System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting health information of a user; means for using a generative AI model to generate a personalized dietary plan based on the health information; means for notifying the user of the dietary plan; means for inputting a diet and a health condition; means for storing and analyzing the inputted diet; means for providing real-time feedback based on the analysis; and means for adjusting the dietary plan based on the feedback.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] Maintaining a healthy lifestyle is a significant challenge in modern society, particularly in consistently determining which diet is best suited to one's health goals and physical constitution. Accurately understanding one's own health status and making appropriate dietary choices based on that information places a significant burden on users. Furthermore, many lack the skills and knowledge to monitor their health status and adjust their diet plans. This creates a problem by providing no clear guidelines for living a healthy life. [Means for solving the problem]

[0005] The present invention provides a system for proposing a personalized meal plan based on health information and dietary preferences. Specifically, the system includes a means for inputting a user's health information, a means for using a generative AI model to generate a personalized meal plan based on the health information, a means for notifying the user of the meal plan, a means for inputting daily dietary content and health status, a means for storing and analyzing the input daily data, a means for providing real-time feedback based on the analysis results, and a means for adjusting the meal plan based on the feedback. This system allows users to easily understand their own health information and obtain specific guidelines for living a healthy diet.

[0006] "User's health information" refers to data relating to the user's physical information and health condition, such as height, weight, blood pressure, and blood sugar level.

[0007] A "personalized meal plan" is a plan in which a generative AI model suggests optimal meal plans for each user based on their individual health information and dietary preferences.

[0008] A "generative AI model" is an artificial intelligence model that analyzes input data and generates a meal plan suitable for the user.

[0009] The "notification means" refers to a technical means for transmitting the generated meal plan and feedback to the user terminal so that the user can check it.

[0010] "Daily dietary content" is information about the meals the user takes every day.

[0011] "Health status" refers to health-related data including measurements such as the user's daily weight, blood pressure, and blood sugar level.

[0012] The "means for inputting" is an interface that allows the user to input their own health information and dietary details into the terminal.

[0013] "Means for storing" refers to the technical means for storing and maintaining the data entered by the user in a database.

[0014] "Means for analyzing" refers to the technical means for analyzing the stored data to assess the user's health status and trends.

[0015] "Means for providing feedback in real time" refers to technical means for giving immediate advice or instructions to the user based on the analysis results.

[0016] "Means for adjusting" refers to technical means for appropriately modifying the meal plan based on said feedback to provide a more effective plan. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system that supports daily health management by proposing a personalized meal plan based on a user's health information and dietary preferences. As an embodiment of the present invention, a system using a user terminal, a server, and a generative AI model will be described.

[0039] System Overview

[0040] In this system, users input their health information and dietary preferences using a device, and a generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary and health data, provides real-time feedback, and adjusts the meal plan as needed.

[0041] Program processing

[0042] 1. User registration and initial settings

[0043] The user launches the app and enters their health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarian, allergy information) into the registration form.

[0044] The terminal transmits the input information to the server.

[0045] The server stores the received user information in a database.

[0046] 2. Generate personalized meal plans

[0047] The server generates an optimal meal plan using a generative AI model based on the stored user information.

[0048] The generative AI model creates optimal meal plans for each individual user (e.g., Breakfast: oatmeal and fruit, Lunch: salad and tofu steak, Dinner: vegetable stew).

[0049] The server transmits the generated meal plan to the terminal.

[0050] The plan will be displayed on the device and the user will be notified.

[0051] 3. Daily diet and health monitoring

[0052] The user inputs their daily diet and health status into the terminal (e.g., eats oatmeal and fruit for breakfast, measures and inputs their weight and blood pressure).

[0053] The terminal transmits the input data to the server.

[0054] The server stores the received data in a database.

[0055] 4. Providing real-time feedback and adjusting meal plans

[0056] The server analyzes the stored monitoring data and evaluates the user's health condition.

[0057] Based on the analysis results, the server generates a feedback message for the user (e.g., "You walked a lot yesterday, so let's increase the calories in your dinner plan tonight.").

[0058] The server adjusts the next day's meal plan based on the feedback.

[0059] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[0060] The terminal displays the received feedback and plan to the user.

[0061] Specific examples

[0062] Example 1: User registration and initial settings

[0063] The user launches the app for the first time and enters their height (170cm), weight (70kg), blood pressure (130 / 80) and vegetarian preference into the device.

[0064] The terminal sends this information to the server, which stores it in a database.

[0065] Example 2: Generating a meal plan

[0066] The server runs a generative AI model based on user information to generate an optimal meal plan.

[0067] For example, a user can create a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner, and the server sends it to the terminal, which then displays it to the user.

[0068] Example 3: Daily monitoring and feedback

[0069] A user eats oatmeal and fruit for breakfast and enters that information, along with their weight and blood pressure, into a terminal.

[0070] The terminal sends this data to the server, which stores it in a database.

[0071] The server analyzes the data, generates feedback to the user (e.g., "You're losing weight well. Keep it up!"), and sends it to the device along with an adjusted meal plan.

[0072] The device displays the feedback and plan to the user.

[0073] This allows users to efficiently manage their health status and obtain specific guidelines for achieving their health goals.

[0074] The processing flow will be explained below.

[0075] Program processing

[0076] User registration and initial settings

[0077] Step 1:

[0078] When a user launches the app, a user registration form is displayed on the device.

[0079] Here, the user inputs their own health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarian, allergy information).

[0080] Step 2:

[0081] The terminal transmits the health information and dietary preference data entered by the user to the server.

[0082] Step 3:

[0083] The server stores the received user information in a database.

[0084] Generate personalized meal plans

[0085] Step 4:

[0086] The server runs a generative AI model based on user information stored in a database.

[0087] Step 5:

[0088] The generative AI model automatically generates optimal meal plans for users, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[0089] Step 6:

[0090] The server transmits the generated meal plan to the terminal.

[0091] Step 7:

[0092] The terminal displays the received meal plan to the user.

[0093] Daily diet and health monitoring

[0094] Step 8:

[0095] The user inputs their daily diet and health status (e.g., weight, blood pressure) into the device. Specifically, after eating oatmeal and fruit for breakfast, they record their weight and blood pressure.

[0096] Step 9:

[0097] The terminal transmits the input data to the server.

[0098] Step 10:

[0099] The server stores the received daily data in a database.

[0100] Providing real-time feedback and adjusting meal plans

[0101] Step 11:

[0102] The server analyzes the stored monitoring data and evaluates the user's current health status.

[0103] Step 12:

[0104] The server generates a feedback message for the user based on the analysis results, such as "You walked a lot yesterday, so let's increase the calories in your dinner plan today."

[0105] Step 13:

[0106] The server will adjust the meal plan for the next day as needed.

[0107] Step 14:

[0108] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[0109] Step 15:

[0110] The terminal displays the received feedback and meal plan to the user.

[0111] This allows users to effectively manage their health and implement a tailored diet plan.

[0112] Example 1

[0113] 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."

[0114] Conventional health management systems have difficulty proposing meal plans that appropriately reflect a user's health information and dietary preferences, and lack the ability to provide real-time feedback on changes in daily dietary habits and health status, making it difficult to effectively support users in achieving their health goals.

[0115] 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.

[0116] In this invention, the server includes: a means for inputting a user's health information; a means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences; a means for notifying the user of the meal plan; a means for inputting daily meal content and health status; a means for storing and analyzing the input daily data; a means for providing real-time feedback based on the analysis results; a means for adjusting the next day's meal plan based on the feedback; and a means for reusing the generative AI model to generate and adjust the meal plan. This allows users to easily obtain an individually optimized meal plan and receive real-time feedback based on their daily health status, thereby effectively achieving their health management goals.

[0117] A "user" is an entity that utilizes the system to input health information and dietary preferences and receive a personalized meal plan.

[0118] "Health information" refers to physiological data such as the user's height, weight, blood pressure, and blood sugar level.

[0119] "Dietary preferences" refers to a user's preferred ingredients, allergy information, and specific dietary requirements such as vegetarianism.

[0120] A "terminal" is a device (e.g., smartphone, tablet, PC, etc.) that a user uses to access the system, enter information, and receive feedback.

[0121] "Server" refers to a computer system that generates user information and meal plans, stores and analyzes data, and provides real-time feedback to users.

[0122] The "database" is a system for storing data on a user's health information, dietary preferences, daily dietary content, and health status.

[0123] "Generative AI model" means an artificial intelligence model for generating personalized meal plans based on a user's health information and dietary preferences.

[0124] "Feedback" refers to advice and information provided by the server based on the results of analyzing the user's daily data.

[0125] A "meal plan" is a specific meal suggestion generated based on a user's health information and dietary preferences.

[0126] The present invention relates to a system that generates a personalized meal plan based on a user's health information and dietary preferences to support daily health management. The system includes a process in which information entered by a user using a terminal is sent to a server, and a generative AI model is used to generate and adjust the meal plan.

[0127] Hardware and software used

[0128] User devices: Devices such as smartphones, tablets, and PCs are used.

[0129] Server: Uses a high-performance computer system (e.g., Linux server).

[0130] Database: The system used to store user information (e.g. MySQL, PostgreSQL).

[0131] Generative AI model: The artificial intelligence model (e.g., OpenAI GPT-4) used to generate meal plans based on the user's health information and dietary preferences.

[0132] Network: Data communication between user terminals and servers uses the Internet or local networks.

[0133] Program processing

[0134] The system helps users manage their health through a process that includes user registration and initial setup, generating a personalized meal plan, monitoring daily diet and health status, providing real-time feedback, and adjusting the meal plan.

[0135] Specific examples

[0136] Example 1: User registration and initial settings

[0137] 1. The user launches the app and enters their height (170cm), weight (70kg), blood pressure (130 / 80) and vegetarian preference.

[0138] 2. The device sends this information to the server, which stores it in a database.

[0139] Example prompt sentence:

[0140] User information registration: Height 170cm, weight 70kg, blood pressure 130 / 80, dietary preference vegetarian.

[0141] Example 2: Generating a personalized meal plan

[0142] 1. The server reads the user information and runs the generative AI model to generate an optimal meal plan.

[0143] For example, suggest oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner.

[0144] 2. The server sends the generated plan to the terminal, which displays it to the user.

[0145] Example prompt sentence:

[0146] Generate meal plans based on the user's health information and dietary preferences.

[0147] Example 3: Daily diet and health monitoring

[0148] 1. A user eats oatmeal and fruit for breakfast and enters that information, along with their weight (70 kg) and blood pressure (130 / 80) into the terminal.

[0149] 2. The device sends this data to the server, which stores it in a database.

[0150] Example prompt sentence:

[0151] Today's diet: Oatmeal and fruit for breakfast. I weigh 70kg and my blood pressure is 130 / 80.

[0152] Example 4: Providing real-time feedback and adjusting meal plans

[0153] 1. The server analyzes the data and generates feedback to the user.

[0154] For example, you could send a message like, "You walked a lot yesterday, so let's add a few more calories to your dinner plan today."

[0155] 2. The server sends the feedback along with the adjusted meal plan for the next day to the device, which displays it to the user.

[0156] Example prompt sentence:

[0157] Generate real-time feedback based on the user's health data and adjust the next day's meal plan.

[0158] This allows users to efficiently manage their health status and obtain specific guidelines for achieving their health goals.

[0159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0160] Step 1: User registration and initial setup

[0161] Input: The user enters health information such as height, weight, blood pressure, and dietary preferences into the app.

[0162] Operation:

[0163] 1. The user launches the app and enters their health information and dietary preferences.

[0164] 2. The device sends the entered information to the server, where the data is encrypted using a communication protocol such as HTTPS.

[0165] 3. The server validates the received user information and stores it in the database.

[0166] Output: The user's health information and dietary preferences are stored in a database.

[0167] Step 2: Generate a personalized meal plan

[0168] Input: User information and food preferences stored on the server.

[0169] Operation:

[0170] 1. The server reads the target user's health information and dietary preferences from the database.

[0171] 2. The server inputs user information into the generative AI model and generates an optimal meal plan. For example, it sends a prompt to the generative AI model saying, "Please generate a meal plan based on the user's health information and dietary preferences."

[0172] 3. The generative AI model generates a personalized meal plan.

[0173] 4. The server sends the generated meal plan to the device.

[0174] 5. The device notifies the user of the received meal plan.

[0175] Output: A personalized meal plan displayed on the user's device.

[0176] Step 3: Monitor your daily diet and health

[0177] Input: The user enters their daily diet and health status into the app.

[0178] Operation:

[0179] 1. The user uses the app to enter the day's diet (e.g., oatmeal and fruit for breakfast) and health status (e.g., weight 70 kg, blood pressure 130 / 80).

[0180] 2. The terminal sends the entered data to the server.

[0181] 3. The server stores the received daily data in a database.

[0182] Output: Daily dietary and health status data is stored in a database.

[0183] Step 4: Provide real-time feedback and adjust meal plans

[0184] Input: Daily data and data saved from the previous day.

[0185] Operation:

[0186] 1. The server analyzes daily data and evaluates the user's health condition.

[0187] 2. The server generates a feedback message for the user based on the analysis results (e.g., "You walked a lot yesterday, so let's increase the calories in today's dinner plan.").

[0188] 3. The server re-runs the generative AI model based on the feedback and adjusts the meal plan for the next day.

[0189] 4. The server sends the generated feedback and adjusted meal plan to the device.

[0190] 5. The device displays the received feedback and the adjusted meal plan to the user.

[0191] Output: Real-time feedback and adjusted meal plan displayed on the user's device.

[0192] These are the specific processing steps of this system, which helps users effectively achieve their health goals by providing personalized meal plans that are adjusted appropriately based on the user's health information and dietary preferences.

[0193] (Application example 1)

[0194] 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."

[0195] In modern society, user health management is an important issue, and providing personalized meal plans is particularly effective. However, in physical stores, it is difficult for users to select optimal ingredients and recipes based on their own health information. There is also a need for a method to measure health data in real time in physical stores and adjust meal plans based on that data. Therefore, a system is needed that provides personalized meal plans based on users' health information and dietary preferences, measures health data in physical stores, and provides real-time feedback.

[0196] 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.

[0197] In this invention, the server includes means for suggesting ingredient lists and recipes based on the user's health information and dietary preferences when shopping at a physical store, means for measuring health data at checkpoints in the physical store and transmitting the measured health data to the server, and means for adjusting meal plans based on the feedback, thereby enabling the user to select optimal ingredients and recipes in accordance with their own health condition in the physical store and receive real-time feedback based on the measured health data.

[0198] "User's health information" is data indicating the user's health condition, such as height, weight, blood pressure, and blood sugar level.

[0199] "Dietary preferences" is data that indicates the user's dietary preferences and restrictions (e.g., vegetarianism, allergy information, etc.).

[0200] A "personalized meal plan" is a meal plan that is individually customized based on a user's health information and dietary preferences.

[0201] A "generative AI model" is an artificial intelligence program that generates optimal meal plans based on a user's health information and dietary preferences.

[0202] A "physical store" is a place where a user visits in person to purchase goods, and includes, for example, a supermarket or grocery store.

[0203] The "ingredient list" is a list of ingredients that the user needs to purchase at a physical store.

[0204] A "recipe" is information that indicates specific steps and necessary ingredients for a user to cook a dish.

[0205] A "checkpoint" is the location of health measurement equipment installed in a physical store, where users can measure their health data.

[0206] "Health data" refers to data relating to the physical condition of the user, such as weight, blood pressure, etc.

[0207] "Feedback" is specific advice or information provided to the user based on analyzed health data.

[0208] "Inventory information" refers to data regarding the current inventory status of products sold in physical stores.

[0209] A "database" is a collection of digital data that stores a user's health information, dietary preferences, daily input data, and so on.

[0210] The present invention relates to a system that supports daily health management by proposing personalized meal plans based on a user's health information and dietary preferences. To implement the present invention, a system using a user terminal, a server, and a generative AI model is applied.

[0211] First, the user enters their health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarianism, allergy information) into the user terminal. The terminal also inputs health data (e.g., weight, blood pressure) acquired at checkpoints when the user visits a physical store. All of this data is sent to the server and stored in a database. The server uses a generative AI model to generate an optimal ingredient list and recipes from the input data, allowing it to provide the user with a personalized meal plan. The server also compares the physical store's inventory information with the user's ingredient list and displays a list of ingredients that the user can purchase.

[0212] The generated meal plan is then sent to the user's device (smartphone, smart glasses, head-mounted display). For example, a plan might suggest a green smoothie for breakfast, tofu salad for lunch, and tomato pasta for dinner. The user measures their health data at checkpoints within the physical store, and the data is automatically sent to the server. The server analyzes this data and generates real-time feedback based on the analysis results. For example, a user with high blood pressure might be given a comment such as "reduce your salt intake." This feedback is displayed on the user's device, and the meal plan is adjusted as necessary.

[0213] The hardware and software used includes:

[0214] Server: Stores data, analyzes it, and runs generative AI models. Uses a web framework such as Flask.

[0215] User devices: Smartphones, smart glasses, and head-mounted displays are used for user input and display of notifications.

[0216] Checkpoint: Devices that measure health data, including scales and blood pressure monitors.

[0217] Generative AI model: An AI program for generating personalized meal plans and feedback.

[0218] For example, the following prompt might be used: "The user launches the app for the first time and enters their height (165cm), weight (60kg), blood pressure (120 / 80), and preference for vegetarianism."

[0219] The system of the present invention enhances the in-store shopping experience by helping users select optimal ingredients and recipes based on their health status, and by providing real-time feedback, users can always get a meal plan tailored to their health status.

[0220] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0221] Step 1:

[0222] The user launches the app and enters their health information (e.g., height, weight, blood pressure) and dietary preferences (e.g., vegetarian, allergy information) into the device. The entered data is sent from the device to the server.

[0223] Step 2:

[0224] The server receives the entered health information and dietary preferences and stores them in a database, at which point the server checks the integrity of the data and makes sure there is no missing information.

[0225] Step 3:

[0226] The server uses a generative AI model to generate a personalized meal plan based on user information stored in a database. The generative AI model analyzes the input data and creates ingredients and recipes suitable for breakfast, lunch, and dinner. The generated meal plan is then sent from the server to the user's device.

[0227] Step 4:

[0228] The user terminal displays the received meal plan and notifies the user, who then begins shopping at a physical store according to the presented meal plan.

[0229] Step 5:

[0230] At health measurement stations installed at checkpoints within physical stores, users measure their health data, such as weight and blood pressure, which is automatically sent from the measuring device to a server.

[0231] Step 6:

[0232] The server analyzes the received health data and evaluates the user's latest health status. Based on this analysis, it generates real-time feedback, such as "Your blood pressure is high, so try to limit your salt intake."

[0233] Step 7:

[0234] The server sends the generated feedback to the user terminal and adjusts the meal plan as needed, and the adjusted meal plan is also sent back to the user terminal from the server.

[0235] Step 8:

[0236] The user device displays the received feedback and the adjusted meal plan and notifies the user, who can then adjust their food selection and meal method in the physical store based on the displayed information.

[0237] Through this series of processing steps, users can obtain optimal meal plans based on their health status, improving their shopping experience in physical stores.

[0238] 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.

[0239] The present invention relates to a system that supports daily health management by proposing a personalized meal plan taking into account a user's health information, dietary preferences, and emotions. In particular, by combining a generative AI model and an emotion engine, the system provides feedback and adjusts the meal plan according to the user's emotional state. As an embodiment of the present invention, a system using a user terminal, a server, a generative AI model, and an emotion engine will be described.

[0240] System Overview

[0241] In this system, users input their health information, dietary preferences, and emotional information using a device, and a generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary content, health status, and emotional information, providing real-time feedback and adjusting the meal plan as needed.

