System

Abstract

A system is provided.SOLUTION: A system includes means for inputting health information and preference data of a user, means for analyzing the health information and the preference data of the user to create a health profile, means for creating a personalized recipe using a generated artificial intelligence model based on the created health profile, and means for automatically ordering necessary ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with an ingredient delivery service.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, the number of people living alone is increasing, and health consciousness is particularly on the rise. However, many people find it difficult to allocate the time and effort to preparing and cooking meals. While personalized meal suggestions based on individual health conditions and dietary preferences are required, efficient inventory management of ingredients and budget adjustments are also necessary. For these reasons, it is currently difficult to realize a healthy and convenient dietary lifestyle for each user. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including: a means for inputting a user's health information and preference data; a means for analyzing the user's health information and preference data to create a health profile; a means for generating personalized recipes using a generative artificial intelligence model based on the created health profile; and a means for automatically ordering ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with a food delivery service. Furthermore, by adding a means for managing the user's ingredient inventory and optimizing the recipe based on the inventory information and the user's budget, and a means for automatically collecting lifestyle habit data from the user's health management device and analyzing the lifestyle habit data to update the health profile, more accurate personalized meal suggestions are possible.

[0006] "User's health information" is information that indicates the user's physical health condition, and is data that includes allergies, nutritional restrictions, medical history, and the like.

[0007] "Preference data" is information about a user's food preferences and disliked foods, including specific cooking styles and seasoning preferences.

[0008] A "health profile" is comprehensive information that indicates a user's overall health status, generated based on the user's health information and lifestyle data.

[0009] A "generative artificial intelligence model" is a machine learning algorithm or artificial intelligence system that generates appropriate recipes or meal plans based on collected data.

[0010] A "personalized recipe" is a cooking recipe that is individually created based on the user's health information, preference data, and inventory information.

[0011] The "food delivery service" is an online service that delivers food to a specified address in response to a user's order.

[0012] "Ingredient inventory" is a list of food and ingredients that the user has on hand, and is data that indicates the current inventory status.

[0013] The "budget" indicates a limit on the cost of food that is set in advance by the user.

[0014] "Lifestyle data" is data related to the user's daily life, including information such as the amount of exercise, sleep patterns, and dietary habits. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system that generates individual recipes based on a user's health information and preferences, and provides meal plans that take into account ingredient availability and budget. The system also works in conjunction with a health management device to suggest meals to maintain the user's optimal health.

[0037] System configuration

[0038] This system is broadly composed of the following four modules:

[0039] 1. Data Collection and Analysis Module

[0040] 2. Recipe Generation Module

[0041] 3. Food inventory and budget management module

[0042] 4. Delivery Management Module

[0043] Data Collection and Analysis Module

[0044] Users can input their health information and dietary preferences through a dedicated device app or web interface. For example, users can input allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (such as smartwatches and fitness trackers) is also automatically collected, and the user's health profile is updated in real time.

[0045] Specific examples

[0046] The user opens the device app and enters their allergy information (e.g., nut allergy) and favorite food (e.g., Italian food). The health management device also automatically sends information to the server, such as running for 45 minutes three times a week.

[0047] Recipe Generation Module

[0048] The server uses information from the data collection and analysis module to create a user's health profile and uses a generative artificial intelligence model to generate personalized recipes that are optimized not only based on the user's health status, but also on their food availability and budget.

[0049] Specific examples

[0050] The server takes into account the user's allergy information and exercise level, and uses a generative AI model to suggest a low-salt, calorie-controlled "tomato and basil pasta."

[0051] Food inventory and budget management module

[0052] Users can manage their food inventory, and the server generates a list of ingredients based on this information. It also optimizes food purchasing plans taking into account the user's budget. This allows for efficient management of necessary ingredients and eliminates waste.

[0053] Specific examples

[0054] The user inputs a list of ingredients they have at home into the app (e.g., two tomatoes, one bunch of basil). The server determines what ingredients are missing based on the proposed recipe and creates a shopping list within the user's budget.

[0055] Delivery Management Module

[0056] The server connects to an online food delivery service and automatically orders the necessary ingredients. Once the order is confirmed, the ingredients are delivered to the user's specified address. This eliminates the need for the user to spend time shopping for ingredients.

[0057] Specific examples

[0058] Based on the recipe proposed by the server, any missing ingredients (e.g., cheese, pasta) are automatically ordered from a food delivery service. The delivery service delivers the ingredients to the user's home, and the user can check the delivery status in real time on the app.

[0059] This system allows users to easily access personalized recipes based on health information and preferences, saving time and effort while enjoying a healthy diet.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] Users open a dedicated terminal app or web interface and enter their health and preference data, such as allergies (e.g., nut allergies), dietary restrictions (e.g., low salt), favorite dishes (e.g., Italian food), and disliked ingredients (e.g., celery).

[0063] Step 2:

[0064] The device collects the entered health information and preference data and sends it to a server, which stores the received data in a database and prepares it for creating the user's health profile.

[0065] Step 3:

[0066] Health management AI automatically collects lifestyle data (e.g., exercise volume, sleep patterns) from the user's health management device (e.g., smartwatch) and sends it to a server. The server receives this data and updates the user's health profile.

[0067] Step 4:

[0068] The server analyzes the collected health information, preference data, and lifestyle data to create a health profile for the user, which assesses the user's overall health and includes appropriate dietary restrictions and nutritional balance.

[0069] Step 5:

[0070] The server uses the health profile to activate a generative artificial intelligence model (generative AI model) that generates personalized recipes for the user. The generative AI model takes into account the user's preferences, health status, and dietary restrictions to suggest the most suitable recipes.

[0071] Step 6:

[0072] The server provides the generated recipe as feedback to the user. The device displays the recipe details (e.g., ingredients, cooking instructions, and nutritional information) to the user. The user reviews the suggested recipes and selects one.

[0073] Step 7:

[0074] To obtain the user's food inventory data, the server asks the user to input the inventory information via the terminal. The user inputs the ingredients they have on hand (e.g., two tomatoes, one bunch of basil) into the terminal.

[0075] Step 8:

[0076] The server compares the user's inventory information with the recipe's ingredient list to identify the ingredients needed. Any missing ingredients (e.g., cheese, pasta) are listed.

[0077] Step 9:

[0078] The server checks the user's budget information and automatically adds the necessary ingredients to the order list within that budget. Once the order list is complete, the order data is sent to the online food delivery service.

[0079] Step 10:

[0080] The online food delivery service receives the order and delivers the food to the user's specified address, allowing the user to track the delivery status in real time via their device.

[0081] Step 11:

[0082] Users can then use the delivered ingredients to prepare healthy meals based on personalized recipe suggestions. Users can then follow the recipes to enjoy healthy and delicious meals.

[0083] In this way, users can utilize the system to efficiently create personalized meal plans.

[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 life, maintaining a balance between health management and diet is extremely important, but planning meals and managing ingredients to suit each individual's health condition and preferences can be time-consuming. Furthermore, finding the time to purchase the right ingredients and prepare meals in a busy daily life can be difficult. Furthermore, efficiently preparing meals within a budget while reducing food waste is a challenging task. Conventional systems manage a user's health information, preferences, inventory information, budget, etc. separately, making it difficult to provide an integrated meal plan.

[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 inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for automatically collecting lifestyle habit data from a health management device to update the health profile in real time, means for generating personalized recipes using a generative AI model based on the created health profile, means for the user to input ingredient inventory and budget using a terminal app, means for generating a required ingredient list according to the personalized recipe, means for automatically ordering ingredients based on the required ingredient list and delivering the ingredients to the user in cooperation with a food delivery service, and means for providing a terminal app for checking delivery status in real time. This enables efficient and personalized meal planning based on the user's health information and preferences, significantly saving time and effort while maintaining a healthy diet.

[0089] "User" refers to a person who uses the system to input health information and preference data to manage and optimize their diet.

[0090] "Device app" refers to software that users use to input health information, preference data, food inventory, budget information, etc.

[0091] "Health management device" refers to a device (e.g., a smartwatch or fitness tracker) that monitors a user's lifestyle data and automatically transmits that data to a server.

[0092] "Server" refers to the central system that receives and analyzes user-submitted data, generates and updates health profiles, and generates personalized recipes.

[0093] The "data collection means" refers to a method for inputting the user's health information and preference data and automatically collecting lifestyle habit data from the health management device.

[0094] "Health profile" refers to an individual health information profile generated by integrating a user's health condition, preferences, lifestyle data, etc.

[0095] "Generative AI Model" refers to the artificial intelligence model used to generate personalized recipes based on a health profile.

[0096] "Personalized recipes" refer to cooking recipes optimized for a user, generated by a generative AI model based on the user's health profile.

[0097] "Ingredient inventory management means" refers to a method by which a user inputs and manages inventory information of ingredients on hand.

[0098] "Budget input means" refers to a method by which a user inputs budget information for purchasing ingredients.

[0099] "Ingredient list generator" refers to a method for generating a list of ingredients required based on a personalized recipe.

[0100] "Online grocery delivery service" refers to an external delivery service used to deliver required groceries to a user.

[0101] "Delivery status confirmation means" refers to a method used by a user to check the delivery status of ingredients in real time.

[0102] The present invention is a system that generates individual recipes based on a user's health information and preferences, and provides meal plans that take into account ingredient availability and budget. This system works in conjunction with a health management device to suggest meals that will help the user maintain optimal health. The detailed configuration and operation of the system are described below.

[0103] System configuration

[0104] This system is broadly composed of the following modules:

[0105] 1. Data Collection and Analysis Module

[0106] 2. Recipe Generation Module

[0107] 3. Food inventory and budget management module

[0108] 4. Delivery Management Module

[0109] Data Collection and Analysis Module

[0110] Users enter their health information and dietary preferences through a dedicated device app or web interface. For example, users can enter allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (e.g., smartwatches and fitness trackers) is also automatically collected and sent to the server. This allows the user's health profile to be updated in real time.

[0111] Examples:

[0112] The user opens the device app and enters information such as "I have a nut allergy" and "I like Italian food." The health management device also automatically sends information such as "I run for 45 minutes three times a week" to the server.

[0113] Recipe Generation Module

[0114] The server creates a user's health profile based on information obtained from the data collection and analysis module. Based on the health profile, a generative AI model is used to generate personalized recipes. These recipes are optimized taking into account the user's health status, ingredient availability, and budget.

[0115] Examples:

[0116] The server takes into account the user's information about their nut allergy and running three times a week, and uses a generative AI model to suggest a low-salt, calorie-controlled tomato and basil pasta.

[0117] Example prompt for a generative AI model:

[0118] "Suggest low-sodium, calorie-controlled recipes for someone who has a nut allergy, loves Italian food, and runs three times a week."

[0119] Food inventory and budget management module

[0120] The user inputs their current inventory and budget information using a terminal app. The server uses this information to generate a list of ingredients needed for the proposed recipe. This provides an optimal food purchasing plan that takes into account the user's inventory and budget.

[0121] Examples:

[0122] The user enters inventory information such as "two tomatoes" and "one bunch of basil" and a budget of "5,000 yen" for purchasing ingredients into the app. The server checks the proposed recipe and adds the missing items "cheese" and "pasta" to the shopping list.

