System

Abstract

A system is provided.SOLUTION: The system includes an input unit configured to input user information, a transmission unit configured to transmit the input user information to a server, a storage unit configured to store the user information, a generation unit configured to generate a personalized meal menu based on the stored user information, a transmission unit configured to transmit the generated meal menu to a user terminal, a recording unit configured to record meal contents actually eaten by the user, an analysis unit configured to analyze the recorded meal contents and evaluate a progress status of the user, and a providing unit configured to provide advice and feedback to the user based on an evaluation result.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] In modern society, many people need individual nutritional management and meal plans due to busy lifestyles and increased health consciousness. However, managing one's own diet while considering appropriate nutritional balance is not easy and requires specialized knowledge. It is also difficult for people with illnesses or dietary restrictions to enjoy healthy meals with peace of mind. This invention aims to provide a system that utilizes a generative AI model to provide users with personalized meal plans and support appropriate dietary management while considering nutritional balance. [Means for solving the problem]

[0005] The system of the present invention includes the following means.

[0006] The system provides an input means for inputting user information and a transmission means for transmitting the input user information to a server, where the user information includes physical information, goals, preferences, restrictions, etc.

[0007] The system includes a storage means for storing the transmitted user information, and a generation means for generating a personalized meal menu based on the stored user information. The generation means proposes a nutritionally balanced meal menu while taking into consideration the user's physical information, goals, and preferences.

[0008] The generated meal menu is transmitted to a user terminal, and a recording means is provided for recording the meal contents that the user actually ate.

[0009] Furthermore, the app is equipped with an analytical tool that analyzes the recorded dietary information and evaluates the user's progress. It also includes a provision tool that provides personalized advice and feedback to improve motivation based on the evaluation results.

[0010] "User Information" refers to data such as your physical information, goals, preferences, and limitations.

[0011] "Input means" refers to the interface through which a user inputs their information into the system.

[0012] "Transmission means" refers to a function for transmitting input user information to a server.

[0013] "Storage means" refers to the function for storing transmitted user information in a database, etc.

[0014] "Generator" refers to the AI ​​model or algorithm that generates personalized meal menus based on stored user information.

[0015] "Recording means" refers to the interface or function that allows the user to record the contents of the meal they actually ate in the system.

[0016] "Analysis means" refers to the function for analyzing the recorded meal contents and evaluating the user's progress.

[0017] "Provision means" refers to the function for providing advice and feedback to users based on the evaluation results.

[0018] "Meal menu" refers to meal suggestions generated based on the user's physical information, goals, and preferences.

[0019] "Terminal" refers to the device used by a user to enter information and view meal menus.

[0020] "Server" refers to a computer system that stores user information and performs processes such as analysis and menu generation. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram 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

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

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

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] This invention relates to a system that manages a user's food log and helps monitor progress. The system uses a generative AI model to suggest personalized meal plans to users, helping them to adopt healthy eating habits.

[0043] System Overview

[0044] The system consists of the following main components:

[0045] 1. User interface (terminal): The interface through which the user inputs information.

[0046] 2. Database (server): This is a database that stores user information, meal records, and recipe data.

[0047] 3. Generative AI model (server): An AI that generates meal menus based on user information.

[0048] 4. Monitoring function (server): A function that analyzes the user's progress.

[0049] 5. Notification and feedback function (server): A function that provides advice and feedback to improve motivation.

[0050] Program processing overview

[0051] 1. Entering and saving user information

[0052] User: Launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0053] Terminal: Sends the entered information to the server.

[0054] Server: Stores the received user information in a database and generates a user ID.

[0055] 2. Generate personalized meal menus

[0056] Users: Send meal requests to the app based on their preferences and goals.

[0057] Terminal: Sends request information to the server.

[0058] Server: Retrieves user information from the database and sends it to the generative AI model.

[0059] Generative AI model: Generates a personalized meal menu based on user information and returns it to the server.

[0060] Server: Sends the generated menu to the device.

[0061] Device: Shows the user a meal menu.

[0062] 3. Enter and save the user's food record

[0063] User: Record the food they actually ate in the app.

[0064] Device: Sends recorded meal data to the server.

[0065] Server: Stores the received meal data in a database.

[0066] 4. Monitoring progress and providing feedback

[0067] Server: Periodically retrieves the user's food record data from the database.

[0068] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[0069] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[0070] Server: Sends feedback to the device.

[0071] Terminal: Show feedback to the user.

[0072] Specific use cases

[0073] As an example, consider a 52-year-old male user. This man has moderate diabetes and wants to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the app. Based on this information, the system uses a generative AI model to generate a personalized, nutritionally balanced, diabetes-friendly meal plan. Specific menu suggestions include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[0074] By doing this and continuing to enter food records, the system will monitor progress and provide appropriate advice to help users live a healthy life toward achieving their goals. In this way, users can enjoy individually customized meal plans and effectively manage their health.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The user launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0078] Step 2:

[0079] The terminal transmits the entered user information to the server.

[0080] Step 3:

[0081] The server stores the received user information in a database and generates a user ID.

[0082] Step 4:

[0083] Users submit meal requests to the app based on their preferences and goals.

[0084] Step 5:

[0085] The terminal sends the request information to the server.

[0086] Step 6:

[0087] The server retrieves user information from the database and sends it to the generative AI model.

[0088] Step 7:

[0089] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[0090] Step 8:

[0091] The server sends the generated menu to the terminal.

[0092] Step 9:

[0093] The device displays a meal menu to the user.

[0094] Step 10:

[0095] The app records the meals that users actually eat.

[0096] Step 11:

[0097] The device transmits the recorded meal data to the server.

[0098] Step 12:

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

[0100] Step 13:

[0101] The server periodically retrieves the user's food record data from the database.

[0102] Step 14:

[0103] The monitoring function analyzes the data acquired and evaluates the user's progress, calculating nutritional balance and goal achievement.

[0104] Step 15:

[0105] The notification and feedback function generates advice and motivational messages for users based on the evaluation results.

[0106] Step 16:

[0107] The server sends the feedback to the device.

[0108] Step 17:

[0109] The device displays feedback to the user.

[0110] Example 1

[0111] 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."

[0112] In today's busy lifestyles, it is challenging for individuals to select appropriate meal plans while taking into account their health status and nutritional balance. It is also difficult for users to accurately manage their own dietary records and consistently monitor their progress. Existing systems lack the ability to provide personalized meal plans or continuous advice based on dietary records, making it difficult for users to maintain a healthy lifestyle.

[0113] 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.

[0114] In this invention, the server includes input means for inputting user information, transmission means for transmitting the input user information to the data processing device, storage means for saving the user information, generation means for generating a personalized meal menu based on the saved user information, transmission means for transmitting the generated meal menu to the user terminal, recording means for recording the meal contents actually consumed by the user, analysis means for analyzing the recorded meal contents and evaluating the user's progress, and provision means for providing the user with advice and feedback based on the evaluation results. This allows the user to easily obtain a personalized meal menu that takes their nutritional balance into consideration, and to consistently record their meals and monitor their progress.

[0115] "Input means" refers to an interface for users to input information, including forms for inputting user attributes and goals.

[0116] "Transmission means" refers to a function for transmitting information input by a user to a data processing device, and includes a process for transferring data via a network.

[0117] "Storage means" refers to a database or storage system for storing transmitted user information and meal records.

[0118] "Generative means" refers to a process, including an algorithm or generative AI model, for generating personalized meal menus based on stored user information.

[0119] "User terminal" means a device through which a user uses the interface, and includes hardware such as a smartphone, tablet, or computer.

[0120] "Recording means" refers to the interface or application function that allows the user to input the details of the food they have actually consumed and store that information in a database.

[0121] "Analysis means" includes algorithms and analytical processes for analyzing the recorded dietary content and evaluating nutritional balance and goal achievement.

[0122] "Delivery means" refers to the process or feedback system for generating advice or feedback to users based on the analysis results and notifying them.

[0123] MODE FOR CARRYING OUT THE INVENTION

[0124] This invention relates to a system that helps users manage their dietary records and monitor their progress in order to lead a healthy diet. The system utilizes a generative AI model to suggest personalized meal plans to users, helping them improve their dietary habits in line with their individual lifestyles.

[0125] System configuration

[0126] The system consists of the following main components:

[0127] 1. User Interface (Terminal): This is the interface through which users input information and view the generated meal menu and feedback. Devices such as smartphones, tablets, and computers are used.

[0128] 2. Database (server): A system for storing user information, meal records, and recipe data, and saving data required for subsequent processing.

[0129] 3. Generative AI model (server): An algorithm that generates individually customized meal menus based on user information.

[0130] 4. Monitoring function (server): This function analyzes the user's food records and evaluates their progress.

[0131] 5. Notification and feedback function (server): A system for providing users with advice and motivational feedback.

[0132] Program processing overview

[0133] The system program performs the following processing.

[0134] 1. Entering and saving user information

[0135] Users enter their name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked ingredients into the app's input screen. The device sends the entered information to the server, which stores it in a database. At this time, a unique user ID is generated and stored along with the information.

[0136] 2. Generate personalized meal menus

[0137] The user requests a meal menu and sends the request information to the server via the device. The server retrieves the user information from the database and sends it to the generative AI model to generate a customized meal menu. The generated menu is then sent from the server to the device, which displays it to the user.

[0138] 3. Enter and save the user's food record

[0139] The user records the details of the food they actually ate in the app, and the device sends the recorded data to the server, which then stores the received food data in a database.

[0140] 4. Monitoring progress and providing feedback

[0141] The server periodically retrieves the user's food records from the database and analyzes them using the monitoring function. Based on the analysis results, the notification and feedback function generates advice and motivational messages for the user, which are then sent to the user's device and displayed.

[0142] Specific examples

[0143] For example, consider a 52-year-old male user with moderate diabetes who wants to lose weight. He enters his weight, height, age, and diet goal (lose 5 kg in 3 months) into the app. The system uses this information to create a personalized, nutritionally balanced, diabetes-friendly meal plan using a generative AI model. For example, specific menu suggestions might include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[0144] Prompt Sentence Examples

[0145] "52-year-old male with moderate diabetes, diet goal: lose 5 kg in 3 months"

[0146] This allows users to easily get personalized meal plans that take their nutritional balance into account, and to consistently record their meals and monitor their progress.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Step 1: Enter and save user information

[0149] User: Launches the app and enters their name, age, gender, height, weight, diet goal, allergy information, and favorite and disliked ingredients.

[0150] Input: User's basic information (e.g., name, age, gender) and individual parameters (e.g., diet goals, allergy information).

[0151] Terminal: Converts the input information into an appropriate data format, such as JSON, and sends it to the server.

[0152] Output: A data packet containing the user's basic information and individual parameters.

[0153] Server: When saving the received data packet in the user information table of the database, generate a unique user ID and record it in the database.

[0154] What happens: The server uses an INSERT command to save the user information in the database and retrieves the generated user ID.

[0155] Step 2: Generate a personalized meal menu

[0156] User: Presses a button in the app to request a meal.

[0157] Input: User ID and meal request (e.g., preferred ingredients, meal purpose).

[0158] Terminal: The user ID and request details are sent together to the server.

[0159] Output: A data packet containing the request information.

[0160] Server: Retrieves user information from the database and sends it along with the request information to the generative AI model.

[0161] Input: User information (e.g. age, gender, diet goal) and request information.

[0162] Generative AI model: Generates specific meal plans based on input information. It uses algorithms to process the data and output optimal meal plans.

[0163] Output: A personalized meal menu.

[0164] Server: Sends the meal menu to the device.

[0165] Device: Display the received meal menu to the user.

[0166] Specific operation: The server receives the response from the generative AI model, saves the meal menu along with the user ID, and sends it to the user's device.

[0167] Step 3: Enter and save the user's food record

[0168] User: Record the food they actually ate in the app.

[0169] Input: Meal details (e.g., ingredients eaten, amount, time).

[0170] Device: Sends recorded meal data to the server.

[0171] Output: A data packet containing the food log data.

[0172] Server: Stores the received meal data in the meal record table in the database.

[0173] Specific operation: The server saves the meal record in the database using the INSERT command and sends a confirmation message to the terminal that the record has been saved.

[0174] Step 4: Monitor progress and provide feedback

[0175] Server: Periodically retrieves the user's food record data from the database.

[0176] Input: Food record data (e.g., what you ate in the past week).

[0177] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[0178] Output: Analysis results (e.g., nutritional balance, goal achievement).

[0179] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[0180] Specific operation: The server generates a feedback message based on the analysis results and sends it to the terminal along with the user ID.

[0181] Server: Sends feedback messages to devices.

[0182] Terminal: Display a feedback message to the user. Examples include "Your meal today is very balanced! Keep it up" or "Try to eat more protein."

[0183] (Application example 1)

[0184] 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."

[0185] In today's world, many people desire personalized meal plans to save valuable time and maintain a healthy lifestyle. However, implementing such plans requires specialized knowledge, and preparing meals yourself is time-consuming and laborious. Therefore, there is a need for a system that supports the implementation of individually customized meal plans. In particular, there is a need for a way to automate and simplify meal preparation.

[0186] 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.

[0187] In this invention, the server includes input means for inputting user information, transmission means for transmitting the input user information to the server, storage means for saving the user information, generation means for generating a personalized meal menu based on the saved user information, transmission means for transmitting the generated meal menu to the user terminal, recording means for recording the contents of meals actually eaten by the user, analysis means for analyzing the recorded meal contents and evaluating the user's progress, provision means for providing the user with advice and feedback based on the evaluation results, and ordering means for ordering meals based on the generated meal menu. This enables users to easily lead a healthy diet by obtaining a meal plan tailored to their health goals and automatically using a delivery service.

[0188] "User Information" refers to data such as a user's basic personal information, health status, dietary preferences, and allergy information.

[0189] "Input means" is a function that provides an interface for users to input their information into the application.

[0190] The "transmission means" is a function for transmitting the input user information to the server.

[0191] The "storage means" is a function that stores the transmitted user information in the server and keeps it accessible as needed.

[0192] The "generation means" is a function that generates a personalized meal menu based on stored user information.

[0193] "User terminal" refers to a device (smartphone, PC, etc.) that a user uses to access applications and manipulate information.

[0194] The "recording means" is a function that allows the user to input and save the details of the meals they actually ate.

[0195] The "analysis means" is a function that analyzes the recorded meal contents and evaluates the user's progress and health condition.

[0196] "Provision means" is a function for notifying users of advice and feedback based on the analysis results.

[0197] The "ordering means" is a function for ordering meals from a delivery service based on the generated meal menu.

[0198] A "server" is a central processing unit that stores user information, generates personalized meal menus, analyzes progress, provides feedback, etc.

[0199] The present invention provides a system that provides a user with a meal plan tailored to their health goals and supports them in carrying out the plan in a simple manner. Specific embodiments of the present invention will be described below.

[0200] The server includes an input means for inputting user information, a transmission means for transmitting the input user information to the server, a storage means for saving the user information, a generation means for generating a personalized meal menu based on the saved user information, a transmission means for transmitting the generated meal menu to the user terminal, a recording means for recording the meal contents actually eaten by the user, an analysis means for analyzing the recorded meal contents and evaluating the user's progress, a provision means for providing advice and feedback to the user based on the evaluation results, and an ordering means for ordering meals based on the generated meal menu.

[0201] Processing Overview

[0202] The server stores user information in a database and sends it to a generative AI model to generate a personalized meal menu, which is then ordered from a food delivery service and sent to the user's device. The system then records the meal the user has eaten, monitors their progress, and provides feedback as needed.

[0203] Hardware and software used

[0204] Server: A central processing unit that stores data, processes data, and generates AI models. Examples include AWS (Amazon Web Services) and Google Cloud Platform.

[0205] Generative AI model: A machine learning model for generating personalized meal menus. For example, we use RandomForestRegressor.

[0206] User device: A device that a user uses to operate an application, such as a smartphone, tablet, or PC. Examples include iOS and Android devices.

[0207] Database: A relational database to store user information and meal records. For example, MySQL or PostgreSQL is used.

[0208] Specific examples

[0209] As an example, consider a 35-year-old male user who has a peanut allergy and wants to lose 5 kg in three months. The user enters information into the application, such as his health information (height: 170 cm, weight: 68 kg) and his favorite foods (chicken and vegetables). This information is sent to the server and stored in a database.

[0210] The server then uses the stored information to generate a meal menu tailored to the user using a generative AI model. This menu is then automatically ordered from a food delivery service at the specified time. The generated menu is then sent to the user's device, where the user can view it.

[0211] Users record the food they eat in the application, and the information is sent to and stored on a server. The server periodically analyzes the recorded data, evaluates their progress, and provides appropriate advice and feedback to the user.

[0212] Prompt Sentence Examples

[0213] User information registration: "35-year-old male, height 170cm, weight 68kg, peanut allergy, likes chicken and vegetables, wants to lose 5kg in 3 months."

[0214] Delivery Order: "I'll order the suggested grilled chicken salad for lunch tomorrow."

[0215] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0216] Step 1:

[0217] The user launches the application and enters their health information (age, height, weight, allergy information, favorite foods, diet goals, etc.). This is the input data. The device sends this user information to the server. To process the input data, the information is converted into JSON format and sent to the server via an HTTP request. The server stores the received information in a database.

[0218] Step 2:

[0219] The server inputs user data into a generative AI model based on the saved user information to generate a personalized meal menu. The generative AI model uses a machine learning algorithm (e.g., RandomForestRegressor) to analyze the input data and calculate the optimal meal menu. The output is a personalized meal menu, which is returned to the server in JSON format.

[0220] Step 3:

[0221] The server sends the generated meal menu to the terminal. The terminal displays the received meal menu to the user. If the user wishes, they can place a delivery order directly based on the meal menu. The input data for ordering is the meal menu and the user's delivery address information. The ordering means uses this information to send an HTTP request to the food delivery service and saves the order information in a database.

[0222] Step 4:

[0223] The user records the food they actually ate. For example, they may record the difference between the food they actually ate and the menu suggested by the app. This record becomes input data. The device sends the recorded data to the server, which stores it in a database.

[0224] Step 5:

[0225] The server periodically retrieves the user's dietary records from the database and analyzes their progress. It uses analytical tools to calculate nutritional balance and goal achievement. This analysis involves calculations based on the nutritional data of the diet. The output is data showing the user's health status and diet progress.

[0226] Step 6:

[0227] The server provides feedback to the user based on the progress evaluation results. For example, it generates messages suggesting improvements to dietary habits or motivating users. The generated feedback and advice is output data, which is sent to the device using notification means. The device then displays the feedback message to the user.

[0228] Prompt Sentence Examples

[0229] User information registration: "35-year-old male, height 170cm, weight 68kg, peanut allergy, likes chicken and vegetables, wants to lose 5kg in 3 months."

[0230] Delivery Order: "I'll order the suggested grilled chicken salad for lunch tomorrow."

[0231] 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.

[0232] This invention relates to a system that manages a user's food log, supports progress monitoring, and combines it with an emotion engine that recognizes the user's emotional state. It uses a generative AI model to suggest personalized meal plans to users and the emotion engine to provide advice that takes into account the user's psychological state, helping users to lead a healthy diet.

[0233] System Overview

[0234] The system consists of the following main components:

[0235] 1. User interface (terminal): The interface through which the user inputs information.

[0236] 2. Database (server): This is a database that stores user information, meal records, and recipe data.

[0237] 3. Generative AI model (server): An AI that generates meal menus based on user information.

[0238] 4. Monitoring function (server): A function that analyzes the user's progress.

[0239] 5. Notification and feedback function (server): A function that provides advice and feedback to improve motivation.

[0240] 6. Emotion engine (server): A function that recognizes the user's emotions and dynamically adjusts the advice and feedback content based on them.

[0241] Program processing overview

[0242] 1. Entering and saving user information

[0243] User: Launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0244] Terminal: Sends the entered information to the server.

[0245] Server: Stores the received user information in a database and generates a user ID.

[0246] 2. Generate personalized meal menus

[0247] Users: Send meal requests to the app based on their preferences and goals.

[0248] Terminal: Sends request information to the server.

[0249] Server: Retrieves user information from the database and sends it to the generative AI model.

[0250] Generative AI model: Generates a personalized meal menu based on user information and returns it to the server.

[0251] Server: Sends the generated menu to the device.

[0252] Device: Shows the user a meal menu.

[0253] 3. Enter and save the user's food record

[0254] User: Record the food they actually ate in the app.

[0255] Device: Sends recorded meal data to the server.

[0256] Server: Stores the received meal data in a database.

[0257] 4. Monitoring progress and providing feedback

[0258] Server: Periodically retrieves the user's food record data from the database.

[0259] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[0260] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[0261] Server: Sends feedback to the device.