[0242] Program processing

[0243] 1. User registration and initial settings

[0244] The user launches the app and fills in the registration form with their health information (e.g., height, weight, blood pressure, blood sugar level), food preferences (e.g., vegetarian, allergy information), and emotional information (e.g., feeling good, feeling stressed).

[0245] The terminal transmits the input information to the server.

[0246] The server stores the received user information in a database.

[0247] 2. Generate personalized meal plans

[0248] The server runs a generative AI model based on user information stored in a database.

[0249] The generative AI model automatically generates optimal meal plans for users, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[0250] The server transmits the generated meal plan to the terminal.

[0251] The plan will be displayed on the device and the user will be notified.

[0252] 3. Daily diet and health monitoring

[0253] The user inputs their daily diet, health status (e.g., weight, blood pressure), and emotional information into the device. Specifically, after eating oatmeal and fruit for breakfast, they record their weight, blood pressure, and emotional state (e.g., feeling relaxed).

[0254] The terminal transmits the input data to the server.

[0255] The server stores the received daily data in a database.

[0256] 4. Providing real-time feedback and adjusting meal plans

[0257] The server analyzes the stored monitoring data and evaluates the user's current health and emotional state based on the analysis results.

[0258] The emotion engine analyzes the emotional information entered by the user and generates a feedback message according to that state, such as "On stressful days, we recommend a relaxing herbal tea."

[0259] The server generates a feedback message for the user based on the analysis results and emotional information.

[0260] The server will adjust the meal plan for the next day as needed.

[0261] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[0262] The terminal displays the received feedback and plan to the user.

[0263] Specific examples

[0264] Example 1: User registration and initial settings

[0265] The user launches the app for the first time and enters their height (170 cm), weight (70 kg), blood pressure (130 / 80), dietary preference (vegetarian) and emotional information (feeling good) into the device.

[0266] The terminal sends this information to the server, which stores it in a database.

[0267] Example 2: Generating a meal plan

[0268] The server runs a generative AI model based on user information to generate an optimal meal plan.

[0269] For example, a user can create a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner, and the server sends it to the terminal, which then displays it to the user.

[0270] Example 3: Daily monitoring and feedback

[0271] The user eats oatmeal and fruit for breakfast and enters that information, along with their weight, blood pressure, and emotional status (relaxed) into the terminal.

[0272] The terminal sends this data to the server, which stores it in a database.

[0273] The server analyzes the data, generates feedback to the user (e.g., "You're losing weight well. Keep it up!"), and sends it to the device along with an adjusted meal plan.

[0274] The emotion engine analyzes the user's emotional information and generates feedback according to the emotion (e.g., "You are feeling less stressed, so continue your diet as planned").

[0275] The server notifies the terminal of the feedback and plan, which is then displayed to the user.

[0276] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[0277] The processing flow will be explained below.

[0278] Program processing

[0279] User registration and initial settings

[0280] Step 1:

[0281] The user launches the app and a user registration form appears on the device.

[0282] Users input health information such as height, weight, blood pressure, and blood sugar level, dietary preferences such as vegetarianism or allergies, and emotional information (e.g., feeling good, feeling stressed).

[0283] Step 2:

[0284] The terminal transmits the input health information, dietary preferences, and emotional information to the server.

[0285] Step 3:

[0286] The server stores the received user information in a database.

[0287] Generate personalized meal plans

[0288] Step 4:

[0289] The server runs a generative AI model based on user information stored in a database.

[0290] Step 5:

[0291] The generative AI model takes into account the user's health information and food preferences to generate an optimal meal plan, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[0292] Step 6:

[0293] The server transmits the generated meal plan to the terminal.

[0294] Step 7:

[0295] The terminal displays the received meal plan to the user.

[0296] Daily diet, health and emotional monitoring

[0297] Step 8:

[0298] The user inputs daily dietary information, health status (e.g., weight, blood pressure), and emotional information into the device. For example, after eating oatmeal and fruit for breakfast, the user records their weight, blood pressure, and emotional state (relaxed).

[0299] Step 9:

[0300] The terminal transmits the input data to the server.

[0301] Step 10:

[0302] The server stores the received daily data in a database.

[0303] Providing real-time feedback and adjusting meal plans

[0304] Step 11:

[0305] The server analyzes the stored monitoring data and assesses the user's current health and emotional state.

[0306] Step 12:

[0307] The emotion engine analyzes the emotion information entered by the user and generates feedback messages based on the user's emotional state. For example, if the user is feeling stressed, it generates a message such as "I recommend some herbal tea to help you relax."

[0308] Step 13:

[0309] The server generates a comprehensive feedback message for the user based on the analysis results and the feedback of the emotion engine.

[0310] Step 14:

[0311] The server will adjust the meal plan for the next day as needed, for example, by encouraging more light meals and stress-reducing foods if the emotion engine's analysis indicates high stress levels.

[0312] Step 15:

[0313] The server transmits the generated feedback message and the adjusted meal plan to the terminal.

[0314] Step 16:

[0315] The terminal displays the received feedback message and meal plan to the user.

[0316] This allows users to effectively manage their health and emotions and achieve their health goals through individually tailored meal plans.

[0317] Example 2

[0318] 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."

[0319] Health management requires providing personalized meal plans that comprehensively consider a user's health information, food preferences, and emotional state. Conventional systems have struggled to analyze this information in real time and provide optimal feedback and meal plan adjustments for individual users. In particular, feedback and meal plan adjustments that take into account a user's emotional state must be performed in a meticulous manner according to daily changes, but conventional technologies have been unable to achieve this.

[0320] 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.

[0321] In this invention, the server includes: means for inputting a user's health information and dietary preferences; means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences; means for generating and executing prompts for the generative AI model; means for notifying the user of the meal plan; means for inputting daily meal content, health status, and emotional information; means for storing and analyzing the input daily data; means for evaluating the user's health status and emotional status based on the stored data; means for analyzing the emotional information and providing feedback according to the user's emotions; and means for adjusting the meal plan based on the feedback and analysis results. This makes it possible to provide and adjust an optimal individual meal plan in real time by comprehensively considering the user's health information, dietary preferences, and emotional status.

[0322] "User's health information" refers to physical data such as the user's height, weight, blood pressure, and blood sugar level.

[0323] "Dietary preferences" refers to data about a user's preferences, such as the types of ingredients and dishes they like, allergy information, and specific dietary restrictions.

[0324] "Generative AI model" refers to an artificial intelligence model that automatically generates personalized meal plans based on input data.

[0325] "Emotional information" refers to data related to the user's emotional state, such as the user's mood, stress level, or happiness.

[0326] A "prompt statement" is a statement that describes input data in the format required to run a generative AI model.

[0327] "Notification Method" means the mechanism for notifying the user of the generated meal plan and feedback, including methods such as in-app notifications, email, and SMS.

[0328] "Monitoring" refers to the continuous observation and collection of data on a user's daily diet, health status, and emotional information.

[0329] "Means for providing real-time feedback" refers to a mechanism that generates instant feedback based on the user's most recent data and notifies the user of that information.

[0330] "Adjusting meal plans" refers to making appropriate changes to existing meal plans based on the user's daily data and feedback.

[0331] "Database" refers to a digital storage system for storing and managing a user's health information, dietary preferences, daily data, etc.

[0332] This invention relates to a system that supports daily health management by proposing a personalized meal plan based on the user's health information, food preferences, and emotional state. By combining a generative AI model and an emotion engine, this system can provide feedback and adjust the meal plan according to the user's emotional state.

[0333] Hardware and software used

[0334] The hardware and software used in this system are as follows:

[0335] User device: A mobile device, such as a smartphone or tablet, that a user uses to enter information.

[0336] Server: A cloud or on-premise server that stores data, analyzes it, and runs generative AI models.

[0337] Generative AI model: An artificial intelligence model for generating personalized meal plans from user data.

[0338] Emotion engine: An analysis engine for analyzing the user's emotional information and generating appropriate feedback.

[0339] System Overview

[0340] Users input their health information, dietary preferences, and emotional information using their device, and the generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary content, health status, and emotional information, providing real-time feedback and adjusting the meal plan as needed.

[0341] Specific examples

[0342] 1. User registration and initial settings

[0343] The user starts the app for the first time and enters their height (170cm), weight (70kg), blood pressure (130 / 80), food preference (vegetarian) and emotional information (feeling good). The device sends this information to the server, which stores it in a database.

[0344] 2. Generate a meal plan

[0345] The server runs a generative AI model based on user information to generate an optimal meal plan. For example, it creates a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner. The server sends the plan to the device, which displays it to the user.

[0346] 3. Daily monitoring and feedback

[0347] The user eats oatmeal and fruit for breakfast and enters that information, along with their weight, blood pressure, and emotional state (relaxed) into the device. The device sends this data to the server, which stores it in a database. The server analyzes the data, generates feedback for the user, and sends it back to the device along with an adjusted meal plan.

[0348] Prompt Sentence Examples

[0349] "Create a personalized meal plan based on your health and emotional information. Specifically, I'm 170cm tall, weigh 70kg, feel great, and am a vegetarian."

[0350] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[0351] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0352] Step 1: User registration and initial setup

[0353] Input: User's health information (e.g., height 170cm, weight 70kg, blood pressure 130 / 80), food preferences (e.g., vegetarian), emotional information (e.g., feeling good)

[0354] Specific operation: A user launches the app, enters the required information in the registration form, and presses the submit button. The device converts the entered information into JSON format and sends an HTTP POST request to the server.

[0355] Output: User information sent to the server

[0356] Data processing / calculation: The server parses the received JSON data and executes an INSERT query to the database to save the user information.

[0357] Step 2: Generate a personalized meal plan

[0358] Input: User information stored in the database

[0359] Specific operation: The server retrieves user information from the database using a SELECT query and passes it to the generative AI model as a prompt statement.

[0360] Output: A personalized meal plan returned by the generative AI model

[0361] Data processing / calculation: The server generates a prompt and calls the API of the generative AI model to generate a meal plan. The generative AI model analyzes the prompt and generates a corresponding meal plan.

[0362] Step 3: Meal plan notification

[0363] Input: Generated meal plan

[0364] Specific operation: The server encodes the generated meal plan in JSON format and sends an HTTP POST request to the device. The device decodes the received JSON data, displays it in the UI, and notifies the user of the plan using the notification function.

[0365] Output: Meal plan notified to user

[0366] Step 4: Monitor your daily diet and health

[0367] Input: User's diet, health status (e.g., weight, blood pressure), emotional information

[0368] Specific operation: The user inputs daily dietary information, health status, and emotional information into the device. The device converts this data into JSON format and sends an HTTP POST request to the server.

[0369] Output: Daily data sent to the server

[0370] Data processing / calculation: The server parses the incoming data and executes INSERT queries in the database to store the daily data.

[0371] Step 5: Data analysis and evaluation

[0372] Input: Daily data stored in a database

[0373] What it does: The server retrieves daily data from the database using SELECT queries and runs analytical algorithms to assess the user's current health and emotional state.

[0374] Output: Analysis results and evaluation data

[0375] Data processing / calculation: The server analyzes the received data and evaluates the user's health and emotional state.

[0376] Step 6: Generate emotional feedback

[0377] Input: User's emotional information

[0378] How it works: The emotion engine analyzes the user's emotional information and generates feedback messages according to their state. For example, it generates feedback such as, "On stressful days, we recommend a relaxing herbal tea."

[0379] Output: Feedback message

[0380] Data processing / calculation: The emotion engine analyzes emotional information and generates appropriate feedback.

[0381] Step 7: Adjust your meal plan

[0382] Input: Analysis results and feedback messages

[0383] What it does: Based on the analysis results and feedback messages, the server determines whether the next day's meal plan needs to be adjusted, and if necessary, re-runs the generative AI model to adjust the meal plan.

[0384] Output: Tailored meal plan

[0385] Data processing / calculation: Based on the analysis results, the server runs the generative AI model again to generate a new meal plan.

[0386] Step 8: Communicate feedback and adjusted plans

[0387] Input: Adjusted meal plan and feedback message

[0388] Specific operation: The server encodes the generated feedback and adjusted meal plan in JSON format and sends an HTTP POST request to the device. The device decodes the received JSON data, displays it in the UI, and notifies the user using the notification function.

[0389] Output: User-informed feedback and adjusted plan

[0390] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[0391] (Application example 2)

[0392] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0393] Conventional health management systems have difficulty reflecting a user's emotional state and daily changing health information in real time and proposing individually personalized meal plans when visiting a store. For this reason, there is a need for support that helps users maintain their health while avoiding confusion when choosing meals when out and in physical stores. In addition, a system is needed that enables more accurate and effective health management by linking daily health data with emotional information.

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

[0395] In this invention, the server includes: means for inputting a user's health information; means for using a generative AI model to generate a personalized meal plan based on the health information; means for notifying the user of the meal plan; means for detecting the user's store visit using a location recognition device installed in the physical store; means for suggesting optimal foods and meal menus at the physical store based on the detection results; means for inputting daily meal content and health status; means for storing and analyzing the input daily data; means for providing real-time feedback based on the analysis results; and means for adjusting the meal plan based on the feedback. This allows the user to receive meal suggestions optimal for their health status and mood when visiting the physical store, enabling more effective daily health management.

[0396] "User's health information" refers to data and information indicating the user's individual health condition, such as the user's height, weight, blood pressure, blood sugar level, etc.

[0397] A "generative AI model" refers to a computational model that uses artificial intelligence to perform a specific task based on input data, in this case generating a personalized meal plan based on health information.

[0398] "Location-aware devices installed in physical stores" refers to devices that use technologies such as beacons, Wi-Fi, and GPS to identify a user's location within a store and their visit.

[0399] "Personalized meal plans" refer to plans and suggestions that provide optimal meal menus for each user based on each user's individual health information and dietary preferences.

[0400] "Providing feedback in real time" means responding to the user immediately based on the input data and analysis results, and providing advice and information.

[0401] "Daily dietary content and health status" refers to the food the user eats on a daily basis and health-related data such as weight, blood pressure, and emotional state on that day.

[0402] "Storing and analyzing data" refers to temporarily or long-term storage of data collected from users and performing calculations or analysis based on that data.

[0403] "Suggesting optimal foods and meal menus" refers to recommending foods and meal contents that are considered most desirable at that time based on the user's health condition and emotional information.

[0404] This invention relates to a system that provides personalized meal plans based on a user's health information, dietary preferences, and emotional information, thereby supporting users in managing their health appropriately even when visiting a physical store.

[0405] System Configuration

[0406] This system consists of the following main hardware and software components:

[0407] 1. Smartphone App

[0408] It provides an interface for users to input their health information, food preferences, and emotional information and receive personalized suggestions at physical stores.

[0409] 2. Cloud Server

[0410] It is responsible for storing and analyzing data and running generative AI models.

[0411] 3. Beacon Devices

[0412] It is installed in physical stores and used to detect when users visit the store.

[0413] 4. Generative AI Models

[0414] It is used to generate personalized meal plans based on user input data.

[0415] 5. Emotion Engine

[0416] It is used to analyze the user's emotional information and generate appropriate feedback.

[0417] Specific operation of the system

[0418] User registration and initial settings

[0419] The user launches the smartphone app and inputs information such as height, weight, blood pressure, dietary preferences, and emotional state. The input information is sent to a cloud server and stored in a database.

[0420] Generate personalized meal plans

[0421] The cloud server runs a generative AI model based on the stored user information to generate an optimal meal plan for each individual user, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[0422] Individual proposals in physical stores

[0423] When a beacon device installed in a physical store detects a user's visit, a notification is sent to a smartphone app. The cloud server then uses the user's location and health information to suggest the most suitable food and meal menu for the store. For example, if the user is having a stressful day, the app will suggest relaxing herbal tea or foods with a calming effect.

[0424] Daily monitoring and feedback

[0425] Users enter their daily diet, health status, and emotional information into a smartphone app. The data is sent to a cloud server and stored in a database. The cloud server analyzes the daily data, provides real-time feedback based on the analysis results, and adjusts the next day's meal plan as needed.

[0426] Specific examples

[0427] User registration and initial setup:

[0428] A user launches the app for the first time, enters their height (170cm), weight (70kg), blood pressure (130 / 80), dietary preference (vegetarian), and emotional information (feeling good), and submits it.

[0429] Generate a meal plan:

[0430] The cloud server runs the generative AI model, generates a plan for breakfast consisting of oatmeal and fruit, lunch consisting of kale salad and tofu steak, and dinner consisting of vegetable stew, and notifies the user.

[0431] Personalized offers in-store:

[0432] When a user visits a physical store, a beacon device detects their visit, and a cloud server suggests relaxing herbal teas for stressful days.

[0433] Examples of prompt statements

[0434] Consider the user's health information and daily emotional state to suggest the best meal plan for today.

[0435] User information: Height 170cm, weight 70kg, blood pressure 130 / 80, vegetarian, feels good.

[0436] Emotional state: Relaxed. Low stress over the past 3 days.

[0437] This allows users to receive appropriate meal suggestions when visiting a physical store, regardless of their condition, making health management more effective.

[0438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0439] Step 1:

[0440] User registration and initial settings

[0441] (Input) The user launches the smartphone app and inputs health information such as height, weight, blood pressure, dietary preferences, and emotional information.

[0442] (Data processing) The health information entered by the user is organized in JSON format and immediately sent to the server.

[0443] (Output) The server saves the received user information in the database.

[0444] (Specific operation) In the smartphone app's user interface, you enter information using text boxes and selection lists, and then press the "Send" button, which sends the information to the server.

[0445] Step 2:

[0446] Generate personalized meal plans

[0447] (Input) User's health information stored on the server.

[0448] (Data processing) The server inputs user information into the generative AI model, which then generates the optimal meal plan for the user.

[0449] (Output) The generated meal plan is sent to a smartphone app and notified to the user.

[0450] (Specific operation) Using a pre-trained generative AI model (e.g., GPT-4) on the server, the system generates and executes prompts based on the user's health information, and then sends the resulting suggested meal plan to the app.

[0451] Step 3:

[0452] Individual proposals in physical stores

[0453] (Input) A user visits a physical store, and a beacon device installed in the store detects the user's visit.

[0454] (Data processing) The beacon device's detection information is sent to a server, which then identifies the user's current location. Using a generative AI model, the server generates optimal food and meal menus based on the user's health information and location within the store.

[0455] (Output) The generated proposal is notified to the smartphone app and presented to the user.

[0456] (Specific operation) The beacon device communicates with the user's smartphone and sends the information to the server. The server generates suggestions based on the user's location and health information and notifies the app.

[0457] Step 4:

[0458] Monitoring and input of daily dietary and health conditions

[0459] (Input) The user inputs daily dietary information, health status, and emotional information into a smartphone app.

[0460] (Data processing) The entered information is organized in JSON format and sent to the server.

[0461] (Output) The received data is stored in a database and prepared for analysis.

[0462] (Specific operation) The user enters the contents of breakfast, lunch, and dinner, as well as that day's weight, blood pressure, emotional information, etc., into the app's dedicated input screen, and when they press the "Send" button, the information is sent to the server.

[0463] Step 5:

[0464] Analysis and real-time feedback

[0465] (Input) Daily dietary and health status data.

[0466] (Data processing) The server analyzes the received data and generates feedback messages based on the user's current health and emotional state. It uses an emotion engine to analyze the emotional information in detail.

[0467] (Output) Send the generated feedback message and adjusted meal plan to the smartphone app.

[0468] (Specific operation) The server generates feedback using pattern recognition algorithms and generative AI models, and sends the generated messages and adjusted plans to the app, where the user can view them in real time.

[0469] Examples of prompts:

[0470] Consider the user's health information and daily emotional state to suggest the best meal plan for today.

