System

Abstract

A system is provided.SOLUTION: Means for providing an interface for inputting personal information of a user, means for transmitting the personal information to a server and storing the personal information in a database by the server, means for generating an optimal meal suggestion for the user by the server using generative artificial intelligence based on the personal information, means for transmitting the generated meal suggestion to a terminal of the user and displaying the meal suggestion by the terminal, and means for transmitting meal information selected by the user to a server and storing the selected meal information in a database by the server; Means for periodically monitoring a health condition of the user based on the data stored in the server and transmitting an alert if there is an abnormality.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] Conventional health management systems lack sufficient functionality to provide meal suggestions tailored to an individual's health condition and lifestyle. Many users find it difficult to manage their own diet in their busy daily lives, making it difficult to maintain their health and prevent lifestyle-related diseases. Furthermore, the system lacks a mechanism for reflecting dietary choices and their results in subsequent suggestions. Therefore, there is a need for systems that efficiently provide personalized meal suggestions for each user. [Means for solving the problem]

[0005] The present invention solves the above problems by providing the following means: a means for providing an interface for inputting a user's personal information; a means for transmitting the personal information to a server and for the server to store the personal information in a database; a means for the server to generate optimal meal suggestions for the user based on the personal information using artificial intelligence; and a means for transmitting the generated meal suggestions to the user's terminal and for the terminal to display the meal suggestions. The system further includes a means for transmitting meal information selected by the user to the server and for the server to store the selected meal information in a database; and a means for the server to periodically monitor the user's health status based on the stored data and send an alert if an abnormality is detected. This allows users to receive meal suggestions based on their individual health information and maintain or improve a healthy lifestyle.

[0006] "Interface" refers to the means by which a user inputs information into a system.

[0007] "Server" refers to a computer system that processes information received from users and stores it in a database.

[0008] "Database" refers to a system for storing and managing information in an organized manner.

[0009] "Generative artificial intelligence" refers to a system that uses specific algorithms or models to generate optimal information (in this case, meal suggestions) for users.

[0010] "Meal Suggestions" refers to a list of recommended meals based on a user's personal information and lifestyle.

[0011] "Terminal" refers to a device that is directly operated by a user (e.g., smartphone, PC).

[0012] "Health monitoring" refers to the system's ability to regularly check health data based on the user's daily routine and dietary choices and send alerts if there are any abnormalities.

[0013] "Feedback" refers to the system's ability to improve its next suggestions based on user behavior and choices. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention provides a personalized diet recommendation system based on the health information of each user. Hereinafter, embodiments of the present invention will be described in detail.

[0036] User Data Collection

[0037] The user opens the application on the device and enters their personal information (age, weight, height, sleep time, exercise, etc.) The device sends this information to the server, which then stores it in a database.

[0038] Specific examples

[0039] Users use a smartphone app to enter information such as age 30, weight 70kg, height 175cm, sleep seven hours, and jog three times a week.

[0040] Data storage and analysis

[0041] The server securely stores the information received from the user in a database, and then uses generative artificial intelligence to analyze the user's health information and generate personalized meal suggestions.

[0042] Specific examples

[0043] The server analyzes the user's exercise volume and other health data based on the user information stored in the database, and suggests optimal healthy diets.

[0044] Generating meal suggestions

[0045] The server's AI generates an optimal meal suggestion list based on personal information and additional data such as seasonal information. This meal suggestion list is sent to the terminal and displayed to the user.

[0046] Specific examples

[0047] Meal suggestions for the fall season include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[0048] Recipe provision and data monitoring

[0049] The device displays the list of meal suggestions received from the server, and the user selects one of them and sends it to the server, which records the selection and uses it as feedback to improve the next suggestion.

[0050] Specific examples

[0051] The user selects "grilled chicken and stir-fried vegetables" and sends the selection from their device to the server, which records this information in a database and uses it for future suggestions.

[0052] Health monitoring and alerts

[0053] The server periodically monitors the user's health status based on the stored data and sends an alert if there is an abnormality. This alert is then sent to the user's device, providing necessary advice and suggestions.

[0054] Specific examples

[0055] If a user consistently chooses high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0056] The system of the present invention allows users to efficiently receive appropriate dietary suggestions based on health information, which supports the maintenance of a healthy lifestyle and the prevention of lifestyle-related diseases, making it easier for users to manage their own lifestyle.

[0057] ---

[0058] The system described in the embodiment of the present invention provides personalized meal suggestions according to the user's health condition and supports continuous health management, allowing the user to maintain their health efficiently and effectively.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user opens the application on their device and enters their personal information (age, weight, height, sleep time, exercise details, etc.).

[0062] Step 2:

[0063] The device sends the user's personal information entered into the application to the server.

[0064] Step 3:

[0065] The server stores the personal information received from the user in a database.

[0066] Step 4:

[0067] The server uses artificial intelligence to analyze data based on the user's stored personal information.

[0068] Step 5:

[0069] Generative AI takes into account the user's personal information and seasonal information to generate a personalized list of meal suggestions.

[0070] Step 6:

[0071] The server transmits the generated meal suggestion list to the terminal.

[0072] Step 7:

[0073] The device displays the received meal suggestion list to the user.

[0074] Step 8:

[0075] The user selects their preferred meal from the displayed list of meal suggestions.

[0076] Step 9:

[0077] The device sends the meal information selected by the user to the server.

[0078] Step 10:

[0079] The server stores the received selected meal information in a database.

[0080] Step 11:

[0081] The server periodically monitors the user's health data and selected dietary information, and generates alerts if necessary if an abnormality is detected.

[0082] Step 12:

[0083] The server sends the generated alert to the terminal, and the terminal notifies the user of the alert.

[0084] Example 1

[0085] 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."

[0086] In modern society, health problems such as lifestyle-related diseases are on the rise, and individual users are being called upon to select optimal diets and manage their health based on their own health information. However, there are currently no systems that allow users to receive appropriate dietary suggestions based on their own health information. Therefore, it is an important challenge to provide a system that makes personalized dietary suggestions for each individual.

[0087] 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.

[0088] In this invention, the server includes means for providing an interface for inputting a user's health information, means for transmitting the health information to the server and storing the health information in a database, means for the server to generate optimal dietary suggestions for the user based on the health information using artificial intelligence, means for transmitting the generated dietary suggestions to the user's terminal and displaying the dietary suggestions, means for transmitting dietary information selected by the user to the server and storing the selected dietary information in a database, and means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected. This enables personalized dietary suggestions based on the user's health information, as well as feedback and monitoring of the suggestions.

[0089] A "user" is a user who accesses the system, inputs their own health information, and receives dietary suggestions.

[0090] "Health information" refers to personal data entered by the user, such as age, weight, height, sleep time, and exercise details.

[0091] The "server" is a computer system that stores health information received from users and generates dietary suggestions using generative artificial intelligence.

[0092] "Database" means a data storage system that allows the server to store and manage health information, dietary suggestions, and user selection data.

[0093] "Generative AI" refers to algorithms or programs that generate meal suggestions tailored to the user based on input data.

[0094] "Meal Suggestions" are personalized meal recommendations provided to users as a result of analysis by the generative artificial intelligence.

[0095] "Terminal" means the device used by a user to enter health information, receive dietary suggestions, and submit selected information.

[0096] "Preference information" is data about the particular meal menu a user selects from a presented list of meal suggestions.

[0097] "Monitoring" refers to the act of the server periodically monitoring stored user data to check for any abnormalities.

[0098] An "alert" is a warning or notification sent from the server to the user's device when an abnormality is detected as a result of monitoring.

[0099] The present invention is a personalized diet recommendation system based on individual user health information. The system uses generative artificial intelligence (generative AI model) to generate optimal diet recommendations based on the health information provided by the user, and then makes recommendations to the user. Specific embodiments for implementing the present invention are described in detail below.

[0100] User Data Collection

[0101] The user opens the application on their device (e.g., smartphone or tablet) and enters their health information (age, weight, height, sleep time, exercise, etc.). The entered health information is sent from the device to the server, which then securely stores the received information in a database.

[0102] Specific examples

[0103] Users use a smartphone app to input information such as age 30, weight 70kg, height 175cm, sleep 7 hours, and jog three times a week, which is then sent to a server via the device and stored in a database.

[0104] Data storage and analysis

[0105] The server stores the health information received from the user in a database and then analyzes it using a generative AI model, which then generates optimal dietary recommendations based on the user's health information.

[0106] Specific examples

[0107] The server analyzes the user's exercise and lifestyle habits based on the user information stored in the database, and automatically generates appropriate meal suggestions. Specifically, it uses a generative AI model implemented in Python to input user data and obtain meal suggestions.

[0108] Generating meal suggestions

[0109] The server's generative AI model takes into account the user's personal information as well as additional data (e.g., seasonal information) to generate an optimal meal recommendation list, which is then sent to the device and displayed to the user.

[0110] Specific examples

[0111] For example, to suggest meals suited to the autumn season, the generative AI model is used to create "pumpkin soup," "grilled chicken and stir-fried vegetables," and "quinoa and avocado salad," and these are then recommended to the user.

[0112] Recipe provision and data monitoring

[0113] When a user selects a meal from the list of suggestions, the information is sent from the device to the server, where it is recorded. This feedback information is used to improve the accuracy of the next meal suggestions.

[0114] Specific examples

[0115] When a user selects "grilled chicken and stir-fried vegetables," the selection information is sent to the server via the device and recorded in the database. It is used as feedback for future suggestions.

[0116] Health monitoring and alerts

[0117] The server periodically monitors the stored health information and, if an abnormality is detected, sends an alert to the user's device, allowing the user to receive appropriate advice and warnings in a timely manner.

[0118] Specific examples

[0119] If a user continues to choose high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0120] Prompt Sentence Examples

[0121] Here are some example prompts to get a generative AI model to analyze user data:

[0122] Generate personalized meal suggestions based on the user's health information (age, weight, height, sleep duration, exercise, etc.) Example: Provide the optimal meal plan for autumn for a user who is 30 years old, 70kg, 175cm tall, and jogs 3 times a week.

[0123] Using these prompts, the generative AI model provides specific dietary suggestions tailored to the user, supporting individual health management. This system allows users to efficiently receive appropriate dietary recommendations based on their own health information, helping them maintain a healthy lifestyle and prevent lifestyle-related diseases.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1: Entering User Data

[0126] The user enters their health information into the device. The input fields include age, weight, height, sleep time, exercise, etc. After the user enters all the information, they press the "Send" button. The input data is displayed on the device and prepared for transmission.

[0127] Input: Health information entered by the user (age, weight, height, sleep time, exercise details)

[0128] Output: Input information displayed on the terminal, ready to send

[0129] Step 2: Sending data

[0130] When the user presses the "Submit" button, the device sends the input data to the server via an HTTP POST request, which encodes the data in JSON format and transmits it over the network.

[0131] Input: Health information entered by the user, click of the submit button

[0132] Output: Health information sent to the server in JSON format

[0133] Step 3: Save your data

[0134] The server analyzes the received health information and saves it in a database. The database stores profile information for each user. The server saves this information in the user table using an INSERT statement.

[0135] Input: Health information in JSON format sent to the server

[0136] Output: Health information stored in a database

[0137] Step 4: Analyze the data

[0138] The server retrieves the user's health information from the database and analyzes it using a generative AI model. The generative AI model generates optimal dietary recommendations based on the user's health information. The server uses a Python script to call the generative AI model and obtain the analysis results.

[0139] Input: Health information stored in a database

[0140] Output: Analysis results from the generative AI model (optimal meal suggestions)

[0141] Step 5: Generate and submit a meal suggestion list

[0142] The server generates a list of meal suggestions based on the analysis results obtained from the generative AI model. This list is encoded in JSON format and sent to the device as an HTTP response. The device then displays the received data to the user.

[0143] Input: Analysis results from generative AI model

[0144] Output: List of meal suggestions sent to the device, meal suggestions displayed to the user

[0145] Step 6: User Choice Feedback

[0146] The user selects one from the list of meal suggestions presented. The user's selection information is sent back to the server from the device, and the server records this data in a database. This data will be used to improve the next meal suggestion.

[0147] Input: User-selected meal information

[0148] Output: Selections sent to the server, selections recorded in the database

[0149] Step 7: Monitoring your health information

[0150] The server periodically monitors the user's health information and food selection history stored in the database, and if any abnormal patterns are detected, an alert is generated and sent to the user's device.

[0151] Input: Health information and dietary choice history stored in a database

[0152] Output: Alerts if anomalies are detected, notifications sent to the device

[0153] Specific operation example

[0154] When a user uses a smartphone app to input and submit health information, the device sends the data in JSON format to a server. The server stores the information in a database and invokes a generative AI model for analysis. As a result of the analysis, optimal meal suggestions for autumn are generated, suggesting pumpkin soup, grilled chicken and stir-fried vegetables, and quinoa and avocado salad. When the user selects "grilled chicken and stir-fried vegetables," the selection information is sent to the server and recorded in the database. The server periodically monitors the data, and if an abnormality is detected, an alert is sent to the device. In this way, users can receive personalized meal suggestions and manage their health.

[0155] Prompt Sentence Examples

[0156] Generate personalized meal suggestions based on the user's health information (age, weight, height, sleep duration, exercise, etc.) Example: Provide the optimal meal plan for autumn for a user who is 30 years old, 70kg, 175cm tall, and jogs 3 times a week.

[0157] (Application example 1)

[0158] 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."

[0159] While existing meal recommendation systems provide personalized suggestions based on the user's health information, they lack a mechanism for linking these with actual meal ordering and delivery. As a result, users must prepare the suggested meals themselves, which is time-consuming and laborious, resulting in low effectiveness of the suggestions. Furthermore, there is little mechanism for incorporating feedback on whether the suggested meals are appropriate, making it unclear whether there is room for improvement in future suggestions.

[0160] 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.

[0161] In this invention, the server includes: means for providing an interface for inputting a user's personal information; means for transmitting the personal information to the server and storing the personal information in a database; means for the server to generate optimal meal suggestions for the user based on the personal information using artificial intelligence; means for transmitting the generated meal suggestions to the user's terminal and displaying the meal suggestions; means for transmitting meal information selected by the user to the server and storing the selected meal information in a database; means for the server to periodically monitor the user's health status based on the stored data and send an alert if an abnormality is detected; means for linking the meal suggestions based on the user's health information with a food delivery ordering function; and means having a feedback function for analyzing the food delivery order history to improve the next suggestion. This allows the user to easily actually order the suggested meals, and the selection results are reflected in subsequent suggestions, enabling more effective health management for the user.

[0162] "User personal information" refers to data related to the user's health and lifestyle, such as age, weight, height, sleep time, and exercise frequency.

[0163] "Interface" means the portion of the software that a user uses to manipulate and input data.

[0164] A "server" is a computer system for storing and processing data received from users.

[0165] "Database" means a system for systematically storing and managing users' personal information and selection information.

[0166] "Generative AI" refers to machine learning models and related technologies that generate optimal meal suggestions based on given data.

[0167] "Meal Suggestions" are a list of recommended meals based on the user's health data.

[0168] "Device" means the device (e.g., smartphone, tablet, etc.) used by a User to view meal suggestions.

[0169] "Selection information" is data on a specific meal menu selected by the user from the meal suggestion list.

[0170] "Monitoring" is the process by which the server periodically analyzes the user's health data and monitors their health status.

[0171] An "alert" is a warning message sent to a device when an abnormality in the user's health condition is detected.

[0172] "Food delivery" is a service that delivers meals ordered by users to a specified location.

[0173] The "feedback function" is a function that reflects the user's selection information in the next meal suggestion to improve the accuracy of the suggestion.

[0174] System program generation

[0175] The system based on this invention consists of the following main components: an interface for inputting users' personal information, a server for storing and analyzing data, a terminal for displaying meal suggestions, a food delivery ordering function, a health monitoring and alert function, and a feedback function. The entire system links these functions to provide users with individually optimized meal suggestions and food delivery services.

[0176] A natural language description of what the program does

[0177] 1. Collection of User's Personal Information:

[0178] Users operate the interface using a smartphone, tablet, or other device and enter their personal information (age, weight, height, sleep duration, exercise frequency, etc.) This information is sent from the device to a server and stored in a database.

[0179] 2. Data storage and analysis:

[0180] The server securely stores data from users and uses a generative AI model to analyze their individual health information, which then generates optimal dietary recommendations for them.

[0181] 3. Generating and displaying meal suggestions:

[0182] The generated meal suggestions are sent from the server to the user's device, which displays them to the user, who then selects from the suggested menu.

[0183] 4. Transmission and Storage of Selected Information:

[0184] The meal information selected by the user is sent from the device to the server and stored in a database, which is used as feedback to improve the next recommendation.

[0185] 5. Food delivery ordering feature:

[0186] It will provide a food delivery service linked to meal suggestions, allowing users to order directly from the suggestions displayed, and the selected meal will be delivered to the specified location.

[0187] 6. Health monitoring and alert features:

[0188] The server periodically analyzes the user's selection information and stored data to monitor their health status, and if an abnormality is detected, the server will send an alert to the device and provide appropriate advice.

[0189] Specific examples

[0190] An example of a usage scenario

[0191] Users download the app and enter personal information such as age, weight, height, sleep duration, and exercise frequency when they first launch it. The device then sends this information to a server, which then uses a generative AI model to analyze the data and create optimal meal recommendations for the day. For example, in the fall, menu suggestions might include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[0192] The user selects "grilled chicken and stir-fried vegetables" and confirms the order. The selection information is sent to the server and stored. The server uses the selection results to improve the next recommendation. If high-calorie meals are consistently selected, an alert is sent to the device, displaying a message saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0193] Prompt Sentence Examples

[0194] Enter your user information: age, weight, height, sleep time, exercise frequency

[0195] Example: 30 years old, 70 kg, 175 cm, 7 hours, 3 times a week

[0196]

[0197] Please select a suggested meal below:

[0198] 1. Pumpkin soup

[0199] 2. Grilled chicken and stir-fried vegetables

[0200] 3. Quinoa and avocado salad

[0201]

[0202] Your recent diet choices are high in calories. Try incorporating lower calorie options.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] Users use their devices to input personal information, such as age, weight, height, sleep duration, and exercise frequency, into the application interface, which is then temporarily stored on the device.

[0206] Input: Age, weight, height, sleep time, exercise frequency

[0207] Output: User's personal information is stored on the device

[0208] Step 2:

[0209] The device sends the user's personal information to the server. The data is serialized in JSON format and sent using a secure communication protocol (e.g., HTTPS). After sending, the server receives the data and stores it in a database.

[0210] Input: Personal information data in JSON format

[0211] Output: User's personal information stored in the server's database

[0212] Step 3:

[0213] The server uses a generative AI model to analyze the user's personal information stored in a database. Specifically, it evaluates the user's health based on data such as age, weight, height, sleep duration, and exercise frequency, and generates optimal dietary recommendations. Seasonal information is also taken into account.

[0214] Input: Personal information and seasonal information in the database

[0215] Output: Meal suggestions from a generative AI model

[0216] Step 4:

[0217] The server sends the generated meal suggestions in JSON format to the device, which receives the data and displays a list of meal suggestions to the user.

[0218] Input: Meal suggestions from a generative AI model

[0219] Output: A list of meal suggestions displayed on the device

[0220] Step 5:

[0221] The user selects one menu from the meal suggestion list, and the selected menu information is sent back to the server in JSON format and recorded in the database.

[0222] Input: User's selected meal menu

[0223] Output: Selection information saved on the server

[0224] Step 6:

[0225] The server provides feedback to the generative AI model based on the selection information to improve the next meal suggestion, which is then used to improve the accuracy of the next meal suggestion.