[0262] Terminal: Show feedback to the user.

[0263] 5. Emotion recognition and feedback adjustment by emotion engine

[0264] Emotion engine: Recognizes the user's emotional state based on user input, behavioral data, or sensors.

[0265] Server: Obtains recognized emotion data and sends it to the generative AI model.

[0266] Generative AI model: Generates personalized meal menus that take emotional data into account.

[0267] Notification and feedback function: Dynamically adjusts advice and feedback content based on emotional data and provides it to users.

[0268] Server: Sends adjusted feedback to the device.

[0269] Device: Show users emotional feedback.

[0270] Specific use cases

[0271] As an example, consider a 52-year-old male user. This man has moderate diabetes and wants to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the app. Based on this information, the system uses a generative AI model to generate a personalized, nutritionally balanced, diabetes-friendly meal plan. Specific menu suggestions include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[0272] Furthermore, the emotion engine analyzes the user's emotions. For example, if the user is feeling stressed, it will incorporate suggestions for ingredients and dishes that have a relaxing effect. Based on the evaluation results, it will also provide advice on how to manage stress and messages to increase motivation. By doing this and continuing to enter food records, the system will monitor the user's progress and provide appropriate advice, helping them live a healthy life while working towards achieving their goals. In this way, users can enjoy individually customized meal plans and advice that takes their psychological state into account, enabling them to effectively manage their health.

[0273] The processing flow will be explained below.

[0274] Step 1:

[0275] The user launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0276] Step 2:

[0277] The terminal transmits the entered user information to the server.

[0278] Step 3:

[0279] The server stores the received user information in a database and generates a user ID.

[0280] Step 4:

[0281] Users submit meal requests to the app based on their preferences and goals.

[0282] Step 5:

[0283] The terminal sends the request information to the server.

[0284] Step 6:

[0285] The server retrieves user information from the database and sends it to the generative AI model.

[0286] Step 7:

[0287] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[0288] Step 8:

[0289] The server sends the generated menu to the terminal.

[0290] Step 9:

[0291] The device displays a meal menu to the user.

[0292] Step 10:

[0293] The app records the meals that users actually eat.

[0294] Step 11:

[0295] The device transmits the recorded meal data to the server.

[0296] Step 12:

[0297] The server stores the received meal data in a database.

[0298] Step 13:

[0299] The server periodically retrieves the user's food record data from the database.

[0300] Step 14:

[0301] The monitoring function analyzes the data acquired and evaluates the user's progress, calculating nutritional balance and goal achievement.

[0302] Step 15:

[0303] The notification and feedback function generates advice and motivational messages for users based on the evaluation results.

[0304] Step 16:

[0305] The server sends the feedback to the device.

[0306] Step 17:

[0307] The device displays feedback to the user.

[0308] Step 18:

[0309] The emotion engine recognizes the user's emotional state based on user input, behavioral data, or sensors.

[0310] Step 19:

[0311] The server sends the recognized emotion data to the generative AI model.

[0312] Step 20:

[0313] The generative AI model takes into account the emotional data to generate a personalized meal menu and returns it to the server.

[0314] Step 21:

[0315] The notification and feedback function dynamically adjusts the content of advice and feedback based on emotional data.

[0316] Step 22:

[0317] The server sends the adjusted feedback to the device.

[0318] Step 23:

[0319] The device displays emotional feedback to the user.

[0320] Example 2

[0321] 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."

[0322] In today's modern living environment, it is important to properly manage one's diet for health maintenance, weight loss, and specific disease management. However, it is difficult to create a meal menu based on one's own judgment and manage one's diet based on one's eating habits and health condition, and conventional systems have limited accuracy and personalization. Furthermore, the selection of healthy foods and management of motivation based on the user's emotional state tend to be overlooked. A system that can solve these issues and achieve more effective and personalized health management is needed.

[0323] 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.

[0324] In this invention, the server includes a storage means for storing user information, a generation means for generating a personalized meal menu based on the stored user information, and a transmission means for transmitting the generated meal menu to the user terminal. This makes it possible to generate and provide a personalized meal menu for each user. Furthermore, an emotion recognition means is used to recognize the user's emotional state and dynamically adjust the feedback content based on that information. This enables health management that takes the user's psychological state into consideration, and realizes the provision of more effective advice and feedback.

[0325] "User information" refers to personal data entered by the user, such as name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0326] "Input means" refers to an interface through which a user inputs their information into the system.

[0327] "Transmission means" refers to a device or software that has the function of transmitting input user information and request data to the server.

[0328] The "storage means" is a mechanism for storing the received user information in a storage device such as a database.

[0329] The "generation means" refers to an algorithm or AI model that generates a personalized meal menu based on stored user information.

[0330] "Recording means" refers to a device or software that has the function of recording the food that the user actually ate.

[0331] "Analysis means" refers to a method or device that analyzes the recorded dietary content and evaluates the user's progress.

[0332] "Provision means" refers to devices or software that have the function of providing advice and feedback to users based on the analysis results.

[0333] An "emotion recognition means" is a mechanism that recognizes the user's emotional state and adjusts the feedback content based on that information.

[0334] A "user terminal" is a device that a user uses to access the system, such as an information processing device such as a smartphone or tablet.

[0335] This invention relates to a system that generates personalized meal menus based on user information and supports health management taking into account the user's emotional state. The system consists of the following main components: a user interface, a database, a generative AI model, a monitoring function, a notification and feedback function, and an emotion engine.

[0336] User Interface (Terminal)

[0337] The user interface is a device through which the user inputs information. Specifically, a smartphone or tablet is used. The user inputs their name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked ingredients. This information is sent to the server via the device's app.

[0338] server

[0339] The server stores the received user information in a database, generates a user ID, and provides the user information to the generative AI model. The server then sends the generated meal menu to the device, which is used to analyze the user's meal record data and emotional data.

[0340] Database

[0341] The database serves to store user information, meal records, and recipe data, which is accessed by the server as needed to generate personalized meal menus and monitor the user's progress.

[0342] Generative AI Models

[0343] The generative AI model uses algorithms and AI techniques to generate personalized meal plans based on user information, taking into account the user's nutritional balance and health status.

[0344] For example, the following prompt is sent to the generative AI model:

[0345] User Information:

[0346] Name: Ichiro Tanaka

[0347] Age: 52

[0348] Gender: Male

[0349] Height: 170cm

[0350] Weight: 80kg

[0351] Diet goal: Lose 5kg in 3 months

[0352] Allergy Information: Nuts

[0353] Favorite ingredients: chicken, fish

[0354] Disliked food: Eggplant

[0355] Use this information to generate a personalized diabetes-friendly meal plan.

[0356] Monitoring Function

[0357] The monitoring function is used to analyze and evaluate the user's progress. Specifically, it analyzes food record data obtained from the database and evaluates the user's nutritional balance and goal achievement.

[0358] Notification and feedback function

[0359] The notification and feedback function provides users with advice and motivational messages based on the analysis results. These messages are created based on the user's nutritional status and progress.

[0360] Emotion Engine

[0361] The emotion engine is a function that recognizes the user's emotional state and dynamically adjusts the feedback content. By analyzing the user's emotional state based on input data, behavioral data, and information obtained from sensors, and reflecting this in the generative AI model, it provides more appropriate advice and feedback.

[0362] The system aims to provide users with individually customized meal plans and advice that takes into account their psychological state, helping them to effectively manage their health.

[0363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0364] Step 1: Enter your user information

[0365] User: The user launches the app and enters personal information such as name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked foods.

[0366] Input: Data that a user enters into a form in your app.

[0367] Specific action: A user enters information into an app on a smartphone or tablet.

[0368] Step 2: Submit user information

[0369] Terminal: The terminal sends the entered user information to the server. This transmission is done in a data-encrypted state.

[0370] Input: User information entered in step 1.

[0371] Output: User information sent to the server.

[0372] What happens: The device generates an API request in the background and sends data to the server.

[0373] Step 3: Save user information

[0374] Server: The server stores the received user information in a database and generates a user ID.

[0375] Input: User information sent from the device.

[0376] Output: User information and user ID stored in the database.

[0377] What happens: The server executes an INSERT statement in the database, saving the user information in a new row.

[0378] Step 4: Request a meal

[0379] User: A user requests a personalized meal within the app.

[0380] Input: Meal request and user ID.

[0381] Output: A request to generate a meal menu.

[0382] What happens: The user clicks the "Generate Meal Menu" button.

[0383] Step 5: Feed the generative AI model with data

[0384] Server: The server retrieves user information from a database and sends it to the generative AI model.

[0385] Input: Food request and user information.

[0386] Output: A prompt to the generative AI model.

[0387] Specific operation: The server executes an SQL SELECT statement to obtain user information data and provides a prompt statement to the generative AI model.

[0388] Step 6: Generate the meal menu

[0389] Generative AI model: The generative AI model generates personalized meal menus based on user information.

[0390] Input: A prompt for the generative AI model.

[0391] Output: A personalized meal menu.

[0392] How it works: The AI ​​model uses data processing and algorithms to generate a meal menu and returns the results to the server.

[0393] Step 7: Submit your meal menu

[0394] Server: The server sends the generated meal menu to the user's device.

[0395] Input: A personalized meal menu from a generative AI model.

[0396] Output: Meal menu sent to user device.

[0397] Specific operation: The server sends the meal menu in JSON format to the device.

[0398] Step 8: View the food menu

[0399] Terminal: The terminal displays the received meal menu to the user.

[0400] Input: A meal menu sent by the server.

[0401] Output: The meal menu displayed to the user.

[0402] Specific behavior: The device displays the received meal menu on the user interface.

[0403] Step 9: Enter your food log

[0404] User: The user records the food they eat in the app.

[0405] Input: Food data logged by the user.

[0406] Output: Food data entered into the app.

[0407] What happens: The user enters their meal details into the app's "Food Log" page.

[0408] Step 10: Submit your food log

[0409] Device: The device sends the recorded meal data to the server.

[0410] Input: Meal data entered by the user.

[0411] Output: Meal data sent to the server.

[0412] Specific operation: The device sends meal data to the server in the background.

[0413] Step 11: Keep a food diary

[0414] Server: The server stores the received meal data in a database.

[0415] Input: Meal data sent from the device.

[0416] Output: Food records stored in a database.

[0417] Specific behavior: The server executes an INSERT statement in the database, saving the meal record in a new row.

[0418] Step 12: Monitor progress

[0419] Server: The server periodically retrieves the user's food record data from the database.

[0420] Input: Food record data from the database.

[0421] Output: The data obtained for analysis.

[0422] Specific behavior: The server extracts the data using an SQL SELECT statement.

[0423] Step 13: Analyze your progress

[0424] Monitoring function: Analyzes the acquired data and evaluates the user's progress.

[0425] Input: Food log data.

[0426] Output: Evaluation results of nutritional balance and goal achievement.

[0427] What it does: The monitoring feature uses data analysis algorithms to assess your nutrition and progress.

[0428] Step 14: Generate feedback

[0429] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[0430] Input: Progress assessment results.

[0431] Output: The feedback message.

[0432] What it does: The feedback function generates a message using a template.

[0433] Step 15: Submit your feedback

[0434] Server: The server sends the generated feedback to the device.

[0435] Input: Message via notification and feedback function.

[0436] Output: Feedback sent to the user device.

[0437] Specific operation: The server sends feedback data in JSON format to the device.

[0438] Step 16: Viewing Feedback

[0439] Terminal: The terminal displays feedback to the user.

[0440] Input: The feedback message sent by the server.

[0441] Output: The feedback displayed to the user.

[0442] What happens: Your device will display feedback as a pop-up notification or message.

[0443] Step 17: Recognizing your emotional state

[0444] Emotion engine: Recognizes the user's emotional state based on user input, behavioral data, or sensors.

[0445] Input: User input data, behavioral data, and sensor information.

[0446] Output: Recognized emotion data.

[0447] How it works: The emotion engine uses analytical algorithms to recognize emotional states.

[0448] Step 18: Adjusting Emotional Feedback

[0449] Server: The server acquires the recognized emotion data and sends it to the generative AI model.

[0450] Input: Emotion data.

[0451] Output: Emotion data to a generative AI model.

[0452] Specific operation: The server sends emotion data to the generative AI model.

[0453] Step 19: Emotion-Based Menu Generation

[0454] Generative AI model: Generates personalized meal menus that take emotional data into account.

[0455] Input: Emotion data.

[0456] Output: A personalized meal menu that takes emotions into account.

[0457] How it works: The AI ​​model generates a meal menu based on emotional data.

[0458] Step 20: Generate emotion-based feedback

[0459] Notification and feedback function: Dynamically adjusts advice and feedback content based on emotional data and provides it to users.

[0460] Enter: an emotionally informed meal menu.

[0461] Output: Regulated feedback.

[0462] Specific behavior: The feedback function generates advice that takes emotional data into account.

[0463] Step 21: Send and view sentiment-based feedback

[0464] Server: Sends adjusted feedback to the device.

[0465] Input: Calibrated feedback.

[0466] Output: Feedback sent to the user device.

[0467] Device: Show users emotional feedback.

[0468] Specific behavior: The server sends feedback data in JSON format to the device, and the device displays the feedback as a popup notification or message.

[0469] (Application example 2)

[0470] 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."

[0471] In modern society, support systems for individual users to lead healthy eating habits are important. However, conventional systems simply manage users' food records and progress, but do not provide dynamic meal plans or advice based on the user's emotional state. As a result, advice that does not take the user's psychological state into account can make it difficult to maintain motivation and make it difficult to achieve long-term goals.

[0472] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an emotion recognition means that recognizes the emotional state of the user and adjusts the advice and feedback content based on the emotion, a generation means that dynamically generates a personalized meal menu based on the emotional state and progress, and a transmission means that transmits the generated menu to the user terminal. This makes it possible to provide individual advice and meal plans that take the user's psychological state into consideration, allowing the user to work more effectively toward achieving their health goals.

[0473] "User Information" refers to basic data about each user of the system, such as name, age, gender, height, weight, health status, dietary preferences and allergy information.

[0474] "Input means" refers to the interface that allows users to input personal information, food records, emotional state, etc. into the system.

[0475] "Transmission means" refers to a function for transmitting input data to a server.

[0476] "Storage means" refers to the mechanism for storing user input information and recorded data on the server.

[0477] "Generation means" refers to the AI ​​model or algorithm used to generate personalized meal menus based on stored user information.

[0478] "Recording means" refers to a function that allows a user to record the meals they have actually eaten and their contents.

[0479] "Analysis means" refers to the component within the system that analyzes and evaluates the recorded diet and the user's progress.

[0480] "Means of provision" refers to the function of providing advice and feedback to users based on the analysis results.

[0481] "Emotion recognition means" is a system function that recognizes the emotional state of the user at that time from the data and behavioral data entered by the user.

[0482] "User Device" means a device (e.g., a smartphone, tablet, etc.) that a User uses to interface with the System.

[0483] This invention is a system that manages a user's dietary records, progress, and emotional state in an integrated manner through a smartphone application, and provides personalized advice and feedback based on this information.

[0484] System Overview

[0485] This system consists of the following main components:

[0486] 1. User Interface (Terminal)

[0487] Users use a smartphone application to enter necessary information, including name, age, gender, height, weight, health goals, allergy information, and food preferences.

[0488] 2. Database (server)

[0489] The server stores user information, food records, and emotional states, as well as recipe data and nutritional information.

[0490] 3. Generative AI model (server)

[0491] The server-based generative AI model uses user information to generate personalized meal menus, including specific meal plans based on the user's nutritional balance and health goals.

[0492] 4. Emotion Recognition Engine (Server)

[0493] The emotion recognition engine recognizes the emotional state from input data and user behavioral data, and dynamically adjusts the content of advice and feedback based on that information.

[0494] 5. Notification and feedback function (server)

[0495] The server uses the analysis results to provide users with advice and feedback, including motivational messages about health and diet.

[0496] Processing Description

[0497] In this system, processing is carried out in the following procedure.

[0498] 1. Entering and saving user information

[0499] Users launch the smartphone application and enter their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0500] The entered information is sent from the smartphone to a server, which stores it in a database.

[0501] 2. Generating a meal menu

[0502] Users submit meal requests to the application based on their preferences and goals.

[0503] The server retrieves user information from the database and sends it to the generative AI model.

[0504] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[0505] The server sends the generated menu to the smartphone and presents it to the user.

[0506] 3. Enter and save your food record

[0507] Users record the food they eat in the application.

[0508] The recorded meal data is sent from the smartphone to a server and stored in a database.

[0509] 4. Progress monitoring and feedback

[0510] The server periodically retrieves the user's food record data from the database and analyzes it using the monitoring function.

[0511] Based on the analysis results, the system evaluates the user's nutritional balance and goal achievement, and generates advice and motivational messages using notification and feedback functions.

[0512] The server sends the feedback to the smartphone and displays it to the user.

[0513] 5. Emotion recognition and feedback regulation

[0514] The emotion engine recognizes the emotional state from user input and behavioral data.

[0515] The server retrieves the recognized emotion data and sends it to the generative AI model.

[0516] The generative AI model generates personalized meal menus that take emotional data into account.

[0517] The notification and feedback function dynamically adjusts the content of advice and feedback based on emotional data and provides it to users.

[0518] Specific examples

[0519] As a concrete example, consider a 30-year-old male user whose goal is to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the application. Based on this information, the system uses a generative AI model to generate a personalized meal plan that takes nutritional balance into account.

[0520] Use the following example prompt to request a meal plan from a generative AI model:

[0521] User: 30-year-old male

[0522] Age: 30

[0523] Gender: Male

[0524] Height: 175cm

[0525] Weight: 70kg

[0526] Goal: Lose 5kg in 3 months

[0527] Allergens: nuts

[0528] Favorite foods: Fish, vegetables

[0529] Use this information to suggest a personalized meal plan.

[0530] The generated meal plan may include specific menus such as "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled fish" for dinner.

[0531] Furthermore, the emotion engine analyzes the user's emotions and, if the user is feeling stressed, incorporates suggestions for ingredients and dishes that have a relaxing effect into the recommendations. Based on the evaluation results, the system also provides advice on how to deal with stress and messages to increase motivation. By doing this and continuing to enter food records, the system can monitor the user's progress and provide appropriate advice, helping them live a healthy life while working towards achieving their goals.

[0532] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0533] Step 1:

[0534] A user launches a smartphone application and enters information such as name, age, gender, height, weight, diet goals, allergy information, favorite and disliked foods, etc. This information is sent from the smartphone to a server. The server stores the received information in a database and generates a user ID. The input data is in text format, and the output is the creation of a database entry.

[0535] Step 2:

[0536] A user submits a meal menu request based on their health goals through a smartphone application. The device sends this request to a server. The server retrieves user information from a database and sends it to a generative AI model. The generative AI model uses this information to generate a personalized meal menu and returns it to the server. The generated menu is in text format, and the output is a specific meal plan.

[0537] Step 3:

[0538] The server sends the generated personalized meal menu to the smartphone device. The device receives this information and displays it to the user. The user confirms the displayed meal menu and proceeds to the next step. The input data is the text information of the generated menu, and the output is the display on the user device.

[0539] Step 4:

[0540] The user records the details of the meal they actually ate in a smartphone application. The device then sends this record to a server. The server then stores the received meal data in a database. The input data is the user's meal record (text information), and the output is stored in the database.

[0541] Step 5:

[0542] The server periodically retrieves the user's food record data from the database. This data is analyzed using a monitoring function to evaluate the user's progress. The analysis results include nutritional balance and goal achievement. The input data is the food record, and the output is an evaluation of nutritional balance and goal achievement.

[0543] Step 6:

[0544] The server executes a notification and feedback function that generates advice and motivational messages based on the evaluation results. The generated feedback is sent to the user's smartphone in the form of a notification. The input data is the evaluation results, and the output is advice and feedback messages.

[0545] Step 7:

[0546] The emotion engine analyzes the user's input data and behavioral patterns to recognize their emotional state at that time. The server sends this emotional state to the generative AI model, which then takes the emotional data into account to regenerate a personalized meal menu based on the user's psychological state. The input data is the emotional state, and the output is the regenerated meal menu.

[0547] Step 8:

[0548] The notification and feedback function dynamically adjusts the content of advice and feedback that takes into account the user's emotional state and sends it to the smartphone. The user can receive this feedback and reflect it in their next actions. The input data is emotion, progress, and feedback content, and the output is a dynamically adjusted feedback message.

[0549] Examples of prompt statements

[0550] User: 30-year-old male

[0551] Age: 30

[0552] Gender: Male

[0553] Height: 175cm

[0554] Weight: 70kg

[0555] Goal: Lose 5kg in 3 months

[0556] Allergens: nuts

[0557] Favorite foods: Fish, vegetables

[0558] Use this information to suggest a personalized meal plan.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] [Second embodiment]

[0563] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] In the smart glasses 214, 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.