[0123] Delivery Management Module

[0124] The server connects to an online food delivery service and automatically orders the necessary ingredients. Users can also check the delivery status of their ingredients in real time through the device app. This means that users can receive the ingredients they need simply by waiting at home.

[0125] Examples:

[0126] The server automatically orders "cheese" and "pasta" from a food delivery service, which then delivers them to the user's home. The user can open the "Delivery Status" tab in the device app and check the delivery status (e.g., "Delivering" or "Delivery Complete") in real time.

[0127] In this way, users can create efficient and personalized meal plans based on their health information and preferences, saving them significant time and effort while maintaining a healthy diet.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1: Enter your health information and preferences

[0130] Users enter their health information and dietary preferences using a dedicated device app or web interface. Specifically, a user opens the app on their smartphone, enters information such as "I have a nut allergy" or "I like Italian food" into a form, and then presses the submit button.

[0131] Input: Allergy information, favorite dishes, disliked dishes

[0132] Output: The entered health information and preference data is sent to the server.

[0133] Step 2: Automatically collect data from health management devices

[0134] The device monitors the user's lifestyle data and automatically transmits it to a server at regular intervals. Specifically, the smartwatch sends data such as heart rate and exercise volume to the server every hour via Bluetooth or Wi-Fi.

[0135] Input: Lifestyle data such as heart rate and exercise amount

[0136] Output: Lifestyle data is sent to the server

[0137] Step 3: Creating a user's health profile

[0138] The server integrates the user's health information and preference data with the lifestyle data sent from the device to generate a health profile for the user, and then updates the user's information in the database to reflect their health status in real time.

[0139] Input: Health information, preference data, lifestyle data

[0140] Output: Creation and updating of a unified health profile

[0141] Step 4: Generate a personalized recipe

[0142] The server uses the generative AI model to generate personalized recipes based on the user's health profile. Specifically, the server inputs data such as "nut allergy" and "running three times a week" as prompts into the generative AI model, and generates appropriate recipes.

[0143] Input: Health profile, prompt (e.g., Suggest low-sodium, calorie-controlled recipes for someone who has a nut allergy, loves Italian food, and runs three times a week)

[0144] Output: A personalized recipe (e.g., pasta with tomatoes and basil)

[0145] Step 5: Enter your food inventory and budget

[0146] The user uses the device app to input the inventory and budget information for ingredients at home. Specifically, the user opens the app's inventory management screen, inputs items such as "2 tomatoes" and "1 bunch of basil," and enters "5,000 yen" in the budget field.

[0147] Input: Ingredient inventory information, budget information

[0148] Output: Inventory and budget information sent to the server

[0149] Step 6: Generate a list of ingredients needed

[0150] The server generates a list of ingredients based on the proposed recipe and the inventory and budget information entered by the user. Specifically, the server compares the information in the database, determines that "the proposed recipe requires tomatoes, but they are already in stock, so cheese and pasta are needed," and generates a shopping list.

[0151] Input: Personalized recipe, inventory information, budget information

[0152] Output: List of ingredients needed (e.g. cheese and pasta)

[0153] Step 7: Integrate with online food delivery services and automate ordering

[0154] The server connects to an online food delivery service and automatically orders the necessary ingredients. Specifically, the server calls the delivery service's API, sends the generated shopping list, and confirms the order.

[0155] Input: List of ingredients needed

[0156] Output: Order sent to online grocery delivery service

[0157] Step 8: Check delivery status

[0158] Users can check the delivery status of their groceries in real time through the app. Specifically, users open the "Delivery Status" tab in the app and view the delivery status (e.g., "Departed," "Delivering," "Delivery completed") updated in real time.

[0159] Input: Delivery status confirmation request

[0160] Output: Real-time delivery status

[0161] (Application example 1)

[0162] 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."

[0163] Conventional meal recommendation systems lack the functionality to propose optimal recipes by comprehensively considering the user's health information, preferences, ingredient availability, budget, etc. However, comprehensive and personalized meal recommendations are necessary to maintain optimal health. In addition, since systems lack the functionality to arrange ingredients by linking with food delivery services or online stores based on the proposed recipes, user convenience must also be improved.

[0164] 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.

[0165] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for generating personalized recipes using a generative artificial intelligence model based on the created health profile, means for automatically ordering ingredients needed according to the personalized recipe and delivering the ingredients to the user in cooperation with a food delivery service, means for placing an order through a food delivery service based on the suggested recipe, and means for automatically ordering missing ingredients from an online store. This allows users to easily obtain personalized recipes based on their health information and preferences, saving time and effort while enjoying a healthy diet.

[0166] "Health information" refers to data related to a user's allergies, medical history, and daily lifestyle habits.

[0167] "Preference data" refers to information such as the user's preferred dishes, ingredients, and eating style.

[0168] A "health profile" is a collection of information that indicates a user's individual health condition, created by analyzing health information and preference data.

[0169] A "generative artificial intelligence model" is an algorithm that generates personalized recipes based on collected data.

[0170] "Personalized recipes" are customized meal suggestions based on a user's health profile.

[0171] The "food delivery service" is an online service that delivers the necessary ingredients to the user based on a suggested recipe.

[0172] A "food delivery service" is a service that delivers cooked food directly from a restaurant to the user.

[0173] An "online store" is a system that provides a service for ordering and delivering food ingredients via the Internet.

[0174] "Ingredient inventory" refers to a list of ingredients currently in the user's home.

[0175] A "health management device" is a device that records and manages a user's daily lifestyle and physical activity. Examples include smartwatches and fitness trackers.

[0176] A "health management report" is a document that analyzes a user's health data and periodically provides an overview of the user's health status and suggestions for improvement.

[0177] The following system configuration and program are required to implement this invention. This system provides consistent support from the stage where the user inputs health information and preference data, to creating personalized recipes and actually arranging the ingredients.

[0178] 1. System Configuration

[0179] Hardware

[0180] Smartphone: A device that allows users to input health and preference data and receive recipe suggestions.

[0181] Smartwatch: A device for collecting data on a user's lifestyle habits.

[0182] Server: The main hardware used to analyze data, create health profiles, generate recipes, and manage ingredients and delivery. Uses cloud services (e.g., AWS (registered trademark), Azure (registered trademark)).

[0183] software

[0184] Smartphone app: Has the function of inputting health information and preference data, and displaying recipe suggestions (iOS / ANDROID (registered trademark) app).

[0185] Health management data collection platform: Collects data from smartwatches (e.g., HealthKit, GOOGLE FI®).

[0186] Generative AI models: Generate personalized recipes based on collected data (e.g., OpenAI® GPT-3®).

[0187] Database management system: Stores and manages user health information, preference data, food inventory, budget, etc. (e.g., Firebase).

[0188] 2. System Operation

[0189] Collection of health and preference data

[0190] Users enter their own health information (allergy information, medical history, etc.) and preference data (favorite dishes, disliked ingredients, etc.) through a smartphone app. In addition, lifestyle data (e.g., amount of exercise, sleep time, etc.) is automatically collected from the smartwatch and sent to a health management data collection platform.

[0191] Generate personalized recipes

[0192] The server analyzes the collected health and preference data and uses a generative AI model to generate a personalized recipe that is optimal for the user. Below are some example prompts for the generative AI model:

[0193] Example prompt sentence:

[0194] User profile:

[0195] Allergies: Nut allergies

[0196] Preferences: Italian cuisine

[0197] Recent exercise data: Running for 45 minutes three times a week

[0198] Based on these, please suggest some low-calorie, low-salt recipes.

[0199] Food arrangement and food delivery service

[0200] The necessary ingredients are automatically ordered according to the generated recipe. Any missing ingredients are automatically ordered from the online store, and an affiliated food delivery service delivers them to the user. In addition, an order is placed with a food delivery service based on the suggested recipe, and cooked food is delivered to the user. The user can check the delivery progress in real time through the app.

[0201] Generate health management reports

[0202] The server periodically analyzes the user's health data and uses a generative AI model to create a health management report, which is provided to the user via a smartphone app and reflected in the user's next meal recommendations.

[0203] For example, a user inputs health information such as a nut allergy and preference data such as a love of Italian food, and recent exercise data is collected from the smartwatch. Based on this information, the server recommends a low-calorie, low-sodium "tomato and basil pasta" and arranges for the necessary ingredients and pre-cooked food from a food delivery service and online store. Periodically generated health management reports are reflected in the next meal recommendations, helping to maintain optimal health for the user.

[0204] In this way, users receive comprehensive and personalized meal suggestions, enabling them to maintain a healthy diet.

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] The user opens the smartphone app and enters their health information (allergies, medical history, etc.) and preference data (favorite dishes, disliked ingredients, etc.). The device collects this data within the app and sends it to the server.

[0208] Input: User's health information, preference data

[0209] Output: Send data to the server

[0210] Step 2:

[0211] The smartwatch automatically collects the user's lifestyle data (such as exercise volume and sleep time) and sends it to a health management data collection platform, which then transfers the data to a server.

[0212] Input: User's lifestyle data

[0213] Output: Send data to the server

[0214] Step 3:

[0215] The server creates a user's health profile based on their health information, preference data, and lifestyle data. Data analysis algorithms analyze this data and generate a health profile.

[0216] Input: Health information, preference data, lifestyle data

[0217] Output: Health Profile

[0218] Step 4:

[0219] The server uses the generative AI model to generate personalized recipes based on the health profile. The server inputs prompts into the generative AI model to obtain appropriate recipes.

[0220] Example prompt sentence:

[0221] User profile:

[0222] Allergies: Nut allergies

[0223] Preferences: Italian cuisine

[0224] Recent exercise data: Running for 45 minutes three times a week

[0225] Based on these, please suggest some low-calorie, low-salt recipes.

[0226] Input: Health profile, prompt text

[0227] Output: Personalized recipe

[0228] Step 5:

[0229] The server automatically orders the necessary ingredients based on the recipe. If any ingredients are missing, they are automatically ordered from the online store and delivered to the user by the food delivery service.

[0230] Input: personalized recipes, ingredient inventory information

[0231] Output: List of food orders, order request to food delivery service

[0232] Step 6:

[0233] Based on the suggested recipe, the server places an order with a food delivery service, and the prepared food is delivered to the user from a partner restaurant.

[0234] Input: Personalized Recipe

[0235] Output: Food delivery service order request, delivery status tracking information

[0236] Step 7:

[0237] The server periodically analyzes the user's health data and generates a health management report, which is provided to the user via a smartphone app.

[0238] Input: Past health data, lifestyle data

[0239] Output: Health management report, display in app

[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 aims to provide a system that integrates a user's health information, preference data, and an emotion engine that recognizes the user's emotional state, and provides an optimal meal plan for the user. This system allows users to easily achieve an efficient, personalized meal plan that takes into account their emotional state.

[0242] System configuration

[0243] This system is broadly composed of the following five modules:

[0244] 1. Data Collection and Analysis Module

[0245] 2. Emotion Recognition Engine

[0246] 3. Recipe Generation Module

[0247] 4. Food inventory and budget management module

[0248] 5. Delivery Management Module

[0249] Data Collection and Analysis Module

[0250] Users enter their health information and preference data through a dedicated device app or web interface. For example, users can enter allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (e.g., smartwatches and fitness trackers) is also automatically collected. This data is sent to a server, where a health profile is created.