[0226] Input: Selection information, health data

[0227] Output: Improved meal suggestions

[0228] Step 7:

[0229] The user places an order for the selected meal menu with the food delivery service from the terminal. The terminal sends the order information to the food delivery service, and delivery begins.

[0230] Input: Selected meal menu

[0231] Output: Food delivery order

[0232] Step 8:

[0233] The server periodically monitors the stored user health data and generates an alert if an abnormality is detected, which is then sent to the device to notify the user.

[0234] Input: Stored health data

[0235] Output: The alert displayed on the terminal

[0236] Step 9:

[0237] Users can adjust their behavior based on alerts and suggestions they receive through their device. For example, if they have been consistently choosing high-calorie meals, they can adjust their diet based on an alert that says, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0238] Input: Alert message

[0239] Output: Improved user behavior

[0240] 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.

[0241] The present invention is a personalized diet recommendation system based on the health information of each user, and provides further adaptability by combining an emotion engine. Below, specific embodiments for carrying out the present invention will be described.

[0242] User Data Collection

[0243] The user opens the application on the device and enters their personal information (age, weight, height, sleep time, exercise details, etc.). The device sends this information to the server, which then stores the received information in a database.

[0244] Specific examples

[0245] Users use a smartphone app to enter information such as age 30, weight 70kg, height 175cm, sleep seven hours, and jog three times a week.

[0246] Data storage and analysis

[0247] The server securely stores the information received from the user in a database, and then uses generative artificial intelligence to analyze the user's health information and generate personalized meal suggestions.

[0248] Specific examples

[0249] The server analyzes the user's exercise volume and other health data based on the user information stored in the database, and suggests optimal healthy diets.

[0250] Generating meal suggestions

[0251] The server's AI generates an optimal meal suggestion list based on personal information and additional data such as seasonal information. This meal suggestion list is sent to the terminal and displayed to the user.

[0252] Specific examples

[0253] Meal suggestions for the fall season include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[0254] Emotion recognition and dietary suggestion adjustment

[0255] The emotion engine analyzes the user's facial expression and voice data to recognize their emotions, and the server further adjusts the meal suggestions based on this recognition result.

[0256] Specific examples

[0257] If the user looks tired, the emotion engine will recognize this and add food suggestions suitable for replenishing energy (e.g., banana pancakes, protein smoothies).

[0258] Recipe provision and data monitoring

[0259] The device displays the list of meal suggestions received from the server, and the user selects one of them and sends it to the server, which records the selection and uses it as feedback to improve the next suggestion.

[0260] Specific examples

[0261] The user selects "grilled chicken and stir-fried vegetables" and sends the selection from their device to the server, which records this information in a database and uses it for future suggestions.

[0262] Health monitoring and alerts

[0263] The server periodically monitors the user's health status based on the stored data and sends an alert if there is an abnormality. This alert is then sent to the user's device, providing necessary advice and suggestions.

[0264] Specific examples

[0265] If a user consistently chooses high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0266] The system of the present invention allows users to efficiently receive appropriate dietary suggestions based on health information. Furthermore, the use of an emotion engine enables detailed responses based on the user's emotional state, supporting the maintenance of a healthy lifestyle and the prevention of lifestyle-related diseases. This makes it easier for users to manage themselves, and health support tailored to individual needs can be realized.

[0267] The processing flow will be explained below.

[0268] Step 1:

[0269] The user opens the application on their device and enters their personal information (age, weight, height, sleep time, exercise details, etc.).

[0270] Step 2:

[0271] The device sends the user's personal information entered into the application to the server.

[0272] Step 3:

[0273] The server stores the personal information received from the user in a database.

[0274] Step 4:

[0275] The server uses artificial intelligence to analyze data based on the user's stored personal information.

[0276] Step 5:

[0277] Generative artificial intelligence takes into account the user's personal information and seasonal information to generate a personalized list of meal suggestions.

[0278] Step 6:

[0279] The server transmits the generated meal suggestion list to the terminal.

[0280] Step 7:

[0281] The device displays the received meal suggestion list to the user.

[0282] Step 8:

[0283] The user selects their preferred meal from the displayed list of meal suggestions.

[0284] Step 9:

[0285] The device sends the meal information selected by the user to the server.

[0286] Step 10:

[0287] The server stores the received selected meal information in a database.

[0288] Step 11:

[0289] The server periodically monitors the user's health data and selected dietary information.

[0290] Step 12:

[0291] The server receives the user's voice data and facial expression data and recognizes the user's emotions using an emotion engine.

[0292] Step 13:

[0293] The emotion engine analyzes the user's emotional data and adjusts the meal suggestion list.

[0294] Step 14:

[0295] The server sends the adjusted meal suggestion list to the terminal.

[0296] Step 15:

[0297] The device displays a tailored list of meal suggestions to the user.

[0298] Step 16:

[0299] The server generates alerts as needed and sends them to the device.

[0300] Step 17:

[0301] Notify the user of any alerts received by the device.

[0302] Example 2

[0303] 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."

[0304] Currently, many people find it difficult to choose the right diet to maintain a healthy lifestyle. In particular, selecting meals based on individual health conditions and daily emotions is a time-consuming and labor-intensive task using conventional methods. Furthermore, there is a lack of appropriate systems for users to utilize their own health data to receive specific dietary recommendations. Given this background, there is a need for a system that can provide personalized dietary recommendations that address individual needs and also take into account the user's emotional state.

[0305] 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.

[0306] In this invention, the server includes means for providing an interface for inputting the user's personal information, means for transmitting the personal information to the server and storing the personal information in a database, means for the server to generate optimal meal suggestions for the user using an artificial intelligence model based on the personal information, means for transmitting the generated meal suggestions to a user terminal and displaying the meal suggestions, means for transmitting meal information selected by the user to the server and storing the selected meal information in a database, means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected, and means for further adjusting the meal suggestions using an emotion engine that collects and analyzes the user's emotion data. This allows the user to efficiently receive personalized meal suggestions based on their health condition and further enables flexible responses according to their emotional state.

[0307] "User" refers to a person who uses the system.

[0308] "Personal information" refers to data entered by users, such as age, weight, height, sleep time, and exercise details.

[0309] "Interface" refers to the screen or input means provided for users to enter personal information.

[0310] "Server" refers to a computer system that receives, stores, and analyzes data sent by users.

[0311] "Database" refers to a system for securely storing personal information and other data within a server.

[0312] "Artificial Intelligence Model" refers to the algorithm and its implementation for generating optimal meal suggestions based on a user's personal information.

[0313] "Meal Suggestions" refers to a list of specific meal suggestions based on the user's health and personal information.

[0314] "User Terminal" means the device (e.g., smartphone or tablet) used by a User to access the System and receive meal suggestions.

[0315] An "emotion engine" refers to a system that determines a user's emotional state by collecting and analyzing their facial expression and voice data.

[0316] The "feedback function" refers to a function that improves the next meal suggestion based on the meal information selected by the user.

[0317] "Alert" refers to the function in which the server monitors the user's health status and notifies the user if an abnormality occurs.

[0318] The present invention is a system that provides personalized meal suggestions based on a user's health information, and provides further adaptability by combining it with an emotion engine. Specific means for implementing the present invention are described below.

[0319] User Data Collection

[0320] The user opens the application on their device (such as a smartphone or tablet) and enters personal information such as age, weight, height, sleep time, exercise details, etc. The device then sends the entered personal information to the server, which then stores the received information in a database.

[0321] Specific examples

[0322] A user opens the application and enters information such as "30 years old, 70 kg, 175 cm, 7 hours of sleep, jog three times a week."

[0323] Data storage and analysis

[0324] The server securely stores the information submitted by the user in a database, and then uses an artificial intelligence model (e.g., using a generative AI model) to analyze the user's health information and generate personalized dietary recommendations.

[0325] Specific examples

[0326] The server generates meal suggestions based on the user's age and activity level based on the user's information stored in the database. For example, it might suggest "grilled chicken and stir-fried vegetables" as a "high-protein food" for a specific user.

[0327] Generating meal suggestions

[0328] The server's artificial intelligence model takes into account the user's personal information and additional data such as seasonal information to generate an optimal meal suggestion list, which is then sent to the device and displayed to the user.

[0329] Specific examples

[0330] Meal suggestions for the fall season include pumpkin soup, grilled chicken and stir-fried vegetables, and quinoa and avocado salad.

[0331] Emotion data collection and analysis

[0332] The emotion engine collects and analyzes the user's facial expression and voice data to recognize their emotional state, based on which the server further adjusts the meal suggestions.

[0333] Specific examples

[0334] If a user uses the device's camera to show a "tired expression," the emotion engine will recognize the "feeling of fatigue" and add "banana pancakes, protein smoothies" that are suitable for replenishing energy.

[0335] Recipe provision and data monitoring

[0336] The device displays a list of meal suggestions sent from the server, and the user can select from them. The selected meal information is then sent back to the server, which uses this information as feedback to reflect in the next suggestion.

[0337] Specific examples

[0338] The user selects "grilled chicken and stir-fried vegetables" and sends that selection to the server, which records the selection in its database and improves its suggestions next time.

[0339] Health Monitoring and Alerts

[0340] The server regularly monitors the user's health status based on the stored data, and if it detects any abnormalities, it sends an alert to the user's device.

[0341] Specific examples

[0342] If the user continues to choose high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try choosing low-calorie options as well."

[0343] This allows users to receive appropriate and personalized meal suggestions based on their health and emotional state. The entire system is highly adaptable, responding dynamically to the user's choices and emotions.

[0344] Prompt Sentence Examples

[0345] "Generate personalized meal suggestions for the fall season for a user who is 30 years old, 70kg, 175cm tall, sleeps 7 hours, and jogs 3 times a week."

[0346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0347] Step 1:

[0348] The user launches the application on their device and enters personal information such as age, weight, height, sleep time, exercise details, etc. The entered information is then sent to the server by the user's device.

[0349] Specifically, the user enters "30 years old, 70 kg, 175 cm, 7 hours of sleep, jogging three times a week" into the application's input form and presses the "Submit" button.

[0350] Input: Age, weight, height, sleep time, exercise details

[0351] Output: Personal information data sent to the server

[0352] Step 2:

[0353] The server receives personal information data sent from the user terminal and stores it in a secure database.

[0354] Specifically, the server encrypts the received data and executes a process to store it in a database.

[0355] Input: Received personal information data

[0356] Output: Personal information stored in the database

[0357] Step 3:

[0358] The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze the personal information data stored in the database, and generates personalized meal recommendations based on the analysis results.

[0359] Specifically, the server inputs the user's health data into the generated AI model, which then generates meal suggestions such as "grilled chicken is good for users who need high-protein foods."

[0360] Input: Personal information stored in a database

[0361] Output: Personalized meal suggestions

[0362] Step 4:

[0363] The server transmits the generated meal suggestion list to the user terminal, which displays the list to the user.

[0364] Specifically, the server generates a list of dishes such as "pumpkin soup, grilled chicken and vegetable stir-fry, quinoa and avocado salad" and sends it to the device, which then displays it on the device screen.

[0365] Input: Generated meal suggestions

[0366] Output: A list of meal suggestions displayed on the user's device.

[0367] Step 5:

[0368] The user selects the desired menu from a list of meal suggestions, and the user's device sends the selection to the server.

[0369] Specifically, the user selects "grilled chicken and stir-fried vegetables," and the device sends this to the server.

[0370] Input: User's menu selection

[0371] Output: Selection data sent to the server

[0372] Step 6:

[0373] The server stores the received user menu selection data in a database and uses it as feedback to improve the next meal suggestion.

[0374] Specifically, the server saves the selected data and reflects it in the next analysis algorithm.

[0375] Input: Selection data sent to the server

[0376] Output: Selection data stored in the database as feedback

[0377] Step 7:

[0378] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, it sends an alert to the user's device. Specifically, if the server detects a user who has consistently chosen high-calorie meals, it sends an alert saying, "Your recent meals have been high in calories. Try incorporating low-calorie options."

[0379] Input: Saved data

[0380] Output: Alert notification sent to terminal

[0381] Step 8:

[0382] The emotion engine recognizes emotions by collecting and analyzing the user's facial and voice data, which is then sent to a server via the device and used to adjust meal suggestions.

[0383] Specifically, the user uses the device's camera and microphone to record their "tired facial expression" and "tired voice," which are then sent to the server. The server then analyzes the data using its emotion engine, recognizes the "feeling of fatigue," and offers "banana pancakes and protein smoothies" to replenish the energy.

[0384] Input: facial expression data, voice data

[0385] Output: Tailored meal suggestions

[0386] Step 9:

[0387] The device then displays the adjusted meal suggestions to the user again, from which the user can make a meal selection.

[0388] Specifically, the device displays new meal suggestions and the user selects again.

[0389] Input: Tailored meal suggestions

[0390] Output: User selects again

[0391] (Application example 2)

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

[0393] Conventional meal recommendation systems only make recommendations based on a user's personal information and do not take into account the user's emotional state. As a result, they are unable to make meal recommendations that are optimal for the user's emotions and circumstances at any given time, and are insufficient for improving the user's health management and satisfaction. The present invention aims to solve these problems and provide a personalized meal recommendation system that takes into account the user's emotional state as well as their personal information.

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

[0395] In this invention, the server includes means for providing an interface for inputting the user's personal information, means for transmitting the personal information to the server and storing the personal information in a database, means for the server to generate optimal meal suggestions for the user based on the personal information using a generative artificial intelligence, means for transmitting the generated meal suggestions to the user's terminal and displaying the meal suggestions on the terminal, means for adjusting the meal suggestions using an emotion engine that recognizes the user's emotions, means for transmitting meal information selected by the user to the server and storing the selected meal information in a database, and means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected. This enables personalized meal suggestions that comprehensively consider the user's health condition and emotional state.

[0396] "User personal information" refers to data related to the user's individual health status, such as age, weight, height, sleep time, and exercise.

[0397] "Means for providing an interface" refers to technology that provides an input screen for an application or web page that allows users to input personal information.

[0398] "Generative AI" is a system that uses machine learning and deep learning technologies to generate optimal meal suggestions based on a user's personal information.

[0399] The "emotion engine" is a technology that analyzes the user's facial expressions and voice data to recognize their emotional state at any given time.

[0400] "Meal Suggestion" refers to the suggestion of specific meals or dishes to consume based on the user's health information and emotional state.

[0401] A "terminal" refers to a device that is capable of processing information, such as a smartphone or tablet, used by a user.

[0402] "Database" means an integrated system for securely storing and managing users' personal information and food selection information.

[0403] "Health monitoring" is a system that periodically checks the user's stored health information and notifies them if any abnormalities are found.

[0404] "Means for sending alerts" refers to technology that sends health status notifications and warnings to the user's device.

[0405] The "feedback function" is a mechanism that reflects the meal information selected by the user in the next suggestions.

[0406] "Personalized" means specific to an individual user and tailored to their individual characteristics and preferences.

[0407] MODE FOR CARRYING OUT THE INVENTION

[0408] The present invention is a personalized meal recommendation system based on a user's personal information and emotional state. Specific embodiments of the present invention are described below.

[0409] User Data Collection

[0410] Users use a smartphone app to enter their personal information (age, weight, height, sleep time, exercise details, etc.) The device then sends the information to a server, which then securely stores it in a database.

[0411] Data storage and analysis

[0412] The server analyzes the data using a generative AI model (e.g., OpenAI GPT-3) based on the stored user information to generate optimal meal suggestions for the user.

[0413] Generating meal suggestions

[0414] The generative AI model uses the user's personal information and additional data, such as seasonal information, to generate a list of optimal meal suggestions, including specific meal plans and recipes, which are then sent to the user's device and displayed in the app.

[0415] Emotion recognition and dietary suggestion adjustment

[0416] The emotion engine uses the smartphone camera and microphone to collect facial and voice data from the user and analyzes their emotions. Based on the analysis results, the server further adjusts the meal suggestions. For example, if the user shows signs of fatigue, it will suggest meals suitable for replenishing energy.

[0417] Recipe provision and data monitoring

[0418] The meal information that the user selects from the suggested meal list is sent from the device to the server, which records this information in a database and uses it as feedback to improve the next meal suggestion.

[0419] Health monitoring and alerts

[0420] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, the server sends an alert to the user's device and provides necessary advice and suggestions.

[0421] Hardware and software used

[0422] Smartphone: Used for entering user information and emotion recognition.

[0423] Server: Used to host the database and operate the generative AI model.

[0424] EmotionRecognition module: As an emotion engine, it recognizes emotions using the smartphone camera and microphone.

[0425] HealthDataAnalysis module: Analyzes the user's health data and generates personalized dietary suggestions.

[0426] OpenAI GPT-3 API: Used as generative artificial intelligence.

[0427] The requests library: Used to send and receive HTTP requests.

[0428] Example prompt

[0429] For example, a prompt to a generative AI model might look like this:

[0430] plaintext

[0431] User information: Age 30, Weight 70kg, Height 175cm, Sleep time 7 hours, Exercise: Jogging 3 times a week

[0432] Emotion: Tired

[0433] Menu: Pumpkin soup, grilled chicken and vegetable stir-fry, quinoa and avocado salad, banana pancakes, protein smoothie

[0434] Season: Autumn

[0435] Generate optimal meal suggestions.

[0436] Based on this prompt, the generative AI model creates optimal meal suggestions for the user.

[0437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0438] Step 1:

[0439] The user opens the smartphone app and enters their personal information (age, weight, height, sleep duration, exercise, etc.) This information is collected through the application interface and sent by the device to the server.

[0440] Input: User's personal information

[0441] Output: Personal information data received by the server

[0442] Step 2:

[0443] The server stores the personal information in a database that uniquely identifies each user and is used for subsequent analysis.

[0444] Input: Personal information data received by the server

[0445] Output: User information stored in the database

[0446] Step 3:

[0447] The server uses a generative AI model (e.g., OpenAI GPT-3) to analyze the stored user information and generate personalized meal suggestions, taking into account the user's personal information and seasonal information.

[0448] Input: User and seasonal information stored in the database

[0449] Output: A list of meal suggestions generated by the generative AI model

[0450] Step 4:

[0451] The generated meal suggestion list is sent to the terminal and displayed to the user on the application.

[0452] Input: A list of meal suggestions generated by a generative AI model

[0453] Output: A list of meal suggestions displayed on the user's device

[0454] Step 5:

[0455] The emotion engine uses the smartphone camera and microphone to collect facial expression and voice data from the user, and analyzes their current emotions. The emotion recognition results are sent to the server.

[0456] Input: User facial expression and voice data collected by smartphone camera and microphone

[0457] Output: Emotion recognition data received by the server

[0458] Step 6:

[0459] The server further refines the generated meal suggestions based on emotion recognition data, for example, suggesting additional energy-replenishing meals if the user looks tired.

[0460] Input: Emotion recognition data and initial meal suggestion list

[0461] Output: Tailored meal suggestion list

[0462] Step 7:

[0463] The user makes a selection from the list of suggested meals and the selection is sent from the device to the server, which records the selection in a database.

[0464] Input: Meal information selected by the user

[0465] Output: Selected meal information stored in a database

[0466] Step 8:

[0467] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, an alert is sent to the user's device and appropriate advice or suggestions are provided.

[0468] Input: User health information stored in a database

[0469] Output: Alert notification sent to user's device

[0470] 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.

[0471] 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.

[0472] 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.

[0473] [Second embodiment]

[0474] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0485] 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."

