System

Abstract

A system is provided.SOLUTION: A system comprising: means for storing profile information and nutritional needs input by a user in a database; means for taking pictures of meals and analyzing the meal content using image recognition technology; means for calculating nutritional information and updating the user's meal records based on the analyzed meal content; means for generating a personalized meal plan based on the user's past meal records and nutritional needs; means for notifying the user of the generated meal plan; and means for the user to continuously input meal records and monitor their progress.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a need to solve the dietary problems faced by a diverse range of target groups, including people with dietary restrictions, people with specific nutritional needs, busy business people, and the elderly. Conventional dietary management methods have difficulty providing personalized services that meet the characteristics and needs of each individual user. This makes it difficult for users to easily eat a nutritionally balanced diet that suits their health condition. It is also difficult to select an appropriate menu when eating out. Given this background, there is a need for a system that provides meal plans tailored to individual needs and provides ongoing support for healthy eating. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means: The profile information and nutritional needs entered by the user are stored in a database, and based on this, photos of meals are taken and the meal contents are analyzed using image recognition technology. Nutritional information is then calculated based on the analyzed meal contents, and the user's meal record is updated. A personalized meal plan is generated based on the updated meal record and nutritional needs, and notified to the user. Furthermore, a means is provided for the user to continuously input meal records and monitor their progress. For users with specific nutritional needs, a means is also provided for generating meal plans that address those restrictions and suggesting optimal restaurant menus to the user when dining out. The system also has a function for generating and suggesting recipes using seasonal ingredients, periodically analyzing the user's meal progress, and adjusting the meal plan as needed. This provides a system that continuously supports the user in maintaining a diet optimal for their health.

[0006] "User" refers to an individual who uses the system to record food and manage their health.

[0007] "Profile information" is data that includes basic health information such as a user's age, sex, weight, and height.

[0008] "Nutritional Needs" refers to the nutrients and dietary requirements of a User based on specific health goals and restrictions.

[0009] A "database" is a system for systematically storing and managing user information, food records, nutritional information, and the like.

[0010] "Photo of food" refers to image data of a meal that a user takes using a terminal while eating or having eaten.

[0011] "Image recognition technology" is a technology that uses AI to analyze image data and identify ingredients and their respective quantities.

[0012] "Meal details" refers to information including the ingredients and food and drink consumed by the user and the amounts.

[0013] "Nutritional information" refers to calculated data on nutrients such as calories, protein, carbohydrates, and lipids obtained from a specific food ingredient.

[0014] A "diet record" is a collection of data that records the user's dietary details and nutritional information on a daily basis.

[0015] A "personalized meal plan" is a customized meal recommendation created based on a user's individual profile information and nutritional needs.

[0016] "Notifying" refers to the act of sending information or suggestions to a user using a terminal.

[0017] "Progress" refers to the status of the user's diet record and changes or improvements in health status.

[0018] "Monitoring" refers to the act of continually watching a user's food records and progress, and analyzing and adjusting as needed.

[0019] "Specific nutritional needs" refers to special nutritional requirements based on a user's unique health conditions, such as diabetes, allergies, etc.

[0020] "Restaurant menu" refers to a list of meals served when dining out, including the nutritional information contained in each dish.

[0021] "Seasonal ingredients" refer to ingredients that are most abundant and nutritious during a particular season.

[0022] A "recipe" refers to a list of instructions and ingredients for making a particular dish.

[0023] "System" refers to the entire computer program and related devices for managing a user's profile information and meal records and providing appropriate meal plans. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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."

[0045] The system provides users with personalized meal plans for the purpose of managing their diet and supporting their health. Users access the system through their devices and input their profile information and nutritional needs, and are then presented with a meal plan based on their individual health conditions.

[0046] User registration and initial settings

[0047] Users first create an account through a web or app interface, entering basic information such as their name, email address, and password, and then entering health information such as age, gender, weight, and height, as well as dietary preferences, allergies, and specific nutritional needs (e.g., diabetes or low-carb diet).

[0048] The server stores the entered information in a database, creates a user account, and sends a confirmation email to the user.

[0049] Food record entry and analysis

[0050] After the user has eaten a meal, the user uses the terminal to take a photo of the meal.

[0051] The device then sends the photo to an AI food recognition module, which analyzes the image to identify ingredients and their quantities. This analysis determines the type and quantity of ingredients.

[0052] The server calculates nutritional information based on the analyzed data, including calorie intake, protein, carbohydrates, and fats, and updates the resulting nutritional information as the user's dietary record in a database.

[0053] Generate personalized meal plans

[0054] The server analyzes the user's past meal records and input profile information and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs and dietary restrictions.

[0055] The device notifies the user of the generated meal plan and provides detailed menus and recipes for carrying it out.

[0056] Monitor progress and adjust plans

[0057] The user follows the suggested meal plan, eats meals and updates their food log as they go.

[0058] The server continuously monitors the user's meal log, analyzes the user's progress, adjusts the meal plan as needed, and notifies the user of the adjusted meal plan.

[0059] Meeting specific nutritional needs

[0060] For example, if a user has diabetes, the server can generate a low-carb meal plan, or when dining out, it can suggest the best options from a particular restaurant menu.

[0061] As a concrete example, the flow when User A, who has diabetes, uses this system is shown below.

[0062] Specific examples

[0063] 1. User A enters initial registration information into the system (e.g., age, gender, height, weight, diabetes).

[0064] 2. After User A has eaten breakfast, he / she takes a photo of it with his / her device and uploads it to the system.

[0065] 3. The server uses AI to analyze the photo and identify the ingredients and their quantities, such as "50g oatmeal, 1 banana, 200ml milk."

[0066] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[0067] 5. The server generates a low-carb lunch menu based on User A's past meal records and initial setting information, and notifies User A of this via the terminal.

[0068] 6. User A follows the lunch menu provided, eats lunch, and records the contents again.

[0069] 7. The server analyzes the food record again and monitors User A's nutritional balance and goal achievement status.

[0070] By using this system, users can efficiently and continuously consume meals that suit their health condition and nutritional needs. It also helps users select appropriate menu items when eating out, making daily dietary management easier.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] When users first access the system, they create an account via the web or app, entering basic information such as their name, email address, and password.

[0074] Step 2:

[0075] The server stores the entered account information in a database and sends the user a confirmation email.

[0076] Step 3:

[0077] Users click a link in the confirmation email to activate their account, then enter their profile information (age, gender, weight, height, etc.) and nutritional needs (e.g., diabetes, allergies, etc.).

[0078] Step 4:

[0079] The server stores the entered profile information and nutritional needs in a database and generates a basic meal plan based on this.

[0080] Step 5:

[0081] After eating a meal, the user uses the device to take a photo of the meal.

[0082] Step 6:

[0083] The device sends a photo of the meal to an AI meal recognition module, which analyzes the image to determine the ingredients and their quantities.

[0084] Step 7:

[0085] The server receives the analyzed meal content and calculates nutritional information (e.g., calories, protein, carbohydrates, fat, etc.) based on it.

[0086] Step 8:

[0087] The server adds the calculated nutritional information to the database as the user's dietary record.

[0088] Step 9:

[0089] The server uses an AI algorithm to generate a new personalized meal plan based on the user's past meal records and profile information.

[0090] Step 10:

[0091] The device will notify the user of the newly generated meal plan and display a detailed menu and its recipes.

[0092] Step 11:

[0093] The user eats according to the meal plan notified to them, then takes a photo of the meal again to update the record.

[0094] Step 12:

[0095] The server continuously monitors the user's food log, analyzes their progress, and adjusts their meal plan as needed.

[0096] Step 13:

[0097] The device will notify the user of the adjusted meal plan and assist the user in continuing to eat according to the new plan.

[0098] Step 14:

[0099] For users with specific nutritional needs, the server provides meal plans tailored to their restrictions and suggests optimal restaurant menus when dining out.

[0100] Step 15:

[0101] The device also suggests recipes to users using seasonal ingredients, helping them to enjoy healthy and timeless meals.

[0102] Example 1

[0103] 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."

[0104] In modern society, dietary management tailored to individual health conditions is becoming increasingly important. However, few general dietary management systems are able to adequately address the individual nutritional needs of users. Furthermore, many systems lack the functionality to allow users to easily and accurately record their dietary information and generate personalized meal plans based on the analysis results. Furthermore, there are issues with suggesting appropriate menus when dining out at restaurants and providing recipes using seasonal ingredients. This makes it difficult for users to optimally manage their diet based on their health conditions.

[0105] 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.

[0106] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously enter meal records and monitor the progress, and means for adjusting the meal plan as needed, thereby enabling the user to efficiently and accurately manage their diet according to their health condition and nutritional needs.

[0107] A "user" is an individual who utilizes the diet management system to receive a meal plan tailored to their health and nutritional needs.

[0108] "Profile information" refers to personal information such as a user's name, age, gender, weight, height, food preferences, and allergy information.

[0109] "Nutrition needs" refers to the types and amounts of nutrients a user requires to maintain good health or manage a particular health condition (e.g., diabetes).

[0110] "Database" means a computer system for storing and managing data such as user profile information, nutritional needs, and dietary records.

[0111] "Image recognition technology" is a technology that uses machine learning algorithms to identify ingredients and their quantities from photographs of meals.

[0112] "Meal contents" refers to the ingredients and amounts consumed by the user.

[0113] "Nutritional information" refers to information on nutrients such as calories, protein, carbohydrates, and lipids calculated based on the contents of a meal.

[0114] A "diet record" is a record that includes the contents of meals consumed by a user and nutritional information calculated based on the meals.

[0115] A "personalized meal plan" is a meal plan that is customized based on a user's profile information and nutritional needs.

[0116] The "notification means" is a system for notifying users of updates to the meal plan or meal record. Specifically, a messaging service is used for notifications.

[0117] "Monitoring" refers to the act of watching the dietary information continuously recorded by the user and evaluating their health condition and nutritional balance.

[0118] "Adjustment" refers to making changes to the user's meal plan as needed based on the monitoring results.

[0119] A "generative model" is a machine learning algorithm that creates optimal meal plans based on a user's past data and specific prompts.

[0120] A "prompt" is an instruction given to a generative AI model, which then generates a meal plan.

[0121] "Dining Menu" refers to a list of meals offered at a restaurant or other establishment, including suggestions for options that best suit the user's nutritional needs.

[0122] "Recipe" refers to the cooking instructions and ingredient list required for a user to execute a meal plan.

[0123] The present invention is a system that provides personalized meal plans for the purpose of dietary management and health support for users. The system is accessed by users via a terminal and proposes meal plans based on individual health conditions.

[0124] First, a user creates an account through the web or app interface. This includes basic information such as name, email address, and password, as well as health information such as age, gender, weight, height, food preferences, allergies, and specific nutritional needs. The server stores this information in a database, creates a user account, and sends a confirmation email. For this purpose, Firebase Authentication is used for user management and MySQL is used for the database.

[0125] Next, after the user has eaten, they use the device to take a photo of the meal. The device sends the photo to the Google Cloud Vision API, which performs image analysis. The analysis results identify the names and amounts of ingredients and send them to the server. The server then calculates nutritional information based on this information and updates the database as the user's meal record. A script written in Python is used to calculate the nutritional information.

[0126] The server analyzes the user's past meal records and input profile information, and generates a personalized meal plan using an AI algorithm powered by TensorFlow. This meal plan takes into account the user's nutritional needs and dietary restrictions. The generated meal plan is then sent to the device, along with detailed menus and recipes for implementing the plan. Notifications are sent using Firebase Cloud Messaging.

[0127] The user then follows the proposed meal plan, eating meals and updating their meal log on their device. The server continuously monitors these meal logs and analyzes the user's progress. If necessary, the meal plan is adjusted and the user is notified again.

[0128] For users with specific nutritional needs, the server can generate meal plans that accommodate those restrictions. For example, a low-carb meal plan can be generated for a user with diabetes, and when dining out, the server can suggest optimal options from a specific restaurant menu. The generative AI model generates the optimal meal plan based on a prompt. For example, the following prompt is used: "Given that the user has diabetes, generate a prompt for the AI ​​model to suggest a low-carb meal plan based on past food records. Consider the following information: Breakfast (50g oatmeal, 1 banana, 200ml milk), user profile (age: 35, gender: male, diabetes)."

[0129] This allows users to efficiently and accurately consume meals that suit their health and nutritional needs, and also provides support for choosing appropriate menu items when eating out.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] Users create an account through a web or app interface. Information entered includes name, email address, password, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs. The entered information is validated by the device. If validation is successful, the information is encrypted and sent to the server, where the user's personal profile information and nutritional needs are entered.

[0133] Step 2:

[0134] The server stores the user's profile information and nutritional needs received from the device in a database. The database uses "MySQL." The server also creates a user account and sends a confirmation email to the user, confirming that the user's information has been saved in the database and that the user's account has been activated.

[0135] Step 3:

[0136] After a user eats a meal, they use the device to take a photo of the meal. The device then sends the photo to the Google Cloud Vision API. The input is the captured image, and the output is the analyzed names and quantities of ingredients. Specifically, the device sends the image to the API and receives the analysis results in return.

[0137] Step 4:

[0138] The server calculates nutritional information based on the analysis results received from the device. The input is the analyzed ingredient name and amount, and the output is nutritional information (calories, protein, carbohydrates, fat, etc.). The system performs this calculation using a script written in Python. The calculated nutritional information is saved in a database and updated as the user's dietary record.

[0139] Step 5:

[0140] The server performs analysis based on the user's past meal records and input profile information. The input is the user's past meal records and profile information, and the output is a personalized meal plan. The server generates the meal plan using a generative AI model using TensorFlow. This meal plan takes into account the user's nutritional needs and dietary restrictions.

[0141] Step 6:

[0142] The server sends the generated meal plan to the terminal. The input is the generated meal plan, and the output is a message to notify the user. Firebase Cloud Messaging is used for the notification, allowing the user to receive a personalized meal plan.

[0143] Step 7:

[0144] The user eats according to the proposed meal plan and updates the meal record on the device each time. The input is the details of the meal the user ate, and the output is the updated meal record. The meal record entered by the user is sent to the server via the device.

[0145] Step 8:

[0146] The server continuously monitors the user's food log. The input is the user's most recent food log, and the output is an analysis of their progress. The server uses this data to assess the user's nutritional balance and progress toward their goals, and adjusts the meal plan as needed.

[0147] Step 9:

[0148] The server sends the adjusted meal plan back to the terminal and notifies the user. The input is the adjusted meal plan, and the output is a new notification to the user. This allows the user to always eat according to the latest meal plan.

[0149] (Application example 1)

[0150] 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."

[0151] With the recent rise in interest in health management and dieting, there is a demand for systems that provide meal plans tailored to individual nutritional needs. In particular, since it is difficult to propose personalized meal plans even when eating out, users need support in selecting the optimal menu that meets their health condition and nutritional needs. There is also a demand for technology that can continuously monitor users' meal records and update their progress in real time.

[0152] 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.

[0153] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for suggesting optimal menus for physical stores, and means for the user to continuously enter meal records and monitor their progress. This enables the user to efficiently and continuously eat meals that suit their health condition and nutritional needs, and provides support for selecting appropriate menus when eating out.

[0154] "User" means an individual who utilizes the system and who inputs profile information and nutritional needs to receive a meal plan.

[0155] "Profile information" refers to data including a user's basic personal and health information, such as age, gender, weight, height, dietary preferences, and allergy information.

[0156] "Nutrition needs" refers to information that indicates the nutrients and dietary restrictions a user needs based on a particular health condition or goal, such as diabetes or a low-carb diet.

[0157] The "database" is an information management system for managing and storing various data such as user profile information and meal records.

[0158] "Image recognition technology" is a technology used to analyze photographs of food and identify ingredients and their quantities.

[0159] "Nutritional information" is data on nutrients such as calories, protein, carbohydrates, and lipids calculated from the dietary content.

[0160] A "food record" is data that stores the details of meals a user has eaten and their nutritional information in chronological order.

[0161] "Personalized Meal Plan" refers to a meal menu or diet plan that is individually customized based on a user's profile information and nutritional needs.

[0162] "Notification" refers to the act of informing the user of the generated meal plan and progress in real time or periodically.

[0163] "Physical store" refers to a restaurant or retail store that a user actually visits.

[0164] An "optimal menu" is a meal menu selected based on the user's profile information and nutritional needs to maintain optimal health.

[0165] "Monitoring" refers to the act of continuously watching the dietary data recorded by the user and evaluating progress.

[0166] The "AI Food Recognition Module" is a module that uses artificial intelligence technology to analyze photographs of food.

[0167] "Generative AI model" refers to an artificial intelligence algorithm that generates personalized meal plans based on a user's eating history and profile information.

[0168] A "smartphone" is a mobile terminal with communication functions and advanced processing capabilities, and in this invention, the camera and internet connection functions are mainly used.

[0169] "Analysis" refers to data processing to identify ingredients and their amounts from the photographs of meals taken.

[0170] "Nutrition balance" refers to the overall condition that indicates whether the user is properly consuming each nutrient they require.

[0171] The present invention provides a system for providing a user with a personalized meal plan and supporting health management. An embodiment of the system will be described in detail below.

[0172] System Overview

[0173] The system stores a user's profile information and nutritional needs in a database, takes photos of meals, and analyzes them using image recognition technology. It calculates nutritional information based on the analyzed meal content and updates the user's meal record. It also generates a personalized meal plan based on the user's past meal records and nutritional needs, and notifies the user of the plan. It also suggests optimal menus for physical restaurants, and allows users to continuously enter their meal records and monitor their progress.

[0174] Hardware and software used

[0175] Smartphone: Used by users to enter information and take photos of their meals.

[0176] Server: Responsible for processing and storing data, and generating meal plans using generative AI models.

[0177] Image recognition technology: Using TensorFlow, it analyzes photos of meals to identify ingredients and their quantities.

[0178] Database: MongoDB is used to store user profile information, food records, and nutrition information.

[0179] Generative AI model: GPT-3.5 is used to generate personalized meal plans.

[0180] System processing flow

[0181] 1. User registration and initial settings

[0182] Users use their smartphones to install the app, create an account, and enter their profile information and nutritional needs, which are then stored in a database by the server. The information entered is then used to generate a personalized meal plan based on the user's individual nutritional needs.

[0183] 2. Entering and analyzing food records

[0184] After eating, users take a photo of their meal with their smartphone. The photo is sent to a server, where the ingredients and their amounts are analyzed using TensorFlow. Based on the analysis results, nutritional information is calculated and updated in a database as a meal record.

[0185] 3. Personalized meal plan generation and notifications

[0186] The server uses a generative AI model (GPT-3.5) to generate a personalized meal plan based on the user's past meal records and input profile information. This meal plan is then sent to the user via their smartphone. The notification includes specific menu items and instructions on how to follow them.

[0187] 4. Progress monitoring and adjustment

[0188] As the user continues to enter their food records, the server monitors their progress, automatically adjusting the meal plan as needed and notifying the user again.

[0189] Specific examples

[0190] For example, if a user with diabetes uses this system, the following occurs:

[0191] 1. The user enters initial registration information (age, gender, height, weight, diabetes, etc.).

[0192] 2. After having breakfast, take a photo of the meal with your smartphone and upload it to the system.

[0193] 3. The server uses TensorFlow to analyze the photo and identify the ingredients and their quantities (e.g., "50g oatmeal, 1 banana, 200ml milk").

[0194] 4. Based on the analysis results, calculate nutritional information (e.g., "calorie intake: 300 kcal, carbohydrates: 45 g, protein: 10 g, fat: 5 g") and record it in the database.

[0195] 5. Based on the user's past meal records and initial settings, a generative AI model (GPT-3.5) will suggest a personalized low-carb lunch menu, such as "chicken salad, whole-grain bread, and vegetable soup."

[0196] 6. The user eats lunch according to the suggested lunch menu and records the contents again.

[0197] Prompt Sentence Examples

[0198] "Please suggest the best menu for diabetes."

[0199] "Generate a new meal plan based on your recent food logs."

[0200] By implementing this invention, users can efficiently and continuously consume meals that suit their health condition and nutritional needs, and can receive support in selecting appropriate menus when eating out.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1:

[0203] Users enter their profile information and nutritional needs, which are then sent to the server via the app using their device. This includes information such as name, age, gender, weight, height, and allergy information. The server receives this information and stores it in a database. The input data completes the initial setup and serves as the basis for generating an individually customized meal plan.

[0204] Step 2:

[0205] After a user eats a meal, they take a photo of the meal with their smartphone camera and upload it to the server via their device. The server receives the photo and uses image recognition technology (TensorFlow) to analyze the ingredients and their quantities. The input is the photo of the meal, and the output is the analyzed ingredient information (e.g., "50g oatmeal, 1 banana, 200ml milk").

[0206] Step 3:

[0207] The server calculates the nutritional information (calories, protein, carbohydrates, fat, etc.) for each ingredient based on the analyzed ingredient information. This is done based on the nutritional data of ingredients that has been stored in advance in a database. The input is ingredient information, and the output is detailed nutritional information (e.g., "calories 300kcal, carbohydrates 45g, protein 10g, fat 5g"). The calculated nutritional information is saved in the database as the user's dietary record.

[0208] Step 4:

[0209] The server analyzes the user's past meal records and input profile information and uses a generative AI model (GPT-3.5) to generate a personalized meal plan that addresses the user's specific nutritional needs. The input is the user's profile and meal records, and the output is a customized meal plan.

[0210] Step 5:

[0211] The server notifies the user of the generated meal plan via the terminal. The notification content includes specific menus and recipes for implementing them. The input is the generated meal plan, and the output is a notification message to the user.

