System

Abstract

A system is provided.SOLUTION: Means for inputting personalized body information and goal data, means for analyzing the inputted personalized body information and goal data and generating a personalized meal plan and exercise plan, means for transmitting the generated personalized meal plan and exercise plan to a user terminal, means for receiving meal records from the user terminal and analyzing the received meal records to generate feedback, and through cooperation with a healthcare application, A system includes a means for acquiring exercise data and body weight data, a means for visualizing the progress of a user on the basis of the acquired data, and a means for acquiring seasonal food material information corresponding to seasons and generating a meal plan on the basis of the information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a wide variety of information and methods regarding dieting and health management, making it difficult for individuals to choose the best plan. Women, who lead busy daily lives and find it difficult to choose the best option from the vast amount of information available, are particularly targeted, and there is a need for customized meal plans and reliable information based on individual physical information and goals. Furthermore, maintaining ongoing motivation and visualizing progress are also important challenges. [Means for solving the problem]

[0005] To solve this problem, the present invention provides a system including the following means: a means for inputting individual physical information and goal data, a means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan, a means for transmitting the generated personalized meal plan and exercise plan to a user terminal, a means for receiving a meal record from the user terminal and analyzing the received meal record to generate feedback, a means for acquiring exercise data and weight data through collaboration with a healthcare application, a means for visualizing the user's progress based on the acquired data, and a means for acquiring information on seasonal ingredients according to the season and generating a meal plan based on the acquired data. This system allows users to easily obtain optimal meal plans and exercise plans and easily maintain ongoing motivation. Furthermore, the present invention also includes a means for presenting the generated personalized meal plan and exercise plan to the user as graphs or charts, generating a weight loss prediction image, and a means for analyzing individual trends based on the user's input and adjusting the meal content and exercise plan, thereby enabling more accurate health management.

[0006] "Individual physical information" is data that indicates the user's individual physical characteristics, such as height, weight, age, and sex.

[0007] "Goal data" is data indicating goals set by the user regarding dieting and health management, such as a target weight and exercise frequency.

[0008] A "personalized meal plan" is a personalized meal menu suggestion generated based on a user's individual physical information and goal data.

[0009] An "exercise plan" is a schedule of specific exercise content and exercise frequency that the user is to achieve.

[0010] A "user terminal" is a device used by a user, such as a smartphone or tablet.

[0011] "Diet record" is data for recording the contents and amounts of food consumed by the user.

[0012] "Feedback" refers to evaluations and advice generated based on data entered by the user.

[0013] A "healthcare application" is an application for recording and managing a user's health management data.

[0014] "Exercise data" refers to data that indicates the type, duration, number of steps, etc. of exercise performed by the user.

[0015] "Weight data" is data that indicates the measured weight of the user.

[0016] "Means for visualizing progress" refers to a method or system for visually displaying a user's weight change, exercise status, etc.

[0017] "Information about seasonal ingredients" is information about ingredients that are nutritious and in abundant supply during a particular season.

[0018] "Generative AI" is an artificial intelligence technology that generates optimal meal plans and exercise plans based on input data. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention relates to a system that provides personalized diet plans in which a generation AI proposes optimal meal menus and exercise plans based on input of individual physical information and goal data. This system records the meals eaten by the user, allowing for an understanding of individual eating habits and further customization. In addition, by linking with healthcare applications, the system visualizes the user's progress and sense of accomplishment, contributing to increased motivation.

[0041] 1. Collection of User Information

[0042] When a user launches the Smart Diet app, the device prompts the user to enter their individual physical information (age, gender, height, weight, etc.) and goal (e.g., lose 5 kg in 3 months). After the user enters this information, the device sends the collected information to a server.

[0043] The server stores the received user information in a database.

[0044] 2. Generative AI creates personalized plans

[0045] The server uses generative AI to generate personalized meal plans and exercise plans based on the stored user information. For example, it might suggest a 1,500 kcal meal plan per day and three aerobic exercise sessions per week, taking into account calorie and nutritional balance based on the user's input.

[0046] The server transmits the generated plan to the user's terminal, and the terminal displays the personalized plan to the user.

[0047] 3. Food Record and Evaluation

[0048] Users record their daily meals in the app, take photos of their meals, and enter supplementary text descriptions. The device then sends the meal information to the server.

[0049] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. The evaluation results are sent to the user's device as feedback. For example, the server provides feedback such as "Total calories are 1400kcal, well-balanced."

[0050] 4. Data linkage with healthcare applications

[0051] When a user configures the smart diet app to link with a healthcare application, the device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[0052] Based on the acquired data, the server grasps the user's activity status and adjusts meal plans and exercise plans as necessary.

[0053] 5. Visualize your progress and stay motivated

[0054] The server analyzes the user's weight change and activity status, generates data that visually displays the results as graphs and charts, and also visualizes weight loss predictions and sends them to the user's device.

[0055] The device displays this data to the user and notifies them with notifications such as "3kg left until you reach your goal" to increase motivation.

[0056] 6. Proposals using seasonal ingredients

[0057] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[0058] For example, the server can suggest a recipe using bamboo shoots and asparagus, which are in season in spring, and send it to the device. The device then displays the recipe to the user and suggests new ingredients.

[0059] For example, if a user enters the following information: "29 years old, male, 175 cm tall, 80 kg weight, lose 5 kg in 3 months," the server will generate a plan based on this information: "1800 kcal / day, 3 times a week aerobic exercise plan." This plan is sent to the user's device, and the user records their daily meals and receives feedback based on the results. Furthermore, by linking with a healthcare application, daily exercise volume and weight data are automatically acquired, and the server analyzes this information and adjusts the plan accordingly. Progress is visualized in graphs and predictive images, helping users to stay motivated. Furthermore, new recipes using seasonal spring ingredients are suggested, allowing users to continue their diet while having fun.

[0060] As a result, the system of the present invention can provide effective diet support to users by providing customized plans based on individual physical information and goals, visualizing progress, maintaining motivation, and suggesting the use of seasonal ingredients.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The user launches the smart diet app and enters their physical information (age, gender, height, weight, etc.) and goal (e.g., lose 5 kg in 3 months). This information is stored on the device.

[0064] Step 2:

[0065] The terminal sends the entered user information to the server, which immediately stores the received information in a database.

[0066] Step 3:

[0067] The server uses the stored user information to generate personalized meal and exercise plans using generative AI, such as a meal plan targeting 1800 kcal per day and a plan to exercise three times a week.

[0068] Step 4:

[0069] The server sends the generated personalized plan to the user's terminal, which displays the received plan for the user to confirm.

[0070] Step 5:

[0071] Users take photos of their daily meals, supplement them with text, and record the meal details in the app, which is then saved on the device.

[0072] Step 6:

[0073] The device sends the recorded meal information to a server, which then uses the received information to analyze the meal contents using AI and calculate calorie intake and nutritional balance.

[0074] Step 7:

[0075] The server generates a feedback based on the evaluation of the meal content and sends it to the device, which then displays the feedback to the user, for example, notifying them that the total calories were 1400 kcal and were well balanced.

[0076] Step 8:

[0077] The user configures the app to link with the healthcare application. The device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[0078] Step 9:

[0079] The server analyzes the user's progress based on the acquired exercise and weight data, adjusts meal plans and exercise schedules as needed, and sends the results to the device.

[0080] Step 10:

[0081] The server visualizes the user's weight change and exercise data as graphs and charts, and also generates a video of the predicted weight loss. The device displays this data to the user and notifies them, such as "You're 3kg away from your goal."

[0082] Step 11:

[0083] The server collects information on seasonal ingredients and generates new meal plans based on that information. For example, it suggests a menu using bamboo shoots and asparagus, which are seasonal ingredients in spring.

[0084] Step 12:

[0085] The server sends new recipes using seasonal ingredients to the user's device, which then displays the suggested recipes to the user, introducing new ingredients and providing recipes.

[0086] The above processing flow allows the user to diet effectively and continuously.

[0087] Example 1

[0088] 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."

[0089] In modern society, there is a growing need for personalized health management and diet plans. However, conventional methods often use generic approaches, making it difficult to provide optimal plans based on individual physical information and goals. In particular, they lack the functionality to record and manage diet and exercise, suggest seasonal ingredients, and visualize each individual's progress, which can lead to a decrease in user motivation. Furthermore, there is a lack of data integration with healthcare programs and automatic plan adjustments. To solve these issues, an advanced, personalized system tailored to individual needs is required.

[0090] 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.

[0091] In this invention, the server includes a means for inputting individual personal information and goal data, a means for analyzing the input individual personal information and goal data to generate a personalized meal plan and exercise plan, and a means for transmitting the generated personalized meal plan and exercise plan to the user terminal. This enables the provision of highly personalized plans tailored to the user's individual needs. The server also includes a means for receiving a meal record from the user terminal, analyzing the received meal record, and generating feedback, a means for acquiring exercise data and weight data through collaboration with a healthcare program, a means for visualizing the user's progress based on the acquired data, and a means for acquiring seasonal food information and generating a meal plan based on the acquired data. This enables more detailed feedback and maintains motivation. The server also includes a means for optimizing the meal and exercise plan using a generative AI model, and a means for analyzing the dietary information acquired from the user using the generative AI model to evaluate calories and nutrients. This enables efficient and accurate evaluation and advice.

[0092] "Individual personal information" refers to data specific to an individual, such as the user's age, sex, height, and weight.

[0093] "Goal data" refers to specific target values ​​and periods set by the user, such as "lose 5 kg in 3 months."

[0094] A "personalized meal plan" is a meal plan optimized for an individual, generated based on the user's individual personal information and goal data.

[0095] An "exercise plan" is an exercise plan that is optimized for an individual, generated based on the user's individual personal information and goal data.

[0096] A "user terminal" is a device used by a user to input information and display results, such as a smartphone, tablet, or PC.

[0097] A "food record" is data in which the user records the contents of the meals they eat each day using photos and text.

[0098] "Feedback" refers to evaluations and advice provided by the server based on the results of analyzing the food records received from the user.

[0099] The "Healthcare Program" is an application for collecting and managing health-related information such as exercise volume and weight data.

[0100] "Progress visualization" refers to displaying a user's health and diet progress in visual formats such as graphs, charts, and videos.

[0101] "Information on seasonal ingredients" refers to information on ingredients that are considered to be most delicious and nutritious in a particular season.

[0102] A "generative AI model" is an artificial intelligence model that generates personalized plans and assessments based on input data.

[0103] "Calorie and nutrient evaluation" refers to analyzing the calorie content and nutritional balance of the food consumed by the user and displaying the results.

[0104] This invention relates to a system that utilizes generative AI models to propose personalized meal and exercise plans based on individual personal information and goal data. The system visualizes the user's progress through subsequent meal records and health care programs, helping to maintain motivation.

[0105] Hardware and software used

[0106] User device: Smartphone, tablet, PC, etc. Install the Smart Diet app as an application.

[0107] Server: Database server and application server deployed on the cloud, which processes data analysis and AI generation.

[0108] Generative AI models: Artificial intelligence models such as OpenAI GPT-4 that provide personalized suggestions based on user information.

[0109] System Configuration and Operation

[0110] 1. Collection of User Information

[0111] Device: When a user launches the smart diet app, the device prompts the user to enter their personal information (age, gender, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months).

[0112] User: The user follows the app's instructions and enters the required personal information.

[0113] Terminal: Sends the entered information to the server as an HTTP POST request.

[0114] Server: Stores the received information in a database, such as a relational database.

[0115] 2. Generative AI creates personalized plans

[0116] Server: Retrieves stored user information from the database and generates a personalized meal menu and exercise plan using a generative AI model.

[0117] Server: Enter the following prompt into the generative AI model:

[0118] User Information:

[0119] Age: 29

[0120] Gender: Male

[0121] Height: 175cm

[0122] Weight: 80kg

[0123] Goal: Lose 5kg in 3 months

[0124] Please suggest the best diet plan for you.

[0125] Generative AI model: Based on the prompt, it generates a plan for "calorie intake of 1800 kcal / day and aerobic exercise three times a week." The result is returned to the server in JSON format.

[0126] Server: Sends the generated plan to the user's device.

[0127] Device: Show the user a personalized plan.

[0128] 3. Food Record and Evaluation

[0129] User: The user logs their daily meals in the app, taking photos of the food and adding text descriptions.

[0130] Device: Sends meal information to the server via an HTTP POST request.

[0131] Server: Receives meal information and analyzes it using a generative AI model. It evaluates calories and nutrients.

[0132] Server: Generates feedback such as "Total calories are 1400kcal, well balanced" and sends it to the device.

[0133] Terminal: Displays the evaluation results to the user.

[0134] 4. Data linkage with healthcare applications

[0135] User: Set up integration with the healthcare application.

[0136] Device: Periodically acquires health data and sends it to the server.

[0137] Server: Analyzes the data and adjusts meal and exercise plans as needed.

[0138] 5. Visualize your progress and stay motivated

[0139] Server: Analyzes the user's weight change and activity data and generates data to be displayed visually.

[0140] Server: Generates data that displays weight loss predictions and progress status for each case, and sends it to the device.

[0141] On the device: Shows users their progress and provides notifications to keep them motivated.

[0142] 6. Proposals using seasonal ingredients

[0143] Server: Collects seasonal food information and stores it in a database.

[0144] Server: Uses generative AI models to create new recipes to suggest to users.

[0145] Server: For example, suggest a recipe using bamboo shoots and asparagus, which are seasonal spring ingredients, and send it to the device.

[0146] Terminal: Display the new recipe to the user.

[0147] In this way, the system of the present invention utilizes a generative AI model based on individual personal information and goal data to provide effective diet support to users. It has various functions, such as adjusting plans based on the user's progress and health care data, and suggesting the use of seasonal ingredients.

[0148] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0149] System program processing flow

[0150] Step 1: Collect user information

[0151] 1. Input: The user launches the smart diet app and enters their personal information (age, gender, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months).

[0152] 2. Specific operation: The terminal receives this information through the input form. When the user has finished entering the data, he or she presses the send button.

[0153] 3. Data processing: The terminal structures the input data and converts it into JSON format.

[0154] 4. Output: The device sends the formatted data to the server as an HTTP POST request.

[0155] Step 2: Data storage and analysis

[0156] 1. Input: User information and goal data received via an HTTP POST request.

[0157] 2. Specific operation: The server analyzes the received data and stores it in the database.

[0158] 3. Data processing: The received data is stored in the user profile table of the relational database.

[0159] 4. Output: A confirmation response that the save is complete is sent back to the terminal.

[0160] Step 3: Generative AI creates a personalized plan

[0161] 1. Input: User information stored in the database.

[0162] 2. Specific operation: The server generates a prompt sentence to input user information into the generated AI model.

[0163] 3. Example prompt:

[0164] User Information:

[0165] Age: 29

[0166] Gender: Male

[0167] Height: 175cm

[0168] Weight: 80kg

[0169] Goal: Lose 5kg in 3 months

[0170] Please suggest the best diet plan for you.

[0171] 4. Data processing: The prompts are fed into a generative AI model to generate a personalized meal and exercise plan.

[0172] 5. Output: The generated plan is returned to the server in JSON format.

[0173] Step 4: Send your personalized plan

[0174] 1. Input: The personalization plan returned by the generative AI model.

[0175] 2. Specific operation: The server formats the generated plan and sends it to the user's device.

[0176] 3. Data processing: Send the plan in JSON format as an HTTP response.

[0177] 4. Output: The personalized plan is sent to the device.

[0178] Step 5: Collect and analyze food records

[0179] 1. Input: Daily food details (photos and text) recorded by the user using the Smart Diet app.

[0180] 2. Specific operation: The device converts the food record into JSON format and sends it to the server.

[0181] 3. Data processing: The server analyzes the food records received using a generative AI model to evaluate calories and nutrients.

[0182] 4. Output: The evaluation result (e.g., "Total calories are 1400 kcal, well balanced") is sent to the device in JSON format.

[0183] Step 6: Provide feedback

[0184] 1. Input: Evaluation results from the generative AI model.

[0185] 2. Specific operation: The device displays the evaluation results to the user.

[0186] 3. Data processing: Visualize the evaluation results and convert them into a format that can be displayed on the notification and feedback screens.

[0187] 4. Output: The evaluation results are displayed to the user.

[0188] Step 7: Data integration with healthcare applications

[0189] 1. Input: Exercise and weight data obtained from the healthcare application.

[0190] 2. Specific operation: The device periodically acquires this data and sends it to the server.

[0191] 3. Data processing: The server analyzes the received data and adjusts the plan as needed.

[0192] 4. Output: The adjusted plan is sent to the user's device.

[0193] Step 8: Visualize and communicate progress

[0194] 1. Input: User's weight change and activity data collected by the server.

[0195] 2. Specific operation: The server analyzes this data and generates graphs, charts, and a video showing predicted weight loss.

[0196] 3. Data processing: Generate data for visual display and format it into JSON format.

[0197] 4. Output: The generated data is sent to the terminal and displayed to the user. An example notification would be "3kg remaining until goal is reached".

[0198] Step 9: Proposals using seasonal ingredients

[0199] 1. Input: Information on seasonal ingredients.

[0200] 2. Specific operation: The server collects this information and stores it in a database.

[0201] 3. Data processing: Based on information on seasonal ingredients, new recipes are created using a generative AI model.

[0202] 4. Output: The new recipe is sent to the terminal and displayed to the user.

[0203] The above is the processing flow and specific operation of the system program. Each step makes it possible to provide personalized diet support to users.

[0204] (Application example 1)

[0205] 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."

[0206] In modern society, there is a need for diet plans tailored to individual physical information and goals. However, many existing systems lack personalization and are unable to provide effective meal and exercise plans. Furthermore, there is a lack of a way to visually check users' progress and receive real-time feedback, making it difficult to maintain motivation. Furthermore, there are few systems that offer intuitive, real-time data input and display capabilities using visual output devices.

[0207] 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.

[0208] In this invention, the server includes means for inputting individual physical information and goal data, means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan, means for transmitting the generated personalized meal plan and exercise plan to a user terminal, means for receiving a meal record from the user terminal and analyzing the received meal record to generate feedback, means for acquiring exercise data and weight data through cooperation with a sensor device, means for visualizing the user's progress based on the acquired data, means for acquiring information on seasonal ingredients according to the season and generating a meal plan based thereon, and means for displaying the personalized meal plan and exercise plan in real time using a visual output device and inputting meal record and progress data. This makes it possible to provide users with highly customizable and effective diet plans and support maintaining motivation through progress visualization and real-time feedback.

[0209] "Individual physical information" refers to data relating to the body that is specific to an individual user, such as the user's age, sex, height, and weight.

[0210] "Goal data" is data indicating a specific goal set by the user (for example, how many kilograms the user wants to lose over what period of time).

[0211] A "personalized meal plan" is a meal menu plan that is individually customized and suggested by the generative AI based on the user's individual physical information and goal data.

[0212] An "exercise plan" is a plan that includes specific exercise content and schedules proposed to help the user achieve their goals.

[0213] A "user terminal" refers to a portable electronic device used by a user, such as a smartphone, tablet PC, or smart glasses.

[0214] A "meal record" is data about the contents of a user's meals that is entered through an application.

[0215] "Feedback" refers to evaluations and advice provided to users based on analyzed data.

[0216] A "sensor device" is a device (e.g., a smartwatch or a weight scale) used to acquire a user's exercise data or weight data.

[0217] "Means for visualizing progress" refers to means that include a function that displays the user's progress in achieving their goals and their exercise and diet progress in graphs and charts.

[0218] "Information on seasonal ingredients" is data on fresh ingredients that are available at that time of year.

[0219] A "visual output device" is a device that displays information directly into the user's field of vision (e.g., smart glasses).

[0220] This invention relates to a system that provides a personalized diet plan in which a generating AI proposes optimal meal menus and exercise plans when individual physical information and goal data are input. This system allows users to understand their individual eating habits by recording their meals, and by linking with a healthcare application, visualizes progress and increases user motivation.

[0221] The system uses the following hardware and software:

[0222] User devices such as smartphones, tablet PCs, and smart glasses

[0223] Sensor devices (e.g. smartwatches, weight scales)

[0224] server

[0225] Generative AI Models

[0226] First, the user launches the smart diet app via their device and inputs their individual physical information (e.g., age, gender, height, weight) and goal (e.g., lose 5 kg in 3 months). This information is sent to the server and analyzed by the generative AI model. As a result of the analysis, an individually optimized meal menu and exercise plan is generated and sent to the user's device.

[0227] Users record their daily meals in the app and enter photos of the meals and supplemental text descriptions, which are then sent to the server. The server analyzes the received meal information and evaluates the calories and nutrients. The evaluation results are provided as feedback to the user's device, which displays an evaluation of the day's meal and advice.

[0228] Furthermore, by using the sensor device and setting it up to link with a healthcare application, the user can periodically send exercise and weight data to a server. This data is used to understand the user's activity status, and meal and exercise plans can be adjusted as needed. The user's progress is also visualized in graphs and charts, and notifications such as "3kg left to reach your goal" are sent to motivate the user.

[0229] Information on seasonal ingredients is also collected, and nutritionally balanced menus are generated based on this information. For example, recipes using ingredients in season in spring are suggested, allowing users to continue their diet while enjoying new ingredients.

[0230] As a concrete example, if a 29-year-old male user (height 175 cm, weight 80 kg) aims to lose 5 kg in three months, the server will suggest a diet plan of 1800 kcal per day and aerobic exercise three times a week. This plan is displayed in real time through the smart glasses, and the user can easily enter their diet and exercise records.

[0231] Prompt Sentence Examples

[0232] "Based on individual physical information (age, gender, height, weight) and goals (e.g., lose 5 kg in 3 months), generative AI will propose optimal meal menus and exercise plans. Users can view the personalized plan in real time through smart glasses and record their diet and exercise. For example, if a 29-year-old man (height 175 cm, weight 80 kg) aims to lose 5 kg in 3 months, he will be presented with a plan to consume 1800 kcal per day and do aerobic exercise three times a week. Imagine a system that visualizes dietary and exercise progress and provides feedback."

[0233] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0234] Step 1:

[0235] The user launches the smart diet app and inputs their individual physical information (age, sex, height, weight) and goal (e.g., lose 5 kg in 3 months). The device sends this input information to the server. The input data is the user's individual information and goal. The output is the sent user information.

[0236] Step 2:

[0237] The server stores the received user information in a database and analyzes it using a generative AI model. This analysis generates a personalized meal plan and exercise plan. The input is user information data, and the output is the generated plan data. The server creates the optimal plan, taking into account calorie calculations and nutritional balance.

[0238] Step 3:

[0239] The server sends the generated personalized plan to the user's terminal, which displays it to the user. The input is the generated plan data, and the output is the transmission to the user's terminal. The plan displayed on the terminal can be viewed in real time through the user's visual output device.

[0240] Step 4:

[0241] The user records their daily meals in the app. By taking photos of the meals and adding text descriptions, meal information is generated. The device sends this information to the server. The input is the user's meal record data, and the output is the data sent to the server.

[0242] Step 5:

[0243] The server analyzes the received food records using a generative AI model and evaluates calories and nutrients. The server generates the evaluation results as feedback and sends it to the user's device. The input is the food record data, and the output is feedback of the evaluation results. The feedback includes specific advice and evaluation details.

