted data to the server as a POST request. The server receives this data and stores it in a database (e.g., MySQL).

[1628] Output: User's weight, body fat percentage, target weight, and target body fat percentage data recorded in the database.

[1629] Step 3: Analyze the data

[1630] Input: The user's current weight and body fat percentage, target weight and target body fat percentage data stored on the server.

[1631] How it works: The server analyzes this data using data analysis tools such as Python and R. It calculates the gap between the user's current state (e.g., weight 70 kg, body fat percentage 25%) and their goal (e.g., weight 65 kg, body fat percentage 20%).

[1632] Output: Calculated gap (e.g. weight gap 5kg, body fat gap 5%).

[1633] Step 4: Create an exercise and meal plan

[1634] Input: Analysis result gap data and user emotion data.

[1635] How it works: The server generates an optimal exercise and meal plan based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise (e.g., 30 minutes of running, 20 squats), and the meal plan includes nutritionally balanced menus. The plan is also personalized using machine learning libraries such as Scikit-learn.

[1636] Output: Generated exercise and meal plans.

[1637] Step 5: Provide feedback

[1638] Input: User exercise and diet results, emotional data.

[1639] How it works: The server evaluates the results and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to generate messages of encouragement and advice based on the user's emotions. For example, if the user enters "low motivation," an encouraging message will be generated.

[1640] Output: Generated feedback and adjusted next day plan.

[1641] Step 6: Daily data entry and submission

[1642] Input: Daily user diet, exercise, and emotional state.

[1643] How it works: Users enter these daily data into a terminal, which formats the data and prepares it for transmission to the server.

[1644] Output: Daily data stored on the device and sent to the server.

[1645] Step 7: Daily data analysis and feedback updates

[1646] Input: User's daily data, emotional state data.

[1647] How it works: The server receives and analyzes this data. Based on the received diet, exercise, and emotional data, it updates and optimizes the next day's exercise and meal plan. It also uses an emotion engine to generate appropriate feedback and messages based on the user's emotional state.

[1648] Output: Updated exercise and meal plans, encouraging and advice feedback.

[1649] (Application example 2)

[1650] 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."

[1651] The present invention aims to provide a system that not only provides diet and exercise plans to help users achieve their target weight and body fat percentage, but also more effectively supports health management by suggesting healthy product selections and meal menus in physical stores. Another objective of the present invention is to provide a system that maintains and improves the user's motivation by taking into account the user's emotional state.

[1652] 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: means for calculating the gap between the user's current state and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing dietary and exercise information input daily by the user; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the generated exercise and meal plan to the user; means for receiving the results of the user's exercise and meal plans and evaluating the progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for providing a real-time health rating for a product when the user scans it in a store; means for providing a health rating for a menu item when the user selects it at a restaurant in the store; and means for recognizing the user's emotional state and providing feedback based on the selected menu item. This enables effective health management of the user even in the store and maintains motivation according to the user's emotional state.

[1653] "Weight" refers to the user's current body weight.

[1654] "Body fat percentage" is a percentage that indicates the proportion of fat in relation to the user's total body weight.

[1655] "Goal weight" refers to the final weight that a user wishes to achieve.

[1656] "Target body fat percentage" refers to the final body fat percentage that the user wishes to achieve.

[1657] The "gap" refers to the difference between the user's current state and the achievement of their goal.

[1658] "Dietary information" is information about the contents, calories, and nutrients of the meals that the user takes in every day.

[1659] "Exercise information" is information relating to the type, duration, intensity, etc. of exercise that the user performs daily.

[1660] The "analysis results" are the results of analyzing the data entered by the user, and indicate the gap between the current state and the achievement of the goal.

[1661] An "exercise plan" is a plan of the type, intensity, and duration of exercise suggested to help the user achieve their goals.

[1662] A "meal plan" is a plan of suggested meal content, calories, and nutritional balance to help the user achieve their goals.

[1663] "Progress" refers to the progress a user makes toward achieving a goal.

[1664] "Health rating" refers to the calorie and nutritional balance rating of the product scanned by the user or the meal menu selected by the user.

[1665] "Emotional state" is information about the user's daily mood, stress level, motivation, etc.

[1666] "Feedback" refers to messages of advice or encouragement provided to the user.

[1667] "Real-time" refers to processing and information being provided immediately without delay.

[1668] A "brick and mortar store" refers to a physical location where consumers visit in person to purchase goods or use services.

[1669] The present invention is a system that suggests optimal exercise and dietary methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. Furthermore, by combining it with an emotion engine that recognizes the user's emotions and adjusts the plan accordingly, motivation is maintained and improved. Specific embodiments of the present invention are described below.

[1670] System Configuration

[1671] This system consists of three main components: a server, a terminal, and a user, and includes an emotion engine that connects these components. Its main roles are as follows:

[1672] server

[1673] The server has the following functions:

[1674] 1. Data reception: The server receives the weight, body fat percentage, target weight, and target body fat percentage entered by the user and stores them in a database.

[1675] 2. Data analysis: The server calculates the gap between the user's current status and their goal based on the received data, and also analyzes the user's daily diet and exercise information.

[1676] 3. Plan Generation: The server generates an optimal exercise and meal plan based on the analysis results. Furthermore, the emotion engine adjusts the plan to take into account the user's emotional state.

[1677] 4. Feedback generation: The server evaluates the results of the exercise and diet performed by the user and generates feedback to be reflected in the plan for the next day.

[1678] Terminal

[1679] The terminal has the following functions:

[1680] 1. Data input: Provides an interface for users to input their weight, body fat percentage, dietary habits, exercise habits, and emotional state. Prepares the input data to be sent to the server.

[1681] 2. Data transmission: The device transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[1682] 3. Plan presentation: The device displays the exercise and meal plan received from the server in an easy-to-understand manner to the user.

[1683] User

[1684] Users use the system to manage their own weight and body fat percentage.

[1685] 1. Initial setup: The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal.

[1686] 2. Daily input: The user inputs their daily diet, exercise, and emotional state into the device.