[0212] Step 6:

[0213] The user continuously enters food records and updates their progress on the device. The server monitors this in real time and analyzes the progress. For example, if a new food record is added, the server uses this to reevaluate the nutritional balance and adjust the meal plan as necessary. The input is the new food record, and the output is the updated progress and adjusted meal plan.

[0214] Step 7:

[0215] In a physical store, a user takes a photo of their meal with their smartphone and uploads it to the server. The server uses image recognition technology to analyze the photo and extract ingredient information. Based on this, the server calculates nutritional information and suggests appropriate menu items. The input is the photo of the meal, and the output is the analysis results and suggested menu items.

[0216] 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.

[0217] To further enhance users' dietary management and health support, the system provides personalized meal plans combined with an emotional engine that recognizes the user's emotional state and dynamically adjusts meal plans based on that, providing motivational messages.

[0218] User registration and initial settings

[0219] When users first access the system, they create an account via the web or app, entering basic information such as their name, email address, and password. They then enter health information such as age, gender, weight, and height, as well as dietary preferences, allergies, and specific nutritional needs (e.g., diabetes or low-carb diet).

[0220] The server stores the entered information in a database, creates a user account, and sends a confirmation email to the user.

[0221] Food record entry and analysis

[0222] After the user has eaten a meal, the user uses the terminal to take a photo of the meal.

[0223] The device sends the captured photo to an AI meal recognition module, which analyzes the image to determine the ingredients and their quantities.

[0224] The server calculates nutritional information (e.g., calorie intake, protein, carbohydrates, fat, etc.) based on the analyzed data and updates the database as the user's dietary record.

[0225] Recognizing and recording emotional states

[0226] The user inputs their emotional state (e.g., happy, sad, stressed, excited, etc.) through the device, and the emotion engine recognizes the user's emotional state through facial expression and voice analysis.

[0227] The server stores the user's input or the emotional state recognized by the emotion engine in a database.

[0228] Generate personalized meal plans

[0229] The server analyzes the user's past food logs, emotional state, and entered profile information, and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs, dietary restrictions, and emotional state.

[0230] The device notifies the user of the generated meal plan and displays a detailed menu and recipes for carrying it out.

[0231] Monitor progress and adjust plans

[0232] The user follows the suggested meal plan, eats the meal, and then takes a photo of the meal again to update the record.

[0233] The server continuously monitors the user's dietary records, analyzes their progress, and dynamically adjusts their meal plan, taking into account the user's emotional state. The adjusted meal plan is then notified to the user.

[0234] Providing emotional motivation

[0235] The server generates appropriate motivational messages based on the user's emotional state as recognized by the emotion engine. For example, if the user is feeling stressed, it will provide a menu using ingredients with a relaxation effect and an encouraging message to boost motivation.

[0236] Meeting specific nutritional needs

[0237] For example, if a user has diabetes, the server can generate a low-carb meal plan, or when dining out, it can suggest the best options from a particular restaurant menu.

[0238] Specific examples

[0239] As a concrete example, the flow when User A, who has diabetes and is easily stressed, uses this system is shown below.

[0240] 1. User A enters initial registration information into the system (e.g., age, gender, height, weight, diabetes).

[0241] 2. After User A has eaten breakfast, he / she takes a photo of it with his / her device and uploads it to the system.

[0242] 3. The server uses AI to analyze the photo and identify the ingredients and their quantities, such as "50g oatmeal, 1 banana, 200ml milk."

[0243] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[0244] 5. User A inputs their emotional state (e.g., stress) into the device. The emotion engine analyzes their facial expressions and voice to recognize their emotional state.

[0245] 6. The server generates a lunch menu using low-carb, relaxing ingredients based on the food record and profile information, including emotional state, and notifies User A of this via the terminal.

[0246] 7. User A follows the lunch menu provided, eats lunch, and records the contents again.

[0247] 8. The server again analyzes the food log and emotional state to monitor progress.

[0248] 9. The server adjusts the next meal plan to include an encouraging message and relaxation effect for user A, since he is feeling stressed, and notifies him again.

[0249] This system enables users to efficiently and continuously consume meals that suit their health condition, nutritional needs, and even emotional state, and by providing appropriate motivation, users' health management becomes even more effective.

[0250] The processing flow will be explained below.

[0251] Step 1:

[0252] Users create an account through the system's web or app interface, providing the required information: name, email address, and password.

[0253] Step 2:

[0254] The server stores the entered account information in a database and sends a confirmation email to the user.

[0255] Step 3:

[0256] The user clicks on the link in the confirmation email to activate their account and log in to the system.

[0257] Step 4:

[0258] Users enter profile information (age, gender, weight, height, favorite foods, allergy information, etc.) and specific nutritional needs (e.g., diabetes, low-carb diet).

[0259] Step 5:

[0260] The server stores the entered profile information and nutritional needs in a database and generates a basic meal plan.

[0261] Step 6:

[0262] After eating, the user takes a photo of the meal on their device and uploads it to the system.

[0263] Step 7:

[0264] The device sends the captured photo to an AI food recognition module, which performs image analysis to identify ingredients and their quantities.

[0265] Step 8:

[0266] The server receives the analyzed meal contents and calculates nutritional information (calories, protein, carbohydrates, fat, etc.).

[0267] Step 9:

[0268] The server updates the calculated nutritional information in the database as the user's dietary record.

[0269] Step 10:

[0270] The user inputs their emotional state (e.g., happy, sad, stressed, excited) using the device, and the emotion engine automatically recognizes the user's emotional state through facial expression or voice analysis.

[0271] Step 11:

[0272] The server stores the emotional state recognized by the emotion engine or the emotional state directly input by the user in a database.

[0273] Step 12:

[0274] The server uses an AI algorithm to generate a new personalized meal plan based on the user's past meal records, emotional state, and profile information.

[0275] Step 13:

[0276] The device notifies the user of the generated meal plan and displays a detailed menu and its recipes.

[0277] Step 14:

[0278] The user eats according to the meal plan notified to them, then takes a photo of the meal again to update the record.

[0279] Step 15:

[0280] The server continuously monitors the user's food log and emotional state, analyzes the progress, and, in particular, dynamically adjusts the meal plan taking into account the user's emotional state.

[0281] Step 16:

[0282] The server generates appropriate motivational messages based on the user's emotional state and notifies them through the device. For example, if the user is feeling stressed, the server will provide a menu containing ingredients with a relaxation effect and an encouraging message.

[0283] Step 17:

[0284] The device also provides recipes using seasonal ingredients recommended by the emotion engine, helping users enjoy healthy and timeless meals.

[0285] Step 18:

[0286] The server will suggest restaurant menus that are optimal for dining out to users with specific nutritional needs, for example, suggesting low-carb options to a diabetic user.

[0287] In this way, the system generates and provides personalized meal plans by comprehensively considering the user's profile information, nutritional needs, and emotional state. It also dynamically adjusts meal plans based on the user's emotional state and provides appropriate motivational messages, making dietary management and health support more effective.

[0288] Example 2

[0289] 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."

[0290] In the past, health and diet management required individual data entry, recording, and management, which was time-consuming and burdensome. It was also difficult to create meal plans that took emotional states into account, and there was a lack of effective ways to maintain motivation. Furthermore, it was difficult to maintain a proper nutritional balance when eating out.

[0291] 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.

[0292] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously input meal records and monitor progress, means for the user to input emotional states and for an emotion engine to analyze facial expressions and voice to recognize and record the emotional states, and means for generating motivational messages based on the emotional states and notifying the user. This enables efficient health and dietary management of the user and provides personalized support that takes emotional states into account, thereby maintaining motivation and maintaining an appropriate nutritional balance even when eating out.

[0293] "Profile Information" refers to information about a user's individual identity, such as the user's name, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs.

[0294] "Database" refers to an information system for centrally managing and storing data such as user profile information, nutritional needs, food records, and emotional state.

[0295] "Image recognition technology" refers to artificial intelligence and machine learning technology that analyzes photos of meals and recognizes specific ingredients and their quantities.

[0296] "Nutritional information" refers to information about the nutritional components of a meal, such as calories, protein, carbohydrates, and fats.

[0297] "Personalized Meal Plan" refers to an optimal meal plan created for you based on your profile information, nutritional needs, past food history, and emotional state.

[0298] A "motivational message" refers to a message that is generated based on the user's emotional state and is intended to increase the user's motivation.

[0299] "Emotion engine" refers to artificial intelligence technology that analyzes a user's facial expressions and voice to recognize their emotional state.

[0300] "Progress" refers to accumulated data on the contents of meals that the user has eaten according to the meal plan and their nutritional information, and is information that indicates the progress of health management.

[0301] This system centrally manages a user's profile information, food records, emotional state, etc., and provides personalized meal plans. This allows for efficient health management for users, while also providing support that takes emotional state into consideration and maintaining motivation. The operation of this system is explained in detail below.

[0302] User registration and initial settings

[0303] When a user first accesses the system, they create an account via the web or app, entering their profile information such as name, email address, password, age, gender, weight, height, food preferences, allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet). The server stores this information in a database and sends a confirmation email to the user.

[0304] Food record entry and analysis

[0305] After a user eats a meal, they take a photo of the meal using a device such as a smartphone or tablet and upload it to the system. The server sends this photo to an AI meal recognition module, which uses image recognition technology to analyze the ingredients and their amounts. For example, it may identify the meal as "50g of oatmeal, 1 banana, 200ml of milk." The server then calculates nutritional information based on the analysis results and updates the database as a meal record. Specific nutritional information might include "calorie intake 300kcal, carbohydrates 45g, protein 10g, fat 5g."

[0306] Recognizing and recording emotional states

[0307] After eating, the user inputs their emotional state (happiness, sadness, stress, excitement, etc.) into the device. The emotion engine then analyzes their facial expressions and voice to recognize the user's emotional state. The server then stores this emotional data in a database.

[0308] Personalized meal plan generation and notifications

[0309] The server analyzes the user's profile information, past meal records, and emotional state, and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs, dietary restrictions, and emotional state. The device notifies the user of the generated meal plan and displays detailed menus and recipes.

[0310] Monitor progress and adjust plans

[0311] The user follows the proposed meal plan, eats the meal, and then takes a photo of the meal on their device to update their meal log. The server continuously monitors the meal log and analyzes the progress. It dynamically adjusts the meal plan, taking into account the user's emotional state. The user is then notified of the adjusted meal plan.

[0312] Providing emotional motivation

[0313] The server generates motivational messages based on the emotional state recognized by the emotion engine and notifies the user via the device. For example, if the user is feeling stressed, it will provide a menu using ingredients with a relaxation effect and an encouraging message.

[0314] Meeting specific nutritional needs

[0315] For example, the server could generate a low-carb meal plan for a diabetic user, or suggest the best options from a specific restaurant menu when dining out via the device.

[0316] Specific examples

[0317] The flow when User A, who has diabetes and is easily stressed, uses this system is as follows.

[0318] 1. User A enters information such as age, gender, height, weight, and diabetes into the system.

[0319] 2. After User A has eaten breakfast, he / she takes a photo of the breakfast on his / her device and uploads it to the system.

[0320] 3. The server analyzes the photo and identifies the ingredients and their quantities, for example, "50g oatmeal, 1 banana, 200ml milk."

[0321] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[0322] 5. User A inputs their emotional state (e.g., stress) into the device. The emotion engine analyzes and recognizes their facial expressions and voice.

[0323] 6. Based on this information, the server generates a low-carb, relaxing lunch menu and notifies User A via the terminal.

[0324] 7. User A follows the lunch menu provided, eats lunch, and records the contents again.

[0325] 8. The server analyzes your new food log and emotional state to monitor your progress.

[0326] 9. The server determines that User A is feeling stressed and adjusts the next meal plan to have a relaxation effect, and notifies the user again with an encouraging message.

[0327] Example prompt sentence:

[0328] "I have diabetes and get stressed easily. For breakfast, I had 50g of oatmeal, one banana, and 200ml of milk. Can you suggest a low-carb lunch that will help me relax?"

[0329] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0330] Step 1: User registration and initial setup

[0331] When users first access the system, they create an account via the web or app by entering their profile information, including their name, email address, password, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet).

[0332] Input: Profile information (name, email address, password, age, gender, weight, height, food preferences, allergy information, nutritional needs)

[0333] Output: Save to database, send confirmation email

[0334] The server receives this information and stores it in a database. After the information is stored, a confirmation email is sent to the user.

[0335] Step 2: Enter and analyze your food records

[0336] After a user has eaten a meal, they take a photo of the meal using a device such as a smartphone or tablet and upload it to the system.

[0337] Input: Food photo

[0338] Output: Analyzed dietary information and nutritional information

[0339] The device sends the captured photo to the AI ​​meal recognition module. The server uses the AI ​​module to apply image recognition technology to analyze the ingredients and their amounts. For example, it might analyze the meal as "50g of oatmeal, 1 banana, 200ml of milk." Based on the analysis results, nutritional information (calories, protein, carbohydrates, fat, etc.) is calculated and updated in the database as a meal record.

[0340] Step 3: Recognize and record your emotional state

[0341] After eating, the user inputs their emotional state (happiness, sadness, stress, excitement, etc.) into the device, and the emotion engine analyzes facial expressions and voice to recognize the user's emotional state.

[0342] Input: Self-input of emotional state, facial expressions and voice data

[0343] Output: Recognized emotional state, stored in a database

[0344] The server receives this emotion data and stores it in a database.

[0345] Step 4: Generate and notify your personalized meal plan

[0346] The server analyzes the user's profile information, past meal records, and emotional state, and uses AI algorithms to generate a personalized meal plan.

[0347] Input: Profile information, past meal records, emotional state

[0348] Output: Meal plan, recipe guide

[0349] The meal plan is created taking into account the user's nutritional needs, dietary restrictions, and emotional state. The device notifies the user of the generated meal plan and displays detailed menus and recipes.

[0350] Step 5: Monitor progress and adjust your plan

[0351] The user follows the proposed meal plan, eats the meal, and then takes a photo of it on the device and uploads it to the system.

[0352] Input: Another meal photo

[0353] Output: Updated food log, adjusted meal plan

[0354] The device sends new meal photos to the server, which analyzes and continuously monitors the new meal log, taking into account progress and emotional state, dynamically adjusting the meal plan as needed, and notifying the user again of the adjustments.

[0355] Step 6: Provide emotional motivation

[0356] The server generates motivational messages based on the emotional state recognized by the emotion engine.

[0357] Input: Perceived emotional state

[0358] Output: Motivation message notification

[0359] For example, if a user is feeling stressed, the device will provide a menu using ingredients with a relaxation effect and an encouraging message.

[0360] Step 7: Addressing specific nutritional needs

[0361] For example, if a user has diabetes, the server will generate a low-carb meal plan, and when dining out, the device will suggest the best options from a particular restaurant menu.

[0362] Inputs: specific nutritional needs, restaurant choices when eating out

[0363] Output: Low-carb meal plan, optimal restaurant menu suggestions

[0364] (Application example 2)

[0365] 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."

[0366] Many people today require highly personalized meal plans for maintaining their health and nutritional management. However, existing systems struggle to provide meal plans tailored to the user's emotional state and individual health needs, and lack mechanisms to sustain user motivation. Furthermore, health-related product recommendations in virtual stores do not take into account the user's nutritional information or emotional state, making truly personalized product recommendations difficult. Therefore, a system is needed that can provide more personalized meal plans and appropriate messages to increase motivation while also recommending health-related products in virtual stores based on the user's nutritional information and emotional state.

[0367] 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.

[0368] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal content using image recognition technology, means for calculating nutritional information based on the analyzed meal content and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal record and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously enter a meal record and monitor the progress of the meal record, means for analyzing the user's emotional state using an emotion recognition engine and saving the data, means for providing motivational messages based on the emotional state, and means for recommending health-related products in a virtual store based on the analyzed nutritional information and emotional state. This makes it possible to provide personalized meal plans and motivational messages based on the user's health needs and emotional state, and further realizes the recommendation of optimal health-related products in the virtual store.

[0369] "Profile Information" refers to specific information about a User, such as the User's name, age, gender, height, weight, allergy information, and specific nutritional needs.

[0370] "Nutritional Needs" refers to the nutritional or dietary requirements a User has for health maintenance or a specific treatment.

[0371] A "database" is a part of a computer system that organizes information systematically and enables efficient searching and management.

[0372] "Image recognition technology" refers to the technology in which a computer analyzes image data and identifies its contents.

[0373] "Nutritional information" refers to the numerical values ​​of nutrients such as calories, protein, carbohydrates, and fat contained in a meal.

[0374] "Diet record" refers to the history of the meals a user has eaten and the nutritional information based on them.

[0375] "Meal Plan" refers to a meal plan that is appropriate for a user based on their needs and preferences.

[0376] An "emotion recognition engine" refers to a technical system that analyzes a user's facial expressions and voice to identify their emotional state.

[0377] "Motivational messages" refer to text or notifications that provide encouragement or suggestions to improve a user's behavior or psychological state.

[0378] A "virtual store" refers to an online shop that sells products over the Internet.

[0379] "Product recommendation" refers to a system that suggests the most suitable product based on the user's needs and condition.

[0380] This invention is a system for effectively managing a user's health and meal planning. A specific implementation method and its processing details are described below. This system provides personalized meal plans that are dynamically adjusted based on the user's profile information, food records, and emotional state, and emotionally-based health product recommendations.

[0381] The server stores the profile information entered by the user during registration in a database. The profile information includes basic user information (name, age, gender, height, weight), allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet). This database is managed using an SQL-based data management system, which allows for efficient data storage and retrieval.

[0382] After the user eats a meal, the device takes a photo of the meal and sends it to a server. The server then uses image recognition technologies such as TensorFlow and Keras to extract ingredient information from the photo. For example, an AI model can identify ingredients and their amounts in a photo taken by the user, such as "50g of oatmeal, 1 banana, 200ml of milk," and calculate nutritional information (calories, protein, carbohydrates, fat, etc.) based on this information. This nutritional information is then stored in a database as the user's dietary record.

[0383] The user's emotional state is input through the device or analyzed by the emotion recognition engine from photos taken by the device or recorded voice. Emotion recognition uses OpenCV and facial expression recognition algorithms to identify the user's emotional state from their facial expressions and voice. For example, if the user is recognized as feeling stressed, that data is stored in a database.

[0384] The server uses an AI algorithm to generate a personalized meal plan based on the user's past meal records, emotional state, and profile information. The meal plan takes into account the user's nutritional needs, food preferences, and emotional state. The generated meal plan is then sent to the user via their device. For example, it may suggest a menu that includes herbal tea to relieve stress or ingredients with a relaxing effect.

[0385] The device allows users to continuously enter food records, and the server monitors their progress. Based on this, the server adjusts the meal plan in real time and notifies the user again. It also provides motivational messages based on emotional state. For example, if the user is feeling stressed, it will send a message such as, "Don't push yourself too hard, eat foods that will help you relax."

[0386] Furthermore, the analyzed nutritional information and emotional state are used to recommend health-related products in the virtual store, enabling personalized product suggestions and helping users efficiently choose products that meet their health needs.

[0387] As an example, the following prompt sentence is used:

[0388] "I want to develop an AI system that recommends personalized health foods and supplements based on a user's food records and emotional state. Build a model to analyze the food photos uploaded by users and their emotional state, so that a virtual store can suggest products that meet the user's nutritional needs."

[0389] Input: Food photo, emotional state, user profile information (e.g., age, gender, height, weight, allergy information, specific nutritional needs)

[0390] Output: A personalized product recommendation list

[0391] Through the above steps, the present invention is a system that effectively supports the user's health management, improves motivation based on emotional state, and recommends appropriate products in a virtual store.

[0392] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0393] Step 1:

[0394] Registration of profile information entered by users

[0395] Users enter basic information such as name, age, gender, height, weight, allergy information, and specific nutritional needs. This information is sent to the server and stored in a database. This process registers the user's basic health information.

[0396] Step 2:

[0397] Taking and uploading food photos

[0398] After eating a meal, the user takes a photo of the meal with the device's camera and uploads it to the server. The uploaded photo is sent to the server's image recognition engine, which then obtains the initial data of the meal's contents.

[0399] Step 3:

[0400] Image analysis of food content

[0401] The server analyzes the uploaded meal photos using TensorFlow and Keras. It identifies the ingredients and their amounts and extracts data such as "50g of oatmeal, 1 banana, 200ml of milk." The results of this analysis become the input data for calculating nutritional information. Image recognition is used to obtain data for each ingredient.

[0402] Step 4:

[0403] Nutritional information calculation

[0404] The server calculates the nutritional information of the meal (calories, protein, carbohydrates, fat, etc.) based on the image analysis results. Using a nutritional information calculation algorithm, it generates numerical data of the constituent components and stores this in a database as a dietary record. In this step, specific nutrients are quantified.

[0405] Step 5:

[0406] Input or recognition of emotional states

[0407] Users can manually input their current emotional state through their device, or they can take photos or record audio using the device's camera and microphone, which are then sent to the server. The server then analyzes the input data using OpenCV and emotion recognition algorithms to identify the user's emotional state, resulting in emotion labels such as "stress," "happy," and "sad."

[0408] Step 6:

[0409] Storing Emotional Data

[0410] The server stores the recognized emotional state data in a database, which can be used for future meal planning and product recommendations. The emotional information can then be tracked.

[0411] Step 7:

[0412] Generate personalized meal plans

[0413] The server uses AI algorithms to generate a personalized meal plan based on the user's past food records, emotional state, and profile information. The meal plan takes into account the user's nutritional needs and emotional state. The plan is then presented as a detailed menu and recipes.

[0414] Step 8:

[0415] Meal plan notifications

[0416] The device notifies the user of the generated meal plan. The menu and recipes are displayed on the device screen and can be executed by the user. In this step, the user knows what meal to eat next.