[0486] The present invention provides a personalized diet recommendation system based on the health information of each user. Hereinafter, embodiments of the present invention will be described in detail.

[0487] User Data Collection

[0488] The user opens the application on the device and enters their personal information (age, weight, height, sleep time, exercise, etc.) The device sends this information to the server, which then stores it in a database.

[0489] Specific examples

[0490] Users use a smartphone app to enter information such as age 30, weight 70kg, height 175cm, sleep seven hours, and jog three times a week.

[0491] Data storage and analysis

[0492] The server securely stores the information received from the user in a database, and then uses generative artificial intelligence to analyze the user's health information and generate personalized meal suggestions.

[0493] Specific examples

[0494] The server analyzes the user's exercise volume and other health data based on the user information stored in the database, and suggests optimal healthy diets.

[0495] Generating meal suggestions

[0496] The server's AI generates an optimal meal suggestion list based on personal information and additional data such as seasonal information. This meal suggestion list is sent to the terminal and displayed to the user.

[0497] Specific examples

[0498] Meal suggestions for the fall season include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[0499] Recipe provision and data monitoring

[0500] The device displays the list of meal suggestions received from the server, and the user selects one of them and sends it to the server, which records the selection and uses it as feedback to improve the next suggestion.

[0501] Specific examples

[0502] The user selects "grilled chicken and stir-fried vegetables" and sends the selection from their device to the server, which records this information in a database and uses it for future suggestions.

[0503] Health monitoring and alerts

[0504] The server periodically monitors the user's health status based on the stored data and sends an alert if there is an abnormality. This alert is then sent to the user's device, providing necessary advice and suggestions.

[0505] Specific examples

[0506] If a user consistently chooses high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0507] The system of the present invention allows users to efficiently receive appropriate dietary suggestions based on health information, which supports the maintenance of a healthy lifestyle and the prevention of lifestyle-related diseases, making it easier for users to manage their own lifestyle.

[0508] ---

[0509] The system described in the embodiment of the present invention provides personalized meal suggestions according to the user's health condition and supports continuous health management, allowing the user to maintain their health efficiently and effectively.

[0510] The processing flow will be explained below.

[0511] Step 1:

[0512] The user opens the application on their device and enters their personal information (age, weight, height, sleep time, exercise details, etc.).

[0513] Step 2:

[0514] The device sends the user's personal information entered into the application to the server.

[0515] Step 3:

[0516] The server stores the personal information received from the user in a database.

[0517] Step 4:

[0518] The server uses artificial intelligence to analyze data based on the user's stored personal information.

[0519] Step 5:

[0520] Generative AI takes into account the user's personal information and seasonal information to generate a personalized list of meal suggestions.

[0521] Step 6:

[0522] The server transmits the generated meal suggestion list to the terminal.

[0523] Step 7:

[0524] The device displays the received meal suggestion list to the user.

[0525] Step 8:

[0526] The user selects their preferred meal from the displayed list of meal suggestions.

[0527] Step 9:

[0528] The device sends the meal information selected by the user to the server.

[0529] Step 10:

[0530] The server stores the received selected meal information in a database.

[0531] Step 11:

[0532] The server periodically monitors the user's health data and selected dietary information, and generates alerts if necessary if an abnormality is detected.

[0533] Step 12:

[0534] The server sends the generated alert to the terminal, and the terminal notifies the user of the alert.

[0535] Example 1

[0536] 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."

[0537] In modern society, health problems such as lifestyle-related diseases are on the rise, and individual users are being called upon to select optimal diets and manage their health based on their own health information. However, there are currently no systems that allow users to receive appropriate dietary suggestions based on their own health information. Therefore, it is an important challenge to provide a system that makes personalized dietary suggestions for each individual.

[0538] 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.

[0539] In this invention, the server includes means for providing an interface for inputting a user's health information, means for transmitting the health information to the server and storing the health information in a database, means for the server to generate optimal dietary suggestions for the user based on the health information using artificial intelligence, means for transmitting the generated dietary suggestions to the user's terminal and displaying the dietary suggestions, means for transmitting dietary information selected by the user to the server and storing the selected dietary information in a database, and means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected. This enables personalized dietary suggestions based on the user's health information, as well as feedback and monitoring of the suggestions.

[0540] A "user" is a user who accesses the system, inputs their own health information, and receives dietary suggestions.

[0541] "Health information" refers to personal data entered by the user, such as age, weight, height, sleep time, and exercise details.

[0542] The "server" is a computer system that stores health information received from users and generates dietary suggestions using generative artificial intelligence.

[0543] "Database" means a data storage system that allows the server to store and manage health information, dietary suggestions, and user selection data.

[0544] "Generative AI" refers to algorithms or programs that generate meal suggestions tailored to the user based on input data.

[0545] "Meal Suggestions" are personalized meal recommendations provided to users as a result of analysis by the generative artificial intelligence.

[0546] "Terminal" means the device used by a user to enter health information, receive dietary suggestions, and submit selected information.

[0547] "Preference information" is data about the particular meal menu a user selects from a presented list of meal suggestions.

[0548] "Monitoring" refers to the act of the server periodically monitoring stored user data to check for any abnormalities.

[0549] An "alert" is a warning or notification sent from the server to the user's device when an abnormality is detected as a result of monitoring.

[0550] The present invention is a personalized diet recommendation system based on individual user health information. The system uses generative artificial intelligence (generative AI model) to generate optimal diet recommendations based on the health information provided by the user, and then makes recommendations to the user. Specific embodiments for implementing the present invention are described in detail below.

[0551] User Data Collection

[0552] The user opens the application on their device (e.g., smartphone or tablet) and enters their health information (age, weight, height, sleep time, exercise, etc.). The entered health information is sent from the device to the server, which then securely stores the received information in a database.

[0553] Specific examples

[0554] Users use a smartphone app to input information such as age 30, weight 70kg, height 175cm, sleep 7 hours, and jog three times a week, which is then sent to a server via the device and stored in a database.

[0555] Data storage and analysis

[0556] The server stores the health information received from the user in a database and then analyzes it using a generative AI model, which then generates optimal dietary recommendations based on the user's health information.

[0557] Specific examples

[0558] The server analyzes the user's exercise and lifestyle habits based on the user information stored in the database, and automatically generates appropriate meal suggestions. Specifically, it uses a generative AI model implemented in Python to input user data and obtain meal suggestions.

[0559] Generating meal suggestions

[0560] The server's generative AI model takes into account the user's personal information as well as additional data (e.g., seasonal information) to generate an optimal meal recommendation list, which is then sent to the device and displayed to the user.

[0561] Specific examples

[0562] For example, to suggest meals suited to the autumn season, the generative AI model is used to create "pumpkin soup," "grilled chicken and stir-fried vegetables," and "quinoa and avocado salad," and these are then recommended to the user.

[0563] Recipe provision and data monitoring

[0564] When a user selects a meal from the list of suggestions, the information is sent from the device to the server, where it is recorded. This feedback information is used to improve the accuracy of the next meal suggestions.

[0565] Specific examples

[0566] When a user selects "grilled chicken and stir-fried vegetables," the selection information is sent to the server via the device and recorded in the database. It is used as feedback for future suggestions.

[0567] Health monitoring and alerts

[0568] The server periodically monitors the stored health information and, if an abnormality is detected, sends an alert to the user's device, allowing the user to receive appropriate advice and warnings in a timely manner.

[0569] Specific examples

[0570] If a user continues to choose high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0571] Prompt Sentence Examples

[0572] Here are some example prompts to get a generative AI model to analyze user data:

[0573] Generate personalized meal suggestions based on the user's health information (age, weight, height, sleep duration, exercise, etc.) Example: Provide the optimal meal plan for autumn for a user who is 30 years old, 70kg, 175cm tall, and jogs 3 times a week.

[0574] Using these prompts, the generative AI model provides specific dietary suggestions tailored to the user, supporting individual health management. This system allows users to efficiently receive appropriate dietary recommendations based on their own health information, helping them maintain a healthy lifestyle and prevent lifestyle-related diseases.

[0575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0576] Step 1: Entering User Data

[0577] The user enters their health information into the device. The input fields include age, weight, height, sleep time, exercise, etc. After the user enters all the information, they press the "Send" button. The input data is displayed on the device and prepared for transmission.

[0578] Input: Health information entered by the user (age, weight, height, sleep time, exercise details)

[0579] Output: Input information displayed on the terminal, ready to send

[0580] Step 2: Sending data

[0581] When the user presses the "Submit" button, the device sends the input data to the server via an HTTP POST request, which encodes the data in JSON format and transmits it over the network.

[0582] Input: Health information entered by the user, click of the submit button

[0583] Output: Health information sent to the server in JSON format

[0584] Step 3: Save your data

[0585] The server analyzes the received health information and saves it in a database. The database stores profile information for each user. The server saves this information in the user table using an INSERT statement.

[0586] Input: Health information in JSON format sent to the server

[0587] Output: Health information stored in a database

[0588] Step 4: Analyze the data

[0589] The server retrieves the user's health information from the database and analyzes it using a generative AI model. The generative AI model generates optimal dietary recommendations based on the user's health information. The server uses a Python script to call the generative AI model and obtain the analysis results.

[0590] Input: Health information stored in a database

[0591] Output: Analysis results from the generative AI model (optimal meal suggestions)

[0592] Step 5: Generate and submit a meal suggestion list

[0593] The server generates a list of meal suggestions based on the analysis results obtained from the generative AI model. This list is encoded in JSON format and sent to the device as an HTTP response. The device then displays the received data to the user.

[0594] Input: Analysis results from generative AI model

[0595] Output: List of meal suggestions sent to the device, meal suggestions displayed to the user

[0596] Step 6: User Choice Feedback

[0597] The user selects one from the list of meal suggestions presented. The user's selection information is sent back to the server from the device, and the server records this data in a database. This data will be used to improve the next meal suggestion.

[0598] Input: User-selected meal information

[0599] Output: Selections sent to the server, selections recorded in the database

[0600] Step 7: Monitoring your health information

[0601] The server periodically monitors the user's health information and food selection history stored in the database, and if any abnormal patterns are detected, an alert is generated and sent to the user's device.

[0602] Input: Health information and dietary choice history stored in a database

[0603] Output: Alerts if anomalies are detected, notifications sent to the device

[0604] Specific operation example

[0605] When a user uses a smartphone app to input and submit health information, the device sends the data in JSON format to a server. The server stores the information in a database and invokes a generative AI model for analysis. As a result of the analysis, optimal meal suggestions for autumn are generated, suggesting pumpkin soup, grilled chicken and stir-fried vegetables, and quinoa and avocado salad. When the user selects "grilled chicken and stir-fried vegetables," the selection information is sent to the server and recorded in the database. The server periodically monitors the data, and if an abnormality is detected, an alert is sent to the device. In this way, users can receive personalized meal suggestions and manage their health.

[0606] Prompt Sentence Examples

[0607] Generate personalized meal suggestions based on the user's health information (age, weight, height, sleep duration, exercise, etc.) Example: Provide the optimal meal plan for autumn for a user who is 30 years old, 70kg, 175cm tall, and jogs 3 times a week.

[0608] (Application example 1)

[0609] 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."

[0610] While existing meal recommendation systems provide personalized suggestions based on the user's health information, they lack a mechanism for linking these with actual meal ordering and delivery. As a result, users must prepare the suggested meals themselves, which is time-consuming and laborious, resulting in low effectiveness of the suggestions. Furthermore, there is little mechanism for incorporating feedback on whether the suggested meals are appropriate, making it unclear whether there is room for improvement in future suggestions.

[0611] 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.

[0612] In this invention, the server includes: means for providing an interface for inputting a user's personal information; means for transmitting the personal information to the server and storing the personal information in a database; means for the server to generate optimal meal suggestions for the user based on the personal information using artificial intelligence; means for transmitting the generated meal suggestions to the user's terminal and displaying the meal suggestions; means for transmitting meal information selected by the user to the server and storing the selected meal information in a database; means for the server to periodically monitor the user's health status based on the stored data and send an alert if an abnormality is detected; means for linking the meal suggestions based on the user's health information with a food delivery ordering function; and means having a feedback function for analyzing the food delivery order history to improve the next suggestion. This allows the user to easily actually order the suggested meals, and the selection results are reflected in subsequent suggestions, enabling more effective health management for the user.

[0613] "User personal information" refers to data related to the user's health and lifestyle, such as age, weight, height, sleep time, and exercise frequency.

[0614] "Interface" means the portion of the software that a user uses to manipulate and input data.

[0615] A "server" is a computer system for storing and processing data received from users.

[0616] "Database" means a system for systematically storing and managing users' personal information and selection information.

[0617] "Generative AI" refers to machine learning models and related technologies that generate optimal meal suggestions based on given data.

[0618] "Meal Suggestions" are a list of recommended meals based on the user's health data.

[0619] "Device" means the device (e.g., smartphone, tablet, etc.) used by a User to view meal suggestions.

[0620] "Selection information" is data on a specific meal menu selected by the user from the meal suggestion list.

[0621] "Monitoring" is the process by which the server periodically analyzes the user's health data and monitors their health status.

[0622] An "alert" is a warning message sent to a device when an abnormality in the user's health condition is detected.

[0623] "Food delivery" is a service that delivers meals ordered by users to a specified location.

[0624] The "feedback function" is a function that reflects the user's selection information in the next meal suggestion to improve the accuracy of the suggestion.

[0625] System program generation

[0626] The system based on this invention consists of the following main components: an interface for inputting users' personal information, a server for storing and analyzing data, a terminal for displaying meal suggestions, a food delivery ordering function, a health monitoring and alert function, and a feedback function. The entire system links these functions to provide users with individually optimized meal suggestions and food delivery services.

[0627] A natural language description of what the program does

[0628] 1. Collection of User's Personal Information:

[0629] Users operate the interface using a smartphone, tablet, or other device and enter their personal information (age, weight, height, sleep duration, exercise frequency, etc.) This information is sent from the device to a server and stored in a database.

[0630] 2. Data storage and analysis:

[0631] The server securely stores data from users and uses a generative AI model to analyze their individual health information, which then generates optimal dietary recommendations for them.

[0632] 3. Generating and displaying meal suggestions:

[0633] The generated meal suggestions are sent from the server to the user's device, which displays them to the user, who then selects from the suggested menu.

[0634] 4. Transmission and Storage of Selected Information:

[0635] The meal information selected by the user is sent from the device to the server and stored in a database, which is used as feedback to improve the next recommendation.

[0636] 5. Food delivery ordering feature:

[0637] It will provide a food delivery service linked to meal suggestions, allowing users to order directly from the suggestions displayed, and the selected meal will be delivered to the specified location.

[0638] 6. Health monitoring and alert features:

[0639] The server periodically analyzes the user's selection information and stored data to monitor their health status, and if an abnormality is detected, the server will send an alert to the device and provide appropriate advice.

[0640] Specific examples

[0641] An example of a usage scenario

[0642] Users download the app and enter personal information such as age, weight, height, sleep duration, and exercise frequency when they first launch it. The device then sends this information to a server, which then uses a generative AI model to analyze the data and create optimal meal recommendations for the day. For example, in the fall, menu suggestions might include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[0643] The user selects "grilled chicken and stir-fried vegetables" and confirms the order. The selection information is sent to the server and stored. The server uses the selection results to improve the next recommendation. If high-calorie meals are consistently selected, an alert is sent to the device, displaying a message saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0644] Prompt Sentence Examples

[0645] Enter your user information: age, weight, height, sleep time, exercise frequency

[0646] Example: 30 years old, 70 kg, 175 cm, 7 hours, 3 times a week

[0647]

[0648] Please select a suggested meal below:

[0649] 1. Pumpkin soup

[0650] 2. Grilled chicken and stir-fried vegetables

[0651] 3. Quinoa and avocado salad

[0652]

[0653] Your recent diet choices are high in calories. Try incorporating lower calorie options.

[0654] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0655] Step 1:

[0656] Users use their devices to input personal information, such as age, weight, height, sleep duration, and exercise frequency, into the application interface, which is then temporarily stored on the device.

[0657] Input: Age, weight, height, sleep time, exercise frequency

[0658] Output: User's personal information is stored on the device

[0659] Step 2:

[0660] The device sends the user's personal information to the server. The data is serialized in JSON format and sent using a secure communication protocol (e.g., HTTPS). After sending, the server receives the data and stores it in a database.

[0661] Input: Personal information data in JSON format

[0662] Output: User's personal information stored in the server's database

[0663] Step 3:

[0664] The server uses a generative AI model to analyze the user's personal information stored in a database. Specifically, it evaluates the user's health based on data such as age, weight, height, sleep duration, and exercise frequency, and generates optimal dietary recommendations. Seasonal information is also taken into account.

[0665] Input: Personal information and seasonal information in the database

[0666] Output: Meal suggestions from a generative AI model

[0667] Step 4:

[0668] The server sends the generated meal suggestions in JSON format to the device, which receives the data and displays a list of meal suggestions to the user.

[0669] Input: Meal suggestions from a generative AI model

[0670] Output: A list of meal suggestions displayed on the device

[0671] Step 5:

[0672] The user selects one menu from the meal suggestion list, and the selected menu information is sent back to the server in JSON format and recorded in the database.

[0673] Input: User's selected meal menu

[0674] Output: Selection information saved on the server

[0675] Step 6:

[0676] The server provides feedback to the generative AI model based on the selection information to improve the next meal suggestion, which is then used to improve the accuracy of the next meal suggestion.

[0677] Input: Selection information, health data

[0678] Output: Improved meal suggestions

[0679] Step 7:

[0680] The user places an order for the selected meal menu with the food delivery service from the terminal. The terminal sends the order information to the food delivery service, and delivery begins.

[0681] Input: Selected meal menu

[0682] Output: Food delivery order

[0683] Step 8:

[0684] The server periodically monitors the stored user health data and generates an alert if an abnormality is detected, which is then sent to the device to notify the user.

[0685] Input: Stored health data

[0686] Output: The alert displayed on the terminal

[0687] Step 9:

[0688] Users can adjust their behavior based on alerts and suggestions they receive through their device. For example, if they have been consistently choosing high-calorie meals, they can adjust their diet based on an alert that says, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0689] Input: Alert message

[0690] Output: Improved user behavior

[0691] 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.

[0692] The present invention is a personalized diet recommendation system based on the health information of each user, and provides further adaptability by combining an emotion engine. Below, specific embodiments for carrying out the present invention will be described.

[0693] User Data Collection

[0694] The user opens the application on the device and enters their personal information (age, weight, height, sleep time, exercise details, etc.). The device sends this information to the server, which then stores the received information in a database.

[0695] Specific examples

[0696] Users use a smartphone app to enter information such as age 30, weight 70kg, height 175cm, sleep seven hours, and jog three times a week.

[0697] Data storage and analysis

[0698] The server securely stores the information received from the user in a database, and then uses generative artificial intelligence to analyze the user's health information and generate personalized meal suggestions.

[0699] Specific examples

[0700] The server analyzes the user's exercise volume and other health data based on the user information stored in the database, and suggests optimal healthy diets.

[0701] Generating meal suggestions

[0702] The server's AI generates an optimal meal suggestion list based on personal information and additional data such as seasonal information. This meal suggestion list is sent to the terminal and displayed to the user.

[0703] Specific examples

[0704] Meal suggestions for the fall season include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[0705] Emotion recognition and dietary suggestion adjustment

[0706] The emotion engine analyzes the user's facial expression and voice data to recognize their emotions, and the server further adjusts the meal suggestions based on this recognition result.