[0244] Step 6:

[0245] When a user uses a sensor device and links it to a healthcare application, exercise data and weight data are periodically sent to a server. The input is exercise data and weight data from the sensor device, and the output is data sent to the server.

[0246] Step 7:

[0247] The server analyzes the user's activity status based on the acquired exercise and weight data. Based on the analysis results, it adjusts the meal plan and exercise plan as needed. The input is exercise and weight data, and the output is the adjusted plan data.

[0248] Step 8:

[0249] The server generates graphs and charts to visualize the user's progress and sends them to the user's device. The input is data related to the progress, and the output is the visualized progress data. The device notifies the user of the displayed progress data.

[0250] Step 9:

[0251] The server collects seasonal ingredient information according to the season and stores it in a database. Based on the acquired seasonal ingredient information, a personalized meal plan is generated and sent to the user's device. The input is seasonal ingredient information data, and the output is plan data based on that data. Users can enjoy new recipes using seasonal ingredients.

[0252] Prompt Sentence Examples

[0253] "Based on individual physical information (age, gender, height, weight) and goals (e.g., lose 5 kg in 3 months), generative AI will propose optimal meal menus and exercise plans. Users can view the personalized plan in real time through smart glasses and record their diet and exercise. For example, if a 29-year-old man (height 175 cm, weight 80 kg) aims to lose 5 kg in 3 months, he will be presented with a plan to consume 1800 kcal per day and do aerobic exercise three times a week. Imagine a system that visualizes dietary and exercise progress and provides feedback."

[0254] 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.

[0255] This invention provides a personalized diet plan in which a generation AI proposes optimal meal menus and exercise plans based on individual physical information and goal data. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system provides feedback and motivation based on the user's emotional state, achieving more effective diet support.

[0256] 1. Collection of User Information

[0257] The user starts the smart diet app and enters their personal physical information (age, gender, height, weight, etc.) and goal (e.g., to lose 5 kg in 3 months). This information is stored on the device, which then sends it to the server.

[0258] The server stores the received user information in a database.

[0259] 2. Generative AI creates personalized plans

[0260] The server uses AI to generate personalized meal plans and exercise plans based on the stored user information. For example, it suggests a meal plan targeting 1800 kcal per day and an exercise plan for three times a week.

[0261] The server transmits the generated plan to the user's terminal, and the terminal displays the personalized plan to the user.

[0262] 3. Food Record and Evaluation

[0263] Users record their daily meals in the app, take photos of their meals, and enter supplementary text descriptions. The device then sends the meal information to the server.

[0264] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. This is then sent to the user's device as feedback. For example, the server provides feedback such as "Total calories are 1400kcal, well-balanced."

[0265] 4. Data linkage with healthcare applications

[0266] The user configures the Smart Diet app to link with the healthcare application. The device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[0267] The server uses the collected data to understand the user's activity status and adjusts their meal plan or exercise plan as needed. For example, if they are not getting enough exercise, it will suggest that they increase their exercise plan.

[0268] 5. Visualize your progress and stay motivated

[0269] The server analyzes the user's weight change and activity status, generates data to visually display the results as graphs and charts, and generates a video of the user's weight loss prediction and sends it to the user's device.

[0270] The device displays this data to the user and notifies them with notifications such as "3kg left until you reach your goal" to increase motivation.

[0271] 6. Proposals using seasonal ingredients

[0272] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[0273] For example, a recipe using bamboo shoots and asparagus, which are in season in spring, is suggested, and the server sends this to the terminal. The terminal displays the recipe to the user and suggests new ingredients.

[0274] 7. Emotion analysis and feedback using an emotion engine

[0275] Users can input their daily emotional state into the app or it can be automatically acquired through emotion recognition technology. This data is stored on the device and sent to a server.

[0276] The server uses an emotion engine to analyze the user's emotional data, and if the user is feeling stressed, for example, it will suggest activities or meals that will have a relaxing effect.

[0277] The server generates a feedback message based on the emotion data and sends it to the user's device, such as "Take a break and relax today."

[0278] As a concrete example, suppose a user inputs information such as "29 years old, male, 175cm tall, 80kg weight, lose 5kg in 3 months" and records their daily meals. Based on this, the server generates a plan for "calorie intake of 1800kcal / day, aerobic exercise 3 times a week" and sends it to the user's device. Once the user records their meals and the information is sent to the server, the generating AI analyzes the calories and nutrients and provides feedback.

[0279] Additionally, if the user's emotional data indicates "stress," the server will analyze it using an emotion engine and generate a suggestion such as "try drinking herbal tea for relaxation," which will be sent to the device. This will make it easier for users to manage not only their physical health but also their mental health.

[0280] As a result, the system of the present invention can provide customized plans based on individual physical information and goals, visualize progress, maintain motivation, and provide feedback and suggestions that correspond to emotional states, thereby providing more effective diet support to users.

[0281] The processing flow will be explained below.

[0282] Step 1:

[0283] The user starts the smart diet app and enters their personal physical information (age, gender, height, weight) and goal data (lose 5 kg in 3 months). The device then sends this information to the server.

[0284] Step 2:

[0285] The server stores the received user information in a database, including the user's individual physical information and goal data.

[0286] Step 3:

[0287] The server uses AI to generate personalized meal and exercise plans based on the saved user information. For example, it suggests a meal plan targeting 1800 kcal per day and aerobic exercise three times a week.

[0288] Step 4:

[0289] The server sends the generated personalized plan to the user's terminal, which displays the received plan for the user to confirm.

[0290] Step 5:

[0291] Users take photos of their daily meals, supplement them with text, and record the meal details in the app. The device then sends the recorded meal information to the server.

[0292] Step 6:

[0293] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. The evaluation results are generated as feedback and sent to the user's device. For example, feedback such as "Total calories are 1400kcal, well-balanced" is provided.

[0294] Step 7:

[0295] The user configures the app to link with the healthcare application, allowing the device to periodically obtain exercise and weight data from the healthcare application.

[0296] Step 8:

[0297] The device sends the acquired exercise and weight data to a server, which uses this information to understand the user's progress and adjusts their meal and exercise plans as needed. For example, if the user is not getting enough exercise, the server may suggest increasing the amount of exercise they need.

[0298] Step 9:

[0299] The server generates data that visualizes the user's weight change and exercise data as graphs and charts, and also generates a video of predicted weight loss and sends it to the user's device.

[0300] Step 10:

[0301] The device displays visualized data and predicted images to the user and notifies them with notifications such as "3kg left until you reach your goal."

[0302] Step 11:

[0303] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[0304] Step 12:

[0305] The server sends the new recipes using the generated seasonal ingredients to the device, which then displays the suggested recipes to the user, introducing new ingredients and suggesting menu items.

[0306] Step 13:

[0307] Users can input their emotional state into the app, or emotion recognition technology can be used to automatically obtain emotional data, which the device then sends to a server.

[0308] Step 14:

[0309] The server uses an emotion engine to analyze the user's emotional data. For example, if it determines that the user is under stress, it will suggest activities or meals that will help them relax.

[0310] Step 15:

[0311] The server generates a feedback message based on the emotion data and sends it to the user's device, for example, a message saying, "Take a break and relax today."

[0312] Through the above processing flow, users can follow a diet plan based on their physical information and goals, while also receiving support tailored to their emotional state, allowing them to diet more effectively and continuously.

[0313] Example 2

[0314] 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."

[0315] Conventional diet support systems provide personalized plans based on individual physical information and goal data, but it is difficult to provide optimal support that takes into account many variables, such as the user's emotional state and seasonal food information.In addition, there are issues with insufficient visualization of progress and maintaining motivation, making it difficult for users to continue dieting over the long term.

[0316] 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.

[0317] In this invention, the server includes: means for inputting individual physical information and goal data; means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan using a generative AI model; means for transmitting the generated personalized meal plan and exercise plan to a user terminal; means for receiving a meal record from the user terminal and analyzing the received meal record using a generative AI model to generate feedback; means for acquiring exercise data and weight data through collaboration with a healthcare application; means for visualizing the user's progress based on the acquired data; means for acquiring information on seasonal ingredients according to the season and generating a personalized meal plan based on the acquired data; and means for analyzing the user's emotional state using an emotion engine and generating feedback based on the emotion data. This enables effective diet support based on the user's physical information and goals, as well as optimal feedback and suggestions that take into account emotional state and seasonal factors.

[0318] 1. "Individual physical information" refers to information about a user's individual body, such as age, gender, height, weight, and body fat percentage.

[0319] 2. "Goal Data" refers to specific goals set by the user, such as weight loss goals or exercise goals.

[0320] 3. "Generative AI model" refers to a model that uses artificial intelligence to generate optimal output from specific input data.

[0321] 4. "Personalized Meal Plan" refers to a meal menu that is customized based on a user's individual physical information and goal data.

[0322] 5. "Exercise Plan" refers to an exercise routine or schedule created based on a user's fitness goals.

[0323] 6. "User terminal" refers to an electronic device capable of executing programs, such as a smartphone or tablet used by a user.

[0324] 7. "Food record" refers to information that a user records about their daily meals, such as the type of food, amount, calories, etc.

[0325] 8. "Healthcare Application" refers to application software used for health management purposes.

[0326] 9. "Exercise Data" refers to information such as the type and duration of exercise performed by the user, and calories burned.

[0327] 10. "Weight Data" refers to measurement information regarding a User's weight.

[0328] 11. "Progress Visualization" refers to the visual display of a user's diet or fitness progress.

[0329] 12. "Information on seasonal ingredients" refers to information about fresh ingredients harvested each season.

[0330] 13. "Emotion Engine" means a program or technology for analyzing a user's emotional state.

[0331] 14. "Emotional Data" means information that represents a user's emotional state.

[0332] 15. "Feedback" refers to evaluations and advice provided to users.

[0333] The present invention is a system that utilizes a generative AI model based on individual physical information and goal data to propose optimal meal menus and exercise plans, and provides feedback and motivation based on the user's emotional state. This system includes the following components and processes.

[0334] Components

[0335] 1. User Device

[0336] Electronic devices such as smartphones and tablets are used to input user information, record meals, input emotional data, and receive feedback.

[0337] 2. Server

[0338] Equipped with a database and generative AI models, it analyzes user information and generates personalized meal and exercise plans.

[0339] Hardware and software used

[0340] Hardware:

[0341] Smartphones, tablets, servers, cloud storage

[0342] software:

[0343] Smart diet apps, healthcare applications, generative AI models (e.g., GPT-3), databases (e.g., MySQL), emotion engines

[0344] Data processing and calculation

[0345] 1. Collection and Transmission of User Information

[0346] The user inputs age, gender, height, weight, and goal data through the smart diet app. For example, "29 years old, male, height 175 cm, weight 80 kg, lose 5 kg in 3 months."

[0347] The device sends this information to the server in JSON format.

[0348] 2. Generate a personalized plan

[0349] The server stores the user information in a database and sends prompts to the generative AI model to generate personalized meal and exercise plans, for example, "Generate the optimal meal and exercise plan for a 29-year-old male, 175 cm tall, 80 kg weight, with a goal of losing 5 kg in 3 months."

[0350] The server sends the generated plan to the terminal, which displays the plan to the user.

[0351] 3. Food Record and Feedback

[0352] The user records their daily diet and enters details with photos and text. The device sends this information to a server, which analyzes it and generates feedback. For example, "Total calories are 1400kcal, well balanced."

[0353] 4. Integration with healthcare applications

[0354] The device periodically retrieves exercise and weight data from the health app and sends it to the server, which then analyzes the user's progress and adjusts their diet and exercise plans as needed.

[0355] 5. Visualize progress

[0356] The server generates graphs and predicted images based on the analysis of exercise and weight data, and sends them to the device. The device displays them to the user, notifying them with a message such as, "You're 3kg away from your goal."

[0357] 6. Proposal of seasonal ingredients

[0358] The server collects information on seasonal ingredients and proposes appropriate meal plans to users based on that information. For example, "Recipes using bamboo shoots and asparagus, which are seasonal ingredients in spring."

[0359] 7. Sentiment Analysis and Feedback

[0360] The user inputs emotional data or it is automatically acquired using emotion recognition technology. This data is sent to the server and analyzed by the emotion engine. For example, a suggestion such as "If you are under a lot of stress, try drinking herbal tea for relaxation" is generated and sent to the device.

[0361] This allows the present invention to provide optimal diet support based on individual physical information and goals, and to provide feedback that takes into account the user's emotional state and seasonal factors.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1:

[0364] Enter and submit user information

[0365] The user launches the Smart Diet app and enters their age, gender, height, weight, and goal data (e.g., "29 years old, male, height 175cm, weight 80kg, lose 5kg in 3 months"). This input is saved on the device as JSON format data.

[0366] The device sends the saved user information to the server in the form of an HTTP request, which is then stored in a database.

[0367] Step 2:

[0368] Generate a personalized plan

[0369] The server retrieves the received user information from a database (e.g., MySQL) and generates a prompt based on it. Example prompt: "Generate the optimal meal menu and exercise plan for a 29-year-old male, 175 cm tall, 80 kg weight, with a goal of losing 5 kg in 3 months."

[0370] The server sends this prompt to a generative AI model (e.g., GPT-3) and obtains a personalized meal plan and exercise plan. The output is something like "Intake 1800kcal / day" and "Exercise 3 times a week."

[0371] The server sends this personalized plan in JSON format to the device.

[0372] Step 3:

[0373] View Plans

[0374] The device analyzes the received personalized plan and displays it on the screen in a format that is easy for the user to view, such as a list of daily meal menus and exercise plans.

[0375] Step 4:

[0376] Enter and submit your food record

[0377] Users record their daily meals in the app by taking photos of the food and adding text descriptions.

[0378] The device sends the meal information in JSON format to the server, and the image data is uploaded to cloud storage (e.g., Amazon S3), along with the URL.

[0379] Step 5:

[0380] Analysis of food information and feedback generation

[0381] The server analyzes the received meal information using a generative AI model and evaluates calories and nutrients, generating feedback such as "calorie intake is 1400kcal, well balanced."

[0382] The server sends the generated feedback in JSON format to the device.

[0383] Step 6:

[0384] View Feedback

[0385] The device displays the received feedback to the user, for example, using notifications to provide real-time feedback.

[0386] Step 7:

[0387] Data linkage with healthcare applications

[0388] Users can set up the Smart Diet app to link with other healthcare apps (e.g., Apple Health, Google Fit).

[0389] The device periodically obtains exercise and weight data from the health app and sends it to the server in JSON format.

[0390] Step 8:

[0391] Analyze data and adjust plans

[0392] The server analyzes the acquired exercise and weight data and adjusts the meal plan and exercise plan as needed. Example: "You are not getting enough exercise, so we suggest increasing your exercise plan to four times a week."

[0393] The server then sends the adjusted plan back to the terminal.

[0394] Step 9:

[0395] Progress visualization

[0396] The server analyzes the user's weight change and activity status, and generates graphs and charts, as well as a video showing predicted weight loss.

[0397] The server sends the generated visualization data to the terminal, which then displays it to the user. Example: A notification saying "3kg left until goal."

[0398] Step 10:

[0399] Suggestions for seasonal ingredients

[0400] The server collects information on seasonal ingredients and stores it in a database.

[0401] The server generates a personalized meal plan based on seasonal ingredients and sends, for example, a "recipe using bamboo shoots and asparagus" to the device.

[0402] The terminal displays this to the user.

[0403] Step 11:

[0404] Entering and sending emotional data

[0405] Users can input their daily emotional state into the app, or it can be automatically obtained through emotion recognition technology.

[0406] The device sends emotion data in JSON format to the server.

[0407] Step 12:

[0408] Emotional data analysis and feedback generation

[0409] The server uses an emotion engine to analyze the user's emotional data and suggests activities and meals that will help them relax if they are feeling stressed or tired. For example, the message might say, "Take a short break and relax today."

[0410] The server sends these proposals to the terminal.

[0411] Step 13:

[0412] Displaying sentiment-based feedback

[0413] The device will then display feedback to the user based on the emotions received, for example, using notifications to offer relaxation and stress reduction techniques.

[0414] (Application example 2)

[0415] 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."

[0416] In modern society, there is a demand for providing diet plans tailored to individual users. However, conventional diet systems have difficulty providing personalized feedback that takes into account individual physical information and emotional states. Furthermore, there is no easy way to purchase the suggested ingredients and supplements, which reduces user convenience. Furthermore, providing feedback and motivation based on the user's emotional state is difficult with conventional technologies.

[0417] 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 inputting individual physical information and goal data, means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan, means for transmitting the generated personalized meal plan and exercise plan to the user terminal, means for inputting emotional data, analyzing it using an emotion analysis engine, and providing feedback and motivation based on the emotional state, and means for purchasing the generated plan and suggested ingredients and supplements in cooperation with an electronic payment service. This enables personalized feedback and suggestions based on the user's individual physical information and emotional state, achieving convenient and effective diet support.

[0418] "Individual physical information" refers to physical data specific to each individual user, such as the user's age, gender, height, and weight.

[0419] "Goal data" is data relating to a specific goal that a user wishes to achieve, such as weight loss or improvement in a particular health condition.

[0420] A "personalized meal plan" is a meal schedule and menu suggestion that is optimal for a user, generated based on the user's individual physical information and goal data.

[0421] An "exercise plan" is a proposal of the optimal exercise method and frequency for a user, generated based on the user's individual physical information and goal data.

[0422] "Emotion data" is data that represents the user's current emotional state, and includes, for example, stress, happiness, fatigue, and the like.

[0423] An "emotion analysis engine" is software or an algorithm that analyzes input emotion data and understands the user's emotional state.

[0424] "Feedback" is information that indicates the next action or areas for improvement based on the user's actions and status.

[0425] "Motivation" is an act or means of providing psychologically encouraging messages or suggestions to help a user achieve a set goal.

[0426] A "healthcare application" is a software application for managing a user's health condition and exercise data.

[0427] An "electronic payment service" is a system for electronically paying for goods and services over the Internet.

[0428] "Visualizing progress" means displaying a user's progress in dieting or health improvement in a visual format such as a graph or chart.

[0429] "Dietary records" are data that record the contents of meals consumed by a user in text and photographs.

[0430] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Details of each element and their interactions will be explained below.

[0431] Server Features

[0432] The server performs the following main functions:

[0433] 1. Processing of individual body information and target data

[0434] Individual physical information (age, gender, height, weight, etc.) and goal data (e.g., weight loss goals or health improvement goals) entered by the user through a smartphone application are sent to a server.

[0435] The server stores this data in a database.

[0436] 2. Generate personalized meal and exercise plans

[0437] The server uses a generative AI model to generate optimal meal and exercise plans based on the individual's stored physical information and goal data.

[0438] For example, if a user submits the data "29 years old, male, 175 cm tall, 80 kg weight, lose 5 kg in 3 months," the server will generate a plan for "calorie intake of 1800 kcal / day, aerobic exercise 3 times a week."

[0439] 3. Generate feedback

[0440] Users record their daily meals through the application and send the information to a server, which then analyzes the received meal records using a generative AI model to evaluate calories and nutrients.

[0441] For example, feedback such as "Total calories are 1400 kcal, well balanced" is generated and sent to the user terminal.

[0442] 4. Emotional Data Analysis and Feedback

[0443] Emotion data is either entered by the user into the application or automatically obtained using emotion recognition technology, and this data is also sent to the server.

[0444] The server uses an emotion analysis engine to analyze the emotional data and, if it indicates stress, generates suggestions such as "Try drinking herbal tea for relaxation."

[0445] 5. Collaboration with electronic payment services

[0446] The server then connects the generated plan and the suggested ingredients and supplements to an electronic payment service, creating an environment where users can easily purchase them. Users can complete the purchase process directly from the application.

[0447] 6. Visualize progress

[0448] The server analyzes the user's progress based on the exercise and weight data acquired and visualizes the results in graphs and charts.

[0449] It provides users with motivation through notifications such as "3kg left until your goal."

[0450] Device Features

[0451] The user device (e.g., smartphone) mainly performs the following operations:

[0452] 1. Data Entry

[0453] The user inputs individual physical information and goal data.

[0454] Enter your daily food records and emotional data.

[0455] 2. Displaying Information

[0456] View personalized meal and exercise plans.

[0457] Feedback and progress indication.

[0458] 3. Use of payment functions

[0459] Purchase suggested foods and supplements.

[0460] Examples of prompt statements

[0461] Prompt: "Male, 29 years old, 175cm tall, 80kg weight. I want to lose 5kg in 3 months. What is the best diet and exercise plan?"

[0462] Prompt: "Does this meal contain anything that has a relaxing effect?"

[0463] This invention allows users to receive personalized feedback and suggestions based on their individual physical information and emotional state. It also allows users to easily purchase the suggested products, improving convenience. Furthermore, notifications and feedback are provided to keep users motivated, enabling more effective diet support.

[0464] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0465] Step 1:

[0466] The user launches the smartphone application, enters their individual physical information (age, sex, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months), and saves the data on the device.

[0467] Input: Age, Gender, Height, Weight, Weight Loss Goal

[0468] Output: Individual physical information and goal data stored on the user's device

[0469] How it works: A user enters information into an app form and clicks the "Save" button.

[0470] Step 2:

[0471] The device transmits the individual physical information and goal data acquired in step 1 to the server.

[0472] Input: Individual physical information and goal data stored on the user's device

[0473] Output: Individual body information and goal data sent to the server

[0474] How it works: The device sends data to the server's API endpoint as a POST request.

[0475] Step 3:

[0476] The server stores the received individual physical information and goal data in a database and uses a generative AI model to generate personalized meal and exercise plans.

[0477] Input: Individual physical information and goal data sent to the server

[0478] Output: Generated personalized meal and exercise plans

[0479] How it works: The server inputs data into the generative AI model and runs the process to generate the optimal plan.

[0480] Step 4:

[0481] The server transmits the generated personalized meal plan and exercise plan to the user terminal.

[0482] Input: Generated personalized meal and exercise plan

[0483] Output: Personalized meal and exercise plans delivered to the user's device

[0484] Operation: The generated plan is sent as a POST request to the user's device via the server's API endpoint.

[0485] Step 5:

[0486] Users record their daily dietary habits on a smartphone app, take photos of their meals, and enter supplementary information. This information is then sent from the device to the server.

[0487] Input: Meal details recorded by the user (photos and supplementary descriptions)

[0488] Output: Meal record sent to the server

[0489] How it works: A user uses the app's food log feature to enter information and presses the "Submit" button.

[0490] Step 6:

[0491] The server analyzes the received food records using a generative AI model to evaluate calories and nutrients.

[0492] Input: Food record sent to the server

[0493] Output: Calorie and nutrient rating feedback

[0494] Operation: The server analyzes the food log and performs processing to generate ratings and feedback.

[0495] Step 7:

[0496] The server transmits the generated feedback to the user terminal.

[0497] Input: Calorie and nutrient rating feedback

[0498] Output: Feedback delivered to the user device

[0499] Operation: Feedback information is sent to the user's device as a POST request via the server's API.

[0500] Step 8:

[0501] The user inputs emotion data via an application, or emotion data automatically acquired through emotion recognition technology is sent from the terminal to the server.

[0502] Input: User emotion data

[0503] Output: Emotion data sent to the server

[0504] Operation: The user inputs emotion data, and the device automatically sends the data to the server.