[0471] User information: Height 170cm, weight 70kg, blood pressure 130 / 80, vegetarian, feels good.

[0472] Emotional state: Relaxed. Low stress over the past 3 days.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] [Second embodiment]

[0477] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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).

[0483] 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.

[0484] 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.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] 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."

[0489] The present invention relates to a system that supports daily health management by proposing a personalized meal plan based on a user's health information and dietary preferences. As an embodiment of the present invention, a system using a user terminal, a server, and a generative AI model will be described.

[0490] System Overview

[0491] In this system, users input their health information and dietary preferences using a device, and a generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary and health data, provides real-time feedback, and adjusts the meal plan as needed.

[0492] Program processing

[0493] 1. User registration and initial settings

[0494] The user launches the app and enters their health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarian, allergy information) into the registration form.

[0495] The terminal transmits the input information to the server.

[0496] The server stores the received user information in a database.

[0497] 2. Generate personalized meal plans

[0498] The server generates an optimal meal plan using a generative AI model based on the stored user information.

[0499] The generative AI model creates optimal meal plans for each individual user (e.g., Breakfast: oatmeal and fruit, Lunch: salad and tofu steak, Dinner: vegetable stew).

[0500] The server transmits the generated meal plan to the terminal.

[0501] The plan will be displayed on the device and the user will be notified.

[0502] 3. Daily diet and health monitoring

[0503] The user inputs their daily diet and health status into the terminal (e.g., eats oatmeal and fruit for breakfast, measures and inputs their weight and blood pressure).

[0504] The terminal transmits the input data to the server.

[0505] The server stores the received data in a database.

[0506] 4. Providing real-time feedback and adjusting meal plans

[0507] The server analyzes the stored monitoring data and evaluates the user's health condition.

[0508] Based on the analysis results, the server generates a feedback message for the user (e.g., "You walked a lot yesterday, so let's increase the calories in your dinner plan tonight.").

[0509] The server adjusts the next day's meal plan based on the feedback.

[0510] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[0511] The terminal displays the received feedback and plan to the user.

[0512] Specific examples

[0513] Example 1: User registration and initial settings

[0514] The user launches the app for the first time and enters their height (170cm), weight (70kg), blood pressure (130 / 80) and vegetarian preference into the device.

[0515] The terminal sends this information to the server, which stores it in a database.

[0516] Example 2: Generating a meal plan

[0517] The server runs a generative AI model based on user information to generate an optimal meal plan.

[0518] For example, a user can create a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner, and the server sends it to the terminal, which then displays it to the user.

[0519] Example 3: Daily monitoring and feedback

[0520] A user eats oatmeal and fruit for breakfast and enters that information, along with their weight and blood pressure, into a terminal.

[0521] The terminal sends this data to the server, which stores it in a database.

[0522] The server analyzes the data, generates feedback to the user (e.g., "You're losing weight well. Keep it up!"), and sends it to the device along with an adjusted meal plan.

[0523] The device displays the feedback and plan to the user.

[0524] This allows users to efficiently manage their health status and obtain specific guidelines for achieving their health goals.

[0525] The processing flow will be explained below.

[0526] Program processing

[0527] User registration and initial settings

[0528] Step 1:

[0529] When a user launches the app, a user registration form is displayed on the device.

[0530] Here, the user inputs their own health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarian, allergy information).

[0531] Step 2:

[0532] The terminal transmits the health information and dietary preference data entered by the user to the server.

[0533] Step 3:

[0534] The server stores the received user information in a database.

[0535] Generate personalized meal plans

[0536] Step 4:

[0537] The server runs a generative AI model based on user information stored in a database.

[0538] Step 5:

[0539] The generative AI model automatically generates optimal meal plans for users, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[0540] Step 6:

[0541] The server transmits the generated meal plan to the terminal.

[0542] Step 7:

[0543] The terminal displays the received meal plan to the user.

[0544] Daily diet and health monitoring

[0545] Step 8:

[0546] The user inputs their daily diet and health status (e.g., weight, blood pressure) into the device. Specifically, after eating oatmeal and fruit for breakfast, they record their weight and blood pressure.

[0547] Step 9:

[0548] The terminal transmits the input data to the server.

[0549] Step 10:

[0550] The server stores the received daily data in a database.

[0551] Providing real-time feedback and adjusting meal plans

[0552] Step 11:

[0553] The server analyzes the stored monitoring data and evaluates the user's current health status.

[0554] Step 12:

[0555] The server generates a feedback message for the user based on the analysis results, such as "You walked a lot yesterday, so let's increase the calories in your dinner plan today."

[0556] Step 13:

[0557] The server will adjust the meal plan for the next day as needed.

[0558] Step 14:

[0559] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[0560] Step 15:

[0561] The terminal displays the received feedback and meal plan to the user.

[0562] This allows users to effectively manage their health and implement a tailored diet plan.

[0563] Example 1

[0564] 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."

[0565] Conventional health management systems have difficulty proposing meal plans that appropriately reflect a user's health information and dietary preferences, and lack the ability to provide real-time feedback on changes in daily dietary habits and health status, making it difficult to effectively support users in achieving their health goals.

[0566] 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.

[0567] In this invention, the server includes: a means for inputting a user's health information; a means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences; a means for notifying the user of the meal plan; a means for inputting daily meal content and health status; a means for storing and analyzing the input daily data; a means for providing real-time feedback based on the analysis results; a means for adjusting the next day's meal plan based on the feedback; and a means for reusing the generative AI model to generate and adjust the meal plan. This allows users to easily obtain an individually optimized meal plan and receive real-time feedback based on their daily health status, thereby effectively achieving their health management goals.

[0568] A "user" is an entity that utilizes the system to input health information and dietary preferences and receive a personalized meal plan.

[0569] "Health information" refers to physiological data such as the user's height, weight, blood pressure, and blood sugar level.

[0570] "Dietary preferences" refers to a user's preferred ingredients, allergy information, and specific dietary requirements such as vegetarianism.

[0571] A "terminal" is a device (e.g., smartphone, tablet, PC, etc.) that a user uses to access the system, enter information, and receive feedback.

[0572] "Server" refers to a computer system that generates user information and meal plans, stores and analyzes data, and provides real-time feedback to users.

[0573] The "database" is a system for storing data on a user's health information, dietary preferences, daily dietary content, and health status.

[0574] "Generative AI model" means an artificial intelligence model for generating personalized meal plans based on a user's health information and dietary preferences.

[0575] "Feedback" refers to advice and information provided by the server based on the results of analyzing the user's daily data.

[0576] A "meal plan" is a specific meal suggestion generated based on a user's health information and dietary preferences.

[0577] The present invention relates to a system that generates a personalized meal plan based on a user's health information and dietary preferences to support daily health management. The system includes a process in which information entered by a user using a terminal is sent to a server, and a generative AI model is used to generate and adjust the meal plan.

[0578] Hardware and software used

[0579] User devices: Devices such as smartphones, tablets, and PCs are used.

[0580] Server: Uses a high-performance computer system (e.g., Linux server).

[0581] Database: The system used to store user information (e.g. MySQL, PostgreSQL).

[0582] Generative AI model: The artificial intelligence model (e.g., OpenAI GPT-4) used to generate meal plans based on the user's health information and dietary preferences.

[0583] Network: Data communication between user terminals and servers uses the Internet or local networks.

[0584] Program processing

[0585] The system helps users manage their health through a process that includes user registration and initial setup, generating a personalized meal plan, monitoring daily diet and health status, providing real-time feedback, and adjusting the meal plan.

[0586] Specific examples

[0587] Example 1: User registration and initial settings

[0588] 1. The user launches the app and enters their height (170cm), weight (70kg), blood pressure (130 / 80) and vegetarian preference.

[0589] 2. The device sends this information to the server, which stores it in a database.

[0590] Example prompt sentence:

[0591] User information registration: Height 170cm, weight 70kg, blood pressure 130 / 80, dietary preference vegetarian.

[0592] Example 2: Generating a personalized meal plan

[0593] 1. The server reads the user information and runs the generative AI model to generate an optimal meal plan.

[0594] For example, suggest oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner.

[0595] 2. The server sends the generated plan to the terminal, which displays it to the user.

[0596] Example prompt sentence:

[0597] Generate meal plans based on the user's health information and dietary preferences.

[0598] Example 3: Daily diet and health monitoring

[0599] 1. A user eats oatmeal and fruit for breakfast and enters that information, along with their weight (70 kg) and blood pressure (130 / 80) into the terminal.

[0600] 2. The device sends this data to the server, which stores it in a database.

[0601] Example prompt sentence:

[0602] Today's diet: Oatmeal and fruit for breakfast. I weigh 70kg and my blood pressure is 130 / 80.

[0603] Example 4: Providing real-time feedback and adjusting meal plans

[0604] 1. The server analyzes the data and generates feedback to the user.

[0605] For example, you could send a message like, "You walked a lot yesterday, so let's add a few more calories to your dinner plan today."

[0606] 2. The server sends the feedback along with the adjusted meal plan for the next day to the device, which displays it to the user.

[0607] Example prompt sentence:

[0608] Generate real-time feedback based on the user's health data and adjust the next day's meal plan.

[0609] This allows users to efficiently manage their health status and obtain specific guidelines for achieving their health goals.

[0610] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0611] Step 1: User registration and initial setup

[0612] Input: The user enters health information such as height, weight, blood pressure, and dietary preferences into the app.

[0613] Operation:

[0614] 1. The user launches the app and enters their health information and dietary preferences.

[0615] 2. The device sends the entered information to the server, where the data is encrypted using a communication protocol such as HTTPS.

[0616] 3. The server validates the received user information and stores it in the database.

[0617] Output: The user's health information and dietary preferences are stored in a database.

[0618] Step 2: Generate a personalized meal plan

[0619] Input: User information and food preferences stored on the server.

[0620] Operation:

[0621] 1. The server reads the target user's health information and dietary preferences from the database.

[0622] 2. The server inputs user information into the generative AI model and generates an optimal meal plan. For example, it sends a prompt to the generative AI model saying, "Please generate a meal plan based on the user's health information and dietary preferences."

[0623] 3. The generative AI model generates a personalized meal plan.

[0624] 4. The server sends the generated meal plan to the device.

[0625] 5. The device notifies the user of the received meal plan.

[0626] Output: A personalized meal plan displayed on the user's device.

[0627] Step 3: Monitor your daily diet and health

[0628] Input: The user enters their daily diet and health status into the app.

[0629] Operation:

[0630] 1. The user uses the app to enter the day's diet (e.g., oatmeal and fruit for breakfast) and health status (e.g., weight 70 kg, blood pressure 130 / 80).

[0631] 2. The terminal sends the entered data to the server.

[0632] 3. The server stores the received daily data in a database.

[0633] Output: Daily dietary and health status data is stored in a database.

[0634] Step 4: Provide real-time feedback and adjust meal plans

[0635] Input: Daily data and data saved from the previous day.

[0636] Operation:

[0637] 1. The server analyzes daily data and evaluates the user's health condition.

[0638] 2. The server generates a feedback message for the user based on the analysis results (e.g., "You walked a lot yesterday, so let's increase the calories in today's dinner plan.").

[0639] 3. The server re-runs the generative AI model based on the feedback and adjusts the meal plan for the next day.

[0640] 4. The server sends the generated feedback and adjusted meal plan to the device.

[0641] 5. The device displays the received feedback and the adjusted meal plan to the user.

[0642] Output: Real-time feedback and adjusted meal plan displayed on the user's device.

[0643] These are the specific processing steps of this system, which helps users effectively achieve their health goals by providing personalized meal plans that are adjusted appropriately based on the user's health information and dietary preferences.

[0644] (Application example 1)

[0645] 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."

[0646] In modern society, user health management is an important issue, and providing personalized meal plans is particularly effective. However, in physical stores, it is difficult for users to select optimal ingredients and recipes based on their own health information. There is also a need for a method to measure health data in real time in physical stores and adjust meal plans based on that data. Therefore, a system is needed that provides personalized meal plans based on users' health information and dietary preferences, measures health data in physical stores, and provides real-time feedback.

[0647] 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.

[0648] In this invention, the server includes means for suggesting ingredient lists and recipes based on the user's health information and dietary preferences when shopping at a physical store, means for measuring health data at checkpoints in the physical store and transmitting the measured health data to the server, and means for adjusting meal plans based on the feedback, thereby enabling the user to select optimal ingredients and recipes in accordance with their own health condition in the physical store and receive real-time feedback based on the measured health data.

[0649] "User's health information" is data indicating the user's health condition, such as height, weight, blood pressure, and blood sugar level.

[0650] "Dietary preferences" is data that indicates the user's dietary preferences and restrictions (e.g., vegetarianism, allergy information, etc.).

[0651] A "personalized meal plan" is a meal plan that is individually customized based on a user's health information and dietary preferences.

[0652] A "generative AI model" is an artificial intelligence program that generates optimal meal plans based on a user's health information and dietary preferences.

[0653] A "physical store" is a place where a user visits in person to purchase goods, and includes, for example, a supermarket or grocery store.

[0654] The "ingredient list" is a list of ingredients that the user needs to purchase at a physical store.

[0655] A "recipe" is information that indicates specific steps and necessary ingredients for a user to cook a dish.

[0656] A "checkpoint" is the location of health measurement equipment installed in a physical store, where users can measure their health data.

[0657] "Health data" refers to data relating to the physical condition of the user, such as weight, blood pressure, etc.

[0658] "Feedback" is specific advice or information provided to the user based on analyzed health data.

[0659] "Inventory information" refers to data regarding the current inventory status of products sold in physical stores.

[0660] A "database" is a collection of digital data that stores a user's health information, dietary preferences, daily input data, and so on.

[0661] The present invention relates to a system that supports daily health management by proposing personalized meal plans based on a user's health information and dietary preferences. To implement the present invention, a system using a user terminal, a server, and a generative AI model is applied.

[0662] First, the user enters their health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarianism, allergy information) into the user terminal. The terminal also inputs health data (e.g., weight, blood pressure) acquired at checkpoints when the user visits a physical store. All of this data is sent to the server and stored in a database. The server uses a generative AI model to generate an optimal ingredient list and recipes from the input data, allowing it to provide the user with a personalized meal plan. The server also compares the physical store's inventory information with the user's ingredient list and displays a list of ingredients that the user can purchase.

[0663] The generated meal plan is then sent to the user's device (smartphone, smart glasses, head-mounted display). For example, a plan might suggest a green smoothie for breakfast, tofu salad for lunch, and tomato pasta for dinner. The user measures their health data at checkpoints within the physical store, and the data is automatically sent to the server. The server analyzes this data and generates real-time feedback based on the analysis results. For example, a user with high blood pressure might be given a comment such as "reduce your salt intake." This feedback is displayed on the user's device, and the meal plan is adjusted as necessary.

[0664] The hardware and software used includes:

[0665] Server: Stores data, analyzes it, and runs generative AI models. Uses a web framework such as Flask.

[0666] User devices: Smartphones, smart glasses, and head-mounted displays are used for user input and display of notifications.

[0667] Checkpoint: Devices that measure health data, including scales and blood pressure monitors.

[0668] Generative AI model: An AI program for generating personalized meal plans and feedback.

[0669] For example, the following prompt might be used: "The user launches the app for the first time and enters their height (165cm), weight (60kg), blood pressure (120 / 80), and preference for vegetarianism."

[0670] The system of the present invention enhances the in-store shopping experience by helping users select optimal ingredients and recipes based on their health status, and by providing real-time feedback, users can always get a meal plan tailored to their health status.

[0671] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0672] Step 1:

[0673] The user launches the app and enters their health information (e.g., height, weight, blood pressure) and dietary preferences (e.g., vegetarian, allergy information) into the device. The entered data is sent from the device to the server.

[0674] Step 2:

[0675] The server receives the entered health information and dietary preferences and stores them in a database, at which point the server checks the integrity of the data and makes sure there is no missing information.

[0676] Step 3:

[0677] The server uses a generative AI model to generate a personalized meal plan based on user information stored in a database. The generative AI model analyzes the input data and creates ingredients and recipes suitable for breakfast, lunch, and dinner. The generated meal plan is then sent from the server to the user's device.

[0678] Step 4:

[0679] The user terminal displays the received meal plan and notifies the user, who then begins shopping at a physical store according to the presented meal plan.

[0680] Step 5:

[0681] At health measurement stations installed at checkpoints within physical stores, users measure their health data, such as weight and blood pressure, which is automatically sent from the measuring device to a server.

[0682] Step 6:

[0683] The server analyzes the received health data and evaluates the user's latest health status. Based on this analysis, it generates real-time feedback, such as "Your blood pressure is high, so try to limit your salt intake."

[0684] Step 7:

[0685] The server sends the generated feedback to the user terminal and adjusts the meal plan as needed, and the adjusted meal plan is also sent back to the user terminal from the server.

[0686] Step 8:

[0687] The user device displays the received feedback and the adjusted meal plan and notifies the user, who can then adjust their food selection and meal method in the physical store based on the displayed information.

[0688] Through this series of processing steps, users can obtain optimal meal plans based on their health status, improving their shopping experience in physical stores.

[0689] 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.

[0690] The present invention relates to a system that supports daily health management by proposing a personalized meal plan taking into account a user's health information, dietary preferences, and emotions. In particular, by combining a generative AI model and an emotion engine, the system provides feedback and adjusts the meal plan according to the user's emotional state. As an embodiment of the present invention, a system using a user terminal, a server, a generative AI model, and an emotion engine will be described.

[0691] System Overview

[0692] In this system, users input their health information, dietary preferences, and emotional information using a device, and a generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary content, health status, and emotional information, providing real-time feedback and adjusting the meal plan as needed.

[0693] Program processing

[0694] 1. User registration and initial settings

[0695] The user launches the app and fills in the registration form with their health information (e.g., height, weight, blood pressure, blood sugar level), food preferences (e.g., vegetarian, allergy information), and emotional information (e.g., feeling good, feeling stressed).

[0696] The terminal transmits the input information to the server.

[0697] The server stores the received user information in a database.

[0698] 2. Generate personalized meal plans

[0699] The server runs a generative AI model based on user information stored in a database.

[0700] The generative AI model automatically generates optimal meal plans for users, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[0701] The server transmits the generated meal plan to the terminal.

[0702] The plan will be displayed on the device and the user will be notified.

[0703] 3. Daily diet and health monitoring

[0704] The user inputs their daily diet, health status (e.g., weight, blood pressure), and emotional information into the device. Specifically, after eating oatmeal and fruit for breakfast, they record their weight, blood pressure, and emotional state (e.g., feeling relaxed).

[0705] The terminal transmits the input data to the server.

[0706] The server stores the received daily data in a database.

[0707] 4. Providing real-time feedback and adjusting meal plans

[0708] The server analyzes the stored monitoring data and evaluates the user's current health and emotional state based on the analysis results.

[0709] The emotion engine analyzes the emotional information entered by the user and generates a feedback message according to that state, such as "On stressful days, we recommend a relaxing herbal tea."

[0710] The server generates a feedback message for the user based on the analysis results and emotional information.

[0711] The server will adjust the meal plan for the next day as needed.

[0712] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[0713] The terminal displays the received feedback and plan to the user.

[0714] Specific examples

[0715] Example 1: User registration and initial settings

[0716] The user launches the app for the first time and enters their height (170 cm), weight (70 kg), blood pressure (130 / 80), dietary preference (vegetarian) and emotional information (feeling good) into the device.

[0717] The terminal sends this information to the server, which stores it in a database.

[0718] Example 2: Generating a meal plan

[0719] The server runs a generative AI model based on user information to generate an optimal meal plan.

[0720] For example, a user can create a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner, and the server sends it to the terminal, which then displays it to the user.