[1687] 3. Task execution: The user follows the exercise and meal plan provided by the server.

[1688] 4. Enter results: Enter the results of your exercise and meals into the device.

[1689] Emotion Engine

[1690] The emotion engine recognizes and analyzes the user's emotional state.

[1691] 1. Emotion recognition: Analyzes the emotional data entered by the user to understand their emotional state for the day.

[1692] 2. Feedback adjustment: The emotion engine generates feedback according to the user's state.

[1693] 3. Plan Adjustment: Adjust exercise and meal plans based on the user's emotional state.

[1694] Hardware and software used

[1695] Hardware: Smartphones, smart glasses

[1696] Software: Python, Flask, SQLite

[1697] Library: emotion_engine

[1698] Specific examples

[1699] For example, suppose a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user inputs initial data such as weight, body fat percentage, target weight, and target body fat percentage into the terminal and registers it in the system. Next, the user inputs their daily diet and exercise information.

[1700] When users visit a physical store and scan an item, they are given a real-time health rating for that item, when they select a meal at an in-store restaurant, they are given a health rating for that menu item, and they can input their emotional state and receive real-time feedback based on that.

[1701] Prompt Sentence Examples

[1702] "I'm very tired today"

[1703] "I'm stressed out at work"

[1704] "Exercise feels fun"

[1705] An example prompt is:

[1706] "Generate appropriate feedback based on user input emotion. Input: {emotion_input}. Feedback: "

[1707] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1708] Step 1:

[1709] The user inputs their weight, body fat percentage, target weight, target body fat percentage, dietary information, exercise information, and emotional information into the device. The device stores this data in a local database and prepares it for transmission to the server. The user inputs the data directly by operating the device, and the input data is converted into XML or JSON format. When the data is ready to be sent to the server, the send command is executed.

[1710] Step 2:

[1711] The terminal sends the data entered by the user to the server. Specifically, it sends the data stored in the terminal to the server using the HTTP protocol. The server processes the received data using a reception module and stores it in the necessary database. The input here is the user's input data, and the output is the result stored in the server's database. After transmission is complete, the terminal waits for a response from the server.

[1712] Step 3:

[1713] The server calculates the gap between the user's current status and their goal based on the received data. To do this, it uses an algorithm that matches the user's weight, body fat percentage, target weight, and target body fat percentage. Specifically, it calculates the difference between the current and target values ​​for weight and body fat percentage, and lists the items the user needs to achieve. The input is the user's weight, body fat percentage, target weight, and target body fat percentage, and the output is the gap calculation result.

[1714] Step 4:

[1715] The server analyzes daily input data (diet, exercise, and emotional information) and generates an optimal exercise and meal plan. Here, the received data is filtered, compared with a health database, and then an algorithm is applied to suggest an appropriate exercise and meal plan. The input is the user's diet, exercise, and emotional information, and the output is an exercise and meal plan. The generated plan is sent to the user.

[1716] Step 5:

[1717] The device displays the exercise and meal plan received from the server to the user. The input here is the plan data sent from the server, and the output is the user's plan information displayed on the device's display. The device notifies the user whenever the plan is updated.

[1718] Step 6:

[1719] The user inputs the results of their exercise and diet into the terminal. Here, the user inputs the duration and type of exercise they performed, the type of meal they ate, their emotional state, etc. The input data is prepared for transmission to the server again. The input is the user's exercise results, diet results, and emotional state data, and the output is the data prepared for transmission to the server.

[1720] Step 7:

[1721] The device sends the results of the user's exercise and diet to the server. Specifically, the device sends the previously entered data to the server as an HTTP request, and the server receives the data and registers it in a database. The input is the user's exercise results, diet results, and emotional state data, and the output is the registration results in the server's database.

[1722] Step 8:

[1723] The server evaluates the user's progress based on the received data and adjusts the exercise and meal plan for the next day. This evaluation process uses analytical algorithms to generate an optimal plan based on the user's progress. The input is the user's progress data, and the output is the adjusted plan for the next day.

[1724] Step 9:

[1725] The server sends the generated new plan to the terminal, which then presents it to the user. The input is the new exercise and meal plan, and the output is the user's new plan information displayed on the terminal.

[1726] Step 10:

[1727] When a user visits a physical store and scans a product or selects an item from the restaurant menu, the terminal sends the product barcode or menu information to the server. The server receives this data and performs a real-time health assessment. The input is the product or menu information, and the output is the health assessment result. The health assessment result is immediately returned to the terminal and displayed to the user.

[1728] Step 11:

[1729] When the user's emotional state is input, the server uses an emotion engine to perform emotion analysis and generate feedback based on the results. The input is the user's emotional data, and the output is a feedback message. The generated feedback is sent to the terminal and presented to the user.

[1730] Example prompt sentences

[1731] For example, it generates appropriate feedback based on emotional input such as "I feel very tired today," "I'm stressed at work," or "I enjoy exercising." Examples of prompt sentences are as follows:

[1732] "Generate appropriate feedback based on user input emotion. Input: {emotion_input}. Feedback: "

[1733] 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.

[1734] 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.

[1735] 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.

[1736] [Fourth embodiment]

[1737] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1738] 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.

[1739] 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).

[1740] 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.

[1741] 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.

[1742] 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).

[1743] 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.

[1744] 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.

[1745] 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.

[1746] 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.

[1747] 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.

[1748] 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.

[1749] 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."

[1750] The present invention is a system that proposes optimal exercise and dietary methods based on dietary and exercise information to help a user achieve their target weight and body fat percentage. An embodiment of this system will be described in detail below.

[1751] System Overview

[1752] This system calculates and analyzes the gap between the user's current condition and the achievement of their goal based on the weight and body fat percentage data entered by the user. Based on the analysis results, it generates an optimal exercise and meal plan and provides it to the user. It also receives the results of the user's exercise and meal, evaluates their progress, and adjusts the plan for the next day.

[1753] This system consists of three main components: a "server," a "terminal," and a "user."

[1754] Server Roles

[1755] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[1756] 1. Data Reception

[1757] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the terminal and stores the data in a database.