[0505] Step 9:

[0506] The server uses an emotion analysis engine to analyze the emotion data and generate feedback and motivational messages based on the emotional state.

[0507] Input: Emotion data sent to the server

[0508] Output: Feedback and motivational messages based on the generated emotional state

[0509] How it works: The server runs a sentiment analysis engine, analyzes the sentiment data, and generates an appropriate message.

[0510] Step 10:

[0511] The server transmits the generated emotion feedback message to the user terminal.

[0512] Input: Feedback and motivational messages based on generated emotional states

[0513] Output: Emotion feedback message delivered to the user device

[0514] Operation: An emotional feedback message is sent to the user device as a POST request via the server's API.

[0515] Step 11:

[0516] The user can purchase the suggested ingredients and supplements from the application by completing payment procedures using an electronic payment service.

[0517] Input: Generated food and supplement suggestions and user purchase information

[0518] Output: Notification of purchase completion via electronic payment

[0519] How it works: A user clicks a purchase button within an application and completes payment through an electronic payment service.

[0520] Step 12:

[0521] The server analyzes the user's progress based on the acquired exercise and weight data and visualizes it as graphs and charts.

[0522] Input: Exercise data and weight data obtained through the Health app

[0523] Output: Visualized progress information (graphs and charts)

[0524] How it works: The server analyzes the data and generates graphs and charts for visualization.

[0525] Step 13:

[0526] The server transmits the visualized progress information to the user terminal, notifying the user of the progress toward the goal.

[0527] Input: Visualized progress information

[0528] Output: Progress notification delivered to the user's device

[0529] Operation: The visualized progress information is sent to the user's device as a POST request via the server's API.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] [Second embodiment]

[0534] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0535] 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.

[0536] 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).

[0537] 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.

[0538] 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.

[0539] 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).

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] 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."

[0546] This invention relates to a system that provides personalized diet plans in which a generation AI proposes optimal meal menus and exercise plans based on input of individual physical information and goal data. This system records the meals eaten by the user, allowing for an understanding of individual eating habits and further customization. In addition, by linking with healthcare applications, the system visualizes the user's progress and sense of accomplishment, contributing to increased motivation.

[0547] 1. Collection of User Information

[0548] When a user launches the Smart Diet app, the device prompts the user to enter their individual physical information (age, gender, height, weight, etc.) and goal (e.g., lose 5 kg in 3 months). After the user enters this information, the device sends the collected information to a server.

[0549] The server stores the received user information in a database.

[0550] 2. Generative AI creates personalized plans

[0551] The server uses generative AI to generate personalized meal plans and exercise plans based on the stored user information. For example, it might suggest a 1,500 kcal meal plan per day and three aerobic exercise sessions per week, taking into account calorie and nutritional balance based on the user's input.

[0552] The server transmits the generated plan to the user's terminal, and the terminal displays the personalized plan to the user.

[0553] 3. Food Record and Evaluation

[0554] Users record their daily meals in the app, take photos of their meals, and enter supplementary text descriptions. The device then sends the meal information to the server.

[0555] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. The evaluation results are sent to the user's device as feedback. For example, the server provides feedback such as "Total calories are 1400kcal, well-balanced."

[0556] 4. Data linkage with healthcare applications

[0557] When a user configures the smart diet app to link with a healthcare application, the device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[0558] Based on the acquired data, the server grasps the user's activity status and adjusts meal plans and exercise plans as necessary.

[0559] 5. Visualize your progress and stay motivated

[0560] The server analyzes the user's weight change and activity status, generates data that visually displays the results as graphs and charts, and also visualizes weight loss predictions and sends them to the user's device.

[0561] The device displays this data to the user and notifies them with notifications such as "3kg left until you reach your goal" to increase motivation.

[0562] 6. Proposals using seasonal ingredients

[0563] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[0564] For example, the server can suggest a recipe using bamboo shoots and asparagus, which are in season in spring, and send it to the device. The device then displays the recipe to the user and suggests new ingredients.

[0565] For example, if a user enters the following information: "29 years old, male, 175 cm tall, 80 kg weight, lose 5 kg in 3 months," the server will generate a plan based on this information: "1800 kcal / day, 3 times a week aerobic exercise plan." This plan is sent to the user's device, and the user records their daily meals and receives feedback based on the results. Furthermore, by linking with a healthcare application, daily exercise volume and weight data are automatically acquired, and the server analyzes this information and adjusts the plan accordingly. Progress is visualized in graphs and predictive images, helping users to stay motivated. Furthermore, new recipes using seasonal spring ingredients are suggested, allowing users to continue their diet while having fun.

[0566] As a result, the system of the present invention can provide effective diet support to users by providing customized plans based on individual physical information and goals, visualizing progress, maintaining motivation, and suggesting the use of seasonal ingredients.

[0567] The processing flow will be explained below.

[0568] Step 1:

[0569] The user launches the smart diet app and enters their physical information (age, gender, height, weight, etc.) and goal (e.g., lose 5 kg in 3 months). This information is stored on the device.

[0570] Step 2:

[0571] The terminal sends the entered user information to the server, which immediately stores the received information in a database.

[0572] Step 3:

[0573] The server uses the stored user information to generate personalized meal and exercise plans using generative AI, such as a meal plan targeting 1800 kcal per day and a plan to exercise three times a week.

[0574] Step 4:

[0575] The server sends the generated personalized plan to the user's terminal, which displays the received plan for the user to confirm.

[0576] Step 5:

[0577] Users take photos of their daily meals, supplement them with text, and record the meal details in the app, which is then saved on the device.

[0578] Step 6:

[0579] The device sends the recorded meal information to a server, which then uses the received information to analyze the meal contents using AI and calculate calorie intake and nutritional balance.

[0580] Step 7:

[0581] The server generates a feedback based on the evaluation of the meal content and sends it to the device, which then displays the feedback to the user, for example, notifying them that the total calories were 1400 kcal and were well balanced.

[0582] Step 8:

[0583] The user configures the app to link with the healthcare application. The device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[0584] Step 9:

[0585] The server analyzes the user's progress based on the acquired exercise and weight data, adjusts meal plans and exercise schedules as needed, and sends the results to the device.

[0586] Step 10:

[0587] The server visualizes the user's weight change and exercise data as graphs and charts, and also generates a video of the predicted weight loss. The device displays this data to the user and notifies them, such as "You're 3kg away from your goal."

[0588] Step 11:

[0589] The server collects information on seasonal ingredients and generates new meal plans based on that information. For example, it suggests a menu using bamboo shoots and asparagus, which are seasonal ingredients in spring.

[0590] Step 12:

[0591] The server sends new recipes using seasonal ingredients to the user's device, which then displays the suggested recipes to the user, introducing new ingredients and providing recipes.

[0592] The above processing flow allows the user to diet effectively and continuously.

[0593] Example 1

[0594] 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."

[0595] In modern society, there is a growing need for personalized health management and diet plans. However, conventional methods often use generic approaches, making it difficult to provide optimal plans based on individual physical information and goals. In particular, they lack the functionality to record and manage diet and exercise, suggest seasonal ingredients, and visualize each individual's progress, which can lead to a decrease in user motivation. Furthermore, there is a lack of data integration with healthcare programs and automatic plan adjustments. To solve these issues, an advanced, personalized system tailored to individual needs is required.

[0596] 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.

[0597] In this invention, the server includes a means for inputting individual personal information and goal data, a means for analyzing the input individual personal information and goal data to generate a personalized meal plan and exercise plan, and a means for transmitting the generated personalized meal plan and exercise plan to the user terminal. This enables the provision of highly personalized plans tailored to the user's individual needs. The server also includes a means for receiving a meal record from the user terminal, analyzing the received meal record, and generating feedback, a means for acquiring exercise data and weight data through collaboration with a healthcare program, a means for visualizing the user's progress based on the acquired data, and a means for acquiring seasonal food information and generating a meal plan based on the acquired data. This enables more detailed feedback and maintains motivation. The server also includes a means for optimizing the meal and exercise plan using a generative AI model, and a means for analyzing the dietary information acquired from the user using the generative AI model to evaluate calories and nutrients. This enables efficient and accurate evaluation and advice.

[0598] "Individual personal information" refers to data specific to an individual, such as the user's age, sex, height, and weight.

[0599] "Goal data" refers to specific target values ​​and periods set by the user, such as "lose 5 kg in 3 months."

[0600] A "personalized meal plan" is a meal plan optimized for an individual, generated based on the user's individual personal information and goal data.

[0601] An "exercise plan" is an exercise plan that is optimized for an individual, generated based on the user's individual personal information and goal data.

[0602] A "user terminal" is a device used by a user to input information and display results, such as a smartphone, tablet, or PC.

[0603] A "food record" is data in which the user records the contents of the meals they eat each day using photos and text.

[0604] "Feedback" refers to evaluations and advice provided by the server based on the results of analyzing the food records received from the user.

[0605] The "Healthcare Program" is an application for collecting and managing health-related information such as exercise volume and weight data.

[0606] "Progress visualization" refers to displaying a user's health and diet progress in visual formats such as graphs, charts, and videos.

[0607] "Information on seasonal ingredients" refers to information on ingredients that are considered to be most delicious and nutritious in a particular season.

[0608] A "generative AI model" is an artificial intelligence model that generates personalized plans and assessments based on input data.

[0609] "Calorie and nutrient evaluation" refers to analyzing the calorie content and nutritional balance of the food consumed by the user and displaying the results.

[0610] This invention relates to a system that utilizes generative AI models to propose personalized meal and exercise plans based on individual personal information and goal data. The system visualizes the user's progress through subsequent meal records and health care programs, helping to maintain motivation.

[0611] Hardware and software used

[0612] User device: Smartphone, tablet, PC, etc. Install the Smart Diet app as an application.

[0613] Server: Database server and application server deployed on the cloud, which processes data analysis and AI generation.

[0614] Generative AI models: Artificial intelligence models such as OpenAI GPT-4 that provide personalized suggestions based on user information.

[0615] System Configuration and Operation

[0616] 1. Collection of User Information

[0617] Device: When a user launches the smart diet app, the device prompts the user to enter their personal information (age, gender, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months).

[0618] User: The user follows the app's instructions and enters the required personal information.

[0619] Terminal: Sends the entered information to the server as an HTTP POST request.

[0620] Server: Stores the received information in a database, such as a relational database.

[0621] 2. Generative AI creates personalized plans

[0622] Server: Retrieves stored user information from the database and generates a personalized meal menu and exercise plan using a generative AI model.

[0623] Server: Enter the following prompt into the generative AI model:

[0624] User Information:

[0625] Age: 29

[0626] Gender: Male

[0627] Height: 175cm

[0628] Weight: 80kg

[0629] Goal: Lose 5kg in 3 months

[0630] Please suggest the best diet plan for you.

[0631] Generative AI model: Based on the prompt, it generates a plan for "calorie intake of 1800 kcal / day and aerobic exercise three times a week." The result is returned to the server in JSON format.

[0632] Server: Sends the generated plan to the user's device.

[0633] Device: Show the user a personalized plan.

[0634] 3. Food Record and Evaluation

[0635] User: The user logs their daily meals in the app, taking photos of the food and adding text descriptions.

[0636] Device: Sends meal information to the server via an HTTP POST request.

[0637] Server: Receives meal information and analyzes it using a generative AI model. It evaluates calories and nutrients.

[0638] Server: Generates feedback such as "Total calories are 1400kcal, well balanced" and sends it to the device.

[0639] Terminal: Displays the evaluation results to the user.

[0640] 4. Data linkage with healthcare applications

[0641] User: Set up integration with the healthcare application.

[0642] Device: Periodically acquires health data and sends it to the server.

[0643] Server: Analyzes the data and adjusts meal and exercise plans as needed.

[0644] 5. Visualize your progress and stay motivated

[0645] Server: Analyzes the user's weight change and activity data and generates data to be displayed visually.

[0646] Server: Generates data that displays weight loss predictions and progress status for each case, and sends it to the device.

[0647] On the device: Shows users their progress and provides notifications to keep them motivated.

[0648] 6. Proposals using seasonal ingredients

[0649] Server: Collects seasonal food information and stores it in a database.

[0650] Server: Uses generative AI models to create new recipes to suggest to users.

[0651] Server: For example, suggest a recipe using bamboo shoots and asparagus, which are seasonal spring ingredients, and send it to the device.

[0652] Terminal: Display the new recipe to the user.

[0653] In this way, the system of the present invention utilizes a generative AI model based on individual personal information and goal data to provide effective diet support to users. It has various functions, such as adjusting plans based on the user's progress and health care data, and suggesting the use of seasonal ingredients.

[0654] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0655] System program processing flow

[0656] Step 1: Collect user information

[0657] 1. Input: The user launches the smart diet app and enters their personal information (age, gender, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months).

[0658] 2. Specific operation: The terminal receives this information through the input form. When the user has finished entering the data, he or she presses the send button.

[0659] 3. Data processing: The terminal structures the input data and converts it into JSON format.

[0660] 4. Output: The device sends the formatted data to the server as an HTTP POST request.

[0661] Step 2: Data storage and analysis

[0662] 1. Input: User information and goal data received via an HTTP POST request.

[0663] 2. Specific operation: The server analyzes the received data and stores it in the database.

[0664] 3. Data processing: The received data is stored in the user profile table of the relational database.

[0665] 4. Output: A confirmation response that the save is complete is sent back to the terminal.

[0666] Step 3: Generative AI creates a personalized plan

[0667] 1. Input: User information stored in the database.

[0668] 2. Specific operation: The server generates a prompt sentence to input user information into the generated AI model.

[0669] 3. Example prompt:

[0670] User Information:

[0671] Age: 29

[0672] Gender: Male

[0673] Height: 175cm

[0674] Weight: 80kg

[0675] Goal: Lose 5kg in 3 months

[0676] Please suggest the best diet plan for you.

[0677] 4. Data processing: The prompts are fed into a generative AI model to generate a personalized meal and exercise plan.

[0678] 5. Output: The generated plan is returned to the server in JSON format.

[0679] Step 4: Send your personalized plan

[0680] 1. Input: The personalization plan returned by the generative AI model.

[0681] 2. Specific operation: The server formats the generated plan and sends it to the user's device.

[0682] 3. Data processing: Send the plan in JSON format as an HTTP response.

[0683] 4. Output: The personalized plan is sent to the device.

[0684] Step 5: Collect and analyze food records

[0685] 1. Input: Daily food details (photos and text) recorded by the user using the Smart Diet app.

[0686] 2. Specific operation: The device converts the food record into JSON format and sends it to the server.

[0687] 3. Data processing: The server analyzes the food records received using a generative AI model to evaluate calories and nutrients.

[0688] 4. Output: The evaluation result (e.g., "Total calories are 1400 kcal, well balanced") is sent to the device in JSON format.

[0689] Step 6: Provide feedback

[0690] 1. Input: Evaluation results from the generative AI model.

[0691] 2. Specific operation: The device displays the evaluation results to the user.

[0692] 3. Data processing: Visualize the evaluation results and convert them into a format that can be displayed on the notification and feedback screens.

[0693] 4. Output: The evaluation results are displayed to the user.

[0694] Step 7: Data integration with healthcare applications

[0695] 1. Input: Exercise and weight data obtained from the healthcare application.

[0696] 2. Specific operation: The device periodically acquires this data and sends it to the server.

[0697] 3. Data processing: The server analyzes the received data and adjusts the plan as needed.

[0698] 4. Output: The adjusted plan is sent to the user's device.

[0699] Step 8: Visualize and communicate progress

[0700] 1. Input: User's weight change and activity data collected by the server.

[0701] 2. Specific operation: The server analyzes this data and generates graphs, charts, and a video showing predicted weight loss.

[0702] 3. Data processing: Generate data for visual display and format it into JSON format.

[0703] 4. Output: The generated data is sent to the terminal and displayed to the user. An example notification would be "3kg remaining until goal is reached".

[0704] Step 9: Proposals using seasonal ingredients

[0705] 1. Input: Information on seasonal ingredients.

[0706] 2. Specific operation: The server collects this information and stores it in a database.

[0707] 3. Data processing: Based on information on seasonal ingredients, new recipes are created using a generative AI model.

[0708] 4. Output: The new recipe is sent to the terminal and displayed to the user.

[0709] The above is the processing flow and specific operation of the system program. Each step makes it possible to provide personalized diet support to users.

[0710] (Application example 1)

[0711] 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."

[0712] In modern society, there is a need for diet plans tailored to individual physical information and goals. However, many existing systems lack personalization and are unable to provide effective meal and exercise plans. Furthermore, there is a lack of a way to visually check users' progress and receive real-time feedback, making it difficult to maintain motivation. Furthermore, there are few systems that offer intuitive, real-time data input and display capabilities using visual output devices.

[0713] 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.

[0714] In this invention, the server includes means for inputting individual physical information and goal data, means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan, means for transmitting the generated personalized meal plan and exercise plan to a user terminal, means for receiving a meal record from the user terminal and analyzing the received meal record to generate feedback, means for acquiring exercise data and weight data through cooperation with a sensor device, means for visualizing the user's progress based on the acquired data, means for acquiring information on seasonal ingredients according to the season and generating a meal plan based thereon, and means for displaying the personalized meal plan and exercise plan in real time using a visual output device and inputting meal record and progress data. This makes it possible to provide users with highly customizable and effective diet plans and support maintaining motivation through progress visualization and real-time feedback.

[0715] "Individual physical information" refers to data relating to the body that is specific to an individual user, such as the user's age, sex, height, and weight.

[0716] "Goal data" is data indicating a specific goal set by the user (for example, how many kilograms the user wants to lose over what period of time).

[0717] A "personalized meal plan" is a meal menu plan that is individually customized and suggested by the generative AI based on the user's individual physical information and goal data.

[0718] An "exercise plan" is a plan that includes specific exercise content and schedules proposed to help the user achieve their goals.

[0719] A "user terminal" refers to a portable electronic device used by a user, such as a smartphone, tablet PC, or smart glasses.

[0720] A "meal record" is data about the contents of a user's meals that is entered through an application.

[0721] "Feedback" refers to evaluations and advice provided to users based on analyzed data.

[0722] A "sensor device" is a device (e.g., a smartwatch or a weight scale) used to acquire a user's exercise data or weight data.

[0723] "Means for visualizing progress" refers to means that include a function that displays the user's progress in achieving their goals and their exercise and diet progress in graphs and charts.

[0724] "Information on seasonal ingredients" is data on fresh ingredients that are available at that time of year.

[0725] A "visual output device" is a device that displays information directly into the user's field of vision (e.g., smart glasses).

[0726] This invention relates to a system that provides a personalized diet plan in which a generating AI proposes optimal meal menus and exercise plans when individual physical information and goal data are input. This system allows users to understand their individual eating habits by recording their meals, and by linking with a healthcare application, visualizes progress and increases user motivation.

[0727] The system uses the following hardware and software:

[0728] User devices such as smartphones, tablet PCs, and smart glasses

[0729] Sensor devices (e.g. smartwatches, weight scales)

[0730] server

[0731] Generative AI Models

[0732] First, the user launches the smart diet app via their device and inputs their individual physical information (e.g., age, gender, height, weight) and goal (e.g., lose 5 kg in 3 months). This information is sent to the server and analyzed by the generative AI model. As a result of the analysis, an individually optimized meal menu and exercise plan is generated and sent to the user's device.

[0733] Users record their daily meals in the app and enter photos of the meals and supplemental text descriptions, which are then sent to the server. The server analyzes the received meal information and evaluates the calories and nutrients. The evaluation results are provided as feedback to the user's device, which displays an evaluation of the day's meal and advice.

[0734] Furthermore, by using the sensor device and setting it up to link with a healthcare application, the user can periodically send exercise and weight data to a server. This data is used to understand the user's activity status, and meal and exercise plans can be adjusted as needed. The user's progress is also visualized in graphs and charts, and notifications such as "3kg left to reach your goal" are sent to motivate the user.

[0735] Information on seasonal ingredients is also collected, and nutritionally balanced menus are generated based on this information. For example, recipes using ingredients in season in spring are suggested, allowing users to continue their diet while enjoying new ingredients.

[0736] As a concrete example, if a 29-year-old male user (height 175 cm, weight 80 kg) aims to lose 5 kg in three months, the server will suggest a diet plan of 1800 kcal per day and aerobic exercise three times a week. This plan is displayed in real time through the smart glasses, and the user can easily enter their diet and exercise records.

[0737] Prompt Sentence Examples

[0738] "Based on individual physical information (age, gender, height, weight) and goals (e.g., lose 5 kg in 3 months), generative AI will propose optimal meal menus and exercise plans. Users can view the personalized plan in real time through smart glasses and record their diet and exercise. For example, if a 29-year-old man (height 175 cm, weight 80 kg) aims to lose 5 kg in 3 months, he will be presented with a plan to consume 1800 kcal per day and do aerobic exercise three times a week. Imagine a system that visualizes dietary and exercise progress and provides feedback."

[0739] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0740] Step 1:

[0741] The user launches the smart diet app and inputs their individual physical information (age, sex, height, weight) and goal (e.g., lose 5 kg in 3 months). The device sends this input information to the server. The input data is the user's individual information and goal. The output is the sent user information.

[0742] Step 2:

[0743] The server stores the received user information in a database and analyzes it using a generative AI model. This analysis generates a personalized meal plan and exercise plan. The input is user information data, and the output is the generated plan data. The server creates the optimal plan, taking into account calorie calculations and nutritional balance.

[0744] Step 3:

[0745] The server sends the generated personalized plan to the user's terminal, which displays it to the user. The input is the generated plan data, and the output is the transmission to the user's terminal. The plan displayed on the terminal can be viewed in real time through the user's visual output device.

[0746] Step 4:

[0747] The user records their daily meals in the app. By taking photos of the meals and adding text descriptions, meal information is generated. The device sends this information to the server. The input is the user's meal record data, and the output is the data sent to the server.

[0748] Step 5:

[0749] The server analyzes the received food records using a generative AI model and evaluates calories and nutrients. The server generates the evaluation results as feedback and sends it to the user's device. The input is the food record data, and the output is feedback of the evaluation results. The feedback includes specific advice and evaluation details.

[0750] Step 6:

[0751] When a user uses a sensor device and links it to a healthcare application, exercise data and weight data are periodically sent to a server. The input is exercise data and weight data from the sensor device, and the output is data sent to the server.

[0752] Step 7:

[0753] The server analyzes the user's activity status based on the acquired exercise and weight data. Based on the analysis results, it adjusts the meal plan and exercise plan as needed. The input is exercise and weight data, and the output is the adjusted plan data.

[0754] Step 8:

[0755] The server generates graphs and charts to visualize the user's progress and sends them to the user's device. The input is data related to the progress, and the output is the visualized progress data. The device notifies the user of the displayed progress data.

[0756] Step 9:

[0757] The server collects seasonal ingredient information according to the season and stores it in a database. Based on the acquired seasonal ingredient information, a personalized meal plan is generated and sent to the user's device. The input is seasonal ingredient information data, and the output is plan data based on that data. Users can enjoy new recipes using seasonal ingredients.