[0707] Specific examples

[0708] If the user looks tired, the emotion engine will recognize this and add food suggestions suitable for replenishing energy (e.g., banana pancakes, protein smoothies).

[0709] Recipe provision and data monitoring

[0710] The device displays the list of meal suggestions received from the server, and the user selects one of them and sends it to the server, which records the selection and uses it as feedback to improve the next suggestion.

[0711] Specific examples

[0712] The user selects "grilled chicken and stir-fried vegetables" and sends the selection from their device to the server, which records this information in a database and uses it for future suggestions.

[0713] Health monitoring and alerts

[0714] The server periodically monitors the user's health status based on the stored data and sends an alert if there is an abnormality. This alert is then sent to the user's device, providing necessary advice and suggestions.

[0715] Specific examples

[0716] If a user consistently chooses high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0717] The system of the present invention allows users to efficiently receive appropriate dietary suggestions based on health information. Furthermore, the use of an emotion engine enables detailed responses based on the user's emotional state, supporting the maintenance of a healthy lifestyle and the prevention of lifestyle-related diseases. This makes it easier for users to manage themselves, and health support tailored to individual needs can be realized.

[0718] The processing flow will be explained below.

[0719] Step 1:

[0720] The user opens the application on their device and enters their personal information (age, weight, height, sleep time, exercise details, etc.).

[0721] Step 2:

[0722] The device sends the user's personal information entered into the application to the server.

[0723] Step 3:

[0724] The server stores the personal information received from the user in a database.

[0725] Step 4:

[0726] The server uses artificial intelligence to analyze data based on the user's stored personal information.

[0727] Step 5:

[0728] Generative artificial intelligence takes into account the user's personal information and seasonal information to generate a personalized list of meal suggestions.

[0729] Step 6:

[0730] The server transmits the generated meal suggestion list to the terminal.

[0731] Step 7:

[0732] The device displays the received meal suggestion list to the user.

[0733] Step 8:

[0734] The user selects their preferred meal from the displayed list of meal suggestions.

[0735] Step 9:

[0736] The device sends the meal information selected by the user to the server.

[0737] Step 10:

[0738] The server stores the received selected meal information in a database.

[0739] Step 11:

[0740] The server periodically monitors the user's health data and selected dietary information.

[0741] Step 12:

[0742] The server receives the user's voice data and facial expression data and recognizes the user's emotions using an emotion engine.

[0743] Step 13:

[0744] The emotion engine analyzes the user's emotional data and adjusts the meal suggestion list.

[0745] Step 14:

[0746] The server sends the adjusted meal suggestion list to the terminal.

[0747] Step 15:

[0748] The device displays a tailored list of meal suggestions to the user.

[0749] Step 16:

[0750] The server generates alerts as needed and sends them to the device.

[0751] Step 17:

[0752] Notify the user of any alerts received by the device.

[0753] Example 2

[0754] 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."

[0755] Currently, many people find it difficult to choose the right diet to maintain a healthy lifestyle. In particular, selecting meals based on individual health conditions and daily emotions is a time-consuming and labor-intensive task using conventional methods. Furthermore, there is a lack of appropriate systems for users to utilize their own health data to receive specific dietary recommendations. Given this background, there is a need for a system that can provide personalized dietary recommendations that address individual needs and also take into account the user's emotional state.

[0756] 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.

[0757] In this invention, the server includes means for providing an interface for inputting the user's personal information, means for transmitting the personal information to the server and storing the personal information in a database, means for the server to generate optimal meal suggestions for the user using an artificial intelligence model based on the personal information, means for transmitting the generated meal suggestions to a user terminal and displaying the meal suggestions, means for transmitting meal information selected by the user to the server and storing the selected meal information in a database, means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected, and means for further adjusting the meal suggestions using an emotion engine that collects and analyzes the user's emotion data. This allows the user to efficiently receive personalized meal suggestions based on their health condition and further enables flexible responses according to their emotional state.

[0758] "User" refers to a person who uses the system.

[0759] "Personal information" refers to data entered by users, such as age, weight, height, sleep time, and exercise details.

[0760] "Interface" refers to the screen or input means provided for users to enter personal information.

[0761] "Server" refers to a computer system that receives, stores, and analyzes data sent by users.

[0762] "Database" refers to a system for securely storing personal information and other data within a server.

[0763] "Artificial Intelligence Model" refers to the algorithm and its implementation for generating optimal meal suggestions based on a user's personal information.

[0764] "Meal Suggestions" refers to a list of specific meal suggestions based on the user's health and personal information.

[0765] "User Terminal" means the device (e.g., smartphone or tablet) used by a User to access the System and receive meal suggestions.

[0766] An "emotion engine" refers to a system that determines a user's emotional state by collecting and analyzing their facial expression and voice data.

[0767] The "feedback function" refers to a function that improves the next meal suggestion based on the meal information selected by the user.

[0768] "Alert" refers to the function in which the server monitors the user's health status and notifies the user if an abnormality occurs.

[0769] The present invention is a system that provides personalized meal suggestions based on a user's health information, and provides further adaptability by combining it with an emotion engine. Specific means for implementing the present invention are described below.

[0770] User Data Collection

[0771] The user opens the application on their device (such as a smartphone or tablet) and enters personal information such as age, weight, height, sleep time, exercise details, etc. The device then sends the entered personal information to the server, which then stores the received information in a database.

[0772] Specific examples

[0773] A user opens the application and enters information such as "30 years old, 70 kg, 175 cm, 7 hours of sleep, jog three times a week."

[0774] Data storage and analysis

[0775] The server securely stores the information submitted by the user in a database, and then uses an artificial intelligence model (e.g., using a generative AI model) to analyze the user's health information and generate personalized dietary recommendations.

[0776] Specific examples

[0777] The server generates meal suggestions based on the user's age and activity level based on the user's information stored in the database. For example, it might suggest "grilled chicken and stir-fried vegetables" as a "high-protein food" for a specific user.

[0778] Generating meal suggestions

[0779] The server's artificial intelligence model takes into account the user's personal information and additional data such as seasonal information to generate an optimal meal suggestion list, which is then sent to the device and displayed to the user.

[0780] Specific examples

[0781] Meal suggestions for the fall season include pumpkin soup, grilled chicken and stir-fried vegetables, and quinoa and avocado salad.

[0782] Emotion data collection and analysis

[0783] The emotion engine collects and analyzes the user's facial expression and voice data to recognize their emotional state, based on which the server further adjusts the meal suggestions.

[0784] Specific examples

[0785] If a user uses the device's camera to show a "tired expression," the emotion engine will recognize the "feeling of fatigue" and add "banana pancakes, protein smoothies" that are suitable for replenishing energy.

[0786] Recipe provision and data monitoring

[0787] The device displays a list of meal suggestions sent from the server, and the user can select from them. The selected meal information is then sent back to the server, which uses this information as feedback to reflect in the next suggestion.

[0788] Specific examples

[0789] The user selects "grilled chicken and stir-fried vegetables" and sends that selection to the server, which records the selection in its database and improves its suggestions next time.

[0790] Health Monitoring and Alerts

[0791] The server regularly monitors the user's health status based on the stored data, and if it detects any abnormalities, it sends an alert to the user's device.

[0792] Specific examples

[0793] If the user continues to choose high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try choosing low-calorie options as well."

[0794] This allows users to receive appropriate and personalized meal suggestions based on their health and emotional state. The entire system is highly adaptable, responding dynamically to the user's choices and emotions.

[0795] Prompt Sentence Examples

[0796] "Generate personalized meal suggestions for the fall season for a user who is 30 years old, 70kg, 175cm tall, sleeps 7 hours, and jogs 3 times a week."

[0797] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0798] Step 1:

[0799] The user launches the application on their device and enters personal information such as age, weight, height, sleep time, exercise details, etc. The entered information is then sent to the server by the user's device.

[0800] Specifically, the user enters "30 years old, 70 kg, 175 cm, 7 hours of sleep, jogging three times a week" into the application's input form and presses the "Submit" button.

[0801] Input: Age, weight, height, sleep time, exercise details

[0802] Output: Personal information data sent to the server

[0803] Step 2:

[0804] The server receives personal information data sent from the user terminal and stores it in a secure database.

[0805] Specifically, the server encrypts the received data and executes a process to store it in a database.

[0806] Input: Received personal information data

[0807] Output: Personal information stored in the database

[0808] Step 3:

[0809] The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze the personal information data stored in the database, and generates personalized meal recommendations based on the analysis results.

[0810] Specifically, the server inputs the user's health data into the generated AI model, which then generates meal suggestions such as "grilled chicken is good for users who need high-protein foods."

[0811] Input: Personal information stored in a database

[0812] Output: Personalized meal suggestions

[0813] Step 4:

[0814] The server transmits the generated meal suggestion list to the user terminal, which displays the list to the user.

[0815] Specifically, the server generates a list of dishes such as "pumpkin soup, grilled chicken and vegetable stir-fry, quinoa and avocado salad" and sends it to the device, which then displays it on the device screen.

[0816] Input: Generated meal suggestions

[0817] Output: A list of meal suggestions displayed on the user's device.

[0818] Step 5:

[0819] The user selects the desired menu from a list of meal suggestions, and the user's device sends the selection to the server.

[0820] Specifically, the user selects "grilled chicken and stir-fried vegetables," and the device sends this to the server.

[0821] Input: User's menu selection

[0822] Output: Selection data sent to the server

[0823] Step 6:

[0824] The server stores the received user menu selection data in a database and uses it as feedback to improve the next meal suggestion.

[0825] Specifically, the server saves the selected data and reflects it in the next analysis algorithm.

[0826] Input: Selection data sent to the server

[0827] Output: Selection data stored in the database as feedback

[0828] Step 7:

[0829] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, it sends an alert to the user's device. Specifically, if the server detects a user who has consistently chosen high-calorie meals, it sends an alert saying, "Your recent meals have been high in calories. Try incorporating low-calorie options."

[0830] Input: Saved data

[0831] Output: Alert notification sent to terminal

[0832] Step 8:

[0833] The emotion engine recognizes emotions by collecting and analyzing the user's facial and voice data, which is then sent to a server via the device and used to adjust meal suggestions.

[0834] Specifically, the user uses the device's camera and microphone to record their "tired facial expression" and "tired voice," which are then sent to the server. The server then analyzes the data using its emotion engine, recognizes the "feeling of fatigue," and offers "banana pancakes and protein smoothies" to replenish the energy.

[0835] Input: facial expression data, voice data

[0836] Output: Tailored meal suggestions

[0837] Step 9:

[0838] The device then displays the adjusted meal suggestions to the user again, from which the user can make a meal selection.

[0839] Specifically, the device displays new meal suggestions and the user selects again.

[0840] Input: Tailored meal suggestions

[0841] Output: User selects again

[0842] (Application example 2)

[0843] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0844] Conventional meal recommendation systems only make recommendations based on a user's personal information and do not take into account the user's emotional state. As a result, they are unable to make meal recommendations that are optimal for the user's emotions and circumstances at any given time, and are insufficient for improving the user's health management and satisfaction. The present invention aims to solve these problems and provide a personalized meal recommendation system that takes into account the user's emotional state as well as their personal information.

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

[0846] In this invention, the server includes means for providing an interface for inputting the user's personal information, means for transmitting the personal information to the server and storing the personal information in a database, means for the server to generate optimal meal suggestions for the user based on the personal information using a generative artificial intelligence, means for transmitting the generated meal suggestions to the user's terminal and displaying the meal suggestions on the terminal, means for adjusting the meal suggestions using an emotion engine that recognizes the user's emotions, means for transmitting meal information selected by the user to the server and storing the selected meal information in a database, and means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected. This enables personalized meal suggestions that comprehensively consider the user's health condition and emotional state.

[0847] "User personal information" refers to data related to the user's individual health status, such as age, weight, height, sleep time, and exercise.

[0848] "Means for providing an interface" refers to technology that provides an input screen for an application or web page that allows users to input personal information.

[0849] "Generative AI" is a system that uses machine learning and deep learning technologies to generate optimal meal suggestions based on a user's personal information.

[0850] The "emotion engine" is a technology that analyzes the user's facial expressions and voice data to recognize their emotional state at any given time.

[0851] "Meal Suggestion" refers to the suggestion of specific meals or dishes to consume based on the user's health information and emotional state.

[0852] A "terminal" refers to a device that is capable of processing information, such as a smartphone or tablet, used by a user.

[0853] "Database" means an integrated system for securely storing and managing users' personal information and food selection information.

[0854] "Health monitoring" is a system that periodically checks the user's stored health information and notifies them if any abnormalities are found.

[0855] "Means for sending alerts" refers to technology that sends health status notifications and warnings to the user's device.

[0856] The "feedback function" is a mechanism that reflects the meal information selected by the user in the next suggestions.

[0857] "Personalized" means specific to an individual user and tailored to their individual characteristics and preferences.

[0858] MODE FOR CARRYING OUT THE INVENTION

[0859] The present invention is a personalized meal recommendation system based on a user's personal information and emotional state. Specific embodiments of the present invention are described below.

[0860] User Data Collection

[0861] Users use a smartphone app to enter their personal information (age, weight, height, sleep time, exercise details, etc.) The device then sends the information to a server, which then securely stores it in a database.

[0862] Data storage and analysis

[0863] The server analyzes the data using a generative AI model (e.g., OpenAI GPT-3) based on the stored user information to generate optimal meal suggestions for the user.

[0864] Generating meal suggestions

[0865] The generative AI model uses the user's personal information and additional data, such as seasonal information, to generate a list of optimal meal suggestions, including specific meal plans and recipes, which are then sent to the user's device and displayed in the app.

[0866] Emotion recognition and dietary suggestion adjustment

[0867] The emotion engine uses the smartphone camera and microphone to collect facial and voice data from the user and analyzes their emotions. Based on the analysis results, the server further adjusts the meal suggestions. For example, if the user shows signs of fatigue, it will suggest meals suitable for replenishing energy.

[0868] Recipe provision and data monitoring

[0869] The meal information that the user selects from the suggested meal list is sent from the device to the server, which records this information in a database and uses it as feedback to improve the next meal suggestion.

[0870] Health monitoring and alerts

[0871] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, the server sends an alert to the user's device and provides necessary advice and suggestions.

[0872] Hardware and software used

[0873] Smartphone: Used for entering user information and emotion recognition.

[0874] Server: Used to host the database and operate the generative AI model.

[0875] EmotionRecognition module: As an emotion engine, it recognizes emotions using the smartphone camera and microphone.

[0876] HealthDataAnalysis module: Analyzes the user's health data and generates personalized dietary suggestions.

[0877] OpenAI GPT-3 API: Used as generative artificial intelligence.

[0878] The requests library: Used to send and receive HTTP requests.

[0879] Example prompt

[0880] For example, a prompt to a generative AI model might look like this:

[0881] plaintext

[0882] User information: Age 30, Weight 70kg, Height 175cm, Sleep time 7 hours, Exercise: Jogging 3 times a week

[0883] Emotion: Tired

[0884] Menu: Pumpkin soup, grilled chicken and vegetable stir-fry, quinoa and avocado salad, banana pancakes, protein smoothie

[0885] Season: Autumn

[0886] Generate optimal meal suggestions.

[0887] Based on this prompt, the generative AI model creates optimal meal suggestions for the user.

[0888] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0889] Step 1:

[0890] The user opens the smartphone app and enters their personal information (age, weight, height, sleep duration, exercise, etc.) This information is collected through the application interface and sent by the device to the server.

[0891] Input: User's personal information

[0892] Output: Personal information data received by the server

[0893] Step 2:

[0894] The server stores the personal information in a database that uniquely identifies each user and is used for subsequent analysis.

[0895] Input: Personal information data received by the server

[0896] Output: User information stored in the database

[0897] Step 3:

[0898] The server uses a generative AI model (e.g., OpenAI GPT-3) to analyze the stored user information and generate personalized meal suggestions, taking into account the user's personal information and seasonal information.

[0899] Input: User and seasonal information stored in the database

[0900] Output: A list of meal suggestions generated by the generative AI model

[0901] Step 4:

[0902] The generated meal suggestion list is sent to the terminal and displayed to the user on the application.

[0903] Input: A list of meal suggestions generated by a generative AI model

[0904] Output: A list of meal suggestions displayed on the user's device

[0905] Step 5:

[0906] The emotion engine uses the smartphone camera and microphone to collect facial expression and voice data from the user, and analyzes their current emotions. The emotion recognition results are sent to the server.

[0907] Input: User facial expression and voice data collected by smartphone camera and microphone

[0908] Output: Emotion recognition data received by the server

[0909] Step 6:

[0910] The server further refines the generated meal suggestions based on emotion recognition data, for example, suggesting additional energy-replenishing meals if the user looks tired.

[0911] Input: Emotion recognition data and initial meal suggestion list

[0912] Output: Tailored meal suggestion list

[0913] Step 7:

[0914] The user makes a selection from the list of suggested meals and the selection is sent from the device to the server, which records the selection in a database.

[0915] Input: Meal information selected by the user

[0916] Output: Selected meal information stored in a database

[0917] Step 8:

[0918] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, an alert is sent to the user's device and appropriate advice or suggestions are provided.

[0919] Input: User health information stored in a database

[0920] Output: Alert notification sent to user's device

[0921] 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.

[0922] 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.

[0923] 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.

[0924] [Third embodiment]

[0925] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0926] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0927] 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).

[0928] 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.

[0929] 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.

[0930] 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).

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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."

[0937] The present invention provides a personalized diet recommendation system based on the health information of each user. Hereinafter, embodiments of the present invention will be described in detail.

[0938] User Data Collection

[0939] The user opens the application on the device and enters their personal information (age, weight, height, sleep time, exercise, etc.) The device sends this information to the server, which then stores it in a database.

[0940] Specific examples

[0941] Users use a smartphone app to enter information such as age 30, weight 70kg, height 175cm, sleep seven hours, and jog three times a week.

[0942] Data storage and analysis

[0943] The server securely stores the information received from the user in a database, and then uses generative artificial intelligence to analyze the user's health information and generate personalized meal suggestions.

[0944] Specific examples

[0945] The server analyzes the user's exercise volume and other health data based on the user information stored in the database, and suggests optimal healthy diets.

[0946] Generating meal suggestions

[0947] The server's AI generates an optimal meal suggestion list based on personal information and additional data such as seasonal information. This meal suggestion list is sent to the terminal and displayed to the user.

[0948] Specific examples

[0949] Meal suggestions for the fall season include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[0950] Recipe provision and data monitoring

[0951] The device displays the list of meal suggestions received from the server, and the user selects one of them and sends it to the server, which records the selection and uses it as feedback to improve the next suggestion.

[0952] Specific examples

[0953] The user selects "grilled chicken and stir-fried vegetables" and sends the selection from their device to the server, which records this information in a database and uses it for future suggestions.

[0954] Health monitoring and alerts

[0955] The server periodically monitors the user's health status based on the stored data and sends an alert if there is an abnormality. This alert is then sent to the user's device, providing necessary advice and suggestions.

[0956] Specific examples

[0957] If a user consistently chooses high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[0958] The system of the present invention allows users to efficiently receive appropriate dietary suggestions based on health information, which supports the maintenance of a healthy lifestyle and the prevention of lifestyle-related diseases, making it easier for users to manage their own lifestyle.

[0959] ---

[0960] The system described in the embodiment of the present invention provides personalized meal suggestions according to the user's health condition and supports continuous health management, allowing the user to maintain their health efficiently and effectively.

[0961] The processing flow will be explained below.