[0574] 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."

[0575] This invention relates to a system that manages a user's food log and helps monitor progress. The system uses a generative AI model to suggest personalized meal plans to users, helping them to adopt healthy eating habits.

[0576] System Overview

[0577] The system consists of the following main components:

[0578] 1. User interface (terminal): The interface through which the user inputs information.

[0579] 2. Database (server): This is a database that stores user information, meal records, and recipe data.

[0580] 3. Generative AI model (server): An AI that generates meal menus based on user information.

[0581] 4. Monitoring function (server): A function that analyzes the user's progress.

[0582] 5. Notification and feedback function (server): A function that provides advice and feedback to improve motivation.

[0583] Program processing overview

[0584] 1. Entering and saving user information

[0585] User: Launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0586] Terminal: Sends the entered information to the server.

[0587] Server: Stores the received user information in a database and generates a user ID.

[0588] 2. Generate personalized meal menus

[0589] Users: Send meal requests to the app based on their preferences and goals.

[0590] Terminal: Sends request information to the server.

[0591] Server: Retrieves user information from the database and sends it to the generative AI model.

[0592] Generative AI model: Generates a personalized meal menu based on user information and returns it to the server.

[0593] Server: Sends the generated menu to the device.

[0594] Device: Shows the user a meal menu.

[0595] 3. Enter and save the user's food record

[0596] User: Record the food they actually ate in the app.

[0597] Device: Sends recorded meal data to the server.

[0598] Server: Stores the received meal data in a database.

[0599] 4. Monitoring progress and providing feedback

[0600] Server: Periodically retrieves the user's food record data from the database.

[0601] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[0602] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[0603] Server: Sends feedback to the device.

[0604] Terminal: Show feedback to the user.

[0605] Specific use cases

[0606] As an example, consider a 52-year-old male user. This man has moderate diabetes and wants to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the app. Based on this information, the system uses a generative AI model to generate a personalized, nutritionally balanced, diabetes-friendly meal plan. Specific menu suggestions include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[0607] By doing this and continuing to enter food records, the system will monitor progress and provide appropriate advice to help users live a healthy life toward achieving their goals. In this way, users can enjoy individually customized meal plans and effectively manage their health.

[0608] The processing flow will be explained below.

[0609] Step 1:

[0610] The user launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0611] Step 2:

[0612] The terminal transmits the entered user information to the server.

[0613] Step 3:

[0614] The server stores the received user information in a database and generates a user ID.

[0615] Step 4:

[0616] Users submit meal requests to the app based on their preferences and goals.

[0617] Step 5:

[0618] The terminal sends the request information to the server.

[0619] Step 6:

[0620] The server retrieves user information from the database and sends it to the generative AI model.

[0621] Step 7:

[0622] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[0623] Step 8:

[0624] The server sends the generated menu to the terminal.

[0625] Step 9:

[0626] The device displays a meal menu to the user.

[0627] Step 10:

[0628] The app records the meals that users actually eat.

[0629] Step 11:

[0630] The device transmits the recorded meal data to the server.

[0631] Step 12:

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

[0633] Step 13:

[0634] The server periodically retrieves the user's food record data from the database.

[0635] Step 14:

[0636] The monitoring function analyzes the data acquired and evaluates the user's progress, calculating nutritional balance and goal achievement.

[0637] Step 15:

[0638] The notification and feedback function generates advice and motivational messages for users based on the evaluation results.

[0639] Step 16:

[0640] The server sends the feedback to the device.

[0641] Step 17:

[0642] The device displays feedback to the user.

[0643] Example 1

[0644] 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."

[0645] In today's busy lifestyles, it is challenging for individuals to select appropriate meal plans while taking into account their health status and nutritional balance. It is also difficult for users to accurately manage their own dietary records and consistently monitor their progress. Existing systems lack the ability to provide personalized meal plans or continuous advice based on dietary records, making it difficult for users to maintain a healthy lifestyle.

[0646] 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.

[0647] In this invention, the server includes input means for inputting user information, transmission means for transmitting the input user information to the data processing device, storage means for saving the user information, generation means for generating a personalized meal menu based on the saved user information, transmission means for transmitting the generated meal menu to the user terminal, recording means for recording the meal contents actually consumed by the user, analysis means for analyzing the recorded meal contents and evaluating the user's progress, and provision means for providing the user with advice and feedback based on the evaluation results. This allows the user to easily obtain a personalized meal menu that takes their nutritional balance into consideration, and to consistently record their meals and monitor their progress.

[0648] "Input means" refers to an interface for users to input information, including forms for inputting user attributes and goals.

[0649] "Transmission means" refers to a function for transmitting information input by a user to a data processing device, and includes a process for transferring data via a network.

[0650] "Storage means" refers to a database or storage system for storing transmitted user information and meal records.

[0651] "Generative means" refers to a process, including an algorithm or generative AI model, for generating personalized meal menus based on stored user information.

[0652] "User terminal" means a device through which a user uses the interface, and includes hardware such as a smartphone, tablet, or computer.

[0653] "Recording means" refers to the interface or application function that allows the user to input the details of the food they have actually consumed and store that information in a database.

[0654] "Analysis means" includes algorithms and analytical processes for analyzing the recorded dietary content and evaluating nutritional balance and goal achievement.

[0655] "Delivery means" refers to the process or feedback system for generating advice or feedback to users based on the analysis results and notifying them.

[0656] MODE FOR CARRYING OUT THE INVENTION

[0657] This invention relates to a system that helps users manage their dietary records and monitor their progress in order to lead a healthy diet. The system utilizes a generative AI model to suggest personalized meal plans to users, helping them improve their dietary habits in line with their individual lifestyles.

[0658] System configuration

[0659] The system consists of the following main components:

[0660] 1. User Interface (Terminal): This is the interface through which users input information and view the generated meal menu and feedback. Devices such as smartphones, tablets, and computers are used.

[0661] 2. Database (server): A system for storing user information, meal records, and recipe data, and saving data required for subsequent processing.

[0662] 3. Generative AI model (server): An algorithm that generates individually customized meal menus based on user information.

[0663] 4. Monitoring function (server): This function analyzes the user's food records and evaluates their progress.

[0664] 5. Notification and feedback function (server): A system for providing users with advice and motivational feedback.

[0665] Program processing overview

[0666] The system program performs the following processing.

[0667] 1. Entering and saving user information

[0668] Users enter their name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked ingredients into the app's input screen. The device sends the entered information to the server, which stores it in a database. At this time, a unique user ID is generated and stored along with the information.

[0669] 2. Generate personalized meal menus

[0670] The user requests a meal menu and sends the request information to the server via the device. The server retrieves the user information from the database and sends it to the generative AI model to generate a customized meal menu. The generated menu is then sent from the server to the device, which displays it to the user.

[0671] 3. Enter and save the user's food record

[0672] The user records the details of the food they actually ate in the app, and the device sends the recorded data to the server, which then stores the received food data in a database.

[0673] 4. Monitoring progress and providing feedback

[0674] The server periodically retrieves the user's food records from the database and analyzes them using the monitoring function. Based on the analysis results, the notification and feedback function generates advice and motivational messages for the user, which are then sent to the user's device and displayed.

[0675] Specific examples

[0676] For example, consider a 52-year-old male user with moderate diabetes who wants to lose weight. He enters his weight, height, age, and diet goal (lose 5 kg in 3 months) into the app. The system uses this information to create a personalized, nutritionally balanced, diabetes-friendly meal plan using a generative AI model. For example, specific menu suggestions might include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[0677] Prompt Sentence Examples

[0678] "52-year-old male with moderate diabetes, diet goal: lose 5 kg in 3 months"

[0679] This allows users to easily get personalized meal plans that take their nutritional balance into account, and to consistently record their meals and monitor their progress.

[0680] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0681] Step 1: Enter and save user information

[0682] User: Launches the app and enters their name, age, gender, height, weight, diet goal, allergy information, and favorite and disliked ingredients.

[0683] Input: User's basic information (e.g., name, age, gender) and individual parameters (e.g., diet goals, allergy information).

[0684] Terminal: Converts the input information into an appropriate data format, such as JSON, and sends it to the server.

[0685] Output: A data packet containing the user's basic information and individual parameters.

[0686] Server: When saving the received data packet in the user information table of the database, generate a unique user ID and record it in the database.

[0687] What happens: The server uses an INSERT command to save the user information in the database and retrieves the generated user ID.

[0688] Step 2: Generate a personalized meal menu

[0689] User: Presses a button in the app to request a meal.

[0690] Input: User ID and meal request (e.g., preferred ingredients, meal purpose).

[0691] Terminal: The user ID and request details are sent together to the server.

[0692] Output: A data packet containing the request information.

[0693] Server: Retrieves user information from the database and sends it along with the request information to the generative AI model.

[0694] Input: User information (e.g. age, gender, diet goal) and request information.

[0695] Generative AI model: Generates specific meal plans based on input information. It uses algorithms to process the data and output optimal meal plans.

[0696] Output: A personalized meal menu.

[0697] Server: Sends the meal menu to the device.

[0698] Device: Display the received meal menu to the user.

[0699] Specific operation: The server receives the response from the generative AI model, saves the meal menu along with the user ID, and sends it to the user's device.

[0700] Step 3: Enter and save the user's food record

[0701] User: Record the food they actually ate in the app.

[0702] Input: Meal details (e.g., ingredients eaten, amount, time).

[0703] Device: Sends recorded meal data to the server.

[0704] Output: A data packet containing the food log data.

[0705] Server: Stores the received meal data in the meal record table in the database.

[0706] Specific operation: The server saves the meal record in the database using the INSERT command and sends a confirmation message to the terminal that the record has been saved.

[0707] Step 4: Monitor progress and provide feedback

[0708] Server: Periodically retrieves the user's food record data from the database.

[0709] Input: Food record data (e.g., what you ate in the past week).

[0710] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[0711] Output: Analysis results (e.g., nutritional balance, goal achievement).

[0712] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[0713] Specific operation: The server generates a feedback message based on the analysis results and sends it to the terminal along with the user ID.

[0714] Server: Sends feedback messages to devices.

[0715] Terminal: Display a feedback message to the user. Examples include "Your meal today is very balanced! Keep it up" or "Try to eat more protein."

[0716] (Application example 1)

[0717] 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."

[0718] In today's world, many people desire personalized meal plans to save valuable time and maintain a healthy lifestyle. However, implementing such plans requires specialized knowledge, and preparing meals yourself is time-consuming and laborious. Therefore, there is a need for a system that supports the implementation of individually customized meal plans. In particular, there is a need for a way to automate and simplify meal preparation.

[0719] 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.

[0720] In this invention, the server includes input means for inputting user information, transmission means for transmitting the input user information to the server, storage means for saving the user information, generation means for generating a personalized meal menu based on the saved user information, transmission means for transmitting the generated meal menu to the user terminal, recording means for recording the contents of meals actually eaten by the user, analysis means for analyzing the recorded meal contents and evaluating the user's progress, provision means for providing the user with advice and feedback based on the evaluation results, and ordering means for ordering meals based on the generated meal menu. This enables users to easily lead a healthy diet by obtaining a meal plan tailored to their health goals and automatically using a delivery service.

[0721] "User Information" refers to data such as a user's basic personal information, health status, dietary preferences, and allergy information.

[0722] "Input means" is a function that provides an interface for users to input their information into the application.

[0723] The "transmission means" is a function for transmitting the input user information to the server.

[0724] The "storage means" is a function that stores the transmitted user information in the server and keeps it accessible as needed.

[0725] The "generation means" is a function that generates a personalized meal menu based on stored user information.

[0726] "User terminal" refers to a device (smartphone, PC, etc.) that a user uses to access applications and manipulate information.

[0727] The "recording means" is a function that allows the user to input and save the details of the meals they actually ate.

[0728] The "analysis means" is a function that analyzes the recorded meal contents and evaluates the user's progress and health condition.

[0729] "Provision means" is a function for notifying users of advice and feedback based on the analysis results.

[0730] The "ordering means" is a function for ordering meals from a delivery service based on the generated meal menu.

[0731] A "server" is a central processing unit that stores user information, generates personalized meal menus, analyzes progress, provides feedback, etc.

[0732] The present invention provides a system that provides a user with a meal plan tailored to their health goals and supports them in carrying out the plan in a simple manner. Specific embodiments of the present invention will be described below.

[0733] The server includes an input means for inputting user information, a transmission means for transmitting the input user information to the server, a storage means for saving the user information, a generation means for generating a personalized meal menu based on the saved user information, a transmission means for transmitting the generated meal menu to the user terminal, a recording means for recording the meal contents actually eaten by the user, an analysis means for analyzing the recorded meal contents and evaluating the user's progress, a provision means for providing advice and feedback to the user based on the evaluation results, and an ordering means for ordering meals based on the generated meal menu.

[0734] Processing Overview

[0735] The server stores user information in a database and sends it to a generative AI model to generate a personalized meal menu, which is then ordered from a food delivery service and sent to the user's device. The system then records the meal the user has eaten, monitors their progress, and provides feedback as needed.

[0736] Hardware and software used

[0737] Server: A central processing unit that stores data, processes data, and generates AI models. Examples include AWS (Amazon Web Services) and Google Cloud Platform.

[0738] Generative AI model: A machine learning model for generating personalized meal menus. For example, we use RandomForestRegressor.

[0739] User device: A device that a user uses to operate an application, such as a smartphone, tablet, or PC. Examples include iOS and Android devices.

[0740] Database: A relational database to store user information and meal records. For example, MySQL or PostgreSQL is used.

[0741] Specific examples

[0742] As an example, consider a 35-year-old male user who has a peanut allergy and wants to lose 5 kg in three months. The user enters information into the application, such as his health information (height: 170 cm, weight: 68 kg) and his favorite foods (chicken and vegetables). This information is sent to the server and stored in a database.

[0743] The server then uses the stored information to generate a meal menu tailored to the user using a generative AI model. This menu is then automatically ordered from a food delivery service at the specified time. The generated menu is then sent to the user's device, where the user can view it.

[0744] Users record the food they eat in the application, and the information is sent to and stored on a server. The server periodically analyzes the recorded data, evaluates their progress, and provides appropriate advice and feedback to the user.

[0745] Prompt Sentence Examples

[0746] User information registration: "35-year-old male, height 170cm, weight 68kg, peanut allergy, likes chicken and vegetables, wants to lose 5kg in 3 months."

[0747] Delivery Order: "I'll order the suggested grilled chicken salad for lunch tomorrow."

[0748] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0749] Step 1:

[0750] The user launches the application and enters their health information (age, height, weight, allergy information, favorite foods, diet goals, etc.). This is the input data. The device sends this user information to the server. To process the input data, the information is converted into JSON format and sent to the server via an HTTP request. The server stores the received information in a database.

[0751] Step 2:

[0752] The server inputs user data into a generative AI model based on the saved user information to generate a personalized meal menu. The generative AI model uses a machine learning algorithm (e.g., RandomForestRegressor) to analyze the input data and calculate the optimal meal menu. The output is a personalized meal menu, which is returned to the server in JSON format.

[0753] Step 3:

[0754] The server sends the generated meal menu to the terminal. The terminal displays the received meal menu to the user. If the user wishes, they can place a delivery order directly based on the meal menu. The input data for ordering is the meal menu and the user's delivery address information. The ordering means uses this information to send an HTTP request to the food delivery service and saves the order information in a database.

[0755] Step 4:

[0756] The user records the food they actually ate. For example, they may record the difference between the food they actually ate and the menu suggested by the app. This record becomes input data. The device sends the recorded data to the server, which stores it in a database.

[0757] Step 5:

[0758] The server periodically retrieves the user's dietary records from the database and analyzes their progress. It uses analytical tools to calculate nutritional balance and goal achievement. This analysis involves calculations based on the nutritional data of the diet. The output is data showing the user's health status and diet progress.

[0759] Step 6:

[0760] The server provides feedback to the user based on the progress evaluation results. For example, it generates messages suggesting improvements to dietary habits or motivating users. The generated feedback and advice is output data, which is sent to the device using notification means. The device then displays the feedback message to the user.

[0761] Prompt Sentence Examples

[0762] User information registration: "35-year-old male, height 170cm, weight 68kg, peanut allergy, likes chicken and vegetables, wants to lose 5kg in 3 months."

[0763] Delivery Order: "I'll order the suggested grilled chicken salad for lunch tomorrow."

[0764] 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.

[0765] This invention relates to a system that manages a user's food log, supports progress monitoring, and combines it with an emotion engine that recognizes the user's emotional state. It uses a generative AI model to suggest personalized meal plans to users and the emotion engine to provide advice that takes into account the user's psychological state, helping users to lead a healthy diet.

[0766] System Overview

[0767] The system consists of the following main components:

[0768] 1. User interface (terminal): The interface through which the user inputs information.

[0769] 2. Database (server): This is a database that stores user information, meal records, and recipe data.

[0770] 3. Generative AI model (server): An AI that generates meal menus based on user information.

[0771] 4. Monitoring function (server): A function that analyzes the user's progress.

[0772] 5. Notification and feedback function (server): A function that provides advice and feedback to improve motivation.

[0773] 6. Emotion engine (server): A function that recognizes the user's emotions and dynamically adjusts the advice and feedback content based on them.

[0774] Program processing overview

[0775] 1. Entering and saving user information

[0776] User: Launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0777] Terminal: Sends the entered information to the server.

[0778] Server: Stores the received user information in a database and generates a user ID.

[0779] 2. Generate personalized meal menus

[0780] Users: Send meal requests to the app based on their preferences and goals.

[0781] Terminal: Sends request information to the server.

[0782] Server: Retrieves user information from the database and sends it to the generative AI model.

[0783] Generative AI model: Generates a personalized meal menu based on user information and returns it to the server.

[0784] Server: Sends the generated menu to the device.

[0785] Device: Shows the user a meal menu.

[0786] 3. Enter and save the user's food record

[0787] User: Record the food they actually ate in the app.

[0788] Device: Sends recorded meal data to the server.

[0789] Server: Stores the received meal data in a database.

[0790] 4. Monitoring progress and providing feedback

[0791] Server: Periodically retrieves the user's food record data from the database.

[0792] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[0793] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[0794] Server: Sends feedback to the device.

[0795] Terminal: Show feedback to the user.

[0796] 5. Emotion recognition and feedback adjustment by emotion engine

[0797] Emotion engine: Recognizes the user's emotional state based on user input, behavioral data, or sensors.

[0798] Server: Obtains recognized emotion data and sends it to the generative AI model.

[0799] Generative AI model: Generates personalized meal menus that take emotional data into account.

[0800] Notification and feedback function: Dynamically adjusts advice and feedback content based on emotional data and provides it to users.

[0801] Server: Sends adjusted feedback to the device.

[0802] Device: Show users emotional feedback.

[0803] Specific use cases

[0804] As an example, consider a 52-year-old male user. This man has moderate diabetes and wants to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the app. Based on this information, the system uses a generative AI model to generate a personalized, nutritionally balanced, diabetes-friendly meal plan. Specific menu suggestions include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[0805] Furthermore, the emotion engine analyzes the user's emotions. For example, if the user is feeling stressed, it will incorporate suggestions for ingredients and dishes that have a relaxing effect. Based on the evaluation results, it will also provide advice on how to manage stress and messages to increase motivation. By doing this and continuing to enter food records, the system will monitor the user's progress and provide appropriate advice, helping them live a healthy life while working towards achieving their goals. In this way, users can enjoy individually customized meal plans and advice that takes their psychological state into account, enabling them to effectively manage their health.

[0806] The processing flow will be explained below.

[0807] Step 1:

[0808] The user launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0809] Step 2:

[0810] The terminal transmits the entered user information to the server.

[0811] Step 3:

[0812] The server stores the received user information in a database and generates a user ID.

[0813] Step 4:

[0814] Users submit meal requests to the app based on their preferences and goals.

[0815] Step 5:

[0816] The terminal sends the request information to the server.

[0817] Step 6:

[0818] The server retrieves user information from the database and sends it to the generative AI model.

[0819] Step 7:

[0820] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[0821] Step 8:

[0822] The server sends the generated menu to the terminal.

[0823] Step 9:

[0824] The device displays a meal menu to the user.

[0825] Step 10:

[0826] The app records the meals that users actually eat.

[0827] Step 11:

[0828] The device transmits the recorded meal data to the server.

[0829] Step 12:

[0830] The server stores the received meal data in a database.

[0831] Step 13:

[0832] The server periodically retrieves the user's food record data from the database.

[0833] Step 14:

[0834] The monitoring function analyzes the data acquired and evaluates the user's progress, calculating nutritional balance and goal achievement.