[0417] Step 9:

[0418] Continuously record your food and monitor your progress

[0419] The user follows the suggested meal plan and then takes photos of the meal to update the record. The server continuously monitors the user's meal log and analyzes their progress, allowing them to continuously manage their health.

[0420] Step 10:

[0421] Generate product recommendation list

[0422] The server recommends health-related products in the virtual store based on the analyzed nutritional information and emotional state, and the recommendation list includes products optimized for the user's needs.

[0423] Step 11:

[0424] Product recommendation notifications

[0425] The terminal notifies the user of the generated product recommendation list, which allows the user to efficiently select products that meet their health needs and promotes purchases in the virtual store.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] [Second embodiment]

[0430] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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."

[0442] The system provides users with personalized meal plans for the purpose of managing their diet and supporting their health. Users access the system through their devices and input their profile information and nutritional needs, and are then presented with a meal plan based on their individual health conditions.

[0443] User registration and initial settings

[0444] Users first create an account through a web or app interface, entering basic information such as their name, email address, and password, and then entering health information such as age, gender, weight, and height, as well as dietary preferences, allergies, and specific nutritional needs (e.g., diabetes or low-carb diet).

[0445] The server stores the entered information in a database, creates a user account, and sends a confirmation email to the user.

[0446] Food record entry and analysis

[0447] After the user has eaten a meal, the user uses the terminal to take a photo of the meal.

[0448] The device then sends the photo to an AI food recognition module, which analyzes the image to identify ingredients and their quantities. This analysis determines the type and quantity of ingredients.

[0449] The server calculates nutritional information based on the analyzed data, including calorie intake, protein, carbohydrates, and fats, and updates the resulting nutritional information as the user's dietary record in a database.

[0450] Generate personalized meal plans

[0451] The server analyzes the user's past meal records and input profile information and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs and dietary restrictions.

[0452] The device notifies the user of the generated meal plan and provides detailed menus and recipes for carrying it out.

[0453] Monitor progress and adjust plans

[0454] The user follows the suggested meal plan, eats meals and updates their food log as they go.

[0455] The server continuously monitors the user's meal log, analyzes the user's progress, adjusts the meal plan as needed, and notifies the user of the adjusted meal plan.

[0456] Meeting specific nutritional needs

[0457] For example, if a user has diabetes, the server can generate a low-carb meal plan, or when dining out, it can suggest the best options from a particular restaurant menu.

[0458] As a concrete example, the flow when User A, who has diabetes, uses this system is shown below.

[0459] Specific examples

[0460] 1. User A enters initial registration information into the system (e.g., age, gender, height, weight, diabetes).

[0461] 2. After User A has eaten breakfast, he / she takes a photo of it with his / her device and uploads it to the system.

[0462] 3. The server uses AI to analyze the photo and identify the ingredients and their quantities, such as "50g oatmeal, 1 banana, 200ml milk."

[0463] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[0464] 5. The server generates a low-carb lunch menu based on User A's past meal records and initial setting information, and notifies User A of this via the terminal.

[0465] 6. User A follows the lunch menu provided, eats lunch, and records the contents again.

[0466] 7. The server analyzes the food record again and monitors User A's nutritional balance and goal achievement status.

[0467] By using this system, users can efficiently and continuously consume meals that suit their health condition and nutritional needs. It also helps users select appropriate menu items when eating out, making daily dietary management easier.

[0468] The processing flow will be explained below.

[0469] Step 1:

[0470] When users first access the system, they create an account via the web or app, entering basic information such as their name, email address, and password.

[0471] Step 2:

[0472] The server stores the entered account information in a database and sends the user a confirmation email.

[0473] Step 3:

[0474] Users click a link in the confirmation email to activate their account, then enter their profile information (age, gender, weight, height, etc.) and nutritional needs (e.g., diabetes, allergies, etc.).

[0475] Step 4:

[0476] The server stores the entered profile information and nutritional needs in a database and generates a basic meal plan based on this.

[0477] Step 5:

[0478] After eating a meal, the user uses the device to take a photo of the meal.

[0479] Step 6:

[0480] The device sends a photo of the meal to an AI meal recognition module, which analyzes the image to determine the ingredients and their quantities.

[0481] Step 7:

[0482] The server receives the analyzed meal content and calculates nutritional information (e.g., calories, protein, carbohydrates, fat, etc.) based on it.

[0483] Step 8:

[0484] The server adds the calculated nutritional information to the database as the user's dietary record.

[0485] Step 9:

[0486] The server uses an AI algorithm to generate a new personalized meal plan based on the user's past meal records and profile information.

[0487] Step 10:

[0488] The device will notify the user of the newly generated meal plan and display a detailed menu and its recipes.

[0489] Step 11:

[0490] The user eats according to the meal plan notified to them, then takes a photo of the meal again to update the record.

[0491] Step 12:

[0492] The server continuously monitors the user's food log, analyzes their progress, and adjusts their meal plan as needed.

[0493] Step 13:

[0494] The device will notify the user of the adjusted meal plan and assist the user in continuing to eat according to the new plan.

[0495] Step 14:

[0496] For users with specific nutritional needs, the server provides meal plans tailored to their restrictions and suggests optimal restaurant menus when dining out.

[0497] Step 15:

[0498] The device also suggests recipes to users using seasonal ingredients, helping them to enjoy healthy and timeless meals.

[0499] Example 1

[0500] 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."

[0501] In modern society, dietary management tailored to individual health conditions is becoming increasingly important. However, few general dietary management systems are able to adequately address the individual nutritional needs of users. Furthermore, many systems lack the functionality to allow users to easily and accurately record their dietary information and generate personalized meal plans based on the analysis results. Furthermore, there are issues with suggesting appropriate menus when dining out at restaurants and providing recipes using seasonal ingredients. This makes it difficult for users to optimally manage their diet based on their health conditions.

[0502] 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.

[0503] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously enter meal records and monitor the progress, and means for adjusting the meal plan as needed, thereby enabling the user to efficiently and accurately manage their diet according to their health condition and nutritional needs.

[0504] A "user" is an individual who utilizes the diet management system to receive a meal plan tailored to their health and nutritional needs.

[0505] "Profile information" refers to personal information such as a user's name, age, gender, weight, height, food preferences, and allergy information.

[0506] "Nutrition needs" refers to the types and amounts of nutrients a user requires to maintain good health or manage a particular health condition (e.g., diabetes).

[0507] "Database" means a computer system for storing and managing data such as user profile information, nutritional needs, and dietary records.

[0508] "Image recognition technology" is a technology that uses machine learning algorithms to identify ingredients and their quantities from photographs of meals.

[0509] "Meal contents" refers to the ingredients and amounts consumed by the user.

[0510] "Nutritional information" refers to information on nutrients such as calories, protein, carbohydrates, and lipids calculated based on the contents of a meal.

[0511] A "diet record" is a record that includes the contents of meals consumed by a user and nutritional information calculated based on the meals.

[0512] A "personalized meal plan" is a meal plan that is customized based on a user's profile information and nutritional needs.

[0513] The "notification means" is a system for notifying users of updates to the meal plan or meal record. Specifically, a messaging service is used for notifications.

[0514] "Monitoring" refers to the act of watching the dietary information continuously recorded by the user and evaluating their health condition and nutritional balance.

[0515] "Adjustment" refers to making changes to the user's meal plan as needed based on the monitoring results.

[0516] A "generative model" is a machine learning algorithm that creates optimal meal plans based on a user's past data and specific prompts.

[0517] A "prompt" is an instruction given to a generative AI model, which then generates a meal plan.

[0518] "Dining Menu" refers to a list of meals offered at a restaurant or other establishment, including suggestions for options that best suit the user's nutritional needs.

[0519] "Recipe" refers to the cooking instructions and ingredient list required for a user to execute a meal plan.

[0520] The present invention is a system that provides personalized meal plans for the purpose of dietary management and health support for users. The system is accessed by users via a terminal and proposes meal plans based on individual health conditions.

[0521] First, a user creates an account through the web or app interface. This includes basic information such as name, email address, and password, as well as health information such as age, gender, weight, height, food preferences, allergies, and specific nutritional needs. The server stores this information in a database, creates a user account, and sends a confirmation email. For this purpose, Firebase Authentication is used for user management and MySQL is used for the database.

[0522] Next, after the user has eaten, they use the device to take a photo of the meal. The device sends the photo to the Google Cloud Vision API, which performs image analysis. The analysis results identify the names and amounts of ingredients and send them to the server. The server then calculates nutritional information based on this information and updates the database as the user's meal record. A script written in Python is used to calculate the nutritional information.

[0523] The server analyzes the user's past meal records and input profile information, and generates a personalized meal plan using an AI algorithm powered by TensorFlow. This meal plan takes into account the user's nutritional needs and dietary restrictions. The generated meal plan is then sent to the device, along with detailed menus and recipes for implementing the plan. Notifications are sent using Firebase Cloud Messaging.

[0524] The user then follows the proposed meal plan, eating meals and updating their meal log on their device. The server continuously monitors these meal logs and analyzes the user's progress. If necessary, the meal plan is adjusted and the user is notified again.

[0525] For users with specific nutritional needs, the server can generate meal plans that accommodate those restrictions. For example, a low-carb meal plan can be generated for a user with diabetes, and when dining out, the server can suggest optimal options from a specific restaurant menu. The generative AI model generates the optimal meal plan based on a prompt. For example, the following prompt is used: "Given that the user has diabetes, generate a prompt for the AI ​​model to suggest a low-carb meal plan based on past food records. Consider the following information: Breakfast (50g oatmeal, 1 banana, 200ml milk), user profile (age: 35, gender: male, diabetes)."

[0526] This allows users to efficiently and accurately consume meals that suit their health and nutritional needs, and also provides support for choosing appropriate menu items when eating out.

[0527] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0528] Step 1:

[0529] Users create an account through a web or app interface. Information entered includes name, email address, password, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs. The entered information is validated by the device. If validation is successful, the information is encrypted and sent to the server, where the user's personal profile information and nutritional needs are entered.

[0530] Step 2:

[0531] The server stores the user's profile information and nutritional needs received from the device in a database. The database uses "MySQL." The server also creates a user account and sends a confirmation email to the user, confirming that the user's information has been saved in the database and that the user's account has been activated.

[0532] Step 3:

[0533] After a user eats a meal, they use the device to take a photo of the meal. The device then sends the photo to the Google Cloud Vision API. The input is the captured image, and the output is the analyzed names and quantities of ingredients. Specifically, the device sends the image to the API and receives the analysis results in return.

[0534] Step 4:

[0535] The server calculates nutritional information based on the analysis results received from the device. The input is the analyzed ingredient name and amount, and the output is nutritional information (calories, protein, carbohydrates, fat, etc.). The system performs this calculation using a script written in Python. The calculated nutritional information is saved in a database and updated as the user's dietary record.

[0536] Step 5:

[0537] The server performs analysis based on the user's past meal records and input profile information. The input is the user's past meal records and profile information, and the output is a personalized meal plan. The server generates the meal plan using a generative AI model using TensorFlow. This meal plan takes into account the user's nutritional needs and dietary restrictions.

[0538] Step 6:

[0539] The server sends the generated meal plan to the terminal. The input is the generated meal plan, and the output is a message to notify the user. Firebase Cloud Messaging is used for the notification, allowing the user to receive a personalized meal plan.

[0540] Step 7:

[0541] The user eats according to the proposed meal plan and updates the meal record on the device each time. The input is the details of the meal the user ate, and the output is the updated meal record. The meal record entered by the user is sent to the server via the device.

[0542] Step 8:

[0543] The server continuously monitors the user's food log. The input is the user's most recent food log, and the output is an analysis of their progress. The server uses this data to assess the user's nutritional balance and progress toward their goals, and adjusts the meal plan as needed.

[0544] Step 9:

[0545] The server sends the adjusted meal plan back to the terminal and notifies the user. The input is the adjusted meal plan, and the output is a new notification to the user. This allows the user to always eat according to the latest meal plan.

[0546] (Application example 1)

[0547] 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."

[0548] With the recent rise in interest in health management and dieting, there is a demand for systems that provide meal plans tailored to individual nutritional needs. In particular, since it is difficult to propose personalized meal plans even when eating out, users need support in selecting the optimal menu that meets their health condition and nutritional needs. There is also a demand for technology that can continuously monitor users' meal records and update their progress in real time.

[0549] 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.

[0550] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for suggesting optimal menus for physical stores, and means for the user to continuously enter meal records and monitor their progress. This enables the user to efficiently and continuously eat meals that suit their health condition and nutritional needs, and provides support for selecting appropriate menus when eating out.

[0551] "User" means an individual who utilizes the system and who inputs profile information and nutritional needs to receive a meal plan.

[0552] "Profile information" refers to data including a user's basic personal and health information, such as age, gender, weight, height, dietary preferences, and allergy information.

[0553] "Nutrition needs" refers to information that indicates the nutrients and dietary restrictions a user needs based on a particular health condition or goal, such as diabetes or a low-carb diet.

[0554] The "database" is an information management system for managing and storing various data such as user profile information and meal records.

[0555] "Image recognition technology" is a technology used to analyze photographs of food and identify ingredients and their quantities.

[0556] "Nutritional information" is data on nutrients such as calories, protein, carbohydrates, and lipids calculated from the dietary content.

[0557] A "food record" is data that stores the details of meals a user has eaten and their nutritional information in chronological order.

[0558] "Personalized Meal Plan" refers to a meal menu or diet plan that is individually customized based on a user's profile information and nutritional needs.

[0559] "Notification" refers to the act of informing the user of the generated meal plan and progress in real time or periodically.

[0560] "Physical store" refers to a restaurant or retail store that a user actually visits.

[0561] An "optimal menu" is a meal menu selected based on the user's profile information and nutritional needs to maintain optimal health.

[0562] "Monitoring" refers to the act of continuously watching the dietary data recorded by the user and evaluating progress.

[0563] The "AI Food Recognition Module" is a module that uses artificial intelligence technology to analyze photographs of food.

[0564] "Generative AI model" refers to an artificial intelligence algorithm that generates personalized meal plans based on a user's eating history and profile information.

[0565] A "smartphone" is a mobile terminal with communication functions and advanced processing capabilities, and in this invention, the camera and internet connection functions are mainly used.

[0566] "Analysis" refers to data processing to identify ingredients and their amounts from the photographs of meals taken.

[0567] "Nutrition balance" refers to the overall condition that indicates whether the user is properly consuming each nutrient they require.

[0568] The present invention provides a system for providing a user with a personalized meal plan and supporting health management. An embodiment of the system will be described in detail below.

[0569] System Overview

[0570] The system stores a user's profile information and nutritional needs in a database, takes photos of meals, and analyzes them using image recognition technology. It calculates nutritional information based on the analyzed meal content and updates the user's meal record. It also generates a personalized meal plan based on the user's past meal records and nutritional needs, and notifies the user of the plan. It also suggests optimal menus for physical restaurants, and allows users to continuously enter their meal records and monitor their progress.

[0571] Hardware and software used

[0572] Smartphone: Used by users to enter information and take photos of their meals.

[0573] Server: Responsible for processing and storing data, and generating meal plans using generative AI models.

[0574] Image recognition technology: Using TensorFlow, it analyzes photos of meals to identify ingredients and their quantities.

[0575] Database: MongoDB is used to store user profile information, food records, and nutrition information.

[0576] Generative AI model: GPT-3.5 is used to generate personalized meal plans.

[0577] System processing flow

[0578] 1. User registration and initial settings

[0579] Users use their smartphones to install the app, create an account, and enter their profile information and nutritional needs, which are then stored in a database by the server. The information entered is then used to generate a personalized meal plan based on the user's individual nutritional needs.

[0580] 2. Entering and analyzing food records

[0581] After eating, users take a photo of their meal with their smartphone. The photo is sent to a server, where the ingredients and their amounts are analyzed using TensorFlow. Based on the analysis results, nutritional information is calculated and updated in a database as a meal record.

[0582] 3. Personalized meal plan generation and notifications

[0583] The server uses a generative AI model (GPT-3.5) to generate a personalized meal plan based on the user's past meal records and input profile information. This meal plan is then sent to the user via smartphone. The notification includes specific menu items and instructions on how to follow them.

[0584] 4. Progress monitoring and adjustment

[0585] As the user continues to enter their food records, the server monitors their progress, automatically adjusting the meal plan as needed and notifying the user again.

[0586] Specific examples

[0587] For example, if a user with diabetes uses this system, the following occurs:

[0588] 1. The user enters initial registration information (age, gender, height, weight, diabetes, etc.).

[0589] 2. After having breakfast, take a photo of the meal with your smartphone and upload it to the system.

[0590] 3. The server uses TensorFlow to analyze the photo and identify the ingredients and their quantities (e.g., "50g oatmeal, 1 banana, 200ml milk").

[0591] 4. Based on the analysis results, calculate nutritional information (e.g., "calorie intake: 300 kcal, carbohydrates: 45 g, protein: 10 g, fat: 5 g") and record it in the database.

[0592] 5. Based on the user's past meal records and initial settings, a generative AI model (GPT-3.5) will suggest personalized low-carb lunch menus, such as "chicken salad, whole-grain bread, and vegetable soup."

[0593] 6. The user eats lunch according to the suggested lunch menu and records the contents again.

[0594] Prompt Sentence Examples

[0595] "Please suggest the best menu for diabetes."

[0596] "Generate a new meal plan based on your recent food logs."

[0597] By implementing this invention, users can efficiently and continuously consume meals that suit their health condition and nutritional needs, and can receive support in selecting appropriate menus when eating out.

[0598] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0599] Step 1:

[0600] Users enter their profile information and nutritional needs, which are then sent to the server via the app using their device. This includes information such as name, age, gender, weight, height, and allergy information. The server receives this information and stores it in a database. The input data completes the initial setup and serves as the basis for generating an individually customized meal plan.

[0601] Step 2:

[0602] After a user eats a meal, they take a photo of the meal with their smartphone camera and upload it to the server via their device. The server receives the photo and uses image recognition technology (TensorFlow) to analyze the ingredients and their quantities. The input is the photo of the meal, and the output is the analyzed ingredient information (e.g., "50g oatmeal, 1 banana, 200ml milk").

[0603] Step 3:

[0604] The server calculates the nutritional information (calories, protein, carbohydrates, fat, etc.) for each ingredient based on the analyzed ingredient information. This is done based on the nutritional data of ingredients that has been stored in advance in a database. The input is ingredient information, and the output is detailed nutritional information (e.g., "calories 300kcal, carbohydrates 45g, protein 10g, fat 5g"). The calculated nutritional information is saved in the database as the user's dietary record.

[0605] Step 4:

[0606] The server analyzes the user's past meal records and input profile information and uses a generative AI model (GPT-3.5) to generate a personalized meal plan that addresses the user's specific nutritional needs. The input is the user's profile and meal records, and the output is a customized meal plan.

[0607] Step 5:

[0608] The server notifies the user of the generated meal plan via the terminal. The notification content includes specific menus and recipes for implementing them. The input is the generated meal plan, and the output is a notification message to the user.

[0609] Step 6:

[0610] The user continuously enters food records and updates their progress on the device. The server monitors this in real time and analyzes the progress. For example, if a new food record is added, the server uses this to reevaluate the nutritional balance and adjust the meal plan as necessary. The input is the new food record, and the output is the updated progress and adjusted meal plan.

[0611] Step 7:

[0612] In a physical store, a user takes a photo of their meal with their smartphone and uploads it to the server. The server uses image recognition technology to analyze the photo and extract ingredient information. Based on this, the server calculates nutritional information and suggests appropriate menu items. The input is the photo of the meal, and the output is the analysis results and suggested menu items.

[0613] 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.

[0614] To further enhance users' dietary management and health support, the system provides personalized meal plans combined with an emotional engine that recognizes the user's emotional state and dynamically adjusts meal plans based on that, providing motivational messages.

[0615] User registration and initial settings

[0616] When users first access the system, they create an account via the web or app, entering basic information such as their name, email address, and password. They then enter health information such as age, gender, weight, and height, as well as dietary preferences, allergies, and specific nutritional needs (e.g., diabetes or low-carb diet).

[0617] The server stores the entered information in a database, creates a user account, and sends a confirmation email to the user.

[0618] Food record entry and analysis

[0619] After the user has eaten a meal, the user uses the terminal to take a photo of the meal.

[0620] The device sends the captured photo to an AI meal recognition module, which analyzes the image to determine the ingredients and their quantities.

[0621] The server calculates nutritional information (e.g., calorie intake, protein, carbohydrates, fat, etc.) based on the analyzed data and updates the database as the user's dietary record.

[0622] Recognizing and recording emotional states

[0623] The user inputs their emotional state (e.g., happy, sad, stressed, excited, etc.) through the device, and the emotion engine recognizes the user's emotional state through facial expression and voice analysis.

[0624] The server stores the user's input or the emotional state recognized by the emotion engine in a database.

[0625] Generate personalized meal plans

[0626] The server analyzes the user's past food logs, emotional state, and entered profile information, and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs, dietary restrictions, and emotional state.

[0627] The device notifies the user of the generated meal plan and displays a detailed menu and recipes for carrying it out.

[0628] Monitor progress and adjust plans

[0629] The user follows the suggested meal plan, eats the meal, and then takes a photo of the meal again to update the record.

[0630] The server continuously monitors the user's dietary records, analyzes their progress, and dynamically adjusts their meal plan, taking into account the user's emotional state. The adjusted meal plan is then notified to the user.

[0631] Providing emotional motivation

[0632] The server generates appropriate motivational messages based on the user's emotional state as recognized by the emotion engine. For example, if the user is feeling stressed, it will provide a menu using ingredients with a relaxation effect and an encouraging message to boost motivation.

[0633] Meeting specific nutritional needs

[0634] For example, if a user has diabetes, the server can generate a low-carb meal plan, or when dining out, it can suggest the best options from a particular restaurant menu.