[0758] Prompt Sentence Examples

[0759] "Based on individual physical information (age, gender, height, weight) and goals (e.g., lose 5 kg in 3 months), generative AI will propose optimal meal menus and exercise plans. Users can view the personalized plan in real time through smart glasses and record their diet and exercise. For example, if a 29-year-old man (height 175 cm, weight 80 kg) aims to lose 5 kg in 3 months, he will be presented with a plan to consume 1800 kcal per day and do aerobic exercise three times a week. Imagine a system that visualizes dietary and exercise progress and provides feedback."

[0760] 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.

[0761] This invention provides a personalized diet plan in which a generation AI proposes optimal meal menus and exercise plans based on individual physical information and goal data. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system provides feedback and motivation based on the user's emotional state, achieving more effective diet support.

[0762] 1. Collection of User Information

[0763] The user starts the smart diet app and enters their personal physical information (age, gender, height, weight, etc.) and goal (e.g., to lose 5 kg in 3 months). This information is stored on the device, which then sends it to the server.

[0764] The server stores the received user information in a database.

[0765] 2. Generative AI creates personalized plans

[0766] The server uses AI to generate personalized meal plans and exercise plans based on the stored user information. For example, it suggests a meal plan targeting 1800 kcal per day and an exercise plan for three times a week.

[0767] The server transmits the generated plan to the user's terminal, and the terminal displays the personalized plan to the user.

[0768] 3. Food Record and Evaluation

[0769] Users record their daily meals in the app, take photos of their meals, and enter supplementary text descriptions. The device then sends the meal information to the server.

[0770] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. This is then sent to the user's device as feedback. For example, the server provides feedback such as "Total calories are 1400kcal, well-balanced."

[0771] 4. Data linkage with healthcare applications

[0772] The user configures the Smart Diet app to link with the healthcare application. The device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[0773] The server uses the collected data to understand the user's activity status and adjusts their meal plan or exercise plan as needed. For example, if they are not getting enough exercise, it will suggest that they increase their exercise plan.

[0774] 5. Visualize your progress and stay motivated

[0775] The server analyzes the user's weight change and activity status, generates data to visually display the results as graphs and charts, and generates a video of the user's weight loss prediction and sends it to the user's device.

[0776] The device displays this data to the user and notifies them with notifications such as "3kg left until you reach your goal" to increase motivation.

[0777] 6. Proposals using seasonal ingredients

[0778] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[0779] For example, a recipe using bamboo shoots and asparagus, which are in season in spring, is suggested, and the server sends this to the terminal. The terminal displays the recipe to the user and suggests new ingredients.

[0780] 7. Emotion analysis and feedback using an emotion engine

[0781] Users can input their daily emotional state into the app or it can be automatically acquired through emotion recognition technology. This data is stored on the device and sent to a server.

[0782] The server uses an emotion engine to analyze the user's emotional data, and if the user is feeling stressed, for example, it will suggest activities or meals that will have a relaxing effect.

[0783] The server generates a feedback message based on the emotion data and sends it to the user's device, such as "Take a break and relax today."

[0784] As a concrete example, suppose a user inputs information such as "29 years old, male, 175cm tall, 80kg weight, lose 5kg in 3 months" and records their daily meals. Based on this, the server generates a plan for "calorie intake of 1800kcal / day, aerobic exercise 3 times a week" and sends it to the user's device. Once the user records their meals and the information is sent to the server, the generating AI analyzes the calories and nutrients and provides feedback.

[0785] Additionally, if the user's emotional data indicates "stress," the server will analyze it using an emotion engine and generate a suggestion such as "try drinking herbal tea for relaxation," which will be sent to the device. This will make it easier for users to manage not only their physical health but also their mental health.

[0786] As a result, the system of the present invention can provide customized plans based on individual physical information and goals, visualize progress, maintain motivation, and provide feedback and suggestions that correspond to emotional states, thereby providing more effective diet support to users.

[0787] The processing flow will be explained below.

[0788] Step 1:

[0789] The user starts the smart diet app and enters their personal physical information (age, gender, height, weight) and goal data (lose 5 kg in 3 months). The device then sends this information to the server.

[0790] Step 2:

[0791] The server stores the received user information in a database, including the user's individual physical information and goal data.

[0792] Step 3:

[0793] The server uses AI to generate personalized meal and exercise plans based on the saved user information. For example, it suggests a meal plan targeting 1800 kcal per day and aerobic exercise three times a week.

[0794] Step 4:

[0795] The server sends the generated personalized plan to the user's terminal, which displays the received plan for the user to confirm.

[0796] Step 5:

[0797] Users take photos of their daily meals, supplement them with text, and record the meal details in the app. The device then sends the recorded meal information to the server.

[0798] Step 6:

[0799] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. The evaluation results are generated as feedback and sent to the user's device. For example, feedback such as "Total calories are 1400kcal, well-balanced" is provided.

[0800] Step 7:

[0801] The user configures the app to link with the healthcare application, allowing the device to periodically obtain exercise and weight data from the healthcare application.

[0802] Step 8:

[0803] The device sends the acquired exercise and weight data to a server, which uses this information to understand the user's progress and adjusts their meal and exercise plans as needed. For example, if the user is not getting enough exercise, the server may suggest increasing the amount of exercise they need.

[0804] Step 9:

[0805] The server generates data that visualizes the user's weight change and exercise data as graphs and charts, and also generates a video of predicted weight loss and sends it to the user's device.

[0806] Step 10:

[0807] The device displays visualized data and predicted images to the user and notifies them with notifications such as "3kg left until you reach your goal."

[0808] Step 11:

[0809] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[0810] Step 12:

[0811] The server sends the new recipes using the generated seasonal ingredients to the device, which then displays the suggested recipes to the user, introducing new ingredients and suggesting menu items.

[0812] Step 13:

[0813] Users can input their emotional state into the app, or emotion recognition technology can be used to automatically obtain emotional data, which the device then sends to a server.

[0814] Step 14:

[0815] The server uses an emotion engine to analyze the user's emotional data. For example, if it determines that the user is under stress, it will suggest activities or meals that will help them relax.

[0816] Step 15:

[0817] The server generates a feedback message based on the emotion data and sends it to the user's device, for example, a message saying, "Take a break and relax today."

[0818] Through the above processing flow, users can follow a diet plan based on their physical information and goals, while also receiving support tailored to their emotional state, allowing them to diet more effectively and continuously.

[0819] Example 2

[0820] 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."

[0821] Conventional diet support systems provide personalized plans based on individual physical information and goal data, but it is difficult to provide optimal support that takes into account many variables, such as the user's emotional state and seasonal food information.In addition, there are issues with insufficient visualization of progress and maintaining motivation, making it difficult for users to continue dieting over the long term.

[0822] 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.

[0823] In this invention, the server includes: means for inputting individual physical information and goal data; means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan using a generative AI model; means for transmitting the generated personalized meal plan and exercise plan to a user terminal; means for receiving a meal record from the user terminal and analyzing the received meal record using a generative AI model to generate feedback; means for acquiring exercise data and weight data through collaboration with a healthcare application; means for visualizing the user's progress based on the acquired data; means for acquiring information on seasonal ingredients according to the season and generating a personalized meal plan based on the acquired data; and means for analyzing the user's emotional state using an emotion engine and generating feedback based on the emotion data. This enables effective diet support based on the user's physical information and goals, as well as optimal feedback and suggestions that take into account emotional state and seasonal factors.

[0824] 1. "Individual physical information" refers to information about a user's individual body, such as age, gender, height, weight, and body fat percentage.

[0825] 2. "Goal Data" refers to specific goals set by the user, such as weight loss goals or exercise goals.

[0826] 3. "Generative AI model" refers to a model that uses artificial intelligence to generate optimal output from specific input data.

[0827] 4. "Personalized Meal Plan" refers to a meal menu that is customized based on a user's individual physical information and goal data.

[0828] 5. "Exercise Plan" refers to an exercise routine or schedule created based on a user's fitness goals.

[0829] 6. "User terminal" refers to an electronic device capable of executing programs, such as a smartphone or tablet used by a user.

[0830] 7. "Food record" refers to information that a user records about their daily meals, such as the type of food, amount, calories, etc.

[0831] 8. "Healthcare Application" refers to application software used for health management purposes.

[0832] 9. "Exercise Data" refers to information such as the type and duration of exercise performed by the user, and calories burned.

[0833] 10. "Weight Data" refers to measurement information regarding a User's weight.

[0834] 11. "Progress Visualization" refers to the visual display of a user's diet or fitness progress.

[0835] 12. "Information on seasonal ingredients" refers to information about fresh ingredients harvested each season.

[0836] 13. "Emotion Engine" means a program or technology for analyzing a user's emotional state.

[0837] 14. "Emotional Data" means information that represents a user's emotional state.

[0838] 15. "Feedback" refers to evaluations and advice provided to users.

[0839] The present invention is a system that utilizes a generative AI model based on individual physical information and goal data to propose optimal meal menus and exercise plans, and provides feedback and motivation based on the user's emotional state. This system includes the following components and processes.

[0840] Components

[0841] 1. User Device

[0842] Electronic devices such as smartphones and tablets are used to input user information, record meals, input emotional data, and receive feedback.

[0843] 2. Server

[0844] Equipped with a database and generative AI models, it analyzes user information and generates personalized meal and exercise plans.

[0845] Hardware and software used

[0846] Hardware:

[0847] Smartphones, tablets, servers, cloud storage

[0848] software:

[0849] Smart diet apps, healthcare applications, generative AI models (e.g., GPT-3), databases (e.g., MySQL), emotion engines

[0850] Data processing and calculation

[0851] 1. Collection and Transmission of User Information

[0852] The user inputs age, gender, height, weight, and goal data through the smart diet app. For example, "29 years old, male, height 175 cm, weight 80 kg, lose 5 kg in 3 months."

[0853] The device sends this information to the server in JSON format.

[0854] 2. Generate a personalized plan

[0855] The server stores the user information in a database and sends prompts to the generative AI model to generate personalized meal and exercise plans, for example, "Generate the optimal meal and exercise plan for a 29-year-old male, 175 cm tall, 80 kg weight, with a goal of losing 5 kg in 3 months."

[0856] The server sends the generated plan to the terminal, which displays the plan to the user.

[0857] 3. Food Record and Feedback

[0858] The user records their daily diet and enters details with photos and text. The device sends this information to a server, which analyzes it and generates feedback. For example, "Total calories are 1400kcal, well balanced."

[0859] 4. Integration with healthcare applications

[0860] The device periodically retrieves exercise and weight data from the health app and sends it to the server, which then analyzes the user's progress and adjusts their diet and exercise plans as needed.

[0861] 5. Visualize progress

[0862] The server generates graphs and predicted images based on the analysis of exercise and weight data, and sends them to the device. The device displays them to the user, notifying them with a message such as, "You're 3kg away from your goal."

[0863] 6. Proposal of seasonal ingredients

[0864] The server collects information on seasonal ingredients and proposes appropriate meal plans to users based on that information. For example, "Recipes using bamboo shoots and asparagus, which are seasonal ingredients in spring."

[0865] 7. Sentiment Analysis and Feedback

[0866] The user inputs emotional data or it is automatically acquired using emotion recognition technology. This data is sent to the server and analyzed by the emotion engine. For example, a suggestion such as "If you are under a lot of stress, try drinking herbal tea for relaxation" is generated and sent to the device.

[0867] This allows the present invention to provide optimal diet support based on individual physical information and goals, and to provide feedback that takes into account the user's emotional state and seasonal factors.

[0868] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0869] Step 1:

[0870] Enter and submit user information

[0871] The user launches the Smart Diet app and enters their age, gender, height, weight, and goal data (e.g., "29 years old, male, height 175cm, weight 80kg, lose 5kg in 3 months"). This input is saved on the device as JSON format data.

[0872] The device sends the saved user information to the server in the form of an HTTP request, which is then stored in a database.

[0873] Step 2:

[0874] Generate a personalized plan

[0875] The server retrieves the received user information from a database (e.g., MySQL) and generates a prompt based on it. Example prompt: "Generate the optimal meal menu and exercise plan for a 29-year-old male, 175 cm tall, 80 kg weight, with a goal of losing 5 kg in 3 months."

[0876] The server sends this prompt to a generative AI model (e.g., GPT-3) and obtains a personalized meal plan and exercise plan. The output is something like "Intake 1800kcal / day" and "Exercise 3 times a week."

[0877] The server sends this personalized plan in JSON format to the device.

[0878] Step 3:

[0879] View Plans

[0880] The device analyzes the received personalized plan and displays it on the screen in a format that is easy for the user to view, such as a list of daily meal menus and exercise plans.

[0881] Step 4:

[0882] Enter and submit your food record

[0883] Users record their daily meals in the app by taking photos of the food and adding text descriptions.

[0884] The device sends the meal information in JSON format to the server, and the image data is uploaded to cloud storage (e.g., Amazon S3), along with the URL.

[0885] Step 5:

[0886] Analysis of food information and feedback generation

[0887] The server analyzes the received meal information using a generative AI model and evaluates calories and nutrients, generating feedback such as "calorie intake is 1400kcal, well balanced."

[0888] The server sends the generated feedback in JSON format to the device.

[0889] Step 6:

[0890] View Feedback

[0891] The device displays the received feedback to the user, for example, using notifications to provide real-time feedback.

[0892] Step 7:

[0893] Data linkage with healthcare applications

[0894] Users can set up the Smart Diet app to link with other healthcare apps (e.g., Apple Health, Google Fit).

[0895] The device periodically obtains exercise and weight data from the health app and sends it to the server in JSON format.

[0896] Step 8:

[0897] Analyze data and adjust plans

[0898] The server analyzes the acquired exercise and weight data and adjusts the meal plan and exercise plan as needed. Example: "You are not getting enough exercise, so we suggest increasing your exercise plan to four times a week."

[0899] The server then sends the adjusted plan back to the terminal.

[0900] Step 9:

[0901] Progress visualization

[0902] The server analyzes the user's weight change and activity status, and generates graphs and charts, as well as a video showing predicted weight loss.

[0903] The server sends the generated visualization data to the terminal, which then displays it to the user. Example: A notification saying "3kg left until goal."

[0904] Step 10:

[0905] Suggestions for seasonal ingredients

[0906] The server collects information on seasonal ingredients and stores it in a database.

[0907] The server generates a personalized meal plan based on seasonal ingredients and sends, for example, a "recipe using bamboo shoots and asparagus" to the device.

[0908] The terminal displays this to the user.

[0909] Step 11:

[0910] Entering and sending emotional data

[0911] Users can input their daily emotional state into the app, or it can be automatically obtained through emotion recognition technology.

[0912] The device sends emotion data in JSON format to the server.

[0913] Step 12:

[0914] Emotional data analysis and feedback generation

[0915] The server uses an emotion engine to analyze the user's emotional data and suggests activities and meals that will help them relax if they are feeling stressed or tired. For example, the message might say, "Take a short break and relax today."

[0916] The server sends these proposals to the terminal.

[0917] Step 13:

[0918] Displaying sentiment-based feedback

[0919] The device will then display feedback to the user based on the emotions received, for example, using notifications to offer relaxation and stress reduction techniques.

[0920] (Application example 2)

[0921] 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."

[0922] In modern society, there is a demand for providing diet plans tailored to individual users. However, conventional diet systems have difficulty providing personalized feedback that takes into account individual physical information and emotional states. Furthermore, there is no easy way to purchase the suggested ingredients and supplements, which reduces user convenience. Furthermore, providing feedback and motivation based on the user's emotional state is difficult with conventional technologies.

[0923] 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 inputting individual physical information and goal data, means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan, means for transmitting the generated personalized meal plan and exercise plan to the user terminal, means for inputting emotional data, analyzing it using an emotion analysis engine, and providing feedback and motivation based on the emotional state, and means for purchasing the generated plan and suggested ingredients and supplements in cooperation with an electronic payment service. This enables personalized feedback and suggestions based on the user's individual physical information and emotional state, achieving convenient and effective diet support.

[0924] "Individual physical information" refers to physical data specific to each individual user, such as the user's age, gender, height, and weight.

[0925] "Goal data" is data relating to a specific goal that a user wishes to achieve, such as weight loss or improvement in a particular health condition.

[0926] A "personalized meal plan" is a meal schedule and menu suggestion that is optimal for a user, generated based on the user's individual physical information and goal data.

[0927] An "exercise plan" is a proposal of the optimal exercise method and frequency for a user, generated based on the user's individual physical information and goal data.

[0928] "Emotion data" is data that represents the user's current emotional state, and includes, for example, stress, happiness, fatigue, and the like.

[0929] An "emotion analysis engine" is software or an algorithm that analyzes input emotion data and understands the user's emotional state.

[0930] "Feedback" is information that indicates the next action or areas for improvement based on the user's actions and status.

[0931] "Motivation" is an act or means of providing psychologically encouraging messages or suggestions to help a user achieve a set goal.

[0932] A "healthcare application" is a software application for managing a user's health condition and exercise data.

[0933] An "electronic payment service" is a system for electronically paying for goods and services over the Internet.

[0934] "Visualizing progress" means displaying a user's progress in dieting or health improvement in a visual format such as a graph or chart.

[0935] "Dietary records" are data that record the contents of meals consumed by a user in text and photographs.

[0936] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Details of each element and their interactions will be explained below.

[0937] Server Features

[0938] The server performs the following main functions:

[0939] 1. Processing of individual body information and target data

[0940] Individual physical information (age, gender, height, weight, etc.) and goal data (e.g., weight loss goals or health improvement goals) entered by the user through a smartphone application are sent to a server.

[0941] The server stores this data in a database.

[0942] 2. Generate personalized meal and exercise plans

[0943] The server uses a generative AI model to generate optimal meal and exercise plans based on the individual's stored physical information and goal data.

[0944] For example, if a user submits the data "29 years old, male, 175 cm tall, 80 kg weight, lose 5 kg in 3 months," the server will generate a plan for "calorie intake of 1800 kcal / day, aerobic exercise 3 times a week."

[0945] 3. Generate feedback

[0946] Users record their daily meals through the application and send the information to a server, which then analyzes the received meal records using a generative AI model to evaluate calories and nutrients.

[0947] For example, feedback such as "Total calories are 1400 kcal, well balanced" is generated and sent to the user terminal.

[0948] 4. Emotional Data Analysis and Feedback

[0949] Emotion data is either entered by the user into the application or automatically obtained using emotion recognition technology, and this data is also sent to the server.

[0950] The server uses an emotion analysis engine to analyze the emotional data and, if it indicates stress, generates suggestions such as "Try drinking herbal tea for relaxation."

[0951] 5. Collaboration with electronic payment services

[0952] The server then connects the generated plan and the suggested ingredients and supplements to an electronic payment service, creating an environment where users can easily purchase them. Users can complete the purchase process directly from the application.

[0953] 6. Visualize progress

[0954] The server analyzes the user's progress based on the exercise and weight data acquired and visualizes the results in graphs and charts.

[0955] It provides users with motivation through notifications such as "3kg left until your goal."

[0956] Device Features

[0957] The user device (e.g., smartphone) mainly performs the following operations:

[0958] 1. Data Entry

[0959] The user inputs individual physical information and goal data.

[0960] Enter your daily food records and emotional data.

[0961] 2. Displaying Information

[0962] View personalized meal and exercise plans.

[0963] Feedback and progress indication.

[0964] 3. Use of payment functions

[0965] Purchase suggested foods and supplements.

[0966] Examples of prompt statements

[0967] Prompt: "Male, 29 years old, 175cm tall, 80kg weight. I want to lose 5kg in 3 months. What is the best diet and exercise plan?"

[0968] Prompt: "Does this meal contain anything that has a relaxing effect?"

[0969] This invention allows users to receive personalized feedback and suggestions based on their individual physical information and emotional state. It also allows users to easily purchase the suggested products, improving convenience. Furthermore, notifications and feedback are provided to keep users motivated, enabling more effective diet support.

[0970] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0971] Step 1:

[0972] The user launches the smartphone application, enters their individual physical information (age, sex, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months), and saves the data on the device.

[0973] Input: Age, Gender, Height, Weight, Weight Loss Goal

[0974] Output: Individual physical information and goal data stored on the user's device

[0975] How it works: A user enters information into an app form and clicks the "Save" button.

[0976] Step 2:

[0977] The device transmits the individual physical information and goal data acquired in step 1 to the server.

[0978] Input: Individual physical information and goal data stored on the user's device

[0979] Output: Individual body information and goal data sent to the server

[0980] How it works: The device sends data to the server's API endpoint as a POST request.

[0981] Step 3:

[0982] The server stores the received individual physical information and goal data in a database and uses a generative AI model to generate personalized meal and exercise plans.

[0983] Input: Individual physical information and goal data sent to the server

[0984] Output: Generated personalized meal and exercise plans

[0985] How it works: The server inputs data into the generative AI model and runs the process to generate the optimal plan.

[0986] Step 4:

[0987] The server transmits the generated personalized meal plan and exercise plan to the user terminal.

[0988] Input: Generated personalized meal and exercise plan

[0989] Output: Personalized meal and exercise plans delivered to the user's device

[0990] Operation: The generated plan is sent as a POST request to the user's device via the server's API endpoint.

[0991] Step 5:

[0992] Users record their daily dietary habits on a smartphone app, take photos of their meals, and enter supplementary information. This information is then sent from the device to the server.

[0993] Input: Meal details recorded by the user (photos and supplementary descriptions)

[0994] Output: Meal record sent to the server

[0995] How it works: A user uses the app's food log feature to enter information and presses the "Submit" button.

[0996] Step 6:

[0997] The server analyzes the received food records using a generative AI model to evaluate calories and nutrients.

[0998] Input: Food record sent to the server

[0999] Output: Calorie and nutrient rating feedback

[1000] Operation: The server analyzes the food log and performs processing to generate ratings and feedback.

[1001] Step 7:

[1002] The server transmits the generated feedback to the user terminal.

[1003] Input: Calorie and nutrient rating feedback

[1004] Output: Feedback delivered to the user device

[1005] Operation: Feedback information is sent to the user's device as a POST request via the server's API.

[1006] Step 8:

[1007] The user inputs emotion data via an application, or emotion data automatically acquired through emotion recognition technology is sent from the terminal to the server.

[1008] Input: User emotion data

[1009] Output: Emotion data sent to the server

[1010] Operation: The user inputs emotion data, and the device automatically sends the data to the server.

[1011] Step 9:

[1012] The server uses an emotion analysis engine to analyze the emotion data and generate feedback and motivational messages based on the emotional state.

[1013] Input: Emotion data sent to the server

[1014] Output: Feedback and motivational messages based on the generated emotional state

[1015] How it works: The server runs a sentiment analysis engine, analyzes the sentiment data, and generates an appropriate message.

[1016] Step 10:

[1017] The server transmits the generated emotion feedback message to the user terminal.

[1018] Input: Feedback and motivational messages based on generated emotional states

[1019] Output: Emotion feedback message delivered to the user device

[1020] Operation: An emotional feedback message is sent to the user device as a POST request via the server's API.

[1021] Step 11:

[1022] The user can purchase the suggested ingredients and supplements from the application by completing payment procedures using an electronic payment service.

[1023] Input: Generated food and supplement suggestions and user purchase information

[1024] Output: Notification of purchase completion via electronic payment

[1025] How it works: A user clicks a purchase button within an application and completes payment through an electronic payment service.