[0251] Specific examples

[0252] The user opens the device app and enters their allergy information (nut allergy) and favorite food (Italian food). The health management device automatically sends information to the server, such as running for 45 minutes three times a week.

[0253] Emotion Recognition Engine

[0254] The emotion recognition engine analyzes the user's current emotional state using technology that recognizes emotions from the user's tone of voice, facial expressions, text messages, etc. This data is combined with health and preference data to deliver more accurate and personalized meal suggestions.

[0255] Specific examples

[0256] The user types "I'm tired today" into the device. The emotion recognition engine analyzes the user's tone of voice and text content and detects that they are feeling very tired.

[0257] Recipe Generation Module

[0258] The server uses a generative artificial intelligence model to generate personalized recipes based on data from the health profile and emotion recognition engine, and the generated recipes are optimized to take into account the user's health status, emotional state, and preferences.

[0259] Specific examples

[0260] For users who feel extremely tired, the server uses a generative AI model to suggest "nutritious chicken soup that can be made in a short time."

[0261] Food inventory and budget management module

[0262] The server acquires the user's food inventory data and creates a list of necessary ingredients. It also optimizes food purchasing plans based on budget information. This allows for efficient management of necessary ingredients and eliminates waste.

[0263] Specific examples

[0264] The user inputs inventory information (two tomatoes, one bunch of basil) into the terminal, and the server lists the ingredients that are in short supply (chicken, soup base) and creates a purchasing list within the budget.

[0265] Delivery Management Module

[0266] The server connects to an online food delivery service and automatically orders the necessary ingredients. Once the order is confirmed, the ingredients are delivered to the user's specified address. This eliminates the need for the user to spend time shopping for ingredients.

[0267] Specific examples

[0268] The server automatically places an order with a food delivery service, which then delivers the food to the user's home. The user can check the delivery status in real time via their device.

[0269] In this way, the system provides personalized meal plans that take into account not only the user's health and preference data, but also their emotional state, making it easy for users to achieve a healthy and emotionally appropriate diet.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] Users open a dedicated device app or web interface and enter their health information (e.g., allergies, dietary restrictions) and preference data (e.g., favorite dishes, disliked ingredients).

[0273] Step 2:

[0274] The device collects the health and preference data entered and transmits it in real time to a server, which then creates a health profile for the user based on the data received.

[0275] Step 3:

[0276] A health management device (e.g., a smartwatch) automatically collects and transmits user lifestyle data (e.g., exercise volume, sleep patterns) to a server, which then imports the data and updates the user's health profile.

[0277] Step 4:

[0278] The user inputs their emotional state (e.g., "I'm tired today") into the device. The emotion recognition engine analyzes the text, tone of voice, facial expressions, etc. to recognize the user's current emotional state.

[0279] Step 5:

[0280] The server integrates the health information, preference data, lifestyle data, and emotional state data to create a comprehensive health profile for the user.

[0281] Step 6:

[0282] The server uses a generative artificial intelligence model to generate personalized recipes based on the health profile created, adjusting the difficulty and cooking time of the recipe depending on the emotional state.

[0283] Step 7:

[0284] The server sends the generated recipe to the user's device, which displays the recipe details (e.g., ingredients, cooking instructions, and nutritional information) to the user.

[0285] Step 8:

[0286] The server checks the user's food inventory data by having the user input the inventory information via the terminal. The user inputs the ingredients they have on hand (e.g., 2 tomatoes, 1 bunch of basil).

[0287] Step 9:

[0288] The server compares the recipe with the inventory of ingredients, lists the ingredients that are in short supply, and optimizes the purchasing plan for the ingredients based on the user's budget information.

[0289] Step 10:

[0290] The server connects to an online food delivery service to automatically order the necessary ingredients, which then receives the order and delivers the ingredients to the specified address.

[0291] Step 11:

[0292] Users can check the delivery status in real time through their device, and once the ingredients arrive, they can start cooking based on the suggested recipe and enjoy a healthy meal that suits their mood.

[0293] This detailed processing step allows the user to execute an efficient and personalized meal plan through the system.

[0294] Example 2

[0295] 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."

[0296] In modern society, many users find it difficult to create personalized meal plans due to their busy lifestyles. Furthermore, there is a lack of systems that provide appropriate meal plans that take into account emotional states in addition to health information and preference data. Furthermore, there is a need for a system that can not only suggest recipes based on a user's health information and preference data, but also suggest meals according to their emotional state. This necessitates a system that can provide comprehensive support, including food inventory management and budget management.

[0297] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's health information and preference data, means for analyzing the health information and preference data to create a health profile, means for recognizing an emotional state, means for generating a personalized recipe using a generative artificial intelligence model based on the health profile and the emotional state, and means for automatically ordering ingredients required according to the personalized recipe and delivering the ingredients to the user in cooperation with a delivery service. This allows the user to easily create an optimal meal plan based on their health information, preference data, and emotional state.

[0298] "Health information" refers to information such as the user's physical condition, medical history, allergy information, and daily health data.

[0299] "Preference data" refers to data that includes information about a user's favorite foods and dishes, disliked foods, and allergies.

[0300] "Health Profile" refers to a comprehensive health record generated based on a User's health information and preference data.

[0301] "Emotional state" refers to information that indicates the user's current emotional or mental state.

[0302] A "generative AI model" refers to an AI algorithm or model that is optimized for a specific purpose (in this case, generating recipes) based on collected data.

[0303] "Personalized recipes" refer to cooking recipes that are individually optimized based on the user's health information, preference data, and emotional state.

[0304] "Delivery Service" refers to an external service that delivers the necessary ingredients to the address specified by the user.

[0305] "Lifestyle data" refers to activity data in the user's daily life and data collected from health management devices.

[0306] "Inventory information" refers to information about the remaining amount and type of ingredients that the user owns.

[0307] This invention is a system that proposes personalized meal plans by comprehensively analyzing a user's health information, preference data, and emotional state. This system mainly consists of three entities: a server, a terminal, and the user.

[0308] 1. Data collection and input

[0309] Users enter health information (e.g., allergy information) and preference data (e.g., favorite foods) through a device app or web interface. This data is sent by the device to the server. In addition, lifestyle data (e.g., exercise frequency) is automatically collected from the user's health management devices (e.g., smartwatches, fitness trackers) and sent to the server. The server then creates a comprehensive health profile.

[0310] 2. Recognizing emotional states

[0311] The user inputs their emotional state through the device, for example, by entering text such as "I feel tired today." The device then sends this data to the server, which uses an emotion recognition engine to analyze the user's emotional state from the input text. The analysis results are added to the health profile.

[0312] 3. Recipe Generation

[0313] The server uses a generative AI model to generate personalized recipes based on the user's health profile and emotional state. This process takes into account the user's health profile, preference data, and emotional state. For example, a user who is feeling very tired might be recommended a nutritious chicken soup that can be made quickly.

[0314] 4. Inventory and budget management

[0315] The user inputs the inventory information of ingredients at home through the device. The device then sends this inventory information to the server, which then creates a list of ingredients needed for a personalized recipe. The server then takes into account the user's budget information and optimizes the shopping list.

[0316] 5. Automatic food delivery

[0317] The server connects to an online delivery service and automatically orders the necessary ingredients. The order information is sent to the delivery service, and the ingredients are delivered to the user's address. The user can check the delivery status in real time from their device.

[0318] Specific examples

[0319] The user uses a device app to input their allergy information and favorite dishes. The health management device sends the user's exercise frequency data to the server, which creates a health profile based on this information. The user enters "I feel tired today" into the device, and the server analyzes the level of fatigue using an emotion recognition engine. The server uses a generative AI model to generate and recommend a "nutritious chicken soup that can be made in a short time." The user enters inventory information, and the server lists ingredients that are in short supply and creates a shopping list within a budget. The server orders the necessary ingredients from an online delivery service, the delivery is made, and the user checks the delivery status on their device.

[0320] An example prompt might look like this:

[0321] "Generate recipes that are effective for fatigue recovery based on the user's health information and preference data."

[0322] In this way, the present invention realizes a system that provides a personalized meal plan that comprehensively takes into account a user's health information, preference data, and emotional state, thereby enabling the user to efficiently achieve a healthy and individualized diet.

[0323] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0324] Step 1: Enter and collect user data

[0325] Users input health information and preference data through a device app or web interface. For example, this includes inputting allergy information (nut allergy) or favorite cuisine (Italian food). This input data is sent by the device to the server. In parallel, lifestyle data (e.g., running for 45 minutes three times a week) is automatically collected from the user's health management device (e.g., smartwatch, fitness tracker) and sent to the server. The server then creates a health profile based on the input and collected data.

[0326] input:

[0327] Health and preference data (user input)

[0328] Lifestyle data (health management device)

[0329] output:

[0330] Health profile (created on the server)

[0331] Step 2: Recognizing your emotional state

[0332] The user inputs their current emotional state through the device. For example, this includes inputting the text "I feel tired today." This emotional data is sent by the device to the server. The server uses an emotion recognition engine to analyze the text data and detect the input emotional state. The server adds this emotional data to a health profile to build more accurate user information.

[0333] input:

[0334] Emotional state text data (user input)

[0335] output:

[0336] Updated health profile (stored on server)

[0337] Step 3: Generate a personalized recipe

[0338] The server acquires the user's health profile and emotional state data and uses a generative AI model to generate personalized recipes. For example, based on the user's health profile and emotional data indicating "high fatigue," it generates a recipe such as "a quick and nutritious chicken soup." The generated recipe is then sent from the server to the user's device.

[0339] input:

[0340] User health profile and emotional state data

[0341] output:

[0342] Personalized recipes (sent to your device)

[0343] Step 4: Manage your food inventory and budget

[0344] The user inputs the inventory information of ingredients they currently own through their device. For example, this includes inputting "two tomatoes, one bunch of basil." The device then sends this inventory information to the server, which then creates a list of ingredients needed for the recipe. The server then optimizes the shopping list by taking into account the user's budget information.

[0345] input:

[0346] Ingredient inventory information (user input)

[0347] Budget information (user settings)

[0348] output:

[0349] Optimized shopping list (created on the server)

[0350] Step 5: Automated delivery of ingredients

[0351] The server connects with an online delivery service and automatically orders the necessary ingredients based on the optimized shopping list. The order information is sent to the delivery service, and the ingredients are delivered to the user's specified address. The user can check the delivery status in real time via their device.

[0352] input:

[0353] Optimized shopping list (created on the server)

[0354] output:

[0355] Delivery status (check on device)

[0356] Through these steps, users can easily achieve a personalized meal plan based on health information, preference data and emotional state.

[0357] (Application example 2)

[0358] 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."

[0359] In today's busy lifestyles, it is challenging to appropriately design and efficiently provide meal plans that take into account individual health conditions, preferences, and even emotional states. Conventional meal plan delivery systems offer personalized meals based on health information and preference data, but they fail to fully enhance user satisfaction because they do not take into account the user's emotional state. In addition, insufficient food inventory and budget management led to problems of waste. Furthermore, few systems offer integrated food delivery services, making it difficult for users to receive ingredients without hassle.

[0360] 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.

[0361] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for analyzing the user's emotional state from voice and facial expression data, means for generating a personalized recipe using a generative artificial intelligence model based on the created health profile and emotional state, and means for automatically ordering ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with a food delivery service. This allows the user to easily receive a meal plan that reflects the user's health information, preference data, and emotional state, and to receive appropriate ingredients without hassle while minimizing food waste.