[0635] Specific examples

[0636] As a concrete example, the flow when User A, who has diabetes and is easily stressed, uses this system is shown below.

[0637] 1. User A enters initial registration information into the system (e.g., age, gender, height, weight, diabetes).

[0638] 2. After User A has eaten breakfast, he / she takes a photo of it with his / her device and uploads it to the system.

[0639] 3. The server uses AI to analyze the photo and identify the ingredients and their quantities, such as "50g oatmeal, 1 banana, 200ml milk."

[0640] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[0641] 5. User A inputs their emotional state (e.g., stress) into the device. The emotion engine analyzes their facial expressions and voice to recognize their emotional state.

[0642] 6. The server generates a lunch menu using low-carb, relaxing ingredients based on the food record and profile information, including emotional state, and notifies User A of this via the terminal.

[0643] 7. User A follows the lunch menu provided, eats lunch, and records the contents again.

[0644] 8. The server again analyzes the food log and emotional state to monitor progress.

[0645] 9. The server adjusts the next meal plan to include an encouraging message and relaxation effect for user A, since he is feeling stressed, and notifies him again.

[0646] This system enables users to efficiently and continuously consume meals that suit their health condition, nutritional needs, and even emotional state, and by providing appropriate motivation, users' health management becomes even more effective.

[0647] The processing flow will be explained below.

[0648] Step 1:

[0649] Users create an account through the system's web or app interface, providing the required information: name, email address, and password.

[0650] Step 2:

[0651] The server stores the entered account information in a database and sends a confirmation email to the user.

[0652] Step 3:

[0653] The user clicks on the link in the confirmation email to activate their account and log in to the system.

[0654] Step 4:

[0655] Users enter profile information (age, gender, weight, height, favorite foods, allergy information, etc.) and specific nutritional needs (e.g., diabetes, low-carb diet).

[0656] Step 5:

[0657] The server stores the entered profile information and nutritional needs in a database and generates a basic meal plan.

[0658] Step 6:

[0659] After eating, the user takes a photo of the meal on their device and uploads it to the system.

[0660] Step 7:

[0661] The device sends the captured photo to an AI food recognition module, which performs image analysis to identify ingredients and their quantities.

[0662] Step 8:

[0663] The server receives the analyzed meal contents and calculates nutritional information (calories, protein, carbohydrates, fat, etc.).

[0664] Step 9:

[0665] The server updates the calculated nutritional information in the database as the user's dietary record.

[0666] Step 10:

[0667] The user inputs their emotional state (e.g., happy, sad, stressed, excited) using the device, and the emotion engine automatically recognizes the user's emotional state through facial expression or voice analysis.

[0668] Step 11:

[0669] The server stores the emotional state recognized by the emotion engine or the emotional state directly input by the user in a database.

[0670] Step 12:

[0671] The server uses an AI algorithm to generate a new personalized meal plan based on the user's past meal records, emotional state, and profile information.

[0672] Step 13:

[0673] The device notifies the user of the generated meal plan and displays a detailed menu and its recipes.

[0674] Step 14:

[0675] The user eats according to the meal plan notified to them, then takes a photo of the meal again to update the record.

[0676] Step 15:

[0677] The server continuously monitors the user's food log and emotional state, analyzes the progress, and, in particular, dynamically adjusts the meal plan taking into account the user's emotional state.

[0678] Step 16:

[0679] The server generates appropriate motivational messages based on the user's emotional state and notifies them through the device. For example, if the user is feeling stressed, the server will provide a menu containing ingredients with a relaxation effect and an encouraging message.

[0680] Step 17:

[0681] The device also provides recipes using seasonal ingredients recommended by the emotion engine, helping users enjoy healthy and timeless meals.

[0682] Step 18:

[0683] The server will suggest restaurant menus that are optimal for dining out to users with specific nutritional needs, for example, suggesting low-carb options to a diabetic user.

[0684] In this way, the system generates and provides personalized meal plans by comprehensively considering the user's profile information, nutritional needs, and emotional state. It also dynamically adjusts meal plans based on the user's emotional state and provides appropriate motivational messages, making dietary management and health support more effective.

[0685] Example 2

[0686] 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."

[0687] In the past, health and diet management required individual data entry, recording, and management, which was time-consuming and burdensome. It was also difficult to create meal plans that took emotional states into account, and there was a lack of effective ways to maintain motivation. Furthermore, it was difficult to maintain a proper nutritional balance when eating out.

[0688] 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.

[0689] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously input meal records and monitor progress, means for the user to input emotional states and for an emotion engine to analyze facial expressions and voice to recognize and record the emotional states, and means for generating motivational messages based on the emotional states and notifying the user. This enables efficient health and dietary management of the user and provides personalized support that takes emotional states into account, thereby maintaining motivation and maintaining an appropriate nutritional balance even when eating out.

[0690] "Profile Information" refers to information about a user's individual identity, such as the user's name, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs.

[0691] "Database" refers to an information system for centrally managing and storing data such as user profile information, nutritional needs, food records, and emotional state.

[0692] "Image recognition technology" refers to artificial intelligence and machine learning technology that analyzes photos of meals and recognizes specific ingredients and their quantities.

[0693] "Nutritional information" refers to information about the nutritional components of a meal, such as calories, protein, carbohydrates, and fats.

[0694] "Personalized Meal Plan" refers to an optimal meal plan created for you based on your profile information, nutritional needs, past food history, and emotional state.

[0695] A "motivational message" refers to a message that is generated based on the user's emotional state and is intended to increase the user's motivation.

[0696] "Emotion engine" refers to artificial intelligence technology that analyzes a user's facial expressions and voice to recognize their emotional state.

[0697] "Progress" refers to accumulated data on the contents of meals that the user has eaten according to the meal plan and their nutritional information, and is information that indicates the progress of health management.

[0698] This system centrally manages a user's profile information, food records, emotional state, etc., and provides personalized meal plans. This allows for efficient health management for users, while also providing support that takes emotional state into consideration and maintaining motivation. The operation of this system is explained in detail below.

[0699] User registration and initial settings

[0700] When a user first accesses the system, they create an account via the web or app, entering their profile information such as name, email address, password, age, gender, weight, height, food preferences, allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet). The server stores this information in a database and sends a confirmation email to the user.

[0701] Food record entry and analysis

[0702] After a user eats a meal, they take a photo of the meal using a device such as a smartphone or tablet and upload it to the system. The server sends this photo to an AI meal recognition module, which uses image recognition technology to analyze the ingredients and their amounts. For example, it may identify the meal as "50g of oatmeal, 1 banana, 200ml of milk." The server then calculates nutritional information based on the analysis results and updates the database as a meal record. Specific nutritional information might include "calorie intake 300kcal, carbohydrates 45g, protein 10g, fat 5g."

[0703] Recognizing and recording emotional states

[0704] After eating, the user inputs their emotional state (happiness, sadness, stress, excitement, etc.) into the device. The emotion engine then analyzes their facial expressions and voice to recognize the user's emotional state. The server then stores this emotional data in a database.

[0705] Personalized meal plan generation and notifications

[0706] The server analyzes the user's profile information, past meal records, and emotional state, and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs, dietary restrictions, and emotional state. The device notifies the user of the generated meal plan and displays detailed menus and recipes.

[0707] Monitor progress and adjust plans

[0708] The user follows the proposed meal plan, eats the meal, and then takes a photo of the meal on their device to update their meal log. The server continuously monitors the meal log and analyzes the progress. It dynamically adjusts the meal plan, taking into account the user's emotional state. The user is then notified of the adjusted meal plan.

[0709] Providing emotional motivation

[0710] The server generates motivational messages based on the emotional state recognized by the emotion engine and notifies the user via the device. For example, if the user is feeling stressed, it will provide a menu using ingredients with a relaxation effect and an encouraging message.

[0711] Meeting specific nutritional needs

[0712] For example, the server could generate a low-carb meal plan for a diabetic user, or suggest the best options from a specific restaurant menu when dining out via the device.

[0713] Specific examples

[0714] The flow when User A, who has diabetes and is easily stressed, uses this system is as follows.

[0715] 1. User A enters information such as age, gender, height, weight, and diabetes into the system.

[0716] 2. After User A has eaten breakfast, he / she takes a photo of the breakfast on his / her device and uploads it to the system.

[0717] 3. The server analyzes the photo and identifies the ingredients and their quantities, for example, "50g oatmeal, 1 banana, 200ml milk."

[0718] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[0719] 5. User A inputs their emotional state (e.g., stress) into the device. The emotion engine analyzes and recognizes their facial expressions and voice.

[0720] 6. Based on this information, the server generates a low-carb, relaxing lunch menu and notifies User A via the terminal.

[0721] 7. User A follows the lunch menu provided, eats lunch, and records the contents again.

[0722] 8. The server analyzes your new food log and emotional state to monitor your progress.

[0723] 9. The server determines that User A is feeling stressed and adjusts the next meal plan to have a relaxation effect, and notifies the user again with an encouraging message.

[0724] Example prompt sentence:

[0725] "I have diabetes and get stressed easily. For breakfast, I had 50g of oatmeal, one banana, and 200ml of milk. Can you suggest a low-carb lunch that will help me relax?"

[0726] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0727] Step 1: User registration and initial setup

[0728] When users first access the system, they create an account via the web or app by entering their profile information, including their name, email address, password, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet).

[0729] Input: Profile information (name, email address, password, age, gender, weight, height, food preferences, allergy information, nutritional needs)

[0730] Output: Save to database, send confirmation email

[0731] The server receives this information and stores it in a database. After the information is stored, a confirmation email is sent to the user.

[0732] Step 2: Enter and analyze your food records

[0733] After a user has eaten a meal, they take a photo of the meal using a device such as a smartphone or tablet and upload it to the system.

[0734] Input: Food photo

[0735] Output: Analyzed dietary information and nutritional information

[0736] The device sends the captured photo to the AI ​​meal recognition module. The server uses the AI ​​module to apply image recognition technology to analyze the ingredients and their amounts. For example, it might analyze the meal as "50g of oatmeal, 1 banana, 200ml of milk." Based on the analysis results, nutritional information (calories, protein, carbohydrates, fat, etc.) is calculated and updated in the database as a meal record.

[0737] Step 3: Recognize and record your emotional state

[0738] After eating, the user inputs their emotional state (happiness, sadness, stress, excitement, etc.) into the device, and the emotion engine analyzes facial expressions and voice to recognize the user's emotional state.

[0739] Input: Self-input of emotional state, facial expressions and voice data

[0740] Output: Recognized emotional state, stored in a database

[0741] The server receives this emotion data and stores it in a database.

[0742] Step 4: Generate and notify your personalized meal plan

[0743] The server analyzes the user's profile information, past meal records, and emotional state, and uses AI algorithms to generate a personalized meal plan.

[0744] Input: Profile information, past meal records, emotional state

[0745] Output: Meal plan, recipe guide

[0746] The meal plan is created taking into account the user's nutritional needs, dietary restrictions, and emotional state. The device notifies the user of the generated meal plan and displays detailed menus and recipes.

[0747] Step 5: Monitor progress and adjust your plan

[0748] The user follows the proposed meal plan, eats the meal, and then takes a photo of it on the device and uploads it to the system.

[0749] Input: Another meal photo

[0750] Output: Updated food log, adjusted meal plan

[0751] The device sends new meal photos to the server, which analyzes and continuously monitors the new meal log, taking into account progress and emotional state, dynamically adjusting the meal plan as needed, and notifying the user again of the adjustments.

[0752] Step 6: Provide emotional motivation

[0753] The server generates motivational messages based on the emotional state recognized by the emotion engine.

[0754] Input: Perceived emotional state

[0755] Output: Motivation message notification

[0756] For example, if a user is feeling stressed, the device will provide a menu using ingredients with a relaxation effect and an encouraging message.

[0757] Step 7: Addressing specific nutritional needs

[0758] For example, if a user has diabetes, the server will generate a low-carb meal plan, and when dining out, the device will suggest the best options from a particular restaurant menu.

[0759] Inputs: specific nutritional needs, restaurant choices when eating out

[0760] Output: Low-carb meal plan, optimal restaurant menu suggestions

[0761] (Application example 2)

[0762] 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."

[0763] Many people today require highly personalized meal plans for maintaining their health and nutritional management. However, existing systems struggle to provide meal plans tailored to the user's emotional state and individual health needs, and lack mechanisms to sustain user motivation. Furthermore, health-related product recommendations in virtual stores do not take into account the user's nutritional information or emotional state, making truly personalized product recommendations difficult. Therefore, a system is needed that can provide more personalized meal plans and appropriate messages to increase motivation while also recommending health-related products in virtual stores based on the user's nutritional information and emotional state.

[0764] 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.

[0765] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal content using image recognition technology, means for calculating nutritional information based on the analyzed meal content and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal record and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously enter a meal record and monitor the progress of the meal record, means for analyzing the user's emotional state using an emotion recognition engine and saving the data, means for providing motivational messages based on the emotional state, and means for recommending health-related products in a virtual store based on the analyzed nutritional information and emotional state. This makes it possible to provide personalized meal plans and motivational messages based on the user's health needs and emotional state, and further realizes the recommendation of optimal health-related products in the virtual store.

[0766] "Profile Information" refers to specific information about a User, such as the User's name, age, gender, height, weight, allergy information, and specific nutritional needs.

[0767] "Nutritional Needs" refers to the nutritional or dietary requirements a User has for health maintenance or a specific treatment.

[0768] A "database" is a part of a computer system that organizes information systematically and enables efficient searching and management.

[0769] "Image recognition technology" refers to the technology in which a computer analyzes image data and identifies its contents.

[0770] "Nutritional information" refers to the numerical values ​​of nutrients such as calories, protein, carbohydrates, and fat contained in a meal.

[0771] "Diet record" refers to the history of the meals a user has eaten and the nutritional information based on them.

[0772] "Meal Plan" refers to a meal plan that is appropriate for a user based on their needs and preferences.

[0773] An "emotion recognition engine" refers to a technical system that analyzes a user's facial expressions and voice to identify their emotional state.

[0774] "Motivational messages" refer to text or notifications that provide encouragement or suggestions to improve a user's behavior or psychological state.

[0775] A "virtual store" refers to an online shop that sells products over the Internet.

[0776] "Product recommendation" refers to a system that suggests the most suitable product based on the user's needs and condition.

[0777] This invention is a system for effectively managing a user's health and meal planning. A specific implementation method and its processing details are described below. This system provides personalized meal plans that are dynamically adjusted based on the user's profile information, food records, and emotional state, and emotionally-based health product recommendations.

[0778] The server stores the profile information entered by the user during registration in a database. The profile information includes basic user information (name, age, gender, height, weight), allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet). This database is managed using an SQL-based data management system, which allows for efficient data storage and retrieval.

[0779] After the user eats a meal, the device takes a photo of the meal and sends it to a server. The server then uses image recognition technologies such as TensorFlow and Keras to extract ingredient information from the photo. For example, an AI model can identify ingredients and their amounts in a photo taken by the user, such as "50g of oatmeal, 1 banana, 200ml of milk," and calculate nutritional information (calories, protein, carbohydrates, fat, etc.) based on this information. This nutritional information is then stored in a database as the user's dietary record.

[0780] The user's emotional state is input through the device or analyzed by the emotion recognition engine from photos taken by the device or recorded voice. Emotion recognition uses OpenCV and facial expression recognition algorithms to identify the user's emotional state from their facial expressions and voice. For example, if the user is recognized as feeling stressed, that data is stored in a database.

[0781] The server uses an AI algorithm to generate a personalized meal plan based on the user's past meal records, emotional state, and profile information. The meal plan takes into account the user's nutritional needs, food preferences, and emotional state. The generated meal plan is then sent to the user via their device. For example, it may suggest a menu that includes herbal tea to relieve stress or ingredients with a relaxing effect.

[0782] The device allows users to continuously enter food records, and the server monitors their progress. Based on this, the server adjusts the meal plan in real time and notifies the user again. It also provides motivational messages based on emotional state. For example, if the user is feeling stressed, it will send a message such as, "Don't push yourself too hard, eat foods that will help you relax."

[0783] Furthermore, the analyzed nutritional information and emotional state are used to recommend health-related products in the virtual store, enabling personalized product suggestions and helping users efficiently choose products that meet their health needs.

[0784] As an example, the following prompt sentence is used:

[0785] "I want to develop an AI system that recommends personalized health foods and supplements based on a user's food records and emotional state. Build a model to analyze the food photos uploaded by users and their emotional state, so that a virtual store can suggest products that meet the user's nutritional needs."

[0786] Input: Food photo, emotional state, user profile information (e.g., age, gender, height, weight, allergy information, specific nutritional needs)

[0787] Output: A personalized product recommendation list

[0788] Through the above steps, the present invention is a system that effectively supports the user's health management, improves motivation based on emotional state, and recommends appropriate products in a virtual store.

[0789] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0790] Step 1:

[0791] Registration of profile information entered by users

[0792] Users enter basic information such as name, age, gender, height, weight, allergy information, and specific nutritional needs. This information is sent to the server and stored in a database. This process registers the user's basic health information.

[0793] Step 2:

[0794] Taking and uploading food photos

[0795] After eating a meal, the user takes a photo of the meal with the device's camera and uploads it to the server. The uploaded photo is sent to the server's image recognition engine, which then obtains the initial data of the meal's contents.

[0796] Step 3:

[0797] Image analysis of food content

[0798] The server analyzes the uploaded meal photos using TensorFlow and Keras. It identifies the ingredients and their amounts and extracts data such as "50g of oatmeal, 1 banana, 200ml of milk." The results of this analysis become the input data for calculating nutritional information. Image recognition is used to obtain data for each ingredient.

[0799] Step 4:

[0800] Nutritional information calculation

[0801] The server calculates the nutritional information of the meal (calories, protein, carbohydrates, fat, etc.) based on the image analysis results. Using a nutritional information calculation algorithm, it generates numerical data of the constituent components and stores this in a database as a dietary record. In this step, specific nutrients are quantified.

[0802] Step 5:

[0803] Input or recognition of emotional states

[0804] Users can manually input their current emotional state through their device, or they can take photos or record audio using the device's camera and microphone, which are then sent to the server. The server then analyzes the input data using OpenCV and emotion recognition algorithms to identify the user's emotional state, resulting in emotion labels such as "stress," "happy," and "sad."

[0805] Step 6:

[0806] Storing Emotional Data

[0807] The server stores the recognized emotional state data in a database, which can be used for future meal planning and product recommendations. The emotional information can then be tracked.

[0808] Step 7:

[0809] Generate personalized meal plans

[0810] The server uses AI algorithms to generate a personalized meal plan based on the user's past food records, emotional state, and profile information. The meal plan takes into account the user's nutritional needs and emotional state. The plan is then presented as a detailed menu and recipes.

[0811] Step 8:

[0812] Meal plan notifications

[0813] The device notifies the user of the generated meal plan. The menu and recipes are displayed on the device screen and can be executed by the user. In this step, the user knows what meal to eat next.

[0814] Step 9:

[0815] Continuously record your food and monitor your progress

[0816] The user follows the suggested meal plan and then takes photos of the meal to update the record. The server continuously monitors the user's meal log and analyzes their progress, allowing them to continuously manage their health.

[0817] Step 10:

[0818] Generate product recommendation list

[0819] The server recommends health-related products in the virtual store based on the analyzed nutritional information and emotional state, and the recommendation list includes products optimized for the user's needs.

[0820] Step 11:

[0821] Product recommendation notifications

[0822] The terminal notifies the user of the generated product recommendation list, which allows the user to efficiently select products that meet their health needs and promotes purchases in the virtual store.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] [Third embodiment]

[0827] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0828] 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.

[0829] 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).

[0830] 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.

[0831] 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.

[0832] 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).

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] The system provides users with personalized meal plans for the purpose of managing their diet and supporting their health. Users access the system through their devices and input their profile information and nutritional needs, and are then presented with a meal plan based on their individual health conditions.

[0840] User registration and initial settings

[0841] Users first create an account through a web or app interface, entering basic information such as their name, email address, and password, and then entering health information such as age, gender, weight, and height, as well as dietary preferences, allergies, and specific nutritional needs (e.g., diabetes or low-carb diet).

[0842] The server stores the entered information in a database, creates a user account, and sends a confirmation email to the user.

[0843] Food record entry and analysis

[0844] After the user has eaten a meal, the user uses the terminal to take a photo of the meal.

[0845] The device then sends the photo to an AI food recognition module, which analyzes the image to identify ingredients and their quantities. This analysis determines the type and quantity of ingredients.

[0846] The server calculates nutritional information based on the analyzed data, including calorie intake, protein, carbohydrates, and fats, and updates the resulting nutritional information as the user's dietary record in a database.

[0847] Generate personalized meal plans

[0848] The server analyzes the user's past meal records and input profile information and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs and dietary restrictions.

[0849] The device notifies the user of the generated meal plan and provides detailed menus and recipes for carrying it out.

[0850] Monitor progress and adjust plans

[0851] The user follows the suggested meal plan, eats meals and updates their food log as they go.

[0852] The server continuously monitors the user's meal log, analyzes the user's progress, adjusts the meal plan as needed, and notifies the user of the adjusted meal plan.

[0853] Meeting specific nutritional needs

[0854] For example, if a user has diabetes, the server can generate a low-carb meal plan, or when dining out, it can suggest the best options from a particular restaurant menu.

[0855] As a concrete example, the flow when User A, who has diabetes, uses this system is shown below.

[0856] Specific examples

[0857] 1. User A enters initial registration information into the system (e.g., age, gender, height, weight, diabetes).

[0858] 2. After User A has eaten breakfast, he / she takes a photo of it with his / her device and uploads it to the system.