[1026] Step 12:

[1027] The server analyzes the user's progress based on the acquired exercise and weight data and visualizes it as graphs and charts.

[1028] Input: Exercise data and weight data obtained through the Health app

[1029] Output: Visualized progress information (graphs and charts)

[1030] How it works: The server analyzes the data and generates graphs and charts for visualization.

[1031] Step 13:

[1032] The server transmits the visualized progress information to the user terminal, notifying the user of the progress toward the goal.

[1033] Input: Visualized progress information

[1034] Output: Progress notification delivered to the user's device

[1035] Operation: The visualized progress information is sent to the user's device as a POST request via the server's API.

[1036] 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.

[1037] 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.

[1038] 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.

[1039] [Third embodiment]

[1040] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1041] 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.

[1042] 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).

[1043] 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.

[1044] 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.

[1045] 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).

[1046] 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.

[1047] 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.

[1048] 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.

[1049] 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.

[1050] 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.

[1051] 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."

[1052] This invention relates to a system that provides personalized diet plans in which a generation AI proposes optimal meal menus and exercise plans based on input of individual physical information and goal data. This system records the meals eaten by the user, allowing for an understanding of individual eating habits and further customization. In addition, by linking with healthcare applications, the system visualizes the user's progress and sense of accomplishment, contributing to increased motivation.

[1053] 1. Collection of User Information

[1054] When a user launches the Smart Diet app, the device prompts the user to enter their individual physical information (age, gender, height, weight, etc.) and goal (e.g., lose 5 kg in 3 months). After the user enters this information, the device sends the collected information to a server.

[1055] The server stores the received user information in a database.

[1056] 2. Generative AI creates personalized plans

[1057] The server uses generative AI to generate personalized meal plans and exercise plans based on the stored user information. For example, it might suggest a 1,500 kcal meal plan per day and three aerobic exercise sessions per week, taking into account calorie and nutritional balance based on the user's input.

[1058] The server transmits the generated plan to the user's terminal, and the terminal displays the personalized plan to the user.

[1059] 3. Food Record and Evaluation

[1060] Users record their daily meals in the app, take photos of their meals, and enter supplementary text descriptions. The device then sends the meal information to the server.

[1061] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. The evaluation results are sent to the user's device as feedback. For example, the server provides feedback such as "Total calories are 1400kcal, well-balanced."

[1062] 4. Data linkage with healthcare applications

[1063] When a user configures the smart diet app to link with a healthcare application, the device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[1064] Based on the acquired data, the server grasps the user's activity status and adjusts meal plans and exercise plans as necessary.

[1065] 5. Visualize your progress and stay motivated

[1066] The server analyzes the user's weight change and activity status, generates data that visually displays the results as graphs and charts, and also visualizes weight loss predictions and sends them to the user's device.

[1067] The device displays this data to the user and notifies them with notifications such as "3kg left until you reach your goal" to increase motivation.

[1068] 6. Proposals using seasonal ingredients

[1069] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[1070] For example, the server can suggest a recipe using bamboo shoots and asparagus, which are in season in spring, and send it to the device. The device then displays the recipe to the user and suggests new ingredients.

[1071] For example, if a user enters the following information: "29 years old, male, 175 cm tall, 80 kg weight, lose 5 kg in 3 months," the server will generate a plan based on this information: "1800 kcal / day, 3 times a week aerobic exercise plan." This plan is sent to the user's device, and the user records their daily meals and receives feedback based on the results. Furthermore, by linking with a healthcare application, daily exercise volume and weight data are automatically acquired, and the server analyzes this information and adjusts the plan accordingly. Progress is visualized in graphs and predictive images, helping users to stay motivated. Furthermore, new recipes using seasonal spring ingredients are suggested, allowing users to continue their diet while having fun.

[1072] As a result, the system of the present invention can provide effective diet support to users by providing customized plans based on individual physical information and goals, visualizing progress, maintaining motivation, and suggesting the use of seasonal ingredients.

[1073] The processing flow will be explained below.

[1074] Step 1:

[1075] The user launches the smart diet app and enters their physical information (age, gender, height, weight, etc.) and goal (e.g., lose 5 kg in 3 months). This information is stored on the device.

[1076] Step 2:

[1077] The terminal sends the entered user information to the server, which immediately stores the received information in a database.

[1078] Step 3:

[1079] The server uses the stored user information to generate personalized meal and exercise plans using generative AI, such as a meal plan targeting 1800 kcal per day and a plan to exercise three times a week.

[1080] Step 4:

[1081] The server sends the generated personalized plan to the user's terminal, which displays the received plan for the user to confirm.

[1082] Step 5:

[1083] Users take photos of their daily meals, supplement them with text, and record the meal details in the app, which is then saved on the device.

[1084] Step 6:

[1085] The device sends the recorded meal information to a server, which then uses the received information to analyze the meal contents using AI and calculate calorie intake and nutritional balance.

[1086] Step 7:

[1087] The server generates a feedback based on the evaluation of the meal content and sends it to the device, which then displays the feedback to the user, for example, notifying them that the total calories were 1400 kcal and were well balanced.

[1088] Step 8:

[1089] The user configures the app to link with the healthcare application. The device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[1090] Step 9:

[1091] The server analyzes the user's progress based on the acquired exercise and weight data, adjusts meal plans and exercise schedules as needed, and sends the results to the device.

[1092] Step 10:

[1093] The server visualizes the user's weight change and exercise data as graphs and charts, and also generates a video of the predicted weight loss. The device displays this data to the user and notifies them, such as "You're 3kg away from your goal."

[1094] Step 11:

[1095] The server collects information on seasonal ingredients and generates new meal plans based on that information. For example, it suggests a menu using bamboo shoots and asparagus, which are seasonal ingredients in spring.

[1096] Step 12:

[1097] The server sends new recipes using seasonal ingredients to the user's device, which then displays the suggested recipes to the user, introducing new ingredients and providing recipes.

[1098] The above processing flow allows the user to diet effectively and continuously.

[1099] Example 1

[1100] 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."

[1101] In modern society, there is a growing need for personalized health management and diet plans. However, conventional methods often use generic approaches, making it difficult to provide optimal plans based on individual physical information and goals. In particular, they lack the functionality to record and manage diet and exercise, suggest seasonal ingredients, and visualize each individual's progress, which can lead to a decrease in user motivation. Furthermore, there is a lack of data integration with healthcare programs and automatic plan adjustments. To solve these issues, an advanced, personalized system tailored to individual needs is required.

[1102] 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.

[1103] In this invention, the server includes a means for inputting individual personal information and goal data, a means for analyzing the input individual personal information and goal data to generate a personalized meal plan and exercise plan, and a means for transmitting the generated personalized meal plan and exercise plan to the user terminal. This enables the provision of highly personalized plans tailored to the user's individual needs. The server also includes a means for receiving a meal record from the user terminal, analyzing the received meal record, and generating feedback, a means for acquiring exercise data and weight data through collaboration with a healthcare program, a means for visualizing the user's progress based on the acquired data, and a means for acquiring seasonal food information and generating a meal plan based on the acquired data. This enables more detailed feedback and maintains motivation. The server also includes a means for optimizing the meal and exercise plan using a generative AI model, and a means for analyzing the dietary information acquired from the user using the generative AI model to evaluate calories and nutrients. This enables efficient and accurate evaluation and advice.

[1104] "Individual personal information" refers to data specific to an individual, such as the user's age, sex, height, and weight.

[1105] "Goal data" refers to specific target values ​​and periods set by the user, such as "lose 5 kg in 3 months."

[1106] A "personalized meal plan" is a meal plan optimized for an individual, generated based on the user's individual personal information and goal data.

[1107] An "exercise plan" is an exercise plan that is optimized for an individual, generated based on the user's individual personal information and goal data.

[1108] A "user terminal" is a device used by a user to input information and display results, such as a smartphone, tablet, or PC.

[1109] A "food record" is data in which the user records the contents of the meals they eat each day using photos and text.

[1110] "Feedback" refers to evaluations and advice provided by the server based on the results of analyzing the food records received from the user.

[1111] The "Healthcare Program" is an application for collecting and managing health-related information such as exercise volume and weight data.

[1112] "Progress visualization" refers to displaying a user's health and diet progress in visual formats such as graphs, charts, and videos.

[1113] "Information on seasonal ingredients" refers to information on ingredients that are considered to be most delicious and nutritious in a particular season.

[1114] A "generative AI model" is an artificial intelligence model that generates personalized plans and assessments based on input data.

[1115] "Calorie and nutrient evaluation" refers to analyzing the calorie content and nutritional balance of the food consumed by the user and displaying the results.

[1116] This invention relates to a system that utilizes generative AI models to propose personalized meal and exercise plans based on individual personal information and goal data. The system visualizes the user's progress through subsequent meal records and health care programs, helping to maintain motivation.

[1117] Hardware and software used

[1118] User device: Smartphone, tablet, PC, etc. Install the Smart Diet app as an application.

[1119] Server: Database server and application server deployed on the cloud, which processes data analysis and AI generation.

[1120] Generative AI models: Artificial intelligence models such as OpenAI GPT-4 that provide personalized suggestions based on user information.

[1121] System Configuration and Operation

[1122] 1. Collection of User Information

[1123] Device: When a user launches the smart diet app, the device prompts the user to enter their personal information (age, gender, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months).

[1124] User: The user follows the app's instructions and enters the required personal information.

[1125] Terminal: Sends the entered information to the server as an HTTP POST request.

[1126] Server: Stores the received information in a database, such as a relational database.

[1127] 2. Generative AI creates personalized plans

[1128] Server: Retrieves stored user information from the database and generates a personalized meal menu and exercise plan using a generative AI model.

[1129] Server: Enter the following prompt into the generative AI model:

[1130] User Information:

[1131] Age: 29

[1132] Gender: Male

[1133] Height: 175cm

[1134] Weight: 80kg

[1135] Goal: Lose 5kg in 3 months

[1136] Please suggest the best diet plan for you.

[1137] Generative AI model: Based on the prompt, it generates a plan for "calorie intake of 1800 kcal / day and aerobic exercise three times a week." The result is returned to the server in JSON format.

[1138] Server: Sends the generated plan to the user's device.

[1139] Device: Show the user a personalized plan.

[1140] 3. Food Record and Evaluation

[1141] User: The user logs their daily meals in the app, taking photos of the food and adding text descriptions.

[1142] Device: Sends meal information to the server via an HTTP POST request.

[1143] Server: Receives meal information and analyzes it using a generative AI model. It evaluates calories and nutrients.

[1144] Server: Generates feedback such as "Total calories are 1400kcal, well balanced" and sends it to the device.

[1145] Terminal: Displays the evaluation results to the user.

[1146] 4. Data linkage with healthcare applications

[1147] User: Set up integration with the healthcare application.

[1148] Device: Periodically acquires health data and sends it to the server.

[1149] Server: Analyzes the data and adjusts meal and exercise plans as needed.

[1150] 5. Visualize your progress and stay motivated

[1151] Server: Analyzes the user's weight change and activity data and generates data to be displayed visually.

[1152] Server: Generates data that displays weight loss predictions and progress status for each case, and sends it to the device.

[1153] On the device: Shows users their progress and provides notifications to keep them motivated.

[1154] 6. Proposals using seasonal ingredients

[1155] Server: Collects seasonal food information and stores it in a database.

[1156] Server: Uses generative AI models to create new recipes to suggest to users.

[1157] Server: For example, suggest a recipe using bamboo shoots and asparagus, which are seasonal spring ingredients, and send it to the device.

[1158] Terminal: Display the new recipe to the user.

[1159] In this way, the system of the present invention utilizes a generative AI model based on individual personal information and goal data to provide effective diet support to users. It has various functions, such as adjusting plans based on the user's progress and health care data, and suggesting the use of seasonal ingredients.

[1160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1161] System program processing flow

[1162] Step 1: Collect user information

[1163] 1. Input: The user launches the smart diet app and enters their personal information (age, gender, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months).

[1164] 2. Specific operation: The terminal receives this information through the input form. When the user has finished entering the data, he or she presses the send button.

[1165] 3. Data processing: The terminal structures the input data and converts it into JSON format.

[1166] 4. Output: The device sends the formatted data to the server as an HTTP POST request.

[1167] Step 2: Data storage and analysis

[1168] 1. Input: User information and goal data received via an HTTP POST request.

[1169] 2. Specific operation: The server analyzes the received data and stores it in the database.

[1170] 3. Data processing: The received data is stored in the user profile table of the relational database.

[1171] 4. Output: A confirmation response that the save is complete is sent back to the terminal.

[1172] Step 3: Generative AI creates a personalized plan

[1173] 1. Input: User information stored in the database.

[1174] 2. Specific operation: The server generates a prompt sentence to input user information into the generated AI model.

[1175] 3. Example prompt:

[1176] User Information:

[1177] Age: 29

[1178] Gender: Male

[1179] Height: 175cm

[1180] Weight: 80kg

[1181] Goal: Lose 5kg in 3 months

[1182] Please suggest the best diet plan for you.

[1183] 4. Data processing: The prompts are fed into a generative AI model to generate a personalized meal and exercise plan.

[1184] 5. Output: The generated plan is returned to the server in JSON format.

[1185] Step 4: Send your personalized plan

[1186] 1. Input: The personalization plan returned by the generative AI model.

[1187] 2. Specific operation: The server formats the generated plan and sends it to the user's device.

[1188] 3. Data processing: Send the plan in JSON format as an HTTP response.

[1189] 4. Output: The personalized plan is sent to the device.

[1190] Step 5: Collect and analyze food records

[1191] 1. Input: Daily food details (photos and text) recorded by the user using the Smart Diet app.

[1192] 2. Specific operation: The device converts the food record into JSON format and sends it to the server.

[1193] 3. Data processing: The server analyzes the food records received using a generative AI model to evaluate calories and nutrients.

[1194] 4. Output: The evaluation result (e.g., "Total calories are 1400 kcal, well balanced") is sent to the device in JSON format.

[1195] Step 6: Provide feedback

[1196] 1. Input: Evaluation results from the generative AI model.

[1197] 2. Specific operation: The device displays the evaluation results to the user.

[1198] 3. Data processing: Visualize the evaluation results and convert them into a format that can be displayed on the notification and feedback screens.

[1199] 4. Output: The evaluation results are displayed to the user.

[1200] Step 7: Data integration with healthcare applications

[1201] 1. Input: Exercise and weight data obtained from the healthcare application.

[1202] 2. Specific operation: The device periodically acquires this data and sends it to the server.

[1203] 3. Data processing: The server analyzes the received data and adjusts the plan as needed.

[1204] 4. Output: The adjusted plan is sent to the user's device.

[1205] Step 8: Visualize and communicate progress

[1206] 1. Input: User's weight change and activity data collected by the server.

[1207] 2. Specific operation: The server analyzes this data and generates graphs, charts, and a video showing predicted weight loss.

[1208] 3. Data processing: Generate data for visual display and format it into JSON format.

[1209] 4. Output: The generated data is sent to the terminal and displayed to the user. An example notification would be "3kg remaining until goal is reached".

[1210] Step 9: Proposals using seasonal ingredients

[1211] 1. Input: Information on seasonal ingredients.

[1212] 2. Specific operation: The server collects this information and stores it in a database.

[1213] 3. Data processing: Based on information on seasonal ingredients, new recipes are created using a generative AI model.

[1214] 4. Output: The new recipe is sent to the terminal and displayed to the user.

[1215] The above is the processing flow and specific operation of the system program. Each step makes it possible to provide personalized diet support to users.

[1216] (Application example 1)

[1217] 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."

[1218] In modern society, there is a need for diet plans tailored to individual physical information and goals. However, many existing systems lack personalization and are unable to provide effective meal and exercise plans. Furthermore, there is a lack of a way to visually check users' progress and receive real-time feedback, making it difficult to maintain motivation. Furthermore, there are few systems that offer intuitive, real-time data input and display capabilities using visual output devices.

[1219] 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.

[1220] In this invention, the server includes means for inputting individual physical information and goal data, means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan, means for transmitting the generated personalized meal plan and exercise plan to a user terminal, means for receiving a meal record from the user terminal and analyzing the received meal record to generate feedback, means for acquiring exercise data and weight data through cooperation with a sensor device, means for visualizing the user's progress based on the acquired data, means for acquiring information on seasonal ingredients according to the season and generating a meal plan based thereon, and means for displaying the personalized meal plan and exercise plan in real time using a visual output device and inputting meal record and progress data. This makes it possible to provide users with highly customizable and effective diet plans and support maintaining motivation through progress visualization and real-time feedback.

[1221] "Individual physical information" refers to data relating to the body that is specific to an individual user, such as the user's age, sex, height, and weight.

[1222] "Goal data" is data indicating a specific goal set by the user (for example, how many kilograms the user wants to lose over what period of time).

[1223] A "personalized meal plan" is a meal menu plan that is individually customized and suggested by the generative AI based on the user's individual physical information and goal data.

[1224] An "exercise plan" is a plan that includes specific exercise content and schedules proposed to help the user achieve their goals.

[1225] A "user terminal" refers to a portable electronic device used by a user, such as a smartphone, tablet PC, or smart glasses.

[1226] A "meal record" is data about the contents of a user's meals that is entered through an application.

[1227] "Feedback" refers to evaluations and advice provided to users based on analyzed data.

[1228] A "sensor device" is a device (e.g., a smartwatch or a weight scale) used to acquire a user's exercise data or weight data.

[1229] "Means for visualizing progress" refers to means that include a function that displays the user's progress in achieving their goals and their exercise and diet progress in graphs and charts.

[1230] "Information on seasonal ingredients" is data on fresh ingredients that are available at that time of year.

[1231] A "visual output device" is a device that displays information directly into the user's field of vision (e.g., smart glasses).

[1232] This invention relates to a system that provides a personalized diet plan in which a generating AI proposes optimal meal menus and exercise plans when individual physical information and goal data are input. This system allows users to understand their individual eating habits by recording their meals, and by linking with a healthcare application, visualizes progress and increases user motivation.

[1233] The system uses the following hardware and software:

[1234] User devices such as smartphones, tablet PCs, and smart glasses

[1235] Sensor devices (e.g. smartwatches, weight scales)

[1236] server

[1237] Generative AI Models

[1238] First, the user launches the smart diet app via their device and inputs their individual physical information (e.g., age, gender, height, weight) and goal (e.g., lose 5 kg in 3 months). This information is sent to the server and analyzed by the generative AI model. As a result of the analysis, an individually optimized meal menu and exercise plan is generated and sent to the user's device.

[1239] Users record their daily meals in the app and enter photos of the meals and supplemental text descriptions, which are then sent to the server. The server analyzes the received meal information and evaluates the calories and nutrients. The evaluation results are provided as feedback to the user's device, which displays an evaluation of the day's meal and advice.

[1240] Furthermore, by using the sensor device and setting it up to link with a healthcare application, the user can periodically send exercise and weight data to a server. This data is used to understand the user's activity status, and meal and exercise plans can be adjusted as needed. The user's progress is also visualized in graphs and charts, and notifications such as "3kg left to reach your goal" are sent to motivate the user.

[1241] Information on seasonal ingredients is also collected, and nutritionally balanced menus are generated based on this information. For example, recipes using ingredients in season in spring are suggested, allowing users to continue their diet while enjoying new ingredients.

[1242] As a concrete example, if a 29-year-old male user (height 175 cm, weight 80 kg) aims to lose 5 kg in three months, the server will suggest a diet plan of 1800 kcal per day and aerobic exercise three times a week. This plan is displayed in real time through the smart glasses, and the user can easily enter their diet and exercise records.

[1243] Prompt Sentence Examples

[1244] "Based on individual physical information (age, gender, height, weight) and goals (e.g., lose 5 kg in 3 months), generative AI will propose optimal meal menus and exercise plans. Users can view the personalized plan in real time through smart glasses and record their diet and exercise. For example, if a 29-year-old man (height 175 cm, weight 80 kg) aims to lose 5 kg in 3 months, he will be presented with a plan to consume 1800 kcal per day and do aerobic exercise three times a week. Imagine a system that visualizes dietary and exercise progress and provides feedback."

[1245] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1246] Step 1:

[1247] The user launches the smart diet app and inputs their individual physical information (age, sex, height, weight) and goal (e.g., lose 5 kg in 3 months). The device sends this input information to the server. The input data is the user's individual information and goal. The output is the sent user information.

[1248] Step 2:

[1249] The server stores the received user information in a database and analyzes it using a generative AI model. This analysis generates a personalized meal plan and exercise plan. The input is user information data, and the output is the generated plan data. The server creates the optimal plan, taking into account calorie calculations and nutritional balance.

[1250] Step 3:

[1251] The server sends the generated personalized plan to the user's terminal, which displays it to the user. The input is the generated plan data, and the output is the transmission to the user's terminal. The plan displayed on the terminal can be viewed in real time through the user's visual output device.

[1252] Step 4:

[1253] The user records their daily meals in the app. By taking photos of the meals and adding text descriptions, meal information is generated. The device sends this information to the server. The input is the user's meal record data, and the output is the data sent to the server.

[1254] Step 5:

[1255] The server analyzes the received food records using a generative AI model and evaluates calories and nutrients. The server generates the evaluation results as feedback and sends it to the user's device. The input is the food record data, and the output is feedback of the evaluation results. The feedback includes specific advice and evaluation details.

[1256] Step 6:

[1257] When a user uses a sensor device and links it to a healthcare application, exercise data and weight data are periodically sent to a server. The input is exercise data and weight data from the sensor device, and the output is data sent to the server.

[1258] Step 7:

[1259] The server analyzes the user's activity status based on the acquired exercise and weight data. Based on the analysis results, it adjusts the meal plan and exercise plan as needed. The input is exercise and weight data, and the output is the adjusted plan data.

[1260] Step 8:

[1261] The server generates graphs and charts to visualize the user's progress and sends them to the user's device. The input is data related to the progress, and the output is the visualized progress data. The device notifies the user of the displayed progress data.

[1262] Step 9:

[1263] The server collects seasonal ingredient information according to the season and stores it in a database. Based on the acquired seasonal ingredient information, a personalized meal plan is generated and sent to the user's device. The input is seasonal ingredient information data, and the output is plan data based on that data. Users can enjoy new recipes using seasonal ingredients.

[1264] Prompt Sentence Examples

[1265] "Based on individual physical information (age, gender, height, weight) and goals (e.g., lose 5 kg in 3 months), generative AI will propose optimal meal menus and exercise plans. Users can view the personalized plan in real time through smart glasses and record their diet and exercise. For example, if a 29-year-old man (height 175 cm, weight 80 kg) aims to lose 5 kg in 3 months, he will be presented with a plan to consume 1800 kcal per day and do aerobic exercise three times a week. Imagine a system that visualizes dietary and exercise progress and provides feedback."

[1266] 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.

[1267] This invention provides a personalized diet plan in which a generation AI proposes optimal meal menus and exercise plans based on individual physical information and goal data. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system provides feedback and motivation based on the user's emotional state, achieving more effective diet support.

[1268] 1. Collection of User Information

[1269] The user starts the smart diet app and enters their personal physical information (age, gender, height, weight, etc.) and goal (e.g., to lose 5 kg in 3 months). This information is stored on the device, which then sends it to the server.

[1270] The server stores the received user information in a database.