[1758] 2. Data Analysis

[1759] The server calculates the user's current status and the gap between them and achieving their goals based on the received data, and also evaluates the user's progress based on the user's past data, generating an optimal exercise and meal plan.

[1760] 3. Plan Generation

[1761] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, and the meal plan includes a menu that takes nutritional balance into consideration.

[1762] 4. Feedback Generation

[1763] It evaluates the results of the user's exercise and diet and generates feedback that is reflected in the next day's plan, including advice on how to improve the user's progress.

[1764] Device Role

[1765] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[1766] 1. Data Entry

[1767] The user inputs their weight, body fat percentage, dietary habits, and exercise habits into the device, which then stores this data in a local database and prepares it for transmission to the server.

[1768] 2. Data Transmission

[1769] The terminal transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[1770] 3. Plan presentation

[1771] The device displays the exercise and meal plans received from the server in a user-friendly format, allowing the user to carry out their daily tasks.

[1772] User Roles

[1773] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[1774] 1. Initial Setup

[1775] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[1776] 2. Daily entries

[1777] The user inputs daily diet and exercise data into the device, including the type, duration, and intensity of exercise, as well as the type, calories, and nutritional balance of the food.

[1778] 3. Task execution

[1779] The user performs daily tasks according to the exercise and meal plan presented by the server.

[1780] 4. Enter results

[1781] Users input their exercise and diet results into the device, and the system generates feedback to improve their plan for the next day.

[1782] Specific examples

[1783] For example, a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, they may have eaten oatmeal for breakfast and run for 30 minutes. This data is sent from the device to the server and analyzed.

[1784] The server generates an optimal exercise and meal plan for the next day based on the user's data. For example, the server suggests a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[1785] The user follows the plan, performs the tasks for the day, and then enters the results back into the device. The server analyzes these results and further optimizes the plan for the next day. By continuing this cycle, the user can achieve their goals without difficulty.

[1786] The processing flow will be explained below.

[1787] Step 1:

[1788] The user launches the app and enters basic information (current weight, body fat percentage, target weight, target body fat percentage) into the device on the new registration screen.

[1789] Step 2:

[1790] The device stores the entered basic information in a local database and generates JSON format data to send to the server.

[1791] Step 3:

[1792] The terminal sends the generated JSON format data to the server.

[1793] Step 4:

[1794] The server stores the basic data of the user received from the terminal in a database and registers it as the initial state.

[1795] Step 5:

[1796] Users input their daily meal details (breakfast, lunch, dinner, snacks) and exercise details (time, type, intensity) into the terminal.

[1797] Step 6:

[1798] The device stores daily diet and exercise information in a local database and generates JSON format data to send to the server.

[1799] Step 7:

[1800] The terminal sends the generated JSON format data to the server.

[1801] Step 8:

[1802] Based on the received data, the server calculates the calorie intake and expenditure for each day and analyzes the user's condition and the gap between them and achieving their goal.

[1803] Step 9:

[1804] Based on the analysis results, the server generates an optimal exercise and meal plan for the user, determining, for example, the duration, intensity, and type of exercise, as well as a menu that takes into account nutritional balance.

[1805] Step 10:

[1806] The server sends the generated exercise and meal plan to the device in JSON format.

[1807] Step 11:

[1808] The terminal interprets the JSON formatted plan data received from the server and displays it in a user-friendly format.

[1809] Step 12:

[1810] The user follows the presented plan and performs the exercise and diet tasks.

[1811] Step 13:

[1812] The user inputs the results of the exercise and diet he or she performed into the terminal, such as the type and duration of the exercise performed, and the details of the diet he or she ate.

[1813] Step 14:

[1814] The terminal stores the execution results in a local database and generates JSON format data to send to the server.

[1815] Step 15:

[1816] The terminal sends the generated JSON format data to the server.

[1817] Step 16:

[1818] The server receives and analyzes the execution result data to assess the user's progress, including progress towards achieving the goal and recommendations.

[1819] Step 17:

[1820] The server adjusts the next day's exercise and meal plan based on the evaluation results and generates feedback.

[1821] Step 18:

[1822] The server sends the generated feedback in JSON format to the device.

[1823] Step 19:

[1824] The terminal displays the feedback received from the server to the user.

[1825] Step 20:

[1826] The user checks the feedback and starts taking action according to the plan for the next day.

[1827] Example 1

[1828] 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."

[1829] Current fitness management systems often lack consistency and accuracy when providing optimal exercise and meal plans for specific user goals, such as weight and body fat percentage. They also lack the ability to analyze users' progress data in real time and incorporate it into the next day's plan. This leaves users with a lack of efficient support to achieve their goals.

[1830] 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.

[1831] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing the user's daily input dietary and exercise information; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the user with the generated exercise and meal plan; means for receiving the user's exercise and meal results again and evaluating progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for storing dietary and exercise information, calorie information, and nutritional balance in a database; means for analyzing the data and generating optimal feedback; and means for using a generative artificial intelligence model to generate meal and exercise plans. This enables highly accurate and efficient support for the user's health management.

[1832] "Weight" is a numerical value that indicates the total weight of the human body.

[1833] "Body fat percentage" is a numerical value that indicates the percentage of fat in the human body.

[1834] "Target weight" is a numerical value indicating the weight that the user wishes to achieve.

[1835] "Target body fat percentage" is a numerical value that indicates the percentage of body fat that the user wishes to achieve.

[1836] A "database" is a system for efficiently storing, managing, and retrieving digital information.

[1837] "Analysis" is the process of processing input data to derive useful information or conclusions.

[1838] "Dietary information" refers to information such as the content, amount, calories, and nutrients of the food consumed.

[1839] "Exercise information" refers to information such as the type, intensity, duration, and number of times of exercise performed.

[1840] "Feedback" is the evaluation and guidance for improvement that the system provides to the user based on past behavior.

[1841] A "generative artificial intelligence model" is an artificial intelligence technology that has the ability to learn from large amounts of data and generate optimal exercise and meal plans.