[0859] 3. The server uses AI to analyze the photo and identify the ingredients and their quantities, such as "50g oatmeal, 1 banana, 200ml milk."

[0860] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[0861] 5. The server generates a low-carb lunch menu based on User A's past meal records and initial setting information, and notifies User A of this via the terminal.

[0862] 6. User A follows the lunch menu provided, eats lunch, and records the contents again.

[0863] 7. The server analyzes the food record again and monitors User A's nutritional balance and goal achievement status.

[0864] By using this system, users can efficiently and continuously consume meals that suit their health condition and nutritional needs. It also helps users select appropriate menu items when eating out, making daily dietary management easier.

[0865] The processing flow will be explained below.

[0866] Step 1:

[0867] When users first access the system, they create an account via the web or app, entering basic information such as their name, email address, and password.

[0868] Step 2:

[0869] The server stores the entered account information in a database and sends the user a confirmation email.

[0870] Step 3:

[0871] Users click a link in the confirmation email to activate their account, then enter their profile information (age, gender, weight, height, etc.) and nutritional needs (e.g., diabetes, allergies, etc.).

[0872] Step 4:

[0873] The server stores the entered profile information and nutritional needs in a database and generates a basic meal plan based on this.

[0874] Step 5:

[0875] After eating a meal, the user uses the device to take a photo of the meal.

[0876] Step 6:

[0877] The device sends a photo of the meal to an AI meal recognition module, which analyzes the image to determine the ingredients and their quantities.

[0878] Step 7:

[0879] The server receives the analyzed meal content and calculates nutritional information (e.g., calories, protein, carbohydrates, fat, etc.) based on it.

[0880] Step 8:

[0881] The server adds the calculated nutritional information to the database as the user's dietary record.

[0882] Step 9:

[0883] The server uses an AI algorithm to generate a new personalized meal plan based on the user's past meal records and profile information.

[0884] Step 10:

[0885] The device will notify the user of the newly generated meal plan and display a detailed menu and its recipes.

[0886] Step 11:

[0887] The user eats according to the meal plan notified to them, then takes a photo of the meal again to update the record.

[0888] Step 12:

[0889] The server continuously monitors the user's food log, analyzes their progress, and adjusts their meal plan as needed.

[0890] Step 13:

[0891] The device will notify the user of the adjusted meal plan and assist the user in continuing to eat according to the new plan.

[0892] Step 14:

[0893] For users with specific nutritional needs, the server provides meal plans tailored to their restrictions and suggests optimal restaurant menus when dining out.

[0894] Step 15:

[0895] The device also suggests recipes to users using seasonal ingredients, helping them to enjoy healthy and timeless meals.

[0896] Example 1

[0897] 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."

[0898] In modern society, dietary management tailored to individual health conditions is becoming increasingly important. However, few general dietary management systems are able to adequately address the individual nutritional needs of users. Furthermore, many systems lack the functionality to allow users to easily and accurately record their dietary information and generate personalized meal plans based on the analysis results. Furthermore, there are issues with suggesting appropriate menus when dining out at restaurants and providing recipes using seasonal ingredients. This makes it difficult for users to optimally manage their diet based on their health conditions.

[0899] 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.

[0900] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously enter meal records and monitor the progress, and means for adjusting the meal plan as needed, thereby enabling the user to efficiently and accurately manage their diet according to their health condition and nutritional needs.

[0901] A "user" is an individual who utilizes the diet management system to receive a meal plan tailored to their health and nutritional needs.

[0902] "Profile information" refers to personal information such as a user's name, age, gender, weight, height, food preferences, and allergy information.

[0903] "Nutrition needs" refers to the types and amounts of nutrients a user requires to maintain good health or manage a particular health condition (e.g., diabetes).

[0904] "Database" means a computer system for storing and managing data such as user profile information, nutritional needs, and dietary records.

[0905] "Image recognition technology" is a technology that uses machine learning algorithms to identify ingredients and their quantities from photographs of meals.

[0906] "Meal contents" refers to the ingredients and amounts consumed by the user.

[0907] "Nutritional information" refers to information on nutrients such as calories, protein, carbohydrates, and lipids calculated based on the contents of a meal.

[0908] A "diet record" is a record that includes the contents of meals consumed by a user and nutritional information calculated based on the meals.

[0909] A "personalized meal plan" is a meal plan that is customized based on a user's profile information and nutritional needs.

[0910] The "notification means" is a system for notifying users of updates to the meal plan or meal record. Specifically, a messaging service is used for notifications.

[0911] "Monitoring" refers to the act of watching the dietary information continuously recorded by the user and evaluating their health condition and nutritional balance.

[0912] "Adjustment" refers to making changes to the user's meal plan as needed based on the monitoring results.

[0913] A "generative model" is a machine learning algorithm that creates optimal meal plans based on a user's past data and specific prompts.

[0914] A "prompt" is an instruction given to a generative AI model, which then generates a meal plan.

[0915] "Dining Menu" refers to a list of meals offered at a restaurant or other establishment, including suggestions for options that best suit the user's nutritional needs.

[0916] "Recipe" refers to the cooking instructions and ingredient list required for a user to execute a meal plan.

[0917] The present invention is a system that provides personalized meal plans for the purpose of dietary management and health support for users. The system is accessed by users via a terminal and proposes meal plans based on individual health conditions.

[0918] First, a user creates an account through the web or app interface. This includes basic information such as name, email address, and password, as well as health information such as age, gender, weight, height, food preferences, allergies, and specific nutritional needs. The server stores this information in a database, creates a user account, and sends a confirmation email. For this purpose, Firebase Authentication is used for user management and MySQL is used for the database.

[0919] Next, after the user has eaten, they use the device to take a photo of the meal. The device sends the photo to the Google Cloud Vision API, which performs image analysis. The analysis results identify the names and amounts of ingredients and send them to the server. The server then calculates nutritional information based on this information and updates the database as the user's meal record. A script written in Python is used to calculate the nutritional information.

[0920] The server analyzes the user's past meal records and input profile information, and generates a personalized meal plan using an AI algorithm powered by TensorFlow. This meal plan takes into account the user's nutritional needs and dietary restrictions. The generated meal plan is then sent to the device, along with detailed menus and recipes for implementing the plan. Notifications are sent using Firebase Cloud Messaging.

[0921] The user then follows the proposed meal plan, eating meals and updating their meal log on their device. The server continuously monitors these meal logs and analyzes the user's progress. If necessary, the meal plan is adjusted and the user is notified again.

[0922] For users with specific nutritional needs, the server can generate meal plans that accommodate those restrictions. For example, a low-carb meal plan can be generated for a user with diabetes, and when dining out, the server can suggest optimal options from a specific restaurant menu. The generative AI model generates the optimal meal plan based on a prompt. For example, the following prompt is used: "Given that the user has diabetes, generate a prompt for the AI ​​model to suggest a low-carb meal plan based on past food records. Consider the following information: Breakfast (50g oatmeal, 1 banana, 200ml milk), user profile (age: 35, gender: male, diabetes)."

[0923] This allows users to efficiently and accurately consume meals that suit their health and nutritional needs, and also provides support for choosing appropriate menu items when eating out.

[0924] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0925] Step 1:

[0926] Users create an account through a web or app interface. Information entered includes name, email address, password, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs. The entered information is validated by the device. If validation is successful, the information is encrypted and sent to the server, where the user's personal profile information and nutritional needs are entered.

[0927] Step 2:

[0928] The server stores the user's profile information and nutritional needs received from the device in a database. The database uses "MySQL." The server also creates a user account and sends a confirmation email to the user, confirming that the user's information has been saved in the database and that the user's account has been activated.

[0929] Step 3:

[0930] After a user eats a meal, they use the device to take a photo of the meal. The device then sends the photo to the Google Cloud Vision API. The input is the captured image, and the output is the analyzed names and quantities of ingredients. Specifically, the device sends the image to the API and receives the analysis results in return.

[0931] Step 4:

[0932] The server calculates nutritional information based on the analysis results received from the device. The input is the analyzed ingredient name and amount, and the output is nutritional information (calories, protein, carbohydrates, fat, etc.). The system performs this calculation using a script written in Python. The calculated nutritional information is saved in a database and updated as the user's dietary record.

[0933] Step 5:

[0934] The server performs analysis based on the user's past meal records and input profile information. The input is the user's past meal records and profile information, and the output is a personalized meal plan. The server generates the meal plan using a generative AI model using TensorFlow. This meal plan takes into account the user's nutritional needs and dietary restrictions.

[0935] Step 6:

[0936] The server sends the generated meal plan to the terminal. The input is the generated meal plan, and the output is a message to notify the user. Firebase Cloud Messaging is used for the notification, allowing the user to receive a personalized meal plan.

[0937] Step 7:

[0938] The user eats according to the proposed meal plan and updates the meal record on the device each time. The input is the details of the meal the user ate, and the output is the updated meal record. The meal record entered by the user is sent to the server via the device.

[0939] Step 8:

[0940] The server continuously monitors the user's food log. The input is the user's most recent food log, and the output is an analysis of their progress. The server uses this data to assess the user's nutritional balance and progress toward their goals, and adjusts the meal plan as needed.

[0941] Step 9:

[0942] The server sends the adjusted meal plan back to the terminal and notifies the user. The input is the adjusted meal plan, and the output is a new notification to the user. This allows the user to always eat according to the latest meal plan.

[0943] (Application example 1)

[0944] 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."

[0945] With the recent rise in interest in health management and dieting, there is a demand for systems that provide meal plans tailored to individual nutritional needs. In particular, since it is difficult to propose personalized meal plans even when eating out, users need support in selecting the optimal menu that meets their health condition and nutritional needs. There is also a demand for technology that can continuously monitor users' meal records and update their progress in real time.

[0946] 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.

[0947] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for suggesting optimal menus for physical stores, and means for the user to continuously enter meal records and monitor their progress. This enables the user to efficiently and continuously eat meals that suit their health condition and nutritional needs, and provides support for selecting appropriate menus when eating out.

[0948] "User" means an individual who utilizes the system and who inputs profile information and nutritional needs to receive a meal plan.

[0949] "Profile information" refers to data including a user's basic personal and health information, such as age, gender, weight, height, dietary preferences, and allergy information.

[0950] "Nutrition needs" refers to information that indicates the nutrients and dietary restrictions a user needs based on a particular health condition or goal, such as diabetes or a low-carb diet.

[0951] The "database" is an information management system for managing and storing various data such as user profile information and meal records.

[0952] "Image recognition technology" is a technology used to analyze photographs of food and identify ingredients and their quantities.

[0953] "Nutritional information" is data on nutrients such as calories, protein, carbohydrates, and lipids calculated from the dietary content.

[0954] A "food record" is data that stores the details of meals a user has eaten and their nutritional information in chronological order.

[0955] "Personalized Meal Plan" refers to a meal menu or diet plan that is individually customized based on a user's profile information and nutritional needs.

[0956] "Notification" refers to the act of informing the user of the generated meal plan and progress in real time or periodically.

[0957] "Physical store" refers to a restaurant or retail store that a user actually visits.

[0958] An "optimal menu" is a meal menu selected based on the user's profile information and nutritional needs to maintain optimal health.

[0959] "Monitoring" refers to the act of continuously watching the dietary data recorded by the user and evaluating progress.

[0960] The "AI Food Recognition Module" is a module that uses artificial intelligence technology to analyze photographs of food.

[0961] "Generative AI model" refers to an artificial intelligence algorithm that generates personalized meal plans based on a user's eating history and profile information.

[0962] A "smartphone" is a mobile terminal with communication functions and advanced processing capabilities, and in this invention, the camera and internet connection functions are mainly used.

[0963] "Analysis" refers to data processing to identify ingredients and their amounts from the photographs of meals taken.

[0964] "Nutrition balance" refers to the overall condition that indicates whether the user is properly consuming each nutrient they require.

[0965] The present invention provides a system for providing a user with a personalized meal plan and supporting health management. An embodiment of the system will be described in detail below.

[0966] System Overview

[0967] The system stores a user's profile information and nutritional needs in a database, takes photos of meals, and analyzes them using image recognition technology. It calculates nutritional information based on the analyzed meal content and updates the user's meal record. It also generates a personalized meal plan based on the user's past meal records and nutritional needs, and notifies the user of the plan. It also suggests optimal menus for physical restaurants, and allows users to continuously enter their meal records and monitor their progress.

[0968] Hardware and software used

[0969] Smartphone: Used by users to enter information and take photos of their meals.

[0970] Server: Responsible for processing and storing data, and generating meal plans using generative AI models.

[0971] Image recognition technology: Using TensorFlow, it analyzes photos of meals to identify ingredients and their quantities.

[0972] Database: MongoDB is used to store user profile information, food records, and nutrition information.

[0973] Generative AI model: GPT-3.5 is used to generate personalized meal plans.

[0974] System processing flow

[0975] 1. User registration and initial settings

[0976] Users use their smartphones to install the app, create an account, and enter their profile information and nutritional needs, which are then stored in a database by the server. The information entered is then used to generate a personalized meal plan based on the user's individual nutritional needs.

[0977] 2. Entering and analyzing food records

[0978] After eating, users take a photo of their meal with their smartphone. The photo is sent to a server, where the ingredients and their amounts are analyzed using TensorFlow. Based on the analysis results, nutritional information is calculated and updated in a database as a meal record.

[0979] 3. Personalized meal plan generation and notifications

[0980] The server uses a generative AI model (GPT-3.5) to generate a personalized meal plan based on the user's past meal records and input profile information. This meal plan is then sent to the user via smartphone. The notification includes specific menu items and instructions on how to follow them.

[0981] 4. Progress monitoring and adjustment

[0982] As the user continues to enter their food records, the server monitors their progress, automatically adjusting the meal plan as needed and notifying the user again.

[0983] Specific examples

[0984] For example, if a user with diabetes uses this system, the following occurs:

[0985] 1. The user enters initial registration information (age, gender, height, weight, diabetes, etc.).

[0986] 2. After having breakfast, take a photo of the meal with your smartphone and upload it to the system.

[0987] 3. The server uses TensorFlow to analyze the photo and identify the ingredients and their quantities (e.g., "50g oatmeal, 1 banana, 200ml milk").

[0988] 4. Based on the analysis results, calculate nutritional information (e.g., "calorie intake: 300 kcal, carbohydrates: 45 g, protein: 10 g, fat: 5 g") and record it in the database.

[0989] 5. Based on the user's past meal records and initial settings, a generative AI model (GPT-3.5) will suggest personalized low-carb lunch menus, such as "chicken salad, whole-grain bread, and vegetable soup."

[0990] 6. The user eats lunch according to the suggested lunch menu and records the contents again.

[0991] Prompt Sentence Examples

[0992] "Please suggest the best menu for diabetes."

[0993] "Generate a new meal plan based on your recent food logs."

[0994] By implementing this invention, users can efficiently and continuously consume meals that suit their health condition and nutritional needs, and can receive support in selecting appropriate menus when eating out.

[0995] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0996] Step 1:

[0997] Users enter their profile information and nutritional needs, which are then sent to the server via the app using their device. This includes information such as name, age, gender, weight, height, and allergy information. The server receives this information and stores it in a database. The input data completes the initial setup and serves as the basis for generating an individually customized meal plan.

[0998] Step 2:

[0999] After a user eats a meal, they take a photo of the meal with their smartphone camera and upload it to the server via their device. The server receives the photo and uses image recognition technology (TensorFlow) to analyze the ingredients and their quantities. The input is the photo of the meal, and the output is the analyzed ingredient information (e.g., "50g oatmeal, 1 banana, 200ml milk").

[1000] Step 3:

[1001] The server calculates the nutritional information (calories, protein, carbohydrates, fat, etc.) for each ingredient based on the analyzed ingredient information. This is done based on the nutritional data of ingredients that has been stored in advance in a database. The input is ingredient information, and the output is detailed nutritional information (e.g., "calories 300kcal, carbohydrates 45g, protein 10g, fat 5g"). The calculated nutritional information is saved in the database as the user's dietary record.

[1002] Step 4:

[1003] The server analyzes the user's past meal records and input profile information and uses a generative AI model (GPT-3.5) to generate a personalized meal plan that addresses the user's specific nutritional needs. The input is the user's profile and meal records, and the output is a customized meal plan.

[1004] Step 5:

[1005] The server notifies the user of the generated meal plan via the terminal. The notification content includes specific menus and recipes for implementing them. The input is the generated meal plan, and the output is a notification message to the user.

[1006] Step 6:

[1007] The user continuously enters food records and updates their progress on the device. The server monitors this in real time and analyzes the progress. For example, if a new food record is added, the server uses this to reevaluate the nutritional balance and adjust the meal plan as necessary. The input is the new food record, and the output is the updated progress and adjusted meal plan.

[1008] Step 7:

[1009] In a physical store, a user takes a photo of their meal with their smartphone and uploads it to the server. The server uses image recognition technology to analyze the photo and extract ingredient information. Based on this, the server calculates nutritional information and suggests appropriate menu items. The input is the photo of the meal, and the output is the analysis results and suggested menu items.

[1010] 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.

[1011] To further enhance users' dietary management and health support, the system provides personalized meal plans combined with an emotional engine that recognizes the user's emotional state and dynamically adjusts meal plans based on that, providing motivational messages.

[1012] User registration and initial settings

[1013] When users first access the system, they create an account via the web or app, entering basic information such as their name, email address, and password. They then enter health information such as age, gender, weight, and height, as well as dietary preferences, allergies, and specific nutritional needs (e.g., diabetes or low-carb diet).

[1014] The server stores the entered information in a database, creates a user account, and sends a confirmation email to the user.

[1015] Food record entry and analysis

[1016] After the user has eaten a meal, the user uses the terminal to take a photo of the meal.

[1017] The device sends the captured photo to an AI meal recognition module, which analyzes the image to determine the ingredients and their quantities.

[1018] The server calculates nutritional information (e.g., calorie intake, protein, carbohydrates, fat, etc.) based on the analyzed data and updates the database as the user's dietary record.

[1019] Recognizing and recording emotional states

[1020] The user inputs their emotional state (e.g., happy, sad, stressed, excited, etc.) through the device, and the emotion engine recognizes the user's emotional state through facial expression and voice analysis.

[1021] The server stores the user's input or the emotional state recognized by the emotion engine in a database.

[1022] Generate personalized meal plans

[1023] The server analyzes the user's past food logs, emotional state, and entered profile information, and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs, dietary restrictions, and emotional state.

[1024] The device notifies the user of the generated meal plan and displays a detailed menu and recipes for carrying it out.

[1025] Monitor progress and adjust plans

[1026] The user follows the suggested meal plan, eats the meal, and then takes a photo of the meal again to update the record.

[1027] The server continuously monitors the user's dietary records, analyzes their progress, and dynamically adjusts their meal plan, taking into account the user's emotional state. The adjusted meal plan is then notified to the user.

[1028] Providing emotional motivation

[1029] The server generates appropriate motivational messages based on the user's emotional state as recognized by the emotion engine. For example, if the user is feeling stressed, it will provide a menu using ingredients with a relaxation effect and an encouraging message to boost motivation.

[1030] Meeting specific nutritional needs

[1031] For example, if a user has diabetes, the server can generate a low-carb meal plan, or when dining out, it can suggest the best options from a particular restaurant menu.

[1032] Specific examples

[1033] As a concrete example, the flow when User A, who has diabetes and is easily stressed, uses this system is shown below.

[1034] 1. User A enters initial registration information into the system (e.g., age, gender, height, weight, diabetes).

[1035] 2. After User A has eaten breakfast, he / she takes a photo of it with his / her device and uploads it to the system.

[1036] 3. The server uses AI to analyze the photo and identify the ingredients and their quantities, such as "50g oatmeal, 1 banana, 200ml milk."

[1037] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[1038] 5. User A inputs their emotional state (e.g., stress) into the device. The emotion engine analyzes their facial expressions and voice to recognize their emotional state.

[1039] 6. The server generates a lunch menu using low-carb, relaxing ingredients based on the food record and profile information, including emotional state, and notifies User A of this via the terminal.

[1040] 7. User A follows the lunch menu provided, eats lunch, and records the contents again.

[1041] 8. The server again analyzes the food log and emotional state to monitor progress.

[1042] 9. The server adjusts the next meal plan to include an encouraging message and relaxation effect for user A, since he is feeling stressed, and notifies him again.

[1043] This system enables users to efficiently and continuously consume meals that suit their health condition, nutritional needs, and even emotional state, and by providing appropriate motivation, users' health management becomes even more effective.

[1044] The processing flow will be explained below.

[1045] Step 1:

[1046] Users create an account through the system's web or app interface, providing the required information: name, email address, and password.

[1047] Step 2:

[1048] The server stores the entered account information in a database and sends a confirmation email to the user.

[1049] Step 3:

[1050] The user clicks on the link in the confirmation email to activate their account and log in to the system.

[1051] Step 4:

[1052] Users enter profile information (age, gender, weight, height, favorite foods, allergy information, etc.) and specific nutritional needs (e.g., diabetes, low-carb diet).

[1053] Step 5:

[1054] The server stores the entered profile information and nutritional needs in a database and generates a basic meal plan.

[1055] Step 6:

[1056] After eating, the user takes a photo of the meal on their device and uploads it to the system.

[1057] Step 7:

[1058] The device sends the captured photo to an AI food recognition module, which performs image analysis to identify ingredients and their quantities.

[1059] Step 8:

[1060] The server receives the analyzed meal contents and calculates nutritional information (calories, protein, carbohydrates, fat, etc.).

[1061] Step 9:

[1062] The server updates the calculated nutritional information in the database as the user's dietary record.

[1063] Step 10:

[1064] The user inputs their emotional state (e.g., happy, sad, stressed, excited) using the device, and the emotion engine automatically recognizes the user's emotional state through facial expression or voice analysis.