[0835] Step 15:

[0836] The notification and feedback function generates advice and motivational messages for users based on the evaluation results.

[0837] Step 16:

[0838] The server sends the feedback to the device.

[0839] Step 17:

[0840] The device displays feedback to the user.

[0841] Step 18:

[0842] The emotion engine recognizes the user's emotional state based on user input, behavioral data, or sensors.

[0843] Step 19:

[0844] The server sends the recognized emotion data to the generative AI model.

[0845] Step 20:

[0846] The generative AI model takes into account the emotional data to generate a personalized meal menu and returns it to the server.

[0847] Step 21:

[0848] The notification and feedback function dynamically adjusts the content of advice and feedback based on emotional data.

[0849] Step 22:

[0850] The server sends the adjusted feedback to the device.

[0851] Step 23:

[0852] The device displays emotional feedback to the user.

[0853] Example 2

[0854] 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."

[0855] In today's modern living environment, it is important to properly manage one's diet for health maintenance, weight loss, and specific disease management. However, it is difficult to create a meal menu based on one's own judgment and manage one's diet based on one's eating habits and health condition, and conventional systems have limited accuracy and personalization. Furthermore, the selection of healthy foods and management of motivation based on the user's emotional state tend to be overlooked. A system that can solve these issues and achieve more effective and personalized health management is needed.

[0856] 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.

[0857] In this invention, the server includes a storage means for storing user information, a generation means for generating a personalized meal menu based on the stored user information, and a transmission means for transmitting the generated meal menu to the user terminal. This makes it possible to generate and provide a personalized meal menu for each user. Furthermore, an emotion recognition means is used to recognize the user's emotional state and dynamically adjust the feedback content based on that information. This enables health management that takes the user's psychological state into consideration, and realizes the provision of more effective advice and feedback.

[0858] "User information" refers to personal data entered by the user, such as name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[0859] "Input means" refers to an interface through which a user inputs their information into the system.

[0860] "Transmission means" refers to a device or software that has the function of transmitting input user information and request data to the server.

[0861] The "storage means" is a mechanism for storing the received user information in a storage device such as a database.

[0862] The "generation means" refers to an algorithm or AI model that generates a personalized meal menu based on stored user information.

[0863] "Recording means" refers to a device or software that has the function of recording the food that the user actually ate.

[0864] "Analysis means" refers to a method or device that analyzes the recorded dietary content and evaluates the user's progress.

[0865] "Provision means" refers to devices or software that have the function of providing advice and feedback to users based on the analysis results.

[0866] An "emotion recognition means" is a mechanism that recognizes the user's emotional state and adjusts the feedback content based on that information.

[0867] A "user terminal" is a device that a user uses to access the system, such as an information processing device such as a smartphone or tablet.

[0868] This invention relates to a system that generates personalized meal menus based on user information and supports health management taking into account the user's emotional state. The system consists of the following main components: a user interface, a database, a generative AI model, a monitoring function, a notification and feedback function, and an emotion engine.

[0869] User Interface (Terminal)

[0870] The user interface is a device through which the user inputs information. Specifically, a smartphone or tablet is used. The user inputs their name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked ingredients. This information is sent to the server via the device's app.

[0871] server

[0872] The server stores the received user information in a database, generates a user ID, and provides the user information to the generative AI model. The server then sends the generated meal menu to the device, which is used to analyze the user's meal record data and emotional data.

[0873] Database

[0874] The database serves to store user information, meal records, and recipe data, which is accessed by the server as needed to generate personalized meal menus and monitor the user's progress.

[0875] Generative AI Models

[0876] The generative AI model uses algorithms and AI techniques to generate personalized meal plans based on user information, taking into account the user's nutritional balance and health status.

[0877] For example, the following prompt is sent to the generative AI model:

[0878] User Information:

[0879] Name: Ichiro Tanaka

[0880] Age: 52

[0881] Gender: Male

[0882] Height: 170cm

[0883] Weight: 80kg

[0884] Diet goal: Lose 5kg in 3 months

[0885] Allergy Information: Nuts

[0886] Favorite ingredients: chicken, fish

[0887] Disliked food: Eggplant

[0888] Use this information to generate a personalized diabetes-friendly meal plan.

[0889] Monitoring Function

[0890] The monitoring function is used to analyze and evaluate the user's progress. Specifically, it analyzes food record data obtained from the database and evaluates the user's nutritional balance and goal achievement.

[0891] Notification and feedback function

[0892] The notification and feedback function provides users with advice and motivational messages based on the analysis results. These messages are created based on the user's nutritional status and progress.

[0893] Emotion Engine

[0894] The emotion engine is a function that recognizes the user's emotional state and dynamically adjusts the feedback content. By analyzing the user's emotional state based on input data, behavioral data, and information obtained from sensors, and reflecting this in the generative AI model, it provides more appropriate advice and feedback.

[0895] The system aims to provide users with individually customized meal plans and advice that takes into account their psychological state, helping them to effectively manage their health.

[0896] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0897] Step 1: Enter your user information

[0898] User: The user launches the app and enters personal information such as name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked foods.

[0899] Input: Data that a user enters into a form in your app.

[0900] Specific action: A user enters information into an app on a smartphone or tablet.

[0901] Step 2: Submit user information

[0902] Terminal: The terminal sends the entered user information to the server. This transmission is done in a data-encrypted state.

[0903] Input: User information entered in step 1.

[0904] Output: User information sent to the server.

[0905] What happens: The device generates an API request in the background and sends data to the server.

[0906] Step 3: Save user information

[0907] Server: The server stores the received user information in a database and generates a user ID.

[0908] Input: User information sent from the device.

[0909] Output: User information and user ID stored in the database.

[0910] What happens: The server executes an INSERT statement in the database, saving the user information in a new row.

[0911] Step 4: Request a meal

[0912] User: A user requests a personalized meal within the app.

[0913] Input: Meal request and user ID.

[0914] Output: A request to generate a meal menu.

[0915] What happens: The user clicks the "Generate Meal Menu" button.

[0916] Step 5: Feed the generative AI model with data

[0917] Server: The server retrieves user information from a database and sends it to the generative AI model.

[0918] Input: Food request and user information.

[0919] Output: A prompt to the generative AI model.

[0920] Specific operation: The server executes an SQL SELECT statement to obtain user information data and provides a prompt statement to the generative AI model.

[0921] Step 6: Generate the meal menu

[0922] Generative AI model: The generative AI model generates personalized meal menus based on user information.

[0923] Input: A prompt for the generative AI model.

[0924] Output: A personalized meal menu.

[0925] How it works: The AI ​​model uses data processing and algorithms to generate a meal menu and returns the results to the server.

[0926] Step 7: Submit your meal menu

[0927] Server: The server sends the generated meal menu to the user's device.

[0928] Input: A personalized meal menu from a generative AI model.

[0929] Output: Meal menu sent to user device.

[0930] Specific operation: The server sends the meal menu in JSON format to the device.

[0931] Step 8: View the food menu

[0932] Terminal: The terminal displays the received meal menu to the user.

[0933] Input: A meal menu sent by the server.

[0934] Output: The meal menu displayed to the user.

[0935] Specific behavior: The device displays the received meal menu on the user interface.

[0936] Step 9: Enter your food log

[0937] User: The user records the food they eat in the app.

[0938] Input: Food data logged by the user.

[0939] Output: Food data entered into the app.

[0940] What happens: The user enters their meal details into the app's "Food Log" page.

[0941] Step 10: Submit your food log

[0942] Device: The device sends the recorded meal data to the server.

[0943] Input: Meal data entered by the user.

[0944] Output: Meal data sent to the server.

[0945] Specific operation: The device sends meal data to the server in the background.

[0946] Step 11: Keep a food diary

[0947] Server: The server stores the received meal data in a database.

[0948] Input: Meal data sent from the device.

[0949] Output: Food records stored in a database.

[0950] Specific behavior: The server executes an INSERT statement in the database, saving the meal record in a new row.

[0951] Step 12: Monitor progress

[0952] Server: The server periodically retrieves the user's food record data from the database.

[0953] Input: Food record data from the database.

[0954] Output: The data obtained for analysis.

[0955] Specific behavior: The server extracts the data using an SQL SELECT statement.

[0956] Step 13: Analyze your progress

[0957] Monitoring function: Analyzes the acquired data and evaluates the user's progress.

[0958] Input: Food log data.

[0959] Output: Evaluation results of nutritional balance and goal achievement.

[0960] What it does: The monitoring feature uses data analysis algorithms to assess your nutrition and progress.

[0961] Step 14: Generate feedback

[0962] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[0963] Input: Progress assessment results.

[0964] Output: The feedback message.

[0965] What it does: The feedback function generates a message using a template.

[0966] Step 15: Submit your feedback

[0967] Server: The server sends the generated feedback to the device.

[0968] Input: Message via notification and feedback function.

[0969] Output: Feedback sent to the user device.

[0970] Specific operation: The server sends feedback data in JSON format to the device.

[0971] Step 16: Viewing Feedback

[0972] Terminal: The terminal displays feedback to the user.

[0973] Input: The feedback message sent by the server.

[0974] Output: The feedback displayed to the user.

[0975] What happens: Your device will display feedback as a pop-up notification or message.

[0976] Step 17: Recognizing your emotional state

[0977] Emotion engine: Recognizes the user's emotional state based on user input, behavioral data, or sensors.

[0978] Input: User input data, behavioral data, and sensor information.

[0979] Output: Recognized emotion data.

[0980] How it works: The emotion engine uses analytical algorithms to recognize emotional states.

[0981] Step 18: Adjusting Emotional Feedback

[0982] Server: The server acquires the recognized emotion data and sends it to the generative AI model.

[0983] Input: Emotion data.

[0984] Output: Emotion data to a generative AI model.

[0985] Specific operation: The server sends emotion data to the generative AI model.

[0986] Step 19: Emotion-Based Menu Generation

[0987] Generative AI model: Generates personalized meal menus that take emotional data into account.

[0988] Input: Emotion data.

[0989] Output: A personalized meal menu that takes emotions into account.

[0990] How it works: The AI ​​model generates a meal menu based on emotional data.

[0991] Step 20: Generate emotion-based feedback

[0992] Notification and feedback function: Dynamically adjusts advice and feedback content based on emotional data and provides it to users.

[0993] Enter: an emotionally informed meal menu.

[0994] Output: Regulated feedback.

[0995] Specific behavior: The feedback function generates advice that takes emotional data into account.

[0996] Step 21: Send and view sentiment-based feedback

[0997] Server: Sends adjusted feedback to the device.

[0998] Input: Calibrated feedback.

[0999] Output: Feedback sent to the user device.

[1000] Device: Show users emotional feedback.

[1001] Specific behavior: The server sends feedback data in JSON format to the device, and the device displays the feedback as a popup notification or message.

[1002] (Application example 2)

[1003] 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."

[1004] In modern society, support systems for individual users to lead healthy eating habits are important. However, conventional systems simply manage users' food records and progress, but do not provide dynamic meal plans or advice based on the user's emotional state. As a result, advice that does not take the user's psychological state into account can make it difficult to maintain motivation and make it difficult to achieve long-term goals.

[1005] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an emotion recognition means that recognizes the emotional state of the user and adjusts the advice and feedback content based on the emotion, a generation means that dynamically generates a personalized meal menu based on the emotional state and progress, and a transmission means that transmits the generated menu to the user terminal. This makes it possible to provide individual advice and meal plans that take the user's psychological state into consideration, allowing the user to work more effectively toward achieving their health goals.

[1006] "User Information" refers to basic data about each user of the system, such as name, age, gender, height, weight, health status, dietary preferences and allergy information.

[1007] "Input means" refers to the interface that allows users to input personal information, food records, emotional state, etc. into the system.

[1008] "Transmission means" refers to a function for transmitting input data to a server.

[1009] "Storage means" refers to the mechanism for storing user input information and recorded data on the server.

[1010] "Generation means" refers to the AI ​​model or algorithm used to generate personalized meal menus based on stored user information.

[1011] "Recording means" refers to a function that allows a user to record the meals they have actually eaten and their contents.

[1012] "Analysis means" refers to the component within the system that analyzes and evaluates the recorded diet and the user's progress.

[1013] "Means of provision" refers to the function of providing advice and feedback to users based on the analysis results.

[1014] "Emotion recognition means" is a system function that recognizes the emotional state of the user at that time from the data and behavioral data entered by the user.

[1015] "User Device" means a device (e.g., a smartphone, tablet, etc.) that a User uses to interface with the System.

[1016] This invention is a system that manages a user's dietary records, progress, and emotional state in an integrated manner through a smartphone application, and provides personalized advice and feedback based on this information.

[1017] System Overview

[1018] This system consists of the following main components:

[1019] 1. User Interface (Terminal)

[1020] Users use a smartphone application to enter necessary information, including name, age, gender, height, weight, health goals, allergy information, and food preferences.

[1021] 2. Database (server)

[1022] The server stores user information, food records, and emotional states, as well as recipe data and nutritional information.

[1023] 3. Generative AI model (server)

[1024] The server-based generative AI model uses user information to generate personalized meal menus, including specific meal plans based on the user's nutritional balance and health goals.

[1025] 4. Emotion Recognition Engine (Server)

[1026] The emotion recognition engine recognizes the emotional state from input data and user behavioral data, and dynamically adjusts the content of advice and feedback based on that information.

[1027] 5. Notification and feedback function (server)

[1028] The server uses the analysis results to provide users with advice and feedback, including motivational messages about health and diet.

[1029] Processing Description

[1030] In this system, processing is carried out in the following procedure.

[1031] 1. Entering and saving user information

[1032] Users launch the smartphone application and enter their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1033] The entered information is sent from the smartphone to a server, which stores it in a database.

[1034] 2. Generating a meal menu

[1035] Users submit meal requests to the application based on their preferences and goals.

[1036] The server retrieves user information from the database and sends it to the generative AI model.

[1037] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[1038] The server sends the generated menu to the smartphone and presents it to the user.

[1039] 3. Enter and save your food record

[1040] Users record the food they eat in the application.

[1041] The recorded meal data is sent from the smartphone to a server and stored in a database.

[1042] 4. Progress monitoring and feedback

[1043] The server periodically retrieves the user's food record data from the database and analyzes it using the monitoring function.

[1044] Based on the analysis results, the system evaluates the user's nutritional balance and goal achievement, and generates advice and motivational messages using notification and feedback functions.

[1045] The server sends the feedback to the smartphone and displays it to the user.

[1046] 5. Emotion recognition and feedback regulation

[1047] The emotion engine recognizes the emotional state from user input and behavioral data.

[1048] The server retrieves the recognized emotion data and sends it to the generative AI model.

[1049] The generative AI model generates personalized meal menus that take emotional data into account.

[1050] The notification and feedback function dynamically adjusts the content of advice and feedback based on emotional data and provides it to users.

[1051] Specific examples

[1052] As a concrete example, consider a 30-year-old male user whose goal is to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the application. Based on this information, the system uses a generative AI model to generate a personalized meal plan that takes nutritional balance into account.

[1053] Use the following example prompt to request a meal plan from a generative AI model:

[1054] User: 30-year-old male

[1055] Age: 30

[1056] Gender: Male

[1057] Height: 175cm

[1058] Weight: 70kg

[1059] Goal: Lose 5kg in 3 months

[1060] Allergens: nuts

[1061] Favorite foods: Fish, vegetables

[1062] Use this information to suggest a personalized meal plan.

[1063] The generated meal plan may include specific menus such as "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled fish" for dinner.

[1064] Furthermore, the emotion engine analyzes the user's emotions and, if the user is feeling stressed, incorporates suggestions for ingredients and dishes that have a relaxing effect into the recommendations. Based on the evaluation results, the system also provides advice on how to deal with stress and messages to increase motivation. By doing this and continuing to enter food records, the system can monitor the user's progress and provide appropriate advice, helping them live a healthy life while working towards achieving their goals.

[1065] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1066] Step 1:

[1067] A user launches a smartphone application and enters information such as name, age, gender, height, weight, diet goals, allergy information, favorite and disliked foods, etc. This information is sent from the smartphone to a server. The server stores the received information in a database and generates a user ID. The input data is in text format, and the output is the creation of a database entry.

[1068] Step 2:

[1069] A user submits a meal menu request based on their health goals through a smartphone application. The device sends this request to a server. The server retrieves user information from a database and sends it to a generative AI model. The generative AI model uses this information to generate a personalized meal menu and returns it to the server. The generated menu is in text format, and the output is a specific meal plan.

[1070] Step 3:

[1071] The server sends the generated personalized meal menu to the smartphone device. The device receives this information and displays it to the user. The user confirms the displayed meal menu and proceeds to the next step. The input data is the text information of the generated menu, and the output is the display on the user device.

[1072] Step 4:

[1073] The user records the details of the meal they actually ate in a smartphone application. The device then sends this record to a server. The server then stores the received meal data in a database. The input data is the user's meal record (text information), and the output is stored in the database.

[1074] Step 5:

[1075] The server periodically retrieves the user's food record data from the database. This data is analyzed using a monitoring function to evaluate the user's progress. The analysis results include nutritional balance and goal achievement. The input data is the food record, and the output is an evaluation of nutritional balance and goal achievement.

[1076] Step 6:

[1077] The server executes a notification and feedback function that generates advice and motivational messages based on the evaluation results. The generated feedback is sent to the user's smartphone in the form of a notification. The input data is the evaluation results, and the output is advice and feedback messages.

[1078] Step 7:

[1079] The emotion engine analyzes the user's input data and behavioral patterns to recognize their emotional state at that time. The server sends this emotional state to the generative AI model, which then takes the emotional data into account to regenerate a personalized meal menu based on the user's psychological state. The input data is the emotional state, and the output is the regenerated meal menu.

[1080] Step 8:

[1081] The notification and feedback function dynamically adjusts the content of advice and feedback that takes into account the user's emotional state and sends it to the smartphone. The user can receive this feedback and reflect it in their next actions. The input data is emotion, progress, and feedback content, and the output is a dynamically adjusted feedback message.

[1082] Examples of prompt statements

[1083] User: 30-year-old male

[1084] Age: 30

[1085] Gender: Male

[1086] Height: 175cm

[1087] Weight: 70kg

[1088] Goal: Lose 5kg in 3 months

[1089] Allergens: nuts

[1090] Favorite foods: Fish, vegetables

[1091] Use this information to suggest a personalized meal plan.

[1092] 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.

[1093] 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.

[1094] 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.

[1095] [Third embodiment]

[1096] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1097] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1098] 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).

[1099] 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.

[1100] 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.

[1101] 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).

[1102] 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.

[1103] 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.

[1104] 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.

[1105] 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.

[1106] 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.

[1107] 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."

[1108] This invention relates to a system that manages a user's food log and helps monitor progress. The system uses a generative AI model to suggest personalized meal plans to users, helping them to adopt healthy eating habits.

[1109] System Overview

[1110] The system consists of the following main components:

[1111] 1. User interface (terminal): The interface through which the user inputs information.

[1112] 2. Database (server): This is a database that stores user information, meal records, and recipe data.

[1113] 3. Generative AI model (server): An AI that generates meal menus based on user information.

[1114] 4. Monitoring function (server): A function that analyzes the user's progress.

[1115] 5. Notification and feedback function (server): A function that provides advice and feedback to improve motivation.

[1116] Program processing overview

[1117] 1. Entering and saving user information

[1118] User: Launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1119] Terminal: Sends the entered information to the server.

[1120] Server: Stores the received user information in a database and generates a user ID.

[1121] 2. Generate personalized meal menus

[1122] Users: Send meal requests to the app based on their preferences and goals.

[1123] Terminal: Sends request information to the server.

[1124] Server: Retrieves user information from the database and sends it to the generative AI model.

[1125] Generative AI model: Generates a personalized meal menu based on user information and returns it to the server.

[1126] Server: Sends the generated menu to the device.

[1127] Device: Shows the user a meal menu.

[1128] 3. Enter and save the user's food record

[1129] User: Record the food they actually ate in the app.

[1130] Device: Sends recorded meal data to the server.

[1131] Server: Stores the received meal data in a database.

[1132] 4. Monitoring progress and providing feedback

[1133] Server: Periodically retrieves the user's food record data from the database.

[1134] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[1135] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[1136] Server: Sends feedback to the device.

[1137] Terminal: Show feedback to the user.

[1138] Specific use cases

[1139] As an example, consider a 52-year-old male user. This man has moderate diabetes and wants to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the app. Based on this information, the system uses a generative AI model to generate a personalized, nutritionally balanced, diabetes-friendly meal plan. Specific menu suggestions include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[1140] By doing this and continuing to enter food records, the system will monitor progress and provide appropriate advice to help users live a healthy life toward achieving their goals. In this way, users can enjoy individually customized meal plans and effectively manage their health.