[0962] Step 1:

[0963] The user opens the application on their device and enters their personal information (age, weight, height, sleep time, exercise details, etc.).

[0964] Step 2:

[0965] The device sends the user's personal information entered into the application to the server.

[0966] Step 3:

[0967] The server stores the personal information received from the user in a database.

[0968] Step 4:

[0969] The server uses artificial intelligence to analyze data based on the user's stored personal information.

[0970] Step 5:

[0971] Generative AI takes into account the user's personal information and seasonal information to generate a personalized list of meal suggestions.

[0972] Step 6:

[0973] The server transmits the generated meal suggestion list to the terminal.

[0974] Step 7:

[0975] The device displays the received meal suggestion list to the user.

[0976] Step 8:

[0977] The user selects their preferred meal from the displayed list of meal suggestions.

[0978] Step 9:

[0979] The device sends the meal information selected by the user to the server.

[0980] Step 10:

[0981] The server stores the received selected meal information in a database.

[0982] Step 11:

[0983] The server periodically monitors the user's health data and selected dietary information, and generates alerts if necessary if an abnormality is detected.

[0984] Step 12:

[0985] The server sends the generated alert to the terminal, and the terminal notifies the user of the alert.

[0986] Example 1

[0987] 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."

[0988] In modern society, health problems such as lifestyle-related diseases are on the rise, and individual users are being called upon to select optimal diets and manage their health based on their own health information. However, there are currently no systems that allow users to receive appropriate dietary suggestions based on their own health information. Therefore, it is an important challenge to provide a system that makes personalized dietary suggestions for each individual.

[0989] 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.

[0990] In this invention, the server includes means for providing an interface for inputting a user's health information, means for transmitting the health information to the server and storing the health information in a database, means for the server to generate optimal dietary suggestions for the user based on the health information using artificial intelligence, means for transmitting the generated dietary suggestions to the user's terminal and displaying the dietary suggestions, means for transmitting dietary information selected by the user to the server and storing the selected dietary information in a database, and means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected. This enables personalized dietary suggestions based on the user's health information, as well as feedback and monitoring of the suggestions.

[0991] A "user" is a user who accesses the system, inputs their own health information, and receives dietary suggestions.

[0992] "Health information" refers to personal data entered by the user, such as age, weight, height, sleep time, and exercise details.

[0993] The "server" is a computer system that stores health information received from users and generates dietary suggestions using generative artificial intelligence.

[0994] "Database" means a data storage system that allows the server to store and manage health information, dietary suggestions, and user selection data.

[0995] "Generative AI" refers to algorithms or programs that generate meal suggestions tailored to the user based on input data.

[0996] "Meal Suggestions" are personalized meal recommendations provided to users as a result of analysis by the generative artificial intelligence.

[0997] "Terminal" means the device used by a user to enter health information, receive dietary suggestions, and submit selected information.

[0998] "Preference information" is data about the particular meal menu a user selects from a presented list of meal suggestions.

[0999] "Monitoring" refers to the act of the server periodically monitoring stored user data to check for any abnormalities.

[1000] An "alert" is a warning or notification sent from the server to the user's device when an abnormality is detected as a result of monitoring.

[1001] The present invention is a personalized diet recommendation system based on individual user health information. The system uses generative artificial intelligence (generative AI model) to generate optimal diet recommendations based on the health information provided by the user, and then makes recommendations to the user. Specific embodiments for implementing the present invention are described in detail below.

[1002] User Data Collection

[1003] The user opens the application on their device (e.g., smartphone or tablet) and enters their health information (age, weight, height, sleep time, exercise, etc.). The entered health information is sent from the device to the server, which then securely stores the received information in a database.

[1004] Specific examples

[1005] Users use a smartphone app to input information such as age 30, weight 70kg, height 175cm, sleep 7 hours, and jog three times a week, which is then sent to a server via the device and stored in a database.

[1006] Data storage and analysis

[1007] The server stores the health information received from the user in a database and then analyzes it using a generative AI model, which then generates optimal dietary recommendations based on the user's health information.

[1008] Specific examples

[1009] The server analyzes the user's exercise and lifestyle habits based on the user information stored in the database, and automatically generates appropriate meal suggestions. Specifically, it uses a generative AI model implemented in Python to input user data and obtain meal suggestions.

[1010] Generating meal suggestions

[1011] The server's generative AI model takes into account the user's personal information as well as additional data (e.g., seasonal information) to generate an optimal meal recommendation list, which is then sent to the device and displayed to the user.

[1012] Specific examples

[1013] For example, to suggest meals suited to the autumn season, the generative AI model is used to create "pumpkin soup," "grilled chicken and stir-fried vegetables," and "quinoa and avocado salad," and these are then recommended to the user.

[1014] Recipe provision and data monitoring

[1015] When a user selects a meal from the list of suggestions, the information is sent from the device to the server, where it is recorded. This feedback information is used to improve the accuracy of the next meal suggestions.

[1016] Specific examples

[1017] When a user selects "grilled chicken and stir-fried vegetables," the selection information is sent to the server via the device and recorded in the database. It is used as feedback for future suggestions.

[1018] Health monitoring and alerts

[1019] The server periodically monitors the stored health information and, if an abnormality is detected, sends an alert to the user's device, allowing the user to receive appropriate advice and warnings in a timely manner.

[1020] Specific examples

[1021] If a user continues to choose high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[1022] Prompt Sentence Examples

[1023] Here are some example prompts to get a generative AI model to analyze user data:

[1024] Generate personalized meal suggestions based on the user's health information (age, weight, height, sleep duration, exercise, etc.) Example: Provide the optimal meal plan for autumn for a user who is 30 years old, 70kg, 175cm tall, and jogs 3 times a week.

[1025] Using these prompts, the generative AI model provides specific dietary suggestions tailored to the user, supporting individual health management. This system allows users to efficiently receive appropriate dietary recommendations based on their own health information, helping them maintain a healthy lifestyle and prevent lifestyle-related diseases.

[1026] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1027] Step 1: Entering User Data

[1028] The user enters their health information into the device. The input fields include age, weight, height, sleep time, exercise, etc. After the user enters all the information, they press the "Send" button. The input data is displayed on the device and prepared for transmission.

[1029] Input: Health information entered by the user (age, weight, height, sleep time, exercise details)

[1030] Output: Input information displayed on the terminal, ready to send

[1031] Step 2: Sending data

[1032] When the user presses the "Submit" button, the device sends the input data to the server via an HTTP POST request, which encodes the data in JSON format and transmits it over the network.

[1033] Input: Health information entered by the user, click of the submit button

[1034] Output: Health information sent to the server in JSON format

[1035] Step 3: Save your data

[1036] The server analyzes the received health information and saves it in a database. The database stores profile information for each user. The server saves this information in the user table using an INSERT statement.

[1037] Input: Health information in JSON format sent to the server

[1038] Output: Health information stored in a database

[1039] Step 4: Analyze the data

[1040] The server retrieves the user's health information from the database and analyzes it using a generative AI model. The generative AI model generates optimal dietary recommendations based on the user's health information. The server uses a Python script to call the generative AI model and obtain the analysis results.

[1041] Input: Health information stored in a database

[1042] Output: Analysis results from the generative AI model (optimal meal suggestions)

[1043] Step 5: Generate and submit a meal suggestion list

[1044] The server generates a list of meal suggestions based on the analysis results obtained from the generative AI model. This list is encoded in JSON format and sent to the device as an HTTP response. The device then displays the received data to the user.

[1045] Input: Analysis results from generative AI model

[1046] Output: List of meal suggestions sent to the device, meal suggestions displayed to the user

[1047] Step 6: User Choice Feedback

[1048] The user selects one from the list of meal suggestions presented. The user's selection information is sent back to the server from the device, and the server records this data in a database. This data will be used to improve the next meal suggestion.

[1049] Input: User-selected meal information

[1050] Output: Selections sent to the server, selections recorded in the database

[1051] Step 7: Monitoring your health information

[1052] The server periodically monitors the user's health information and food selection history stored in the database, and if any abnormal patterns are detected, an alert is generated and sent to the user's device.

[1053] Input: Health information and dietary choice history stored in a database

[1054] Output: Alerts if anomalies are detected, notifications sent to the device

[1055] Specific operation example

[1056] When a user uses a smartphone app to input and submit health information, the device sends the data in JSON format to a server. The server stores the information in a database and invokes a generative AI model for analysis. As a result of the analysis, optimal meal suggestions for autumn are generated, suggesting pumpkin soup, grilled chicken and stir-fried vegetables, and quinoa and avocado salad. When the user selects "grilled chicken and stir-fried vegetables," the selection information is sent to the server and recorded in the database. The server periodically monitors the data, and if an abnormality is detected, an alert is sent to the device. In this way, users can receive personalized meal suggestions and manage their health.

[1057] Prompt Sentence Examples

[1058] Generate personalized meal suggestions based on the user's health information (age, weight, height, sleep duration, exercise, etc.) Example: Provide the optimal meal plan for autumn for a user who is 30 years old, 70kg, 175cm tall, and jogs 3 times a week.

[1059] (Application example 1)

[1060] 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."

[1061] While existing meal recommendation systems provide personalized suggestions based on the user's health information, they lack a mechanism for linking these with actual meal ordering and delivery. As a result, users must prepare the suggested meals themselves, which is time-consuming and laborious, resulting in low effectiveness of the suggestions. Furthermore, there is little mechanism for incorporating feedback on whether the suggested meals are appropriate, making it unclear whether there is room for improvement in future suggestions.

[1062] 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.

[1063] In this invention, the server includes: means for providing an interface for inputting a user's personal information; means for transmitting the personal information to the server and storing the personal information in a database; means for the server to generate optimal meal suggestions for the user based on the personal information using artificial intelligence; means for transmitting the generated meal suggestions to the user's terminal and displaying the meal suggestions; means for transmitting meal information selected by the user to the server and storing the selected meal information in a database; means for the server to periodically monitor the user's health status based on the stored data and send an alert if an abnormality is detected; means for linking the meal suggestions based on the user's health information with a food delivery ordering function; and means having a feedback function for analyzing the food delivery order history to improve the next suggestion. This allows the user to easily actually order the suggested meals, and the selection results are reflected in subsequent suggestions, enabling more effective health management for the user.

[1064] "User personal information" refers to data related to the user's health and lifestyle, such as age, weight, height, sleep time, and exercise frequency.

[1065] "Interface" means the portion of the software that a user uses to manipulate and input data.

[1066] A "server" is a computer system for storing and processing data received from users.

[1067] "Database" means a system for systematically storing and managing users' personal information and selection information.

[1068] "Generative AI" refers to machine learning models and related technologies that generate optimal meal suggestions based on given data.

[1069] "Meal Suggestions" are a list of recommended meals based on the user's health data.

[1070] "Device" means the device (e.g., smartphone, tablet, etc.) used by a User to view meal suggestions.

[1071] "Selection information" is data on a specific meal menu selected by the user from the meal suggestion list.

[1072] "Monitoring" is the process by which the server periodically analyzes the user's health data and monitors their health status.

[1073] An "alert" is a warning message sent to a device when an abnormality in the user's health condition is detected.

[1074] "Food delivery" is a service that delivers meals ordered by users to a specified location.

[1075] The "feedback function" is a function that reflects the user's selection information in the next meal suggestion to improve the accuracy of the suggestion.

[1076] System program generation

[1077] The system based on this invention consists of the following main components: an interface for inputting users' personal information, a server for storing and analyzing data, a terminal for displaying meal suggestions, a food delivery ordering function, a health monitoring and alert function, and a feedback function. The entire system links these functions to provide users with individually optimized meal suggestions and food delivery services.

[1078] A natural language description of what the program does

[1079] 1. Collection of User's Personal Information:

[1080] Users operate the interface using a smartphone, tablet, or other device and enter their personal information (age, weight, height, sleep duration, exercise frequency, etc.) This information is sent from the device to a server and stored in a database.

[1081] 2. Data storage and analysis:

[1082] The server securely stores data from users and uses a generative AI model to analyze their individual health information, which then generates optimal dietary recommendations for them.

[1083] 3. Generating and displaying meal suggestions:

[1084] The generated meal suggestions are sent from the server to the user's device, which displays them to the user, who then selects from the suggested menu.

[1085] 4. Transmission and Storage of Selected Information:

[1086] The meal information selected by the user is sent from the device to the server and stored in a database, which is used as feedback to improve the next recommendation.

[1087] 5. Food delivery ordering feature:

[1088] It will provide a food delivery service linked to meal suggestions, allowing users to order directly from the suggestions displayed, and the selected meal will be delivered to the specified location.

[1089] 6. Health monitoring and alert features:

[1090] The server periodically analyzes the user's selection information and stored data to monitor their health status, and if an abnormality is detected, the server will send an alert to the device and provide appropriate advice.

[1091] Specific examples

[1092] An example of a usage scenario

[1093] Users download the app and enter personal information such as age, weight, height, sleep duration, and exercise frequency when they first launch it. The device then sends this information to a server, which then uses a generative AI model to analyze the data and create optimal meal recommendations for the day. For example, in the fall, menu suggestions might include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[1094] The user selects "grilled chicken and stir-fried vegetables" and confirms the order. The selection information is sent to the server and stored. The server uses the selection results to improve the next recommendation. If high-calorie meals are consistently selected, an alert is sent to the device, displaying a message saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[1095] Prompt Sentence Examples

[1096] Enter your user information: age, weight, height, sleep time, exercise frequency

[1097] Example: 30 years old, 70 kg, 175 cm, 7 hours, 3 times a week

[1098]

[1099] Please select a suggested meal below:

[1100] 1. Pumpkin soup

[1101] 2. Grilled chicken and stir-fried vegetables

[1102] 3. Quinoa and avocado salad

[1103]

[1104] Your recent diet choices are high in calories. Try incorporating lower calorie options.

[1105] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1106] Step 1:

[1107] Users use their devices to input personal information, such as age, weight, height, sleep duration, and exercise frequency, into the application interface, which is then temporarily stored on the device.

[1108] Input: Age, weight, height, sleep time, exercise frequency

[1109] Output: User's personal information is stored on the device

[1110] Step 2:

[1111] The device sends the user's personal information to the server. The data is serialized in JSON format and sent using a secure communication protocol (e.g., HTTPS). After sending, the server receives the data and stores it in a database.

[1112] Input: Personal information data in JSON format

[1113] Output: User's personal information stored in the server's database

[1114] Step 3:

[1115] The server uses a generative AI model to analyze the user's personal information stored in a database. Specifically, it evaluates the user's health based on data such as age, weight, height, sleep duration, and exercise frequency, and generates optimal dietary recommendations. Seasonal information is also taken into account.

[1116] Input: Personal information and seasonal information in the database

[1117] Output: Meal suggestions from a generative AI model

[1118] Step 4:

[1119] The server sends the generated meal suggestions in JSON format to the device, which receives the data and displays a list of meal suggestions to the user.

[1120] Input: Meal suggestions from a generative AI model

[1121] Output: A list of meal suggestions displayed on the device

[1122] Step 5:

[1123] The user selects one menu from the meal suggestion list, and the selected menu information is sent back to the server in JSON format and recorded in the database.

[1124] Input: User's selected meal menu

[1125] Output: Selection information saved on the server

[1126] Step 6:

[1127] The server provides feedback to the generative AI model based on the selection information to improve the next meal suggestion, which is then used to improve the accuracy of the next meal suggestion.

[1128] Input: Selection information, health data

[1129] Output: Improved meal suggestions

[1130] Step 7:

[1131] The user places an order for the selected meal menu with the food delivery service from the terminal. The terminal sends the order information to the food delivery service, and delivery begins.

[1132] Input: Selected meal menu

[1133] Output: Food delivery order

[1134] Step 8:

[1135] The server periodically monitors the stored user health data and generates an alert if an abnormality is detected, which is then sent to the device to notify the user.

[1136] Input: Stored health data

[1137] Output: The alert displayed on the terminal

[1138] Step 9:

[1139] Users can adjust their behavior based on alerts and suggestions they receive through their device. For example, if they have been consistently choosing high-calorie meals, they can adjust their diet based on an alert that says, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[1140] Input: Alert message

[1141] Output: Improved user behavior

[1142] 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.

[1143] The present invention is a personalized diet recommendation system based on the health information of each user, and provides further adaptability by combining an emotion engine. Below, specific embodiments for carrying out the present invention will be described.

[1144] User Data Collection

[1145] The user opens the application on the device and enters their personal information (age, weight, height, sleep time, exercise details, etc.). The device sends this information to the server, which then stores the received information in a database.

[1146] Specific examples

[1147] Users use a smartphone app to enter information such as age 30, weight 70kg, height 175cm, sleep seven hours, and jog three times a week.

[1148] Data storage and analysis

[1149] The server securely stores the information received from the user in a database, and then uses generative artificial intelligence to analyze the user's health information and generate personalized meal suggestions.

[1150] Specific examples

[1151] The server analyzes the user's exercise volume and other health data based on the user information stored in the database, and suggests optimal healthy diets.

[1152] Generating meal suggestions

[1153] The server's AI generates an optimal meal suggestion list based on personal information and additional data such as seasonal information. This meal suggestion list is sent to the terminal and displayed to the user.

[1154] Specific examples

[1155] Meal suggestions for the fall season include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[1156] Emotion recognition and dietary suggestion adjustment

[1157] The emotion engine analyzes the user's facial expression and voice data to recognize their emotions, and the server further adjusts the meal suggestions based on this recognition result.

[1158] Specific examples

[1159] If the user looks tired, the emotion engine will recognize this and add food suggestions suitable for replenishing energy (e.g., banana pancakes, protein smoothies).

[1160] Recipe provision and data monitoring

[1161] The device displays the list of meal suggestions received from the server, and the user selects one of them and sends it to the server, which records the selection and uses it as feedback to improve the next suggestion.

[1162] Specific examples

[1163] The user selects "grilled chicken and stir-fried vegetables" and sends the selection from their device to the server, which records this information in a database and uses it for future suggestions.

[1164] Health monitoring and alerts

[1165] The server periodically monitors the user's health status based on the stored data and sends an alert if there is an abnormality. This alert is then sent to the user's device, providing necessary advice and suggestions.

[1166] Specific examples

[1167] If a user consistently chooses high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[1168] The system of the present invention allows users to efficiently receive appropriate dietary suggestions based on health information. Furthermore, the use of an emotion engine enables detailed responses based on the user's emotional state, supporting the maintenance of a healthy lifestyle and the prevention of lifestyle-related diseases. This makes it easier for users to manage themselves, and health support tailored to individual needs can be realized.

[1169] The processing flow will be explained below.

[1170] Step 1:

[1171] The user opens the application on their device and enters their personal information (age, weight, height, sleep time, exercise details, etc.).

[1172] Step 2:

[1173] The device sends the user's personal information entered into the application to the server.

[1174] Step 3:

[1175] The server stores the personal information received from the user in a database.

[1176] Step 4:

[1177] The server uses artificial intelligence to analyze data based on the user's stored personal information.

[1178] Step 5:

[1179] Generative artificial intelligence takes into account the user's personal information and seasonal information to generate a personalized list of meal suggestions.

[1180] Step 6:

[1181] The server transmits the generated meal suggestion list to the terminal.

[1182] Step 7:

[1183] The device displays the received meal suggestion list to the user.

[1184] Step 8:

[1185] The user selects their preferred meal from the displayed list of meal suggestions.

[1186] Step 9:

[1187] The device sends the meal information selected by the user to the server.

[1188] Step 10:

[1189] The server stores the received selected meal information in a database.