[1842] "Analysis results" refers to information and conclusions obtained through data analysis.

[1843] An "exercise plan" is a plan that includes a specific combination of exercises, number of repetitions, duration, and intensity that are suggested based on the goals that the user wants to achieve.

[1844] A "meal plan" is a nutritionally balanced meal plan suggested based on the goals the user wants to achieve.

[1845] The present invention is a system that proposes optimal exercise and dietary methods based on dietary and exercise information to help a user achieve their target weight and body fat percentage. An embodiment of this system will be described in detail below.

[1846] System Overview

[1847] This system has the function of calculating and analyzing the gap between the user's current condition and the achievement of their goal based on the weight and body fat percentage data entered by the user. Based on the analysis results, it generates an optimal exercise and meal plan and provides it to the user. It also receives the results of the user's exercise and meal, evaluates progress, and adjusts the plan for the next day. This system consists of three main components: a "server," a "terminal," and a "user."

[1848] Server Roles

[1849] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[1850] 1. Data Reception

[1851] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the device and stores it in a database. Data communication uses the HTTP protocol and REST API.

[1852] 2. Data Analysis

[1853] Based on the received data, the server calculates the gap between the user's current state and the achievement of the goal using Python libraries such as NumPy and Pandas. It also evaluates the user's progress based on their past data and manages it in an SQL database. Google Cloud Platform and Amazon Web Services are often used as cloud services.

[1854] 3. Plan Generation

[1855] Based on the analysis results, the server uses AI models (such as TensorFlow and PyTorch) to generate optimal exercise and meal plans. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration.

[1856] 4. Feedback Generation

[1857] The server generates feedback based on the user's exercise and dietary habits to be reflected in the next day's plan, including advice on how to improve the user's progress.

[1858] Device Role

[1859] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[1860] 1. Data Entry

[1861] The user inputs their weight, body fat percentage, dietary habits, and exercise habits into the device. These data are stored in a local database (e.g., SQLite) and prepared for transmission to the server.

[1862] 2. Data Transmission

[1863] The device sends the entered data to the server using an HTTP request, and displays the plan and feedback received from the server to the user.

[1864] 3. Plan presentation

[1865] The device receives the exercise and meal plans from the server and displays them in an easy-to-understand format. The app is developed using Java or Kotlin for Android and Swift for iOS.

[1866] User Roles

[1867] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[1868] 1. Initial Setup

[1869] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[1870] 2. Daily entries

[1871] Users enter their daily diet and exercise data into the device, including the type, calories, and nutritional balance of their meals, as well as the type, duration, and intensity of their exercise.

[1872] 3. Task execution

[1873] The user performs daily tasks according to the exercise and meal plan presented by the server.

[1874] 4. Enter results

[1875] The results of the exercises and meals performed are entered into the device and sent to the server, which then generates feedback to improve the plan for the next day.

[1876] Specific examples

[1877] For example, a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, they may have eaten oatmeal for breakfast and run for 30 minutes. This data is sent from the device to the server and analyzed.

[1878] The server generates an optimal exercise and meal plan for the next day based on the user's data. For example, the server suggests a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is sent to the device and displayed to the user.

[1879] The user follows the plan, performs the tasks for the day, and then enters the results back into the device. The server analyzes these results and further optimizes the plan for the next day. By continuing this cycle, the user can achieve their goals without difficulty.

[1880] Example prompts for generative AI models

[1881] Prompt: "If the user weighs 70 kg and has 25% body fat, and sets a goal of 65 kg and 20% body fat, generate the optimal exercise and meal plan. Also consider the past week's exercise and meal data."

[1882] Example expected output:

[1883] "Based on the user's current data, it will suggest an exercise plan that includes 30 minutes of running and 20 squats, as well as a low-fat, high-protein meal plan."

[1884] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1885] System program processing flow

[1886] Step 1:

[1887] User data initial settings

[1888] 1. Specific explanation

[1889] The user starts the application on the terminal and inputs their current weight, body fat percentage, target weight, and target body fat percentage, which allows the system to recognize the user's goals.

[1890] 2. Input / Output

[1891] Input: Weight, body fat percentage, target weight, and target body fat percentage entered by the user on the device

[1892] Data processing: The entered data is collected using the terminal's form interface and stored in a local database.

[1893] Output: User's physical data stored in a local database

[1894] 3. Specific actions

[1895] When the user enters various data into the designated fields and presses the "Save" button, the data is saved in the local database.

[1896] Step 2:

[1897] Data transmission

[1898] 1. Specific explanation

[1899] The device sends the stored data to the server using an HTTP request, and through this process the server receives the user's initial setting data.

[1900] 2. Input / Output

[1901] Input: User's physical data stored in a local database

[1902] Data processing: The data is encoded into JSON format and sent to the server via an HTTP POST request.

[1903] Output: User preference data sent to the server

[1904] 3. Specific actions

[1905] The device sends data to the specified API endpoint via a POST request, which the server receives and stores in the database.

[1906] Step 3:

[1907] Data reception and storage

[1908] 1. Specific explanation

[1909] The server receives the HTTP POST request sent from the terminal and stores it in a database.

[1910] 2. Input / Output

[1911] Input: User preference data sent in the HTTP POST request

[1912] Data processing: Parse the received JSON data and store it in the corresponding column in the database.

[1913] Output: User preference data stored in the database

[1914] 3. Specific actions

[1915] The server's web framework (e.g., Flask or Django) receives the request and executes the logic to save it to the database.

[1916] Step 4:

[1917] Data analysis

[1918] 1. Specific explanation

[1919] Based on the received data, the server calculates the user's current state and the gap to achieving the goal.

[1920] 2. Input / Output

[1921] Input: User preference data stored in the database

[1922] Data processing: Using Python's NumPy and Pandas, calculate the gap (for example, 5 kg between weights of 70 kg and 65 kg).

[1923] Output: Data showing the gap between the user's current situation and their goal

[1924] 3. Specific actions

[1925] The server calls NumPy and Pandas to process the data, calculate the gap information, and store it in the database.