[0362] "User's health information" refers to data relating to the user's physical and physiological condition, including, for example, fitness data obtained from health management devices, medical history, and allergy information.

[0363] "Preference data" is data that indicates the types of food and drink that a user likes, cooking methods, or preferences for particular dishes.

[0364] "Voice data" refers to data that is the analysis target of audio signals such as the user's speaking voice, and is used to estimate the emotional state.

[0365] "Facial expression data" is data obtained by image analysis of the user's facial expression, and is used to determine the emotional state.

[0366] A "health profile" is a data set that comprehensively represents a user's health status, generated based on the user's health information and preference data.

[0367] "Emotional state" is data that indicates the user's current psychological and emotional state, and is obtained by analyzing voice, facial expressions, etc.

[0368] A "generative artificial intelligence model" is a model developed using machine learning technology that has the ability to generate optimal results based on specific conditions and data.

[0369] A "personalized recipe" is a recipe that includes meal instructions and ingredient lists optimized for a user based on individual data such as the user's health information, preference data, and emotional state.

[0370] "Ingredients" refers to the specific foods and seasonings used in cooking based on a recipe.

[0371] "Automatic ordering" is the process by which the system orders the required items from the supplier without requiring user confirmation or manual intervention.

[0372] "Food delivery service" refers to a service that specializes in delivering ordered food ingredients to a location specified by the user.

[0373] The following system is used as an embodiment of this invention. The system allows users to input and manage health information, preference data, and emotional state using a device such as a smartphone. A server collects and analyzes this data and provides the user with a personalized meal plan. The hardware and software used, as well as the processing involved, are described in detail below.

[0374] Hardware and software used

[0375] Hardware:

[0376] Smartphone (iOS / Android)

[0377] Health management devices (e.g., smart watches, fitness trackers)

[0378] software:

[0379] Server side: Django (backend framework), PostgreSQL (database)

[0380] Machine learning model: TENSORFLOW® (for emotion recognition and generative AI models)

[0381] API integration: RESTful API

[0382] What the program does

[0383] 1. User Information Input and Data Collection:

[0384] Users use a smartphone app to input their health information and preference data. Lifestyle data (e.g., number of steps, heart rate, and sleep time) is automatically sent from the health management device to the server. In addition, the user's emotional state is recognized by analyzing voice and facial expression data. Voice recognition and facial expression recognition are performed using TensorFlow models.

[0385] 2. Data analysis and health profile creation:

[0386] The server analyzes the received health information, preference data, and emotional state to create a health profile, using the Django backend framework to manage the data and storing it in PostgreSQL.

[0387] 3. Generate personalized recipes:

[0388] Based on the created health profile and emotional state, the server generates personalized recipes using a generative AI model, which is fed prompts using OpenAI's API, such as:

[0389] Example: "Generate the best recipes for a user whose emotional state is fatigue and whose health data is 3 times weekly exercise. Preferences: Italian food, nut allergy."

[0390] 4. Automated food ordering and delivery:

[0391] The server creates a list of ingredients based on the generated recipe and automatically places an order with the delivery service. A RESTful API is used to connect with the delivery service. Users can check the status of their order and delivery via their smartphone.

[0392] Examples:

[0393] When a user types "I feel tired today" into a smartphone app, the smartphone's microphone recognizes the user's voice, and a TensorFlow model analyzes the audio data to estimate the emotional state as "fatigue." The fitness tracker automatically sends data on the user's 45-minute runs three times a week. Based on this data, the server uses OpenAI's generative AI model to generate a recipe for "nutritious chicken soup that can be made quickly." The server then lists any missing ingredients (chicken, soup base) and automatically places an order with a delivery service. Once the order is confirmed, the ingredients are delivered to the user's address.

[0394] This allows users to easily receive personalized meal plans that reflect their health information, preference data, and emotional state, while also ensuring they receive the right ingredients while minimizing food waste.

[0395] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0396] Step 1:

[0397] User Data Input and Collection

[0398] Users can use their smartphones to input their health and preference data. For example, they can enter allergy information, favorite foods, and emotional state in text. The devices also automatically collect lifestyle data from health management devices (smartwatches, fitness trackers, etc.) and send it to the server.

[0399] Input: Health information, preference data, lifestyle data

[0400] Output: Send data to the server

[0401] Step 2:

[0402] Recognition of emotional states

[0403] When a user inputs a voice request or text into their smartphone, the device's microphone and camera are used to collect voice and facial expression data. The collected data is then sent to a server, where an emotion recognition engine using TensorFlow analyzes the user's emotional state.

[0404] Input: Voice data, facial expression data

[0405] Output: Emotional state (happy, sad, tired, etc.)

[0406] Step 3:

[0407] Data analysis and health profile creation

[0408] The server analyzes the collected health information, preference data, and emotional state to create a health profile. The data is managed using the Django framework and stored in PostgreSQL.

[0409] Input: Health information, preference data, emotional state

[0410] Output: Health Profile

[0411] Step 4:

[0412] Generate personalized recipes

[0413] The server generates personalized recipes using a generative AI model based on the health profile and emotional state by providing the following prompts to the model using OpenAI's API:

[0414] "Generate the best recipes for a user whose emotional state is fatigue and whose health data is 3 times weekly exercise. Preferences: Italian food, nut allergy"

[0415] Input: Health profile, emotional state

[0416] Output: personalized recipe

[0417] Step 5:

[0418] Ingredient inventory and budget management

[0419] The server obtains the user's food inventory data and lists the ingredients that are in short supply. It also optimizes the purchasing plan based on the user's budget information. It constantly compares the user's inventory data with their budget data to generate an appropriate list.

[0420] Input: Food inventory data, budget information

[0421] Output: Ingredients shopping list

[0422] Step 6:

[0423] Automated food ordering and delivery integration

[0424] Based on the generated recipe, the server automatically orders any missing ingredients from a food delivery service and delivers them to the user's address. The server connects with the delivery service using a RESTful API. Users can check the order status and delivery status in real time from their smartphones.

[0425] Input: Personalized recipes, grocery shopping lists

[0426] Output: Delivered ingredients, user confirmation information

[0427] In this way, the system can provide personalized meal plans and efficiently deliver the necessary ingredients to users, taking into consideration their health information, preference data, and emotional state.

[0428] 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.

[0429] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0430] 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.

[0431] [Second embodiment]

[0432] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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."

[0444] The present invention is a system that generates individual recipes based on a user's health information and preferences, and provides meal plans that take into account ingredient availability and budget. The system also works in conjunction with a health management device to suggest meals to maintain the user's optimal health.

[0445] System configuration

[0446] This system is broadly composed of the following four modules:

[0447] 1. Data Collection and Analysis Module

[0448] 2. Recipe Generation Module

[0449] 3. Food inventory and budget management module

[0450] 4. Delivery Management Module

[0451] Data Collection and Analysis Module

[0452] Users can input their health information and dietary preferences through a dedicated device app or web interface. For example, users can input allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (such as smartwatches and fitness trackers) is also automatically collected, and the user's health profile is updated in real time.

[0453] Specific examples

[0454] The user opens the device app and enters their allergy information (e.g., nut allergy) and favorite food (e.g., Italian food). The health management device also automatically sends information to the server, such as running for 45 minutes three times a week.

[0455] Recipe Generation Module

[0456] The server uses information from the data collection and analysis module to create a user's health profile and uses a generative artificial intelligence model to generate personalized recipes that are optimized not only based on the user's health status, but also on their food availability and budget.

[0457] Specific examples

[0458] The server takes into account the user's allergy information and exercise level, and uses a generative AI model to suggest a low-salt, calorie-controlled "tomato and basil pasta."

[0459] Food inventory and budget management module

[0460] Users can manage their food inventory, and the server generates a list of ingredients based on this information. It also optimizes food purchasing plans taking into account the user's budget. This allows for efficient management of necessary ingredients and eliminates waste.

[0461] Specific examples

[0462] The user inputs a list of ingredients they have at home into the app (e.g., two tomatoes, one bunch of basil). The server determines what ingredients are missing based on the proposed recipe and creates a shopping list within the user's budget.

[0463] Delivery Management Module

[0464] The server connects to an online food delivery service and automatically orders the necessary ingredients. Once the order is confirmed, the ingredients are delivered to the user's specified address. This eliminates the need for the user to spend time shopping for ingredients.

[0465] Specific examples

[0466] Based on the recipe proposed by the server, any missing ingredients (e.g., cheese, pasta) are automatically ordered from a food delivery service. The delivery service delivers the ingredients to the user's home, and the user can check the delivery status in real time on the app.

[0467] This system allows users to easily access personalized recipes based on health information and preferences, saving time and effort while enjoying a healthy diet.

[0468] The processing flow will be explained below.

[0469] Step 1:

[0470] Users open a dedicated terminal app or web interface and enter their health and preference data, such as allergies (e.g., nut allergies), dietary restrictions (e.g., low salt), favorite dishes (e.g., Italian food), and disliked ingredients (e.g., celery).

[0471] Step 2:

[0472] The device collects the entered health information and preference data and sends it to a server, which stores the received data in a database and prepares it for creating the user's health profile.

[0473] Step 3:

[0474] Health management AI automatically collects lifestyle data (e.g., exercise volume, sleep patterns) from the user's health management device (e.g., smartwatch) and sends it to a server. The server receives this data and updates the user's health profile.

[0475] Step 4:

[0476] The server analyzes the collected health information, preference data, and lifestyle data to create a health profile for the user, which assesses the user's overall health and includes appropriate dietary restrictions and nutritional balance.

[0477] Step 5:

[0478] The server uses the health profile to activate a generative artificial intelligence model (generative AI model) that generates personalized recipes for the user. The generative AI model takes into account the user's preferences, health status, and dietary restrictions to suggest the most suitable recipes.

[0479] Step 6:

[0480] The server provides the generated recipe as feedback to the user. The device displays the recipe details (e.g., ingredients, cooking instructions, and nutritional information) to the user. The user reviews the suggested recipes and selects one.

[0481] Step 7:

[0482] To obtain the user's food inventory data, the server asks the user to input the inventory information via the terminal. The user inputs the ingredients they have on hand (e.g., two tomatoes, one bunch of basil) into the terminal.

[0483] Step 8:

[0484] The server compares the user's inventory information with the recipe's ingredient list to identify the ingredients needed. Any missing ingredients (e.g., cheese, pasta) are listed.

[0485] Step 9:

[0486] The server checks the user's budget information and automatically adds the necessary ingredients to the order list within that budget. Once the order list is complete, the order data is sent to the online food delivery service.

[0487] Step 10:

[0488] The online food delivery service receives the order and delivers the food to the user's specified address, allowing the user to track the delivery status in real time via their device.

[0489] Step 11:

[0490] Users can then use the delivered ingredients to prepare healthy meals based on personalized recipe suggestions. Users can then follow the recipes to enjoy healthy and delicious meals.

[0491] In this way, users can utilize the system to efficiently create personalized meal plans.

[0492] Example 1

[0493] 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."