[1065] Step 11:

[1066] The server stores the emotional state recognized by the emotion engine or the emotional state directly input by the user in a database.

[1067] Step 12:

[1068] The server uses an AI algorithm to generate a new personalized meal plan based on the user's past meal records, emotional state, and profile information.

[1069] Step 13:

[1070] The device notifies the user of the generated meal plan and displays a detailed menu and its recipes.

[1071] Step 14:

[1072] The user eats according to the meal plan notified to them, then takes a photo of the meal again to update the record.

[1073] Step 15:

[1074] The server continuously monitors the user's food log and emotional state, analyzes the progress, and, in particular, dynamically adjusts the meal plan taking into account the user's emotional state.

[1075] Step 16:

[1076] The server generates appropriate motivational messages based on the user's emotional state and notifies them through the device. For example, if the user is feeling stressed, the server will provide a menu containing ingredients with a relaxation effect and an encouraging message.

[1077] Step 17:

[1078] The device also provides recipes using seasonal ingredients recommended by the emotion engine, helping users enjoy healthy and timeless meals.

[1079] Step 18:

[1080] The server will suggest restaurant menus that are optimal for dining out to users with specific nutritional needs, for example, suggesting low-carb options to a diabetic user.

[1081] In this way, the system generates and provides personalized meal plans by comprehensively considering the user's profile information, nutritional needs, and emotional state. It also dynamically adjusts meal plans based on the user's emotional state and provides appropriate motivational messages, making dietary management and health support more effective.

[1082] Example 2

[1083] 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."

[1084] In the past, health and diet management required individual data entry, recording, and management, which was time-consuming and burdensome. It was also difficult to create meal plans that took emotional states into account, and there was a lack of effective ways to maintain motivation. Furthermore, it was difficult to maintain a proper nutritional balance when eating out.

[1085] 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.

[1086] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously input meal records and monitor progress, means for the user to input emotional states and for an emotion engine to analyze facial expressions and voice to recognize and record the emotional states, and means for generating motivational messages based on the emotional states and notifying the user. This enables efficient health and dietary management of the user and provides personalized support that takes emotional states into account, thereby maintaining motivation and maintaining an appropriate nutritional balance even when eating out.

[1087] "Profile Information" refers to information about a user's individual identity, such as the user's name, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs.

[1088] "Database" refers to an information system for centrally managing and storing data such as user profile information, nutritional needs, food records, and emotional state.

[1089] "Image recognition technology" refers to artificial intelligence and machine learning technology that analyzes photos of meals and recognizes specific ingredients and their quantities.

[1090] "Nutritional information" refers to information about the nutritional components of a meal, such as calories, protein, carbohydrates, and fats.

[1091] "Personalized Meal Plan" refers to an optimal meal plan created for you based on your profile information, nutritional needs, past food history, and emotional state.

[1092] A "motivational message" refers to a message that is generated based on the user's emotional state and is intended to increase the user's motivation.

[1093] "Emotion engine" refers to artificial intelligence technology that analyzes a user's facial expressions and voice to recognize their emotional state.

[1094] "Progress" refers to accumulated data on the contents of meals that the user has eaten according to the meal plan and their nutritional information, and is information that indicates the progress of health management.

[1095] This system centrally manages a user's profile information, food records, emotional state, etc., and provides personalized meal plans. This allows for efficient health management for users, while also providing support that takes emotional state into consideration and maintaining motivation. The operation of this system is explained in detail below.

[1096] User registration and initial settings

[1097] When a user first accesses the system, they create an account via the web or app, entering their profile information such as name, email address, password, age, gender, weight, height, food preferences, allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet). The server stores this information in a database and sends a confirmation email to the user.

[1098] Food record entry and analysis

[1099] After a user eats a meal, they take a photo of the meal using a device such as a smartphone or tablet and upload it to the system. The server sends this photo to an AI meal recognition module, which uses image recognition technology to analyze the ingredients and their amounts. For example, it may identify the meal as "50g of oatmeal, 1 banana, 200ml of milk." The server then calculates nutritional information based on the analysis results and updates the database as a meal record. Specific nutritional information might include "calorie intake 300kcal, carbohydrates 45g, protein 10g, fat 5g."

[1100] Recognizing and recording emotional states

[1101] After eating, the user inputs their emotional state (happiness, sadness, stress, excitement, etc.) into the device. The emotion engine then analyzes their facial expressions and voice to recognize the user's emotional state. The server then stores this emotional data in a database.

[1102] Personalized meal plan generation and notifications

[1103] The server analyzes the user's profile information, past meal records, and emotional state, and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs, dietary restrictions, and emotional state. The device notifies the user of the generated meal plan and displays detailed menus and recipes.

[1104] Monitor progress and adjust plans

[1105] The user follows the proposed meal plan, eats the meal, and then takes a photo of the meal on their device to update their meal log. The server continuously monitors the meal log and analyzes the progress. It dynamically adjusts the meal plan, taking into account the user's emotional state. The user is then notified of the adjusted meal plan.

[1106] Providing emotional motivation

[1107] The server generates motivational messages based on the emotional state recognized by the emotion engine and notifies the user via the device. For example, if the user is feeling stressed, it will provide a menu using ingredients with a relaxation effect and an encouraging message.

[1108] Meeting specific nutritional needs

[1109] For example, the server could generate a low-carb meal plan for a diabetic user, or suggest the best options from a specific restaurant menu when dining out via the device.

[1110] Specific examples

[1111] The flow when User A, who has diabetes and is easily stressed, uses this system is as follows.

[1112] 1. User A enters information such as age, gender, height, weight, and diabetes into the system.

[1113] 2. After User A has eaten breakfast, he / she takes a photo of the breakfast on his / her device and uploads it to the system.

[1114] 3. The server analyzes the photo and identifies the ingredients and their quantities, for example, "50g oatmeal, 1 banana, 200ml milk."

[1115] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[1116] 5. User A inputs their emotional state (e.g., stress) into the device. The emotion engine analyzes and recognizes their facial expressions and voice.

[1117] 6. Based on this information, the server generates a low-carb, relaxing lunch menu and notifies User A via the terminal.

[1118] 7. User A follows the lunch menu provided, eats lunch, and records the contents again.

[1119] 8. The server analyzes your new food log and emotional state to monitor your progress.

[1120] 9. The server determines that User A is feeling stressed and adjusts the next meal plan to have a relaxation effect, and notifies the user again with an encouraging message.

[1121] Example prompt sentence:

[1122] "I have diabetes and get stressed easily. For breakfast, I had 50g of oatmeal, one banana, and 200ml of milk. Can you suggest a low-carb lunch that will help me relax?"

[1123] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1124] Step 1: User registration and initial setup

[1125] When users first access the system, they create an account via the web or app by entering their profile information, including their name, email address, password, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet).

[1126] Input: Profile information (name, email address, password, age, gender, weight, height, food preferences, allergy information, nutritional needs)

[1127] Output: Save to database, send confirmation email

[1128] The server receives this information and stores it in a database. After the information is stored, a confirmation email is sent to the user.

[1129] Step 2: Enter and analyze your food records

[1130] After a user has eaten a meal, they take a photo of the meal using a device such as a smartphone or tablet and upload it to the system.

[1131] Input: Food photo

[1132] Output: Analyzed dietary information and nutritional information

[1133] The device sends the captured photo to the AI ​​meal recognition module. The server uses the AI ​​module to apply image recognition technology to analyze the ingredients and their amounts. For example, it might analyze the meal as "50g of oatmeal, 1 banana, 200ml of milk." Based on the analysis results, nutritional information (calories, protein, carbohydrates, fat, etc.) is calculated and updated in the database as a meal record.

[1134] Step 3: Recognize and record your emotional state

[1135] After eating, the user inputs their emotional state (happiness, sadness, stress, excitement, etc.) into the device, and the emotion engine analyzes facial expressions and voice to recognize the user's emotional state.

[1136] Input: Self-input of emotional state, facial expressions and voice data

[1137] Output: Recognized emotional state, stored in a database

[1138] The server receives this emotion data and stores it in a database.

[1139] Step 4: Generate and notify your personalized meal plan

[1140] The server analyzes the user's profile information, past meal records, and emotional state, and uses AI algorithms to generate a personalized meal plan.

[1141] Input: Profile information, past meal records, emotional state

[1142] Output: Meal plan, recipe guide

[1143] The meal plan is created taking into account the user's nutritional needs, dietary restrictions, and emotional state. The device notifies the user of the generated meal plan and displays detailed menus and recipes.

[1144] Step 5: Monitor progress and adjust your plan

[1145] The user follows the proposed meal plan, eats the meal, and then takes a photo of it on the device and uploads it to the system.

[1146] Input: Another meal photo

[1147] Output: Updated food log, adjusted meal plan

[1148] The device sends new meal photos to the server, which analyzes and continuously monitors the new meal log, taking into account progress and emotional state, dynamically adjusting the meal plan as needed, and notifying the user again of the adjustments.

[1149] Step 6: Provide emotional motivation

[1150] The server generates motivational messages based on the emotional state recognized by the emotion engine.

[1151] Input: Perceived emotional state

[1152] Output: Motivation message notification

[1153] For example, if a user is feeling stressed, the device will provide a menu using ingredients with a relaxation effect and an encouraging message.

[1154] Step 7: Addressing specific nutritional needs

[1155] For example, if a user has diabetes, the server will generate a low-carb meal plan, and when dining out, the device will suggest the best options from a particular restaurant menu.

[1156] Inputs: specific nutritional needs, restaurant choices when eating out

[1157] Output: Low-carb meal plan, optimal restaurant menu suggestions

[1158] (Application example 2)

[1159] 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."

[1160] Many people today require highly personalized meal plans for maintaining their health and nutritional management. However, existing systems struggle to provide meal plans tailored to the user's emotional state and individual health needs, and lack mechanisms to sustain user motivation. Furthermore, health-related product recommendations in virtual stores do not take into account the user's nutritional information or emotional state, making truly personalized product recommendations difficult. Therefore, a system is needed that can provide more personalized meal plans and appropriate messages to increase motivation while also recommending health-related products in virtual stores based on the user's nutritional information and emotional state.

[1161] 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.

[1162] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal content using image recognition technology, means for calculating nutritional information based on the analyzed meal content and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal record and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously enter a meal record and monitor the progress of the meal record, means for analyzing the user's emotional state using an emotion recognition engine and saving the data, means for providing motivational messages based on the emotional state, and means for recommending health-related products in a virtual store based on the analyzed nutritional information and emotional state. This makes it possible to provide personalized meal plans and motivational messages based on the user's health needs and emotional state, and further realizes the recommendation of optimal health-related products in the virtual store.

[1163] "Profile Information" refers to specific information about a User, such as the User's name, age, gender, height, weight, allergy information, and specific nutritional needs.

[1164] "Nutritional Needs" refers to the nutritional or dietary requirements a User has for health maintenance or a specific treatment.

[1165] A "database" is a part of a computer system that organizes information systematically and enables efficient searching and management.

[1166] "Image recognition technology" refers to the technology in which a computer analyzes image data and identifies its contents.

[1167] "Nutritional information" refers to the numerical values ​​of nutrients such as calories, protein, carbohydrates, and fat contained in a meal.

[1168] "Diet record" refers to the history of the meals a user has eaten and the nutritional information based on them.

[1169] "Meal Plan" refers to a meal plan that is appropriate for a user based on their needs and preferences.

[1170] An "emotion recognition engine" refers to a technical system that analyzes a user's facial expressions and voice to identify their emotional state.

[1171] "Motivational messages" refer to text or notifications that offer encouragement or suggestions to improve a user's behavior or psychological state.

[1172] A "virtual store" refers to an online shop that sells products over the Internet.

[1173] "Product recommendation" refers to a system that suggests the most suitable product based on the user's needs and condition.

[1174] This invention is a system for effectively managing a user's health and meal planning. A specific implementation method and its processing details are described below. This system provides personalized meal plans that are dynamically adjusted based on the user's profile information, food records, and emotional state, and emotionally-based health product recommendations.

[1175] The server stores the profile information entered by the user during registration in a database. The profile information includes basic user information (name, age, gender, height, weight), allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet). This database is managed using an SQL-based data management system, which allows for efficient data storage and retrieval.

[1176] After the user eats a meal, the device takes a photo of the meal and sends it to a server. The server then uses image recognition technologies such as TensorFlow and Keras to extract ingredient information from the photo. For example, an AI model can identify ingredients and their amounts in a photo taken by the user, such as "50g of oatmeal, 1 banana, 200ml of milk," and calculate nutritional information (calories, protein, carbohydrates, fat, etc.) based on this information. This nutritional information is then stored in a database as the user's dietary record.

[1177] The user's emotional state is input through the device or analyzed by the emotion recognition engine from photos taken by the device or recorded voice. Emotion recognition uses OpenCV and facial expression recognition algorithms to identify the user's emotional state from their facial expressions and voice. For example, if the user is recognized as feeling stressed, that data is stored in a database.

[1178] The server uses an AI algorithm to generate a personalized meal plan based on the user's past meal records, emotional state, and profile information. The meal plan takes into account the user's nutritional needs, food preferences, and emotional state. The generated meal plan is then sent to the user via their device. For example, it may suggest a menu that includes herbal tea to relieve stress or ingredients with a relaxing effect.

[1179] The device allows users to continuously enter food records, and the server monitors their progress. Based on this, the server adjusts the meal plan in real time and notifies the user again. It also provides motivational messages based on emotional state. For example, if the user is feeling stressed, it will send a message such as, "Don't push yourself too hard, eat foods that will help you relax."

[1180] Furthermore, the analyzed nutritional information and emotional state are used to recommend health-related products in the virtual store, enabling personalized product suggestions and helping users efficiently choose products that meet their health needs.

[1181] As an example, the following prompt sentence is used:

[1182] "I want to develop an AI system that recommends personalized health foods and supplements based on a user's food records and emotional state. Build a model to analyze the food photos uploaded by users and their emotional state, so that a virtual store can suggest products that meet the user's nutritional needs."

[1183] Input: Food photo, emotional state, user profile information (e.g., age, gender, height, weight, allergy information, specific nutritional needs)

[1184] Output: A personalized product recommendation list

[1185] Through the above steps, the present invention is a system that effectively supports the user's health management, improves motivation based on emotional state, and recommends appropriate products in a virtual store.

[1186] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1187] Step 1:

[1188] Registration of profile information entered by users

[1189] Users enter basic information such as name, age, gender, height, weight, allergy information, and specific nutritional needs. This information is sent to the server and stored in a database. This process registers the user's basic health information.

[1190] Step 2:

[1191] Taking and uploading food photos

[1192] After eating a meal, the user takes a photo of the meal with the device's camera and uploads it to the server. The uploaded photo is sent to the server's image recognition engine, which then obtains the initial data of the meal's contents.

[1193] Step 3:

[1194] Image analysis of food content

[1195] The server analyzes the uploaded meal photos using TensorFlow and Keras. It identifies the ingredients and their amounts and extracts data such as "50g of oatmeal, 1 banana, 200ml of milk." The results of this analysis become the input data for calculating nutritional information. Image recognition is used to obtain data for each ingredient.

[1196] Step 4:

[1197] Nutritional information calculation

[1198] The server calculates the nutritional information of the meal (calories, protein, carbohydrates, fat, etc.) based on the image analysis results. Using a nutritional information calculation algorithm, it generates numerical data of the constituent components and stores this in a database as a dietary record. In this step, specific nutrients are quantified.

[1199] Step 5:

[1200] Input or recognition of emotional states

[1201] Users can manually input their current emotional state through their device, or they can take photos or record audio using the device's camera and microphone, which are then sent to the server. The server then analyzes the input data using OpenCV and emotion recognition algorithms to identify the user's emotional state, resulting in emotion labels such as "stress," "happy," and "sad."

[1202] Step 6:

[1203] Storing Emotional Data

[1204] The server stores the recognized emotional state data in a database, which can be used for future meal planning and product recommendations. The emotional information can then be tracked.

[1205] Step 7:

[1206] Generate personalized meal plans

[1207] The server uses AI algorithms to generate a personalized meal plan based on the user's past food records, emotional state, and profile information. The meal plan takes into account the user's nutritional needs and emotional state. The plan is then presented as a detailed menu and recipes.

[1208] Step 8:

[1209] Meal plan notifications

[1210] The device notifies the user of the generated meal plan. The menu and recipes are displayed on the device screen and can be executed by the user. In this step, the user knows what meal to eat next.

[1211] Step 9:

[1212] Continuously record your food and monitor your progress

[1213] The user follows the suggested meal plan and then takes photos of the meal to update the record. The server continuously monitors the user's meal log and analyzes their progress, allowing them to continuously manage their health.

[1214] Step 10:

[1215] Generate product recommendation list

[1216] The server recommends health-related products in the virtual store based on the analyzed nutritional information and emotional state, and the recommendation list includes products optimized for the user's needs.

[1217] Step 11:

[1218] Product recommendation notifications

[1219] The terminal notifies the user of the generated product recommendation list, which allows the user to efficiently select products that meet their health needs and promotes purchases in the virtual store.

[1220] 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.

[1221] 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.

[1222] 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.

[1223] [Fourth embodiment]

[1224] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1225] 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.

[1226] 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).

[1227] 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.

[1228] 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.

[1229] 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).

[1230] 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.

[1231] 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.

[1232] 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.

[1233] 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.

[1234] 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.

[1235] 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.

[1236] 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."

[1237] The system provides users with personalized meal plans for the purpose of managing their diet and supporting their health. Users access the system through their devices and input their profile information and nutritional needs, and are then presented with a meal plan based on their individual health conditions.

[1238] User registration and initial settings

[1239] Users first create an account through a web or app interface, entering basic information such as their name, email address, and password, and then entering health information such as age, gender, weight, and height, as well as dietary preferences, allergies, and specific nutritional needs (e.g., diabetes or low-carb diet).

[1240] The server stores the entered information in a database, creates a user account, and sends a confirmation email to the user.

[1241] Food record entry and analysis

[1242] After the user has eaten a meal, the user uses the terminal to take a photo of the meal.

[1243] The device then sends the photo to an AI food recognition module, which analyzes the image to identify ingredients and their quantities. This analysis determines the type and quantity of ingredients.

[1244] The server calculates nutritional information based on the analyzed data, including calorie intake, protein, carbohydrates, and fats, and updates the resulting nutritional information as the user's dietary record in a database.

[1245] Generate personalized meal plans

[1246] The server analyzes the user's past meal records and input profile information and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs and dietary restrictions.

[1247] The device notifies the user of the generated meal plan and provides detailed menus and recipes for carrying it out.

[1248] Monitor progress and adjust plans

[1249] The user follows the suggested meal plan, eats meals and updates their food log as they go.

[1250] The server continuously monitors the user's meal log, analyzes the user's progress, adjusts the meal plan as needed, and notifies the user of the adjusted meal plan.

[1251] Meeting specific nutritional needs

[1252] For example, if a user has diabetes, the server can generate a low-carb meal plan, or when dining out, it can suggest the best options from a particular restaurant menu.

[1253] As a concrete example, the flow when User A, who has diabetes, uses this system is shown below.

[1254] Specific examples

[1255] 1. User A enters initial registration information into the system (e.g., age, gender, height, weight, diabetes).

[1256] 2. After User A has eaten breakfast, he / she takes a photo of it with his / her device and uploads it to the system.

[1257] 3. The server uses AI to analyze the photo and identify the ingredients and their quantities, such as "50g oatmeal, 1 banana, 200ml milk."

[1258] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[1259] 5. The server generates a low-carb lunch menu based on User A's past meal records and initial setting information, and notifies User A of this via the terminal.

[1260] 6. User A follows the lunch menu provided, eats lunch, and records the contents again.

[1261] 7. The server analyzes the food record again and monitors User A's nutritional balance and goal achievement status.

[1262] By using this system, users can efficiently and continuously consume meals that suit their health condition and nutritional needs. It also helps users select appropriate menu items when eating out, making daily dietary management easier.

[1263] The processing flow will be explained below.

[1264] Step 1:

[1265] When users first access the system, they create an account via the web or app, entering basic information such as their name, email address, and password.

[1266] Step 2:

[1267] The server stores the entered account information in a database and sends the user a confirmation email.

[1268] Step 3:

[1269] Users click a link in the confirmation email to activate their account, then enter their profile information (age, gender, weight, height, etc.) and nutritional needs (e.g., diabetes, allergies, etc.).

[1270] Step 4:

[1271] The server stores the entered profile information and nutritional needs in a database and generates a basic meal plan based on this.

[1272] Step 5:

[1273] After eating a meal, the user uses the device to take a photo of the meal.

[1274] Step 6:

[1275] The device sends a photo of the meal to an AI meal recognition module, which analyzes the image to determine the ingredients and their quantities.

[1276] Step 7:

[1277] The server receives the analyzed meal content and calculates nutritional information (e.g., calories, protein, carbohydrates, fat, etc.) based on it.

[1278] Step 8:

[1279] The server adds the calculated nutritional information to the database as the user's dietary record.

[1280] Step 9:

[1281] The server uses an AI algorithm to generate a new personalized meal plan based on the user's past meal records and profile information.

[1282] Step 10:

[1283] The device will notify the user of the newly generated meal plan and display a detailed menu and its recipes.

[1284] Step 11:

[1285] The user eats according to the meal plan notified to them, then takes a photo of the meal again to update the record.

[1286] Step 12:

[1287] The server continuously monitors the user's food log, analyzes their progress, and adjusts their meal plan as needed.

[1288] Step 13:

[1289] The device will notify the user of the adjusted meal plan and assist the user in continuing to eat according to the new plan.

[1290] Step 14:

[1291] For users with specific nutritional needs, the server provides meal plans tailored to their restrictions and suggests optimal restaurant menus when dining out.