[1926] Step 5:

[1927] Plan Generation

[1928] 1. Specific explanation

[1929] Based on the analysis results, the server uses an AI model (such as TensorFlow or PyTorch) to generate an optimal exercise and meal plan.

[1930] 2. Input / Output

[1931] Input: Current status and goal gap data, past exercise and diet data

[1932] Data processing: Using AI models, we generate optimal exercise and diet plans, such as 30 minutes of running, 20 squats, and a low-fat, high-protein diet.

[1933] Output: Generated exercise and meal plan

[1934] 3. Specific actions

[1935] The AI ​​model takes user data as input and generates a plan, which is then converted to JSON format and saved.

[1936] Step 6:

[1937] Send Plan

[1938] 1. Specific explanation

[1939] The server transmits the generated exercise and meal plan to the terminal.

[1940] 2. Input / Output

[1941] Input: Generated exercise and meal plan

[1942] Data processing: Encode the plan data into JSON format and send it to the end user as an HTTP response.

[1943] Output: Exercise and meal plan sent to device

[1944] 3. Specific actions

[1945] The server sends the plan data to the device through the specified API endpoint.

[1946] Step 7:

[1947] Show Plan

[1948] 1. Specific explanation

[1949] The terminal analyzes the received plan data and displays it on the user interface.

[1950] 2. Input / Output

[1951] Input: Exercise and meal plan received in HTTP response

[1952] Data processing: Parse the JSON data and convert it into a format that can be displayed in the graphical user interface (GUI).

[1953] Output: Plan information presented visually to the user

[1954] 3. Specific actions

[1955] The device analyzes the received data and visually displays exercise and meal plans on the app's dashboard screen.

[1956] Step 8:

[1957] Daily data entry

[1958] 1. Specific explanation

[1959] The user inputs the results of their daily exercise and diet into the terminal.

[1960] 2. Input / Output

[1961] Input: Exercise and diet information entered by the user into the device (e.g., calories burned, dietary details)

[1962] Data processing: The entered data is stored in the device's local database.

[1963] Output: Daily exercise and diet information stored in a local database

[1964] 3. Specific actions

[1965] The user enters the results of their exercise and diet into the form on the device app and presses the "Save" button to save the data.

[1966] Step 9:

[1967] Sending result data

[1968] 1. Specific explanation

[1969] The terminal sends the input result data to the server via an HTTP POST request.

[1970] 2. Input / Output

[1971] Input: Daily exercise and diet information stored in a local database

[1972] Data processing: The data is again encoded into JSON format and sent to the server via an HTTP POST request.

[1973] Output: Daily results data sent to the server

[1974] 3. Specific actions

[1975] The device sends the result data to the specified API endpoint via a POST request, which the server receives and analyzes.

[1976] Step 10:

[1977] Feedback Generation

[1978] 1. Specific explanation

[1979] The server analyzes the received results data, evaluates the user's progress, and generates feedback to optimize the next day's plan.

[1980] 2. Input / Output

[1981] Input: Daily results data sent to the server

[1982] Data processing: Analyze and process the results data to evaluate the user's progress and generate feedback to optimize exercise and meal plans for the next day.

[1983] Output: Generated feedback and optimal plan for the next day

[1984] 3. Specific actions

[1985] The server performs the analysis, generates feedback, stores it in a database, and updates the plan for the next day. The updated data is then sent back to the device.

[1986] (Application example 1)

[1987] 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."

[1988] Traditional health management systems not only provide users with optimal exercise and meal plans, but also provide feedback based on the results of each day and adjust the plan for the next day. However, these systems lack the functionality to allow users to easily purchase healthy products and fitness gear, which means users have to go through the trouble of searching for and purchasing these products separately.

[1989] 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.

[1990] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the weight, body fat percentage, target weight, and target body fat percentage input by the user; means for receiving and analyzing the dietary and exercise information input by the user daily; means for generating an optimal exercise and meal plan for the user based on the analysis results; and means for suggesting related health products and fitness goods based on the generated exercise and meal plan and allowing the user to purchase them. This allows the user to easily purchase the optimal health products and fitness goods and manage their overall health in a unified manner.

[1991] "Body weight" is an indicator of the total weight of the body.

[1992] "Body fat percentage" is an index showing the proportion of body fat to total body weight.

[1993] "Target weight" is the weight value that the user sets and wants to achieve.

[1994] "Target body fat percentage" is the body fat percentage value that the user sets and wants to achieve.

[1995] A "gap" refers to the difference or difference between the current state and the goal.

[1996] "Dietary information" is data indicating the types of food and drink that a user consumes daily.

[1997] "Exercise information" is data that indicates information such as the type, intensity, and duration of exercise performed by the user each day.

[1998] The "analysis results" are results calculated based on the data entered by the user, showing the difference between the current state and the progress towards achieving the goal.

[1999] An "exercise plan" is a plan that includes specific exercise content, intensity, time, etc. that is proposed to the user.

[2000] A "meal plan" is a plan that takes into consideration specific meal contents and nutritional balance proposed to a user.

[2001] "Evaluation results" refers to feedback generated based on the user's progress.

[2002] "Health products" refer to items that are considered useful for managing or improving the user's health.

[2003] "Fitness goods" refers to equipment and devices used for exercise and training.

[2004] "User" refers to an individual who uses this system to manage their own health.

[2005] "Server" refers to a central system for analyzing data received from users, generating results, and transmitting required information.

[2006] "Terminal" refers to a device through which a user can enter data and view received information.

[2007]

[2008] This system provides a means for users to effectively manage their health. Specific embodiments are described below.

[2009] Server Roles

[2010] The server is responsible for calculating the gap between the user's current condition and the achievement of their goal based on the user's weight, body fat percentage, target weight, and target body fat percentage. It also receives and analyzes the dietary and exercise information entered daily by the user. Based on the analysis results, it generates an optimal exercise and meal plan for the user. Furthermore, based on the generated exercise and meal plan, it suggests related health and fitness products and provides a means for the user to purchase them. This is done using a Python-based web server (such as Flask or Django). Databases such as MySQL or PostgreSQL are used to manage the data. Furthermore, data analysis is performed using numerical calculation libraries such as NumPy and Pandas.