[0494] In modern life, maintaining a balance between health management and diet is extremely important, but planning meals and managing ingredients to suit each individual's health condition and preferences can be time-consuming. Furthermore, finding the time to purchase the right ingredients and prepare meals in a busy daily life can be difficult. Furthermore, efficiently preparing meals within a budget while reducing food waste is a challenging task. Conventional systems manage a user's health information, preferences, inventory information, budget, etc. separately, making it difficult to provide an integrated meal plan.

[0495] 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.

[0496] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for automatically collecting lifestyle habit data from a health management device to update the health profile in real time, means for generating personalized recipes using a generative AI model based on the created health profile, means for the user to input ingredient inventory and budget using a terminal app, means for generating a required ingredient list according to the personalized recipe, means for automatically ordering ingredients based on the required ingredient list and delivering the ingredients to the user in cooperation with a food delivery service, and means for providing a terminal app for checking delivery status in real time. This enables efficient and personalized meal planning based on the user's health information and preferences, significantly saving time and effort while maintaining a healthy diet.

[0497] "User" refers to a person who uses the system to input health information and preference data to manage and optimize their diet.

[0498] "Device app" refers to software that users use to input health information, preference data, food inventory, budget information, etc.

[0499] "Health management device" refers to a device (e.g., a smartwatch or fitness tracker) that monitors a user's lifestyle data and automatically transmits that data to a server.

[0500] "Server" refers to the central system that receives and analyzes user-submitted data, generates and updates health profiles, and generates personalized recipes.

[0501] The "data collection means" refers to a method for inputting the user's health information and preference data and automatically collecting lifestyle habit data from the health management device.

[0502] "Health profile" refers to an individual health information profile generated by integrating a user's health condition, preferences, lifestyle data, etc.

[0503] "Generative AI Model" refers to the artificial intelligence model used to generate personalized recipes based on a health profile.

[0504] "Personalized recipes" refer to cooking recipes optimized for a user, generated by a generative AI model based on the user's health profile.

[0505] "Ingredient inventory management means" refers to a method by which a user inputs and manages inventory information of ingredients on hand.

[0506] "Budget input means" refers to a method by which a user inputs budget information for purchasing ingredients.

[0507] "Ingredient list generator" refers to a method for generating a list of ingredients required based on a personalized recipe.

[0508] "Online grocery delivery service" refers to an external delivery service used to deliver required groceries to a user.

[0509] "Delivery status confirmation means" refers to a method used by a user to check the delivery status of ingredients in real time.

[0510] The present invention is a system that generates individual recipes based on a user's health information and preferences, and provides meal plans that take into account ingredient availability and budget. This system works in conjunction with a health management device to suggest meals that will help the user maintain optimal health. The detailed configuration and operation of the system are described below.

[0511] System configuration

[0512] This system is broadly composed of the following modules:

[0513] 1. Data Collection and Analysis Module

[0514] 2. Recipe Generation Module

[0515] 3. Food inventory and budget management module

[0516] 4. Delivery Management Module

[0517] Data Collection and Analysis Module

[0518] Users enter their health information and dietary preferences through a dedicated device app or web interface. For example, users can enter allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (e.g., smartwatches and fitness trackers) is also automatically collected and sent to the server. This allows the user's health profile to be updated in real time.

[0519] Examples:

[0520] The user opens the device app and enters information such as "I have a nut allergy" and "I like Italian food." The health management device also automatically sends information such as "I run for 45 minutes three times a week" to the server.

[0521] Recipe Generation Module

[0522] The server creates a user's health profile based on information obtained from the data collection and analysis module. Based on the health profile, a generative AI model is used to generate personalized recipes. These recipes are optimized taking into account the user's health status, ingredient availability, and budget.

[0523] Examples:

[0524] The server takes into account the user's information about their nut allergy and running three times a week, and uses a generative AI model to suggest a low-salt, calorie-controlled tomato and basil pasta.

[0525] Example prompt for a generative AI model:

[0526] "Suggest low-sodium, calorie-controlled recipes for someone who has a nut allergy, loves Italian food, and runs three times a week."

[0527] Food inventory and budget management module

[0528] The user inputs their current inventory and budget information using a terminal app. The server uses this information to generate a list of ingredients needed for the proposed recipe. This provides an optimal food purchasing plan that takes into account the user's inventory and budget.

[0529] Examples:

[0530] The user enters inventory information such as "two tomatoes" and "one bunch of basil" and a budget of "5,000 yen" for purchasing ingredients into the app. The server checks the proposed recipe and adds the missing items "cheese" and "pasta" to the shopping list.

[0531] Delivery Management Module

[0532] The server connects to an online food delivery service and automatically orders the necessary ingredients. Users can also check the delivery status of their ingredients in real time through the device app. This means that users can receive the ingredients they need simply by waiting at home.

[0533] Examples:

[0534] The server automatically orders "cheese" and "pasta" from a food delivery service, which then delivers them to the user's home. The user can open the "Delivery Status" tab in the device app and check the delivery status (e.g., "Delivering" or "Delivery Complete") in real time.

[0535] In this way, users can create efficient and personalized meal plans based on their health information and preferences, saving them significant time and effort while maintaining a healthy diet.

[0536] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0537] Step 1: Enter your health information and preferences

[0538] Users enter their health information and dietary preferences using a dedicated device app or web interface. Specifically, a user opens the app on their smartphone, enters information such as "I have a nut allergy" or "I like Italian food" into a form, and then presses the submit button.

[0539] Input: Allergy information, favorite dishes, disliked dishes

[0540] Output: The entered health information and preference data is sent to the server.

[0541] Step 2: Automatically collect data from health management devices

[0542] The device monitors the user's lifestyle data and automatically transmits it to a server at regular intervals. Specifically, the smartwatch sends data such as heart rate and exercise volume to the server every hour via Bluetooth or Wi-Fi.

[0543] Input: Lifestyle data such as heart rate and exercise amount

[0544] Output: Lifestyle data is sent to the server

[0545] Step 3: Creating a user's health profile

[0546] The server integrates the user's health information and preference data with the lifestyle data sent from the device to generate a health profile for the user, and then updates the user's information in the database to reflect their health status in real time.

[0547] Input: Health information, preference data, lifestyle data

[0548] Output: Creation and updating of a unified health profile

[0549] Step 4: Generate a personalized recipe

[0550] The server uses the generative AI model to generate personalized recipes based on the user's health profile. Specifically, the server inputs data such as "nut allergy" and "running three times a week" as prompts into the generative AI model, and generates appropriate recipes.

[0551] Input: Health profile, prompt (e.g., Suggest low-sodium, calorie-controlled recipes for someone who has a nut allergy, loves Italian food, and runs three times a week)

[0552] Output: A personalized recipe (e.g., pasta with tomatoes and basil)

[0553] Step 5: Enter your food inventory and budget

[0554] The user uses the device app to input the inventory and budget information for ingredients at home. Specifically, the user opens the app's inventory management screen, inputs items such as "2 tomatoes" and "1 bunch of basil," and enters "5,000 yen" in the budget field.

[0555] Input: Ingredient inventory information, budget information

[0556] Output: Inventory and budget information sent to the server

[0557] Step 6: Generate a list of ingredients needed

[0558] The server generates a list of ingredients based on the proposed recipe and the inventory and budget information entered by the user. Specifically, the server compares the information in the database, determines that "the proposed recipe requires tomatoes, but they are already in stock, so cheese and pasta are needed," and generates a shopping list.

[0559] Input: Personalized recipe, inventory information, budget information

[0560] Output: List of ingredients needed (e.g. cheese and pasta)

[0561] Step 7: Integrate with online food delivery services and automate ordering

[0562] The server connects to an online food delivery service and automatically orders the necessary ingredients. Specifically, the server calls the delivery service's API, sends the generated shopping list, and confirms the order.

[0563] Input: List of ingredients needed

[0564] Output: Order sent to online grocery delivery service

[0565] Step 8: Check delivery status

[0566] Users can check the delivery status of their groceries in real time through the app. Specifically, users open the "Delivery Status" tab in the app and view the delivery status (e.g., "Departed," "Delivering," "Delivery completed") updated in real time.

[0567] Input: Delivery status confirmation request

[0568] Output: Real-time delivery status

[0569] (Application example 1)

[0570] 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."

[0571] Conventional meal recommendation systems lack the functionality to propose optimal recipes by comprehensively considering the user's health information, preferences, ingredient availability, budget, etc. However, comprehensive and personalized meal recommendations are necessary to maintain optimal health. In addition, since systems lack the functionality to arrange ingredients by linking with food delivery services or online stores based on the proposed recipes, user convenience must also be improved.

[0572] 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.

[0573] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for generating personalized recipes using a generative artificial intelligence model based on the created health profile, means for automatically ordering ingredients needed according to the personalized recipe and delivering the ingredients to the user in cooperation with a food delivery service, means for placing an order through a food delivery service based on the suggested recipe, and means for automatically ordering missing ingredients from an online store. This allows users to easily obtain personalized recipes based on their health information and preferences, saving time and effort while enjoying a healthy diet.

[0574] "Health information" refers to data related to a user's allergies, medical history, and daily lifestyle habits.

[0575] "Preference data" refers to information such as the user's preferred dishes, ingredients, and eating style.

[0576] A "health profile" is a collection of information that indicates a user's individual health condition, created by analyzing health information and preference data.

[0577] A "generative artificial intelligence model" is an algorithm that generates personalized recipes based on collected data.

[0578] "Personalized recipes" are customized meal suggestions based on a user's health profile.

[0579] The "food delivery service" is an online service that delivers the necessary ingredients to the user based on a suggested recipe.

[0580] A "food delivery service" is a service that delivers cooked food directly from a restaurant to the user.

[0581] An "online store" is a system that provides a service for ordering and delivering food ingredients via the Internet.

[0582] "Ingredient inventory" refers to a list of ingredients currently in the user's home.

[0583] A "health management device" is a device that records and manages a user's daily lifestyle and physical activity. Examples include smartwatches and fitness trackers.

[0584] A "health management report" is a document that analyzes a user's health data and periodically provides an overview of the user's health status and suggestions for improvement.

[0585] The following system configuration and program are required to implement this invention. This system provides consistent support from the stage where the user inputs health information and preference data, to creating personalized recipes and actually arranging the ingredients.

[0586] 1. System Configuration

[0587] Hardware

[0588] Smartphone: A device that allows users to input health and preference data and receive recipe suggestions.

[0589] Smartwatch: A device for collecting data on a user's lifestyle habits.

[0590] Server: The main hardware used to analyze data, create health profiles, generate recipes, and manage ingredients and delivery. Uses cloud services (e.g., AWS, Azure).

[0591] software

[0592] Smartphone app: Has the ability to input health information and preference data, and display recipe suggestions (iOS / Android app).

[0593] Health management data collection platform: Collects data from smartwatches (e.g., HealthKit, GOOGLE FIT®).

[0594] Generative AI models: Generate personalized recipes based on collected data (e.g., OpenAI GPT-3).

[0595] Database management system: Stores and manages user health information, preference data, food inventory, budget, etc. (e.g., Firebase).

[0596] 2. System Operation

[0597] Collection of health and preference data

[0598] Users enter their own health information (allergy information, medical history, etc.) and preference data (favorite dishes, disliked ingredients, etc.) through a smartphone app. In addition, lifestyle data (e.g., amount of exercise, sleep time, etc.) is automatically collected from the smartwatch and sent to a health management data collection platform.