[1141] The processing flow will be explained below.

[1142] Step 1:

[1143] The user launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1144] Step 2:

[1145] The terminal transmits the entered user information to the server.

[1146] Step 3:

[1147] The server stores the received user information in a database and generates a user ID.

[1148] Step 4:

[1149] Users submit meal requests to the app based on their preferences and goals.

[1150] Step 5:

[1151] The terminal sends the request information to the server.

[1152] Step 6:

[1153] The server retrieves user information from the database and sends it to the generative AI model.

[1154] Step 7:

[1155] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[1156] Step 8:

[1157] The server sends the generated menu to the terminal.

[1158] Step 9:

[1159] The device displays a meal menu to the user.

[1160] Step 10:

[1161] The app records the meals that users actually eat.

[1162] Step 11:

[1163] The device transmits the recorded meal data to the server.

[1164] Step 12:

[1165] The server stores the received meal data in a database.

[1166] Step 13:

[1167] The server periodically retrieves the user's food record data from the database.

[1168] Step 14:

[1169] The monitoring function analyzes the data acquired and evaluates the user's progress, calculating nutritional balance and goal achievement.

[1170] Step 15:

[1171] The notification and feedback function generates advice and motivational messages for users based on the evaluation results.

[1172] Step 16:

[1173] The server sends the feedback to the device.

[1174] Step 17:

[1175] The device displays feedback to the user.

[1176] Example 1

[1177] 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."

[1178] In today's busy lifestyles, it is challenging for individuals to select appropriate meal plans while taking into account their health status and nutritional balance. It is also difficult for users to accurately manage their own dietary records and consistently monitor their progress. Existing systems lack the ability to provide personalized meal plans or continuous advice based on dietary records, making it difficult for users to maintain a healthy lifestyle.

[1179] 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.

[1180] In this invention, the server includes input means for inputting user information, transmission means for transmitting the input user information to the data processing device, storage means for saving the user information, generation means for generating a personalized meal menu based on the saved user information, transmission means for transmitting the generated meal menu to the user terminal, recording means for recording the meal contents actually consumed by the user, analysis means for analyzing the recorded meal contents and evaluating the user's progress, and provision means for providing the user with advice and feedback based on the evaluation results. This allows the user to easily obtain a personalized meal menu that takes their nutritional balance into consideration, and to consistently record their meals and monitor their progress.

[1181] "Input means" refers to an interface for users to input information, including forms for inputting user attributes and goals.

[1182] "Transmission means" refers to a function for transmitting information input by a user to a data processing device, and includes a process for transferring data via a network.

[1183] "Storage means" refers to a database or storage system for storing transmitted user information and meal records.

[1184] "Generative means" refers to a process, including an algorithm or generative AI model, for generating personalized meal menus based on stored user information.

[1185] "User terminal" means a device through which a user uses the interface, and includes hardware such as a smartphone, tablet, or computer.

[1186] "Recording means" refers to the interface or application function that allows the user to input the details of the food they have actually consumed and store that information in a database.

[1187] "Analysis means" includes algorithms and analytical processes for analyzing the recorded dietary content and evaluating nutritional balance and goal achievement.

[1188] "Delivery means" refers to the process or feedback system for generating advice or feedback to users based on the analysis results and notifying them.

[1189] MODE FOR CARRYING OUT THE INVENTION

[1190] This invention relates to a system that helps users manage their dietary records and monitor their progress in order to lead a healthy diet. The system utilizes a generative AI model to suggest personalized meal plans to users, helping them improve their dietary habits in line with their individual lifestyles.

[1191] System configuration

[1192] The system consists of the following main components:

[1193] 1. User Interface (Terminal): This is the interface through which users input information and view the generated meal menu and feedback. Devices such as smartphones, tablets, and computers are used.

[1194] 2. Database (server): A system for storing user information, meal records, and recipe data, and saving data required for subsequent processing.

[1195] 3. Generative AI model (server): An algorithm that generates individually customized meal menus based on user information.

[1196] 4. Monitoring function (server): This function analyzes the user's food records and evaluates their progress.

[1197] 5. Notification and feedback function (server): A system for providing users with advice and motivational feedback.

[1198] Program processing overview

[1199] The system program performs the following processing.

[1200] 1. Entering and saving user information

[1201] Users enter their name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked ingredients into the app's input screen. The device sends the entered information to the server, which stores it in a database. At this time, a unique user ID is generated and stored along with the information.

[1202] 2. Generate personalized meal menus

[1203] The user requests a meal menu and sends the request information to the server via the device. The server retrieves the user information from the database and sends it to the generative AI model to generate a customized meal menu. The generated menu is then sent from the server to the device, which displays it to the user.

[1204] 3. Enter and save the user's food record

[1205] The user records the details of the food they actually ate in the app, and the device sends the recorded data to the server, which then stores the received food data in a database.

[1206] 4. Monitoring progress and providing feedback

[1207] The server periodically retrieves the user's food records from the database and analyzes them using the monitoring function. Based on the analysis results, the notification and feedback function generates advice and motivational messages for the user, which are then sent to the user's device and displayed.

[1208] Specific examples

[1209] For example, consider a 52-year-old male user with moderate diabetes who wants to lose weight. He enters his weight, height, age, and diet goal (lose 5 kg in 3 months) into the app. The system uses this information to create a personalized, nutritionally balanced, diabetes-friendly meal plan using a generative AI model. For example, specific menu suggestions might include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[1210] Prompt Sentence Examples

[1211] "52-year-old male with moderate diabetes, diet goal: lose 5 kg in 3 months"

[1212] This allows users to easily get personalized meal plans that take their nutritional balance into account, and to consistently record their meals and monitor their progress.

[1213] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1214] Step 1: Enter and save user information

[1215] User: Launches the app and enters their name, age, gender, height, weight, diet goal, allergy information, and favorite and disliked ingredients.

[1216] Input: User's basic information (e.g., name, age, gender) and individual parameters (e.g., diet goals, allergy information).

[1217] Terminal: Converts the input information into an appropriate data format, such as JSON, and sends it to the server.

[1218] Output: A data packet containing the user's basic information and individual parameters.

[1219] Server: When saving the received data packet in the user information table of the database, generate a unique user ID and record it in the database.

[1220] What happens: The server uses an INSERT command to save the user information in the database and retrieves the generated user ID.

[1221] Step 2: Generate a personalized meal menu

[1222] User: Presses a button in the app to request a meal.

[1223] Input: User ID and meal request (e.g., preferred ingredients, meal purpose).

[1224] Terminal: The user ID and request details are sent together to the server.

[1225] Output: A data packet containing the request information.

[1226] Server: Retrieves user information from the database and sends it along with the request information to the generative AI model.

[1227] Input: User information (e.g. age, gender, diet goal) and request information.

[1228] Generative AI model: Generates specific meal plans based on input information. It uses algorithms to process the data and output optimal meal plans.

[1229] Output: A personalized meal menu.

[1230] Server: Sends the meal menu to the device.

[1231] Device: Display the received meal menu to the user.

[1232] Specific operation: The server receives the response from the generative AI model, saves the meal menu along with the user ID, and sends it to the user's device.

[1233] Step 3: Enter and save the user's food record

[1234] User: Record the food they actually ate in the app.

[1235] Input: Meal details (e.g., ingredients eaten, amount, time).

[1236] Device: Sends recorded meal data to the server.

[1237] Output: A data packet containing the food log data.

[1238] Server: Stores the received meal data in the meal record table in the database.

[1239] Specific operation: The server saves the meal record in the database using the INSERT command and sends a confirmation message to the terminal that the record has been saved.

[1240] Step 4: Monitor progress and provide feedback

[1241] Server: Periodically retrieves the user's food record data from the database.

[1242] Input: Food record data (e.g., what you ate in the past week).

[1243] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[1244] Output: Analysis results (e.g., nutritional balance, goal achievement).

[1245] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[1246] Specific operation: The server generates a feedback message based on the analysis results and sends it to the terminal along with the user ID.

[1247] Server: Sends feedback messages to devices.

[1248] Terminal: Display a feedback message to the user. Examples include "Your meal today is very balanced! Keep it up" or "Try to eat more protein."

[1249] (Application example 1)

[1250] 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."

[1251] In today's world, many people desire personalized meal plans to save valuable time and maintain a healthy lifestyle. However, implementing such plans requires specialized knowledge, and preparing meals yourself is time-consuming and laborious. Therefore, there is a need for a system that supports the implementation of individually customized meal plans. In particular, there is a need for a way to automate and simplify meal preparation.

[1252] 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.

[1253] In this invention, the server includes input means for inputting user information, transmission means for transmitting the input user information to the server, storage means for saving the user information, generation means for generating a personalized meal menu based on the saved user information, transmission means for transmitting the generated meal menu to the user terminal, recording means for recording the contents of meals actually eaten by the user, analysis means for analyzing the recorded meal contents and evaluating the user's progress, provision means for providing the user with advice and feedback based on the evaluation results, and ordering means for ordering meals based on the generated meal menu. This enables users to easily lead a healthy diet by obtaining a meal plan tailored to their health goals and automatically using a delivery service.

[1254] "User Information" refers to data such as a user's basic personal information, health status, dietary preferences, and allergy information.

[1255] "Input means" is a function that provides an interface for users to input their information into the application.

[1256] The "transmission means" is a function for transmitting the input user information to the server.

[1257] The "storage means" is a function that stores the transmitted user information in the server and keeps it accessible as needed.

[1258] The "generation means" is a function that generates a personalized meal menu based on stored user information.

[1259] "User terminal" refers to a device (smartphone, PC, etc.) that a user uses to access applications and manipulate information.

[1260] The "recording means" is a function that allows the user to input and save the details of the meals they actually ate.

[1261] The "analysis means" is a function that analyzes the recorded meal contents and evaluates the user's progress and health condition.

[1262] "Provision means" is a function for notifying users of advice and feedback based on the analysis results.

[1263] The "ordering means" is a function for ordering meals from a delivery service based on the generated meal menu.

[1264] A "server" is a central processing unit that stores user information, generates personalized meal menus, analyzes progress, provides feedback, etc.

[1265] The present invention provides a system that provides a user with a meal plan tailored to their health goals and supports them in carrying out the plan in a simple manner. Specific embodiments of the present invention will be described below.

[1266] The server includes an input means for inputting user information, a transmission means for transmitting the input user information to the server, a storage means for saving the user information, a generation means for generating a personalized meal menu based on the saved user information, a transmission means for transmitting the generated meal menu to the user terminal, a recording means for recording the meal contents actually eaten by the user, an analysis means for analyzing the recorded meal contents and evaluating the user's progress, a provision means for providing advice and feedback to the user based on the evaluation results, and an ordering means for ordering meals based on the generated meal menu.

[1267] Processing Overview

[1268] The server stores user information in a database and sends it to a generative AI model to generate a personalized meal menu, which is then ordered from a food delivery service and sent to the user's device. The system then records the meal the user has eaten, monitors their progress, and provides feedback as needed.

[1269] Hardware and software used

[1270] Server: A central processing unit that stores data, processes data, and generates AI models. Examples include AWS (Amazon Web Services) and Google Cloud Platform.

[1271] Generative AI model: A machine learning model for generating personalized meal menus. For example, we use RandomForestRegressor.

[1272] User device: A device that a user uses to operate an application, such as a smartphone, tablet, or PC. Examples include iOS and Android devices.

[1273] Database: A relational database to store user information and meal records. For example, MySQL or PostgreSQL is used.

[1274] Specific examples

[1275] As an example, consider a 35-year-old male user who has a peanut allergy and wants to lose 5 kg in three months. The user enters information into the application, such as his health information (height: 170 cm, weight: 68 kg) and his favorite foods (chicken and vegetables). This information is sent to the server and stored in a database.

[1276] The server then uses the stored information to generate a meal menu tailored to the user using a generative AI model. This menu is then automatically ordered from a food delivery service at the specified time. The generated menu is then sent to the user's device, where the user can view it.

[1277] Users record the food they eat in the application, and the information is sent to and stored on a server. The server periodically analyzes the recorded data, evaluates their progress, and provides appropriate advice and feedback to the user.

[1278] Prompt Sentence Examples

[1279] User information registration: "35-year-old male, height 170cm, weight 68kg, peanut allergy, likes chicken and vegetables, wants to lose 5kg in 3 months."

[1280] Delivery Order: "I'll order the suggested grilled chicken salad for lunch tomorrow."

[1281] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1282] Step 1:

[1283] The user launches the application and enters their health information (age, height, weight, allergy information, favorite foods, diet goals, etc.). This is the input data. The device sends this user information to the server. To process the input data, the information is converted into JSON format and sent to the server via an HTTP request. The server stores the received information in a database.

[1284] Step 2:

[1285] The server inputs user data into a generative AI model based on the saved user information to generate a personalized meal menu. The generative AI model uses a machine learning algorithm (e.g., RandomForestRegressor) to analyze the input data and calculate the optimal meal menu. The output is a personalized meal menu, which is returned to the server in JSON format.

[1286] Step 3:

[1287] The server sends the generated meal menu to the terminal. The terminal displays the received meal menu to the user. If the user wishes, they can place a delivery order directly based on the meal menu. The input data for ordering is the meal menu and the user's delivery address information. The ordering means uses this information to send an HTTP request to the food delivery service and saves the order information in a database.

[1288] Step 4:

[1289] The user records the food they actually ate. For example, they may record the difference between the food they actually ate and the menu suggested by the app. This record becomes input data. The device sends the recorded data to the server, which stores it in a database.

[1290] Step 5:

[1291] The server periodically retrieves the user's dietary records from the database and analyzes their progress. It uses analytical tools to calculate nutritional balance and goal achievement. This analysis involves calculations based on the nutritional data of the diet. The output is data showing the user's health status and diet progress.

[1292] Step 6:

[1293] The server provides feedback to the user based on the progress evaluation results. For example, it generates messages suggesting improvements to dietary habits or motivating users. The generated feedback and advice is output data, which is sent to the device using notification means. The device then displays the feedback message to the user.

[1294] Prompt Sentence Examples

[1295] User information registration: "35-year-old male, height 170cm, weight 68kg, peanut allergy, likes chicken and vegetables, wants to lose 5kg in 3 months."

[1296] Delivery Order: "I'll order the suggested grilled chicken salad for lunch tomorrow."

[1297] 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.

[1298] This invention relates to a system that manages a user's food log, supports progress monitoring, and combines it with an emotion engine that recognizes the user's emotional state. It uses a generative AI model to suggest personalized meal plans to users and the emotion engine to provide advice that takes into account the user's psychological state, helping users to lead a healthy diet.

[1299] System Overview

[1300] The system consists of the following main components:

[1301] 1. User interface (terminal): The interface through which the user inputs information.

[1302] 2. Database (server): This is a database that stores user information, meal records, and recipe data.

[1303] 3. Generative AI model (server): An AI that generates meal menus based on user information.

[1304] 4. Monitoring function (server): A function that analyzes the user's progress.

[1305] 5. Notification and feedback function (server): A function that provides advice and feedback to improve motivation.

[1306] 6. Emotion engine (server): A function that recognizes the user's emotions and dynamically adjusts the advice and feedback content based on them.

[1307] Program processing overview

[1308] 1. Entering and saving user information

[1309] User: Launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1310] Terminal: Sends the entered information to the server.

[1311] Server: Stores the received user information in a database and generates a user ID.

[1312] 2. Generate personalized meal menus

[1313] Users: Send meal requests to the app based on their preferences and goals.

[1314] Terminal: Sends request information to the server.

[1315] Server: Retrieves user information from the database and sends it to the generative AI model.

[1316] Generative AI model: Generates a personalized meal menu based on user information and returns it to the server.

[1317] Server: Sends the generated menu to the device.

[1318] Device: Shows the user a meal menu.

[1319] 3. Enter and save the user's food record

[1320] User: Record the food they actually ate in the app.

[1321] Device: Sends recorded meal data to the server.

[1322] Server: Stores the received meal data in a database.

[1323] 4. Monitoring progress and providing feedback

[1324] Server: Periodically retrieves the user's food record data from the database.

[1325] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[1326] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[1327] Server: Sends feedback to the device.

[1328] Terminal: Show feedback to the user.

[1329] 5. Emotion recognition and feedback adjustment by emotion engine

[1330] Emotion engine: Recognizes the user's emotional state based on user input, behavioral data, or sensors.

[1331] Server: Obtains recognized emotion data and sends it to the generative AI model.

[1332] Generative AI model: Generates personalized meal menus that take emotional data into account.

[1333] Notification and feedback function: Dynamically adjusts advice and feedback content based on emotional data and provides it to users.

[1334] Server: Sends adjusted feedback to the device.

[1335] Device: Show users emotional feedback.

[1336] Specific use cases

[1337] As an example, consider a 52-year-old male user. This man has moderate diabetes and wants to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the app. Based on this information, the system uses a generative AI model to generate a personalized, nutritionally balanced, diabetes-friendly meal plan. Specific menu suggestions include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[1338] Furthermore, the emotion engine analyzes the user's emotions. For example, if the user is feeling stressed, it will incorporate suggestions for ingredients and dishes that have a relaxing effect. Based on the evaluation results, it will also provide advice on how to manage stress and messages to increase motivation. By doing this and continuing to enter food records, the system will monitor the user's progress and provide appropriate advice, helping them live a healthy life while working towards achieving their goals. In this way, users can enjoy individually customized meal plans and advice that takes their psychological state into account, enabling them to effectively manage their health.

[1339] The processing flow will be explained below.

[1340] Step 1:

[1341] The user launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1342] Step 2:

[1343] The terminal transmits the entered user information to the server.

[1344] Step 3:

[1345] The server stores the received user information in a database and generates a user ID.

[1346] Step 4:

[1347] Users submit meal requests to the app based on their preferences and goals.

[1348] Step 5:

[1349] The terminal sends the request information to the server.

[1350] Step 6:

[1351] The server retrieves user information from the database and sends it to the generative AI model.

[1352] Step 7:

[1353] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[1354] Step 8:

[1355] The server sends the generated menu to the terminal.

[1356] Step 9:

[1357] The device displays a meal menu to the user.

[1358] Step 10:

[1359] The app records the meals that users actually eat.

[1360] Step 11:

[1361] The device transmits the recorded meal data to the server.

[1362] Step 12:

[1363] The server stores the received meal data in a database.

[1364] Step 13:

[1365] The server periodically retrieves the user's food record data from the database.

[1366] Step 14:

[1367] The monitoring function analyzes the data acquired and evaluates the user's progress, calculating nutritional balance and goal achievement.

[1368] Step 15:

[1369] The notification and feedback function generates advice and motivational messages for users based on the evaluation results.

[1370] Step 16:

[1371] The server sends the feedback to the device.

[1372] Step 17:

[1373] The device displays feedback to the user.

[1374] Step 18:

[1375] The emotion engine recognizes the user's emotional state based on user input, behavioral data, or sensors.

[1376] Step 19:

[1377] The server sends the recognized emotion data to the generative AI model.

[1378] Step 20:

[1379] The generative AI model takes into account the emotional data to generate a personalized meal menu and returns it to the server.

[1380] Step 21:

[1381] The notification and feedback function dynamically adjusts the content of advice and feedback based on emotional data.

[1382] Step 22:

[1383] The server sends the adjusted feedback to the device.

[1384] Step 23:

[1385] The device displays emotional feedback to the user.

[1386] Example 2

[1387] 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."

[1388] In today's modern living environment, it is important to properly manage one's diet for health maintenance, weight loss, and specific disease management. However, it is difficult to create a meal menu based on one's own judgment and manage one's diet based on one's eating habits and health condition, and conventional systems have limited accuracy and personalization. Furthermore, the selection of healthy foods and management of motivation based on the user's emotional state tend to be overlooked. A system that can solve these issues and achieve more effective and personalized health management is needed.

[1389] 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.

[1390] In this invention, the server includes a storage means for storing user information, a generation means for generating a personalized meal menu based on the stored user information, and a transmission means for transmitting the generated meal menu to the user terminal. This makes it possible to generate and provide a personalized meal menu for each user. Furthermore, an emotion recognition means is used to recognize the user's emotional state and dynamically adjust the feedback content based on that information. This enables health management that takes the user's psychological state into consideration, and realizes the provision of more effective advice and feedback.

[1391] "User information" refers to personal data entered by the user, such as name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1392] "Input means" refers to an interface through which a user inputs their information into the system.

[1393] "Transmission means" refers to a device or software that has the function of transmitting input user information and request data to the server.

[1394] The "storage means" is a mechanism for storing the received user information in a storage device such as a database.

[1395] The "generation means" refers to an algorithm or AI model that generates a personalized meal menu based on stored user information.