[1190] Step 11:

[1191] The server periodically monitors the user's health data and selected dietary information.

[1192] Step 12:

[1193] The server receives the user's voice data and facial expression data and recognizes the user's emotions using an emotion engine.

[1194] Step 13:

[1195] The emotion engine analyzes the user's emotional data and adjusts the meal suggestion list.

[1196] Step 14:

[1197] The server sends the adjusted meal suggestion list to the terminal.

[1198] Step 15:

[1199] The device displays a tailored list of meal suggestions to the user.

[1200] Step 16:

[1201] The server generates alerts as needed and sends them to the device.

[1202] Step 17:

[1203] Notify the user of any alerts received by the device.

[1204] Example 2

[1205] 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."

[1206] Currently, many people find it difficult to choose the right diet to maintain a healthy lifestyle. In particular, selecting meals based on individual health conditions and daily emotions is a time-consuming and labor-intensive task using conventional methods. Furthermore, there is a lack of appropriate systems for users to utilize their own health data to receive specific dietary recommendations. Given this background, there is a need for a system that can provide personalized dietary recommendations that address individual needs and also take into account the user's emotional state.

[1207] 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.

[1208] In this invention, the server includes means for providing an interface for inputting the user's personal information, means for transmitting the personal information to the server and storing the personal information in a database, means for the server to generate optimal meal suggestions for the user using an artificial intelligence model based on the personal information, means for transmitting the generated meal suggestions to a user terminal and displaying the meal suggestions, means for transmitting meal information selected by the user to the server and storing the selected meal information in a database, means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected, and means for further adjusting the meal suggestions using an emotion engine that collects and analyzes the user's emotion data. This allows the user to efficiently receive personalized meal suggestions based on their health condition and further enables flexible responses according to their emotional state.

[1209] "User" refers to a person who uses the system.

[1210] "Personal information" refers to data entered by users, such as age, weight, height, sleep time, and exercise details.

[1211] "Interface" refers to the screen or input means provided for users to enter personal information.

[1212] "Server" refers to a computer system that receives, stores, and analyzes data sent by users.

[1213] "Database" refers to a system for securely storing personal information and other data within a server.

[1214] "Artificial Intelligence Model" refers to the algorithm and its implementation for generating optimal meal suggestions based on a user's personal information.

[1215] "Meal Suggestions" refers to a list of specific meal suggestions based on the user's health and personal information.

[1216] "User Terminal" means the device (e.g., smartphone or tablet) used by a User to access the System and receive meal suggestions.

[1217] An "emotion engine" refers to a system that determines a user's emotional state by collecting and analyzing their facial expression and voice data.

[1218] The "feedback function" refers to a function that improves the next meal suggestion based on the meal information selected by the user.

[1219] "Alert" refers to the function in which the server monitors the user's health status and notifies the user if an abnormality occurs.

[1220] The present invention is a system that provides personalized meal suggestions based on a user's health information, and provides further adaptability by combining it with an emotion engine. Specific means for implementing the present invention are described below.

[1221] User Data Collection

[1222] The user opens the application on their device (such as a smartphone or tablet) and enters personal information such as age, weight, height, sleep time, exercise details, etc. The device then sends the entered personal information to the server, which then stores the received information in a database.

[1223] Specific examples

[1224] A user opens the application and enters information such as "30 years old, 70 kg, 175 cm, 7 hours of sleep, jog three times a week."

[1225] Data storage and analysis

[1226] The server securely stores the information submitted by the user in a database, and then uses an artificial intelligence model (e.g., using a generative AI model) to analyze the user's health information and generate personalized dietary recommendations.

[1227] Specific examples

[1228] The server generates meal suggestions based on the user's age and activity level based on the user's information stored in the database. For example, it might suggest "grilled chicken and stir-fried vegetables" as a "high-protein food" for a specific user.

[1229] Generating meal suggestions

[1230] The server's artificial intelligence model takes into account the user's personal information and additional data such as seasonal information to generate an optimal meal suggestion list, which is then sent to the device and displayed to the user.

[1231] Specific examples

[1232] Meal suggestions for the fall season include pumpkin soup, grilled chicken and stir-fried vegetables, and quinoa and avocado salad.

[1233] Emotion data collection and analysis

[1234] The emotion engine collects and analyzes the user's facial expression and voice data to recognize their emotional state, based on which the server further adjusts the meal suggestions.

[1235] Specific examples

[1236] If a user uses the device's camera to show a "tired expression," the emotion engine will recognize the "feeling of fatigue" and add "banana pancakes, protein smoothies" that are suitable for replenishing energy.

[1237] Recipe provision and data monitoring

[1238] The device displays a list of meal suggestions sent from the server, and the user can select from them. The selected meal information is then sent back to the server, which uses this information as feedback to reflect in the next suggestion.

[1239] Specific examples

[1240] The user selects "grilled chicken and stir-fried vegetables" and sends that selection to the server, which records the selection in its database and improves its suggestions next time.

[1241] Health Monitoring and Alerts

[1242] The server regularly monitors the user's health status based on the stored data, and if it detects any abnormalities, it sends an alert to the user's device.

[1243] Specific examples

[1244] If the user continues to choose high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try choosing low-calorie options as well."

[1245] This allows users to receive appropriate and personalized meal suggestions based on their health and emotional state. The entire system is highly adaptable, responding dynamically to the user's choices and emotions.

[1246] Prompt Sentence Examples

[1247] "Generate personalized meal suggestions for the fall season for a user who is 30 years old, 70kg, 175cm tall, sleeps 7 hours, and jogs 3 times a week."

[1248] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1249] Step 1:

[1250] The user launches the application on their device and enters personal information such as age, weight, height, sleep time, exercise details, etc. The entered information is then sent to the server by the user's device.

[1251] Specifically, the user enters "30 years old, 70 kg, 175 cm, 7 hours of sleep, jogging three times a week" into the application's input form and presses the "Submit" button.

[1252] Input: Age, weight, height, sleep time, exercise details

[1253] Output: Personal information data sent to the server

[1254] Step 2:

[1255] The server receives personal information data sent from the user terminal and stores it in a secure database.

[1256] Specifically, the server encrypts the received data and executes a process to store it in a database.

[1257] Input: Received personal information data

[1258] Output: Personal information stored in the database

[1259] Step 3:

[1260] The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze the personal information data stored in the database, and generates personalized meal recommendations based on the analysis results.

[1261] Specifically, the server inputs the user's health data into the generated AI model, which then generates meal suggestions such as "grilled chicken is good for users who need high-protein foods."

[1262] Input: Personal information stored in a database

[1263] Output: Personalized meal suggestions

[1264] Step 4:

[1265] The server transmits the generated meal suggestion list to the user terminal, which displays the list to the user.

[1266] Specifically, the server generates a list of dishes such as "pumpkin soup, grilled chicken and vegetable stir-fry, quinoa and avocado salad" and sends it to the device, which then displays it on the device screen.

[1267] Input: Generated meal suggestions

[1268] Output: A list of meal suggestions displayed on the user's device.

[1269] Step 5:

[1270] The user selects the desired menu from a list of meal suggestions, and the user's device sends the selection to the server.

[1271] Specifically, the user selects "grilled chicken and stir-fried vegetables," and the device sends this to the server.

[1272] Input: User's menu selection

[1273] Output: Selection data sent to the server

[1274] Step 6:

[1275] The server stores the received user menu selection data in a database and uses it as feedback to improve the next meal suggestion.

[1276] Specifically, the server saves the selected data and reflects it in the next analysis algorithm.

[1277] Input: Selection data sent to the server

[1278] Output: Selection data stored in the database as feedback

[1279] Step 7:

[1280] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, it sends an alert to the user's device. Specifically, if the server detects a user who has consistently chosen high-calorie meals, it sends an alert saying, "Your recent meals have been high in calories. Try incorporating low-calorie options."

[1281] Input: Saved data

[1282] Output: Alert notification sent to terminal

[1283] Step 8:

[1284] The emotion engine recognizes emotions by collecting and analyzing the user's facial and voice data, which is then sent to a server via the device and used to adjust meal suggestions.

[1285] Specifically, the user uses the device's camera and microphone to record their "tired facial expression" and "tired voice," which are then sent to the server. The server then analyzes the data using its emotion engine, recognizes the "feeling of fatigue," and offers "banana pancakes and protein smoothies" to replenish the energy.

[1286] Input: facial expression data, voice data

[1287] Output: Tailored meal suggestions

[1288] Step 9:

[1289] The device then displays the adjusted meal suggestions to the user again, from which the user can make a meal selection.

[1290] Specifically, the device displays new meal suggestions and the user selects again.

[1291] Input: Tailored meal suggestions

[1292] Output: User selects again

[1293] (Application example 2)

[1294] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1295] Conventional meal recommendation systems only make recommendations based on a user's personal information and do not take into account the user's emotional state. As a result, they are unable to make meal recommendations that are optimal for the user's emotions and circumstances at any given time, and are insufficient for improving the user's health management and satisfaction. The present invention aims to solve these problems and provide a personalized meal recommendation system that takes into account the user's emotional state as well as their personal information.

[1296] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1297] In this invention, the server includes means for providing an interface for inputting the user's personal information, means for transmitting the personal information to the server and storing the personal information in a database, means for the server to generate optimal meal suggestions for the user based on the personal information using a generative artificial intelligence, means for transmitting the generated meal suggestions to the user's terminal and displaying the meal suggestions on the terminal, means for adjusting the meal suggestions using an emotion engine that recognizes the user's emotions, means for transmitting meal information selected by the user to the server and storing the selected meal information in a database, and means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected. This enables personalized meal suggestions that comprehensively consider the user's health condition and emotional state.

[1298] "User personal information" refers to data related to the user's individual health status, such as age, weight, height, sleep time, and exercise.

[1299] "Means for providing an interface" refers to technology that provides an input screen for an application or web page that allows users to input personal information.

[1300] "Generative AI" is a system that uses machine learning and deep learning technologies to generate optimal meal suggestions based on a user's personal information.

[1301] The "emotion engine" is a technology that analyzes the user's facial expressions and voice data to recognize their emotional state at any given time.

[1302] "Meal Suggestion" refers to the suggestion of specific meals or dishes to consume based on the user's health information and emotional state.

[1303] A "terminal" refers to a device that is capable of processing information, such as a smartphone or tablet, used by a user.

[1304] "Database" means an integrated system for securely storing and managing users' personal information and food selection information.

[1305] "Health monitoring" is a system that periodically checks the user's stored health information and notifies them if any abnormalities are found.

[1306] "Means for sending alerts" refers to technology that sends health status notifications and warnings to the user's device.

[1307] The "feedback function" is a mechanism that reflects the meal information selected by the user in the next suggestions.

[1308] "Personalized" means specific to an individual user and tailored to their individual characteristics and preferences.

[1309] MODE FOR CARRYING OUT THE INVENTION

[1310] The present invention is a personalized meal recommendation system based on a user's personal information and emotional state. Specific embodiments of the present invention are described below.

[1311] User Data Collection

[1312] Users use a smartphone app to enter their personal information (age, weight, height, sleep time, exercise details, etc.) The device then sends the information to a server, which then securely stores it in a database.

[1313] Data storage and analysis

[1314] The server analyzes the data using a generative AI model (e.g., OpenAI GPT-3) based on the stored user information to generate optimal meal suggestions for the user.

[1315] Generating meal suggestions

[1316] The generative AI model uses the user's personal information and additional data, such as seasonal information, to generate a list of optimal meal suggestions, including specific meal plans and recipes, which are then sent to the user's device and displayed in the app.

[1317] Emotion recognition and dietary suggestion adjustment

[1318] The emotion engine uses the smartphone camera and microphone to collect facial and voice data from the user and analyzes their emotions. Based on the analysis results, the server further adjusts the meal suggestions. For example, if the user shows signs of fatigue, it will suggest meals suitable for replenishing energy.

[1319] Recipe provision and data monitoring

[1320] The meal information that the user selects from the suggested meal list is sent from the device to the server, which records this information in a database and uses it as feedback to improve the next meal suggestion.

[1321] Health monitoring and alerts

[1322] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, the server sends an alert to the user's device and provides necessary advice and suggestions.

[1323] Hardware and software used

[1324] Smartphone: Used for entering user information and emotion recognition.

[1325] Server: Used to host the database and operate the generative AI model.

[1326] EmotionRecognition module: As an emotion engine, it recognizes emotions using the smartphone camera and microphone.

[1327] HealthDataAnalysis module: Analyzes the user's health data and generates personalized dietary suggestions.

[1328] OpenAI GPT-3 API: Used as generative artificial intelligence.

[1329] The requests library: Used to send and receive HTTP requests.

[1330] Example prompt

[1331] For example, a prompt to a generative AI model might look like this:

[1332] plaintext

[1333] User information: Age 30, Weight 70kg, Height 175cm, Sleep time 7 hours, Exercise: Jogging 3 times a week

[1334] Emotion: Tired

[1335] Menu: Pumpkin soup, grilled chicken and vegetable stir-fry, quinoa and avocado salad, banana pancakes, protein smoothie

[1336] Season: Autumn

[1337] Generate optimal meal suggestions.

[1338] Based on this prompt, the generative AI model creates optimal meal suggestions for the user.

[1339] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1340] Step 1:

[1341] The user opens the smartphone app and enters their personal information (age, weight, height, sleep duration, exercise, etc.) This information is collected through the application interface and sent by the device to the server.

[1342] Input: User's personal information

[1343] Output: Personal information data received by the server

[1344] Step 2:

[1345] The server stores the personal information in a database that uniquely identifies each user and is used for subsequent analysis.

[1346] Input: Personal information data received by the server

[1347] Output: User information stored in the database

[1348] Step 3:

[1349] The server uses a generative AI model (e.g., OpenAI GPT-3) to analyze the stored user information and generate personalized meal suggestions, taking into account the user's personal information and seasonal information.

[1350] Input: User and seasonal information stored in the database

[1351] Output: A list of meal suggestions generated by the generative AI model

[1352] Step 4:

[1353] The generated meal suggestion list is sent to the terminal and displayed to the user on the application.

[1354] Input: A list of meal suggestions generated by a generative AI model

[1355] Output: A list of meal suggestions displayed on the user's device

[1356] Step 5:

[1357] The emotion engine uses the smartphone camera and microphone to collect facial expression and voice data from the user, and analyzes their current emotions. The emotion recognition results are sent to the server.

[1358] Input: User facial expression and voice data collected by smartphone camera and microphone

[1359] Output: Emotion recognition data received by the server

[1360] Step 6:

[1361] The server further refines the generated meal suggestions based on emotion recognition data, for example, suggesting additional energy-replenishing meals if the user looks tired.

[1362] Input: Emotion recognition data and initial meal suggestion list

[1363] Output: Tailored meal suggestion list

[1364] Step 7:

[1365] The user makes a selection from the list of suggested meals and the selection is sent from the device to the server, which records the selection in a database.

[1366] Input: Meal information selected by the user

[1367] Output: Selected meal information stored in a database

[1368] Step 8:

[1369] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, an alert is sent to the user's device and appropriate advice or suggestions are provided.

[1370] Input: User health information stored in a database

[1371] Output: Alert notification sent to user's device

[1372] 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.

[1373] 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.

[1374] 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.

[1375] [Fourth embodiment]

[1376] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1377] 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.

[1378] 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).

[1379] 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.

[1380] 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.

[1381] 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).

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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."

[1389] The present invention provides a personalized diet recommendation system based on the health information of each user. Hereinafter, embodiments of the present invention will be described in detail.

[1390] User Data Collection

[1391] The user opens the application on the device and enters their personal information (age, weight, height, sleep time, exercise, etc.) The device sends this information to the server, which then stores it in a database.

[1392] Specific examples

[1393] Users use a smartphone app to enter information such as age 30, weight 70kg, height 175cm, sleep seven hours, and jog three times a week.

[1394] Data storage and analysis

[1395] The server securely stores the information received from the user in a database, and then uses generative artificial intelligence to analyze the user's health information and generate personalized meal suggestions.

[1396] Specific examples

[1397] The server analyzes the user's exercise volume and other health data based on the user information stored in the database, and suggests optimal healthy diets.

[1398] Generating meal suggestions

[1399] The server's AI generates an optimal meal suggestion list based on personal information and additional data such as seasonal information. This meal suggestion list is sent to the terminal and displayed to the user.

[1400] Specific examples

[1401] Meal suggestions for the fall season include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[1402] Recipe provision and data monitoring

[1403] The device displays the list of meal suggestions received from the server, and the user selects one of them and sends it to the server, which records the selection and uses it as feedback to improve the next suggestion.

[1404] Specific examples

[1405] The user selects "grilled chicken and stir-fried vegetables" and sends the selection from their device to the server, which records this information in a database and uses it for future suggestions.

[1406] Health monitoring and alerts

[1407] The server periodically monitors the user's health status based on the stored data and sends an alert if there is an abnormality. This alert is then sent to the user's device, providing necessary advice and suggestions.

[1408] Specific examples

[1409] If a user consistently chooses high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[1410] The system of the present invention allows users to efficiently receive appropriate dietary suggestions based on health information, which supports the maintenance of a healthy lifestyle and the prevention of lifestyle-related diseases, making it easier for users to manage their own lifestyle.

[1411] ---

[1412] The system described in the embodiment of the present invention provides personalized meal suggestions according to the user's health condition and supports continuous health management, allowing the user to maintain their health efficiently and effectively.

[1413] The processing flow will be explained below.

[1414] Step 1:

[1415] The user opens the application on their device and enters their personal information (age, weight, height, sleep time, exercise details, etc.).

[1416] Step 2:

[1417] The device sends the user's personal information entered into the application to the server.

[1418] Step 3:

[1419] The server stores the personal information received from the user in a database.

[1420] Step 4:

[1421] The server uses artificial intelligence to analyze data based on the user's stored personal information.

[1422] Step 5:

[1423] Generative AI takes into account the user's personal information and seasonal information to generate a personalized list of meal suggestions.

[1424] Step 6:

[1425] The server transmits the generated meal suggestion list to the terminal.

[1426] Step 7:

[1427] The device displays the received meal suggestion list to the user.

[1428] Step 8:

[1429] The user selects their preferred meal from the displayed list of meal suggestions.

[1430] Step 9:

[1431] The device sends the meal information selected by the user to the server.

[1432] Step 10:

[1433] The server stores the received selected meal information in a database.

[1434] Step 11:

[1435] The server periodically monitors the user's health data and selected dietary information, and generates alerts if necessary if an abnormality is detected.

[1436] Step 12:

[1437] The server sends the generated alert to the terminal, and the terminal notifies the user of the alert.

[1438] Example 1

[1439] 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."

[1440] In modern society, health problems such as lifestyle-related diseases are on the rise, and individual users are being called upon to select optimal diets and manage their health based on their own health information. However, there are currently no systems that allow users to receive appropriate dietary suggestions based on their own health information. Therefore, it is an important challenge to provide a system that makes personalized dietary suggestions for each individual.

[1441] 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.

[1442] In this invention, the server includes means for providing an interface for inputting a user's health information, means for transmitting the health information to the server and storing the health information in a database, means for the server to generate optimal dietary suggestions for the user based on the health information using artificial intelligence, means for transmitting the generated dietary suggestions to the user's terminal and displaying the dietary suggestions, means for transmitting dietary information selected by the user to the server and storing the selected dietary information in a database, and means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected. This enables personalized dietary suggestions based on the user's health information, as well as feedback and monitoring of the suggestions.

[1443] A "user" is a user who accesses the system, inputs their own health information, and receives dietary suggestions.