[0721] Example 3: Daily monitoring and feedback

[0722] The user eats oatmeal and fruit for breakfast and enters that information, along with their weight, blood pressure, and emotional status (relaxed) into the terminal.

[0723] The terminal sends this data to the server, which stores it in a database.

[0724] The server analyzes the data, generates feedback to the user (e.g., "You're losing weight well. Keep it up!"), and sends it to the device along with an adjusted meal plan.

[0725] The emotion engine analyzes the user's emotional information and generates feedback according to the emotion (e.g., "You are feeling less stressed, so continue your diet as planned").

[0726] The server notifies the terminal of the feedback and plan, which is then displayed to the user.

[0727] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[0728] The processing flow will be explained below.

[0729] Program processing

[0730] User registration and initial settings

[0731] Step 1:

[0732] The user launches the app and a user registration form appears on the device.

[0733] Users input health information such as height, weight, blood pressure, and blood sugar level, dietary preferences such as vegetarianism or allergies, and emotional information (e.g., feeling good, feeling stressed).

[0734] Step 2:

[0735] The terminal transmits the input health information, dietary preferences, and emotional information to the server.

[0736] Step 3:

[0737] The server stores the received user information in a database.

[0738] Generate personalized meal plans

[0739] Step 4:

[0740] The server runs a generative AI model based on user information stored in a database.

[0741] Step 5:

[0742] The generative AI model takes into account the user's health information and food preferences to generate an optimal meal plan, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[0743] Step 6:

[0744] The server transmits the generated meal plan to the terminal.

[0745] Step 7:

[0746] The terminal displays the received meal plan to the user.

[0747] Daily diet, health and emotional monitoring

[0748] Step 8:

[0749] The user inputs daily dietary information, health status (e.g., weight, blood pressure), and emotional information into the device. For example, after eating oatmeal and fruit for breakfast, the user records their weight, blood pressure, and emotional state (relaxed).

[0750] Step 9:

[0751] The terminal transmits the input data to the server.

[0752] Step 10:

[0753] The server stores the received daily data in a database.

[0754] Providing real-time feedback and adjusting meal plans

[0755] Step 11:

[0756] The server analyzes the stored monitoring data and assesses the user's current health and emotional state.

[0757] Step 12:

[0758] The emotion engine analyzes the emotion information entered by the user and generates feedback messages based on the user's emotional state. For example, if the user is feeling stressed, it generates a message such as "I recommend some herbal tea to help you relax."

[0759] Step 13:

[0760] The server generates a comprehensive feedback message for the user based on the analysis results and the feedback of the emotion engine.

[0761] Step 14:

[0762] The server will adjust the meal plan for the next day as needed, for example, by encouraging more light meals and stress-reducing foods if the emotion engine's analysis indicates high stress levels.

[0763] Step 15:

[0764] The server transmits the generated feedback message and the adjusted meal plan to the terminal.

[0765] Step 16:

[0766] The terminal displays the received feedback message and meal plan to the user.

[0767] This allows users to effectively manage their health and emotions and achieve their health goals through individually tailored meal plans.

[0768] Example 2

[0769] 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."

[0770] Health management requires providing personalized meal plans that comprehensively consider a user's health information, food preferences, and emotional state. Conventional systems have struggled to analyze this information in real time and provide optimal feedback and meal plan adjustments for individual users. In particular, feedback and meal plan adjustments that take into account a user's emotional state must be performed in a meticulous manner according to daily changes, but conventional technologies have been unable to achieve this.

[0771] 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.

[0772] In this invention, the server includes: means for inputting a user's health information and dietary preferences; means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences; means for generating and executing prompts for the generative AI model; means for notifying the user of the meal plan; means for inputting daily meal content, health status, and emotional information; means for storing and analyzing the input daily data; means for evaluating the user's health status and emotional status based on the stored data; means for analyzing the emotional information and providing feedback according to the user's emotions; and means for adjusting the meal plan based on the feedback and analysis results. This makes it possible to provide and adjust an optimal individual meal plan in real time by comprehensively considering the user's health information, dietary preferences, and emotional status.

[0773] "User's health information" refers to physical data such as the user's height, weight, blood pressure, and blood sugar level.

[0774] "Dietary preferences" refers to data about a user's preferences, such as the types of ingredients and dishes they like, allergy information, and specific dietary restrictions.

[0775] "Generative AI model" refers to an artificial intelligence model that automatically generates personalized meal plans based on input data.

[0776] "Emotional information" refers to data related to the user's emotional state, such as the user's mood, stress level, or happiness.

[0777] A "prompt statement" is a statement that describes input data in the format required to run a generative AI model.

[0778] "Notification Method" means the mechanism for notifying the user of the generated meal plan and feedback, including methods such as in-app notifications, email, and SMS.

[0779] "Monitoring" refers to the continuous observation and collection of data on a user's daily diet, health status, and emotional information.

[0780] "Means for providing real-time feedback" refers to a mechanism that generates instant feedback based on the user's most recent data and notifies the user of that information.

[0781] "Adjusting meal plans" refers to making appropriate changes to existing meal plans based on the user's daily data and feedback.

[0782] "Database" refers to a digital storage system for storing and managing a user's health information, dietary preferences, daily data, etc.

[0783] This invention relates to a system that supports daily health management by proposing a personalized meal plan based on the user's health information, food preferences, and emotional state. By combining a generative AI model and an emotion engine, this system can provide feedback and adjust the meal plan according to the user's emotional state.

[0784] Hardware and software used

[0785] The hardware and software used in this system are as follows:

[0786] User device: A mobile device, such as a smartphone or tablet, that a user uses to enter information.

[0787] Server: A cloud or on-premise server that stores data, analyzes it, and runs generative AI models.

[0788] Generative AI model: An artificial intelligence model for generating personalized meal plans from user data.

[0789] Emotion engine: An analysis engine for analyzing the user's emotional information and generating appropriate feedback.

[0790] System Overview

[0791] Users input their health information, dietary preferences, and emotional information using their device, and the generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary content, health status, and emotional information, providing real-time feedback and adjusting the meal plan as needed.

[0792] Specific examples

[0793] 1. User registration and initial settings

[0794] The user starts the app for the first time and enters their height (170cm), weight (70kg), blood pressure (130 / 80), food preference (vegetarian) and emotional information (feeling good). The device sends this information to the server, which stores it in a database.

[0795] 2. Generate a meal plan

[0796] The server runs a generative AI model based on user information to generate an optimal meal plan. For example, it creates a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner. The server sends the plan to the device, which displays it to the user.

[0797] 3. Daily monitoring and feedback

[0798] The user eats oatmeal and fruit for breakfast and enters that information, along with their weight, blood pressure, and emotional state (relaxed) into the device. The device sends this data to the server, which stores it in a database. The server analyzes the data, generates feedback for the user, and sends it back to the device along with an adjusted meal plan.

[0799] Prompt Sentence Examples

[0800] "Create a personalized meal plan based on your health and emotional information. Specifically, I'm 170cm tall, weigh 70kg, feel great, and am a vegetarian."

[0801] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[0802] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0803] Step 1: User registration and initial setup

[0804] Input: User's health information (e.g., height 170cm, weight 70kg, blood pressure 130 / 80), food preferences (e.g., vegetarian), emotional information (e.g., feeling good)

[0805] Specific operation: A user launches the app, enters the required information in the registration form, and presses the submit button. The device converts the entered information into JSON format and sends an HTTP POST request to the server.

[0806] Output: User information sent to the server

[0807] Data processing / calculation: The server parses the received JSON data and executes an INSERT query to the database to save the user information.

[0808] Step 2: Generate a personalized meal plan

[0809] Input: User information stored in the database

[0810] Specific operation: The server retrieves user information from the database using a SELECT query and passes it to the generative AI model as a prompt statement.

[0811] Output: A personalized meal plan returned by the generative AI model

[0812] Data processing / calculation: The server generates a prompt and calls the API of the generative AI model to generate a meal plan. The generative AI model analyzes the prompt and generates a corresponding meal plan.

[0813] Step 3: Meal plan notification

[0814] Input: Generated meal plan

[0815] Specific operation: The server encodes the generated meal plan in JSON format and sends an HTTP POST request to the device. The device decodes the received JSON data, displays it in the UI, and notifies the user of the plan using the notification function.

[0816] Output: Meal plan notified to user

[0817] Step 4: Monitor your daily diet and health

[0818] Input: User's diet, health status (e.g., weight, blood pressure), emotional information

[0819] Specific operation: The user inputs daily dietary information, health status, and emotional information into the device. The device converts this data into JSON format and sends an HTTP POST request to the server.

[0820] Output: Daily data sent to the server

[0821] Data processing / calculation: The server parses the incoming data and executes INSERT queries in the database to store the daily data.

[0822] Step 5: Data analysis and evaluation

[0823] Input: Daily data stored in a database

[0824] What it does: The server retrieves daily data from the database using SELECT queries and runs analytical algorithms to assess the user's current health and emotional state.

[0825] Output: Analysis results and evaluation data

[0826] Data processing / calculation: The server analyzes the received data and evaluates the user's health and emotional state.

[0827] Step 6: Generate emotional feedback

[0828] Input: User's emotional information

[0829] How it works: The emotion engine analyzes the user's emotional information and generates feedback messages according to their state. For example, it generates feedback such as, "On stressful days, we recommend a relaxing herbal tea."

[0830] Output: Feedback message

[0831] Data processing / calculation: The emotion engine analyzes emotional information and generates appropriate feedback.

[0832] Step 7: Adjust your meal plan

[0833] Input: Analysis results and feedback messages

[0834] What it does: Based on the analysis results and feedback messages, the server determines whether the next day's meal plan needs to be adjusted, and if necessary, re-runs the generative AI model to adjust the meal plan.

[0835] Output: Tailored meal plan

[0836] Data processing / calculation: Based on the analysis results, the server runs the generative AI model again to generate a new meal plan.

[0837] Step 8: Communicate feedback and adjusted plans

[0838] Input: Adjusted meal plan and feedback message

[0839] Specific operation: The server encodes the generated feedback and adjusted meal plan in JSON format and sends an HTTP POST request to the device. The device decodes the received JSON data, displays it in the UI, and notifies the user using the notification function.

[0840] Output: User-informed feedback and adjusted plan

[0841] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[0842] (Application example 2)

[0843] 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."

[0844] Conventional health management systems have difficulty reflecting a user's emotional state and daily changing health information in real time and proposing individually personalized meal plans when visiting a store. For this reason, there is a need for support that helps users maintain their health while avoiding confusion when choosing meals when out and in physical stores. In addition, a system is needed that enables more accurate and effective health management by linking daily health data with emotional information.

[0845] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0846] In this invention, the server includes: means for inputting a user's health information; means for using a generative AI model to generate a personalized meal plan based on the health information; means for notifying the user of the meal plan; means for detecting the user's store visit using a location recognition device installed in the physical store; means for suggesting optimal foods and meal menus at the physical store based on the detection results; means for inputting daily meal content and health status; means for storing and analyzing the input daily data; means for providing real-time feedback based on the analysis results; and means for adjusting the meal plan based on the feedback. This allows the user to receive meal suggestions optimal for their health status and mood when visiting the physical store, enabling more effective daily health management.

[0847] "User's health information" refers to data and information indicating the user's individual health condition, such as the user's height, weight, blood pressure, blood sugar level, etc.

[0848] A "generative AI model" refers to a computational model that uses artificial intelligence to perform a specific task based on input data, in this case generating a personalized meal plan based on health information.

[0849] "Location-aware devices installed in physical stores" refers to devices that use technologies such as beacons, Wi-Fi, and GPS to identify a user's location within a store and their visit.

[0850] "Personalized meal plans" refer to plans and suggestions that provide optimal meal menus for each user based on each user's individual health information and dietary preferences.

[0851] "Providing feedback in real time" means responding to the user immediately based on the input data and analysis results, and providing advice and information.

[0852] "Daily dietary content and health status" refers to the food the user eats on a daily basis and health-related data such as weight, blood pressure, and emotional state on that day.

[0853] "Storing and analyzing data" refers to temporarily or long-term storage of data collected from users and performing calculations or analysis based on that data.

[0854] "Suggesting optimal foods and meal menus" refers to recommending foods and meal contents that are considered most desirable at that time based on the user's health condition and emotional information.

[0855] This invention relates to a system that provides personalized meal plans based on a user's health information, dietary preferences, and emotional information, thereby supporting users in managing their health appropriately even when visiting a physical store.

[0856] System Configuration

[0857] This system consists of the following main hardware and software components:

[0858] 1. Smartphone App

[0859] It provides an interface for users to input their health information, food preferences, and emotional information and receive personalized suggestions at physical stores.

[0860] 2. Cloud Server

[0861] It is responsible for storing and analyzing data and running generative AI models.

[0862] 3. Beacon Devices

[0863] It is installed in physical stores and used to detect when users visit the store.

[0864] 4. Generative AI Models

[0865] It is used to generate personalized meal plans based on user input data.

[0866] 5. Emotion Engine

[0867] It is used to analyze the user's emotional information and generate appropriate feedback.

[0868] Specific operation of the system

[0869] User registration and initial settings

[0870] The user launches the smartphone app and inputs information such as height, weight, blood pressure, dietary preferences, and emotional state. The input information is sent to a cloud server and stored in a database.

[0871] Generate personalized meal plans

[0872] The cloud server runs a generative AI model based on the stored user information to generate an optimal meal plan for each individual user, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[0873] Individual proposals in physical stores

[0874] When a beacon device installed in a physical store detects a user's visit, a notification is sent to a smartphone app. The cloud server then uses the user's location and health information to suggest the most suitable food and meal menu for the store. For example, if the user is having a stressful day, the app will suggest relaxing herbal tea or foods with a calming effect.

[0875] Daily monitoring and feedback

[0876] Users enter their daily diet, health status, and emotional information into a smartphone app. The data is sent to a cloud server and stored in a database. The cloud server analyzes the daily data, provides real-time feedback based on the analysis results, and adjusts the next day's meal plan as needed.

[0877] Specific examples

[0878] User registration and initial setup:

[0879] A user launches the app for the first time, enters their height (170cm), weight (70kg), blood pressure (130 / 80), dietary preference (vegetarian), and emotional information (feeling good), and submits it.

[0880] Generate a meal plan:

[0881] The cloud server runs the generative AI model, generates a plan for breakfast consisting of oatmeal and fruit, lunch consisting of kale salad and tofu steak, and dinner consisting of vegetable stew, and notifies the user.

[0882] Personalized offers in-store:

[0883] When a user visits a physical store, a beacon device detects their visit, and a cloud server suggests relaxing herbal teas for stressful days.

[0884] Examples of prompt statements

[0885] Consider the user's health information and daily emotional state to suggest the best meal plan for today.

[0886] User information: Height 170cm, weight 70kg, blood pressure 130 / 80, vegetarian, feels good.

[0887] Emotional state: Relaxed. Low stress over the past 3 days.

[0888] This allows users to receive appropriate meal suggestions when visiting a physical store, regardless of their condition, making health management more effective.

[0889] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0890] Step 1:

[0891] User registration and initial settings

[0892] (Input) The user launches the smartphone app and inputs health information such as height, weight, blood pressure, dietary preferences, and emotional information.

[0893] (Data processing) The health information entered by the user is organized in JSON format and immediately sent to the server.

[0894] (Output) The server saves the received user information in the database.

[0895] (Specific operation) In the smartphone app's user interface, you enter information using text boxes and selection lists, and then press the "Send" button, which sends the information to the server.

[0896] Step 2:

[0897] Generate personalized meal plans

[0898] (Input) User's health information stored on the server.

[0899] (Data processing) The server inputs user information into the generative AI model, which then generates the optimal meal plan for the user.

[0900] (Output) The generated meal plan is sent to a smartphone app and notified to the user.

[0901] (Specific operation) Using a pre-trained generative AI model (e.g., GPT-4) on the server, the system generates and executes prompts based on the user's health information, and then sends the resulting suggested meal plan to the app.

[0902] Step 3:

[0903] Individual proposals in physical stores

[0904] (Input) A user visits a physical store, and a beacon device installed in the store detects the user's visit.

[0905] (Data processing) The beacon device's detection information is sent to a server, which then identifies the user's current location. Using a generative AI model, the server generates optimal food and meal menus based on the user's health information and location within the store.

[0906] (Output) The generated proposal is notified to the smartphone app and presented to the user.

[0907] (Specific operation) The beacon device communicates with the user's smartphone and sends the information to the server. The server generates suggestions based on the user's location and health information and notifies the app.

[0908] Step 4:

[0909] Monitoring and input of daily dietary and health conditions

[0910] (Input) The user inputs daily dietary information, health status, and emotional information into a smartphone app.

[0911] (Data processing) The entered information is organized in JSON format and sent to the server.

[0912] (Output) The received data is stored in a database and prepared for analysis.

[0913] (Specific operation) The user enters the contents of breakfast, lunch, and dinner, as well as that day's weight, blood pressure, emotional information, etc., into the app's dedicated input screen, and when they press the "Send" button, the information is sent to the server.

[0914] Step 5:

[0915] Analysis and real-time feedback

[0916] (Input) Daily dietary and health status data.

[0917] (Data processing) The server analyzes the received data and generates feedback messages based on the user's current health and emotional state. It uses an emotion engine to analyze the emotional information in detail.

[0918] (Output) Send the generated feedback message and adjusted meal plan to the smartphone app.

[0919] (Specific operation) The server generates feedback using pattern recognition algorithms and generative AI models, and sends the generated messages and adjusted plans to the app, where the user can view them in real time.

[0920] Examples of prompts:

[0921] Consider the user's health information and daily emotional state to suggest the best meal plan for today.

[0922] User information: Height 170cm, weight 70kg, blood pressure 130 / 80, vegetarian, feels good.

[0923] Emotional state: Relaxed. Low stress over the past 3 days.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] [Third embodiment]

[0928] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0929] 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.

[0930] 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).

[0931] 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.

[0932] 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.

[0933] 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).

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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."

[0940] The present invention relates to a system that supports daily health management by proposing a personalized meal plan based on a user's health information and dietary preferences. As an embodiment of the present invention, a system using a user terminal, a server, and a generative AI model will be described.

[0941] System Overview

[0942] In this system, users input their health information and dietary preferences using a device, and a generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary and health data, provides real-time feedback, and adjusts the meal plan as needed.

[0943] Program processing

[0944] 1. User registration and initial settings

[0945] The user launches the app and enters their health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarian, allergy information) into the registration form.

[0946] The terminal transmits the input information to the server.

[0947] The server stores the received user information in a database.

[0948] 2. Generate personalized meal plans

[0949] The server generates an optimal meal plan using a generative AI model based on the stored user information.

[0950] The generative AI model creates optimal meal plans for each individual user (e.g., Breakfast: oatmeal and fruit, Lunch: salad and tofu steak, Dinner: vegetable stew).

[0951] The server transmits the generated meal plan to the terminal.

[0952] The plan will be displayed on the device and the user will be notified.

[0953] 3. Daily diet and health monitoring

[0954] The user inputs their daily diet and health status into the terminal (e.g., eating oatmeal and fruit for breakfast, measuring and inputting their weight and blood pressure).

[0955] The terminal transmits the input data to the server.

[0956] The server stores the received data in a database.

[0957] 4. Providing real-time feedback and adjusting meal plans

[0958] The server analyzes the stored monitoring data and evaluates the user's health condition.

[0959] Based on the analysis results, the server generates a feedback message for the user (e.g., "You walked a lot yesterday, so let's increase the calories in your dinner plan tonight.").

[0960] The server adjusts the next day's meal plan based on the feedback.

[0961] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[0962] The terminal displays the received feedback and plan to the user.