[2011] Device Role

[2012] The terminal provides an interface for users to input data and communicate with the server. This terminal is a mobile device such as a smartphone. Smartphone apps are developed using Swift for iOS and Kotlin / Java for Android. Users input their weight, body fat percentage, dietary details, and exercise details through the terminal. The device also receives and displays exercise and meal plans sent from the server, as well as suggestions for related health and fitness products.

[2013] User Roles

[2014] As an initial setting, the user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the device. This allows the system to understand the user's goals and begin analysis. The user then inputs their daily diet and exercise data into the device. They then perform daily tasks according to the exercise and diet plan provided, and enter the results back into the device. The input results are then sent to the server, where evaluation and feedback are provided.

[2015] Specific examples

[2016] For example, suppose a user inputs their current weight of 70 kg and body fat percentage of 25%, and sets their target weight of 65 kg and target body fat percentage of 20%. The user enters their daily diet and exercise data into the device. For example, suppose they ate oatmeal for breakfast and ran for 30 minutes. This data is sent from the device to the server and analyzed.

[2017] The server generates the optimal exercise and meal plan for the next day based on the user's data. For example, the user might be recommended a 30-minute run, 20 squats, and a low-fat, high-protein diet. Healthy products like protein powder and running shoes are also recommended, and can be easily purchased within the app. An example of a prompt to be input into the generative AI model is as follows:

[2018] A user inputs their current weight of 70kg and body fat percentage of 25%, and sets their goal weight of 65kg and body fat percentage of 20%. Generate exercise and meal suggestions. For example, the next day's plan is to run 30 minutes, do 20 squats, and eat chicken breast and salad.

[2019] This allows users to manage their health in a centralized manner and purchase optimal health products, enabling more effective improvements to their health.

[2020] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2021] Step 1:

[2022] The user enters input data into the terminal.

[2023] Users input their current weight, body fat percentage, target weight, and target body fat percentage into a smartphone app, which temporarily stores the input data in a local database on the device.

[2024] Step 2:

[2025] The terminal sends the input data to the server.

[2026] The terminal sends the input data, including weight, body fat percentage, target weight, and target body fat percentage, to the server, which receives it and stores it in a database.

[2027] Step 3:

[2028] The server performs the analysis and calculates the gap.

[2029] The server calculates the gap between the user's current state and the goal based on the received data. For this calculation, it uses a numerical calculation library (NumPy or Pandas). For example, it calculates the change from a weight of 70 kg and a body fat percentage of 25% to a weight of 65 kg and a body fat percentage of 20%.

[2030] Step 4:

[2031] The server generates an optimal exercise and diet plan.

[2032] The server generates an optimal exercise and meal plan for the user based on the calculated gap. The exercise plan includes the type, intensity, and duration of exercise, and the meal plan includes nutritional balance. The generated plan is saved in a database.

[2033] Step 5:

[2034] The server will suggest related health and fitness products.

[2035] The server uses a generative AI model to suggest relevant health and fitness products based on the user's health status and goals, such as protein powder or running shoes, and these suggestions are also stored in a database.

[2036] Step 6:

[2037] The terminal displays the generated plans and proposals to the user.

[2038] The device displays the exercise and meal plans sent from the server, as well as suggestions for health and fitness products, to the user, who can then check this information through the app.

[2039] Step 7:

[2040] The user performs daily tasks and inputs the results into the terminal.

[2041] The user performs daily tasks according to the exercise and meal plan provided by the server. As a result, the user inputs the details of the meals and exercise into the device again. For example, if the user ate oatmeal for breakfast and ran for 30 minutes, the user inputs the details.

[2042] Step 8:

[2043] The terminal sends the input data to the server, which evaluates it.

[2044] The device sends the user's input results (meal and exercise details) to the server, which receives them and evaluates the progress. The evaluation includes comparing with past data and calculating the progress rate.

[2045] Step 9:

[2046] The server adjusts the plan for the next day and generates a new plan.

[2047] The server then adjusts the next day's exercise and meal plan based on the evaluation results. For example, if progress is good, it may extend the exercise time and increase the amount of protein in meals. The new plan is then saved in a database.

[2048] Step 10:

[2049] The terminal displays the new plan to the user and the cycle continues.

[2050] The device displays the new exercise and meal plan sent from the server to the user, helping the user to continue with their daily tasks and maintain a cycle toward achieving their goals.

[2051] 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.

[2052] This system proposes optimal exercise and dietary methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. It also combines an emotion engine that recognizes the user's emotions and adjusts the plan accordingly, thereby maintaining and improving motivation.

[2053] System Overview

[2054] This system consists of three main components: a server, a terminal, and a user, along with an emotion engine that connects them. Based on weight and body fat percentage data entered by the user, the server calculates the gap between the user's current state and their goals, and then analyzes the data to generate an optimal exercise and meal plan. The system also recognizes the user's emotions, adjusting the plan and providing feedback based on those emotions.

[2055] Server Roles

[2056] The server plays a central role in receiving and analyzing data entered by users. Specifically, it has the following functions:

[2057] 1. Data Reception

[2058] The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the terminal and stores the data in a database.

[2059] 2. Data Analysis

[2060] The server calculates the gap between the user's current state and the goal achievement based on the received data, and also analyzes the user's emotional data to evaluate the progress of each individual user.

[2061] 3. Plan Generation

[2062] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. It also makes adjustments based on emotional data to maintain and improve motivation.

[2063] 4. Feedback Generation

[2064] The server evaluates the results of the user's exercise and diet and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to provide messages of encouragement and advice based on the user's emotions.

[2065] Device Role

[2066] The terminal provides an interface for users to input data and communicate with the server, and specifically includes the following functions:

[2067] 1. Data Entry

[2068] The user inputs data about weight, body fat percentage, dietary habits, exercise habits, and emotions into the device, which stores the data in a local database and prepares it for transmission to the server.