[1444] "Health information" refers to personal data entered by the user, such as age, weight, height, sleep time, and exercise details.

[1445] The "server" is a computer system that stores health information received from users and generates dietary suggestions using generative artificial intelligence.

[1446] "Database" means a data storage system that allows the server to store and manage health information, dietary suggestions, and user selection data.

[1447] "Generative AI" refers to algorithms or programs that generate meal suggestions tailored to the user based on input data.

[1448] "Meal Suggestions" are personalized meal recommendations provided to users as a result of analysis by the generative artificial intelligence.

[1449] "Terminal" means the device used by a user to enter health information, receive dietary suggestions, and submit selected information.

[1450] "Preference information" is data about the particular meal menu a user selects from a presented list of meal suggestions.

[1451] "Monitoring" refers to the act of the server periodically monitoring stored user data to check for any abnormalities.

[1452] An "alert" is a warning or notification sent from the server to the user's device when an abnormality is detected as a result of monitoring.

[1453] The present invention is a personalized diet recommendation system based on individual user health information. The system uses generative artificial intelligence (generative AI model) to generate optimal diet recommendations based on the health information provided by the user, and then makes recommendations to the user. Specific embodiments for implementing the present invention are described in detail below.

[1454] User Data Collection

[1455] The user opens the application on their device (e.g., smartphone or tablet) and enters their health information (age, weight, height, sleep time, exercise, etc.). The entered health information is sent from the device to the server, which then securely stores the received information in a database.

[1456] Specific examples

[1457] Users use a smartphone app to input information such as age 30, weight 70kg, height 175cm, sleep 7 hours, and jog three times a week, which is then sent to a server via the device and stored in a database.

[1458] Data storage and analysis

[1459] The server stores the health information received from the user in a database and then analyzes it using a generative AI model, which then generates optimal dietary recommendations based on the user's health information.

[1460] Specific examples

[1461] The server analyzes the user's exercise and lifestyle habits based on the user information stored in the database, and automatically generates appropriate meal suggestions. Specifically, it uses a generative AI model implemented in Python to input user data and obtain meal suggestions.

[1462] Generating meal suggestions

[1463] The server's generative AI model takes into account the user's personal information as well as additional data (e.g., seasonal information) to generate an optimal meal recommendation list, which is then sent to the device and displayed to the user.

[1464] Specific examples

[1465] For example, to suggest meals suited to the autumn season, the generative AI model is used to create "pumpkin soup," "grilled chicken and stir-fried vegetables," and "quinoa and avocado salad," and these are then recommended to the user.

[1466] Recipe provision and data monitoring

[1467] When a user selects a meal from the list of suggestions, the information is sent from the device to the server, where it is recorded. This feedback information is used to improve the accuracy of the next meal suggestions.

[1468] Specific examples

[1469] When a user selects "grilled chicken and stir-fried vegetables," the selection information is sent to the server via the device and recorded in the database. It is used as feedback for future suggestions.

[1470] Health monitoring and alerts

[1471] The server periodically monitors the stored health information and, if an abnormality is detected, sends an alert to the user's device, allowing the user to receive appropriate advice and warnings in a timely manner.

[1472] Specific examples

[1473] If a user continues to choose high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[1474] Prompt Sentence Examples

[1475] Here are some example prompts to get a generative AI model to analyze user data:

[1476] Generate personalized meal suggestions based on the user's health information (age, weight, height, sleep duration, exercise, etc.) Example: Provide the optimal meal plan for autumn for a user who is 30 years old, 70kg, 175cm tall, and jogs 3 times a week.

[1477] Using these prompts, the generative AI model provides specific dietary suggestions tailored to the user, supporting individual health management. This system allows users to efficiently receive appropriate dietary recommendations based on their own health information, helping them maintain a healthy lifestyle and prevent lifestyle-related diseases.

[1478] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1479] Step 1: Entering User Data

[1480] The user enters their health information into the device. The input fields include age, weight, height, sleep time, exercise, etc. After the user enters all the information, they press the "Send" button. The input data is displayed on the device and prepared for transmission.

[1481] Input: Health information entered by the user (age, weight, height, sleep time, exercise details)

[1482] Output: Input information displayed on the terminal, ready to send

[1483] Step 2: Sending data

[1484] When the user presses the "Submit" button, the device sends the input data to the server via an HTTP POST request, which encodes the data in JSON format and transmits it over the network.

[1485] Input: Health information entered by the user, click of the submit button

[1486] Output: Health information sent to the server in JSON format

[1487] Step 3: Save your data

[1488] The server analyzes the received health information and saves it in a database. The database stores profile information for each user. The server saves this information in the user table using an INSERT statement.

[1489] Input: Health information in JSON format sent to the server

[1490] Output: Health information stored in a database

[1491] Step 4: Analyze the data

[1492] The server retrieves the user's health information from the database and analyzes it using a generative AI model. The generative AI model generates optimal dietary recommendations based on the user's health information. The server uses a Python script to call the generative AI model and obtain the analysis results.

[1493] Input: Health information stored in a database

[1494] Output: Analysis results from the generative AI model (optimal meal suggestions)

[1495] Step 5: Generate and submit a meal suggestion list

[1496] The server generates a list of meal suggestions based on the analysis results obtained from the generative AI model. This list is encoded in JSON format and sent to the device as an HTTP response. The device then displays the received data to the user.

[1497] Input: Analysis results from generative AI model

[1498] Output: List of meal suggestions sent to the device, meal suggestions displayed to the user

[1499] Step 6: User Choice Feedback

[1500] The user selects one from the list of meal suggestions presented. The user's selection information is sent back to the server from the device, and the server records this data in a database. This data will be used to improve the next meal suggestion.

[1501] Input: User-selected meal information

[1502] Output: Selections sent to the server, selections recorded in the database

[1503] Step 7: Monitoring your health information

[1504] The server periodically monitors the user's health information and food selection history stored in the database, and if any abnormal patterns are detected, an alert is generated and sent to the user's device.

[1505] Input: Health information and dietary choice history stored in a database

[1506] Output: Alerts if anomalies are detected, notifications sent to the device

[1507] Specific operation example

[1508] When a user uses a smartphone app to input and submit health information, the device sends the data in JSON format to a server. The server stores the information in a database and invokes a generative AI model for analysis. As a result of the analysis, optimal meal suggestions for autumn are generated, suggesting pumpkin soup, grilled chicken and stir-fried vegetables, and quinoa and avocado salad. When the user selects "grilled chicken and stir-fried vegetables," the selection information is sent to the server and recorded in the database. The server periodically monitors the data, and if an abnormality is detected, an alert is sent to the device. In this way, users can receive personalized meal suggestions and manage their health.

[1509] Prompt Sentence Examples

[1510] Generate personalized meal suggestions based on the user's health information (age, weight, height, sleep duration, exercise, etc.) Example: Provide the optimal meal plan for autumn for a user who is 30 years old, 70kg, 175cm tall, and jogs 3 times a week.

[1511] (Application example 1)

[1512] 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."

[1513] While existing meal recommendation systems provide personalized suggestions based on the user's health information, they lack a mechanism for linking these with actual meal ordering and delivery. As a result, users must prepare the suggested meals themselves, which is time-consuming and laborious, resulting in low effectiveness of the suggestions. Furthermore, there is little mechanism for incorporating feedback on whether the suggested meals are appropriate, making it unclear whether there is room for improvement in future suggestions.

[1514] 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.

[1515] In this invention, the server includes: means for providing an interface for inputting a user's personal information; means for transmitting the personal information to the server and storing the personal information in a database; means for the server to generate optimal meal suggestions for the user based on the personal information using artificial intelligence; means for transmitting the generated meal suggestions to the user's terminal and displaying the meal suggestions; means for transmitting meal information selected by the user to the server and storing the selected meal information in a database; means for the server to periodically monitor the user's health status based on the stored data and send an alert if an abnormality is detected; means for linking the meal suggestions based on the user's health information with a food delivery ordering function; and means having a feedback function for analyzing the food delivery order history to improve the next suggestion. This allows the user to easily actually order the suggested meals, and the selection results are reflected in subsequent suggestions, enabling more effective health management for the user.

[1516] "User personal information" refers to data related to the user's health and lifestyle, such as age, weight, height, sleep time, and exercise frequency.

[1517] "Interface" means the portion of the software that a user uses to manipulate and input data.

[1518] A "server" is a computer system for storing and processing data received from users.

[1519] "Database" means a system for systematically storing and managing users' personal information and selection information.

[1520] "Generative AI" refers to machine learning models and related technologies that generate optimal meal suggestions based on given data.

[1521] "Meal Suggestions" are a list of recommended meals based on the user's health data.

[1522] "Device" means the device (e.g., smartphone, tablet, etc.) used by a User to view meal suggestions.

[1523] "Selection information" is data on a specific meal menu selected by the user from the meal suggestion list.

[1524] "Monitoring" is the process by which the server periodically analyzes the user's health data and monitors their health status.

[1525] An "alert" is a warning message sent to a device when an abnormality in the user's health condition is detected.

[1526] "Food delivery" is a service that delivers meals ordered by users to a specified location.

[1527] The "feedback function" is a function that reflects the user's selection information in the next meal suggestion to improve the accuracy of the suggestion.

[1528] System program generation

[1529] The system based on this invention consists of the following main components: an interface for inputting users' personal information, a server for storing and analyzing data, a terminal for displaying meal suggestions, a food delivery ordering function, a health monitoring and alert function, and a feedback function. The entire system links these functions to provide users with individually optimized meal suggestions and food delivery services.

[1530] A natural language description of what the program does

[1531] 1. Collection of User's Personal Information:

[1532] Users operate the interface using a smartphone, tablet, or other device and enter their personal information (age, weight, height, sleep duration, exercise frequency, etc.) This information is sent from the device to a server and stored in a database.

[1533] 2. Data storage and analysis:

[1534] The server securely stores data from users and uses a generative AI model to analyze their individual health information, which then generates optimal dietary recommendations for them.

[1535] 3. Generating and displaying meal suggestions:

[1536] The generated meal suggestions are sent from the server to the user's device, which displays them to the user, who then selects from the suggested menu.

[1537] 4. Transmission and Storage of Selected Information:

[1538] The meal information selected by the user is sent from the device to the server and stored in a database, which is used as feedback to improve the next recommendation.

[1539] 5. Food delivery ordering feature:

[1540] It will provide a food delivery service linked to meal suggestions, allowing users to order directly from the suggestions displayed, and the selected meal will be delivered to the specified location.

[1541] 6. Health monitoring and alert features:

[1542] The server periodically analyzes the user's selection information and stored data to monitor their health status, and if an abnormality is detected, the server will send an alert to the device and provide appropriate advice.

[1543] Specific examples

[1544] An example of a usage scenario

[1545] Users download the app and enter personal information such as age, weight, height, sleep duration, and exercise frequency when they first launch it. The device then sends this information to a server, which then uses a generative AI model to analyze the data and create optimal meal recommendations for the day. For example, in the fall, menu suggestions might include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[1546] The user selects "grilled chicken and stir-fried vegetables" and confirms the order. The selection information is sent to the server and stored. The server uses the selection results to improve the next recommendation. If high-calorie meals are consistently selected, an alert is sent to the device, displaying a message saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[1547] Prompt Sentence Examples

[1548] Enter your user information: age, weight, height, sleep time, exercise frequency

[1549] Example: 30 years old, 70 kg, 175 cm, 7 hours, 3 times a week

[1550]

[1551] Please select a suggested meal below:

[1552] 1. Pumpkin soup

[1553] 2. Grilled chicken and stir-fried vegetables

[1554] 3. Quinoa and avocado salad

[1555]

[1556] Your recent diet choices are high in calories. Try incorporating lower calorie options.

[1557] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1558] Step 1:

[1559] Users use their devices to input personal information, such as age, weight, height, sleep duration, and exercise frequency, into the application interface, which is then temporarily stored on the device.

[1560] Input: Age, weight, height, sleep time, exercise frequency

[1561] Output: User's personal information is stored on the device

[1562] Step 2:

[1563] The device sends the user's personal information to the server. The data is serialized in JSON format and sent using a secure communication protocol (e.g., HTTPS). After sending, the server receives the data and stores it in a database.

[1564] Input: Personal information data in JSON format

[1565] Output: User's personal information stored in the server's database

[1566] Step 3:

[1567] The server uses a generative AI model to analyze the user's personal information stored in a database. Specifically, it evaluates the user's health based on data such as age, weight, height, sleep duration, and exercise frequency, and generates optimal dietary recommendations. Seasonal information is also taken into account.

[1568] Input: Personal information and seasonal information in the database

[1569] Output: Meal suggestions from a generative AI model

[1570] Step 4:

[1571] The server sends the generated meal suggestions in JSON format to the device, which receives the data and displays a list of meal suggestions to the user.

[1572] Input: Meal suggestions from a generative AI model

[1573] Output: A list of meal suggestions displayed on the device

[1574] Step 5:

[1575] The user selects one menu from the meal suggestion list, and the selected menu information is sent back to the server in JSON format and recorded in the database.

[1576] Input: User's selected meal menu

[1577] Output: Selection information saved on the server

[1578] Step 6:

[1579] The server provides feedback to the generative AI model based on the selection information to improve the next meal suggestion, which is then used to improve the accuracy of the next meal suggestion.

[1580] Input: Selection information, health data

[1581] Output: Improved meal suggestions

[1582] Step 7:

[1583] The user places an order for the selected meal menu with the food delivery service from the terminal. The terminal sends the order information to the food delivery service, and delivery begins.

[1584] Input: Selected meal menu

[1585] Output: Food delivery order

[1586] Step 8:

[1587] The server periodically monitors the stored user health data and generates an alert if an abnormality is detected, which is then sent to the device to notify the user.

[1588] Input: Stored health data

[1589] Output: The alert displayed on the terminal

[1590] Step 9:

[1591] Users can adjust their behavior based on alerts and suggestions they receive through their device. For example, if they have been consistently choosing high-calorie meals, they can adjust their diet based on an alert that says, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[1592] Input: Alert message

[1593] Output: Improved user behavior

[1594] 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.

[1595] The present invention is a personalized diet recommendation system based on the health information of each user, and provides further adaptability by combining an emotion engine. Below, specific embodiments for carrying out the present invention will be described.

[1596] User Data Collection

[1597] The user opens the application on the device and enters their personal information (age, weight, height, sleep time, exercise details, etc.). The device sends this information to the server, which then stores the received information in a database.

[1598] Specific examples

[1599] Users use a smartphone app to enter information such as age 30, weight 70kg, height 175cm, sleep seven hours, and jog three times a week.

[1600] Data storage and analysis

[1601] The server securely stores the information received from the user in a database, and then uses generative artificial intelligence to analyze the user's health information and generate personalized meal suggestions.

[1602] Specific examples

[1603] The server analyzes the user's exercise volume and other health data based on the user information stored in the database, and suggests optimal healthy diets.

[1604] Generating meal suggestions

[1605] The server's AI generates an optimal meal suggestion list based on personal information and additional data such as seasonal information. This meal suggestion list is sent to the terminal and displayed to the user.

[1606] Specific examples

[1607] Meal suggestions for the fall season include pumpkin soup, grilled chicken and vegetable stir-fry, and quinoa and avocado salad.

[1608] Emotion recognition and dietary suggestion adjustment

[1609] The emotion engine analyzes the user's facial expression and voice data to recognize their emotions, and the server further adjusts the meal suggestions based on this recognition result.

[1610] Specific examples

[1611] If the user looks tired, the emotion engine will recognize this and add food suggestions suitable for replenishing energy (e.g., banana pancakes, protein smoothies).

[1612] Recipe provision and data monitoring

[1613] The device displays the list of meal suggestions received from the server, and the user selects one of them and sends it to the server, which records the selection and uses it as feedback to improve the next suggestion.

[1614] Specific examples

[1615] The user selects "grilled chicken and stir-fried vegetables" and sends the selection from their device to the server, which records this information in a database and uses it for future suggestions.

[1616] Health monitoring and alerts

[1617] The server periodically monitors the user's health status based on the stored data and sends an alert if there is an abnormality. This alert is then sent to the user's device, providing necessary advice and suggestions.

[1618] Specific examples

[1619] If a user consistently chooses high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try incorporating lower-calorie options."

[1620] The system of the present invention allows users to efficiently receive appropriate dietary suggestions based on health information. Furthermore, the use of an emotion engine enables detailed responses based on the user's emotional state, supporting the maintenance of a healthy lifestyle and the prevention of lifestyle-related diseases. This makes it easier for users to manage themselves, and health support tailored to individual needs can be realized.

[1621] The processing flow will be explained below.

[1622] Step 1:

[1623] The user opens the application on their device and enters their personal information (age, weight, height, sleep time, exercise details, etc.).

[1624] Step 2:

[1625] The device sends the user's personal information entered into the application to the server.

[1626] Step 3:

[1627] The server stores the personal information received from the user in a database.

[1628] Step 4:

[1629] The server uses artificial intelligence to analyze data based on the user's stored personal information.

[1630] Step 5:

[1631] Generative artificial intelligence takes into account the user's personal information and seasonal information to generate a personalized list of meal suggestions.

[1632] Step 6:

[1633] The server transmits the generated meal suggestion list to the terminal.

[1634] Step 7:

[1635] The device displays the received meal suggestion list to the user.

[1636] Step 8:

[1637] The user selects their preferred meal from the displayed list of meal suggestions.

[1638] Step 9:

[1639] The device sends the meal information selected by the user to the server.

[1640] Step 10:

[1641] The server stores the received selected meal information in a database.

[1642] Step 11:

[1643] The server periodically monitors the user's health data and selected dietary information.

[1644] Step 12:

[1645] The server receives the user's voice data and facial expression data and recognizes the user's emotions using an emotion engine.

[1646] Step 13:

[1647] The emotion engine analyzes the user's emotional data and adjusts the meal suggestion list.

[1648] Step 14:

[1649] The server sends the adjusted meal suggestion list to the terminal.

[1650] Step 15:

[1651] The device displays a tailored list of meal suggestions to the user.

[1652] Step 16:

[1653] The server generates alerts as needed and sends them to the device.

[1654] Step 17:

[1655] Notify the user of any alerts received by the device.

[1656] Example 2

[1657] 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."

[1658] Currently, many people find it difficult to choose the right diet to maintain a healthy lifestyle. In particular, selecting meals based on individual health conditions and daily emotions is a time-consuming and labor-intensive task using conventional methods. Furthermore, there is a lack of appropriate systems for users to utilize their own health data to receive specific dietary recommendations. Given this background, there is a need for a system that can provide personalized dietary recommendations that address individual needs and also take into account the user's emotional state.

[1659] 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.

[1660] In this invention, the server includes means for providing an interface for inputting the user's personal information, means for transmitting the personal information to the server and storing the personal information in a database, means for the server to generate optimal meal suggestions for the user using an artificial intelligence model based on the personal information, means for transmitting the generated meal suggestions to a user terminal and displaying the meal suggestions, means for transmitting meal information selected by the user to the server and storing the selected meal information in a database, means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected, and means for further adjusting the meal suggestions using an emotion engine that collects and analyzes the user's emotion data. This allows the user to efficiently receive personalized meal suggestions based on their health condition and further enables flexible responses according to their emotional state.

[1661] "User" refers to a person who uses the system.

[1662] "Personal information" refers to data entered by users, such as age, weight, height, sleep time, and exercise details.

[1663] "Interface" refers to the screen or input means provided for users to enter personal information.

[1664] "Server" refers to a computer system that receives, stores, and analyzes data sent by users.