[1271] 2. Generative AI creates personalized plans

[1272] The server uses AI to generate personalized meal plans and exercise plans based on the stored user information. For example, it suggests a meal plan targeting 1800 kcal per day and an exercise plan for three times a week.

[1273] The server transmits the generated plan to the user's terminal, and the terminal displays the personalized plan to the user.

[1274] 3. Food Record and Evaluation

[1275] Users record their daily meals in the app, take photos of their meals, and enter supplementary text descriptions. The device then sends the meal information to the server.

[1276] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. This is then sent to the user's device as feedback. For example, the server provides feedback such as "Total calories are 1400kcal, well-balanced."

[1277] 4. Data linkage with healthcare applications

[1278] The user configures the Smart Diet app to link with the healthcare application. The device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[1279] The server uses the collected data to understand the user's activity status and adjusts their meal plan or exercise plan as needed. For example, if they are not getting enough exercise, it will suggest that they increase their exercise plan.

[1280] 5. Visualize your progress and stay motivated

[1281] The server analyzes the user's weight change and activity status, generates data to visually display the results as graphs and charts, and generates a video of the user's weight loss prediction and sends it to the user's device.

[1282] The device displays this data to the user and notifies them with notifications such as "3kg left until you reach your goal" to increase motivation.

[1283] 6. Proposals using seasonal ingredients

[1284] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[1285] For example, a recipe using bamboo shoots and asparagus, which are in season in spring, is suggested, and the server sends this to the terminal. The terminal displays the recipe to the user and suggests new ingredients.

[1286] 7. Emotion analysis and feedback using an emotion engine

[1287] Users can input their daily emotional state into the app or it can be automatically acquired through emotion recognition technology. This data is stored on the device and sent to a server.

[1288] The server uses an emotion engine to analyze the user's emotional data, and if the user is feeling stressed, for example, it will suggest activities or meals that will have a relaxing effect.

[1289] The server generates a feedback message based on the emotion data and sends it to the user's device, such as "Take a break and relax today."

[1290] As a concrete example, suppose a user inputs information such as "29 years old, male, 175cm tall, 80kg weight, lose 5kg in 3 months" and records their daily meals. Based on this, the server generates a plan for "calorie intake of 1800kcal / day, aerobic exercise 3 times a week" and sends it to the user's device. Once the user records their meals and the information is sent to the server, the generating AI analyzes the calories and nutrients and provides feedback.

[1291] Additionally, if the user's emotional data indicates "stress," the server will analyze it using an emotion engine and generate a suggestion such as "try drinking herbal tea for relaxation," which will be sent to the device. This will make it easier for users to manage not only their physical health but also their mental health.

[1292] As a result, the system of the present invention can provide customized plans based on individual physical information and goals, visualize progress, maintain motivation, and provide feedback and suggestions that correspond to emotional states, thereby providing more effective diet support to users.

[1293] The processing flow will be explained below.

[1294] Step 1:

[1295] The user starts the smart diet app and enters their personal physical information (age, gender, height, weight) and goal data (lose 5 kg in 3 months). The device then sends this information to the server.

[1296] Step 2:

[1297] The server stores the received user information in a database, including the user's individual physical information and goal data.

[1298] Step 3:

[1299] The server uses AI to generate personalized meal and exercise plans based on the saved user information. For example, it suggests a meal plan targeting 1800 kcal per day and aerobic exercise three times a week.

[1300] Step 4:

[1301] The server sends the generated personalized plan to the user's terminal, which displays the received plan for the user to confirm.

[1302] Step 5:

[1303] Users take photos of their daily meals, supplement them with text, and record the meal details in the app. The device then sends the recorded meal information to the server.

[1304] Step 6:

[1305] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. The evaluation results are generated as feedback and sent to the user's device. For example, feedback such as "Total calories are 1400kcal, well-balanced" is provided.

[1306] Step 7:

[1307] The user configures the app to link with the healthcare application, allowing the device to periodically obtain exercise and weight data from the healthcare application.

[1308] Step 8:

[1309] The device sends the acquired exercise and weight data to a server, which uses this information to understand the user's progress and adjusts their meal and exercise plans as needed. For example, if the user is not getting enough exercise, the server may suggest increasing the amount of exercise they need.

[1310] Step 9:

[1311] The server generates data that visualizes the user's weight change and exercise data as graphs and charts, and also generates a video of predicted weight loss and sends it to the user's device.

[1312] Step 10:

[1313] The device displays visualized data and predicted images to the user and notifies them with notifications such as "3kg left until you reach your goal."

[1314] Step 11:

[1315] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[1316] Step 12:

[1317] The server sends the new recipes using the generated seasonal ingredients to the device, which then displays the suggested recipes to the user, introducing new ingredients and suggesting menu items.

[1318] Step 13:

[1319] Users can input their emotional state into the app, or emotion recognition technology can be used to automatically obtain emotional data, which the device then sends to a server.

[1320] Step 14:

[1321] The server uses an emotion engine to analyze the user's emotional data. For example, if it determines that the user is under stress, it will suggest activities or meals that will help them relax.

[1322] Step 15:

[1323] The server generates a feedback message based on the emotion data and sends it to the user's device, for example, a message saying, "Take a break and relax today."

[1324] Through the above processing flow, users can follow a diet plan based on their physical information and goals, while also receiving support tailored to their emotional state, allowing them to diet more effectively and continuously.

[1325] Example 2

[1326] 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."

[1327] Conventional diet support systems provide personalized plans based on individual physical information and goal data, but it is difficult to provide optimal support that takes into account many variables, such as the user's emotional state and seasonal food information.In addition, there are issues with insufficient visualization of progress and maintaining motivation, making it difficult for users to continue dieting over the long term.

[1328] 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.

[1329] In this invention, the server includes: means for inputting individual physical information and goal data; means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan using a generative AI model; means for transmitting the generated personalized meal plan and exercise plan to a user terminal; means for receiving a meal record from the user terminal and analyzing the received meal record using a generative AI model to generate feedback; means for acquiring exercise data and weight data through collaboration with a healthcare application; means for visualizing the user's progress based on the acquired data; means for acquiring information on seasonal ingredients according to the season and generating a personalized meal plan based on the acquired data; and means for analyzing the user's emotional state using an emotion engine and generating feedback based on the emotion data. This enables effective diet support based on the user's physical information and goals, as well as optimal feedback and suggestions that take into account emotional state and seasonal factors.

[1330] 1. "Individual physical information" refers to information about a user's individual body, such as age, gender, height, weight, and body fat percentage.

[1331] 2. "Goal Data" refers to specific goals set by the user, such as weight loss goals or exercise goals.

[1332] 3. "Generative AI model" refers to a model that uses artificial intelligence to generate optimal output from specific input data.

[1333] 4. "Personalized Meal Plan" refers to a meal menu that is customized based on a user's individual physical information and goal data.

[1334] 5. "Exercise Plan" refers to an exercise routine or schedule created based on a user's fitness goals.

[1335] 6. "User terminal" refers to an electronic device capable of executing programs, such as a smartphone or tablet used by a user.

[1336] 7. "Food record" refers to information that a user records about their daily meals, such as the type of food, amount, calories, etc.

[1337] 8. "Healthcare Application" refers to application software used for health management purposes.

[1338] 9. "Exercise Data" refers to information such as the type and duration of exercise performed by the user, and calories burned.

[1339] 10. "Weight Data" refers to measurement information regarding a User's weight.

[1340] 11. "Progress Visualization" refers to the visual display of a user's diet or fitness progress.

[1341] 12. "Information on seasonal ingredients" refers to information about fresh ingredients harvested each season.

[1342] 13. "Emotion Engine" means a program or technology for analyzing a user's emotional state.

[1343] 14. "Emotional Data" means information that represents a user's emotional state.

[1344] 15. "Feedback" refers to evaluations and advice provided to users.

[1345] The present invention is a system that utilizes a generative AI model based on individual physical information and goal data to propose optimal meal menus and exercise plans, and provides feedback and motivation based on the user's emotional state. This system includes the following components and processes.

[1346] Components

[1347] 1. User Device

[1348] Electronic devices such as smartphones and tablets are used to input user information, record meals, input emotional data, and receive feedback.

[1349] 2. Server

[1350] Equipped with a database and generative AI models, it analyzes user information and generates personalized meal and exercise plans.

[1351] Hardware and software used

[1352] Hardware:

[1353] Smartphones, tablets, servers, cloud storage

[1354] software:

[1355] Smart diet apps, healthcare applications, generative AI models (e.g., GPT-3), databases (e.g., MySQL), emotion engines

[1356] Data processing and calculation

[1357] 1. Collection and Transmission of User Information

[1358] The user inputs age, gender, height, weight, and goal data through the smart diet app. For example, "29 years old, male, height 175 cm, weight 80 kg, lose 5 kg in 3 months."

[1359] The device sends this information to the server in JSON format.

[1360] 2. Generate a personalized plan

[1361] The server stores the user information in a database and sends prompts to the generative AI model to generate personalized meal and exercise plans, for example, "Generate the optimal meal and exercise plan for a 29-year-old male, 175 cm tall, 80 kg weight, with a goal of losing 5 kg in 3 months."

[1362] The server sends the generated plan to the terminal, which displays the plan to the user.

[1363] 3. Food Record and Feedback

[1364] The user records their daily diet and enters details with photos and text. The device sends this information to a server, which analyzes it and generates feedback. For example, "Total calories are 1400kcal, well balanced."

[1365] 4. Integration with healthcare applications

[1366] The device periodically retrieves exercise and weight data from the health app and sends it to the server, which then analyzes the user's progress and adjusts their diet and exercise plans as needed.

[1367] 5. Visualize progress

[1368] The server generates graphs and predicted images based on the analysis of exercise and weight data, and sends them to the device. The device displays them to the user, notifying them with a message such as, "You're 3kg away from your goal."

[1369] 6. Proposal of seasonal ingredients

[1370] The server collects information on seasonal ingredients and proposes appropriate meal plans to users based on that information. For example, "Recipes using bamboo shoots and asparagus, which are seasonal ingredients in spring."

[1371] 7. Sentiment Analysis and Feedback

[1372] The user inputs emotional data or it is automatically acquired using emotion recognition technology. This data is sent to the server and analyzed by the emotion engine. For example, a suggestion such as "If you are under a lot of stress, try drinking herbal tea for relaxation" is generated and sent to the device.

[1373] This allows the present invention to provide optimal diet support based on individual physical information and goals, and to provide feedback that takes into account the user's emotional state and seasonal factors.

[1374] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1375] Step 1:

[1376] Enter and submit user information

[1377] The user launches the Smart Diet app and enters their age, gender, height, weight, and goal data (e.g., "29 years old, male, height 175cm, weight 80kg, lose 5kg in 3 months"). This input is saved on the device as JSON format data.

[1378] The device sends the saved user information to the server in the form of an HTTP request, which is then stored in a database.

[1379] Step 2:

[1380] Generate a personalized plan

[1381] The server retrieves the received user information from a database (e.g., MySQL) and generates a prompt based on it. Example prompt: "Generate the optimal meal menu and exercise plan for a 29-year-old male, 175 cm tall, 80 kg weight, with a goal of losing 5 kg in 3 months."

[1382] The server sends this prompt to a generative AI model (e.g., GPT-3) and obtains a personalized meal plan and exercise plan. The output is something like "Intake 1800kcal / day" and "Exercise 3 times a week."

[1383] The server sends this personalized plan in JSON format to the device.

[1384] Step 3:

[1385] View Plans

[1386] The device analyzes the received personalized plan and displays it on the screen in a format that is easy for the user to view, such as a list of daily meal menus and exercise plans.

[1387] Step 4:

[1388] Enter and submit your food record

[1389] Users record their daily meals in the app by taking photos of the food and adding text descriptions.

[1390] The device sends the meal information in JSON format to the server, and the image data is uploaded to cloud storage (e.g., Amazon S3), along with the URL.

[1391] Step 5:

[1392] Analysis of food information and feedback generation

[1393] The server analyzes the received meal information using a generative AI model and evaluates calories and nutrients, generating feedback such as "calorie intake is 1400kcal, well balanced."

[1394] The server sends the generated feedback in JSON format to the device.

[1395] Step 6:

[1396] View Feedback

[1397] The device displays the received feedback to the user, for example, using notifications to provide real-time feedback.

[1398] Step 7:

[1399] Data linkage with healthcare applications

[1400] Users can set up the Smart Diet app to link with other healthcare apps (e.g., Apple Health, Google Fit).

[1401] The device periodically obtains exercise and weight data from the health app and sends it to the server in JSON format.

[1402] Step 8:

[1403] Analyze data and adjust plans

[1404] The server analyzes the acquired exercise and weight data and adjusts the meal plan and exercise plan as needed. Example: "You are not getting enough exercise, so we suggest increasing your exercise plan to four times a week."

[1405] The server then sends the adjusted plan back to the terminal.

[1406] Step 9:

[1407] Progress visualization

[1408] The server analyzes the user's weight change and activity status, and generates graphs and charts, as well as a video showing predicted weight loss.

[1409] The server sends the generated visualization data to the terminal, which then displays it to the user. Example: A notification saying "3kg left until goal."

[1410] Step 10:

[1411] Suggestions for seasonal ingredients

[1412] The server collects information on seasonal ingredients and stores it in a database.

[1413] The server generates a personalized meal plan based on seasonal ingredients and sends, for example, a "recipe using bamboo shoots and asparagus" to the device.

[1414] The terminal displays this to the user.

[1415] Step 11:

[1416] Entering and sending emotional data

[1417] Users can input their daily emotional state into the app, or it can be automatically obtained through emotion recognition technology.

[1418] The device sends emotion data in JSON format to the server.

[1419] Step 12:

[1420] Emotional data analysis and feedback generation

[1421] The server uses an emotion engine to analyze the user's emotional data and suggests activities and meals that will help them relax if they are feeling stressed or tired. For example, the message might say, "Take a short break and relax today."

[1422] The server sends these proposals to the terminal.

[1423] Step 13:

[1424] Displaying sentiment-based feedback

[1425] The device will then display feedback to the user based on the emotions received, for example, using notifications to offer relaxation and stress reduction techniques.

[1426] (Application example 2)

[1427] 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."

[1428] In modern society, there is a demand for providing diet plans tailored to individual users. However, conventional diet systems have difficulty providing personalized feedback that takes into account individual physical information and emotional states. Furthermore, there is no easy way to purchase the suggested ingredients and supplements, which reduces user convenience. Furthermore, providing feedback and motivation based on the user's emotional state is difficult with conventional technologies.

[1429] 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 inputting individual physical information and goal data, means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan, means for transmitting the generated personalized meal plan and exercise plan to the user terminal, means for inputting emotional data, analyzing it using an emotion analysis engine, and providing feedback and motivation based on the emotional state, and means for purchasing the generated plan and suggested ingredients and supplements in cooperation with an electronic payment service. This enables personalized feedback and suggestions based on the user's individual physical information and emotional state, achieving convenient and effective diet support.

[1430] "Individual physical information" refers to physical data specific to each individual user, such as the user's age, gender, height, and weight.

[1431] "Goal data" is data relating to a specific goal that a user wishes to achieve, such as weight loss or improvement in a particular health condition.

[1432] A "personalized meal plan" is a meal schedule and menu suggestion that is optimal for a user, generated based on the user's individual physical information and goal data.

[1433] An "exercise plan" is a proposal of the optimal exercise method and frequency for a user, generated based on the user's individual physical information and goal data.

[1434] "Emotion data" is data that represents the user's current emotional state, and includes, for example, stress, happiness, fatigue, and the like.

[1435] An "emotion analysis engine" is software or an algorithm that analyzes input emotion data and understands the user's emotional state.

[1436] "Feedback" is information that indicates the next action or areas for improvement based on the user's actions and status.

[1437] "Motivation" is an act or means of providing psychologically encouraging messages or suggestions to help a user achieve a set goal.

[1438] A "healthcare application" is a software application for managing a user's health condition and exercise data.

[1439] An "electronic payment service" is a system for electronically paying for goods and services over the Internet.

[1440] "Visualizing progress" means displaying a user's progress in dieting or health improvement in a visual format such as a graph or chart.

[1441] "Dietary records" are data that record the contents of meals consumed by a user in text and photographs.

[1442] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Details of each element and their interactions will be explained below.

[1443] Server Features

[1444] The server performs the following main functions:

[1445] 1. Processing of individual body information and target data

[1446] Individual physical information (age, gender, height, weight, etc.) and goal data (e.g., weight loss goals or health improvement goals) entered by the user through a smartphone application are sent to a server.

[1447] The server stores this data in a database.

[1448] 2. Generate personalized meal and exercise plans

[1449] The server uses a generative AI model to generate optimal meal and exercise plans based on the individual's stored physical information and goal data.

[1450] For example, if a user submits the data "29 years old, male, 175 cm tall, 80 kg weight, lose 5 kg in 3 months," the server will generate a plan for "calorie intake of 1800 kcal / day, aerobic exercise 3 times a week."

[1451] 3. Generate feedback

[1452] Users record their daily meals through the application and send the information to a server, which then analyzes the received meal records using a generative AI model to evaluate calories and nutrients.

[1453] For example, feedback such as "Total calories are 1400 kcal, well balanced" is generated and sent to the user terminal.

[1454] 4. Emotional Data Analysis and Feedback

[1455] Emotion data is either entered by the user into the application or automatically obtained using emotion recognition technology, and this data is also sent to the server.

[1456] The server uses an emotion analysis engine to analyze the emotional data and, if it indicates stress, generates suggestions such as "Try drinking herbal tea for relaxation."

[1457] 5. Collaboration with electronic payment services

[1458] The server then connects the generated plan and the suggested ingredients and supplements to an electronic payment service, creating an environment where users can easily purchase them. Users can complete the purchase process directly from the application.

[1459] 6. Visualize progress

[1460] The server analyzes the user's progress based on the exercise and weight data acquired and visualizes the results in graphs and charts.

[1461] It provides users with motivation through notifications such as "3kg left until your goal."

[1462] Device Features

[1463] The user device (e.g., smartphone) mainly performs the following operations:

[1464] 1. Data Entry

[1465] The user inputs individual physical information and goal data.

[1466] Enter your daily food records and emotional data.

[1467] 2. Displaying Information

[1468] View personalized meal and exercise plans.

[1469] Feedback and progress indication.

[1470] 3. Use of payment functions

[1471] Purchase suggested foods and supplements.

[1472] Examples of prompt statements

[1473] Prompt: "Male, 29 years old, 175cm tall, 80kg weight. I want to lose 5kg in 3 months. What is the best diet and exercise plan?"

[1474] Prompt: "Does this meal contain anything that has a relaxing effect?"

[1475] This invention allows users to receive personalized feedback and suggestions based on their individual physical information and emotional state. It also allows users to easily purchase the suggested products, improving convenience. Furthermore, notifications and feedback are provided to keep users motivated, enabling more effective diet support.

[1476] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1477] Step 1:

[1478] The user launches the smartphone application, enters their individual physical information (age, sex, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months), and saves the data on the device.

[1479] Input: Age, Gender, Height, Weight, Weight Loss Goal

[1480] Output: Individual physical information and goal data stored on the user's device

[1481] How it works: A user enters information into an app form and clicks the "Save" button.

[1482] Step 2:

[1483] The device transmits the individual physical information and goal data acquired in step 1 to the server.

[1484] Input: Individual physical information and goal data stored on the user's device

[1485] Output: Individual body information and goal data sent to the server

[1486] How it works: The device sends data to the server's API endpoint as a POST request.

[1487] Step 3:

[1488] The server stores the received individual physical information and goal data in a database and uses a generative AI model to generate personalized meal and exercise plans.

[1489] Input: Individual physical information and goal data sent to the server

[1490] Output: Generated personalized meal and exercise plans

[1491] How it works: The server inputs data into the generative AI model and runs the process to generate the optimal plan.

[1492] Step 4:

[1493] The server transmits the generated personalized meal plan and exercise plan to the user terminal.

[1494] Input: Generated personalized meal and exercise plan

[1495] Output: Personalized meal and exercise plans delivered to the user's device

[1496] Operation: The generated plan is sent as a POST request to the user's device via the server's API endpoint.

[1497] Step 5:

[1498] Users record their daily dietary habits on a smartphone app, take photos of their meals, and enter supplementary information. This information is then sent from the device to the server.

[1499] Input: Meal details recorded by the user (photos and supplementary descriptions)

[1500] Output: Meal record sent to the server

[1501] How it works: A user uses the app's food log feature to enter information and presses the "Submit" button.

[1502] Step 6:

[1503] The server analyzes the received food records using a generative AI model to evaluate calories and nutrients.

[1504] Input: Food record sent to the server

[1505] Output: Calorie and nutrient rating feedback

[1506] Operation: The server analyzes the food log and performs processing to generate ratings and feedback.

[1507] Step 7:

[1508] The server transmits the generated feedback to the user terminal.

[1509] Input: Calorie and nutrient rating feedback

[1510] Output: Feedback delivered to the user device

[1511] Operation: Feedback information is sent to the user's device as a POST request via the server's API.

[1512] Step 8:

[1513] The user inputs emotion data via an application, or emotion data automatically acquired through emotion recognition technology is sent from the terminal to the server.

[1514] Input: User emotion data

[1515] Output: Emotion data sent to the server

[1516] Operation: The user inputs emotion data, and the device automatically sends the data to the server.

[1517] Step 9:

[1518] The server uses an emotion analysis engine to analyze the emotion data and generate feedback and motivational messages based on the emotional state.

[1519] Input: Emotion data sent to the server

[1520] Output: Feedback and motivational messages based on the generated emotional state

[1521] How it works: The server runs a sentiment analysis engine, analyzes the sentiment data, and generates an appropriate message.

[1522] Step 10:

[1523] The server transmits the generated emotion feedback message to the user terminal.

[1524] Input: Feedback and motivational messages based on generated emotional states

[1525] Output: Emotion feedback message delivered to the user device

[1526] Operation: An emotional feedback message is sent to the user device as a POST request via the server's API.

[1527] Step 11:

[1528] The user can purchase the suggested ingredients and supplements from the application by completing payment procedures using an electronic payment service.

[1529] Input: Generated food and supplement suggestions and user purchase information

[1530] Output: Notification of purchase completion via electronic payment

[1531] How it works: A user clicks a purchase button within an application and completes payment through an electronic payment service.

[1532] Step 12:

[1533] The server analyzes the user's progress based on the acquired exercise and weight data and visualizes it as graphs and charts.

[1534] Input: Exercise data and weight data obtained through the Health app

[1535] Output: Visualized progress information (graphs and charts)

[1536] How it works: The server analyzes the data and generates graphs and charts for visualization.

[1537] Step 13:

[1538] The server transmits the visualized progress information to the user terminal, notifying the user of the progress toward the goal.

[1539] Input: Visualized progress information

[1540] Output: Progress notification delivered to the user's device

[1541] Operation: The visualized progress information is sent to the user's device as a POST request via the server's API.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] [Fourth embodiment]

[1546] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1547] 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.

[1548] 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).

[1549] 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.

[1550] 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.

[1551] 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).

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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.

[1556] 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.

[1557] 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.

[1558] 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."