[0963] Specific examples

[0964] Example 1: User registration and initial settings

[0965] The user launches the app for the first time and enters their height (170cm), weight (70kg), blood pressure (130 / 80) and vegetarian preference into the device.

[0966] The terminal sends this information to the server, which stores it in a database.

[0967] Example 2: Generating a meal plan

[0968] The server runs a generative AI model based on user information to generate an optimal meal plan.

[0969] For example, a user can create a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner, and the server sends it to the terminal, which then displays it to the user.

[0970] Example 3: Daily monitoring and feedback

[0971] A user eats oatmeal and fruit for breakfast and enters that information, along with their weight and blood pressure, into a terminal.

[0972] The terminal sends this data to the server, which stores it in a database.

[0973] The server analyzes the data, generates feedback to the user (e.g., "You're losing weight well. Keep it up!"), and sends it to the device along with an adjusted meal plan.

[0974] The device displays the feedback and plan to the user.

[0975] This allows users to efficiently manage their health status and obtain specific guidelines for achieving their health goals.

[0976] The processing flow will be explained below.

[0977] Program processing

[0978] User registration and initial settings

[0979] Step 1:

[0980] When a user launches the app, a user registration form is displayed on the device.

[0981] Here, the user enters their own health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarian, allergy information).

[0982] Step 2:

[0983] The terminal transmits the health information and dietary preference data entered by the user to the server.

[0984] Step 3:

[0985] The server stores the received user information in a database.

[0986] Generate personalized meal plans

[0987] Step 4:

[0988] The server runs a generative AI model based on user information stored in a database.

[0989] Step 5:

[0990] The generative AI model automatically generates optimal meal plans for users, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[0991] Step 6:

[0992] The server transmits the generated meal plan to the terminal.

[0993] Step 7:

[0994] The terminal displays the received meal plan to the user.

[0995] Daily diet and health monitoring

[0996] Step 8:

[0997] The user inputs their daily diet and health status (e.g., weight, blood pressure) into the device. Specifically, after eating oatmeal and fruit for breakfast, they record their weight and blood pressure.

[0998] Step 9:

[0999] The terminal transmits the input data to the server.

[1000] Step 10:

[1001] The server stores the received daily data in a database.

[1002] Providing real-time feedback and adjusting meal plans

[1003] Step 11:

[1004] The server analyzes the stored monitoring data and evaluates the user's current health status.

[1005] Step 12:

[1006] The server generates a feedback message for the user based on the analysis results, such as "You walked a lot yesterday, so let's increase the calories in your dinner plan today."

[1007] Step 13:

[1008] The server will adjust the meal plan for the next day as needed.

[1009] Step 14:

[1010] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[1011] Step 15:

[1012] The terminal displays the received feedback and meal plan to the user.

[1013] This allows users to effectively manage their health and implement a tailored diet plan.

[1014] Example 1

[1015] 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."

[1016] Conventional health management systems have difficulty proposing meal plans that appropriately reflect a user's health information and dietary preferences, and lack the ability to provide real-time feedback on changes in daily dietary habits and health status, making it difficult to effectively support users in achieving their health goals.

[1017] 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.

[1018] In this invention, the server includes: a means for inputting a user's health information; a means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences; a means for notifying the user of the meal plan; a means for inputting daily meal content and health status; a means for storing and analyzing the input daily data; a means for providing real-time feedback based on the analysis results; a means for adjusting the next day's meal plan based on the feedback; and a means for reusing the generative AI model to generate and adjust the meal plan. This allows users to easily obtain an individually optimized meal plan and receive real-time feedback based on their daily health status, thereby effectively achieving their health management goals.

[1019] A "user" is an entity that utilizes the system to input health information and dietary preferences and receive a personalized meal plan.

[1020] "Health information" refers to physiological data such as the user's height, weight, blood pressure, and blood sugar level.

[1021] "Dietary preferences" refers to a user's preferred ingredients, allergy information, and specific dietary requirements such as vegetarianism.

[1022] A "terminal" is a device (e.g., smartphone, tablet, PC, etc.) that a user uses to access the system, enter information, and receive feedback.

[1023] "Server" refers to a computer system that generates user information and meal plans, stores and analyzes data, and provides real-time feedback to users.

[1024] The "database" is a system for storing data on a user's health information, dietary preferences, daily dietary content, and health status.

[1025] "Generative AI model" means an artificial intelligence model for generating personalized meal plans based on a user's health information and dietary preferences.

[1026] "Feedback" refers to advice and information provided by the server based on the results of analyzing the user's daily data.

[1027] A "meal plan" is a specific meal suggestion generated based on a user's health information and dietary preferences.

[1028] The present invention relates to a system that generates a personalized meal plan based on a user's health information and dietary preferences to support daily health management. The system includes a process in which information entered by a user using a terminal is sent to a server, and a generative AI model is used to generate and adjust the meal plan.

[1029] Hardware and software used

[1030] User devices: Devices such as smartphones, tablets, and PCs are used.

[1031] Server: Uses a high-performance computer system (e.g., Linux server).

[1032] Database: The system used to store user information (e.g. MySQL, PostgreSQL).

[1033] Generative AI model: The artificial intelligence model (e.g., OpenAI GPT-4) used to generate meal plans based on the user's health information and dietary preferences.

[1034] Network: Data communication between user terminals and servers uses the Internet or local networks.

[1035] Program processing

[1036] The system helps users manage their health through a process that includes user registration and initial setup, generating a personalized meal plan, monitoring daily diet and health status, providing real-time feedback, and adjusting the meal plan.

[1037] Specific examples

[1038] Example 1: User registration and initial settings

[1039] 1. The user launches the app and enters their height (170cm), weight (70kg), blood pressure (130 / 80) and vegetarian preference.

[1040] 2. The device sends this information to the server, which stores it in a database.

[1041] Example prompt sentence:

[1042] User information registration: Height 170cm, weight 70kg, blood pressure 130 / 80, dietary preference vegetarian.

[1043] Example 2: Generating a personalized meal plan

[1044] 1. The server reads the user information and runs the generative AI model to generate an optimal meal plan.

[1045] For example, suggest oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner.

[1046] 2. The server sends the generated plan to the terminal, which displays it to the user.

[1047] Example prompt sentence:

[1048] Generate meal plans based on the user's health information and dietary preferences.

[1049] Example 3: Daily diet and health monitoring

[1050] 1. A user eats oatmeal and fruit for breakfast and enters that information, along with their weight (70 kg) and blood pressure (130 / 80) into the terminal.

[1051] 2. The device sends this data to the server, which stores it in a database.

[1052] Example prompt sentence:

[1053] Today's diet: Oatmeal and fruit for breakfast. I weigh 70kg and my blood pressure is 130 / 80.

[1054] Example 4: Providing real-time feedback and adjusting meal plans

[1055] 1. The server analyzes the data and generates feedback to the user.

[1056] For example, you could send a message like, "You walked a lot yesterday, so let's add a few more calories to your dinner plan today."

[1057] 2. The server sends the feedback along with the adjusted meal plan for the next day to the device, which displays it to the user.

[1058] Example prompt sentence:

[1059] Generate real-time feedback based on the user's health data and adjust the next day's meal plan.

[1060] This allows users to efficiently manage their health status and obtain specific guidelines for achieving their health goals.

[1061] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1062] Step 1: User registration and initial setup

[1063] Input: The user enters health information such as height, weight, blood pressure, and dietary preferences into the app.

[1064] Operation:

[1065] 1. The user launches the app and enters their health information and dietary preferences.

[1066] 2. The device sends the entered information to the server, where the data is encrypted using a communication protocol such as HTTPS.

[1067] 3. The server validates the received user information and stores it in the database.

[1068] Output: The user's health information and dietary preferences are stored in a database.

[1069] Step 2: Generate a personalized meal plan

[1070] Input: User information and food preferences stored on the server.

[1071] Operation:

[1072] 1. The server reads the target user's health information and dietary preferences from the database.

[1073] 2. The server inputs user information into the generative AI model and generates an optimal meal plan. For example, it sends a prompt to the generative AI model saying, "Please generate a meal plan based on the user's health information and dietary preferences."

[1074] 3. The generative AI model generates a personalized meal plan.

[1075] 4. The server sends the generated meal plan to the device.

[1076] 5. The device notifies the user of the received meal plan.

[1077] Output: A personalized meal plan displayed on the user's device.

[1078] Step 3: Monitor your daily diet and health

[1079] Input: The user enters their daily diet and health status into the app.

[1080] Operation:

[1081] 1. The user uses the app to enter the day's diet (e.g., oatmeal and fruit for breakfast) and health status (e.g., weight 70 kg, blood pressure 130 / 80).

[1082] 2. The terminal sends the entered data to the server.

[1083] 3. The server stores the received daily data in a database.

[1084] Output: Daily dietary and health status data is stored in a database.

[1085] Step 4: Provide real-time feedback and adjust meal plans

[1086] Input: Daily data and data saved from the previous day.

[1087] Operation:

[1088] 1. The server analyzes daily data and evaluates the user's health condition.

[1089] 2. The server generates a feedback message for the user based on the analysis results (e.g., "You walked a lot yesterday, so let's increase the calories in today's dinner plan.").

[1090] 3. The server re-runs the generative AI model based on the feedback and adjusts the meal plan for the next day.

[1091] 4. The server sends the generated feedback and adjusted meal plan to the device.

[1092] 5. The device displays the received feedback and the adjusted meal plan to the user.

[1093] Output: Real-time feedback and adjusted meal plan displayed on the user's device.

[1094] These are the specific processing steps of this system, which helps users effectively achieve their health goals by providing personalized meal plans that are adjusted appropriately based on the user's health information and dietary preferences.

[1095] (Application example 1)

[1096] 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."

[1097] In modern society, user health management is an important issue, and providing personalized meal plans is particularly effective. However, in physical stores, it is difficult for users to select optimal ingredients and recipes based on their own health information. There is also a need for a method to measure health data in real time in physical stores and adjust meal plans based on that data. Therefore, a system is needed that provides personalized meal plans based on users' health information and dietary preferences, measures health data in physical stores, and provides real-time feedback.

[1098] 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.

[1099] In this invention, the server includes means for suggesting ingredient lists and recipes based on the user's health information and dietary preferences when shopping at a physical store, means for measuring health data at checkpoints in the physical store and transmitting the measured health data to the server, and means for adjusting meal plans based on the feedback, thereby enabling the user to select optimal ingredients and recipes in accordance with their own health condition in the physical store and receive real-time feedback based on the measured health data.

[1100] "User's health information" is data indicating the user's health condition, such as height, weight, blood pressure, and blood sugar level.

[1101] "Dietary preferences" is data that indicates the user's dietary preferences and restrictions (e.g., vegetarianism, allergy information, etc.).

[1102] A "personalized meal plan" is a meal plan that is individually customized based on a user's health information and dietary preferences.

[1103] A "generative AI model" is an artificial intelligence program that generates optimal meal plans based on a user's health information and dietary preferences.

[1104] A "physical store" is a place where a user visits in person to purchase goods, and includes, for example, a supermarket or grocery store.

[1105] The "ingredient list" is a list of ingredients that the user needs to purchase at a physical store.

[1106] A "recipe" is information that indicates specific steps and necessary ingredients for a user to cook a dish.

[1107] A "checkpoint" is the location of health measurement equipment installed in a physical store, where users can measure their health data.

[1108] "Health data" refers to data relating to the physical condition of the user, such as weight, blood pressure, etc.

[1109] "Feedback" is specific advice or information provided to the user based on analyzed health data.

[1110] "Inventory information" refers to data regarding the current inventory status of products sold in physical stores.

[1111] A "database" is a collection of digital data that stores a user's health information, dietary preferences, daily input data, and so on.

[1112] The present invention relates to a system that supports daily health management by proposing personalized meal plans based on a user's health information and dietary preferences. To implement the present invention, a system using a user terminal, a server, and a generative AI model is applied.

[1113] First, the user enters their health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarianism, allergy information) into the user terminal. The terminal also inputs health data (e.g., weight, blood pressure) acquired at checkpoints when the user visits a physical store. All of this data is sent to the server and stored in a database. The server uses a generative AI model to generate an optimal ingredient list and recipes from the input data, allowing it to provide the user with a personalized meal plan. The server also compares the physical store's inventory information with the user's ingredient list and displays a list of ingredients that the user can purchase.

[1114] The generated meal plan is then sent to the user's device (smartphone, smart glasses, head-mounted display). For example, a plan might suggest a green smoothie for breakfast, tofu salad for lunch, and tomato pasta for dinner. The user measures their health data at checkpoints within the physical store, and the data is automatically sent to the server. The server analyzes this data and generates real-time feedback based on the analysis results. For example, a user with high blood pressure might be given a comment such as "reduce your salt intake." This feedback is displayed on the user's device, and the meal plan is adjusted as necessary.

[1115] The hardware and software used includes:

[1116] Server: Stores data, analyzes it, and runs generative AI models. Uses a web framework such as Flask.

[1117] User devices: Smartphones, smart glasses, and head-mounted displays are used for user input and display of notifications.

[1118] Checkpoint: Devices that measure health data, including scales and blood pressure monitors.

[1119] Generative AI model: An AI program for generating personalized meal plans and feedback.

[1120] For example, the following prompt might be used: "The user launches the app for the first time and enters their height (165cm), weight (60kg), blood pressure (120 / 80), and preference for vegetarianism."

[1121] The system of the present invention enhances the in-store shopping experience by helping users select optimal ingredients and recipes based on their health status, and by providing real-time feedback, users can always get a meal plan tailored to their health status.

[1122] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1123] Step 1:

[1124] The user launches the app and enters their health information (e.g., height, weight, blood pressure) and dietary preferences (e.g., vegetarian, allergy information) into the device. The entered data is sent from the device to the server.

[1125] Step 2:

[1126] The server receives the entered health information and dietary preferences and stores them in a database, at which point the server checks the integrity of the data and makes sure there is no missing information.

[1127] Step 3:

[1128] The server uses a generative AI model to generate a personalized meal plan based on user information stored in a database. The generative AI model analyzes the input data and creates ingredients and recipes suitable for breakfast, lunch, and dinner. The generated meal plan is then sent from the server to the user's device.

[1129] Step 4:

[1130] The user terminal displays the received meal plan and notifies the user, who then begins shopping at a physical store according to the presented meal plan.

[1131] Step 5:

[1132] At health measurement stations installed at checkpoints within physical stores, users measure their health data, such as weight and blood pressure, which is automatically sent from the measuring device to a server.

[1133] Step 6:

[1134] The server analyzes the received health data and evaluates the user's latest health status. Based on this analysis, it generates real-time feedback, such as "Your blood pressure is high, so try to limit your salt intake."

[1135] Step 7:

[1136] The server sends the generated feedback to the user terminal and adjusts the meal plan as needed, and the adjusted meal plan is also sent back to the user terminal from the server.

[1137] Step 8:

[1138] The user device displays the received feedback and the adjusted meal plan and notifies the user, who can then adjust their food selection and meal method in the physical store based on the displayed information.

[1139] Through this series of processing steps, users can obtain optimal meal plans based on their health status, improving their shopping experience in physical stores.

[1140] 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.

[1141] The present invention relates to a system that supports daily health management by proposing a personalized meal plan taking into account a user's health information, dietary preferences, and emotions. In particular, by combining a generative AI model and an emotion engine, the system provides feedback and adjusts the meal plan according to the user's emotional state. As an embodiment of the present invention, a system using a user terminal, a server, a generative AI model, and an emotion engine will be described.

[1142] System Overview

[1143] In this system, users input their health information, dietary preferences, and emotional information using a device, and a generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary content, health status, and emotional information, providing real-time feedback and adjusting the meal plan as needed.

[1144] Program processing

[1145] 1. User registration and initial settings

[1146] The user launches the app and fills in the registration form with their health information (e.g., height, weight, blood pressure, blood sugar level), food preferences (e.g., vegetarian, allergy information), and emotional information (e.g., feeling good, feeling stressed).

[1147] The terminal transmits the input information to the server.

[1148] The server stores the received user information in a database.

[1149] 2. Generate personalized meal plans

[1150] The server runs a generative AI model based on user information stored in a database.

[1151] The generative AI model automatically generates optimal meal plans for users, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[1152] The server transmits the generated meal plan to the terminal.

[1153] The plan will be displayed on the device and the user will be notified.

[1154] 3. Daily diet and health monitoring

[1155] The user inputs their daily diet, health status (e.g., weight, blood pressure), and emotional information into the device. Specifically, after eating oatmeal and fruit for breakfast, they record their weight, blood pressure, and emotional state (e.g., feeling relaxed).

[1156] The terminal transmits the input data to the server.

[1157] The server stores the received daily data in a database.

[1158] 4. Providing real-time feedback and adjusting meal plans

[1159] The server analyzes the stored monitoring data and evaluates the user's current health and emotional state based on the analysis results.

[1160] The emotion engine analyzes the emotional information entered by the user and generates a feedback message according to that state, such as "On stressful days, we recommend a relaxing herbal tea."

[1161] The server generates a feedback message for the user based on the analysis results and emotional information.

[1162] The server will adjust the meal plan for the next day as needed.

[1163] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[1164] The terminal displays the received feedback and plan to the user.

[1165] Specific examples

[1166] Example 1: User registration and initial settings

[1167] The user launches the app for the first time and enters their height (170 cm), weight (70 kg), blood pressure (130 / 80), dietary preference (vegetarian) and emotional information (feeling good) into the device.

[1168] The terminal sends this information to the server, which stores it in a database.

[1169] Example 2: Generating a meal plan

[1170] The server runs a generative AI model based on user information to generate an optimal meal plan.

[1171] For example, a user can create a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner, and the server sends it to the terminal, which then displays it to the user.

[1172] Example 3: Daily monitoring and feedback

[1173] The user eats oatmeal and fruit for breakfast and enters that information, along with their weight, blood pressure, and emotional status (relaxed) into the terminal.

[1174] The terminal sends this data to the server, which stores it in a database.

[1175] The server analyzes the data, generates feedback to the user (e.g., "You're losing weight well. Keep it up!"), and sends it to the device along with an adjusted meal plan.

[1176] The emotion engine analyzes the user's emotional information and generates feedback according to the emotion (e.g., "You are feeling less stressed, so continue your diet as planned").

[1177] The server notifies the terminal of the feedback and plan, which is then displayed to the user.

[1178] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[1179] The processing flow will be explained below.

[1180] Program processing

[1181] User registration and initial settings

[1182] Step 1:

[1183] The user launches the app and a user registration form appears on the device.

[1184] Users input health information such as height, weight, blood pressure, and blood sugar level, dietary preferences such as vegetarianism or allergies, and emotional information (e.g., feeling good, feeling stressed).

[1185] Step 2:

[1186] The terminal transmits the input health information, dietary preferences, and emotional information to the server.

[1187] Step 3:

[1188] The server stores the received user information in a database.

[1189] Generate personalized meal plans

[1190] Step 4:

[1191] The server runs a generative AI model based on user information stored in a database.

[1192] Step 5:

[1193] The generative AI model takes into account the user's health information and food preferences to generate an optimal meal plan, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[1194] Step 6:

[1195] The server transmits the generated meal plan to the terminal.

[1196] Step 7:

[1197] The terminal displays the received meal plan to the user.

[1198] Daily diet, health and emotional monitoring

[1199] Step 8:

[1200] The user inputs daily dietary information, health status (e.g., weight, blood pressure), and emotional information into the device. For example, after eating oatmeal and fruit for breakfast, the user records their weight, blood pressure, and emotional state (relaxed).

[1201] Step 9:

[1202] The terminal transmits the input data to the server.

[1203] Step 10:

[1204] The server stores the received daily data in a database.