[1665] "Database" refers to a system for securely storing personal information and other data within a server.

[1666] "Artificial Intelligence Model" refers to the algorithm and its implementation for generating optimal meal suggestions based on a user's personal information.

[1667] "Meal Suggestions" refers to a list of specific meal suggestions based on the user's health and personal information.

[1668] "User Terminal" means the device (e.g., smartphone or tablet) used by a User to access the System and receive meal suggestions.

[1669] An "emotion engine" refers to a system that determines a user's emotional state by collecting and analyzing their facial expression and voice data.

[1670] The "feedback function" refers to a function that improves the next meal suggestion based on the meal information selected by the user.

[1671] "Alert" refers to the function in which the server monitors the user's health status and notifies the user if an abnormality occurs.

[1672] The present invention is a system that provides personalized meal suggestions based on a user's health information, and provides further adaptability by combining it with an emotion engine. Specific means for implementing the present invention are described below.

[1673] User Data Collection

[1674] The user opens the application on their device (such as a smartphone or tablet) and enters personal information such as age, weight, height, sleep time, exercise details, etc. The device then sends the entered personal information to the server, which then stores the received information in a database.

[1675] Specific examples

[1676] A user opens the application and enters information such as "30 years old, 70 kg, 175 cm, 7 hours of sleep, jog three times a week."

[1677] Data storage and analysis

[1678] The server securely stores the information submitted by the user in a database, and then uses an artificial intelligence model (e.g., using a generative AI model) to analyze the user's health information and generate personalized dietary recommendations.

[1679] Specific examples

[1680] The server generates meal suggestions based on the user's age and activity level based on the user's information stored in the database. For example, it might suggest "grilled chicken and stir-fried vegetables" as a "high-protein food" for a specific user.

[1681] Generating meal suggestions

[1682] The server's artificial intelligence model takes into account the user's personal information and additional data such as seasonal information to generate an optimal meal suggestion list, which is then sent to the device and displayed to the user.

[1683] Specific examples

[1684] Meal suggestions for the fall season include pumpkin soup, grilled chicken and stir-fried vegetables, and quinoa and avocado salad.

[1685] Emotion data collection and analysis

[1686] The emotion engine collects and analyzes the user's facial expression and voice data to recognize their emotional state, based on which the server further adjusts the meal suggestions.

[1687] Specific examples

[1688] If a user uses the device's camera to show a "tired expression," the emotion engine will recognize the "feeling of fatigue" and add "banana pancakes, protein smoothies" that are suitable for replenishing energy.

[1689] Recipe provision and data monitoring

[1690] The device displays a list of meal suggestions sent from the server, and the user can select from them. The selected meal information is then sent back to the server, which uses this information as feedback to reflect in the next suggestion.

[1691] Specific examples

[1692] The user selects "grilled chicken and stir-fried vegetables" and sends that selection to the server, which records the selection in its database and improves its suggestions next time.

[1693] Health Monitoring and Alerts

[1694] The server regularly monitors the user's health status based on the stored data, and if it detects any abnormalities, it sends an alert to the user's device.

[1695] Specific examples

[1696] If the user continues to choose high-calorie meals, the server will detect the anomaly and send an alert to the device saying, "Your recent meals have been high in calories. Try choosing low-calorie options as well."

[1697] This allows users to receive appropriate and personalized meal suggestions based on their health and emotional state. The entire system is highly adaptable, responding dynamically to the user's choices and emotions.

[1698] Prompt Sentence Examples

[1699] "Generate personalized meal suggestions for the fall season for a user who is 30 years old, 70kg, 175cm tall, sleeps 7 hours, and jogs 3 times a week."

[1700] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1701] Step 1:

[1702] The user launches the application on their device and enters personal information such as age, weight, height, sleep time, exercise details, etc. The entered information is then sent to the server by the user's device.

[1703] Specifically, the user enters "30 years old, 70 kg, 175 cm, 7 hours of sleep, jogging three times a week" into the application's input form and presses the "Submit" button.

[1704] Input: Age, weight, height, sleep time, exercise details

[1705] Output: Personal information data sent to the server

[1706] Step 2:

[1707] The server receives personal information data sent from the user terminal and stores it in a secure database.

[1708] Specifically, the server encrypts the received data and executes a process to store it in a database.

[1709] Input: Received personal information data

[1710] Output: Personal information stored in the database

[1711] Step 3:

[1712] The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze the personal information data stored in the database, and generates personalized meal recommendations based on the analysis results.

[1713] Specifically, the server inputs the user's health data into the generated AI model, which then generates meal suggestions such as "grilled chicken is good for users who need high-protein foods."

[1714] Input: Personal information stored in a database

[1715] Output: Personalized meal suggestions

[1716] Step 4:

[1717] The server transmits the generated meal suggestion list to the user terminal, which displays the list to the user.

[1718] Specifically, the server generates a list of dishes such as "pumpkin soup, grilled chicken and vegetable stir-fry, quinoa and avocado salad" and sends it to the device, which then displays it on the device screen.

[1719] Input: Generated meal suggestions

[1720] Output: A list of meal suggestions displayed on the user's device.

[1721] Step 5:

[1722] The user selects the desired menu from a list of meal suggestions, and the user's device sends the selection to the server.

[1723] Specifically, the user selects "grilled chicken and stir-fried vegetables," and the device sends this to the server.

[1724] Input: User's menu selection

[1725] Output: Selection data sent to the server

[1726] Step 6:

[1727] The server stores the received user menu selection data in a database and uses it as feedback to improve the next meal suggestion.

[1728] Specifically, the server saves the selected data and reflects it in the next analysis algorithm.

[1729] Input: Selection data sent to the server

[1730] Output: Selection data stored in the database as feedback

[1731] Step 7:

[1732] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, it sends an alert to the user's device. Specifically, if the server detects a user who has consistently chosen high-calorie meals, it sends an alert saying, "Your recent meals have been high in calories. Try incorporating low-calorie options."

[1733] Input: Saved data

[1734] Output: Alert notification sent to terminal

[1735] Step 8:

[1736] The emotion engine recognizes emotions by collecting and analyzing the user's facial and voice data, which is then sent to a server via the device and used to adjust meal suggestions.

[1737] Specifically, the user uses the device's camera and microphone to record their "tired facial expression" and "tired voice," which are then sent to the server. The server then analyzes the data using its emotion engine, recognizes the "feeling of fatigue," and offers "banana pancakes and protein smoothies" to replenish the energy.

[1738] Input: facial expression data, voice data

[1739] Output: Tailored meal suggestions

[1740] Step 9:

[1741] The device then displays the adjusted meal suggestions to the user again, from which the user can make a meal selection.

[1742] Specifically, the device displays new meal suggestions and the user selects again.

[1743] Input: Tailored meal suggestions

[1744] Output: User selects again

[1745] (Application example 2)

[1746] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1747] Conventional meal recommendation systems only make recommendations based on a user's personal information and do not take into account the user's emotional state. As a result, they are unable to make meal recommendations that are optimal for the user's emotions and circumstances at any given time, and are insufficient for improving the user's health management and satisfaction. The present invention aims to solve these problems and provide a personalized meal recommendation system that takes into account the user's emotional state as well as their personal information.

[1748] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1749] In this invention, the server includes means for providing an interface for inputting the user's personal information, means for transmitting the personal information to the server and storing the personal information in a database, means for the server to generate optimal meal suggestions for the user based on the personal information using a generative artificial intelligence, means for transmitting the generated meal suggestions to the user's terminal and displaying the meal suggestions on the terminal, means for adjusting the meal suggestions using an emotion engine that recognizes the user's emotions, means for transmitting meal information selected by the user to the server and storing the selected meal information in a database, and means for the server to periodically monitor the user's health condition based on the stored data and send an alert if an abnormality is detected. This enables personalized meal suggestions that comprehensively consider the user's health condition and emotional state.

[1750] "User personal information" refers to data related to the user's individual health status, such as age, weight, height, sleep time, and exercise.

[1751] "Means for providing an interface" refers to technology that provides an input screen for an application or web page that allows users to input personal information.

[1752] "Generative AI" is a system that uses machine learning and deep learning technologies to generate optimal meal suggestions based on a user's personal information.

[1753] The "emotion engine" is a technology that analyzes the user's facial expressions and voice data to recognize their emotional state at any given time.

[1754] "Meal Suggestion" refers to the suggestion of specific meals or dishes to consume based on the user's health information and emotional state.

[1755] A "terminal" refers to a device that is capable of processing information, such as a smartphone or tablet, used by a user.

[1756] "Database" means an integrated system for securely storing and managing users' personal information and food selection information.

[1757] "Health monitoring" is a system that periodically checks the user's stored health information and notifies them if any abnormalities are found.

[1758] "Means for sending alerts" refers to technology that sends health status notifications and warnings to the user's device.

[1759] The "feedback function" is a mechanism that reflects the meal information selected by the user in the next suggestions.

[1760] "Personalized" means specific to an individual user and tailored to their individual characteristics and preferences.

[1761] MODE FOR CARRYING OUT THE INVENTION

[1762] The present invention is a personalized meal recommendation system based on a user's personal information and emotional state. Specific embodiments of the present invention are described below.

[1763] User Data Collection

[1764] Users use a smartphone app to enter their personal information (age, weight, height, sleep time, exercise details, etc.) The device then sends the information to a server, which then securely stores it in a database.

[1765] Data storage and analysis

[1766] The server analyzes the data using a generative AI model (e.g., OpenAI GPT-3) based on the stored user information to generate optimal meal suggestions for the user.

[1767] Generating meal suggestions

[1768] The generative AI model uses the user's personal information and additional data, such as seasonal information, to generate a list of optimal meal suggestions, including specific meal plans and recipes, which are then sent to the user's device and displayed in the app.

[1769] Emotion recognition and dietary suggestion adjustment

[1770] The emotion engine uses the smartphone camera and microphone to collect facial and voice data from the user and analyzes their emotions. Based on the analysis results, the server further adjusts the meal suggestions. For example, if the user shows signs of fatigue, it will suggest meals suitable for replenishing energy.

[1771] Recipe provision and data monitoring

[1772] The meal information that the user selects from the suggested meal list is sent from the device to the server, which records this information in a database and uses it as feedback to improve the next meal suggestion.

[1773] Health monitoring and alerts

[1774] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, the server sends an alert to the user's device and provides necessary advice and suggestions.

[1775] Hardware and software used

[1776] Smartphone: Used for entering user information and emotion recognition.

[1777] Server: Used to host the database and operate the generative AI model.

[1778] EmotionRecognition module: As an emotion engine, it recognizes emotions using the smartphone camera and microphone.

[1779] HealthDataAnalysis module: Analyzes the user's health data and generates personalized dietary suggestions.

[1780] OpenAI GPT-3 API: Used as generative artificial intelligence.

[1781] The requests library: Used to send and receive HTTP requests.

[1782] Example prompt

[1783] For example, a prompt to a generative AI model might look like this:

[1784] plaintext

[1785] User information: Age 30, Weight 70kg, Height 175cm, Sleep time 7 hours, Exercise: Jogging 3 times a week

[1786] Emotion: Tired

[1787] Menu: Pumpkin soup, grilled chicken and vegetable stir-fry, quinoa and avocado salad, banana pancakes, protein smoothie

[1788] Season: Autumn

[1789] Generate optimal meal suggestions.

[1790] Based on this prompt, the generative AI model creates optimal meal suggestions for the user.

[1791] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1792] Step 1:

[1793] The user opens the smartphone app and enters their personal information (age, weight, height, sleep duration, exercise, etc.) This information is collected through the application interface and sent by the device to the server.

[1794] Input: User's personal information

[1795] Output: Personal information data received by the server

[1796] Step 2:

[1797] The server stores the personal information in a database that uniquely identifies each user and is used for subsequent analysis.

[1798] Input: Personal information data received by the server

[1799] Output: User information stored in the database

[1800] Step 3:

[1801] The server uses a generative AI model (e.g., OpenAI GPT-3) to analyze the stored user information and generate personalized meal suggestions, taking into account the user's personal information and seasonal information.

[1802] Input: User and seasonal information stored in the database

[1803] Output: A list of meal suggestions generated by the generative AI model

[1804] Step 4:

[1805] The generated meal suggestion list is sent to the terminal and displayed to the user on the application.

[1806] Input: A list of meal suggestions generated by a generative AI model

[1807] Output: A list of meal suggestions displayed on the user's device

[1808] Step 5:

[1809] The emotion engine uses the smartphone camera and microphone to collect facial expression and voice data from the user, and analyzes their current emotions. The emotion recognition results are sent to the server.

[1810] Input: User facial expression and voice data collected by smartphone camera and microphone

[1811] Output: Emotion recognition data received by the server

[1812] Step 6:

[1813] The server further refines the generated meal suggestions based on emotion recognition data, for example, suggesting additional energy-replenishing meals if the user looks tired.

[1814] Input: Emotion recognition data and initial meal suggestion list

[1815] Output: Tailored meal suggestion list

[1816] Step 7:

[1817] The user makes a selection from the list of suggested meals and the selection is sent from the device to the server, which records the selection in a database.

[1818] Input: Meal information selected by the user

[1819] Output: Selected meal information stored in a database

[1820] Step 8:

[1821] The server periodically monitors the user's health status based on the stored data, and if an abnormality is detected, an alert is sent to the user's device and appropriate advice or suggestions are provided.

[1822] Input: User health information stored in a database

[1823] Output: Alert notification sent to user's device

[1824] 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.

[1825] 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.

[1826] 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.

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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).

[1831] 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.

[1832] 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."

[1833] 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.

[1834] 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).

[1835] 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.

[1836] 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.

[1837] 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.

[1838] 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.

[1839] 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.

[1840] 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.

[1841] 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.

[1842] 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.

[1843] 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.

[1844] 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.

[1845] The following is further disclosed regarding the above embodiment.

[1846] (Claim 1)

[1847] means for providing an interface for inputting personal user information;

[1848] means for transmitting the personal information to a server, and the server storing the personal information in a database;

[1849] A means for the server to generate optimal meal suggestions for the user using artificial intelligence based on the personal information;

[1850] means for transmitting the generated meal suggestions to a user's terminal and for the terminal to display the meal suggestions;

[1851] a means for transmitting the meal information selected by the user to a server, and for the server to store the selected meal information in a database;

[1852] A means for the server to periodically monitor the user's health condition based on the stored data and to send an alert if an abnormality is detected;

[1853] A system including:

[1854] (Claim 2)

[1855] The system of claim 1, wherein the generating artificial intelligence generates meal suggestions based on the user's personal information and seasonal information.

[1856] (Claim 3)

[1857] The system according to claim 1, characterized in that it has a feedback function that improves the next meal suggestion based on the meal information selected by the user.

[1858] "Example 1"

[1859] (Claim 1)

[1860] means for providing an interface for inputting a user's health information;

[1861] means for transmitting the health information to a server, and the server storing the health information in a database;

[1862] A means for the server to generate optimal dietary suggestions for the user using artificial intelligence based on the health information;

[1863] means for transmitting the generated meal suggestions to a user's terminal and for the terminal to display the meal suggestions;

[1864] a means for transmitting the meal information selected by the user to a server, and for the server to store the selected meal information in a database;

[1865] A means for the server to periodically monitor the user's health condition based on the stored data and to send an alert if an abnormality is detected;

[1866] A system including:

[1867] (Claim 2)

[1868] The system of claim 1, wherein the generating artificial intelligence generates meal suggestions based on the user's health information and additional data.

[1869] (Claim 3)

[1870] The system according to claim 1, characterized in that it has a feedback function that improves the next meal suggestion based on the meal information selected by the user.

[1871] "Application Example 1"

[1872] (Claim 1)

[1873] means for providing an interface for inputting personal user information;

[1874] means for transmitting the personal information to a server, and the server storing the personal information in a database;

[1875] A means for the server to generate optimal meal suggestions for the user using artificial intelligence based on the personal information;

[1876] means for transmitting the generated meal suggestions to a user's terminal and for the terminal to display the meal suggestions;

[1877] a means for transmitting the meal information selected by the user to a server, and for the server to store the selected meal information in a database;

[1878] A means for the server to periodically monitor the user's health condition based on the stored data and to send an alert if an abnormality is detected;

[1879] A means to link meal suggestions based on users' health information with food delivery ordering functions, and

[1880] A means for analyzing the order history of the food delivery service and having a feedback function for improving the next proposal;

[1881] A system including:

[1882] (Claim 2)

[1883] The system of claim 1, wherein the generating artificial intelligence generates meal suggestions based on the user's personal information and seasonal information.

[1884] (Claim 3)

[1885] 2. The system according to claim 1, wherein the user selects a suggested meal menu and transmits the selection information to the server.

[1886] "Example 2: Combining Emotion Engines"

[1887] (Claim 1)

[1888] means for providing an interface for inputting personal user information;

[1889] means for transmitting the personal information to a server, and the server storing the personal information in a database;

[1890] means for the server to generate optimal meal suggestions for the user using an artificial intelligence model based on the personal information;

[1891] means for transmitting the generated meal suggestions to a user terminal, where the terminal displays the meal suggestions;

[1892] a means for transmitting the meal information selected by the user to a server, and for the server to store the selected meal information in a database;

[1893] A means for the server to periodically monitor the user's health condition based on the stored data and to send an alert if an abnormality is detected;

[1894] a means for further adjusting meal suggestions using an emotion engine that collects and analyzes user emotion data;

[1895] A system including:

[1896] (Claim 2)

[1897] 2. The system of claim 1, wherein the artificial intelligence model generates meal suggestions based on the user's personal information and weather data.

[1898] (Claim 3)

[1899] The system according to claim 1, characterized in that it has a feedback function that improves the next meal suggestion based on the meal information selected by the user.

[1900] "Application example 2 when combining emotion engines"

[1901] New Claims

[1902] (Claim 1)

[1903] means for providing an interface for inputting personal user information;

[1904] means for transmitting the personal information to a server, and the server storing the personal information in a database;

[1905] A means for the server to generate optimal meal suggestions for the user using artificial intelligence based on the personal information;

[1906] means for transmitting the generated meal suggestions to a user's terminal, the terminal displaying the meal suggestions;

[1907] means for adjusting the meal suggestions using an emotion engine that recognizes the user's emotions;

[1908] a means for transmitting the meal information selected by the user to a server, and for the server to store the selected meal information in a database;

[1909] A means for the server to periodically monitor the user's health condition based on the stored data and to send an alert if an abnormality is detected;

[1910] A system including:

[1911] (Claim 2)

[1912] The system of claim 1, wherein the generating artificial intelligence generates meal suggestions based on the user's personal information and seasonal information.

[1913] (Claim 3)

[1914] The system according to claim 1, characterized in that it has a feedback function that improves the next meal suggestion based on the meal information selected by the user. [Explanation of symbols]

[1915] 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. means for providing an interface for inputting personal user information; means for transmitting the personal information to a server, and the server storing the personal information in a database; A means for the server to generate optimal meal suggestions for the user using artificial intelligence based on the personal information; means for transmitting the generated meal suggestions to a user's terminal and for the terminal to display the meal suggestions; a means for transmitting the meal information selected by the user to a server, and for the server to store the selected meal information in a database; A means for the server to periodically monitor the user's health condition based on the stored data and to send an alert if an abnormality is detected; A system including:

2. The system of claim 1, wherein the artificial intelligence generating system generates meal suggestions based on the user's personal information and seasonal information.

3. The system of claim 1, further comprising a feedback function for improving next meal suggestions based on the meal information selected by the user.

Images