[1396] "Recording means" refers to a device or software that has the function of recording the food that the user actually ate.

[1397] "Analysis means" refers to a method or device that analyzes the recorded dietary content and evaluates the user's progress.

[1398] "Provision means" refers to devices or software that have the function of providing advice and feedback to users based on the analysis results.

[1399] An "emotion recognition means" is a mechanism that recognizes the user's emotional state and adjusts the feedback content based on that information.

[1400] A "user terminal" is a device that a user uses to access the system, such as an information processing device such as a smartphone or tablet.

[1401] This invention relates to a system that generates personalized meal menus based on user information and supports health management taking into account the user's emotional state. The system consists of the following main components: a user interface, a database, a generative AI model, a monitoring function, a notification and feedback function, and an emotion engine.

[1402] User Interface (Terminal)

[1403] The user interface is a device through which the user inputs information. Specifically, a smartphone or tablet is used. The user inputs their name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked ingredients. This information is sent to the server via the device's app.

[1404] server

[1405] The server stores the received user information in a database, generates a user ID, and provides the user information to the generative AI model. The server then sends the generated meal menu to the device, which is used to analyze the user's meal record data and emotional data.

[1406] Database

[1407] The database serves to store user information, meal records, and recipe data, which is accessed by the server as needed to generate personalized meal menus and monitor the user's progress.

[1408] Generative AI Models

[1409] The generative AI model uses algorithms and AI techniques to generate personalized meal plans based on user information, taking into account the user's nutritional balance and health status.

[1410] For example, the following prompt is sent to the generative AI model:

[1411] User Information:

[1412] Name: Ichiro Tanaka

[1413] Age: 52

[1414] Gender: Male

[1415] Height: 170cm

[1416] Weight: 80kg

[1417] Diet goal: Lose 5kg in 3 months

[1418] Allergy Information: Nuts

[1419] Favorite ingredients: chicken, fish

[1420] Disliked food: Eggplant

[1421] Use this information to generate a personalized diabetes-friendly meal plan.

[1422] Monitoring Function

[1423] The monitoring function is used to analyze and evaluate the user's progress. Specifically, it analyzes food record data obtained from the database and evaluates the user's nutritional balance and goal achievement.

[1424] Notification and feedback function

[1425] The notification and feedback function provides users with advice and motivational messages based on the analysis results. These messages are created based on the user's nutritional status and progress.

[1426] Emotion Engine

[1427] The emotion engine is a function that recognizes the user's emotional state and dynamically adjusts the feedback content. By analyzing the user's emotional state based on input data, behavioral data, and information obtained from sensors, and reflecting this in the generative AI model, it provides more appropriate advice and feedback.

[1428] The system aims to provide users with individually customized meal plans and advice that takes into account their psychological state, helping them to effectively manage their health.

[1429] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1430] Step 1: Enter your user information

[1431] User: The user launches the app and enters personal information such as name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked foods.

[1432] Input: Data that a user enters into a form in your app.

[1433] Specific action: A user enters information into an app on a smartphone or tablet.

[1434] Step 2: Submit user information

[1435] Terminal: The terminal sends the entered user information to the server. This transmission is done in a data-encrypted state.

[1436] Input: User information entered in step 1.

[1437] Output: User information sent to the server.

[1438] What happens: The device generates an API request in the background and sends data to the server.

[1439] Step 3: Save user information

[1440] Server: The server stores the received user information in a database and generates a user ID.

[1441] Input: User information sent from the device.

[1442] Output: User information and user ID stored in the database.

[1443] What happens: The server executes an INSERT statement in the database, saving the user information in a new row.

[1444] Step 4: Request a meal

[1445] User: A user requests a personalized meal within the app.

[1446] Input: Meal request and user ID.

[1447] Output: A request to generate a meal menu.

[1448] What happens: The user clicks the "Generate Meal Menu" button.

[1449] Step 5: Feed the generative AI model with data

[1450] Server: The server retrieves user information from a database and sends it to the generative AI model.

[1451] Input: Food request and user information.

[1452] Output: A prompt to the generative AI model.

[1453] Specific operation: The server executes an SQL SELECT statement to obtain user information data and provides a prompt statement to the generative AI model.

[1454] Step 6: Generate the meal menu

[1455] Generative AI model: The generative AI model generates personalized meal menus based on user information.

[1456] Input: A prompt for the generative AI model.

[1457] Output: A personalized meal menu.

[1458] How it works: The AI ​​model uses data processing and algorithms to generate a meal menu and returns the results to the server.

[1459] Step 7: Submit your meal menu

[1460] Server: The server sends the generated meal menu to the user's device.

[1461] Input: A personalized meal menu from a generative AI model.

[1462] Output: Meal menu sent to user device.

[1463] Specific operation: The server sends the meal menu in JSON format to the device.

[1464] Step 8: View the food menu

[1465] Terminal: The terminal displays the received meal menu to the user.

[1466] Input: A meal menu sent by the server.

[1467] Output: The meal menu displayed to the user.

[1468] Specific behavior: The device displays the received meal menu on the user interface.

[1469] Step 9: Enter your food log

[1470] User: The user records the food they eat in the app.

[1471] Input: Food data logged by the user.

[1472] Output: Food data entered into the app.

[1473] What happens: The user enters their meal details into the app's "Food Log" page.

[1474] Step 10: Submit your food log

[1475] Device: The device sends the recorded meal data to the server.

[1476] Input: Meal data entered by the user.

[1477] Output: Meal data sent to the server.

[1478] Specific operation: The device sends meal data to the server in the background.

[1479] Step 11: Keep a food diary

[1480] Server: The server stores the received meal data in a database.

[1481] Input: Meal data sent from the device.

[1482] Output: Food records stored in a database.

[1483] Specific behavior: The server executes an INSERT statement in the database, saving the meal record in a new row.

[1484] Step 12: Monitor progress

[1485] Server: The server periodically retrieves the user's food record data from the database.

[1486] Input: Food record data from the database.

[1487] Output: The data obtained for analysis.

[1488] Specific behavior: The server extracts the data using an SQL SELECT statement.

[1489] Step 13: Analyze your progress

[1490] Monitoring function: Analyzes the acquired data and evaluates the user's progress.

[1491] Input: Food log data.

[1492] Output: Evaluation results of nutritional balance and goal achievement.

[1493] What it does: The monitoring feature uses data analysis algorithms to assess your nutrition and progress.

[1494] Step 14: Generate feedback

[1495] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[1496] Input: Progress assessment results.

[1497] Output: The feedback message.

[1498] What it does: The feedback function generates a message using a template.

[1499] Step 15: Submit your feedback

[1500] Server: The server sends the generated feedback to the device.

[1501] Input: Message via notification and feedback function.

[1502] Output: Feedback sent to the user device.

[1503] Specific operation: The server sends feedback data in JSON format to the device.

[1504] Step 16: Viewing Feedback

[1505] Terminal: The terminal displays feedback to the user.

[1506] Input: The feedback message sent by the server.

[1507] Output: The feedback displayed to the user.

[1508] What happens: Your device will display feedback as a pop-up notification or message.

[1509] Step 17: Recognizing your emotional state

[1510] Emotion engine: Recognizes the user's emotional state based on user input, behavioral data, or sensors.

[1511] Input: User input data, behavioral data, and sensor information.

[1512] Output: Recognized emotion data.

[1513] How it works: The emotion engine uses analytical algorithms to recognize emotional states.

[1514] Step 18: Adjusting Emotional Feedback

[1515] Server: The server acquires the recognized emotion data and sends it to the generative AI model.

[1516] Input: Emotion data.

[1517] Output: Emotion data to a generative AI model.

[1518] Specific operation: The server sends emotion data to the generative AI model.

[1519] Step 19: Emotion-Based Menu Generation

[1520] Generative AI model: Generates personalized meal menus that take emotional data into account.

[1521] Input: Emotion data.

[1522] Output: A personalized meal menu that takes emotions into account.

[1523] How it works: The AI ​​model generates a meal menu based on emotional data.

[1524] Step 20: Generate emotion-based feedback

[1525] Notification and feedback function: Dynamically adjusts advice and feedback content based on emotional data and provides it to users.

[1526] Enter: an emotionally informed meal menu.

[1527] Output: Regulated feedback.

[1528] Specific behavior: The feedback function generates advice that takes emotional data into account.

[1529] Step 21: Send and view sentiment-based feedback

[1530] Server: Sends adjusted feedback to the device.

[1531] Input: Calibrated feedback.

[1532] Output: Feedback sent to the user device.

[1533] Device: Show users emotional feedback.

[1534] Specific behavior: The server sends feedback data in JSON format to the device, and the device displays the feedback as a popup notification or message.

[1535] (Application example 2)

[1536] 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."

[1537] In modern society, support systems for individual users to lead healthy eating habits are important. However, conventional systems simply manage users' food records and progress, but do not provide dynamic meal plans or advice based on the user's emotional state. As a result, advice that does not take the user's psychological state into account can make it difficult to maintain motivation and make it difficult to achieve long-term goals.

[1538] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an emotion recognition means that recognizes the emotional state of the user and adjusts the advice and feedback content based on the emotion, a generation means that dynamically generates a personalized meal menu based on the emotional state and progress, and a transmission means that transmits the generated menu to the user terminal. This makes it possible to provide individual advice and meal plans that take the user's psychological state into consideration, allowing the user to work more effectively toward achieving their health goals.

[1539] "User Information" refers to basic data about each user of the system, such as name, age, gender, height, weight, health status, dietary preferences and allergy information.

[1540] "Input means" refers to the interface that allows users to input personal information, food records, emotional state, etc. into the system.

[1541] "Transmission means" refers to a function for transmitting input data to a server.

[1542] "Storage means" refers to the mechanism for storing user input information and recorded data on the server.

[1543] "Generation means" refers to the AI ​​model or algorithm used to generate personalized meal menus based on stored user information.

[1544] "Recording means" refers to a function that allows a user to record the meals they have actually eaten and their contents.

[1545] "Analysis means" refers to the component within the system that analyzes and evaluates the recorded diet and the user's progress.

[1546] "Means of provision" refers to the function of providing advice and feedback to users based on the analysis results.

[1547] "Emotion recognition means" is a system function that recognizes the emotional state of the user at that time from the data and behavioral data entered by the user.

[1548] "User Device" means a device (e.g., a smartphone, tablet, etc.) that a User uses to interface with the System.

[1549] This invention is a system that manages a user's dietary records, progress, and emotional state in an integrated manner through a smartphone application, and provides personalized advice and feedback based on this information.

[1550] System Overview

[1551] This system consists of the following main components:

[1552] 1. User Interface (Terminal)

[1553] Users use a smartphone application to enter necessary information, including name, age, gender, height, weight, health goals, allergy information, and food preferences.

[1554] 2. Database (server)

[1555] The server stores user information, food records, and emotional states, as well as recipe data and nutritional information.

[1556] 3. Generative AI model (server)

[1557] The server-based generative AI model uses user information to generate personalized meal menus, including specific meal plans based on the user's nutritional balance and health goals.

[1558] 4. Emotion Recognition Engine (Server)

[1559] The emotion recognition engine recognizes the emotional state from input data and user behavioral data, and dynamically adjusts the content of advice and feedback based on that information.

[1560] 5. Notification and feedback function (server)

[1561] The server uses the analysis results to provide users with advice and feedback, including motivational messages about health and diet.

[1562] Processing Description

[1563] In this system, processing is carried out in the following procedure.

[1564] 1. Entering and saving user information

[1565] Users launch the smartphone application and enter their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1566] The entered information is sent from the smartphone to a server, which stores it in a database.

[1567] 2. Generating a meal menu

[1568] Users submit meal requests to the application based on their preferences and goals.

[1569] The server retrieves user information from the database and sends it to the generative AI model.

[1570] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[1571] The server sends the generated menu to the smartphone and presents it to the user.

[1572] 3. Enter and save your food record

[1573] Users record the food they eat in the application.

[1574] The recorded meal data is sent from the smartphone to a server and stored in a database.

[1575] 4. Progress monitoring and feedback

[1576] The server periodically retrieves the user's food record data from the database and analyzes it using the monitoring function.

[1577] Based on the analysis results, the system evaluates the user's nutritional balance and goal achievement, and generates advice and motivational messages using notification and feedback functions.

[1578] The server sends the feedback to the smartphone and displays it to the user.

[1579] 5. Emotion recognition and feedback regulation

[1580] The emotion engine recognizes the emotional state from user input and behavioral data.

[1581] The server retrieves the recognized emotion data and sends it to the generative AI model.

[1582] The generative AI model generates personalized meal menus that take emotional data into account.

[1583] The notification and feedback function dynamically adjusts the content of advice and feedback based on emotional data and provides it to users.

[1584] Specific examples

[1585] As a concrete example, consider a 30-year-old male user whose goal is to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the application. Based on this information, the system uses a generative AI model to generate a personalized meal plan that takes nutritional balance into account.

[1586] Use the following example prompt to request a meal plan from a generative AI model:

[1587] User: 30-year-old male

[1588] Age: 30

[1589] Gender: Male

[1590] Height: 175cm

[1591] Weight: 70kg

[1592] Goal: Lose 5kg in 3 months

[1593] Allergens: nuts

[1594] Favorite foods: Fish, vegetables

[1595] Use this information to suggest a personalized meal plan.

[1596] The generated meal plan may include specific menus such as "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled fish" for dinner.

[1597] Furthermore, the emotion engine analyzes the user's emotions and, if the user is feeling stressed, incorporates suggestions for ingredients and dishes that have a relaxing effect into the recommendations. Based on the evaluation results, the system also provides advice on how to deal with stress and messages to increase motivation. By doing this and continuing to enter food records, the system can monitor the user's progress and provide appropriate advice, helping them live a healthy life while working towards achieving their goals.

[1598] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1599] Step 1:

[1600] A user launches a smartphone application and enters information such as name, age, gender, height, weight, diet goals, allergy information, favorite and disliked foods, etc. This information is sent from the smartphone to a server. The server stores the received information in a database and generates a user ID. The input data is in text format, and the output is the creation of a database entry.

[1601] Step 2:

[1602] A user submits a meal menu request based on their health goals through a smartphone application. The device sends this request to a server. The server retrieves user information from a database and sends it to a generative AI model. The generative AI model uses this information to generate a personalized meal menu and returns it to the server. The generated menu is in text format, and the output is a specific meal plan.

[1603] Step 3:

[1604] The server sends the generated personalized meal menu to the smartphone device. The device receives this information and displays it to the user. The user confirms the displayed meal menu and proceeds to the next step. The input data is the text information of the generated menu, and the output is the display on the user device.

[1605] Step 4:

[1606] The user records the details of the meal they actually ate in a smartphone application. The device then sends this record to a server. The server then stores the received meal data in a database. The input data is the user's meal record (text information), and the output is stored in the database.

[1607] Step 5:

[1608] The server periodically retrieves the user's food record data from the database. This data is analyzed using a monitoring function to evaluate the user's progress. The analysis results include nutritional balance and goal achievement. The input data is the food record, and the output is an evaluation of nutritional balance and goal achievement.

[1609] Step 6:

[1610] The server executes a notification and feedback function that generates advice and motivational messages based on the evaluation results. The generated feedback is sent to the user's smartphone in the form of a notification. The input data is the evaluation results, and the output is advice and feedback messages.

[1611] Step 7:

[1612] The emotion engine analyzes the user's input data and behavioral patterns to recognize their emotional state at that time. The server sends this emotional state to the generative AI model, which then takes the emotional data into account to regenerate a personalized meal menu based on the user's psychological state. The input data is the emotional state, and the output is the regenerated meal menu.

[1613] Step 8:

[1614] The notification and feedback function dynamically adjusts the content of advice and feedback that takes into account the user's emotional state and sends it to the smartphone. The user can receive this feedback and reflect it in their next actions. The input data is emotion, progress, and feedback content, and the output is a dynamically adjusted feedback message.

[1615] Examples of prompt statements

[1616] User: 30-year-old male

[1617] Age: 30

[1618] Gender: Male

[1619] Height: 175cm

[1620] Weight: 70kg

[1621] Goal: Lose 5kg in 3 months

[1622] Allergens: nuts

[1623] Favorite foods: Fish, vegetables

[1624] Use this information to suggest a personalized meal plan.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] [Fourth embodiment]

[1629] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1630] 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.

[1631] 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).

[1632] 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.

[1633] 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.

[1634] 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).

[1635] 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.

[1636] 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.

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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."

[1642] This invention relates to a system that manages a user's food log and helps monitor progress. The system uses a generative AI model to suggest personalized meal plans to users, helping them to adopt healthy eating habits.

[1643] System Overview

[1644] The system consists of the following main components:

[1645] 1. User interface (terminal): The interface through which the user inputs information.

[1646] 2. Database (server): This is a database that stores user information, meal records, and recipe data.

[1647] 3. Generative AI model (server): An AI that generates meal menus based on user information.

[1648] 4. Monitoring function (server): A function that analyzes the user's progress.

[1649] 5. Notification and feedback function (server): A function that provides advice and feedback to improve motivation.

[1650] Program processing overview

[1651] 1. Entering and saving user information

[1652] User: Launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1653] Terminal: Sends the entered information to the server.

[1654] Server: Stores the received user information in a database and generates a user ID.

[1655] 2. Generate personalized meal menus

[1656] Users: Send meal requests to the app based on their preferences and goals.

[1657] Terminal: Sends request information to the server.

[1658] Server: Retrieves user information from the database and sends it to the generative AI model.

[1659] Generative AI model: Generates a personalized meal menu based on user information and returns it to the server.

[1660] Server: Sends the generated menu to the device.

[1661] Device: Shows the user a meal menu.

[1662] 3. Enter and save the user's food record

[1663] User: Record the food they actually ate in the app.

[1664] Device: Sends recorded meal data to the server.

[1665] Server: Stores the received meal data in a database.

[1666] 4. Monitoring progress and providing feedback

[1667] Server: Periodically retrieves the user's food record data from the database.

[1668] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[1669] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[1670] Server: Sends feedback to the device.

[1671] Terminal: Show feedback to the user.

[1672] Specific use cases

[1673] As an example, consider a 52-year-old male user. This man has moderate diabetes and wants to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the app. Based on this information, the system uses a generative AI model to generate a personalized, nutritionally balanced, diabetes-friendly meal plan. Specific menu suggestions include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[1674] By doing this and continuing to enter food records, the system will monitor progress and provide appropriate advice to help users live a healthy life toward achieving their goals. In this way, users can enjoy individually customized meal plans and effectively manage their health.

[1675] The processing flow will be explained below.

[1676] Step 1:

[1677] The user launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1678] Step 2:

[1679] The terminal transmits the entered user information to the server.

[1680] Step 3:

[1681] The server stores the received user information in a database and generates a user ID.

[1682] Step 4:

[1683] Users submit meal requests to the app based on their preferences and goals.

[1684] Step 5:

[1685] The terminal sends the request information to the server.

[1686] Step 6:

[1687] The server retrieves user information from the database and sends it to the generative AI model.

[1688] Step 7:

[1689] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[1690] Step 8:

[1691] The server sends the generated menu to the terminal.

[1692] Step 9:

[1693] The device displays a meal menu to the user.

[1694] Step 10:

[1695] The app records the meals that users actually eat.

[1696] Step 11:

[1697] The device transmits the recorded meal data to the server.

[1698] Step 12:

[1699] The server stores the received meal data in a database.

[1700] Step 13:

[1701] The server periodically retrieves the user's food record data from the database.

[1702] Step 14:

[1703] The monitoring function analyzes the data acquired and evaluates the user's progress, calculating nutritional balance and goal achievement.

[1704] Step 15:

[1705] The notification and feedback function generates advice and motivational messages for users based on the evaluation results.

[1706] Step 16:

[1707] The server sends the feedback to the device.

[1708] Step 17:

[1709] The device displays feedback to the user.

[1710] Example 1

[1711] 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."

[1712] In today's busy lifestyles, it is challenging for individuals to select appropriate meal plans while taking into account their health status and nutritional balance. It is also difficult for users to accurately manage their own dietary records and consistently monitor their progress. Existing systems lack the ability to provide personalized meal plans or continuous advice based on dietary records, making it difficult for users to maintain a healthy lifestyle.

[1713] 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.

[1714] In this invention, the server includes input means for inputting user information, transmission means for transmitting the input user information to the data processing device, storage means for saving the user information, generation means for generating a personalized meal menu based on the saved user information, transmission means for transmitting the generated meal menu to the user terminal, recording means for recording the meal contents actually consumed by the user, analysis means for analyzing the recorded meal contents and evaluating the user's progress, and provision means for providing the user with advice and feedback based on the evaluation results. This allows the user to easily obtain a personalized meal menu that takes their nutritional balance into consideration, and to consistently record their meals and monitor their progress.