[1292] Step 15:

[1293] The device also suggests recipes to users using seasonal ingredients, helping them to enjoy healthy and timeless meals.

[1294] Example 1

[1295] 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."

[1296] In modern society, dietary management tailored to individual health conditions is becoming increasingly important. However, few general dietary management systems are able to adequately address the individual nutritional needs of users. Furthermore, many systems lack the functionality to allow users to easily and accurately record their dietary information and generate personalized meal plans based on the analysis results. Furthermore, there are issues with suggesting appropriate menus when dining out at restaurants and providing recipes using seasonal ingredients. This makes it difficult for users to optimally manage their diet based on their health conditions.

[1297] 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.

[1298] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously enter meal records and monitor the progress, and means for adjusting the meal plan as needed, thereby enabling the user to efficiently and accurately manage their diet according to their health condition and nutritional needs.

[1299] A "user" is an individual who utilizes the diet management system to receive a meal plan tailored to their health and nutritional needs.

[1300] "Profile information" refers to personal information such as a user's name, age, gender, weight, height, food preferences, and allergy information.

[1301] "Nutrition needs" refers to the types and amounts of nutrients a user requires to maintain good health or manage a particular health condition (e.g., diabetes).

[1302] "Database" means a computer system for storing and managing data such as user profile information, nutritional needs, and dietary records.

[1303] "Image recognition technology" is a technology that uses machine learning algorithms to identify ingredients and their quantities from photographs of meals.

[1304] "Meal contents" refers to the ingredients and amounts consumed by the user.

[1305] "Nutritional information" refers to information on nutrients such as calories, protein, carbohydrates, and lipids calculated based on the contents of a meal.

[1306] A "diet record" is a record that includes the contents of meals consumed by a user and nutritional information calculated based on the meals.

[1307] A "personalized meal plan" is a meal plan that is customized based on a user's profile information and nutritional needs.

[1308] The "notification means" is a system for notifying users of updates to the meal plan or meal record. Specifically, a messaging service is used for notifications.

[1309] "Monitoring" refers to the act of watching the dietary information continuously recorded by the user and evaluating their health condition and nutritional balance.

[1310] "Adjustment" refers to making changes to the user's meal plan as needed based on the monitoring results.

[1311] A "generative model" is a machine learning algorithm that creates optimal meal plans based on a user's past data and specific prompts.

[1312] A "prompt" is an instruction given to a generative AI model, which then generates a meal plan.

[1313] "Dining Menu" refers to a list of meals offered at a restaurant or other establishment, including suggestions for options that best suit the user's nutritional needs.

[1314] "Recipe" refers to the cooking instructions and ingredient list required for a user to execute a meal plan.

[1315] The present invention is a system that provides personalized meal plans for the purpose of dietary management and health support for users. The system is accessed by users via a terminal and proposes meal plans based on individual health conditions.

[1316] First, a user creates an account through the web or app interface. This includes basic information such as name, email address, and password, as well as health information such as age, gender, weight, height, food preferences, allergies, and specific nutritional needs. The server stores this information in a database, creates a user account, and sends a confirmation email. For this purpose, Firebase Authentication is used for user management and MySQL is used for the database.

[1317] Next, after the user has eaten, they use the device to take a photo of the meal. The device sends the photo to the Google Cloud Vision API, which performs image analysis. The analysis results identify the names and amounts of ingredients and send them to the server. The server then calculates nutritional information based on this information and updates the database as the user's meal record. A script written in Python is used to calculate the nutritional information.

[1318] The server analyzes the user's past meal records and input profile information, and generates a personalized meal plan using an AI algorithm powered by TensorFlow. This meal plan takes into account the user's nutritional needs and dietary restrictions. The generated meal plan is then sent to the device, along with detailed menus and recipes for implementing the plan. Notifications are sent using Firebase Cloud Messaging.

[1319] The user then follows the proposed meal plan, eating meals and updating their meal log on their device. The server continuously monitors these meal logs and analyzes the user's progress. If necessary, the meal plan is adjusted and the user is notified again.

[1320] For users with specific nutritional needs, the server can generate meal plans that accommodate those restrictions. For example, a low-carb meal plan can be generated for a user with diabetes, and when dining out, the server can suggest optimal options from a specific restaurant menu. The generative AI model generates the optimal meal plan based on a prompt. For example, the following prompt is used: "Given that the user has diabetes, generate a prompt for the AI ​​model to suggest a low-carb meal plan based on past food records. Consider the following information: Breakfast (50g oatmeal, 1 banana, 200ml milk), user profile (age: 35, gender: male, diabetes)."

[1321] This allows users to efficiently and accurately consume meals that suit their health and nutritional needs, and also provides support for choosing appropriate menu items when eating out.

[1322] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1323] Step 1:

[1324] Users create an account through a web or app interface. Information entered includes name, email address, password, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs. The entered information is validated by the device. If validation is successful, the information is encrypted and sent to the server, where the user's personal profile information and nutritional needs are entered.

[1325] Step 2:

[1326] The server stores the user's profile information and nutritional needs received from the device in a database. The database uses "MySQL." The server also creates a user account and sends a confirmation email to the user, confirming that the user's information has been saved in the database and that the user's account has been activated.

[1327] Step 3:

[1328] After a user eats a meal, they use the device to take a photo of the meal. The device then sends the photo to the Google Cloud Vision API. The input is the captured image, and the output is the analyzed names and quantities of ingredients. Specifically, the device sends the image to the API and receives the analysis results in return.

[1329] Step 4:

[1330] The server calculates nutritional information based on the analysis results received from the device. The input is the analyzed ingredient name and amount, and the output is nutritional information (calories, protein, carbohydrates, fat, etc.). The system performs this calculation using a script written in Python. The calculated nutritional information is saved in a database and updated as the user's dietary record.

[1331] Step 5:

[1332] The server performs analysis based on the user's past meal records and input profile information. The input is the user's past meal records and profile information, and the output is a personalized meal plan. The server generates the meal plan using a generative AI model using TensorFlow. This meal plan takes into account the user's nutritional needs and dietary restrictions.

[1333] Step 6:

[1334] The server sends the generated meal plan to the terminal. The input is the generated meal plan, and the output is a message to notify the user. Firebase Cloud Messaging is used for the notification, allowing the user to receive a personalized meal plan.

[1335] Step 7:

[1336] The user eats according to the proposed meal plan and updates the meal record on the device each time. The input is the details of the meal the user ate, and the output is the updated meal record. The meal record entered by the user is sent to the server via the device.

[1337] Step 8:

[1338] The server continuously monitors the user's food log. The input is the user's most recent food log, and the output is an analysis of their progress. The server uses this data to assess the user's nutritional balance and progress toward their goals, and adjusts the meal plan as needed.

[1339] Step 9:

[1340] The server sends the adjusted meal plan back to the terminal and notifies the user. The input is the adjusted meal plan, and the output is a new notification to the user. This allows the user to always eat according to the latest meal plan.

[1341] (Application example 1)

[1342] 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."

[1343] With the recent rise in interest in health management and dieting, there is a demand for systems that provide meal plans tailored to individual nutritional needs. In particular, since it is difficult to propose personalized meal plans even when eating out, users need support in selecting the optimal menu that meets their health condition and nutritional needs. There is also a demand for technology that can continuously monitor users' meal records and update their progress in real time.

[1344] 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.

[1345] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for suggesting optimal menus for physical stores, and means for the user to continuously enter meal records and monitor their progress. This enables the user to efficiently and continuously eat meals that suit their health condition and nutritional needs, and provides support for selecting appropriate menus when eating out.

[1346] "User" means an individual who utilizes the system and who inputs profile information and nutritional needs to receive a meal plan.

[1347] "Profile information" refers to data including a user's basic personal and health information, such as age, gender, weight, height, dietary preferences, and allergy information.

[1348] "Nutrition needs" refers to information that indicates the nutrients and dietary restrictions a user needs based on a particular health condition or goal, such as diabetes or a low-carb diet.

[1349] The "database" is an information management system for managing and storing various data such as user profile information and meal records.

[1350] "Image recognition technology" is a technology used to analyze photographs of food and identify ingredients and their quantities.

[1351] "Nutritional information" is data on nutrients such as calories, protein, carbohydrates, and lipids calculated from the dietary content.

[1352] A "food record" is data that stores the details of meals a user has eaten and their nutritional information in chronological order.

[1353] "Personalized Meal Plan" refers to a meal menu or diet plan that is individually customized based on a user's profile information and nutritional needs.

[1354] "Notification" refers to the act of informing the user of the generated meal plan and progress in real time or periodically.

[1355] "Physical store" refers to a restaurant or retail store that a user actually visits.

[1356] An "optimal menu" is a meal menu selected based on the user's profile information and nutritional needs to maintain optimal health.

[1357] "Monitoring" refers to the act of continuously watching the dietary data recorded by the user and evaluating progress.

[1358] The "AI Food Recognition Module" is a module that uses artificial intelligence technology to analyze photographs of food.

[1359] "Generative AI model" refers to an artificial intelligence algorithm that generates personalized meal plans based on a user's eating history and profile information.

[1360] A "smartphone" is a mobile terminal with communication functions and advanced processing capabilities, and in this invention, the camera and internet connection functions are mainly used.

[1361] "Analysis" refers to data processing to identify ingredients and their amounts from the photographs of meals taken.

[1362] "Nutrition balance" refers to the overall condition that indicates whether the user is properly consuming each nutrient they require.

[1363] The present invention provides a system for providing a user with a personalized meal plan and supporting health management. An embodiment of the system will be described in detail below.

[1364] System Overview

[1365] The system stores a user's profile information and nutritional needs in a database, takes photos of meals, and analyzes them using image recognition technology. It calculates nutritional information based on the analyzed meal content and updates the user's meal record. It also generates a personalized meal plan based on the user's past meal records and nutritional needs, and notifies the user of the plan. It also suggests optimal menus for physical restaurants, and allows users to continuously enter their meal records and monitor their progress.

[1366] Hardware and software used

[1367] Smartphone: Used by users to enter information and take photos of their meals.

[1368] Server: Responsible for processing and storing data, and generating meal plans using generative AI models.

[1369] Image recognition technology: Using TensorFlow, it analyzes photos of meals to identify ingredients and their quantities.

[1370] Database: MongoDB is used to store user profile information, food records, and nutrition information.

[1371] Generative AI model: GPT-3.5 is used to generate personalized meal plans.

[1372] System processing flow

[1373] 1. User registration and initial settings

[1374] Users use their smartphones to install the app, create an account, and enter their profile information and nutritional needs, which are then stored in a database by the server. The information entered is then used to generate a personalized meal plan based on the user's individual nutritional needs.

[1375] 2. Entering and analyzing food records

[1376] After eating, users take a photo of their meal with their smartphone. The photo is sent to a server, where the ingredients and their amounts are analyzed using TensorFlow. Based on the analysis results, nutritional information is calculated and updated in a database as a meal record.

[1377] 3. Personalized meal plan generation and notifications

[1378] The server uses a generative AI model (GPT-3.5) to generate a personalized meal plan based on the user's past meal records and input profile information. This meal plan is then sent to the user via smartphone. The notification includes specific menu items and instructions on how to follow them.

[1379] 4. Progress monitoring and adjustment

[1380] As the user continues to enter their food records, the server monitors their progress, automatically adjusting the meal plan as needed and notifying the user again.

[1381] Specific examples

[1382] For example, if a user with diabetes uses this system, the following occurs:

[1383] 1. The user enters initial registration information (age, gender, height, weight, diabetes, etc.).

[1384] 2. After having breakfast, take a photo of the meal with your smartphone and upload it to the system.

[1385] 3. The server uses TensorFlow to analyze the photo and identify the ingredients and their quantities (e.g., "50g oatmeal, 1 banana, 200ml milk").

[1386] 4. Based on the analysis results, calculate nutritional information (e.g., "calorie intake: 300 kcal, carbohydrates: 45 g, protein: 10 g, fat: 5 g") and record it in the database.

[1387] 5. Based on the user's past meal records and initial settings, a generative AI model (GPT-3.5) will suggest personalized low-carb lunch menus, such as "chicken salad, whole-grain bread, and vegetable soup."

[1388] 6. The user eats lunch according to the suggested lunch menu and records the contents again.

[1389] Prompt Sentence Examples

[1390] "Please suggest the best menu for diabetes."

[1391] "Generate a new meal plan based on your recent food logs."

[1392] By implementing this invention, users can efficiently and continuously consume meals that suit their health condition and nutritional needs, and can receive support in selecting appropriate menus when eating out.

[1393] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1394] Step 1:

[1395] Users enter their profile information and nutritional needs, which are then sent to the server via the app using their device. This includes information such as name, age, gender, weight, height, and allergy information. The server receives this information and stores it in a database. The input data completes the initial setup and serves as the basis for generating an individually customized meal plan.

[1396] Step 2:

[1397] After a user eats a meal, they take a photo of the meal with their smartphone camera and upload it to the server via their device. The server receives the photo and uses image recognition technology (TensorFlow) to analyze the ingredients and their quantities. The input is the photo of the meal, and the output is the analyzed ingredient information (e.g., "50g oatmeal, 1 banana, 200ml milk").

[1398] Step 3:

[1399] The server calculates the nutritional information (calories, protein, carbohydrates, fat, etc.) for each ingredient based on the analyzed ingredient information. This is done based on the nutritional data of ingredients that has been stored in advance in a database. The input is ingredient information, and the output is detailed nutritional information (e.g., "calories 300kcal, carbohydrates 45g, protein 10g, fat 5g"). The calculated nutritional information is saved in the database as the user's dietary record.

[1400] Step 4:

[1401] The server analyzes the user's past meal records and input profile information and uses a generative AI model (GPT-3.5) to generate a personalized meal plan that addresses the user's specific nutritional needs. The input is the user's profile and meal records, and the output is a customized meal plan.

[1402] Step 5:

[1403] The server notifies the user of the generated meal plan via the terminal. The notification content includes specific menus and recipes for implementing them. The input is the generated meal plan, and the output is a notification message to the user.

[1404] Step 6:

[1405] The user continuously enters food records and updates their progress on the device. The server monitors this in real time and analyzes the progress. For example, if a new food record is added, the server uses this to reevaluate the nutritional balance and adjust the meal plan as necessary. The input is the new food record, and the output is the updated progress and adjusted meal plan.

[1406] Step 7:

[1407] In a physical store, a user takes a photo of their meal with their smartphone and uploads it to the server. The server uses image recognition technology to analyze the photo and extract ingredient information. Based on this, the server calculates nutritional information and suggests appropriate menu items. The input is the photo of the meal, and the output is the analysis results and suggested menu items.

[1408] 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.

[1409] To further enhance users' dietary management and health support, the system provides personalized meal plans combined with an emotional engine that recognizes the user's emotional state and dynamically adjusts meal plans based on that, providing motivational messages.

[1410] User registration and initial settings

[1411] When users first access the system, they create an account via the web or app, entering basic information such as their name, email address, and password. They then enter health information such as age, gender, weight, and height, as well as dietary preferences, allergies, and specific nutritional needs (e.g., diabetes or low-carb diet).

[1412] The server stores the entered information in a database, creates a user account, and sends a confirmation email to the user.

[1413] Food record entry and analysis

[1414] After the user has eaten a meal, the user uses the terminal to take a photo of the meal.

[1415] The device sends the captured photo to an AI meal recognition module, which analyzes the image to determine the ingredients and their quantities.

[1416] The server calculates nutritional information (e.g., calorie intake, protein, carbohydrates, fat, etc.) based on the analyzed data and updates the database as the user's dietary record.

[1417] Recognizing and recording emotional states

[1418] The user inputs their emotional state (e.g., happy, sad, stressed, excited, etc.) through the device, and the emotion engine recognizes the user's emotional state through facial expression and voice analysis.

[1419] The server stores the user's input or the emotional state recognized by the emotion engine in a database.

[1420] Generate personalized meal plans

[1421] The server analyzes the user's past food logs, emotional state, and entered profile information, and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs, dietary restrictions, and emotional state.

[1422] The device notifies the user of the generated meal plan and displays a detailed menu and recipes for carrying it out.

[1423] Monitor progress and adjust plans

[1424] The user follows the suggested meal plan, eats the meal, and then takes a photo of the meal again to update the record.

[1425] The server continuously monitors the user's dietary records, analyzes their progress, and dynamically adjusts their meal plan, taking into account the user's emotional state. The adjusted meal plan is then notified to the user.

[1426] Providing emotional motivation

[1427] The server generates appropriate motivational messages based on the user's emotional state as recognized by the emotion engine. For example, if the user is feeling stressed, it will provide a menu using ingredients with a relaxation effect and an encouraging message to boost motivation.

[1428] Meeting specific nutritional needs

[1429] For example, if a user has diabetes, the server can generate a low-carb meal plan, or when dining out, it can suggest the best options from a particular restaurant menu.

[1430] Specific examples

[1431] As a concrete example, the flow when User A, who has diabetes and is easily stressed, uses this system is shown below.

[1432] 1. User A enters initial registration information into the system (e.g., age, gender, height, weight, diabetes).

[1433] 2. After User A has eaten breakfast, he / she takes a photo of it with his / her device and uploads it to the system.

[1434] 3. The server uses AI to analyze the photo and identify the ingredients and their quantities, such as "50g oatmeal, 1 banana, 200ml milk."

[1435] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[1436] 5. User A inputs their emotional state (e.g., stress) into the device. The emotion engine analyzes their facial expressions and voice to recognize their emotional state.

[1437] 6. The server generates a lunch menu using low-carb, relaxing ingredients based on the food record and profile information, including emotional state, and notifies User A of this via the terminal.

[1438] 7. User A follows the lunch menu provided, eats lunch, and records the contents again.

[1439] 8. The server again analyzes the food log and emotional state to monitor progress.

[1440] 9. The server adjusts the next meal plan to include an encouraging message and relaxation effect for user A, since he is feeling stressed, and notifies him again.

[1441] This system enables users to efficiently and continuously consume meals that suit their health condition, nutritional needs, and even emotional state, and by providing appropriate motivation, users' health management becomes even more effective.

[1442] The processing flow will be explained below.

[1443] Step 1:

[1444] Users create an account through the system's web or app interface, providing the required information: name, email address, and password.

[1445] Step 2:

[1446] The server stores the entered account information in a database and sends a confirmation email to the user.

[1447] Step 3:

[1448] The user clicks on the link in the confirmation email to activate their account and log in to the system.

[1449] Step 4:

[1450] Users enter profile information (age, gender, weight, height, favorite foods, allergy information, etc.) and specific nutritional needs (e.g., diabetes, low-carb diet).

[1451] Step 5:

[1452] The server stores the entered profile information and nutritional needs in a database and generates a basic meal plan.

[1453] Step 6:

[1454] After eating, the user takes a photo of the meal on their device and uploads it to the system.

[1455] Step 7:

[1456] The device sends the captured photo to an AI food recognition module, which performs image analysis to identify ingredients and their quantities.

[1457] Step 8:

[1458] The server receives the analyzed meal contents and calculates nutritional information (calories, protein, carbohydrates, fat, etc.).

[1459] Step 9:

[1460] The server updates the calculated nutritional information in the database as the user's dietary record.

[1461] Step 10:

[1462] The user inputs their emotional state (e.g., happy, sad, stressed, excited) using the device, and the emotion engine automatically recognizes the user's emotional state through facial expression or voice analysis.

[1463] Step 11:

[1464] The server stores the emotional state recognized by the emotion engine or the emotional state directly input by the user in a database.

[1465] Step 12:

[1466] The server uses an AI algorithm to generate a new personalized meal plan based on the user's past meal records, emotional state, and profile information.

[1467] Step 13:

[1468] The device notifies the user of the generated meal plan and displays a detailed menu and its recipes.

[1469] Step 14:

[1470] The user eats according to the meal plan notified to them, then takes a photo of the meal again to update the record.

[1471] Step 15:

[1472] The server continuously monitors the user's food log and emotional state, analyzes the progress, and, in particular, dynamically adjusts the meal plan taking into account the user's emotional state.

[1473] Step 16:

[1474] The server generates appropriate motivational messages based on the user's emotional state and notifies them through the device. For example, if the user is feeling stressed, the server will provide a menu containing ingredients with a relaxation effect and an encouraging message.

[1475] Step 17:

[1476] The device also provides recipes using seasonal ingredients recommended by the emotion engine, helping users enjoy healthy and timeless meals.

[1477] Step 18:

[1478] The server will suggest restaurant menus that are optimal for dining out to users with specific nutritional needs, for example, suggesting low-carb options to a diabetic user.

[1479] In this way, the system generates and provides personalized meal plans by comprehensively considering the user's profile information, nutritional needs, and emotional state. It also dynamically adjusts meal plans based on the user's emotional state and provides appropriate motivational messages, making dietary management and health support more effective.

[1480] Example 2

[1481] 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."

[1482] In the past, health and diet management required individual data entry, recording, and management, which was time-consuming and burdensome. It was also difficult to create meal plans that took emotional states into account, and there was a lack of effective ways to maintain motivation. Furthermore, it was difficult to maintain a proper nutritional balance when eating out.

[1483] 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.

[1484] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal contents using image recognition technology, means for calculating nutritional information based on the analyzed meal contents and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal records and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously input meal records and monitor progress, means for the user to input emotional states and for an emotion engine to analyze facial expressions and voice to recognize and record the emotional states, and means for generating motivational messages based on the emotional states and notifying the user. This enables efficient health and dietary management of the user and provides personalized support that takes emotional states into account, thereby maintaining motivation and maintaining an appropriate nutritional balance even when eating out.

[1485] "Profile Information" refers to information about a user's individual identity, such as the user's name, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs.

[1486] "Database" refers to an information system for centrally managing and storing data such as user profile information, nutritional needs, food records, and emotional state.