[0599] Generate personalized recipes

[0600] The server analyzes the collected health and preference data and uses a generative AI model to generate a personalized recipe that is optimal for the user. Below are some example prompts for the generative AI model:

[0601] Example prompt sentence:

[0602] User profile:

[0603] Allergies: Nut allergies

[0604] Preferences: Italian cuisine

[0605] Recent exercise data: Running for 45 minutes three times a week

[0606] Based on these, please suggest some low-calorie, low-salt recipes.

[0607] Food arrangement and food delivery service

[0608] The necessary ingredients are automatically ordered according to the generated recipe. Any missing ingredients are automatically ordered from the online store, and an affiliated food delivery service delivers them to the user. In addition, an order is placed with a food delivery service based on the suggested recipe, and cooked food is delivered to the user. The user can check the delivery progress in real time through the app.

[0609] Generate health management reports

[0610] The server periodically analyzes the user's health data and uses a generative AI model to create a health management report, which is provided to the user via a smartphone app and reflected in the user's next meal recommendations.

[0611] For example, a user inputs health information such as a nut allergy and preference data such as a love of Italian food, and recent exercise data is collected from the smartwatch. Based on this information, the server recommends a low-calorie, low-sodium "tomato and basil pasta" and arranges for the necessary ingredients and pre-cooked food from a food delivery service and online store. Periodically generated health management reports are reflected in the next meal recommendations, helping to maintain optimal health for the user.

[0612] In this way, users receive comprehensive and personalized meal suggestions, enabling them to maintain a healthy diet.

[0613] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0614] Step 1:

[0615] The user opens the smartphone app and enters their health information (allergies, medical history, etc.) and preference data (favorite dishes, disliked ingredients, etc.). The device collects this data within the app and sends it to the server.

[0616] Input: User's health information, preference data

[0617] Output: Send data to the server

[0618] Step 2:

[0619] The smartwatch automatically collects the user's lifestyle data (such as exercise volume and sleep time) and sends it to a health management data collection platform, which then transfers the data to a server.

[0620] Input: User's lifestyle data

[0621] Output: Send data to the server

[0622] Step 3:

[0623] The server creates a user's health profile based on their health information, preference data, and lifestyle data. Data analysis algorithms analyze this data and generate a health profile.

[0624] Input: Health information, preference data, lifestyle data

[0625] Output: Health Profile

[0626] Step 4:

[0627] The server uses the generative AI model to generate personalized recipes based on the health profile. The server inputs prompts into the generative AI model to obtain appropriate recipes.

[0628] Example prompt sentence:

[0629] User profile:

[0630] Allergies: Nut allergies

[0631] Preferences: Italian cuisine

[0632] Recent exercise data: Running for 45 minutes three times a week

[0633] Based on these, please suggest some low-calorie, low-salt recipes.

[0634] Input: Health profile, prompt text

[0635] Output: Personalized recipe

[0636] Step 5:

[0637] The server automatically orders the necessary ingredients based on the recipe. If any ingredients are missing, they are automatically ordered from the online store and delivered to the user by the food delivery service.

[0638] Input: personalized recipes, ingredient inventory information

[0639] Output: List of food orders, order request to food delivery service

[0640] Step 6:

[0641] Based on the suggested recipe, the server places an order with a food delivery service, and the prepared food is delivered to the user from a partner restaurant.

[0642] Input: Personalized Recipe

[0643] Output: Food delivery service order request, delivery status tracking information

[0644] Step 7:

[0645] The server periodically analyzes the user's health data and generates a health management report, which is provided to the user via a smartphone app.

[0646] Input: Past health data, lifestyle data

[0647] Output: Health management report, display in app

[0648] 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.

[0649] The present invention aims to provide a system that integrates a user's health information, preference data, and an emotion engine that recognizes the user's emotional state, and provides an optimal meal plan for the user. This system allows users to easily achieve an efficient, personalized meal plan that takes into account their emotional state.

[0650] System configuration

[0651] This system is broadly composed of the following five modules:

[0652] 1. Data Collection and Analysis Module

[0653] 2. Emotion Recognition Engine

[0654] 3. Recipe Generation Module

[0655] 4. Food inventory and budget management module

[0656] 5. Delivery Management Module

[0657] Data Collection and Analysis Module

[0658] Users enter their health information and preference data through a dedicated device app or web interface. For example, users can enter allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (e.g., smartwatches and fitness trackers) is also automatically collected. This data is sent to a server, where a health profile is created.

[0659] Specific examples

[0660] The user opens the device app and enters their allergy information (nut allergy) and favorite food (Italian food). The health management device automatically sends information to the server, such as running for 45 minutes three times a week.

[0661] Emotion Recognition Engine

[0662] The emotion recognition engine analyzes the user's current emotional state using technology that recognizes emotions from the user's tone of voice, facial expressions, text messages, etc. This data is combined with health and preference data to deliver more accurate and personalized meal suggestions.

[0663] Specific examples

[0664] The user types "I'm tired today" into the device. The emotion recognition engine analyzes the user's tone of voice and text content and detects that they are feeling very tired.

[0665] Recipe Generation Module

[0666] The server uses a generative artificial intelligence model to generate personalized recipes based on data from the health profile and emotion recognition engine, and the generated recipes are optimized to take into account the user's health status, emotional state, and preferences.

[0667] Specific examples

[0668] For users who feel extremely tired, the server uses a generative AI model to suggest "nutritious chicken soup that can be made in a short time."

[0669] Food inventory and budget management module

[0670] The server acquires the user's food inventory data and creates a list of necessary ingredients. It also optimizes food purchasing plans based on budget information. This allows for efficient management of necessary ingredients and eliminates waste.

[0671] Specific examples

[0672] The user inputs inventory information (two tomatoes, one bunch of basil) into the terminal, and the server lists the ingredients that are in short supply (chicken, soup base) and creates a purchasing list within the budget.

[0673] Delivery Management Module

[0674] The server connects to an online food delivery service and automatically orders the necessary ingredients. Once the order is confirmed, the ingredients are delivered to the user's specified address. This eliminates the need for the user to spend time shopping for ingredients.

[0675] Specific examples

[0676] The server automatically places an order with a food delivery service, which then delivers the food to the user's home. The user can check the delivery status in real time via their device.

[0677] In this way, the system provides personalized meal plans that take into account not only the user's health and preference data, but also their emotional state, making it easy for users to achieve a healthy and emotionally appropriate diet.

[0678] The processing flow will be explained below.

[0679] Step 1:

[0680] Users open a dedicated device app or web interface and enter their health information (e.g., allergies, dietary restrictions) and preference data (e.g., favorite dishes, disliked ingredients).

[0681] Step 2:

[0682] The device collects the health and preference data entered and transmits it in real time to a server, which then creates a health profile for the user based on the data received.

[0683] Step 3:

[0684] A health management device (e.g., a smartwatch) automatically collects and transmits user lifestyle data (e.g., exercise volume, sleep patterns) to a server, which then imports the data and updates the user's health profile.

[0685] Step 4:

[0686] The user inputs their emotional state (e.g., "I'm tired today") into the device. The emotion recognition engine analyzes the text, tone of voice, facial expressions, etc. to recognize the user's current emotional state.

[0687] Step 5:

[0688] The server integrates the health information, preference data, lifestyle data, and emotional state data to create a comprehensive health profile for the user.

[0689] Step 6:

[0690] The server uses a generative artificial intelligence model to generate personalized recipes based on the health profile created, adjusting the difficulty and cooking time of the recipe depending on the emotional state.

[0691] Step 7:

[0692] The server sends the generated recipe to the user's device, which displays the recipe details (e.g., ingredients, cooking instructions, and nutritional information) to the user.

[0693] Step 8:

[0694] The server checks the user's food inventory data by having the user input the inventory information via the terminal. The user inputs the ingredients they have on hand (e.g., 2 tomatoes, 1 bunch of basil).

[0695] Step 9:

[0696] The server compares the recipe with the inventory of ingredients, lists the ingredients that are in short supply, and optimizes the purchasing plan for the ingredients based on the user's budget information.

[0697] Step 10:

[0698] The server connects to an online food delivery service to automatically order the necessary ingredients, which then receives the order and delivers the ingredients to the specified address.

[0699] Step 11:

[0700] Users can check the delivery status in real time through their device, and once the ingredients arrive, they can start cooking based on the suggested recipe and enjoy a healthy meal that suits their mood.

[0701] This detailed processing step allows the user to execute an efficient and personalized meal plan through the system.

[0702] Example 2

[0703] 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."

[0704] In modern society, many users find it difficult to create personalized meal plans due to their busy lifestyles. Furthermore, there is a lack of systems that provide appropriate meal plans that take into account emotional states in addition to health information and preference data. Furthermore, there is a need for a system that can not only suggest recipes based on a user's health information and preference data, but also suggest meals according to their emotional state. This necessitates a system that can provide comprehensive support, including food inventory management and budget management.

[0705] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's health information and preference data, means for analyzing the health information and preference data to create a health profile, means for recognizing an emotional state, means for generating a personalized recipe using a generative artificial intelligence model based on the health profile and the emotional state, and means for automatically ordering ingredients required according to the personalized recipe and delivering the ingredients to the user in cooperation with a delivery service. This allows the user to easily create an optimal meal plan based on their health information, preference data, and emotional state.

[0706] "Health information" refers to information such as the user's physical condition, medical history, allergy information, and daily health data.

[0707] "Preference data" refers to data that includes information about a user's favorite foods and dishes, disliked foods, and allergies.

[0708] "Health Profile" refers to a comprehensive health record generated based on a User's health information and preference data.

[0709] "Emotional state" refers to information that indicates the user's current emotional or mental state.

[0710] A "generative AI model" refers to an AI algorithm or model that is optimized for a specific purpose (in this case, generating recipes) based on collected data.

[0711] "Personalized recipes" refer to cooking recipes that are individually optimized based on the user's health information, preference data, and emotional state.

[0712] "Delivery Service" refers to an external service that delivers the necessary ingredients to the address specified by the user.

[0713] "Lifestyle data" refers to activity data in the user's daily life and data collected from health management devices.

[0714] "Inventory information" refers to information about the remaining amount and type of ingredients that the user owns.

[0715] This invention is a system that proposes personalized meal plans by comprehensively analyzing a user's health information, preference data, and emotional state. This system mainly consists of three entities: a server, a terminal, and the user.

[0716] 1. Data collection and input

[0717] Users enter health information (e.g., allergy information) and preference data (e.g., favorite foods) through a device app or web interface. This data is sent by the device to the server. In addition, lifestyle data (e.g., exercise frequency) is automatically collected from the user's health management devices (e.g., smartwatches, fitness trackers) and sent to the server. The server then creates a comprehensive health profile.

[0718] 2. Recognizing emotional states

[0719] The user inputs their emotional state through the device, for example, by entering text such as "I feel tired today." The device then sends this data to the server, which uses an emotion recognition engine to analyze the user's emotional state from the input text. The analysis results are added to the health profile.

[0720] 3. Recipe Generation

[0721] The server uses a generative AI model to generate personalized recipes based on the user's health profile and emotional state. This process takes into account the user's health profile, preference data, and emotional state. For example, a user who is feeling very tired might be recommended a nutritious chicken soup that can be made quickly.