[1559] This invention relates to a system that provides personalized diet plans in which a generation AI proposes optimal meal menus and exercise plans based on input of individual physical information and goal data. This system records the meals eaten by the user, allowing for an understanding of individual eating habits and further customization. In addition, by linking with healthcare applications, the system visualizes the user's progress and sense of accomplishment, contributing to increased motivation.

[1560] 1. Collection of User Information

[1561] When a user launches the Smart Diet app, the device prompts the user to enter their individual physical information (age, gender, height, weight, etc.) and goal (e.g., lose 5 kg in 3 months). After the user enters this information, the device sends the collected information to the server.

[1562] The server stores the received user information in a database.

[1563] 2. Generative AI creates personalized plans

[1564] The server uses generative AI to generate personalized meal plans and exercise plans based on the stored user information. For example, it might suggest a 1,500 kcal meal plan per day and three aerobic exercise sessions per week, taking into account calories and nutritional balance based on the user's input.

[1565] The server transmits the generated plan to the user's terminal, and the terminal displays the personalized plan to the user.

[1566] 3. Food Record and Evaluation

[1567] Users record their daily meals in the app, take photos of their meals, and enter supplementary text descriptions. The device then sends the meal information to the server.

[1568] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. The evaluation results are sent to the user's device as feedback. For example, the server provides feedback such as "Total calories are 1400kcal, well-balanced."

[1569] 4. Data linkage with healthcare applications

[1570] When a user configures the smart diet app to link with a healthcare application, the device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[1571] Based on the acquired data, the server grasps the user's activity status and adjusts meal plans and exercise plans as necessary.

[1572] 5. Visualize your progress and stay motivated

[1573] The server analyzes the user's weight change and activity status, generates data that visually displays the results as graphs and charts, and also visualizes weight loss predictions and sends them to the user's device.

[1574] The device displays this data to the user and notifies them with notifications such as "3kg left until you reach your goal" to increase motivation.

[1575] 6. Proposals using seasonal ingredients

[1576] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[1577] For example, the server can suggest a recipe using bamboo shoots and asparagus, which are in season in spring, and send it to the device. The device then displays the recipe to the user and suggests new ingredients.

[1578] For example, if a user enters information such as "29 years old, male, 175 cm tall, 80 kg weight, lose 5 kg in 3 months," the server generates a plan based on this information: "1800 kcal / day, 3 times a week aerobic exercise plan." This plan is sent to the user's device, and the user records their daily meals and receives feedback based on the results. In addition, by linking with a healthcare application, daily exercise volume and weight data are automatically acquired, and the server analyzes this and adjusts the plan accordingly. Progress is visualized in graphs and predictive images, helping users to stay motivated. In addition, new recipes using seasonal spring ingredients are suggested, allowing users to continue their diet while having fun.

[1579] As a result, the system of the present invention can provide effective diet support to users by providing customized plans based on individual physical information and goals, visualizing progress, maintaining motivation, and suggesting the use of seasonal ingredients.

[1580] The processing flow will be explained below.

[1581] Step 1:

[1582] The user launches the smart diet app and enters their physical information (age, gender, height, weight, etc.) and goal (e.g., lose 5 kg in 3 months). This information is stored on the device.

[1583] Step 2:

[1584] The terminal sends the entered user information to the server, which immediately stores the received information in a database.

[1585] Step 3:

[1586] The server uses the stored user information to generate personalized meal and exercise plans using generative AI, such as a meal plan targeting 1800 kcal per day and a plan to exercise three times a week.

[1587] Step 4:

[1588] The server sends the generated personalized plan to the user's terminal, which displays the received plan for the user to confirm.

[1589] Step 5:

[1590] Users take photos of their daily meals, supplement them with text, and record the meal details in the app, which is then saved on the device.

[1591] Step 6:

[1592] The device sends the recorded meal information to a server, which then uses the received information to analyze the meal contents using AI and calculate calorie intake and nutritional balance.

[1593] Step 7:

[1594] The server generates a feedback based on the evaluation of the meal content and sends it to the device, which then displays the feedback to the user, for example, notifying them that the total calories were 1400 kcal and were well balanced.

[1595] Step 8:

[1596] The user configures the app to link with the healthcare application. The device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[1597] Step 9:

[1598] The server analyzes the user's progress based on the acquired exercise and weight data, adjusts meal plans and exercise schedules as needed, and sends the results to the device.

[1599] Step 10:

[1600] The server visualizes the user's weight change and exercise data as graphs and charts, and also generates a video of the predicted weight loss. The device displays this data to the user and notifies them, such as "You're 3kg away from your goal."

[1601] Step 11:

[1602] The server collects information on seasonal ingredients and generates new meal plans based on that information. For example, it suggests a menu using bamboo shoots and asparagus, which are seasonal ingredients in spring.

[1603] Step 12:

[1604] The server sends new recipes using seasonal ingredients to the user's device, which then displays the suggested recipes to the user, introducing new ingredients and providing recipes.

[1605] The above processing flow allows the user to diet effectively and continuously.

[1606] Example 1

[1607] 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."

[1608] In modern society, there is a growing need for personalized health management and diet plans. However, conventional methods often use generic approaches, making it difficult to provide optimal plans based on individual physical information and goals. In particular, they lack the functionality to record and manage diet and exercise, suggest seasonal ingredients, and visualize each individual's progress, which can lead to a decrease in user motivation. Furthermore, there is a lack of data integration with healthcare programs and automatic plan adjustments. To solve these issues, an advanced, personalized system tailored to individual needs is required.

[1609] 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.

[1610] In this invention, the server includes a means for inputting individual personal information and goal data, a means for analyzing the input individual personal information and goal data to generate a personalized meal plan and exercise plan, and a means for transmitting the generated personalized meal plan and exercise plan to the user terminal. This enables the provision of highly personalized plans tailored to the user's individual needs. The server also includes a means for receiving a meal record from the user terminal, analyzing the received meal record, and generating feedback, a means for acquiring exercise data and weight data through collaboration with a healthcare program, a means for visualizing the user's progress based on the acquired data, and a means for acquiring seasonal food information and generating a meal plan based on the acquired data. This enables more detailed feedback and maintains motivation. The server also includes a means for optimizing the meal and exercise plan using a generative AI model, and a means for analyzing the dietary information acquired from the user using the generative AI model to evaluate calories and nutrients. This enables efficient and accurate evaluation and advice.

[1611] "Individual personal information" refers to data specific to an individual, such as the user's age, sex, height, and weight.

[1612] "Goal data" refers to specific target values ​​and periods set by the user, such as "lose 5 kg in 3 months."

[1613] A "personalized meal plan" is a meal plan optimized for an individual, generated based on the user's individual personal information and goal data.

[1614] An "exercise plan" is an exercise plan that is optimized for an individual, generated based on the user's individual personal information and goal data.

[1615] A "user terminal" is a device used by a user to input information and display results, such as a smartphone, tablet, or PC.

[1616] A "food record" is data in which the user records the contents of the meals they eat each day using photos and text.

[1617] "Feedback" refers to evaluations and advice provided by the server based on the results of analyzing the food records received from the user.

[1618] The "Healthcare Program" is an application for collecting and managing health-related information such as exercise volume and weight data.

[1619] "Progress visualization" refers to displaying a user's health and diet progress in visual formats such as graphs, charts, and videos.

[1620] "Information on seasonal ingredients" refers to information on ingredients that are considered to be most delicious and nutritious in a particular season.

[1621] A "generative AI model" is an artificial intelligence model that generates personalized plans and assessments based on input data.

[1622] "Calorie and nutrient evaluation" refers to analyzing the calorie content and nutritional balance of the food consumed by the user and displaying the results.

[1623] This invention relates to a system that utilizes generative AI models to propose personalized meal and exercise plans based on individual personal information and goal data. The system visualizes the user's progress through subsequent meal records and health care programs, helping to maintain motivation.

[1624] Hardware and software used

[1625] User device: Smartphone, tablet, PC, etc. Install the Smart Diet app as an application.

[1626] Server: Database server and application server deployed on the cloud, which processes data analysis and AI generation.

[1627] Generative AI models: Artificial intelligence models such as OpenAI GPT-4 that provide personalized suggestions based on user information.

[1628] System Configuration and Operation

[1629] 1. Collection of User Information

[1630] Device: When a user launches the smart diet app, the device prompts the user to enter their personal information (age, gender, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months).

[1631] User: The user follows the app's instructions and enters the required personal information.

[1632] Terminal: Sends the entered information to the server as an HTTP POST request.

[1633] Server: Stores the received information in a database, such as a relational database.

[1634] 2. Generative AI creates personalized plans

[1635] Server: Retrieves stored user information from the database and generates personalized meal menus and exercise plans using generative AI models.

[1636] Server: Enter the following prompt into the generative AI model:

[1637] User Information:

[1638] Age: 29

[1639] Gender: Male

[1640] Height: 175cm

[1641] Weight: 80kg

[1642] Goal: Lose 5kg in 3 months

[1643] Please suggest the best diet plan for you.

[1644] Generative AI model: Based on the prompt, it generates a plan for "calorie intake of 1800 kcal / day and aerobic exercise three times a week." The result is returned to the server in JSON format.

[1645] Server: Sends the generated plan to the user's device.

[1646] Device: Show the user a personalized plan.

[1647] 3. Food Record and Evaluation

[1648] User: The user logs their daily meals in the app, taking photos of the food and adding text descriptions.

[1649] Device: Sends meal information to the server via an HTTP POST request.

[1650] Server: Receives meal information and analyzes it using a generative AI model. It evaluates calories and nutrients.

[1651] Server: Generates evaluation results such as "Total calories are 1400kcal, well balanced" as feedback and sends them to the device.

[1652] Terminal: Displays the evaluation results to the user.

[1653] 4. Data linkage with healthcare applications

[1654] User: Set up integration with the healthcare application.

[1655] Device: Periodically acquires health data and sends it to the server.

[1656] Server: Analyzes the data and adjusts meal and exercise plans as needed.

[1657] 5. Visualize your progress and stay motivated

[1658] Server: Analyzes the user's weight change and activity data and generates data to be displayed visually.

[1659] Server: Generates data that displays weight loss predictions and progress status for each case, and sends it to the device.

[1660] On the device: Shows users their progress and provides notifications to keep them motivated.

[1661] 6. Proposals using seasonal ingredients

[1662] Server: Collects seasonal food information and stores it in a database.

[1663] Server: Uses generative AI models to create new recipes to suggest to users.

[1664] Server: For example, suggest a recipe using bamboo shoots and asparagus, which are seasonal spring ingredients, and send it to the device.

[1665] Terminal: Display the new recipe to the user.

[1666] In this way, the system of the present invention utilizes a generative AI model based on individual personal information and goal data to provide effective diet support to users. It has various functions, such as adjusting plans based on the user's progress and health care data, and suggesting the use of seasonal ingredients.

[1667] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1668] System program processing flow

[1669] Step 1: Collect user information

[1670] 1. Input: The user launches the smart diet app and enters their personal information (age, gender, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months).

[1671] 2. Specific operation: The terminal receives this information through the input form. When the user has finished entering the data, he or she presses the send button.

[1672] 3. Data processing: The terminal structures the input data and converts it into JSON format.

[1673] 4. Output: The device sends the formatted data to the server as an HTTP POST request.

[1674] Step 2: Data storage and analysis

[1675] 1. Input: User information and goal data received via an HTTP POST request.

[1676] 2. Specific operation: The server analyzes the received data and stores it in the database.

[1677] 3. Data processing: The received data is stored in the user profile table of the relational database.

[1678] 4. Output: A confirmation response that the save is complete is sent back to the terminal.

[1679] Step 3: Generative AI creates a personalized plan

[1680] 1. Input: User information stored in the database.

[1681] 2. Specific operation: The server generates a prompt sentence to input user information into the generated AI model.

[1682] 3. Example prompt:

[1683] User Information:

[1684] Age: 29

[1685] Gender: Male

[1686] Height: 175cm

[1687] Weight: 80kg

[1688] Goal: Lose 5kg in 3 months

[1689] Please suggest the best diet plan for you.

[1690] 4. Data processing: The prompts are fed into a generative AI model to generate a personalized meal and exercise plan.

[1691] 5. Output: The generated plan is returned to the server in JSON format.

[1692] Step 4: Send your personalized plan

[1693] 1. Input: The personalization plan returned by the generative AI model.

[1694] 2. Specific operation: The server formats the generated plan and sends it to the user's device.

[1695] 3. Data processing: Send the plan in JSON format as an HTTP response.

[1696] 4. Output: The personalized plan is sent to the device.

[1697] Step 5: Collect and analyze food records

[1698] 1. Input: Daily food details (photos and text) recorded by the user using the Smart Diet app.

[1699] 2. Specific operation: The device converts the food record into JSON format and sends it to the server.

[1700] 3. Data processing: The server analyzes the food records received using a generative AI model to evaluate calories and nutrients.

[1701] 4. Output: The evaluation result (e.g., "Total calories are 1400 kcal, well balanced") is sent to the device in JSON format.

[1702] Step 6: Provide feedback

[1703] 1. Input: Evaluation results from the generative AI model.

[1704] 2. Specific operation: The device displays the evaluation results to the user.

[1705] 3. Data processing: Visualize the evaluation results and convert them into a format that can be displayed on the notification and feedback screens.

[1706] 4. Output: The evaluation results are displayed to the user.

[1707] Step 7: Data integration with healthcare applications

[1708] 1. Input: Exercise and weight data obtained from the healthcare application.

[1709] 2. Specific operation: The device periodically acquires this data and sends it to the server.

[1710] 3. Data processing: The server analyzes the received data and adjusts the plan as needed.

[1711] 4. Output: The adjusted plan is sent to the user's device.

[1712] Step 8: Visualize and communicate progress

[1713] 1. Input: User's weight change and activity data collected by the server.

[1714] 2. Specific operation: The server analyzes this data and generates graphs, charts, and a video showing predicted weight loss.

[1715] 3. Data processing: Generate data for visual display and format it into JSON format.

[1716] 4. Output: The generated data is sent to the terminal and displayed to the user. An example notification would be "3kg remaining until goal is reached".

[1717] Step 9: Proposals using seasonal ingredients

[1718] 1. Input: Information on seasonal ingredients.

[1719] 2. Specific operation: The server collects this information and stores it in a database.

[1720] 3. Data processing: Based on information on seasonal ingredients, new recipes are created using a generative AI model.

[1721] 4. Output: The new recipe is sent to the terminal and displayed to the user.

[1722] The above is the processing flow and specific operation of the system program. Each step makes it possible to provide personalized diet support to users.

[1723] (Application example 1)

[1724] 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."

[1725] In modern society, there is a need for diet plans tailored to individual physical information and goals. However, many existing systems lack personalization and are unable to provide effective meal and exercise plans. Furthermore, there is a lack of a way to visually check users' progress and receive real-time feedback, making it difficult to maintain motivation. Furthermore, there are few systems that offer intuitive, real-time data input and display capabilities using visual output devices.

[1726] 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.

[1727] In this invention, the server includes means for inputting individual physical information and goal data, means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan, means for transmitting the generated personalized meal plan and exercise plan to a user terminal, means for receiving a meal record from the user terminal and analyzing the received meal record to generate feedback, means for acquiring exercise data and weight data through cooperation with a sensor device, means for visualizing the user's progress based on the acquired data, means for acquiring information on seasonal ingredients according to the season and generating a meal plan based thereon, and means for displaying the personalized meal plan and exercise plan in real time using a visual output device and inputting meal record and progress data. This makes it possible to provide users with highly customizable and effective diet plans and support maintaining motivation through progress visualization and real-time feedback.

[1728] "Individual physical information" refers to data relating to the body that is specific to an individual user, such as the user's age, sex, height, and weight.

[1729] "Goal data" is data indicating a specific goal set by the user (for example, how many kilograms the user wants to lose over what period of time).

[1730] A "personalized meal plan" is a meal menu plan that is individually customized and suggested by the generative AI based on the user's individual physical information and goal data.

[1731] An "exercise plan" is a plan that includes specific exercise content and schedules proposed to help the user achieve their goals.

[1732] A "user terminal" refers to a portable electronic device used by a user, such as a smartphone, tablet PC, or smart glasses.

[1733] A "meal record" is data about the contents of a user's meals that is entered through an application.

[1734] "Feedback" refers to evaluations and advice provided to users based on analyzed data.

[1735] A "sensor device" is a device (e.g., a smartwatch or a weight scale) used to acquire a user's exercise data or weight data.

[1736] "Means for visualizing progress" refers to means that include a function that displays the user's progress in achieving their goals and their exercise and diet progress in graphs and charts.

[1737] "Information on seasonal ingredients" is data on fresh ingredients that are available at that time of year.

[1738] A "visual output device" is a device that displays information directly into the user's field of vision (e.g., smart glasses).

[1739] This invention relates to a system that provides a personalized diet plan in which a generating AI proposes optimal meal menus and exercise plans when individual physical information and goal data are input. This system allows users to understand their individual eating habits by recording their meals, and by linking with a healthcare application, visualizes progress and increases user motivation.

[1740] The system uses the following hardware and software:

[1741] User devices such as smartphones, tablet PCs, and smart glasses

[1742] Sensor devices (e.g. smartwatches, weight scales)

[1743] server

[1744] Generative AI Models

[1745] First, the user launches the smart diet app via their device and inputs their individual physical information (e.g., age, gender, height, weight) and goal (e.g., lose 5 kg in 3 months). This information is sent to the server and analyzed by the generative AI model. As a result of the analysis, an individually optimized meal menu and exercise plan is generated and sent to the user's device.

[1746] Users record their daily meals in the app and enter photos of the meals and supplemental text descriptions, which are then sent to the server. The server analyzes the received meal information and evaluates the calories and nutrients. The evaluation results are provided as feedback to the user's device, which displays an evaluation of the day's meal and advice.

[1747] Furthermore, by using the sensor device and setting it up to link with a healthcare application, the user can periodically send exercise and weight data to a server. This data is used to understand the user's activity status, and meal and exercise plans can be adjusted as needed. The user's progress is also visualized in graphs and charts, and notifications such as "3kg left to reach your goal" are sent to motivate the user.

[1748] Information on seasonal ingredients is also collected, and nutritionally balanced menus are generated based on this information. For example, recipes using ingredients in season in spring are suggested, allowing users to continue their diet while enjoying new ingredients.

[1749] As a concrete example, if a 29-year-old male user (height 175 cm, weight 80 kg) aims to lose 5 kg in three months, the server will suggest a diet plan of 1800 kcal per day and aerobic exercise three times a week. This plan is displayed in real time through the smart glasses, and the user can easily enter their diet and exercise records.

[1750] Prompt Sentence Examples

[1751] "Based on individual physical information (age, gender, height, weight) and goals (e.g., lose 5 kg in 3 months), generative AI will propose optimal meal menus and exercise plans. Users can view the personalized plan in real time through smart glasses and record their diet and exercise. For example, if a 29-year-old man (height 175 cm, weight 80 kg) aims to lose 5 kg in 3 months, he will be presented with a plan to consume 1800 kcal per day and do aerobic exercise three times a week. Imagine a system that visualizes dietary and exercise progress and provides feedback."

[1752] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1753] Step 1:

[1754] The user launches the smart diet app and inputs their individual physical information (age, sex, height, weight) and goal (e.g., lose 5 kg in 3 months). The device sends this input information to the server. The input data is the user's individual information and goal. The output is the sent user information.

[1755] Step 2:

[1756] The server stores the received user information in a database and analyzes it using a generative AI model. This analysis generates a personalized meal plan and exercise plan. The input is user information data, and the output is the generated plan data. The server creates the optimal plan, taking into account calorie calculations and nutritional balance.

[1757] Step 3:

[1758] The server sends the generated personalized plan to the user's terminal, which displays it to the user. The input is the generated plan data, and the output is the transmission to the user's terminal. The plan displayed on the terminal can be viewed in real time through the user's visual output device.

[1759] Step 4:

[1760] The user records their daily meals in the app. By taking photos of the meals and adding text descriptions, meal information is generated. The device sends this information to the server. The input is the user's meal record data, and the output is the data sent to the server.

[1761] Step 5:

[1762] The server analyzes the received food records using a generative AI model and evaluates calories and nutrients. The server generates the evaluation results as feedback and sends it to the user's device. The input is the food record data, and the output is feedback of the evaluation results. The feedback includes specific advice and evaluation details.

[1763] Step 6:

[1764] When a user uses a sensor device and links it to a healthcare application, exercise data and weight data are periodically sent to a server. The input is exercise data and weight data from the sensor device, and the output is data sent to the server.

[1765] Step 7:

[1766] The server analyzes the user's activity status based on the acquired exercise and weight data. Based on the analysis results, it adjusts the meal plan and exercise plan as needed. The input is exercise and weight data, and the output is the adjusted plan data.

[1767] Step 8:

[1768] The server generates graphs and charts to visualize the user's progress and sends them to the user's device. The input is data related to the progress, and the output is the visualized progress data. The device notifies the user of the displayed progress data.

[1769] Step 9:

[1770] The server collects seasonal ingredient information according to the season and stores it in a database. Based on the acquired seasonal ingredient information, a personalized meal plan is generated and sent to the user's device. The input is seasonal ingredient information data, and the output is plan data based on that data. Users can enjoy new recipes using seasonal ingredients.

[1771] Prompt Sentence Examples

[1772] "Based on individual physical information (age, gender, height, weight) and goals (e.g., lose 5 kg in 3 months), generative AI will propose optimal meal menus and exercise plans. Users can view the personalized plan in real time through smart glasses and record their diet and exercise. For example, if a 29-year-old man (height 175 cm, weight 80 kg) aims to lose 5 kg in 3 months, he will be presented with a plan to consume 1800 kcal per day and do aerobic exercise three times a week. Imagine a system that visualizes dietary and exercise progress and provides feedback."

[1773] 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.

[1774] This invention provides a personalized diet plan in which a generation AI proposes optimal meal menus and exercise plans based on individual physical information and goal data. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system provides feedback and motivation based on the user's emotional state, achieving more effective diet support.

[1775] 1. Collection of User Information

[1776] The user starts the smart diet app and enters their personal physical information (age, gender, height, weight, etc.) and goal (e.g., to lose 5 kg in 3 months). This information is stored on the device, which then sends it to the server.

[1777] The server stores the received user information in a database.

[1778] 2. Generative AI creates personalized plans

[1779] The server uses AI to generate personalized meal plans and exercise plans based on the stored user information. For example, it suggests a meal plan targeting 1800 kcal per day and an exercise plan for three times a week.

[1780] The server transmits the generated plan to the user's terminal, and the terminal displays the personalized plan to the user.

[1781] 3. Food Record and Evaluation

[1782] Users record their daily meals in the app, take photos of their meals, and enter supplementary text descriptions. The device then sends the meal information to the server.

[1783] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. This is then sent to the user's device as feedback. For example, the server provides feedback such as "Total calories are 1400kcal, well-balanced."

[1784] 4. Data linkage with healthcare applications

[1785] The user configures the Smart Diet app to link with the healthcare application. The device periodically obtains exercise and weight data from the healthcare application and sends it to the server.

[1786] The server uses the collected data to understand the user's activity status and adjusts their meal plan or exercise plan as needed. For example, if they are not getting enough exercise, it will suggest that they increase their exercise plan.