[1205] Providing real-time feedback and adjusting meal plans

[1206] Step 11:

[1207] The server analyzes the stored monitoring data and assesses the user's current health and emotional state.

[1208] Step 12:

[1209] The emotion engine analyzes the emotion information entered by the user and generates feedback messages based on the user's emotional state. For example, if the user is feeling stressed, it generates a message such as "I recommend some herbal tea to help you relax."

[1210] Step 13:

[1211] The server generates a comprehensive feedback message for the user based on the analysis results and the feedback of the emotion engine.

[1212] Step 14:

[1213] The server will adjust the meal plan for the next day as needed, for example, by encouraging more light meals and stress-reducing foods if the emotion engine's analysis indicates high stress levels.

[1214] Step 15:

[1215] The server transmits the generated feedback message and the adjusted meal plan to the terminal.

[1216] Step 16:

[1217] The terminal displays the received feedback message and meal plan to the user.

[1218] This allows users to effectively manage their health and emotions and achieve their health goals through individually tailored meal plans.

[1219] Example 2

[1220] 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."

[1221] Health management requires providing personalized meal plans that comprehensively consider a user's health information, food preferences, and emotional state. Conventional systems have struggled to analyze this information in real time and provide optimal feedback and meal plan adjustments for individual users. In particular, feedback and meal plan adjustments that take into account a user's emotional state must be performed in a meticulous manner according to daily changes, but conventional technologies have been unable to achieve this.

[1222] 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.

[1223] In this invention, the server includes: means for inputting a user's health information and dietary preferences; means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences; means for generating and executing prompts for the generative AI model; means for notifying the user of the meal plan; means for inputting daily meal content, health status, and emotional information; means for storing and analyzing the input daily data; means for evaluating the user's health status and emotional status based on the stored data; means for analyzing the emotional information and providing feedback according to the user's emotions; and means for adjusting the meal plan based on the feedback and analysis results. This makes it possible to provide and adjust an optimal individual meal plan in real time by comprehensively considering the user's health information, dietary preferences, and emotional status.

[1224] "User's health information" refers to physical data such as the user's height, weight, blood pressure, and blood sugar level.

[1225] "Dietary preferences" refers to data about a user's preferences, such as the types of ingredients and dishes they like, allergy information, and specific dietary restrictions.

[1226] "Generative AI model" refers to an artificial intelligence model that automatically generates personalized meal plans based on input data.

[1227] "Emotional information" refers to data related to the user's emotional state, such as the user's mood, stress level, or happiness.

[1228] A "prompt statement" is a statement that describes input data in the format required to run a generative AI model.

[1229] "Notification Method" means the mechanism for notifying the user of the generated meal plan and feedback, including methods such as in-app notifications, email, and SMS.

[1230] "Monitoring" refers to the continuous observation and collection of data on a user's daily diet, health status, and emotional information.

[1231] "Means for providing real-time feedback" refers to a mechanism that generates instant feedback based on the user's most recent data and notifies the user of that information.

[1232] "Adjusting meal plans" refers to making appropriate changes to existing meal plans based on the user's daily data and feedback.

[1233] "Database" refers to a digital storage system for storing and managing a user's health information, dietary preferences, daily data, etc.

[1234] This invention relates to a system that supports daily health management by proposing a personalized meal plan based on the user's health information, food preferences, and emotional state. By combining a generative AI model and an emotion engine, this system can provide feedback and adjust the meal plan according to the user's emotional state.

[1235] Hardware and software used

[1236] The hardware and software used in this system are as follows:

[1237] User device: A mobile device, such as a smartphone or tablet, that a user uses to enter information.

[1238] Server: A cloud or on-premise server that stores data, analyzes it, and runs generative AI models.

[1239] Generative AI model: An artificial intelligence model for generating personalized meal plans from user data.

[1240] Emotion engine: An analysis engine for analyzing the user's emotional information and generating appropriate feedback.

[1241] System Overview

[1242] Users input their health information, dietary preferences, and emotional information using their device, and the generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary content, health status, and emotional information, providing real-time feedback and adjusting the meal plan as needed.

[1243] Specific examples

[1244] 1. User registration and initial settings

[1245] The user starts the app for the first time and enters their height (170cm), weight (70kg), blood pressure (130 / 80), food preference (vegetarian) and emotional information (feeling good). The device sends this information to the server, which stores it in a database.

[1246] 2. Generate a meal plan

[1247] The server runs a generative AI model based on user information to generate an optimal meal plan. For example, it creates a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner. The server sends the plan to the device, which displays it to the user.

[1248] 3. Daily monitoring and feedback

[1249] The user eats oatmeal and fruit for breakfast and enters that information, along with their weight, blood pressure, and emotional state (relaxed) into the device. The device sends this data to the server, which stores it in a database. The server analyzes the data, generates feedback for the user, and sends it back to the device along with an adjusted meal plan.

[1250] Prompt Sentence Examples

[1251] "Create a personalized meal plan based on your health and emotional information. Specifically, I'm 170cm tall, weigh 70kg, feel great, and am a vegetarian."

[1252] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[1253] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1254] Step 1: User registration and initial setup

[1255] Input: User's health information (e.g., height 170cm, weight 70kg, blood pressure 130 / 80), food preferences (e.g., vegetarian), emotional information (e.g., feeling good)

[1256] Specific operation: A user launches the app, enters the required information in the registration form, and presses the submit button. The device converts the entered information into JSON format and sends an HTTP POST request to the server.

[1257] Output: User information sent to the server

[1258] Data processing / calculation: The server parses the received JSON data and executes an INSERT query to the database to save the user information.

[1259] Step 2: Generate a personalized meal plan

[1260] Input: User information stored in the database

[1261] Specific operation: The server retrieves user information from the database using a SELECT query and passes it to the generative AI model as a prompt statement.

[1262] Output: A personalized meal plan returned by the generative AI model

[1263] Data processing / calculation: The server generates a prompt and calls the API of the generative AI model to generate a meal plan. The generative AI model analyzes the prompt and generates a corresponding meal plan.

[1264] Step 3: Meal plan notification

[1265] Input: Generated meal plan

[1266] Specific operation: The server encodes the generated meal plan in JSON format and sends an HTTP POST request to the device. The device decodes the received JSON data, displays it in the UI, and notifies the user of the plan using the notification function.

[1267] Output: Meal plan notified to user

[1268] Step 4: Monitor your daily diet and health

[1269] Input: User's diet, health status (e.g., weight, blood pressure), emotional information

[1270] Specific operation: The user inputs daily dietary information, health status, and emotional information into the device. The device converts this data into JSON format and sends an HTTP POST request to the server.

[1271] Output: Daily data sent to the server

[1272] Data processing / calculation: The server parses the incoming data and executes INSERT queries in the database to store the daily data.

[1273] Step 5: Data analysis and evaluation

[1274] Input: Daily data stored in a database

[1275] What it does: The server retrieves daily data from the database using SELECT queries and runs analytical algorithms to assess the user's current health and emotional state.

[1276] Output: Analysis results and evaluation data

[1277] Data processing / calculation: The server analyzes the received data and evaluates the user's health and emotional state.

[1278] Step 6: Generate emotional feedback

[1279] Input: User's emotional information

[1280] How it works: The emotion engine analyzes the user's emotional information and generates feedback messages according to their state. For example, it generates feedback such as, "On stressful days, we recommend a relaxing herbal tea."

[1281] Output: Feedback message

[1282] Data processing / calculation: The emotion engine analyzes emotional information and generates appropriate feedback.

[1283] Step 7: Adjust your meal plan

[1284] Input: Analysis results and feedback messages

[1285] What it does: Based on the analysis results and feedback messages, the server determines whether the next day's meal plan needs to be adjusted, and if necessary, re-runs the generative AI model to adjust the meal plan.

[1286] Output: Tailored meal plan

[1287] Data processing / calculation: Based on the analysis results, the server runs the generative AI model again to generate a new meal plan.

[1288] Step 8: Communicate feedback and adjusted plans

[1289] Input: Adjusted meal plan and feedback message

[1290] Specific operation: The server encodes the generated feedback and adjusted meal plan in JSON format and sends an HTTP POST request to the device. The device decodes the received JSON data, displays it in the UI, and notifies the user using the notification function.

[1291] Output: User-informed feedback and adjusted plan

[1292] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[1293] (Application example 2)

[1294] 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."

[1295] Conventional health management systems have difficulty reflecting a user's emotional state and daily changing health information in real time and proposing individually personalized meal plans when visiting a store. For this reason, there is a need for support that helps users maintain their health while avoiding confusion when choosing meals when out and in physical stores. In addition, a system is needed that enables more accurate and effective health management by linking daily health data with emotional information.

[1296] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1297] In this invention, the server includes: means for inputting a user's health information; means for using a generative AI model to generate a personalized meal plan based on the health information; means for notifying the user of the meal plan; means for detecting the user's store visit using a location recognition device installed in the physical store; means for suggesting optimal foods and meal menus at the physical store based on the detection results; means for inputting daily meal content and health status; means for storing and analyzing the input daily data; means for providing real-time feedback based on the analysis results; and means for adjusting the meal plan based on the feedback. This allows the user to receive meal suggestions optimal for their health status and mood when visiting the physical store, enabling more effective daily health management.

[1298] "User's health information" refers to data and information indicating the user's individual health condition, such as the user's height, weight, blood pressure, blood sugar level, etc.

[1299] A "generative AI model" refers to a computational model that uses artificial intelligence to perform a specific task based on input data, in this case generating a personalized meal plan based on health information.

[1300] "Location-aware devices installed in physical stores" refers to devices that use technologies such as beacons, Wi-Fi, and GPS to identify a user's location within a store and their visit.

[1301] "Personalized meal plans" refer to plans and suggestions that provide optimal meal menus for each user based on each user's individual health information and dietary preferences.

[1302] "Providing feedback in real time" means responding to the user immediately based on the input data and analysis results, and providing advice and information.

[1303] "Daily dietary content and health status" refers to the food the user eats on a daily basis and health-related data such as weight, blood pressure, and emotional state on that day.

[1304] "Storing and analyzing data" refers to temporarily or long-term storage of data collected from users and performing calculations or analysis based on that data.

[1305] "Suggesting optimal foods and meal menus" refers to recommending foods and meal contents that are considered most desirable at that time based on the user's health condition and emotional information.

[1306] This invention relates to a system that provides personalized meal plans based on a user's health information, dietary preferences, and emotional information, thereby supporting users in managing their health appropriately even when visiting a physical store.

[1307] System Configuration

[1308] This system consists of the following main hardware and software components:

[1309] 1. Smartphone App

[1310] It provides an interface for users to input their health information, food preferences, and emotional information and receive personalized suggestions at physical stores.

[1311] 2. Cloud Server

[1312] It is responsible for storing and analyzing data and running generative AI models.

[1313] 3. Beacon Devices

[1314] It is installed in physical stores and used to detect when users visit the store.

[1315] 4. Generative AI Models

[1316] It is used to generate personalized meal plans based on user input data.

[1317] 5. Emotion Engine

[1318] It is used to analyze the user's emotional information and generate appropriate feedback.

[1319] Specific operation of the system

[1320] User registration and initial settings

[1321] The user launches the smartphone app and inputs information such as height, weight, blood pressure, dietary preferences, and emotional state. The input information is sent to a cloud server and stored in a database.

[1322] Generate personalized meal plans

[1323] The cloud server runs a generative AI model based on the stored user information to generate an optimal meal plan for each individual user, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[1324] Individual proposals in physical stores

[1325] When a beacon device installed in a physical store detects a user's visit, a notification is sent to a smartphone app. The cloud server then uses the user's location and health information to suggest the most suitable food and meal menu for the store. For example, if the user is having a stressful day, the app will suggest relaxing herbal tea or foods with a calming effect.

[1326] Daily monitoring and feedback

[1327] Users enter their daily diet, health status, and emotional information into a smartphone app. The data is sent to a cloud server and stored in a database. The cloud server analyzes the daily data, provides real-time feedback based on the analysis results, and adjusts the next day's meal plan as needed.

[1328] Specific examples

[1329] User registration and initial setup:

[1330] A user launches the app for the first time, enters their height (170cm), weight (70kg), blood pressure (130 / 80), dietary preference (vegetarian), and emotional information (feeling good), and submits it.

[1331] Generate a meal plan:

[1332] The cloud server runs the generative AI model, generates a plan for breakfast consisting of oatmeal and fruit, lunch consisting of kale salad and tofu steak, and dinner consisting of vegetable stew, and notifies the user.

[1333] Personalized offers in-store:

[1334] When a user visits a physical store, a beacon device detects their visit, and a cloud server suggests relaxing herbal teas for stressful days.

[1335] Examples of prompt statements

[1336] Consider the user's health information and daily emotional state to suggest the best meal plan for today.

[1337] User information: Height 170cm, weight 70kg, blood pressure 130 / 80, vegetarian, feels good.

[1338] Emotional state: Relaxed. Low stress over the past 3 days.

[1339] This allows users to receive appropriate meal suggestions when visiting a physical store, regardless of their condition, making health management more effective.

[1340] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1341] Step 1:

[1342] User registration and initial settings

[1343] (Input) The user launches the smartphone app and inputs health information such as height, weight, blood pressure, dietary preferences, and emotional information.

[1344] (Data processing) The health information entered by the user is organized in JSON format and immediately sent to the server.

[1345] (Output) The server saves the received user information in the database.

[1346] (Specific operation) In the smartphone app's user interface, you enter information using text boxes and selection lists, and then press the "Send" button, which sends the information to the server.

[1347] Step 2:

[1348] Generate personalized meal plans

[1349] (Input) User's health information stored on the server.

[1350] (Data processing) The server inputs user information into the generative AI model, which then generates the optimal meal plan for the user.

[1351] (Output) The generated meal plan is sent to a smartphone app and notified to the user.

[1352] (Specific operation) Using a pre-trained generative AI model (e.g., GPT-4) on the server, the system generates and executes prompts based on the user's health information, and then sends the resulting suggested meal plan to the app.

[1353] Step 3:

[1354] Individual proposals in physical stores

[1355] (Input) A user visits a physical store, and a beacon device installed in the store detects the user's visit.

[1356] (Data processing) The beacon device's detection information is sent to a server, which then identifies the user's current location. Using a generative AI model, the server generates optimal food and meal menus based on the user's health information and location within the store.

[1357] (Output) The generated proposal is notified to the smartphone app and presented to the user.

[1358] (Specific operation) The beacon device communicates with the user's smartphone and sends the information to the server. The server generates suggestions based on the user's location and health information and notifies the app.

[1359] Step 4:

[1360] Monitoring and input of daily dietary and health conditions

[1361] (Input) The user inputs daily dietary information, health status, and emotional information into a smartphone app.

[1362] (Data processing) The entered information is organized in JSON format and sent to the server.

[1363] (Output) The received data is stored in a database and prepared for analysis.

[1364] (Specific operation) The user enters the contents of breakfast, lunch, and dinner, as well as that day's weight, blood pressure, emotional information, etc., into the app's dedicated input screen, and when they press the "Send" button, the information is sent to the server.

[1365] Step 5:

[1366] Analysis and real-time feedback

[1367] (Input) Daily dietary and health status data.

[1368] (Data processing) The server analyzes the received data and generates feedback messages based on the user's current health and emotional state. It uses an emotion engine to analyze the emotional information in detail.

[1369] (Output) Send the generated feedback message and adjusted meal plan to the smartphone app.

[1370] (Specific operation) The server generates feedback using pattern recognition algorithms and generative AI models, and sends the generated messages and adjusted plans to the app, where the user can view them in real time.

[1371] Examples of prompts:

[1372] Consider the user's health information and daily emotional state to suggest the best meal plan for today.

[1373] User information: Height 170cm, weight 70kg, blood pressure 130 / 80, vegetarian, feels good.

[1374] Emotional state: Relaxed. Low stress over the past 3 days.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] [Fourth embodiment]

[1379] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1380] 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.

[1381] 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).

[1382] 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.

[1383] 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.

[1384] 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).

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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."

[1392] The present invention relates to a system that supports daily health management by proposing a personalized meal plan based on a user's health information and dietary preferences. As an embodiment of the present invention, a system using a user terminal, a server, and a generative AI model will be described.

[1393] System Overview

[1394] In this system, users input their health information and dietary preferences using a device, and a generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary and health data, provides real-time feedback, and adjusts the meal plan as needed.

[1395] Program processing

[1396] 1. User registration and initial settings

[1397] The user launches the app and enters their health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarian, allergy information) into the registration form.

[1398] The terminal transmits the input information to the server.

[1399] The server stores the received user information in a database.

[1400] 2. Generate personalized meal plans

[1401] The server generates an optimal meal plan using a generative AI model based on the stored user information.

[1402] The generative AI model creates optimal meal plans for each individual user (e.g., Breakfast: oatmeal and fruit, Lunch: salad and tofu steak, Dinner: vegetable stew).

[1403] The server transmits the generated meal plan to the terminal.

[1404] The plan will be displayed on the device and the user will be notified.

[1405] 3. Daily diet and health monitoring

[1406] The user inputs their daily diet and health status into the terminal (e.g., eating oatmeal and fruit for breakfast, measuring and inputting their weight and blood pressure).

[1407] The terminal transmits the input data to the server.

[1408] The server stores the received data in a database.

[1409] 4. Providing real-time feedback and adjusting meal plans

[1410] The server analyzes the stored monitoring data and evaluates the user's health condition.

[1411] Based on the analysis results, the server generates a feedback message for the user (e.g., "You walked a lot yesterday, so let's increase the calories in your dinner plan tonight.").

[1412] The server adjusts the next day's meal plan based on the feedback.

[1413] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[1414] The terminal displays the received feedback and plan to the user.

[1415] Specific examples

[1416] Example 1: User registration and initial settings

[1417] The user launches the app for the first time and enters their height (170cm), weight (70kg), blood pressure (130 / 80) and vegetarian preference into the device.

[1418] The terminal sends this information to the server, which stores it in a database.

[1419] Example 2: Generating a meal plan

[1420] The server runs a generative AI model based on user information to generate an optimal meal plan.

[1421] For example, a user can create a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner, and the server sends it to the terminal, which then displays it to the user.

[1422] Example 3: Daily monitoring and feedback

[1423] A user eats oatmeal and fruit for breakfast and enters that information, along with their weight and blood pressure, into a terminal.

[1424] The terminal sends this data to the server, which stores it in a database.

[1425] The server analyzes the data, generates feedback to the user (e.g., "You're losing weight well. Keep it up!"), and sends it to the device along with an adjusted meal plan.

[1426] The device displays the feedback and plan to the user.

[1427] This allows users to efficiently manage their health status and obtain specific guidelines for achieving their health goals.

[1428] The processing flow will be explained below.

[1429] Program processing

[1430] User registration and initial settings

[1431] Step 1:

[1432] When a user launches the app, a user registration form is displayed on the device.

[1433] Here, the user enters their own health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarian, allergy information).

[1434] Step 2:

[1435] The terminal transmits the health information and dietary preference data entered by the user to the server.

[1436] Step 3:

[1437] The server stores the received user information in a database.

[1438] Generate personalized meal plans

[1439] Step 4:

[1440] The server runs a generative AI model based on user information stored in a database.

[1441] Step 5:

[1442] The generative AI model automatically generates optimal meal plans for users, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[1443] Step 6:

[1444] The server transmits the generated meal plan to the terminal.

[1445] Step 7:

[1446] The terminal displays the received meal plan to the user.

[1447] Daily diet and health monitoring

[1448] Step 8:

[1449] The user inputs their daily diet and health status (e.g., weight, blood pressure) into the device. Specifically, after eating oatmeal and fruit for breakfast, they record their weight and blood pressure.