[1715] "Input means" refers to an interface for users to input information, including forms for inputting user attributes and goals.

[1716] "Transmission means" refers to a function for transmitting information input by a user to a data processing device, and includes a process for transferring data via a network.

[1717] "Storage means" refers to a database or storage system for storing transmitted user information and meal records.

[1718] "Generative means" refers to a process, including an algorithm or generative AI model, for generating personalized meal menus based on stored user information.

[1719] "User terminal" means a device through which a user uses the interface, and includes hardware such as a smartphone, tablet, or computer.

[1720] "Recording means" refers to the interface or application function that allows the user to input the details of the food they have actually consumed and store that information in a database.

[1721] "Analysis means" includes algorithms and analytical processes for analyzing the recorded dietary content and evaluating nutritional balance and goal achievement.

[1722] "Delivery means" refers to the process or feedback system for generating advice or feedback to users based on the analysis results and notifying them.

[1723] MODE FOR CARRYING OUT THE INVENTION

[1724] This invention relates to a system that helps users manage their dietary records and monitor their progress in order to lead a healthy diet. The system utilizes a generative AI model to suggest personalized meal plans to users, helping them improve their dietary habits in line with their individual lifestyles.

[1725] System configuration

[1726] The system consists of the following main components:

[1727] 1. User Interface (Terminal): This is the interface through which users input information and view the generated meal menu and feedback. Devices such as smartphones, tablets, and computers are used.

[1728] 2. Database (server): A system for storing user information, meal records, and recipe data, and saving data required for subsequent processing.

[1729] 3. Generative AI model (server): An algorithm that generates individually customized meal menus based on user information.

[1730] 4. Monitoring function (server): This function analyzes the user's food records and evaluates their progress.

[1731] 5. Notification and feedback function (server): A system for providing users with advice and motivational feedback.

[1732] Program processing overview

[1733] The system program performs the following processing.

[1734] 1. Entering and saving user information

[1735] Users enter their name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked ingredients into the app's input screen. The device sends the entered information to the server, which stores it in a database. At this time, a unique user ID is generated and stored along with the information.

[1736] 2. Generate personalized meal menus

[1737] The user requests a meal menu and sends the request information to the server via the device. The server retrieves the user information from the database and sends it to the generative AI model to generate a customized meal menu. The generated menu is then sent from the server to the device, which displays it to the user.

[1738] 3. Enter and save the user's food record

[1739] The user records the details of the food they actually ate in the app, and the device sends the recorded data to the server, which then stores the received food data in a database.

[1740] 4. Monitoring progress and providing feedback

[1741] The server periodically retrieves the user's food records from the database and analyzes them using the monitoring function. Based on the analysis results, the notification and feedback function generates advice and motivational messages for the user, which are then sent to the user's device and displayed.

[1742] Specific examples

[1743] For example, consider a 52-year-old male user with moderate diabetes who wants to lose weight. He enters his weight, height, age, and diet goal (lose 5 kg in 3 months) into the app. The system uses this information to create a personalized, nutritionally balanced, diabetes-friendly meal plan using a generative AI model. For example, specific menu suggestions might include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[1744] Prompt Sentence Examples

[1745] "52-year-old male with moderate diabetes, diet goal: lose 5 kg in 3 months"

[1746] This allows users to easily get personalized meal plans that take their nutritional balance into account, and to consistently record their meals and monitor their progress.

[1747] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1748] Step 1: Enter and save user information

[1749] User: Launches the app and enters their name, age, gender, height, weight, diet goal, allergy information, and favorite and disliked ingredients.

[1750] Input: User's basic information (e.g., name, age, gender) and individual parameters (e.g., diet goals, allergy information).

[1751] Terminal: Converts the input information into an appropriate data format, such as JSON, and sends it to the server.

[1752] Output: A data packet containing the user's basic information and individual parameters.

[1753] Server: When saving the received data packet in the user information table of the database, generate a unique user ID and record it in the database.

[1754] What happens: The server uses an INSERT command to save the user information in the database and retrieves the generated user ID.

[1755] Step 2: Generate a personalized meal menu

[1756] User: Presses a button in the app to request a meal.

[1757] Input: User ID and meal request (e.g., preferred ingredients, meal purpose).

[1758] Terminal: The user ID and request details are sent together to the server.

[1759] Output: A data packet containing the request information.

[1760] Server: Retrieves user information from the database and sends it along with the request information to the generative AI model.

[1761] Input: User information (e.g. age, gender, diet goal) and request information.

[1762] Generative AI model: Generates specific meal plans based on input information. It uses algorithms to process the data and output optimal meal plans.

[1763] Output: A personalized meal menu.

[1764] Server: Sends the meal menu to the device.

[1765] Device: Display the received meal menu to the user.

[1766] Specific operation: The server receives the response from the generative AI model, saves the meal menu along with the user ID, and sends it to the user's device.

[1767] Step 3: Enter and save the user's food record

[1768] User: Record the food they actually ate in the app.

[1769] Input: Meal details (e.g., ingredients eaten, amount, time).

[1770] Device: Sends recorded meal data to the server.

[1771] Output: A data packet containing the food log data.

[1772] Server: Stores the received meal data in the meal record table in the database.

[1773] Specific operation: The server saves the meal record in the database using the INSERT command and sends a confirmation message to the terminal that the record has been saved.

[1774] Step 4: Monitor progress and provide feedback

[1775] Server: Periodically retrieves the user's food record data from the database.

[1776] Input: Food record data (e.g., what you ate in the past week).

[1777] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[1778] Output: Analysis results (e.g., nutritional balance, goal achievement).

[1779] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[1780] Specific operation: The server generates a feedback message based on the analysis results and sends it to the terminal along with the user ID.

[1781] Server: Sends feedback messages to devices.

[1782] Terminal: Display a feedback message to the user. Examples include "Your meal today is very balanced! Keep it up" or "Try to eat more protein."

[1783] (Application example 1)

[1784] 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."

[1785] In today's world, many people desire personalized meal plans to save valuable time and maintain a healthy lifestyle. However, implementing such plans requires specialized knowledge, and preparing meals yourself is time-consuming and laborious. Therefore, there is a need for a system that supports the implementation of individually customized meal plans. In particular, there is a need for a way to automate and simplify meal preparation.

[1786] 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.

[1787] In this invention, the server includes input means for inputting user information, transmission means for transmitting the input user information to the server, storage means for saving the user information, generation means for generating a personalized meal menu based on the saved user information, transmission means for transmitting the generated meal menu to the user terminal, recording means for recording the contents of meals actually eaten by the user, analysis means for analyzing the recorded meal contents and evaluating the user's progress, provision means for providing the user with advice and feedback based on the evaluation results, and ordering means for ordering meals based on the generated meal menu. This enables users to easily lead a healthy diet by obtaining a meal plan tailored to their health goals and automatically using a delivery service.

[1788] "User Information" refers to data such as a user's basic personal information, health status, dietary preferences, and allergy information.

[1789] "Input means" is a function that provides an interface for users to input their information into the application.

[1790] The "transmission means" is a function for transmitting the input user information to the server.

[1791] The "storage means" is a function that stores the transmitted user information in the server and keeps it accessible as needed.

[1792] The "generation means" is a function that generates a personalized meal menu based on stored user information.

[1793] "User terminal" refers to a device (smartphone, PC, etc.) that a user uses to access applications and manipulate information.

[1794] The "recording means" is a function that allows the user to input and save the details of the meals they actually ate.

[1795] The "analysis means" is a function that analyzes the recorded meal contents and evaluates the user's progress and health condition.

[1796] "Provision means" is a function for notifying users of advice and feedback based on the analysis results.

[1797] The "ordering means" is a function for ordering meals from a delivery service based on the generated meal menu.

[1798] A "server" is a central processing unit that stores user information, generates personalized meal menus, analyzes progress, provides feedback, etc.

[1799] The present invention provides a system that provides a user with a meal plan tailored to their health goals and supports them in carrying out the plan in a simple manner. Specific embodiments of the present invention will be described below.

[1800] The server includes an input means for inputting user information, a transmission means for transmitting the input user information to the server, a storage means for saving the user information, a generation means for generating a personalized meal menu based on the saved user information, a transmission means for transmitting the generated meal menu to the user terminal, a recording means for recording the meal contents actually eaten by the user, an analysis means for analyzing the recorded meal contents and evaluating the user's progress, a provision means for providing advice and feedback to the user based on the evaluation results, and an ordering means for ordering meals based on the generated meal menu.

[1801] Processing Overview

[1802] The server stores user information in a database and sends it to a generative AI model to generate a personalized meal menu, which is then ordered from a food delivery service and sent to the user's device. The system then records the meal the user has eaten, monitors their progress, and provides feedback as needed.

[1803] Hardware and software used

[1804] Server: A central processing unit that stores data, processes data, and generates AI models. Examples include AWS (Amazon Web Services) and Google Cloud Platform.

[1805] Generative AI model: A machine learning model for generating personalized meal menus. For example, we use RandomForestRegressor.

[1806] User device: A device that a user uses to operate an application, such as a smartphone, tablet, or PC. Examples include iOS and Android devices.

[1807] Database: A relational database to store user information and meal records. For example, MySQL or PostgreSQL is used.

[1808] Specific examples

[1809] As an example, consider a 35-year-old male user who has a peanut allergy and wants to lose 5 kg in three months. The user enters information into the application, such as his health information (height: 170 cm, weight: 68 kg) and his favorite foods (chicken and vegetables). This information is sent to the server and stored in a database.

[1810] The server then uses the stored information to generate a meal menu tailored to the user using a generative AI model. This menu is then automatically ordered from a food delivery service at the specified time. The generated menu is then sent to the user's device, where the user can view it.

[1811] Users record the food they eat in the application, and the information is sent to and stored on a server. The server periodically analyzes the recorded data, evaluates their progress, and provides appropriate advice and feedback to the user.

[1812] Prompt Sentence Examples

[1813] User information registration: "35-year-old male, height 170cm, weight 68kg, peanut allergy, likes chicken and vegetables, wants to lose 5kg in 3 months."

[1814] Delivery Order: "I'll order the suggested grilled chicken salad for lunch tomorrow."

[1815] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1816] Step 1:

[1817] The user launches the application and enters their health information (age, height, weight, allergy information, favorite foods, diet goals, etc.). This is the input data. The device sends this user information to the server. To process the input data, the information is converted into JSON format and sent to the server via an HTTP request. The server stores the received information in a database.

[1818] Step 2:

[1819] The server inputs user data into a generative AI model based on the saved user information to generate a personalized meal menu. The generative AI model uses a machine learning algorithm (e.g., RandomForestRegressor) to analyze the input data and calculate the optimal meal menu. The output is a personalized meal menu, which is returned to the server in JSON format.

[1820] Step 3:

[1821] The server sends the generated meal menu to the terminal. The terminal displays the received meal menu to the user. If the user wishes, they can place a delivery order directly based on the meal menu. The input data for ordering is the meal menu and the user's delivery address information. The ordering means uses this information to send an HTTP request to the food delivery service and saves the order information in a database.

[1822] Step 4:

[1823] The user records the food they actually ate. For example, they may record the difference between the food they actually ate and the menu suggested by the app. This record becomes input data. The device sends the recorded data to the server, which stores it in a database.

[1824] Step 5:

[1825] The server periodically retrieves the user's dietary records from the database and analyzes their progress. It uses analytical tools to calculate nutritional balance and goal achievement. This analysis involves calculations based on the nutritional data of the diet. The output is data showing the user's health status and diet progress.

[1826] Step 6:

[1827] The server provides feedback to the user based on the progress evaluation results. For example, it generates messages suggesting improvements to dietary habits or motivating users. The generated feedback and advice is output data, which is sent to the device using notification means. The device then displays the feedback message to the user.

[1828] Prompt Sentence Examples

[1829] User information registration: "35-year-old male, height 170cm, weight 68kg, peanut allergy, likes chicken and vegetables, wants to lose 5kg in 3 months."

[1830] Delivery Order: "I'll order the suggested grilled chicken salad for lunch tomorrow."

[1831] 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.

[1832] This invention relates to a system that manages a user's food log, supports progress monitoring, and combines it with an emotion engine that recognizes the user's emotional state. It uses a generative AI model to suggest personalized meal plans to users and the emotion engine to provide advice that takes into account the user's psychological state, helping users to lead a healthy diet.

[1833] System Overview

[1834] The system consists of the following main components:

[1835] 1. User interface (terminal): The interface through which the user inputs information.

[1836] 2. Database (server): This is a database that stores user information, meal records, and recipe data.

[1837] 3. Generative AI model (server): An AI that generates meal menus based on user information.

[1838] 4. Monitoring function (server): A function that analyzes the user's progress.

[1839] 5. Notification and feedback function (server): A function that provides advice and feedback to improve motivation.

[1840] 6. Emotion engine (server): A function that recognizes the user's emotions and dynamically adjusts the advice and feedback content based on them.

[1841] Program processing overview

[1842] 1. Entering and saving user information

[1843] User: Launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1844] Terminal: Sends the entered information to the server.

[1845] Server: Stores the received user information in a database and generates a user ID.

[1846] 2. Generate personalized meal menus

[1847] Users: Send meal requests to the app based on their preferences and goals.

[1848] Terminal: Sends request information to the server.

[1849] Server: Retrieves user information from the database and sends it to the generative AI model.

[1850] Generative AI model: Generates a personalized meal menu based on user information and returns it to the server.

[1851] Server: Sends the generated menu to the device.

[1852] Device: Shows the user a meal menu.

[1853] 3. Enter and save the user's food record

[1854] User: Record the food they actually ate in the app.

[1855] Device: Sends recorded meal data to the server.

[1856] Server: Stores the received meal data in a database.

[1857] 4. Monitoring progress and providing feedback

[1858] Server: Periodically retrieves the user's food record data from the database.

[1859] Monitoring function: Analyzes acquired data and evaluates the user's progress. Calculates nutritional balance and goal achievement.

[1860] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[1861] Server: Sends feedback to the device.

[1862] Terminal: Show feedback to the user.

[1863] 5. Emotion recognition and feedback adjustment by emotion engine

[1864] Emotion engine: Recognizes the user's emotional state based on user input, behavioral data, or sensors.

[1865] Server: Obtains recognized emotion data and sends it to the generative AI model.

[1866] Generative AI model: Generates personalized meal menus that take emotional data into account.

[1867] Notification and feedback function: Dynamically adjusts advice and feedback content based on emotional data and provides it to users.

[1868] Server: Sends adjusted feedback to the device.

[1869] Device: Show users emotional feedback.

[1870] Specific use cases

[1871] As an example, consider a 52-year-old male user. This man has moderate diabetes and wants to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the app. Based on this information, the system uses a generative AI model to generate a personalized, nutritionally balanced, diabetes-friendly meal plan. Specific menu suggestions include "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled salmon" for dinner.

[1872] Furthermore, the emotion engine analyzes the user's emotions. For example, if the user is feeling stressed, it will incorporate suggestions for ingredients and dishes that have a relaxing effect. Based on the evaluation results, it will also provide advice on how to manage stress and messages to increase motivation. By doing this and continuing to enter food records, the system will monitor the user's progress and provide appropriate advice, helping them live a healthy life while working towards achieving their goals. In this way, users can enjoy individually customized meal plans and advice that takes their psychological state into account, enabling them to effectively manage their health.

[1873] The processing flow will be explained below.

[1874] Step 1:

[1875] The user launches the app and enters their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1876] Step 2:

[1877] The terminal transmits the entered user information to the server.

[1878] Step 3:

[1879] The server stores the received user information in a database and generates a user ID.

[1880] Step 4:

[1881] Users submit meal requests to the app based on their preferences and goals.

[1882] Step 5:

[1883] The terminal sends the request information to the server.

[1884] Step 6:

[1885] The server retrieves user information from the database and sends it to the generative AI model.

[1886] Step 7:

[1887] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[1888] Step 8:

[1889] The server sends the generated menu to the terminal.

[1890] Step 9:

[1891] The device displays a meal menu to the user.

[1892] Step 10:

[1893] The app records the meals that users actually eat.

[1894] Step 11:

[1895] The device transmits the recorded meal data to the server.

[1896] Step 12:

[1897] The server stores the received meal data in a database.

[1898] Step 13:

[1899] The server periodically retrieves the user's food record data from the database.

[1900] Step 14:

[1901] The monitoring function analyzes the data acquired and evaluates the user's progress, calculating nutritional balance and goal achievement.

[1902] Step 15:

[1903] The notification and feedback function generates advice and motivational messages for users based on the evaluation results.

[1904] Step 16:

[1905] The server sends the feedback to the device.

[1906] Step 17:

[1907] The device displays feedback to the user.

[1908] Step 18:

[1909] The emotion engine recognizes the user's emotional state based on user input, behavioral data, or sensors.

[1910] Step 19:

[1911] The server sends the recognized emotion data to the generative AI model.

[1912] Step 20:

[1913] The generative AI model takes into account the emotional data to generate a personalized meal menu and returns it to the server.

[1914] Step 21:

[1915] The notification and feedback function dynamically adjusts the content of advice and feedback based on emotional data.

[1916] Step 22:

[1917] The server sends the adjusted feedback to the device.

[1918] Step 23:

[1919] The device displays emotional feedback to the user.

[1920] Example 2

[1921] 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."

[1922] In today's modern living environment, it is important to properly manage one's diet for health maintenance, weight loss, and specific disease management. However, it is difficult to create a meal menu based on one's own judgment and manage one's diet based on one's eating habits and health condition, and conventional systems have limited accuracy and personalization. Furthermore, the selection of healthy foods and management of motivation based on the user's emotional state tend to be overlooked. A system that can solve these issues and achieve more effective and personalized health management is needed.

[1923] 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.

[1924] In this invention, the server includes a storage means for storing user information, a generation means for generating a personalized meal menu based on the stored user information, and a transmission means for transmitting the generated meal menu to the user terminal. This makes it possible to generate and provide a personalized meal menu for each user. Furthermore, an emotion recognition means is used to recognize the user's emotional state and dynamically adjust the feedback content based on that information. This enables health management that takes the user's psychological state into consideration, and realizes the provision of more effective advice and feedback.

[1925] "User information" refers to personal data entered by the user, such as name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[1926] "Input means" refers to an interface through which a user inputs their information into the system.

[1927] "Transmission means" refers to a device or software that has the function of transmitting input user information and request data to the server.

[1928] The "storage means" is a mechanism for storing the received user information in a storage device such as a database.

[1929] The "generation means" refers to an algorithm or AI model that generates a personalized meal menu based on stored user information.

[1930] "Recording means" refers to a device or software that has the function of recording the food that the user actually ate.

[1931] "Analysis means" refers to a method or device that analyzes the recorded dietary content and evaluates the user's progress.

[1932] "Provision means" refers to devices or software that have the function of providing advice and feedback to users based on the analysis results.

[1933] An "emotion recognition means" is a mechanism that recognizes the user's emotional state and adjusts the feedback content based on that information.

[1934] A "user terminal" is a device that a user uses to access the system, such as an information processing device such as a smartphone or tablet.

[1935] This invention relates to a system that generates personalized meal menus based on user information and supports health management taking into account the user's emotional state. The system consists of the following main components: a user interface, a database, a generative AI model, a monitoring function, a notification and feedback function, and an emotion engine.

[1936] User Interface (Terminal)

[1937] The user interface is a device through which the user inputs information. Specifically, a smartphone or tablet is used. The user inputs their name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked ingredients. This information is sent to the server via the device's app.

[1938] server

[1939] The server stores the received user information in a database, generates a user ID, and provides the user information to the generative AI model. The server then sends the generated meal menu to the device, which is used to analyze the user's meal record data and emotional data.

[1940] Database

[1941] The database serves to store user information, meal records, and recipe data, which is accessed by the server as needed to generate personalized meal menus and monitor the user's progress.

[1942] Generative AI Models

[1943] The generative AI model uses algorithms and AI techniques to generate personalized meal plans based on user information, taking into account the user's nutritional balance and health status.

[1944] For example, the following prompt is sent to the generative AI model:

[1945] User Information:

[1946] Name: Ichiro Tanaka

[1947] Age: 52

[1948] Gender: Male

[1949] Height: 170cm

[1950] Weight: 80kg

[1951] Diet goal: Lose 5kg in 3 months

[1952] Allergy Information: Nuts

[1953] Favorite ingredients: chicken, fish

[1954] Disliked food: Eggplant

[1955] Use this information to generate a personalized diabetes-friendly meal plan.

[1956] Monitoring Function

[1957] The monitoring function is used to analyze and evaluate the user's progress. Specifically, it analyzes food record data obtained from the database and evaluates the user's nutritional balance and goal achievement.