[1787] 5. Visualize your progress and stay motivated

[1788] The server analyzes the user's weight change and activity status, generates data to visually display the results as graphs and charts, and generates a video of the user's weight loss prediction and sends it to the user's device.

[1789] The device displays this data to the user and notifies them with notifications such as "3kg left until you reach your goal" to increase motivation.

[1790] 6. Proposals using seasonal ingredients

[1791] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[1792] For example, a recipe using bamboo shoots and asparagus, which are in season in spring, is suggested, and the server sends this to the terminal. The terminal displays the recipe to the user and suggests new ingredients.

[1793] 7. Emotion analysis and feedback using an emotion engine

[1794] Users can input their daily emotional state into the app or it can be automatically acquired through emotion recognition technology. This data is stored on the device and sent to a server.

[1795] The server uses an emotion engine to analyze the user's emotional data, and if the user is feeling stressed, for example, it will suggest activities or meals that will have a relaxing effect.

[1796] The server generates a feedback message based on the emotion data and sends it to the user's device, such as "Take a break and relax today."

[1797] As a concrete example, suppose a user inputs information such as "29 years old, male, 175cm tall, 80kg weight, lose 5kg in 3 months" and records their daily meals. Based on this, the server generates a plan for "calorie intake of 1800kcal / day, aerobic exercise 3 times a week" and sends it to the user's device. Once the user records their meals and the information is sent to the server, the generating AI analyzes the calories and nutrients and provides feedback.

[1798] Additionally, if the user's emotional data indicates "stress," the server will analyze it using an emotion engine and generate a suggestion such as "try drinking herbal tea for relaxation," which will be sent to the device. This will make it easier for users to manage not only their physical health but also their mental health.

[1799] As a result, the system of the present invention can provide customized plans based on individual physical information and goals, visualize progress, maintain motivation, and provide feedback and suggestions that correspond to emotional states, thereby providing more effective diet support to users.

[1800] The processing flow will be explained below.

[1801] Step 1:

[1802] The user starts the smart diet app and enters their personal physical information (age, gender, height, weight) and goal data (lose 5 kg in 3 months). The device then sends this information to the server.

[1803] Step 2:

[1804] The server stores the received user information in a database, including the user's individual physical information and goal data.

[1805] Step 3:

[1806] The server uses AI to generate personalized meal and exercise plans based on the saved user information. For example, it suggests a meal plan targeting 1800 kcal per day and aerobic exercise three times a week.

[1807] Step 4:

[1808] The server sends the generated personalized plan to the user's terminal, which displays the received plan for the user to confirm.

[1809] Step 5:

[1810] Users take photos of their daily meals, supplement them with text, and record the meal details in the app. The device then sends the recorded meal information to the server.

[1811] Step 6:

[1812] The server analyzes the received meal information using AI generation and evaluates the calories and nutrients. The evaluation results are generated as feedback and sent to the user's device. For example, feedback such as "Total calories are 1400kcal, well-balanced" is provided.

[1813] Step 7:

[1814] The user configures the app to link with the healthcare application, allowing the device to periodically obtain exercise and weight data from the healthcare application.

[1815] Step 8:

[1816] The device sends the acquired exercise and weight data to a server, which uses this information to understand the user's progress and adjusts their meal and exercise plans as needed. For example, if the user is not getting enough exercise, the server may suggest increasing the amount of exercise they need.

[1817] Step 9:

[1818] The server generates data that visualizes the user's weight change and exercise data as graphs and charts, and also generates a video of predicted weight loss and sends it to the user's device.

[1819] Step 10:

[1820] The device displays visualized data and predicted images to the user and notifies them with notifications such as "3kg left until you reach your goal."

[1821] Step 11:

[1822] The server collects information on seasonal ingredients according to the season and stores it in a database. Based on this information, it generates nutritionally balanced menus that utilize seasonal ingredients within the user's personalized plan.

[1823] Step 12:

[1824] The server sends the new recipes using the generated seasonal ingredients to the device, which then displays the suggested recipes to the user, introducing new ingredients and suggesting menu items.

[1825] Step 13:

[1826] Users can input their emotional state into the app, or emotion recognition technology can be used to automatically obtain emotional data, which the device then sends to a server.

[1827] Step 14:

[1828] The server uses an emotion engine to analyze the user's emotional data. For example, if it determines that the user is under stress, it will suggest activities or meals that will help them relax.

[1829] Step 15:

[1830] The server generates a feedback message based on the emotion data and sends it to the user's device, for example, a message saying, "Take a break and relax today."

[1831] Through the above processing flow, users can follow a diet plan based on their physical information and goals, while also receiving support tailored to their emotional state, allowing them to diet more effectively and continuously.

[1832] Example 2

[1833] 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."

[1834] Conventional diet support systems provide personalized plans based on individual physical information and goal data, but it is difficult to provide optimal support that takes into account many variables, such as the user's emotional state and seasonal food information.In addition, there are issues with insufficient visualization of progress and maintaining motivation, making it difficult for users to continue dieting over the long term.

[1835] 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.

[1836] In this invention, the server includes: means for inputting individual physical information and goal data; means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan using a generative AI model; means for transmitting the generated personalized meal plan and exercise plan to a user terminal; means for receiving a meal record from the user terminal and analyzing the received meal record using a generative AI model to generate feedback; means for acquiring exercise data and weight data through collaboration with a healthcare application; means for visualizing the user's progress based on the acquired data; means for acquiring information on seasonal ingredients according to the season and generating a personalized meal plan based on the acquired data; and means for analyzing the user's emotional state using an emotion engine and generating feedback based on the emotion data. This enables effective diet support based on the user's physical information and goals, as well as optimal feedback and suggestions that take into account emotional state and seasonal factors.

[1837] 1. "Individual physical information" refers to information about a user's individual body, such as age, gender, height, weight, and body fat percentage.

[1838] 2. "Goal Data" refers to specific goals set by the user, such as weight loss goals or exercise goals.

[1839] 3. "Generative AI model" refers to a model that uses artificial intelligence to generate optimal output from specific input data.

[1840] 4. "Personalized Meal Plan" refers to a meal menu that is customized based on a user's individual physical information and goal data.

[1841] 5. "Exercise Plan" refers to an exercise routine or schedule created based on a user's fitness goals.

[1842] 6. "User terminal" refers to an electronic device capable of executing programs, such as a smartphone or tablet used by a user.

[1843] 7. "Food record" refers to information that a user records about their daily meals, such as the type of food, amount, calories, etc.

[1844] 8. "Healthcare Application" refers to application software used for health management purposes.

[1845] 9. "Exercise Data" refers to information such as the type and duration of exercise performed by the user, and calories burned.

[1846] 10. "Weight Data" refers to measurement information regarding a User's weight.

[1847] 11. "Progress Visualization" refers to the visual display of a user's diet or fitness progress.

[1848] 12. "Information on seasonal ingredients" refers to information about fresh ingredients harvested each season.

[1849] 13. "Emotion Engine" means a program or technology for analyzing a user's emotional state.

[1850] 14. "Emotional Data" means information that represents a user's emotional state.

[1851] 15. "Feedback" refers to evaluations and advice provided to users.

[1852] The present invention is a system that utilizes a generative AI model based on individual physical information and goal data to propose optimal meal menus and exercise plans, and provides feedback and motivation based on the user's emotional state. This system includes the following components and processes.

[1853] Components

[1854] 1. User Device

[1855] Electronic devices such as smartphones and tablets are used to input user information, record meals, input emotional data, and receive feedback.

[1856] 2. Server

[1857] Equipped with a database and generative AI models, it analyzes user information and generates personalized meal and exercise plans.

[1858] Hardware and software used

[1859] Hardware:

[1860] Smartphones, tablets, servers, cloud storage

[1861] software:

[1862] Smart diet apps, healthcare applications, generative AI models (e.g., GPT-3), databases (e.g., MySQL), emotion engines

[1863] Data processing and calculation

[1864] 1. Collection and Transmission of User Information

[1865] The user inputs age, gender, height, weight, and goal data through the smart diet app. For example, "29 years old, male, height 175 cm, weight 80 kg, lose 5 kg in 3 months."

[1866] The device sends this information to the server in JSON format.

[1867] 2. Generate a personalized plan

[1868] The server stores the user information in a database and sends prompts to the generative AI model to generate personalized meal and exercise plans, for example, "Generate the optimal meal and exercise plan for a 29-year-old male, 175 cm tall, 80 kg weight, with a goal of losing 5 kg in 3 months."

[1869] The server sends the generated plan to the terminal, which displays the plan to the user.

[1870] 3. Food Record and Feedback

[1871] The user records their daily diet and enters details with photos and text. The device sends this information to a server, which analyzes it and generates feedback. For example, "Total calories are 1400kcal, well balanced."

[1872] 4. Integration with healthcare applications

[1873] The device periodically retrieves exercise and weight data from the health app and sends it to the server, which then analyzes the user's progress and adjusts their diet and exercise plans as needed.

[1874] 5. Visualize progress

[1875] The server generates graphs and predicted images based on the analysis of exercise and weight data, and sends them to the device. The device displays them to the user, notifying them with a message such as, "You're 3kg away from your goal."

[1876] 6. Proposal of seasonal ingredients

[1877] The server collects information on seasonal ingredients and proposes appropriate meal plans to users based on that information. For example, "Recipes using bamboo shoots and asparagus, which are seasonal ingredients in spring."

[1878] 7. Sentiment Analysis and Feedback

[1879] The user inputs emotional data or it is automatically acquired using emotion recognition technology. This data is sent to the server and analyzed by the emotion engine. For example, a suggestion such as "If you are under a lot of stress, try drinking herbal tea for relaxation" is generated and sent to the device.

[1880] This allows the present invention to provide optimal diet support based on individual physical information and goals, and to provide feedback that takes into account the user's emotional state and seasonal factors.

[1881] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1882] Step 1:

[1883] Enter and submit user information

[1884] The user launches the Smart Diet app and enters their age, gender, height, weight, and goal data (e.g., "29 years old, male, height 175cm, weight 80kg, lose 5kg in 3 months"). This input is saved on the device as JSON format data.

[1885] The device sends the saved user information to the server in the form of an HTTP request, which is then stored in a database.

[1886] Step 2:

[1887] Generate a personalized plan

[1888] The server retrieves the received user information from a database (e.g., MySQL) and generates a prompt based on it. Example prompt: "Generate the optimal meal menu and exercise plan for a 29-year-old male, 175 cm tall, 80 kg weight, with a goal of losing 5 kg in 3 months."

[1889] The server sends this prompt to a generative AI model (e.g., GPT-3) and obtains a personalized meal plan and exercise plan. The output is something like "Intake 1800kcal / day" and "Exercise 3 times a week."

[1890] The server sends this personalized plan in JSON format to the device.

[1891] Step 3:

[1892] View Plans

[1893] The device analyzes the received personalized plan and displays it on the screen in a format that is easy for the user to view, such as a list of daily meal menus and exercise plans.

[1894] Step 4:

[1895] Enter and submit your food record

[1896] Users record their daily meals in the app by taking photos of the food and adding text descriptions.

[1897] The device sends the meal information in JSON format to the server, and the image data is uploaded to cloud storage (e.g., Amazon S3), along with the URL.

[1898] Step 5:

[1899] Analysis of food information and feedback generation

[1900] The server analyzes the received meal information using a generative AI model and evaluates calories and nutrients, generating feedback such as "calorie intake is 1400kcal, well balanced."

[1901] The server sends the generated feedback in JSON format to the device.

[1902] Step 6:

[1903] View Feedback

[1904] The device displays the received feedback to the user, for example, using notifications to provide real-time feedback.

[1905] Step 7:

[1906] Data linkage with healthcare applications

[1907] Users can set up the Smart Diet app to link with other healthcare apps (e.g., Apple Health, Google Fit).

[1908] The device periodically obtains exercise and weight data from the health app and sends it to the server in JSON format.

[1909] Step 8:

[1910] Analyze data and adjust plans

[1911] The server analyzes the acquired exercise and weight data and adjusts the meal plan and exercise plan as needed. Example: "You are not getting enough exercise, so we suggest increasing your exercise plan to four times a week."

[1912] The server then sends the adjusted plan back to the terminal.

[1913] Step 9:

[1914] Progress visualization

[1915] The server analyzes the user's weight change and activity status, and generates graphs and charts, as well as a video showing predicted weight loss.

[1916] The server sends the generated visualization data to the terminal, which then displays it to the user. Example: A notification saying "3kg left until goal."

[1917] Step 10:

[1918] Suggestions for seasonal ingredients

[1919] The server collects information on seasonal ingredients and stores it in a database.

[1920] The server generates a personalized meal plan based on seasonal ingredients and sends, for example, a "recipe using bamboo shoots and asparagus" to the device.

[1921] The terminal displays this to the user.

[1922] Step 11:

[1923] Entering and sending emotional data

[1924] Users can input their daily emotional state into the app, or it can be automatically obtained through emotion recognition technology.

[1925] The device sends emotion data in JSON format to the server.

[1926] Step 12:

[1927] Emotional data analysis and feedback generation

[1928] The server uses an emotion engine to analyze the user's emotional data and suggests activities and meals that will help them relax if they are feeling stressed or tired. For example, the message might say, "Take a short break and relax today."

[1929] The server sends these proposals to the terminal.

[1930] Step 13:

[1931] Displaying sentiment-based feedback

[1932] The device will then display feedback to the user based on the emotions received, for example, using notifications to offer relaxation and stress reduction techniques.

[1933] (Application example 2)

[1934] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1935] In modern society, there is a demand for providing diet plans tailored to individual users. However, conventional diet systems have difficulty providing personalized feedback that takes into account individual physical information and emotional states. Furthermore, there is no easy way to purchase the suggested ingredients and supplements, which reduces user convenience. Furthermore, providing feedback and motivation based on the user's emotional state is difficult with conventional technologies.

[1936] 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 inputting individual physical information and goal data, means for analyzing the input individual physical information and goal data and generating a personalized meal plan and exercise plan, means for transmitting the generated personalized meal plan and exercise plan to the user terminal, means for inputting emotional data, analyzing it using an emotion analysis engine, and providing feedback and motivation based on the emotional state, and means for purchasing the generated plan and suggested ingredients and supplements in cooperation with an electronic payment service. This enables personalized feedback and suggestions based on the user's individual physical information and emotional state, achieving convenient and effective diet support.

[1937] "Individual physical information" refers to physical data specific to each individual user, such as the user's age, gender, height, and weight.

[1938] "Goal data" is data relating to a specific goal that a user wishes to achieve, such as weight loss or improvement in a particular health condition.

[1939] A "personalized meal plan" is a meal schedule and menu suggestion that is optimal for a user, generated based on the user's individual physical information and goal data.

[1940] An "exercise plan" is a proposal of the optimal exercise method and frequency for a user, generated based on the user's individual physical information and goal data.

[1941] "Emotion data" is data that represents the user's current emotional state, and includes, for example, stress, happiness, fatigue, and the like.

[1942] An "emotion analysis engine" is software or an algorithm that analyzes input emotion data and understands the user's emotional state.

[1943] "Feedback" is information that indicates the next action or areas for improvement based on the user's actions and status.

[1944] "Motivation" is an act or means of providing psychologically encouraging messages or suggestions to help a user achieve a set goal.

[1945] A "healthcare application" is a software application for managing a user's health condition and exercise data.

[1946] An "electronic payment service" is a system for electronically paying for goods and services over the Internet.

[1947] "Visualizing progress" means displaying a user's progress in dieting or health improvement in a visual format such as a graph or chart.

[1948] "Dietary records" are data that record the contents of meals consumed by a user in text and photographs.

[1949] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. Details of each element and their interactions will be explained below.

[1950] Server Features

[1951] The server performs the following main functions:

[1952] 1. Processing of individual body information and target data

[1953] Individual physical information (age, gender, height, weight, etc.) and goal data (e.g., weight loss goals or health improvement goals) entered by the user through a smartphone application are sent to a server.

[1954] The server stores this data in a database.

[1955] 2. Generate personalized meal and exercise plans

[1956] The server uses a generative AI model to generate optimal meal and exercise plans based on the individual's stored physical information and goal data.

[1957] For example, if a user submits the data "29 years old, male, 175 cm tall, 80 kg weight, lose 5 kg in 3 months," the server will generate a plan for "calorie intake of 1800 kcal / day, aerobic exercise 3 times a week."

[1958] 3. Generate feedback

[1959] Users record their daily meals through the application and send the information to a server, which then analyzes the received meal records using a generative AI model to evaluate calories and nutrients.

[1960] For example, feedback such as "Total calories are 1400 kcal, well balanced" is generated and sent to the user terminal.

[1961] 4. Emotional Data Analysis and Feedback

[1962] Emotion data is either entered by the user into the application or automatically obtained using emotion recognition technology, and this data is also sent to the server.

[1963] The server uses an emotion analysis engine to analyze the emotional data and, if it indicates stress, generates suggestions such as "Try drinking herbal tea for relaxation."

[1964] 5. Collaboration with electronic payment services

[1965] The server then connects the generated plan and the suggested ingredients and supplements to an electronic payment service, creating an environment where users can easily purchase them. Users can complete the purchase process directly from the application.

[1966] 6. Visualize progress

[1967] The server analyzes the user's progress based on the exercise and weight data acquired and visualizes the results in graphs and charts.

[1968] It provides users with motivation through notifications such as "3kg left until your goal."

[1969] Device Features

[1970] The user device (e.g., smartphone) mainly performs the following operations:

[1971] 1. Data Entry

[1972] The user inputs individual physical information and goal data.

[1973] Enter your daily food records and emotional data.

[1974] 2. Displaying Information

[1975] View personalized meal and exercise plans.

[1976] Feedback and progress indication.

[1977] 3. Use of payment functions

[1978] Purchase suggested foods and supplements.

[1979] Examples of prompt statements

[1980] Prompt: "Male, 29 years old, 175cm tall, 80kg weight. I want to lose 5kg in 3 months. What is the best diet and exercise plan?"

[1981] Prompt: "Does this meal contain anything that has a relaxing effect?"

[1982] This invention allows users to receive personalized feedback and suggestions based on their individual physical information and emotional state. It also allows users to easily purchase the suggested products, improving convenience. Furthermore, notifications and feedback are provided to keep users motivated, enabling more effective diet support.

[1983] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1984] Step 1:

[1985] The user launches the smartphone application, enters their individual physical information (age, sex, height, weight, etc.) and goal data (e.g., lose 5 kg in 3 months), and saves the data on the device.

[1986] Input: Age, Gender, Height, Weight, Weight Loss Goal

[1987] Output: Individual physical information and goal data stored on the user's device

[1988] How it works: A user enters information into an app form and clicks the "Save" button.

[1989] Step 2:

[1990] The device transmits the individual physical information and goal data acquired in step 1 to the server.

[1991] Input: Individual physical information and goal data stored on the user's device

[1992] Output: Individual body information and goal data sent to the server

[1993] How it works: The device sends data to the server's API endpoint as a POST request.

[1994] Step 3:

[1995] The server stores the received individual physical information and goal data in a database and uses a generative AI model to generate personalized meal and exercise plans.

[1996] Input: Individual physical information and goal data sent to the server

[1997] Output: Generated personalized meal and exercise plans

[1998] How it works: The server inputs data into the generative AI model and runs the process to generate the optimal plan.

[1999] Step 4:

[2000] The server transmits the generated personalized meal plan and exercise plan to the user terminal.

[2001] Input: Generated personalized meal and exercise plan

[2002] Output: Personalized meal and exercise plans delivered to the user's device

[2003] Operation: The generated plan is sent as a POST request to the user's device via the server's API endpoint.

[2004] Step 5:

[2005] Users record their daily dietary habits on a smartphone app, take photos of their meals, and enter supplementary information. This information is then sent from the device to the server.

[2006] Input: Meal details recorded by the user (photos and supplementary descriptions)

[2007] Output: Meal record sent to the server

[2008] How it works: A user uses the app's food log feature to enter information and presses the "Submit" button.

[2009] Step 6:

[2010] The server analyzes the received food records using a generative AI model to evaluate calories and nutrients.

[2011] Input: Food record sent to the server

[2012] Output: Calorie and nutrient rating feedback

[2013] Operation: The server analyzes the food log and performs processing to generate ratings and feedback.

[2014] Step 7:

[2015] The server transmits the generated feedback to the user terminal.

[2016] Input: Calorie and nutrient rating feedback

[2017] Output: Feedback delivered to the user device

[2018] Operation: Feedback information is sent to the user's device as a POST request via the server's API.

[2019] Step 8:

[2020] The user inputs emotion data via an application, or emotion data automatically acquired through emotion recognition technology is sent from the terminal to the server.

[2021] Input: User emotion data

[2022] Output: Emotion data sent to the server

[2023] Operation: The user inputs emotion data, and the device automatically sends the data to the server.

[2024] Step 9:

[2025] The server uses an emotion analysis engine to analyze the emotion data and generate feedback and motivational messages based on the emotional state.

[2026] Input: Emotion data sent to the server

[2027] Output: Feedback and motivational messages based on the generated emotional state

[2028] How it works: The server runs a sentiment analysis engine, analyzes the sentiment data, and generates an appropriate message.

[2029] Step 10:

[2030] The server transmits the generated emotion feedback message to the user terminal.

[2031] Input: Feedback and motivational messages based on generated emotional states

[2032] Output: Emotion feedback message delivered to the user device

[2033] Operation: An emotional feedback message is sent to the user device as a POST request via the server's API.

[2034] Step 11:

[2035] The user can purchase the suggested ingredients and supplements from the application by completing payment procedures using an electronic payment service.

[2036] Input: Generated food and supplement suggestions and user purchase information

[2037] Output: Notification of purchase completion via electronic payment

[2038] How it works: A user clicks a purchase button within an application and completes payment through an electronic payment service.

[2039] Step 12:

[2040] The server analyzes the user's progress based on the acquired exercise and weight data and visualizes it as graphs and charts.

[2041] Input: Exercise data and weight data obtained through the Health app

[2042] Output: Visualized progress information (graphs and charts)

[2043] How it works: The server analyzes the data and generates graphs and charts for visualization.

[2044] Step 13:

[2045] The server transmits the visualized progress information to the user terminal, notifying the user of the progress toward the goal.

[2046] Input: Visualized progress information

[2047] Output: Progress notification delivered to the user's device

[2048] Operation: The visualized progress information is sent to the user's device as a POST request via the server's API.

[2049] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2050] 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.

[2051] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2052] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2053] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2054] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2055] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2056] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of...

Claims

1. a means for inputting individual physical information and goal data; means for analyzing the input individual body information and goal data to generate a personalized meal plan and exercise regimen; means for transmitting the generated personalized meal plan and exercise plan to a user terminal; means for receiving a meal record from a user terminal and analyzing the received meal record to generate feedback; A means of obtaining exercise and weight data through collaboration with healthcare applications, A means for visualizing the user's progress based on the acquired data; A means to obtain information on seasonal ingredients and generate meal plans based on that information; A system including:

2. 10. The system of claim 1, further comprising: means for presenting the generated personalized meal and exercise plan to the user as graphs and charts, and for generating a visual of the weight loss prediction.

3. 10. The system of claim 1, further comprising: means for analyzing individual trends based on user input and adjusting diet and exercise plans.

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