[1450] Step 9:

[1451] The terminal transmits the input data to the server.

[1452] Step 10:

[1453] The server stores the received daily data in a database.

[1454] Providing real-time feedback and adjusting meal plans

[1455] Step 11:

[1456] The server analyzes the stored monitoring data and evaluates the user's current health status.

[1457] Step 12:

[1458] The server generates a feedback message for the user based on the analysis results, such as "You walked a lot yesterday, so let's increase the calories in your dinner plan today."

[1459] Step 13:

[1460] The server will adjust the meal plan for the next day as needed.

[1461] Step 14:

[1462] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[1463] Step 15:

[1464] The terminal displays the received feedback and meal plan to the user.

[1465] This allows users to effectively manage their health and implement a tailored diet plan.

[1466] Example 1

[1467] 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."

[1468] Conventional health management systems have difficulty proposing meal plans that appropriately reflect a user's health information and dietary preferences, and lack the ability to provide real-time feedback on changes in daily dietary habits and health status, making it difficult to effectively support users in achieving their health goals.

[1469] 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.

[1470] In this invention, the server includes: a means for inputting a user's health information; a means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences; a means for notifying the user of the meal plan; a means for inputting daily meal content and health status; a means for storing and analyzing the input daily data; a means for providing real-time feedback based on the analysis results; a means for adjusting the next day's meal plan based on the feedback; and a means for reusing the generative AI model to generate and adjust the meal plan. This allows users to easily obtain an individually optimized meal plan and receive real-time feedback based on their daily health status, thereby effectively achieving their health management goals.

[1471] A "user" is an entity that utilizes the system to input health information and dietary preferences and receive a personalized meal plan.

[1472] "Health information" refers to physiological data such as the user's height, weight, blood pressure, and blood sugar level.

[1473] "Dietary preferences" refers to a user's preferred ingredients, allergy information, and specific dietary requirements such as vegetarianism.

[1474] A "terminal" is a device (e.g., smartphone, tablet, PC, etc.) that a user uses to access the system, enter information, and receive feedback.

[1475] "Server" refers to a computer system that generates user information and meal plans, stores and analyzes data, and provides real-time feedback to users.

[1476] The "database" is a system for storing data on a user's health information, dietary preferences, daily dietary content, and health status.

[1477] "Generative AI model" means an artificial intelligence model for generating personalized meal plans based on a user's health information and dietary preferences.

[1478] "Feedback" refers to advice and information provided by the server based on the results of analyzing the user's daily data.

[1479] A "meal plan" is a specific meal suggestion generated based on a user's health information and dietary preferences.

[1480] The present invention relates to a system that generates a personalized meal plan based on a user's health information and dietary preferences to support daily health management. The system includes a process in which information entered by a user using a terminal is sent to a server, and a generative AI model is used to generate and adjust the meal plan.

[1481] Hardware and software used

[1482] User devices: Devices such as smartphones, tablets, and PCs are used.

[1483] Server: Uses a high-performance computer system (e.g., Linux server).

[1484] Database: A system for storing user information (e.g. MySQL, PostgreSQL).

[1485] Generative AI model: The artificial intelligence model (e.g., OpenAI GPT-4) used to generate meal plans based on the user's health information and dietary preferences.

[1486] Network: Data communication between user terminals and servers uses the Internet or local networks.

[1487] Program processing

[1488] The system helps users manage their health through a process that includes user registration and initial setup, generating a personalized meal plan, monitoring daily diet and health status, providing real-time feedback, and adjusting the meal plan.

[1489] Specific examples

[1490] Example 1: User registration and initial settings

[1491] 1. The user launches the app and enters their height (170cm), weight (70kg), blood pressure (130 / 80) and vegetarian preference.

[1492] 2. The device sends this information to the server, which stores it in a database.

[1493] Example prompt sentence:

[1494] User information registration: Height 170cm, weight 70kg, blood pressure 130 / 80, dietary preference vegetarian.

[1495] Example 2: Generating a personalized meal plan

[1496] 1. The server reads the user information and runs the generative AI model to generate an optimal meal plan.

[1497] For example, suggest oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner.

[1498] 2. The server sends the generated plan to the terminal, which displays it to the user.

[1499] Example prompt sentence:

[1500] Generate meal plans based on the user's health information and dietary preferences.

[1501] Example 3: Daily diet and health monitoring

[1502] 1. A user eats oatmeal and fruit for breakfast and enters that information, along with their weight (70 kg) and blood pressure (130 / 80) into the terminal.

[1503] 2. The device sends this data to the server, which stores it in a database.

[1504] Example prompt sentence:

[1505] Today's diet: Oatmeal and fruit for breakfast. I weigh 70kg and my blood pressure is 130 / 80.

[1506] Example 4: Providing real-time feedback and adjusting meal plans

[1507] 1. The server analyzes the data and generates feedback to the user.

[1508] For example, you could send a message like, "You walked a lot yesterday, so let's add a few more calories to your dinner plan today."

[1509] 2. The server sends the feedback along with the adjusted meal plan for the next day to the device, which displays it to the user.

[1510] Example prompt sentence:

[1511] Generate real-time feedback based on the user's health data and adjust the next day's meal plan.

[1512] This allows users to efficiently manage their health status and obtain specific guidelines for achieving their health goals.

[1513] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1514] Step 1: User registration and initial setup

[1515] Input: The user enters health information such as height, weight, blood pressure, and dietary preferences into the app.

[1516] Operation:

[1517] 1. The user launches the app and enters their health information and dietary preferences.

[1518] 2. The device sends the entered information to the server, where the data is encrypted using a communication protocol such as HTTPS.

[1519] 3. The server validates the received user information and stores it in the database.

[1520] Output: The user's health information and dietary preferences are stored in a database.

[1521] Step 2: Generate a personalized meal plan

[1522] Input: User information and food preferences stored on the server.

[1523] Operation:

[1524] 1. The server reads the target user's health information and dietary preferences from the database.

[1525] 2. The server inputs user information into the generative AI model and generates an optimal meal plan. For example, it sends a prompt to the generative AI model saying, "Please generate a meal plan based on the user's health information and dietary preferences."

[1526] 3. The generative AI model generates a personalized meal plan.

[1527] 4. The server sends the generated meal plan to the device.

[1528] 5. The device notifies the user of the received meal plan.

[1529] Output: A personalized meal plan displayed on the user's device.

[1530] Step 3: Monitor your daily diet and health

[1531] Input: The user enters their daily diet and health status into the app.

[1532] Operation:

[1533] 1. The user uses the app to enter the day's diet (e.g., oatmeal and fruit for breakfast) and health status (e.g., weight 70 kg, blood pressure 130 / 80).

[1534] 2. The terminal sends the entered data to the server.

[1535] 3. The server stores the received daily data in a database.

[1536] Output: Daily dietary and health status data is stored in a database.

[1537] Step 4: Provide real-time feedback and adjust meal plans

[1538] Input: Daily data and data saved from the previous day.

[1539] Operation:

[1540] 1. The server analyzes daily data and evaluates the user's health condition.

[1541] 2. The server generates a feedback message for the user based on the analysis results (e.g., "You walked a lot yesterday, so let's increase the calories in today's dinner plan.").

[1542] 3. The server re-runs the generative AI model based on the feedback and adjusts the meal plan for the next day.

[1543] 4. The server sends the generated feedback and adjusted meal plan to the device.

[1544] 5. The device displays the received feedback and the adjusted meal plan to the user.

[1545] Output: Real-time feedback and adjusted meal plan displayed on the user's device.

[1546] These are the specific processing steps of this system, which helps users effectively achieve their health goals by providing personalized meal plans that are adjusted appropriately based on the user's health information and dietary preferences.

[1547] (Application example 1)

[1548] 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."

[1549] In modern society, user health management is an important issue, and providing personalized meal plans is particularly effective. However, in physical stores, it is difficult for users to select optimal ingredients and recipes based on their own health information. There is also a need for a method to measure health data in real time in physical stores and adjust meal plans based on that data. Therefore, a system is needed that provides personalized meal plans based on users' health information and dietary preferences, measures health data in physical stores, and provides real-time feedback.

[1550] 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.

[1551] In this invention, the server includes means for suggesting ingredient lists and recipes based on the user's health information and dietary preferences when shopping at a physical store, means for measuring health data at checkpoints in the physical store and transmitting the measured health data to the server, and means for adjusting meal plans based on the feedback, thereby enabling the user to select optimal ingredients and recipes in accordance with their own health condition in the physical store and receive real-time feedback based on the measured health data.

[1552] "User's health information" is data indicating the user's health condition, such as height, weight, blood pressure, and blood sugar level.

[1553] "Dietary preferences" is data that indicates the user's dietary preferences and restrictions (e.g., vegetarianism, allergy information, etc.).

[1554] A "personalized meal plan" is a meal plan that is individually customized based on a user's health information and dietary preferences.

[1555] A "generative AI model" is an artificial intelligence program that generates optimal meal plans based on a user's health information and dietary preferences.

[1556] A "physical store" is a place where a user visits in person to purchase goods, and includes, for example, a supermarket or grocery store.

[1557] The "ingredient list" is a list of ingredients that the user needs to purchase at a physical store.

[1558] A "recipe" is information that indicates specific steps and necessary ingredients for a user to cook a dish.

[1559] A "checkpoint" is the location of health measurement equipment installed in a physical store, where users can measure their health data.

[1560] "Health data" refers to data relating to the physical condition of the user, such as weight, blood pressure, etc.

[1561] "Feedback" refers to specific advice or information provided to the user based on analyzed health data.

[1562] "Inventory information" refers to data regarding the current inventory status of products sold in physical stores.

[1563] A "database" is a collection of digital data that stores a user's health information, dietary preferences, daily input data, and so on.

[1564] The present invention relates to a system that supports daily health management by proposing personalized meal plans based on a user's health information and dietary preferences. To implement the present invention, a system using a user terminal, a server, and a generative AI model is applied.

[1565] First, the user enters their health information (e.g., height, weight, blood pressure, blood sugar level) and dietary preferences (e.g., vegetarianism, allergy information) into the user terminal. The terminal also inputs health data (e.g., weight, blood pressure) acquired at checkpoints when the user visits a physical store. All of this data is sent to the server and stored in a database. The server uses a generative AI model to generate an optimal ingredient list and recipes from the input data, allowing it to provide the user with a personalized meal plan. The server also compares the physical store's inventory information with the user's ingredient list and displays a list of ingredients that the user can purchase.

[1566] The generated meal plan is then sent to the user's device (smartphone, smart glasses, head-mounted display). For example, a plan might suggest a green smoothie for breakfast, tofu salad for lunch, and tomato pasta for dinner. The user measures their health data at checkpoints within the physical store, and the data is automatically sent to the server. The server analyzes this data and generates real-time feedback based on the analysis results. For example, a user with high blood pressure might be given a comment such as "reduce your salt intake." This feedback is displayed on the user's device, and the meal plan is adjusted as necessary.

[1567] The hardware and software used includes:

[1568] Server: Stores data, analyzes it, and runs generative AI models. Uses a web framework such as Flask.

[1569] User devices: Smartphones, smart glasses, and head-mounted displays are used for user input and display of notifications.

[1570] Checkpoint: Devices that measure health data, including scales and blood pressure monitors.

[1571] Generative AI model: An AI program for generating personalized meal plans and feedback.

[1572] For example, the following prompt might be used: "The user launches the app for the first time and enters their height (165cm), weight (60kg), blood pressure (120 / 80), and preference for vegetarianism."

[1573] The system of the present invention enhances the in-store shopping experience by helping users select optimal ingredients and recipes based on their health status, and by providing real-time feedback, users can always get a meal plan tailored to their health status.

[1574] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1575] Step 1:

[1576] The user launches the app and enters their health information (e.g., height, weight, blood pressure) and dietary preferences (e.g., vegetarian, allergy information) into the device. The entered data is sent from the device to the server.

[1577] Step 2:

[1578] The server receives the entered health information and dietary preferences and stores them in a database, at which point the server checks the integrity of the data and makes sure there is no missing information.

[1579] Step 3:

[1580] The server uses a generative AI model to generate a personalized meal plan based on user information stored in a database. The generative AI model analyzes the input data and creates ingredients and recipes suitable for breakfast, lunch, and dinner. The generated meal plan is then sent from the server to the user's device.

[1581] Step 4:

[1582] The user terminal displays the received meal plan and notifies the user, who then begins shopping at a physical store according to the presented meal plan.

[1583] Step 5:

[1584] At health measurement stations installed at checkpoints within physical stores, users measure their health data, such as weight and blood pressure, which is automatically sent from the measuring device to a server.

[1585] Step 6:

[1586] The server analyzes the received health data and evaluates the user's latest health status. Based on the analysis results, it generates real-time feedback, such as "Your blood pressure is high, so try to limit your salt intake."

[1587] Step 7:

[1588] The server transmits the generated feedback to the user terminal and adjusts the meal plan as necessary, and the adjusted meal plan is also transmitted from the server to the user terminal again.

[1589] Step 8:

[1590] The user device displays the received feedback and the adjusted meal plan and notifies the user, who can then adjust their food selection and meal method in the physical store based on the displayed information.

[1591] Through this series of processing steps, users can obtain optimal meal plans based on their health status, improving their shopping experience in physical stores.

[1592] 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.

[1593] The present invention relates to a system that supports daily health management by proposing a personalized meal plan taking into account a user's health information, dietary preferences, and emotions. In particular, by combining a generative AI model and an emotion engine, the system provides feedback and adjusts the meal plan according to the user's emotional state. As an embodiment of the present invention, a system using a user terminal, a server, a generative AI model, and an emotion engine will be described.

[1594] System Overview

[1595] In this system, users input their health information, dietary preferences, and emotional information using a device, and a generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary content, health status, and emotional information, providing real-time feedback and adjusting the meal plan as needed.

[1596] Program processing

[1597] 1. User registration and initial settings

[1598] The user launches the app and fills in the registration form with their health information (e.g., height, weight, blood pressure, blood sugar level), food preferences (e.g., vegetarian, allergy information), and emotional information (e.g., feeling good, feeling stressed).

[1599] The terminal transmits the input information to the server.

[1600] The server stores the received user information in a database.

[1601] 2. Generate personalized meal plans

[1602] The server runs a generative AI model based on user information stored in a database.

[1603] The generative AI model automatically generates optimal meal plans for users, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[1604] The server transmits the generated meal plan to the terminal.

[1605] The plan will be displayed on the device and the user will be notified.

[1606] 3. Daily diet and health monitoring

[1607] The user inputs their daily diet, health status (e.g., weight, blood pressure), and emotional information into the device. Specifically, after eating oatmeal and fruit for breakfast, they record their weight, blood pressure, and emotional state (e.g., feeling relaxed).

[1608] The terminal transmits the input data to the server.

[1609] The server stores the received daily data in a database.

[1610] 4. Providing real-time feedback and adjusting meal plans

[1611] The server analyzes the stored monitoring data and evaluates the user's current health and emotional state based on the analysis results.

[1612] The emotion engine analyzes the emotional information entered by the user and generates a feedback message according to that state, such as "On stressful days, we recommend a relaxing herbal tea."

[1613] The server generates a feedback message for the user based on the analysis results and emotional information.

[1614] The server will adjust the meal plan for the next day as needed.

[1615] The server transmits the generated feedback and the adjusted meal plan to the terminal.

[1616] The terminal displays the received feedback and plan to the user.

[1617] Specific examples

[1618] Example 1: User registration and initial settings

[1619] The user launches the app for the first time and enters their height (170 cm), weight (70 kg), blood pressure (130 / 80), dietary preference (vegetarian) and emotional information (feeling good) into the device.

[1620] The terminal sends this information to the server, which stores it in a database.

[1621] Example 2: Generating a meal plan

[1622] The server runs a generative AI model based on user information to generate an optimal meal plan.

[1623] For example, a user can create a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner, and the server sends it to the terminal, which then displays it to the user.

[1624] Example 3: Daily monitoring and feedback

[1625] The user eats oatmeal and fruit for breakfast and enters that information, along with their weight, blood pressure, and emotional status (relaxed) into the terminal.

[1626] The terminal sends this data to the server, which stores it in a database.

[1627] The server analyzes the data, generates feedback to the user (e.g., "You're losing weight well. Keep it up!"), and sends it to the device along with an adjusted meal plan.

[1628] The emotion engine analyzes the user's emotional information and generates feedback according to the emotion (e.g., "You are feeling less stressed, so continue your diet as planned").

[1629] The server notifies the terminal of the feedback and plan, which is then displayed to the user.

[1630] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[1631] The processing flow will be explained below.

[1632] Program processing

[1633] User registration and initial settings

[1634] Step 1:

[1635] The user launches the app and a user registration form appears on the device.

[1636] Users input health information such as height, weight, blood pressure, and blood sugar level, dietary preferences such as vegetarianism or allergies, and emotional information (e.g., feeling good, feeling stressed).

[1637] Step 2:

[1638] The terminal transmits the input health information, dietary preferences, and emotional information to the server.

[1639] Step 3:

[1640] The server stores the received user information in a database.

[1641] Generate personalized meal plans

[1642] Step 4:

[1643] The server runs a generative AI model based on user information stored in a database.

[1644] Step 5:

[1645] The generative AI model takes into account the user's health information and food preferences to generate an optimal meal plan, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[1646] Step 6:

[1647] The server transmits the generated meal plan to the terminal.

[1648] Step 7:

[1649] The terminal displays the received meal plan to the user.

[1650] Daily diet, health and emotional monitoring

[1651] Step 8:

[1652] The user inputs daily dietary information, health status (e.g., weight, blood pressure), and emotional information into the device. For example, after eating oatmeal and fruit for breakfast, the user records their weight, blood pressure, and emotional state (relaxed).

[1653] Step 9:

[1654] The terminal transmits the input data to the server.

[1655] Step 10:

[1656] The server stores the received daily data in a database.

[1657] Providing real-time feedback and adjusting meal plans

[1658] Step 11:

[1659] The server analyzes the stored monitoring data and assesses the user's current health and emotional state.

[1660] Step 12:

[1661] The emotion engine analyzes the emotion information entered by the user and generates feedback messages based on the user's emotional state. For example, if the user is feeling stressed, it generates a message such as "I recommend some herbal tea to help you relax."

[1662] Step 13:

[1663] The server generates a comprehensive feedback message for the user based on the analysis results and the feedback of the emotion engine.

[1664] Step 14:

[1665] The server will adjust the meal plan for the next day as needed, for example, by encouraging more light meals and stress-reducing foods if the emotion engine's analysis indicates high stress levels.

[1666] Step 15:

[1667] The server transmits the generated feedback message and the adjusted meal plan to the terminal.

[1668] Step 16:

[1669] The terminal displays the received feedback message and meal plan to the user.

[1670] This allows users to effectively manage their health and emotions and achieve their health goals through individually tailored meal plans.

[1671] Example 2

[1672] 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."

[1673] Health management requires providing personalized meal plans that comprehensively consider a user's health information, food preferences, and emotional state. Conventional systems have struggled to analyze this information in real time and provide optimal feedback and meal plan adjustments for individual users. In particular, feedback and meal plan adjustments that take into account a user's emotional state must be performed in a meticulous manner according to daily changes, but conventional technologies have been unable to achieve this.

[1674] 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.

[1675] In this invention, the server includes: means for inputting a user's health information and dietary preferences; means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences; means for generating and executing prompts for the generative AI model; means for notifying the user of the meal plan; means for inputting daily meal content, health status, and emotional information; means for storing and analyzing the input daily data; means for evaluating the user's health status and emotional status based on the stored data; means for analyzing the emotional information and providing feedback according to the user's emotions; and means for adjusting the meal plan based on the feedback and analysis results. This makes it possible to provide and adjust an optimal individual meal plan in real time by comprehensively considering the user's health information, dietary preferences, and emotional status.