[0722] 4. Inventory and budget management

[0723] The user inputs the inventory information of ingredients at home through the device. The device then sends this inventory information to the server, which then creates a list of ingredients needed for a personalized recipe. The server then takes into account the user's budget information and optimizes the shopping list.

[0724] 5. Automatic food delivery

[0725] The server connects to an online delivery service and automatically orders the necessary ingredients. The order information is sent to the delivery service, and the ingredients are delivered to the user's address. The user can check the delivery status in real time from their device.

[0726] Specific examples

[0727] The user uses a device app to input their allergy information and favorite dishes. The health management device sends the user's exercise frequency data to the server, which creates a health profile based on this information. The user enters "I feel tired today" into the device, and the server analyzes the level of fatigue using an emotion recognition engine. The server uses a generative AI model to generate and recommend a "nutritious chicken soup that can be made in a short time." The user enters inventory information, and the server lists ingredients that are in short supply and creates a shopping list within a budget. The server orders the necessary ingredients from an online delivery service, the delivery is made, and the user checks the delivery status on their device.

[0728] An example prompt might look like this:

[0729] "Generate recipes that are effective for fatigue recovery based on the user's health information and preference data."

[0730] In this way, the present invention realizes a system that provides a personalized meal plan that comprehensively takes into account a user's health information, preference data, and emotional state, thereby enabling the user to efficiently achieve a healthy and individualized diet.

[0731] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0732] Step 1: Enter and collect user data

[0733] Users input health information and preference data through a device app or web interface. For example, this includes inputting allergy information (nut allergy) or favorite cuisine (Italian food). This input data is sent by the device to the server. In parallel, lifestyle data (e.g., running for 45 minutes three times a week) is automatically collected from the user's health management device (e.g., smartwatch, fitness tracker) and sent to the server. The server then creates a health profile based on the input and collected data.

[0734] input:

[0735] Health and preference data (user input)

[0736] Lifestyle data (health management device)

[0737] output:

[0738] Health profile (created on the server)

[0739] Step 2: Recognizing your emotional state

[0740] The user inputs their current emotional state through the device. For example, this includes inputting the text "I feel tired today." This emotional data is sent by the device to the server. The server uses an emotion recognition engine to analyze the text data and detect the input emotional state. The server adds this emotional data to a health profile to build more accurate user information.

[0741] input:

[0742] Emotional state text data (user input)

[0743] output:

[0744] Updated health profile (stored on server)

[0745] Step 3: Generate a personalized recipe

[0746] The server acquires the user's health profile and emotional state data and uses a generative AI model to generate personalized recipes. For example, based on the user's health profile and emotional data indicating "high fatigue," it generates a recipe such as "a quick and nutritious chicken soup." The generated recipe is then sent from the server to the user's device.

[0747] input:

[0748] User health profile and emotional state data

[0749] output:

[0750] Personalized recipes (sent to your device)

[0751] Step 4: Manage your food inventory and budget

[0752] The user inputs the inventory information of ingredients they currently own through their device. For example, this includes inputting "two tomatoes, one bunch of basil." The device then sends this inventory information to the server, which then creates a list of ingredients needed for the recipe. The server then optimizes the shopping list by taking into account the user's budget information.

[0753] input:

[0754] Ingredient inventory information (user input)

[0755] Budget information (user settings)

[0756] output:

[0757] Optimized shopping list (created on the server)

[0758] Step 5: Automated delivery of ingredients

[0759] The server connects with an online delivery service and automatically orders the necessary ingredients based on the optimized shopping list. The order information is sent to the delivery service, and the ingredients are delivered to the user's specified address. The user can check the delivery status in real time via their device.

[0760] input:

[0761] Optimized shopping list (created on the server)

[0762] output:

[0763] Delivery status (check on device)

[0764] Through these steps, users can easily achieve a personalized meal plan based on health information, preference data and emotional state.

[0765] (Application example 2)

[0766] 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."

[0767] In today's busy lifestyles, it is challenging to appropriately design and efficiently provide meal plans that take into account individual health conditions, preferences, and even emotional states. Conventional meal plan delivery systems offer personalized meals based on health information and preference data, but they fail to fully enhance user satisfaction because they do not take into account the user's emotional state. In addition, insufficient food inventory and budget management led to problems of waste. Furthermore, few systems offer integrated food delivery services, making it difficult for users to receive ingredients without hassle.

[0768] 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.

[0769] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for analyzing the user's emotional state from voice and facial expression data, means for generating a personalized recipe using a generative artificial intelligence model based on the created health profile and emotional state, and means for automatically ordering ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with a food delivery service. This allows the user to easily receive a meal plan that reflects the user's health information, preference data, and emotional state, and to receive appropriate ingredients without hassle while minimizing food waste.

[0770] "User's health information" refers to data relating to the user's physical and physiological condition, including, for example, fitness data obtained from health management devices, medical history, and allergy information.

[0771] "Preference data" is data that indicates the types of food and drink that a user likes, cooking methods, or preferences for particular dishes.

[0772] "Voice data" refers to data that is the analysis target of audio signals such as the user's speaking voice, and is used to estimate the emotional state.

[0773] "Facial expression data" is data obtained by image analysis of the user's facial expression, and is used to determine the emotional state.

[0774] A "health profile" is a data set that comprehensively represents a user's health status, generated based on the user's health information and preference data.

[0775] "Emotional state" is data that indicates the user's current psychological and emotional state, and is obtained by analyzing voice, facial expressions, etc.

[0776] A "generative artificial intelligence model" is a model developed using machine learning technology that has the ability to generate optimal results based on specific conditions and data.

[0777] A "personalized recipe" is a recipe that includes meal instructions and ingredient lists optimized for a user based on individual data such as the user's health information, preference data, and emotional state.

[0778] "Ingredients" refers to the specific foods and seasonings used in cooking based on a recipe.

[0779] "Automatic ordering" is the process by which the system orders the required items from the supplier without requiring user confirmation or manual intervention.

[0780] "Food delivery service" refers to a service that specializes in delivering ordered food ingredients to a location specified by the user.

[0781] The following system is used as an embodiment of this invention. The system allows users to input and manage health information, preference data, and emotional state using a device such as a smartphone. A server collects and analyzes this data and provides the user with a personalized meal plan. The hardware and software used, as well as the processing involved, are described in detail below.

[0782] Hardware and software used

[0783] Hardware:

[0784] Smartphone (iOS / Android)

[0785] Health management devices (e.g., smart watches, fitness trackers)

[0786] software:

[0787] Server side: Django (backend framework), PostgreSQL (database)

[0788] Machine learning models: TensorFlow (for emotion recognition and generative AI models)

[0789] API integration: RESTful API

[0790] What the program does

[0791] 1. User Information Input and Data Collection:

[0792] Users use a smartphone app to input their health information and preference data. Lifestyle data (e.g., number of steps, heart rate, and sleep time) is automatically sent from the health management device to the server. In addition, the user's emotional state is recognized by analyzing voice and facial expression data. Voice recognition and facial expression recognition are performed using TensorFlow models.

[0793] 2. Data analysis and health profile creation:

[0794] The server analyzes the received health information, preference data, and emotional state to create a health profile, using the Django backend framework to manage the data and storing it in PostgreSQL.

[0795] 3. Generate personalized recipes:

[0796] Based on the created health profile and emotional state, the server generates personalized recipes using a generative AI model, which is fed prompts using OpenAI's API, such as:

[0797] Example: "Generate the best recipes for a user whose emotional state is fatigue and whose health data is 3 times weekly exercise. Preferences: Italian food, nut allergy."

[0798] 4. Automated food ordering and delivery:

[0799] The server creates a list of ingredients based on the generated recipe and automatically places an order with the delivery service. A RESTful API is used to connect with the delivery service. Users can check the status of their order and delivery via their smartphone.

[0800] Examples:

[0801] When a user types "I feel tired today" into a smartphone app, the smartphone's microphone recognizes the user's voice, and a TensorFlow model analyzes the audio data to estimate the emotional state as "fatigue." The fitness tracker automatically sends data on the user's 45-minute runs three times a week. Based on this data, the server uses OpenAI's generative AI model to generate a recipe for "nutritious chicken soup that can be made quickly." The server then lists any missing ingredients (chicken, soup base) and automatically places an order with a delivery service. Once the order is confirmed, the ingredients are delivered to the user's address.

[0802] This allows users to easily receive personalized meal plans that reflect their health information, preference data, and emotional state, while also ensuring they receive the right ingredients while minimizing food waste.

[0803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0804] Step 1:

[0805] User Data Input and Collection

[0806] Users can use their smartphones to input their health and preference data. For example, they can enter allergy information, favorite foods, and emotional state in text. The devices also automatically collect lifestyle data from health management devices (smartwatches, fitness trackers, etc.) and send it to the server.

[0807] Input: Health information, preference data, lifestyle data

[0808] Output: Send data to the server

[0809] Step 2:

[0810] Recognition of emotional states

[0811] When a user inputs a voice request or text into their smartphone, the device's microphone and camera are used to collect voice and facial expression data. The collected data is then sent to a server, where an emotion recognition engine using TensorFlow analyzes the user's emotional state.

[0812] Input: Voice data, facial expression data

[0813] Output: Emotional state (happy, sad, tired, etc.)

[0814] Step 3:

[0815] Data analysis and health profile creation

[0816] The server analyzes the collected health information, preference data, and emotional state to create a health profile. The data is managed using the Django framework and stored in PostgreSQL.

[0817] Input: Health information, preference data, emotional state

[0818] Output: Health Profile

[0819] Step 4:

[0820] Generate personalized recipes

[0821] The server generates personalized recipes using a generative AI model based on the health profile and emotional state by providing the following prompts to the model using OpenAI's API:

[0822] "Generate the best recipes for a user whose emotional state is fatigue and whose health data is 3 times weekly exercise. Preferences: Italian food, nut allergy"

[0823] Input: Health profile, emotional state

[0824] Output: personalized recipe

[0825] Step 5:

[0826] Ingredient inventory and budget management

[0827] The server obtains the user's food inventory data and lists the ingredients that are in short supply. It also optimizes the purchasing plan based on the user's budget information. It constantly compares the user's inventory data with their budget data to generate an appropriate list.

[0828] Input: Food inventory data, budget information

[0829] Output: Ingredients shopping list

[0830] Step 6:

[0831] Automated food ordering and delivery integration

[0832] Based on the generated recipe, the server automatically orders any missing ingredients from a food delivery service and delivers them to the user's address. The server connects with the delivery service using a RESTful API. Users can check the order status and delivery status in real time from their smartphones.

[0833] Input: Personalized recipes, grocery shopping lists

[0834] Output: Delivered ingredients, user confirmation information

[0835] In this way, the system can provide personalized meal plans and efficiently deliver the necessary ingredients to users, taking into consideration their health information, preference data, and emotional state.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] [Third embodiment]

[0840] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0841] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0842] 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).

[0843] 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.

[0844] 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.

[0845] 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).

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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."

[0852] The present invention is a system that generates individual recipes based on a user's health information and preferences, and provides meal plans that take into account ingredient availability and budget. The system also works in conjunction with a health management device to suggest meals to maintain the user's optimal health.