[2069] 2. Data Transmission

[2070] The terminal transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[2071] 3. Plan presentation

[2072] The terminal displays the exercise and meal plan received from the server in a user-friendly format.

[2073] User Roles

[2074] The user uses the system to manage their own weight and body fat percentage by performing the following actions:

[2075] 1. Initial Setup

[2076] The user inputs their current weight, body fat percentage, target weight, and target body fat percentage into the terminal, which allows the system to grasp the user's goals and begin analysis.

[2077] 2. Daily entries

[2078] The user inputs daily diet and exercise data and emotional state into the device, including, for example, the contents of meals, calories, nutritional balance, type of exercise, time, intensity, and mood of the day.

[2079] 3. Task execution

[2080] The user performs daily tasks according to the exercise and meal plan presented by the server.

[2081] 4. Enter results

[2082] Users input their exercise routines, dietary habits, and daily emotional state into the device, and the system generates feedback to improve their plan for the next day.

[2083] The role of the emotional engine

[2084] The emotion engine recognizes and analyzes the user's emotional state from input data. Specifically, it includes the following functions:

[2085] 1. Emotion recognition

[2086] It analyzes the emotional data entered by the user (e.g., mood, stress level, motivation, etc.) to understand their emotional state for the day.

[2087] 2. Feedback adjustment

[2088] The emotion engine generates feedback according to the user's state. For example, if the user's motivation is low, it provides encouraging messages and appropriate advice.

[2089] 3. Plan adjustment

[2090] It adjusts exercise and meal plans based on the user's emotional state, for example suggesting light exercise or recommending nutritious meals if the user is feeling stressed.

[2091] Specific examples

[2092] For example, suppose a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat percentage of 20%. During initial setup, this data is entered into the device and registered in the system. The user then enters their daily diet and exercise data into the device. For example, they may eat oatmeal for breakfast, run for 30 minutes, and enter their mood for the day as "normal."

[2093] The server analyzes this data and generates an optimal exercise and meal plan for the next day. For example, the server might suggest a 30-minute run, 20 squats, and a low-fat, high-protein diet. This information is then sent to the device and presented to the user.

[2094] The user follows the presented plan, performs the day's tasks, and enters the results into the device, such as the type and time of exercise performed, food intake, and daily emotional state. The server analyzes these results and optimizes the plan for the next day. The emotion engine also recognizes when the user's motivation is low on a particular day and provides encouraging messages to restore motivation.

[2095] In this way, the system of the present invention not only supports the user in managing their weight and body fat percentage, but also recognizes their emotional state and provides appropriate feedback to help them maintain their motivation and achieve their goals.

[2096] The processing flow will be explained below.

[2097] Step 1:

[2098] The user launches the app and enters basic information (current weight, body fat percentage, target weight, target body fat percentage) into the device on the new registration screen.

[2099] Step 2:

[2100] The device stores the entered basic information in a local database and generates JSON format data to send to the server.

[2101] Step 3:

[2102] The terminal sends the generated JSON format data to the server.

[2103] Step 4:

[2104] The server stores the basic data of the user received from the terminal in a database and registers it as the initial state.

[2105] Step 5:

[2106] Users input their daily meal details (breakfast, lunch, dinner, snacks), exercise details (time, type, intensity), and emotional data (mood, stress level, motivation) into the device.

[2107] Step 6:

[2108] The device stores daily diet, exercise, and emotional information in a local database and generates JSON format data to send to the server.

[2109] Step 7:

[2110] The terminal sends the generated JSON format data to the server.

[2111] Step 8:

[2112] The server calculates the calorie intake and expenditure for each day based on the received data and analyzes the gap between the user's condition and the achievement of their goal. The emotion engine also analyzes the received emotion data to grasp the user's emotional state for that day.

[2113] Step 9:

[2114] The server then generates an optimal exercise and meal plan for the user based on the analysis results. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. The plan can also be adjusted based on emotional data.

[2115] Step 10:

[2116] The server sends the generated exercise and meal plan to the device in JSON format.

[2117] Step 11:

[2118] The terminal interprets the JSON formatted plan data received from the server and displays it in a user-friendly format.

[2119] Step 12:

[2120] The user follows the presented plan and performs the exercise and diet tasks.

[2121] Step 13:

[2122] The user inputs the exercise and meals they have performed, as well as their daily emotional state, into the terminal, such as the type and duration of exercise they performed, the food they ate, and their mood that day.

[2123] Step 14:

[2124] The terminal stores the execution results in a local database and generates JSON format data to send to the server.

[2125] Step 15:

[2126] The terminal sends the generated JSON format data to the server.

[2127] Step 16:

[2128] The server receives the execution result data, analyzes it, and evaluates the user's progress. The emotion engine reevaluates the user's emotional state based on the emotion data.

[2129] Step 17:

[2130] The server adjusts the next day's exercise and meal plan based on the evaluation results, using an emotion engine to generate encouraging and advice messages according to the user's emotions.

[2131] Step 18:

[2132] The server sends the generated plan for the next day and feedback to the terminal in JSON format.

[2133] Step 19:

[2134] The terminal displays the next day's plan and feedback received from the server to the user.

[2135] Step 20:

[2136] The user checks the feedback and starts taking action according to the plan for the next day.

[2137] Example 2

[2138] 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."

[2139] Conventional weight and body fat management systems provide analysis based on the user's weight and body fat percentage data, as well as exercise and meal plans, but lack feedback or plan adjustments that take into account the user's emotional state, making it difficult to maintain motivation or provide continuous support for achieving goals.In addition, daily data input and feedback of analysis results are often not done consistently across the entire system, making it difficult to provide and implement effective plans.

[2140] 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.