[1958] Notification and feedback function

[1959] The notification and feedback function provides users with advice and motivational messages based on the analysis results. These messages are created based on the user's nutritional status and progress.

[1960] Emotion Engine

[1961] The emotion engine is a function that recognizes the user's emotional state and dynamically adjusts the feedback content. By analyzing the user's emotional state based on input data, behavioral data, and information obtained from sensors, and reflecting this in the generative AI model, it provides more appropriate advice and feedback.

[1962] The system aims to provide users with individually customized meal plans and advice that takes into account their psychological state, helping them to effectively manage their health.

[1963] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1964] Step 1: Enter your user information

[1965] User: The user launches the app and enters personal information such as name, age, gender, height, weight, diet goals, allergy information, and favorite and disliked foods.

[1966] Input: Data that a user enters into a form in your app.

[1967] Specific action: A user enters information into an app on a smartphone or tablet.

[1968] Step 2: Submit user information

[1969] Terminal: The terminal sends the entered user information to the server. This transmission is done in a data-encrypted state.

[1970] Input: User information entered in step 1.

[1971] Output: User information sent to the server.

[1972] What happens: The device generates an API request in the background and sends data to the server.

[1973] Step 3: Save user information

[1974] Server: The server stores the received user information in a database and generates a user ID.

[1975] Input: User information sent from the device.

[1976] Output: User information and user ID stored in the database.

[1977] What happens: The server executes an INSERT statement in the database, saving the user information in a new row.

[1978] Step 4: Request a meal

[1979] User: A user requests a personalized meal within the app.

[1980] Input: Meal request and user ID.

[1981] Output: A request to generate a meal menu.

[1982] What happens: The user clicks the "Generate Meal Menu" button.

[1983] Step 5: Feed the generative AI model with data

[1984] Server: The server retrieves user information from a database and sends it to the generative AI model.

[1985] Input: Food request and user information.

[1986] Output: A prompt to the generative AI model.

[1987] Specific operation: The server executes an SQL SELECT statement to obtain user information data and provides a prompt statement to the generative AI model.

[1988] Step 6: Generate the meal menu

[1989] Generative AI model: The generative AI model generates personalized meal menus based on user information.

[1990] Input: A prompt for the generative AI model.

[1991] Output: A personalized meal menu.

[1992] How it works: The AI ​​model uses data processing and algorithms to generate a meal menu and returns the results to the server.

[1993] Step 7: Submit your meal menu

[1994] Server: The server sends the generated meal menu to the user's device.

[1995] Input: A personalized meal menu from a generative AI model.

[1996] Output: Meal menu sent to user device.

[1997] Specific operation: The server sends the meal menu in JSON format to the device.

[1998] Step 8: View the food menu

[1999] Terminal: The terminal displays the received meal menu to the user.

[2000] Input: A meal menu sent by the server.

[2001] Output: The meal menu displayed to the user.

[2002] Specific behavior: The device displays the received meal menu on the user interface.

[2003] Step 9: Enter your food log

[2004] User: The user records the food they eat in the app.

[2005] Input: Food data logged by the user.

[2006] Output: Food data entered into the app.

[2007] What happens: The user enters their meal details into the app's "Food Log" page.

[2008] Step 10: Submit your food log

[2009] Device: The device sends the recorded meal data to the server.

[2010] Input: Meal data entered by the user.

[2011] Output: Meal data sent to the server.

[2012] Specific operation: The device sends meal data to the server in the background.

[2013] Step 11: Keep a food diary

[2014] Server: The server stores the received meal data in a database.

[2015] Input: Meal data sent from the device.

[2016] Output: Food records stored in a database.

[2017] Specific behavior: The server executes an INSERT statement in the database, saving the meal record in a new row.

[2018] Step 12: Monitor progress

[2019] Server: The server periodically retrieves the user's food record data from the database.

[2020] Input: Food record data from the database.

[2021] Output: The data obtained for analysis.

[2022] Specific behavior: The server extracts the data using an SQL SELECT statement.

[2023] Step 13: Analyze your progress

[2024] Monitoring function: Analyzes the acquired data and evaluates the user's progress.

[2025] Input: Food log data.

[2026] Output: Evaluation results of nutritional balance and goal achievement.

[2027] What it does: The monitoring feature uses data analysis algorithms to assess your nutrition and progress.

[2028] Step 14: Generate feedback

[2029] Notification and feedback function: Based on the evaluation results, advice and motivational messages are generated for the user.

[2030] Input: Progress assessment results.

[2031] Output: The feedback message.

[2032] What it does: The feedback function generates a message using a template.

[2033] Step 15: Submit your feedback

[2034] Server: The server sends the generated feedback to the device.

[2035] Input: Message via notification and feedback function.

[2036] Output: Feedback sent to the user device.

[2037] Specific operation: The server sends feedback data in JSON format to the device.

[2038] Step 16: Viewing Feedback

[2039] Terminal: The terminal displays feedback to the user.

[2040] Input: The feedback message sent by the server.

[2041] Output: The feedback displayed to the user.

[2042] What happens: Your device will display feedback as a pop-up notification or message.

[2043] Step 17: Recognizing your emotional state

[2044] Emotion engine: Recognizes the user's emotional state based on user input, behavioral data, or sensors.

[2045] Input: User input data, behavioral data, and sensor information.

[2046] Output: Recognized emotion data.

[2047] How it works: The emotion engine uses analytical algorithms to recognize emotional states.

[2048] Step 18: Adjusting Emotional Feedback

[2049] Server: The server acquires the recognized emotion data and sends it to the generative AI model.

[2050] Input: Emotion data.

[2051] Output: Emotion data to a generative AI model.

[2052] Specific operation: The server sends emotion data to the generative AI model.

[2053] Step 19: Emotion-Based Menu Generation

[2054] Generative AI model: Generates personalized meal menus that take emotional data into account.

[2055] Input: Emotion data.

[2056] Output: A personalized meal menu that takes emotions into account.

[2057] How it works: The AI ​​model generates a meal menu based on emotional data.

[2058] Step 20: Generate emotion-based feedback

[2059] Notification and feedback function: Dynamically adjusts advice and feedback content based on emotional data and provides it to users.

[2060] Enter: an emotionally informed meal menu.

[2061] Output: Regulated feedback.

[2062] Specific behavior: The feedback function generates advice that takes emotional data into account.

[2063] Step 21: Send and view sentiment-based feedback

[2064] Server: Sends adjusted feedback to the device.

[2065] Input: Calibrated feedback.

[2066] Output: Feedback sent to the user device.

[2067] Device: Show users emotional feedback.

[2068] Specific behavior: The server sends feedback data in JSON format to the device, and the device displays the feedback as a popup notification or message.

[2069] (Application example 2)

[2070] 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."

[2071] In modern society, support systems for individual users to lead healthy eating habits are important. However, conventional systems simply manage users' food records and progress, but do not provide dynamic meal plans or advice based on the user's emotional state. As a result, advice that does not take the user's psychological state into account can make it difficult to maintain motivation and make it difficult to achieve long-term goals.

[2072] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an emotion recognition means that recognizes the emotional state of the user and adjusts the advice and feedback content based on the emotion, a generation means that dynamically generates a personalized meal menu based on the emotional state and progress, and a transmission means that transmits the generated menu to the user terminal. This makes it possible to provide individual advice and meal plans that take the user's psychological state into consideration, allowing the user to work more effectively toward achieving their health goals.

[2073] "User Information" refers to basic data about each user of the system, such as name, age, gender, height, weight, health status, dietary preferences and allergy information.

[2074] "Input means" refers to the interface that allows users to input personal information, food records, emotional state, etc. into the system.

[2075] "Transmission means" refers to a function for transmitting input data to a server.

[2076] "Storage means" refers to the mechanism for storing user input information and recorded data on the server.

[2077] "Generation means" refers to the AI ​​model or algorithm used to generate personalized meal menus based on stored user information.

[2078] "Recording means" refers to a function that allows a user to record the meals they have actually eaten and their contents.

[2079] "Analysis means" refers to the component within the system that analyzes and evaluates the recorded diet and the user's progress.

[2080] "Means of provision" refers to the function of providing advice and feedback to users based on the analysis results.

[2081] "Emotion recognition means" is a system function that recognizes the emotional state of the user at that time from the data and behavioral data entered by the user.

[2082] "User Device" means a device (e.g., a smartphone, tablet, etc.) that a User uses to interface with the System.

[2083] This invention is a system that manages a user's dietary records, progress, and emotional state in an integrated manner through a smartphone application, and provides personalized advice and feedback based on this information.

[2084] System Overview

[2085] This system consists of the following main components:

[2086] 1. User Interface (Terminal)

[2087] Users use a smartphone application to enter necessary information, including name, age, gender, height, weight, health goals, allergy information, and food preferences.

[2088] 2. Database (server)

[2089] The server stores user information, food records, and emotional states, as well as recipe data and nutritional information.

[2090] 3. Generative AI model (server)

[2091] The server-based generative AI model uses user information to generate personalized meal menus, including specific meal plans based on the user's nutritional balance and health goals.

[2092] 4. Emotion Recognition Engine (Server)

[2093] The emotion recognition engine recognizes the emotional state from input data and user behavioral data, and dynamically adjusts the content of advice and feedback based on that information.

[2094] 5. Notification and feedback function (server)

[2095] The server uses the analysis results to provide users with advice and feedback, including motivational messages about health and diet.

[2096] Processing Description

[2097] In this system, processing is carried out in the following procedure.

[2098] 1. Entering and saving user information

[2099] Users launch the smartphone application and enter their name, age, gender, height, weight, diet goals, allergy information, favorite and disliked ingredients, etc.

[2100] The entered information is sent from the smartphone to a server, which stores it in a database.

[2101] 2. Generating a meal menu

[2102] Users submit meal requests to the application based on their preferences and goals.

[2103] The server retrieves user information from the database and sends it to the generative AI model.

[2104] The generative AI model generates a personalized meal menu based on the user's information and returns it to the server.

[2105] The server sends the generated menu to the smartphone and presents it to the user.

[2106] 3. Enter and save your food record

[2107] Users record the food they eat in the application.

[2108] The recorded meal data is sent from the smartphone to a server and stored in a database.

[2109] 4. Progress monitoring and feedback

[2110] The server periodically retrieves the user's food record data from the database and analyzes it using the monitoring function.

[2111] Based on the analysis results, the system evaluates the user's nutritional balance and goal achievement, and generates advice and motivational messages using notification and feedback functions.

[2112] The server sends the feedback to the smartphone and displays it to the user.

[2113] 5. Emotion recognition and feedback regulation

[2114] The emotion engine recognizes the emotional state from user input and behavioral data.

[2115] The server retrieves the recognized emotion data and sends it to the generative AI model.

[2116] The generative AI model generates personalized meal menus that take emotional data into account.

[2117] The notification and feedback function dynamically adjusts the content of advice and feedback based on emotional data and provides it to users.

[2118] Specific examples

[2119] As a concrete example, consider a 30-year-old male user whose goal is to lose weight. He enters his physical information (weight, height, age) and diet goal (lose 5 kg in 3 months) into the application. Based on this information, the system uses a generative AI model to generate a personalized meal plan that takes nutritional balance into account.

[2120] Use the following example prompt to request a meal plan from a generative AI model:

[2121] User: 30-year-old male

[2122] Age: 30

[2123] Gender: Male

[2124] Height: 175cm

[2125] Weight: 70kg

[2126] Goal: Lose 5kg in 3 months

[2127] Allergens: nuts

[2128] Favorite foods: Fish, vegetables

[2129] Use this information to suggest a personalized meal plan.

[2130] The generated meal plan may include specific menus such as "oatmeal and berry yogurt" for breakfast, "grilled chicken salad" for lunch, and "steamed vegetables and grilled fish" for dinner.

[2131] Furthermore, the emotion engine analyzes the user's emotions and, if the user is feeling stressed, incorporates suggestions for ingredients and dishes that have a relaxing effect into the recommendations. Based on the evaluation results, the system also provides advice on how to deal with stress and messages to increase motivation. By doing this and continuing to enter food records, the system can monitor the user's progress and provide appropriate advice, helping them live a healthy life while working towards achieving their goals.

[2132] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2133] Step 1:

[2134] A user launches a smartphone application and enters information such as name, age, gender, height, weight, diet goals, allergy information, favorite and disliked foods, etc. This information is sent from the smartphone to a server. The server stores the received information in a database and generates a user ID. The input data is in text format, and the output is the creation of a database entry.

[2135] Step 2:

[2136] A user submits a meal menu request based on their health goals through a smartphone application. The device sends this request to a server. The server retrieves user information from a database and sends it to a generative AI model. The generative AI model uses this information to generate a personalized meal menu and returns it to the server. The generated menu is in text format, and the output is a specific meal plan.

[2137] Step 3:

[2138] The server sends the generated personalized meal menu to the smartphone device. The device receives this information and displays it to the user. The user confirms the displayed meal menu and proceeds to the next step. The input data is the text information of the generated menu, and the output is the display on the user device.

[2139] Step 4:

[2140] The user records the details of the meal they actually ate in a smartphone application. The device then sends this record to a server. The server then stores the received meal data in a database. The input data is the user's meal record (text information), and the output is stored in the database.

[2141] Step 5:

[2142] The server periodically retrieves the user's food record data from the database. This data is analyzed using a monitoring function to evaluate the user's progress. The analysis results include nutritional balance and goal achievement. The input data is the food record, and the output is an evaluation of nutritional balance and goal achievement.

[2143] Step 6:

[2144] The server executes a notification and feedback function that generates advice and motivational messages based on the evaluation results. The generated feedback is sent to the user's smartphone in the form of a notification. The input data is the evaluation results, and the output is advice and feedback messages.

[2145] Step 7:

[2146] The emotion engine analyzes the user's input data and behavioral patterns to recognize their emotional state at that time. The server sends this emotional state to the generative AI model, which then takes the emotional data into account to regenerate a personalized meal menu based on the user's psychological state. The input data is the emotional state, and the output is the regenerated meal menu.

[2147] Step 8:

[2148] The notification and feedback function dynamically adjusts the content of advice and feedback that takes into account the user's emotional state and sends it to the smartphone. The user can receive this feedback and reflect it in their next actions. The input data is emotion, progress, and feedback content, and the output is a dynamically adjusted feedback message.

[2149] Examples of prompt statements

[2150] User: 30-year-old male

[2151] Age: 30

[2152] Gender: Male

[2153] Height: 175cm

[2154] Weight: 70kg

[2155] Goal: Lose 5kg in 3 months

[2156] Allergens: nuts

[2157] Favorite foods: Fish, vegetables

[2158] Use this information to suggest a personalized meal plan.

[2159] 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.

[2160] 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.

[2161] 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.

[2162] 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.

[2163] 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.

[2164] 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.

[2165] 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).

[2166] 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.

[2167] 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."

[2168] 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.

[2169] 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).

[2170] 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.

[2171] 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.

[2172] 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.

[2173] 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.

[2174] 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.

[2175] 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.

[2176] 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.

[2177] 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.

[2178] 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.

[2179] 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.

[2180] The following is further disclosed regarding the above embodiment.

[2181] (Claim 1)

[2182] an input means for inputting user information;

[2183] a transmitting means for transmitting the input user information to a server;

[2184] a storage means for storing user information;

[2185] a generating means for generating a personalized meal menu based on the stored user information;

[2186] a transmitting means for transmitting the generated meal menu to a user terminal;

[2187] A recording means for recording the meals that the user actually ate;

[2188] an analytical means for analyzing the recorded dietary content and evaluating the user's progress;

[2189] A means for providing advice and feedback to users based on the evaluation results;

[2190] A system including:

[2191] (Claim 2)

[2192] 2. The system according to claim 1, further comprising a generation means for generating a meal menu that takes into consideration nutritional balance based on the stored user information.

[2193] (Claim 3)

[2194] 10. The system according to claim 1, further comprising an analysis means and a provision means for evaluating the progress of the goal based on the user's records and generating personalized advice.

[2195] "Example 1"

[2196] (Claim 1)

[2197] an input means for inputting user information;

[2198] a transmitting means for transmitting the input user information to the data processing device;

[2199] storage means for storing user information;

[2200] a generating means for generating a personalized meal menu based on the stored user information;

[2201] a transmitting means for transmitting the generated meal menu to a user terminal;

[2202] A recording means for recording the food intake actually taken by the user;

[2203] an analytical means for analyzing the recorded dietary content and evaluating the user's progress;

[2204] A means for providing advice and feedback to users based on the evaluation results;

[2205] A system including:

[2206] (Claim 2)

[2207] 2. The system according to claim 1, further comprising a generation means for generating a meal menu that takes into consideration nutritional balance based on the stored user information.

[2208] (Claim 3)

[2209] 10. The system of claim 1, further comprising an analysis means and a provision means for evaluating the progress of the goal based on the user's records and generating personalized advice.

[2210] "Application Example 1"

[2211] (Claim 1)

[2212] an input means for inputting user information;

[2213] a transmitting means for transmitting the input user information to a server;

[2214] a storage means for storing user information;

[2215] a generating means for generating a personalized meal menu based on the stored user information;

[2216] a transmitting means for transmitting the generated meal menu to a user terminal;

[2217] A recording means for recording the meals that the user actually ate;

[2218] an analytical means for analyzing the recorded dietary content and evaluating the user's progress;

[2219] A means for providing advice and feedback to users based on the evaluation results;

[2220] ordering means for ordering a meal based on the generated meal menu;

[2221] A system including:

[2222] (Claim 2)

[2223] 2. The system according to claim 1, further comprising a generation means for generating a meal menu that takes into consideration nutritional balance based on the stored user information.

[2224] (Claim 3)

[2225] 10. The system according to claim 1, further comprising an analysis means and a provision means for evaluating the progress of the goal based on the user's records and generating personalized advice.

[2226] "Example 2: Combining Emotion Engines"

[2227] (Claim 1)

[2228] an input means for inputting user information;

[2229] a transmitting means for transmitting the input user information to a server;

[2230] a storage means for storing user information;

[2231] a generating means for generating a personalized meal menu based on the stored user information;

[2232] a transmitting means for transmitting the generated meal menu to a user terminal;

[2233] A recording means for recording the meals that the user actually ate;

[2234] an analytical means for analyzing the recorded dietary content and evaluating the user's progress;

[2235] A means for providing advice and feedback to users based on the evaluation results;

[2236] The system includes an emotion recognition means for recognizing the user's emotional state and adjusting the feedback content based on that information.

[2237] (Claim 2)

[2238] 2. The system according to claim 1, further comprising a generation means for generating a meal menu that takes into consideration nutritional balance based on the stored user information.

[2239] (Claim 3)

[2240] 10. The system of claim 1, further comprising an analysis means and a provision means for evaluating the progress of a goal based on the user's records, generating personalized advice, and further adjusting the feedback content based on the user's emotional state.

[2241] "Application example 2 when combining emotion engines"

[2242] (Claim 1)

[2243] an input means for inputting user information;

[2244] a transmitting means for transmitting the input user information to a server;

[2245] a storage means for storing user information;

[2246] a generating means for generating a personalized meal menu based on the stored user information;

[2247] a transmitting means for transmitting the generated meal menu to a user terminal;

[2248] A recording means for recording the meals that the user actually ate;

[2249] an analytical means for analyzing the recorded dietary content and evaluating the user's progress;

[2250] A means for providing advice and feedback to users based on the evaluation results;

[2251] an emotion recognition means for recognizing the user's emotional state and adjusting advice or feedback content based on the emotion;

[2252] A generating means for dynamically generating a personalized meal menu based on the emotional state and progress;

[2253] A system including:

[2254] (Claim 2)

[2255] 10. The system of claim 1, further comprising a generating means for generating a nutritionally balanced meal menu based on the stored user information and the recognized emotional state.

[2256] (Claim 3)

[2257] 10. The system of claim 1, further comprising an analysis means and a provision means for evaluating the progress of the goal based on the user's records and emotional state, and generating personalized advice and emotional feedback. [Explanation of symbols]

[2258] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting user information; a transmitting means for transmitting the input user information to a server; a storage means for storing user information; a generating means for generating a personalized meal menu based on the stored user information; a transmitting means for transmitting the generated meal menu to a user terminal; A recording means for recording the meals that the user actually ate; an analytical means for analyzing the recorded dietary content and evaluating the user's progress; A means for providing advice and feedback to users based on the evaluation results; A system including:

2. 2. The system according to claim 1, further comprising a generation unit that generates a meal menu that takes into consideration nutritional balance based on the stored user information.

3. The system according to claim 1, further comprising an analysis means and a provision means for evaluating the degree of goal achievement based on the user's records and generating individualized advice.

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