[1487] "Image recognition technology" refers to artificial intelligence and machine learning technology that analyzes photos of meals and recognizes specific ingredients and their quantities.

[1488] "Nutritional information" refers to information about the nutritional components of a meal, such as calories, protein, carbohydrates, and fats.

[1489] "Personalized Meal Plan" refers to an optimal meal plan created for you based on your profile information, nutritional needs, past food history, and emotional state.

[1490] A "motivational message" refers to a message that is generated based on the user's emotional state and is intended to increase the user's motivation.

[1491] "Emotion engine" refers to artificial intelligence technology that analyzes a user's facial expressions and voice to recognize their emotional state.

[1492] "Progress" refers to accumulated data on the contents of meals that the user has eaten according to the meal plan and their nutritional information, and is information that indicates the progress of health management.

[1493] This system centrally manages a user's profile information, food records, emotional state, etc., and provides personalized meal plans. This allows for efficient health management for users, while also providing support that takes emotional state into consideration and maintaining motivation. The operation of this system is explained in detail below.

[1494] User registration and initial settings

[1495] When a user first accesses the system, they create an account via the web or app, entering their profile information such as name, email address, password, age, gender, weight, height, food preferences, allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet). The server stores this information in a database and sends a confirmation email to the user.

[1496] Food record entry and analysis

[1497] After a user eats a meal, they take a photo of the meal using a device such as a smartphone or tablet and upload it to the system. The server sends this photo to an AI meal recognition module, which uses image recognition technology to analyze the ingredients and their amounts. For example, it may identify the meal as "50g of oatmeal, 1 banana, 200ml of milk." The server then calculates nutritional information based on the analysis results and updates the database as a meal record. Specific nutritional information might include "calorie intake 300kcal, carbohydrates 45g, protein 10g, fat 5g."

[1498] Recognizing and recording emotional states

[1499] After eating, the user inputs their emotional state (happiness, sadness, stress, excitement, etc.) into the device. The emotion engine then analyzes their facial expressions and voice to recognize the user's emotional state. The server then stores this emotional data in a database.

[1500] Personalized meal plan generation and notifications

[1501] The server analyzes the user's profile information, past meal records, and emotional state, and uses AI algorithms to generate a personalized meal plan that takes into account the user's nutritional needs, dietary restrictions, and emotional state. The device notifies the user of the generated meal plan and displays detailed menus and recipes.

[1502] Monitor progress and adjust plans

[1503] The user follows the proposed meal plan, eats the meal, and then takes a photo of the meal on their device to update their meal log. The server continuously monitors the meal log and analyzes the progress. It dynamically adjusts the meal plan, taking into account the user's emotional state. The user is then notified of the adjusted meal plan.

[1504] Providing emotional motivation

[1505] The server generates motivational messages based on the emotional state recognized by the emotion engine and notifies the user via the device. For example, if the user is feeling stressed, it will provide a menu using ingredients with a relaxation effect and an encouraging message.

[1506] Meeting specific nutritional needs

[1507] For example, the server could generate a low-carb meal plan for a diabetic user, or suggest the best options from a specific restaurant menu when dining out via the device.

[1508] Specific examples

[1509] The flow when User A, who has diabetes and is easily stressed, uses this system is as follows.

[1510] 1. User A enters information such as age, gender, height, weight, and diabetes into the system.

[1511] 2. After User A has eaten breakfast, he / she takes a photo of the breakfast on his / her device and uploads it to the system.

[1512] 3. The server analyzes the photo and identifies the ingredients and their quantities, for example, "50g oatmeal, 1 banana, 200ml milk."

[1513] 4. The server calculates nutritional information based on this and adds information such as "calories consumed: 300kcal, carbohydrates: 45g, protein: 10g, fat: 5g" to the food record.

[1514] 5. User A inputs their emotional state (e.g., stress) into the device. The emotion engine analyzes and recognizes their facial expressions and voice.

[1515] 6. Based on this information, the server generates a low-carb, relaxing lunch menu and notifies User A via the terminal.

[1516] 7. User A follows the lunch menu provided, eats lunch, and records the contents again.

[1517] 8. The server analyzes your new food log and emotional state to monitor your progress.

[1518] 9. The server determines that User A is feeling stressed and adjusts the next meal plan to have a relaxation effect, and notifies the user again with an encouraging message.

[1519] Example prompt sentence:

[1520] "I have diabetes and get stressed easily. For breakfast, I had 50g of oatmeal, one banana, and 200ml of milk. Can you suggest a low-carb lunch that will help me relax?"

[1521] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1522] Step 1: User registration and initial setup

[1523] When users first access the system, they create an account via the web or app by entering their profile information, including their name, email address, password, age, gender, weight, height, dietary preferences, allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet).

[1524] Input: Profile information (name, email address, password, age, gender, weight, height, food preferences, allergy information, nutritional needs)

[1525] Output: Save to database, send confirmation email

[1526] The server receives this information and stores it in a database. After the information is stored, a confirmation email is sent to the user.

[1527] Step 2: Enter and analyze your food records

[1528] After a user has eaten a meal, they take a photo of the meal using a device such as a smartphone or tablet and upload it to the system.

[1529] Input: Food photo

[1530] Output: Analyzed dietary information and nutritional information

[1531] The device sends the captured photo to the AI ​​meal recognition module. The server uses the AI ​​module to apply image recognition technology to analyze the ingredients and their amounts. For example, it might analyze the meal as "50g of oatmeal, 1 banana, 200ml of milk." Based on the analysis results, nutritional information (calories, protein, carbohydrates, fat, etc.) is calculated and updated in the database as a meal record.

[1532] Step 3: Recognize and record your emotional state

[1533] After eating, the user inputs their emotional state (happiness, sadness, stress, excitement, etc.) into the device, and the emotion engine analyzes facial expressions and voice to recognize the user's emotional state.

[1534] Input: Self-input of emotional state, facial expressions and voice data

[1535] Output: Recognized emotional state, stored in a database

[1536] The server receives this emotion data and stores it in a database.

[1537] Step 4: Generate and notify your personalized meal plan

[1538] The server analyzes the user's profile information, past meal records, and emotional state, and uses AI algorithms to generate a personalized meal plan.

[1539] Input: Profile information, past meal records, emotional state

[1540] Output: Meal plan, recipe guide

[1541] The meal plan is created taking into account the user's nutritional needs, dietary restrictions, and emotional state. The device notifies the user of the generated meal plan and displays detailed menus and recipes.

[1542] Step 5: Monitor progress and adjust your plan

[1543] The user follows the proposed meal plan, eats the meal, and then takes a photo of it on the device and uploads it to the system.

[1544] Input: Another meal photo

[1545] Output: Updated food log, adjusted meal plan

[1546] The device sends new meal photos to the server, which analyzes and continuously monitors the new meal log, taking into account progress and emotional state, dynamically adjusting the meal plan as needed, and notifying the user again of the adjustments.

[1547] Step 6: Provide emotional motivation

[1548] The server generates motivational messages based on the emotional state recognized by the emotion engine.

[1549] Input: Perceived emotional state

[1550] Output: Motivation message notification

[1551] For example, if a user is feeling stressed, the device will provide a menu using ingredients with a relaxation effect and an encouraging message.

[1552] Step 7: Addressing specific nutritional needs

[1553] For example, if a user has diabetes, the server will generate a low-carb meal plan, and when dining out, the device will suggest the best options from a particular restaurant menu.

[1554] Inputs: specific nutritional needs, restaurant choices when eating out

[1555] Output: Low-carb meal plan, optimal restaurant menu suggestions

[1556] (Application example 2)

[1557] 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."

[1558] Many people today require highly personalized meal plans for maintaining their health and nutritional management. However, existing systems struggle to provide meal plans tailored to the user's emotional state and individual health needs, and lack mechanisms to sustain user motivation. Furthermore, health-related product recommendations in virtual stores do not take into account the user's nutritional information or emotional state, making truly personalized product recommendations difficult. Therefore, a system is needed that can provide more personalized meal plans and appropriate messages to increase motivation while also recommending health-related products in virtual stores based on the user's nutritional information and emotional state.

[1559] 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.

[1560] In this invention, the server includes means for storing the profile information and nutritional needs entered by the user in a database, means for taking photos of meals and analyzing the meal content using image recognition technology, means for calculating nutritional information based on the analyzed meal content and updating the user's meal record, means for generating a personalized meal plan based on the user's past meal record and nutritional needs, means for notifying the user of the generated meal plan, means for the user to continuously enter a meal record and monitor the progress of the meal record, means for analyzing the user's emotional state using an emotion recognition engine and saving the data, means for providing motivational messages based on the emotional state, and means for recommending health-related products in a virtual store based on the analyzed nutritional information and emotional state. This makes it possible to provide personalized meal plans and motivational messages based on the user's health needs and emotional state, and further realizes the recommendation of optimal health-related products in the virtual store.

[1561] "Profile Information" refers to specific information about a User, such as the User's name, age, gender, height, weight, allergy information, and specific nutritional needs.

[1562] "Nutritional Needs" refers to the nutritional or dietary requirements a User has for health maintenance or a specific treatment.

[1563] A "database" is a part of a computer system that organizes information systematically and enables efficient searching and management.

[1564] "Image recognition technology" refers to the technology in which a computer analyzes image data and identifies its contents.

[1565] "Nutritional information" refers to the numerical values ​​of nutrients such as calories, protein, carbohydrates, and fat contained in a meal.

[1566] "Diet record" refers to the history of the meals a user has eaten and the nutritional information based on them.

[1567] "Meal Plan" refers to a meal plan that is appropriate for a user based on their needs and preferences.

[1568] An "emotion recognition engine" refers to a technical system that analyzes a user's facial expressions and voice to identify their emotional state.

[1569] "Motivational messages" refer to text or notifications that provide encouragement or suggestions to improve a user's behavior or psychological state.

[1570] A "virtual store" refers to an online shop that sells products over the Internet.

[1571] "Product recommendation" refers to a system that suggests the most suitable product based on the user's needs and condition.

[1572] This invention is a system for effectively managing a user's health and meal planning. A specific implementation method and its processing details are described below. This system provides personalized meal plans that are dynamically adjusted based on the user's profile information, food records, and emotional state, and emotionally-based health product recommendations.

[1573] The server stores the profile information entered by the user during registration in a database. The profile information includes basic user information (name, age, gender, height, weight), allergy information, and specific nutritional needs (e.g., diabetes or low-carb diet). This database is managed using an SQL-based data management system, which allows for efficient data storage and retrieval.

[1574] After the user eats a meal, the device takes a photo of the meal and sends it to a server. The server then uses image recognition technologies such as TensorFlow and Keras to extract ingredient information from the photo. For example, an AI model can identify ingredients and their amounts in a photo taken by the user, such as "50g of oatmeal, 1 banana, 200ml of milk," and calculate nutritional information (calories, protein, carbohydrates, fat, etc.) based on this information. This nutritional information is then stored in a database as the user's dietary record.

[1575] The user's emotional state is input through the device or analyzed by the emotion recognition engine from photos taken by the device or recorded voice. Emotion recognition uses OpenCV and facial expression recognition algorithms to identify the user's emotional state from their facial expressions and voice. For example, if the user is recognized as feeling stressed, that data is stored in a database.

[1576] The server uses an AI algorithm to generate a personalized meal plan based on the user's past meal records, emotional state, and profile information. The meal plan takes into account the user's nutritional needs, food preferences, and emotional state. The generated meal plan is then sent to the user via their device. For example, it may suggest a menu that includes herbal tea to relieve stress or ingredients with a relaxing effect.

[1577] The device allows users to continuously enter food records, and the server monitors their progress. Based on this, the server adjusts the meal plan in real time and notifies the user again. It also provides motivational messages based on emotional state. For example, if the user is feeling stressed, it will send a message such as, "Don't push yourself too hard, eat foods that will help you relax."

[1578] Furthermore, the analyzed nutritional information and emotional state are used to recommend health-related products in the virtual store, enabling personalized product suggestions and helping users efficiently choose products that meet their health needs.

[1579] As an example, the following prompt sentence is used:

[1580] "I want to develop an AI system that recommends personalized health foods and supplements based on a user's food records and emotional state. Build a model to analyze the food photos uploaded by users and their emotional state, so that a virtual store can suggest products that meet the user's nutritional needs."

[1581] Input: Food photo, emotional state, user profile information (e.g., age, gender, height, weight, allergy information, specific nutritional needs)

[1582] Output: A personalized product recommendation list

[1583] Through the above steps, the present invention is a system that effectively supports the user's health management, improves motivation based on emotional state, and recommends appropriate products in a virtual store.

[1584] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1585] Step 1:

[1586] Registration of profile information entered by users

[1587] Users enter basic information such as name, age, gender, height, weight, allergy information, and specific nutritional needs. This information is sent to the server and stored in a database. This process registers the user's basic health information.

[1588] Step 2:

[1589] Taking and uploading food photos

[1590] After eating a meal, the user takes a photo of the meal with the device's camera and uploads it to the server. The uploaded photo is sent to the server's image recognition engine, which then obtains the initial data of the meal's contents.

[1591] Step 3:

[1592] Image analysis of food content

[1593] The server analyzes the uploaded meal photos using TensorFlow and Keras. It identifies the ingredients and their amounts and extracts data such as "50g of oatmeal, 1 banana, 200ml of milk." The results of this analysis become the input data for calculating nutritional information. Image recognition is used to obtain data for each ingredient.

[1594] Step 4:

[1595] Nutritional information calculation

[1596] The server calculates the nutritional information of the meal (calories, protein, carbohydrates, fat, etc.) based on the image analysis results. Using a nutritional information calculation algorithm, it generates numerical data of the constituent components and stores this in a database as a dietary record. In this step, specific nutrients are quantified.

[1597] Step 5:

[1598] Input or recognition of emotional states

[1599] Users can manually input their current emotional state through their device, or they can take photos or record audio using the device's camera and microphone, which are then sent to the server. The server then analyzes the input data using OpenCV and emotion recognition algorithms to identify the user's emotional state, resulting in emotion labels such as "stress," "happy," and "sad."

[1600] Step 6:

[1601] Storing Emotional Data

[1602] The server stores the recognized emotional state data in a database, which can be used for future meal planning and product recommendations. The emotional information can then be tracked.

[1603] Step 7:

[1604] Generate personalized meal plans

[1605] The server uses AI algorithms to generate a personalized meal plan based on the user's past food records, emotional state, and profile information. The meal plan takes into account the user's nutritional needs and emotional state. The plan is then presented as a detailed menu and recipes.

[1606] Step 8:

[1607] Meal plan notifications

[1608] The device notifies the user of the generated meal plan. The menu and recipes are displayed on the device screen and can be executed by the user. In this step, the user knows what meal to eat next.

[1609] Step 9:

[1610] Continuously record your food and monitor your progress

[1611] The user follows the suggested meal plan and then takes photos of the meal to update the record. The server continuously monitors the user's meal log and analyzes their progress, allowing them to continuously manage their health.

[1612] Step 10:

[1613] Generate product recommendation list

[1614] The server recommends health-related products in the virtual store based on the analyzed nutritional information and emotional state, and the recommendation list includes products optimized for the user's needs.

[1615] Step 11:

[1616] Product recommendation notifications

[1617] The terminal notifies the user of the generated product recommendation list, which allows the user to efficiently select products that meet their health needs and promotes purchases in the virtual store.

[1618] 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.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] 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.

[1623] 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.

[1624] 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).

[1625] 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.

[1626] 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."

[1627] 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.

[1628] 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).

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] 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.

[1636] 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.

[1637] 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.

[1638] 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.

[1639] The following is further disclosed regarding the above embodiment.

[1640] (Claim 1)

[1641] means for storing user-entered profile information and nutritional needs in a database;

[1642] A means for taking a photo of a meal and analyzing the meal contents using image recognition technology;

[1643] a means for calculating nutritional information based on the analyzed dietary content and updating the user's dietary record;

[1644] means for generating a personalized meal plan based on the user's past food records and nutritional needs;

[1645] means for notifying the user of the generated meal plan;

[1646] a means for the user to continually enter food logs and monitor their progress;

[1647] A system including:

[1648] (Claim 2)

[1649] means for generating a meal plan for a user with specific nutritional needs that accommodates the user's restrictions;

[1650] A means to suggest the best restaurant menu to users when dining out;

[1651] The system of claim 1 further comprising:

[1652] (Claim 3)

[1653] A means for generating recipes using seasonal ingredients and suggesting them to users;

[1654] a means for periodically analyzing the user's dietary progress and adjusting the meal plan as needed;

[1655] The system of claim 1 further comprising:

[1656] "Example 1"

[1657] (Claim 1)

[1658] means for storing user-entered profile information and nutritional needs in a database;

[1659] A means for taking a photo of a meal and analyzing the meal contents using image recognition technology;

[1660] a means for calculating nutritional information based on the analyzed dietary content and updating the user's dietary record;

[1661] means for generating a personalized meal plan based on the user's past food records and nutritional needs;

[1662] means for notifying the user of the generated meal plan;

[1663] a means for the user to continually enter food logs and monitor their progress;

[1664] and the means to adjust your meal plan as needed.

[1665] A system including:

[1666] (Claim 2)

[1667] means for generating a meal plan for a user with specific nutritional needs that accommodates the user's restrictions;

[1668] A means to suggest the best restaurant menu to users when dining out;

[1669] means for generating a meal plan based on the prompt sentence using the generative model;

[1670] 10. The system of claim 1, further comprising:

[1671] (Claim 3)

[1672] A means for generating recipes using seasonal ingredients and suggesting them to users;

[1673] a means for periodically analyzing the user's dietary progress and adjusting the meal plan as needed;

[1674] A means to continuously monitor the user's dietary records and nutritional balance using AI algorithms, and

[1675] 10. The system of claim 1, further comprising:

[1676] "Application Example 1"

[1677] New Claims

[1678] (Claim 1)

[1679] means for storing user-entered profile information and nutritional needs in a database;

[1680] A means for taking a photo of a meal and analyzing the meal contents using image recognition technology;

[1681] a means for calculating nutritional information based on the analyzed dietary content and updating the user's dietary record;

[1682] means for generating a personalized meal plan based on the user's past food records and nutritional needs;

[1683] means for notifying the user of the generated meal plan;

[1684] A way to propose the best menu for brick-and-mortar stores,

[1685] a means for the user to continually enter food logs and monitor their progress;

[1686] A system including:

[1687] (Claim 2)

[1688] A method for taking photos of meal contents at a physical store using a smartphone camera and analyzing the images of the meal;

[1689] A means for calculating nutritional information based on the results of dietary analysis and storing this information in a database;

[1690] A means for proposing a personalized menu corresponding to a user in a real store;

[1691] The system of claim 1 further comprising:

[1692] (Claim 3)

[1693] A means of generating meal plans using a generative AI model based on the user's dietary history and profile using the latest AI technology;

[1694] a means for periodically analyzing the user's dietary progress and adjusting the meal plan as needed;

[1695] The system of claim 1 further comprising:

[1696] "Example 2: Combining Emotion Engines"

[1697] (Claim 1)

[1698] means for storing user-entered profile information and nutritional needs in a database;

[1699] A means for taking a photo of a meal and analyzing the meal contents using image recognition technology;

[1700] a means for calculating nutritional information based on the analyzed dietary content and updating the user's dietary record;

[1701] means for generating a personalized meal plan based on the user's past food records and nutritional needs;

[1702] means for notifying the user of the generated meal plan;

[1703] a means for the user to continually enter food logs and monitor their progress;

[1704] A means for a user to input an emotional state, and an emotion engine to analyze facial expressions and voice to recognize and record the emotional state;

[1705] means for generating a motivational message based on the emotional state and notifying the user of the message;

[1706] A system including:

[1707] (Claim 2)

[1708] means for generating a meal plan for a user with specific nutritional needs that accommodates the user's restrictions;

[1709] A means to suggest the best restaurant menu to users when dining out;

[1710] The system of claim 1 further comprising:

[1711] (Claim 3)

[1712] A means for generating recipes using seasonal ingredients and suggesting them to users;

[1713] a means for periodically analyzing the user's dietary progress and adjusting the meal plan as needed;

[1714] The system of claim 1 further comprising:

[1715] "Application example 2 when combining emotion engines"

[1716] (Claim 1)

[1717] means for storing user-entered profile information and nutritional needs in a database;

[1718] A means for taking a photo of a meal and analyzing the meal contents using image recognition technology;

[1719] a means for calculating nutritional information based on the analyzed dietary content and updating the user's dietary record;

[1720] means for generating a personalized meal plan based on the user's past food records and nutritional needs;

[1721] means for notifying the user of the generated meal plan;

[1722] a means for the user to continually enter food logs and monitor their progress;

[1723] means for analyzing the user's emotional state using an emotion recognition engine and storing the data;

[1724] means for providing motivational messages based on the emotional state;

[1725] A means for recommending health-related products in a virtual store based on the analyzed nutritional information and emotional state;

[1726] A system including:

[1727] (Claim 2)

[1728] means for genera...

Claims

1. means for storing user-entered profile information and nutritional needs in a database; A means for taking a photo of a meal and analyzing the meal contents using image recognition technology; a means for calculating nutritional information based on the analyzed dietary content and updating the user's dietary record; means for generating a personalized meal plan based on the user's past food records and nutritional needs; means for notifying the user of the generated meal plan; a means for the user to continually enter food logs and monitor their progress; A system including:

2. means for generating a meal plan for a user with specific nutritional needs that accommodates the user's restrictions; A means to suggest the best restaurant menu to users when dining out; The system of claim 1 further comprising:

3. A means for generating recipes using seasonal ingredients and suggesting them to users; a means for periodically analyzing the user's dietary progress and adjusting the meal plan as needed; The system of claim 1 further comprising:

Images