[1676] "User's health information" refers to physical data such as the user's height, weight, blood pressure, and blood sugar level.

[1677] "Dietary preferences" refers to data about a user's preferences, such as the types of ingredients and dishes they like, allergy information, and specific dietary restrictions.

[1678] "Generative AI model" refers to an artificial intelligence model that automatically generates personalized meal plans based on input data.

[1679] "Emotional information" refers to data related to the user's emotional state, such as the user's mood, stress level, or happiness.

[1680] A "prompt statement" is a statement that describes input data in the format required to run a generative AI model.

[1681] "Notification Method" means the mechanism for notifying the user of the generated meal plan and feedback, including methods such as in-app notifications, email, and SMS.

[1682] "Monitoring" refers to the continuous observation and collection of data on a user's daily diet, health status, and emotional information.

[1683] "Means for providing real-time feedback" refers to a mechanism that generates instant feedback based on the user's most recent data and notifies the user of that information.

[1684] "Adjusting meal plans" refers to making appropriate changes to existing meal plans based on the user's daily data and feedback.

[1685] "Database" refers to a digital storage system for storing and managing a user's health information, dietary preferences, daily data, etc.

[1686] This invention relates to a system that supports daily health management by proposing a personalized meal plan based on the user's health information, food preferences, and emotional state. By combining a generative AI model and an emotion engine, this system can provide feedback and adjust the meal plan according to the user's emotional state.

[1687] Hardware and software used

[1688] The hardware and software used in this system are as follows:

[1689] User device: A mobile device, such as a smartphone or tablet, that a user uses to enter information.

[1690] Server: A cloud or on-premise server that stores data, analyzes it, and runs generative AI models.

[1691] Generative AI model: An artificial intelligence model for generating personalized meal plans from user data.

[1692] Emotion engine: An analysis engine for analyzing the user's emotional information and generating appropriate feedback.

[1693] System Overview

[1694] Users input their health information, dietary preferences, and emotional information using their device, and the generative AI model creates a personalized meal plan based on that information. The server then notifies the user of the meal plan and provides it to them. Furthermore, the system monitors daily dietary content, health status, and emotional information, providing real-time feedback and adjusting the meal plan as needed.

[1695] Specific examples

[1696] 1. User registration and initial settings

[1697] The user starts the app for the first time and enters their height (170cm), weight (70kg), blood pressure (130 / 80), food preference (vegetarian) and emotional information (feeling good). The device sends this information to the server, which stores it in a database.

[1698] 2. Generate a meal plan

[1699] The server runs a generative AI model based on user information to generate an optimal meal plan. For example, it creates a plan that includes oatmeal and fruit for breakfast, kale salad and tofu steak for lunch, and vegetable stew for dinner. The server sends the plan to the device, which displays it to the user.

[1700] 3. Daily monitoring and feedback

[1701] The user eats oatmeal and fruit for breakfast and enters that information, along with their weight, blood pressure, and emotional state (relaxed) into the device. The device sends this data to the server, which stores it in a database. The server analyzes the data, generates feedback for the user, and sends it back to the device along with an adjusted meal plan.

[1702] Prompt Sentence Examples

[1703] "Create a personalized meal plan based on your health and emotional information. Specifically, I'm 170cm tall, weigh 70kg, feel great, and am a vegetarian."

[1704] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[1705] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1706] Step 1: User registration and initial setup

[1707] Input: User's health information (e.g., height 170cm, weight 70kg, blood pressure 130 / 80), food preferences (e.g., vegetarian), emotional information (e.g., feeling good)

[1708] Specific operation: A user launches the app, enters the required information in the registration form, and presses the submit button. The device converts the entered information into JSON format and sends an HTTP POST request to the server.

[1709] Output: User information sent to the server

[1710] Data processing / calculation: The server parses the received JSON data and executes an INSERT query to the database to save the user information.

[1711] Step 2: Generate a personalized meal plan

[1712] Input: User information stored in the database

[1713] Specific operation: The server retrieves user information from the database using a SELECT query and passes it to the generative AI model as a prompt statement.

[1714] Output: A personalized meal plan returned by the generative AI model

[1715] Data processing / calculation: The server generates a prompt and calls the API of the generative AI model to generate a meal plan. The generative AI model analyzes the prompt and generates a corresponding meal plan.

[1716] Step 3: Meal plan notification

[1717] Input: Generated meal plan

[1718] Specific operation: The server encodes the generated meal plan in JSON format and sends an HTTP POST request to the device. The device decodes the received JSON data, displays it in the UI, and notifies the user of the plan using the notification function.

[1719] Output: Meal plan notified to user

[1720] Step 4: Monitor your daily diet and health

[1721] Input: User's diet, health status (e.g., weight, blood pressure), emotional information

[1722] Specific operation: The user inputs daily dietary information, health status, and emotional information into the device. The device converts this data into JSON format and sends an HTTP POST request to the server.

[1723] Output: Daily data sent to the server

[1724] Data processing / calculation: The server parses the incoming data and executes INSERT queries in the database to store the daily data.

[1725] Step 5: Data analysis and evaluation

[1726] Input: Daily data stored in a database

[1727] What it does: The server retrieves daily data from the database using SELECT queries and runs analytical algorithms to assess the user's current health and emotional state.

[1728] Output: Analysis results and evaluation data

[1729] Data processing / calculation: The server analyzes the received data and evaluates the user's health and emotional state.

[1730] Step 6: Generate emotional feedback

[1731] Input: User's emotional information

[1732] How it works: The emotion engine analyzes the user's emotional information and generates feedback messages according to their state. For example, it generates feedback such as, "On stressful days, we recommend a relaxing herbal tea."

[1733] Output: Feedback message

[1734] Data processing / calculation: The emotion engine analyzes emotional information and generates appropriate feedback.

[1735] Step 7: Adjust your meal plan

[1736] Input: Analysis results and feedback messages

[1737] What it does: Based on the analysis results and feedback messages, the server determines whether the next day's meal plan needs to be adjusted, and if necessary, re-runs the generative AI model to adjust the meal plan.

[1738] Output: Tailored meal plan

[1739] Data processing / calculation: Based on the analysis results, the server runs the generative AI model again to generate a new meal plan.

[1740] Step 8: Communicate feedback and adjusted plans

[1741] Input: Adjusted meal plan and feedback message

[1742] Specific operation: The server encodes the generated feedback and adjusted meal plan in JSON format and sends an HTTP POST request to the device. The device decodes the received JSON data, displays it in the UI, and notifies the user using the notification function.

[1743] Output: User-informed feedback and adjusted plan

[1744] This allows users to effectively manage their health and emotions and implement a tailored meal plan.

[1745] (Application example 2)

[1746] 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."

[1747] Conventional health management systems have difficulty reflecting a user's emotional state and daily changing health information in real time and proposing individually personalized meal plans when visiting a store. For this reason, there is a need for support that helps users maintain their health while avoiding confusion when choosing meals when out and in physical stores. In addition, a system is needed that enables more accurate and effective health management by linking daily health data with emotional information.

[1748] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1749] In this invention, the server includes: means for inputting a user's health information; means for using a generative AI model to generate a personalized meal plan based on the health information; means for notifying the user of the meal plan; means for detecting the user's store visit using a location recognition device installed in the physical store; means for suggesting optimal foods and meal menus at the physical store based on the detection results; means for inputting daily meal content and health status; means for storing and analyzing the input daily data; means for providing real-time feedback based on the analysis results; and means for adjusting the meal plan based on the feedback. This allows the user to receive meal suggestions optimal for their health status and mood when visiting the physical store, enabling more effective daily health management.

[1750] "User's health information" refers to data and information indicating the user's individual health condition, such as the user's height, weight, blood pressure, blood sugar level, etc.

[1751] A "generative AI model" refers to a computational model that uses artificial intelligence to perform a specific task based on input data, in this case generating a personalized meal plan based on health information.

[1752] "Location-aware devices installed in physical stores" refers to devices that use technologies such as beacons, Wi-Fi, and GPS to identify a user's location within a store and their visit.

[1753] "Personalized meal plans" refer to plans and suggestions that provide optimal meal menus for each user based on each user's individual health information and dietary preferences.

[1754] "Providing feedback in real time" means responding to the user immediately based on the input data and analysis results, and providing advice and information.

[1755] "Daily dietary content and health status" refers to the food the user eats on a daily basis and health-related data such as weight, blood pressure, and emotional state on that day.

[1756] "Storing and analyzing data" refers to temporarily or long-term storage of data collected from users and performing calculations or analysis based on that data.

[1757] "Suggesting optimal foods and meal menus" refers to recommending foods and meal contents that are considered most desirable at that time based on the user's health condition and emotional information.

[1758] This invention relates to a system that provides personalized meal plans based on a user's health information, dietary preferences, and emotional information, thereby supporting users in managing their health appropriately even when visiting a physical store.

[1759] System Configuration

[1760] This system consists of the following main hardware and software components:

[1761] 1. Smartphone App

[1762] It provides an interface for users to input their health information, food preferences, and emotional information and receive personalized suggestions at physical stores.

[1763] 2. Cloud Server

[1764] It is responsible for storing and analyzing data and running generative AI models.

[1765] 3. Beacon Devices

[1766] It is installed in physical stores and used to detect when users visit the store.

[1767] 4. Generative AI Models

[1768] It is used to generate personalized meal plans based on user input data.

[1769] 5. Emotion Engine

[1770] It is used to analyze the user's emotional information and generate appropriate feedback.

[1771] Specific operation of the system

[1772] User registration and initial settings

[1773] The user launches the smartphone app and inputs information such as height, weight, blood pressure, dietary preferences, and emotional state. The input information is sent to a cloud server and stored in a database.

[1774] Generate personalized meal plans

[1775] The cloud server runs a generative AI model based on the stored user information to generate an optimal meal plan for each individual user, suggesting, for example, oatmeal and fruit for breakfast, salad and tofu steak for lunch, and vegetable stew for dinner.

[1776] Individual proposals in physical stores

[1777] When a beacon device installed in a physical store detects a user's visit, a notification is sent to a smartphone app. The cloud server then uses the user's location and health information to suggest the most suitable food and meal menu for the store. For example, if the user is having a stressful day, the app will suggest relaxing herbal tea or foods with a calming effect.

[1778] Daily monitoring and feedback

[1779] Users enter their daily diet, health status, and emotional information into a smartphone app. The data is sent to a cloud server and stored in a database. The cloud server analyzes the daily data, provides real-time feedback based on the analysis results, and adjusts the next day's meal plan as needed.

[1780] Specific examples

[1781] User registration and initial setup:

[1782] A user launches the app for the first time, enters their height (170cm), weight (70kg), blood pressure (130 / 80), dietary preference (vegetarian), and emotional information (feeling good), and submits it.

[1783] Generate a meal plan:

[1784] The cloud server runs the generative AI model, generates a plan for breakfast consisting of oatmeal and fruit, lunch consisting of kale salad and tofu steak, and dinner consisting of vegetable stew, and notifies the user.

[1785] Personalized offers in-store:

[1786] When a user visits a physical store, a beacon device detects their visit, and a cloud server suggests relaxing herbal teas for stressful days.

[1787] Examples of prompt statements

[1788] Consider the user's health information and daily emotional state to suggest the best meal plan for today.

[1789] User information: Height 170cm, weight 70kg, blood pressure 130 / 80, vegetarian, feels good.

[1790] Emotional state: Relaxed. Low stress over the past 3 days.

[1791] This allows users to receive appropriate meal suggestions when visiting a physical store, regardless of their condition, making health management more effective.

[1792] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1793] Step 1:

[1794] User registration and initial settings

[1795] (Input) The user launches the smartphone app and inputs health information such as height, weight, blood pressure, dietary preferences, and emotional information.

[1796] (Data processing) The health information entered by the user is organized in JSON format and immediately sent to the server.

[1797] (Output) The server saves the received user information in the database.

[1798] (Specific operation) In the smartphone app's user interface, you enter information using text boxes and selection lists, and then press the "Send" button, which sends the information to the server.

[1799] Step 2:

[1800] Generate personalized meal plans

[1801] (Input) User's health information stored on the server.

[1802] (Data processing) The server inputs user information into the generative AI model, which then generates the optimal meal plan for the user.

[1803] (Output) The generated meal plan is sent to a smartphone app and notified to the user.

[1804] (Specific operation) Using a pre-trained generative AI model (e.g., GPT-4) on the server, the system generates and executes prompts based on the user's health information, and then sends the resulting suggested meal plan to the app.

[1805] Step 3:

[1806] Individual proposals in physical stores

[1807] (Input) A user visits a physical store, and a beacon device installed in the store detects the user's visit.

[1808] (Data processing) The beacon device's detection information is sent to the server, which then identifies the user's current location. Using a generative AI model, the server generates optimal food and meal menus based on the user's health information and location within the store.

[1809] (Output) The generated proposal is notified to the smartphone app and presented to the user.

[1810] (Specific operation) The beacon device communicates with the user's smartphone and sends the information to the server. The server generates suggestions based on the user's location and health information and notifies the app.

[1811] Step 4:

[1812] Monitoring and input of daily dietary and health conditions

[1813] (Input) The user inputs daily dietary information, health status, and emotional information into a smartphone app.

[1814] (Data processing) The entered information is organized in JSON format and sent to the server.

[1815] (Output) The received data is stored in a database and prepared for analysis.

[1816] (Specific operation) The user enters the contents of breakfast, lunch, and dinner, as well as that day's weight, blood pressure, emotional information, etc., into the app's dedicated input screen, and when they press the "Send" button, the information is sent to the server.

[1817] Step 5:

[1818] Analysis and real-time feedback

[1819] (Input) Daily dietary and health status data.

[1820] (Data processing) The server analyzes the received data and generates feedback messages based on the user's current health and emotional state. It uses an emotion engine to analyze the emotional information in detail.

[1821] (Output) Send the generated feedback message and adjusted meal plan to the smartphone app.

[1822] (Specific operation) The server generates feedback using pattern recognition algorithms and generative AI models, and sends the generated messages and adjusted plans to the app, where the user can view them in real time.

[1823] Examples of prompts:

[1824] Consider the user's health information and daily emotional state to suggest the best meal plan for today.

[1825] User information: Height 170cm, weight 70kg, blood pressure 130 / 80, vegetarian, feels good.

[1826] Emotional state: Relaxed. Low stress over the past 3 days.

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] 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.

[1833] 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).

[1834] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1835] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1836] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1837] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1838] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1839] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1840] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1841] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1842] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1843] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1844] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1845] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1846] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1847] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1848] The following is further disclosed regarding the above embodiment.

[1849] (Claim 1)

[1850] a means for inputting user health information;

[1851] a means for using a generative AI model to generate a personalized meal plan based on the health information;

[1852] means for informing a user of said meal plan;

[1853] A means for inputting daily dietary information and health status;

[1854] means for storing and analyzing the input daily data;

[1855] means for providing real-time feedback based on the analysis results;

[1856] means for adjusting the meal plan based on said feedback;

[1857] A system including:

[1858] (Claim 2)

[1859] 10. The system of claim 1, further comprising means for generating a database based on the user's health information and dietary preferences.

[1860] (Claim 3)

[1861] The system according to claim 1, further comprising a means for monitoring the daily dietary content and health status and supporting the user in achieving their health goals based on the monitoring.

[1862] "Example 1"

[1863] (Claim 1)

[1864] a means for inputting user health information;

[1865] a means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences;

[1866] means for informing a user of said meal plan;

[1867] A means for inputting daily dietary information and health status;

[1868] means for storing and analyzing the input daily data;

[1869] means for providing real-time feedback based on the analysis results;

[1870] means for adjusting the next day's meal plan based on said feedback;

[1871] means for reusing a generative AI model to generate and adjust said meal plan;

[1872] A system including:

[1873] (Claim 2)

[1874] 10. The system of claim 1, further comprising means for generating a database based on the user's health information and dietary preferences.

[1875] (Claim 3)

[1876] The system according to claim 1, further comprising a means for monitoring the daily dietary content and health status and supporting the user in achieving their health goals based on the monitoring.

[1877] "Application Example 1"

[1878] (Claim 1)

[1879] a means for inputting user health information;

[1880] a means for using a generative AI model to generate a personalized meal plan based on the health information;

[1881] means for informing a user of said meal plan;

[1882] A means for inputting daily dietary information and health status;

[1883] means for storing and analyzing the input daily data;

[1884] means for providing real-time feedback based on the analysis results;

[1885] means for adjusting the meal plan based on said feedback;

[1886] A means to suggest ingredient lists and recipes based on health information and dietary preferences when users shop in physical stores;

[1887] A means for measuring health data at checkpoints within a physical store and transmitting the measured health data to a server;

[1888] A system including:

[1889] (Claim 2)

[1890] 2. The system according to claim 1, further comprising means for generating a database based on the user's health information and dietary preferences, and for comparing the database with inventory information of local stores.

[1891] (Claim 3)

[1892] The system of claim 1 further comprises means for monitoring the user's daily diet and health status, supporting the user in achieving their health goals based on the results, and providing real-time feedback based on health data obtained at a health measurement station in a physical store.

[1893] "Example 2: Combining Emotion Engines"

[1894] (Claim 1)

[1895] a means for inputting a user's health information and dietary preferences;

[1896] a means for using a generative AI model to generate a personalized meal plan based on the health information and dietary preferences;

[1897] means for generating and executing prompt sentences for the generative AI model;

[1898] means for informing a user of said meal plan;

[1899] A means for inputting daily dietary, health and emotional information;

[1900] means for storing and analyzing the input daily data;

[1901] means for assessing the health and emotional state of the user based on the stored data;

[1902] means for analyzing the emotion information and providing feedback according to the emotion of the user;

[1903] means for adjusting the meal plan based on said feedback and analysis results;

[1904] A system including:

[1905] (Claim 2)

[1906] 10. The system of claim 1, further comprising means for generating and storing a database based on the user's health information and dietary preferences.

[1907] (Claim 3)

[1908] The system of claim 1, further comprising means for monitoring the daily dietary content, health status and emotional information and supporting the user in achieving their health goals based thereon.

[1909] "Application example 2 when combining emotion engines"

[1910] (Claim 1)

[1911] a means for inputting user health information;

[1912] a means for using a generative AI model to generate a personalized meal plan based on the health information;

[1913] means for informing a user of said meal plan;

[1914] A means for detecting a user's visit to a store using a location recognition device installed in a physical store;

[1915] A means for proposing optimal foods and meal menus at a physical store based on the detection results;

[1916] A means for inputting daily dietary information and health status;

[1917] means for storing and analyzing the input daily data;

[1918] means for providing real-time feedback based on the analysis results;

[1919] means for adjusting the meal plan based on said feedback;

[1920] A system including:

[1921] (Claim 2)

[1922] 10. The system of claim 1, further comprising means for generating a database based on the user's health information and dietary preferences.

[1923] (Claim 3)

[1924] The system according to claim 1, further comprising a means for monitoring the daily dietary content and health status and supporting the user in achieving their health goals based on the monitoring. [Explanation of symbols]

[1925] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting user health information; a means for using a generative AI model to generate a personalized meal plan based on the health information; means for informing a user of said meal plan; A means for inputting daily dietary information and health status; means for storing and analyzing the input daily data; means for providing real-time feedback based on the analysis results; means for adjusting the meal plan based on said feedback; A system including:

2. The system of claim 1 further comprising means for generating a database based on the user's health information and dietary preferences.

3. The system according to claim 1 , further comprising a means for monitoring the daily dietary content and health status and supporting the user in achieving their health goals based on the monitoring.

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