[2141] In this invention, the server includes: means for calculating the gap between the user's current condition and the achievement of the goal based on the user's input weight, body fat percentage, target weight, and target body fat percentage; means for receiving and analyzing dietary and exercise information input daily by the user; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the generated exercise and meal plan to the user; means for receiving the results of the user's exercise and meal plans and evaluating the progress; means for adjusting the next day's exercise and meal plan based on the evaluation results; means for receiving and analyzing the user's emotional data; and means for adjusting the plan to provide feedback based on the emotional analysis results. This makes it possible to provide a comprehensive exercise and meal plan that also takes the user's emotional state into consideration, thereby maintaining and improving motivation and realizing an effective system for supporting goal achievement.

[2142] "User" refers to an individual who uses this system to manage their weight and body fat percentage.

[2143] "Server" refers to the computer system that receives data sent by users and performs analysis and plan generation.

[2144] "Terminal" refers to a device (such as a smartphone or PC) that provides an interface for users to input data and communicate with a server.

[2145] "Weight" represents data indicating the current weight of the user.

[2146] "Body fat percentage" represents data indicating the percentage of fat in the user's body.

[2147] "Target weight" refers to data indicating the weight that the user wishes to achieve.

[2148] "Target body fat percentage" represents data indicating the body fat percentage that the user wishes to achieve.

[2149] The "gap" represents data that indicates the difference between the user's current state and the progress toward achieving the goal.

[2150] "Dietary information" refers to data that indicates the contents of the foods and drinks that a user consumes daily.

[2151] "Exercise information" refers to data indicating the type, duration, and intensity of exercise that the user performs daily.

[2152] "Analysis" refers to the process by which the server processes the data it receives and evaluates the user's status and progress.

[2153] "Exercise plan" refers to a plan generated by the server that includes the duration, intensity, and type of exercise optimal for the user.

[2154] "Meal plan" refers to a meal menu generated by the server that takes into consideration the optimal nutritional balance for the user.

[2155] "Progress" refers to data indicating the degree of achievement evaluated based on the results of the exercise and diet performed by the user.

[2156] "Emotion data" refers to data entered by the user that indicates their own mood, stress level, and motivation state.

[2157] "Emotion engine" refers to technology that analyzes a user's emotional data and provides appropriate feedback and plan adjustments.

[2158] "Feedback" refers to information generated by the server based on analysis, including evaluations and advice regarding the user's actions.

[2159] This invention is a system that proposes optimal exercise and diet methods based on dietary and exercise information to help users achieve their target weight and body fat percentage. This system includes three main components: a "server," a "terminal," and a "user," as well as an emotion engine that links them.

[2160] Server Roles

[2161] The server plays a central role in receiving and analyzing the data entered by the user. The server processes the data using the following techniques:

[2162] Data reception and storage: The server receives data such as the user's weight, body fat percentage, target weight, and target body fat percentage sent from the device and stores it in a database (e.g., MySQL).

[2163] Data analysis: The server uses data analysis tools such as Python and R to calculate the user's current state and the gap between the goal and the goal based on the received data. It also analyzes emotional data to evaluate the progress of each individual user.

[2164] Plan generation: Based on the analysis results, the server generates an optimal exercise and meal plan for the user. The exercise plan includes the duration, intensity, and type of exercise, while the meal plan includes a menu that takes nutritional balance into consideration. Adjustments are also made based on emotional data to maintain and improve motivation.

[2165] Feedback generation: The server evaluates the results of the user's exercise and diet and generates feedback to be reflected in the next day's plan. It also uses an emotion engine to provide messages of encouragement and advice based on the user's emotions.

[2166] Device Role

[2167] The terminal provides an interface for the user to input data and communicate with the server. The specific operation is as follows.

[2168] Data input: The user inputs data about weight, body fat percentage, diet, exercise, and emotions into the device. The device stores this data in a local database (e.g., SQLite) and prepares it for transmission to the server.

[2169] Data transmission: The device transmits the input data to the server, and displays the plan and feedback received from the server to the user.

[2170] Plan presentation: The device displays the exercise and meal plan received from the server in a user-friendly format. Front-end frameworks such as React and Vue.js are used for the UI.

[2171] User Roles

[2172] Users use the system to manage their own weight and body fat percentage. They utilize the system by performing the following actions:

[2173] Initial setting: The user inputs the current weight, body fat percentage, target weight, and target body fat percentage into the terminal.

[2174] Daily input: The user inputs their daily diet and exercise data, as well as their emotional state, into the device. For example, they may have oatmeal for breakfast, go for a 30-minute run, and enter their mood for the day as "normal."

[2175] Task execution: The user performs daily tasks according to the exercise and meal plan presented by the server.

[2176] Result input: Enter the results of your exercise, diet, and daily emotional state into your device.

[2177] The role of the emotional engine

[2178] The emotion engine recognizes and analyzes the emotional state of the user from input data. This technology has the following functions:

[2179] Emotion recognition: Analyzes the emotional data entered by the user (e.g., mood, stress level, motivation, etc.) to understand their emotional state for the day.

[2180] Feedback adjustment: The emotion engine generates feedback according to the user's state. For example, if the user's motivation is low, it provides encouraging messages or appropriate advice.

[2181] Plan Adjustment: Adjust your exercise and meal plan based on your emotional state. For example, if you're feeling stressed, it might suggest light exercise or recommend a nutritious meal.

[2182] Specific examples

[2183] If a user weighs 70 kg and has a body fat percentage of 25%, and sets their goal to be 65 kg and a body fat perc...

Claims

1. A means for calculating a gap between the user's current state and the achievement of the goal based on the weight, body fat percentage, target weight, and target body fat percentage input by the user; A means for receiving and analyzing dietary information and exercise information input by a user on a daily basis; means for generating an optimal exercise and meal plan for the user based on the analysis results; means for providing the generated exercise and meal plan to the user; means for receiving the results of the exercise and diet performed by the user again and evaluating the progress; a means of adjusting the next day's exercise and meal plan based on the assessment results; A system including:

2. A means for determining the user's exercise intensity, exercise time, and type of exercise based on the analysis results; Further comprising a means for creating a meal plan taking into consideration the user's nutritional balance; The system of claim 1 .

3. receiving the results of the exercise and diet performed by the user; further comprising means for providing feedback to the user based on past results; The system of claim 1 .

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