[0853] System configuration

[0854] This system is broadly composed of the following four modules:

[0855] 1. Data Collection and Analysis Module

[0856] 2. Recipe Generation Module

[0857] 3. Food inventory and budget management module

[0858] 4. Delivery Management Module

[0859] Data Collection and Analysis Module

[0860] Users can input their health information and dietary preferences through a dedicated device app or web interface. For example, users can input allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (such as smartwatches and fitness trackers) is also automatically collected, and the user's health profile is updated in real time.

[0861] Specific examples

[0862] The user opens the device app and enters their allergy information (e.g., nut allergy) and favorite food (e.g., Italian food). The health management device also automatically sends information to the server, such as running for 45 minutes three times a week.

[0863] Recipe Generation Module

[0864] The server uses information from the data collection and analysis module to create a user's health profile and uses a generative artificial intelligence model to generate personalized recipes that are optimized not only based on the user's health status, but also on their food availability and budget.

[0865] Specific examples

[0866] The server takes into account the user's allergy information and exercise level, and uses a generative AI model to suggest a low-salt, calorie-controlled "tomato and basil pasta."

[0867] Food inventory and budget management module

[0868] Users can manage their food inventory, and the server generates a list of ingredients based on this information. It also optimizes food purchasing plans taking into account the user's budget. This allows for efficient management of necessary ingredients and eliminates waste.

[0869] Specific examples

[0870] The user inputs a list of ingredients they have at home into the app (e.g., two tomatoes, one bunch of basil). The server determines what ingredients are missing based on the proposed recipe and creates a shopping list within the user's budget.

[0871] Delivery Management Module

[0872] The server connects to an online food delivery service and automatically orders the necessary ingredients. Once the order is confirmed, the ingredients are delivered to the user's specified address. This eliminates the need for the user to spend time shopping for ingredients.

[0873] Specific examples

[0874] Based on the recipe proposed by the server, any missing ingredients (e.g., cheese, pasta) are automatically ordered from a food delivery service. The delivery service delivers the ingredients to the user's home, and the user can check the delivery status in real time on the app.

[0875] This system allows users to easily access personalized recipes based on health information and preferences, saving time and effort while enjoying a healthy diet.

[0876] The processing flow will be explained below.

[0877] Step 1:

[0878] Users open a dedicated terminal app or web interface and enter their health and preference data, such as allergies (e.g., nut allergies), dietary restrictions (e.g., low salt), favorite dishes (e.g., Italian food), and disliked ingredients (e.g., celery).

[0879] Step 2:

[0880] The device collects the entered health information and preference data and sends it to a server, which stores the received data in a database and prepares it for creating the user's health profile.

[0881] Step 3:

[0882] Health management AI automatically collects lifestyle data (e.g., exercise volume, sleep patterns) from the user's health management device (e.g., smartwatch) and sends it to a server. The server receives this data and updates the user's health profile.

[0883] Step 4:

[0884] The server analyzes the collected health information, preference data, and lifestyle data to create a health profile for the user, which assesses the user's overall health and includes appropriate dietary restrictions and nutritional balance.

[0885] Step 5:

[0886] The server uses the health profile to activate a generative artificial intelligence model (generative AI model) that generates personalized recipes for the user. The generative AI model takes into account the user's preferences, health status, and dietary restrictions to suggest the most suitable recipes.

[0887] Step 6:

[0888] The server provides the generated recipe as feedback to the user. The device displays the recipe details (e.g., ingredients, cooking instructions, and nutritional information) to the user. The user reviews the suggested recipes and selects one.

[0889] Step 7:

[0890] To obtain the user's food inventory data, the server asks the user to input the inventory information via the terminal. The user inputs the ingredients they have on hand (e.g., two tomatoes, one bunch of basil) into the terminal.

[0891] Step 8:

[0892] The server compares the user's inventory information with the recipe's ingredient list to identify the ingredients needed. Any missing ingredients (e.g., cheese, pasta) are listed.

[0893] Step 9:

[0894] The server checks the user's budget information and automatically adds the necessary ingredients to the order list within that budget. Once the order list is complete, the order data is sent to the online food delivery service.

[0895] Step 10:

[0896] The online food delivery service receives the order and delivers the food to the user's specified address, allowing the user to track the delivery status in real time via their device.

[0897] Step 11:

[0898] Users can then use the delivered ingredients to prepare healthy meals based on personalized recipe suggestions. Users can then follow the recipes to enjoy healthy and delicious meals.

[0899] In this way, users can utilize the system to efficiently create personalized meal plans.

[0900] Example 1

[0901] 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."

[0902] In modern life, maintaining a balance between health management and diet is extremely important, but planning meals and managing ingredients to suit each individual's health condition and preferences can be time-consuming. Furthermore, finding the time to purchase the right ingredients and prepare meals in a busy daily life can be difficult. Furthermore, efficiently preparing meals within a budget while reducing food waste is a challenging task. Conventional systems manage a user's health information, preferences, inventory information, budget, etc. separately, making it difficult to provide an integrated meal plan.

[0903] 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.

[0904] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for automatically collecting lifestyle habit data from a health management device to update the health profile in real time, means for generating personalized recipes using a generative AI model based on the created health profile, means for the user to input ingredient inventory and budget using a terminal app, means for generating a required ingredient list according to the personalized recipe, means for automatically ordering ingredients based on the required ingredient list and delivering the ingredients to the user in cooperation with a food delivery service, and means for providing a terminal app for checking delivery status in real time. This enables efficient and personalized meal planning based on the user's health information and preferences, significantly saving time and effort while maintaining a healthy diet.

[0905] "User" refers to a person who uses the system to input health information and preference data to manage and optimize their diet.

[0906] "Device app" refers to software that users use to input health information, preference data, food inventory, budget information, etc.

[0907] "Health management device" refers to a device (e.g., a smartwatch or fitness tracker) that monitors a user's lifestyle data and automatically transmits that data to a server.

[0908] "Server" refers to the central system that receives and analyzes user-submitted data, generates and updates health profiles, and generates personalized recipes.

[0909] The "data collection means" refers to a method for inputting the user's health information and preference data and automatically collecting lifestyle habit data from the health management device.

[0910] "Health profile" refers to an individual health information profile generated by integrating a user's health condition, preferences, lifestyle data, etc.

[0911] "Generative AI Model" refers to the artificial intelligence model used to generate personalized recipes based on a health profile.

[0912] "Personalized recipes" refer to cooking recipes optimized for a user, generated by a generative AI model based on the user's health profile.

[0913] "Ingredient inventory management means" refers to a method by which a user inputs and manages inventory information of ingredients on hand.

[0914] "Budget input means" refers to a method by which a user inputs budget information for purchasing ingredients.

[0915] "Ingredient list generator" refers to a method for generating a list of ingredients required based on a personalized recipe.

[0916] "Online grocery delivery service" refers to an external delivery service used to deliver required groceries to a user.

[0917] "Delivery status confirmation means" refers to a method used by a user to check the delivery status of ingredients in real time.

[0918] The present invention is a system that generates individual recipes based on a user's health information and preferences, and provides meal plans that take into account ingredient availability and budget. This system works in conjunction with a health management device to suggest meals that will help the user maintain optimal health. The detailed configuration and operation of the system are described below.

[0919] System configuration

[0920] This system is broadly composed of the following modules:

[0921] 1. Data Collection and Analysis Module

[0922] 2. Recipe Generation Module

[0923] 3. Food inventory and budget management module

[0924] 4. Delivery Management Module

[0925] Data Collection and Analysis Module

[0926] Users enter their health information and dietary preferences through a dedicated device app or web interface. For example, users can enter allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (e.g., smartwatches and fitness trackers) is also automatically collected and sent to the server. This allows the user's health profile to be updated in real time.

[0927] Examples:

[0928] The user opens the device app and enters information such as "I have a nut allergy" and "I like Italian food." The health management device also automatically sends information such as "I run for 45 minutes three times a week" to the server.

[0929] Recipe Generation Module

[0930] The server creates a user's health profile based on information obtained from the data collection and analysis module. Based on the health profile, a generative AI model is used to generate personalized recipes. These recipes are optimized taking into account the user's health status, ingredient availability, and budget.

[0931] Examples:

[0932] The server takes into account the user's information about their nut allergy and running three times a week, and uses a generative AI model to suggest a low-salt, calorie-controlled tomato and basil pasta.

[0933] Example prompt for a generative AI model:

[0934] "Suggest low-sodium, calorie-controlled recipes for someone who has a nut allergy, loves Italian food, and runs three times a week."

[0935] Food inventory and budget management module

[0936] The user inputs their current inventory and budget information using a terminal app. The server uses this information to generate a list of ingredients needed for the proposed recipe. This provides an optimal food purchasing plan that takes into account the user's inventory and budget.

[0937] Examples:

[0938] The user enters inventory information such as "two tomatoes" and "one bunch of basil" and a budget of "5,000 yen" for purchasing ingredients into the app. The server checks the proposed recipe and adds the missing items "cheese" and "pasta" to the shopping list.

[0939] Delivery Management Module

[0940] The server connects to an online food delivery service and automatically orders the necessary ingredients. Users can also check the delivery status of their ingredients in real time through the device app. This means that users can receive the ingredients they need simply by waiting at home.

[0941] Examples:

[0942] The server automatically orders "cheese" and "pasta" from a food delivery service, which then delivers them to the user's home. The user can open the "Delivery Status" tab in the device app and check the delivery status (e.g., "Delivering" or "Delivery Complete") in real time.

[0943] In this way, users can create efficient and personalized meal plans based on their health information and preferences, saving them significant time and effort while maintaining a healthy diet.

[0944] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0945] Step 1: Enter your health information and preferences

[0946] Users enter their health information and dietary preferences using a dedicated device app or web interface. Specifically, a user opens the app on their smartphone, enters information such as "I have a nut allergy" or "I like Italian food" into a form, and then presses the submit button.

[0947] Input: Allergy information, favorite dishes, disliked dishes

[0948] Output: The entered health information and preference data is sent to the server.

[0949] Step 2: Automatically collect data from health management devices

[0950] The device monitors the user's lifestyle data and automatically transmits it to a server at regular intervals. Specifically, the smartwatch sends data such as heart rate and exercise volume to the server every hour via Bluetooth or Wi-Fi.

[0951] Input: Lifestyle data such as heart rate and exercise amount

[0952] Output: Lifestyle data is sent to the server

[0953] Step 3: Creating a user's health profile

[0954] The server integrates the user's health information and preference data with the lifestyle data sent from the device to generate a health profile for the user, and then updates the user's information in the database to reflect their health status in real time.

[0955] Input: Health information, preference data, lifestyle data

[0956] Output: Creation and updating of a unified health profile

[0957] Step 4: Generate a personalized recipe

[0958] The server uses the generative AI model to generate personalized recipes based on the user's health profile. Specifically, the server inputs data such as "nut allergy" and "running three times a week" as prompts into the generative AI model, and generates appropriate recipes.

[0959] Input: Health profile, prompt (e.g., Suggest low-sodium, calorie-controlled recipes for someone who has a nut allergy, loves Italian food, and runs three times a week)

[0960] Output: A personalized recipe (e.g., pasta with tomatoes and basil)

[0961] Step 5: Enter your food inventory and budget

[0962] The user uses the device app to input the inventory and budget information for ingredients at home. Specifically, the user opens the app's inventory management screen, inputs items such as "2 tomatoes" and "1 bunch of basil," and enters "5,000 yen" in the budget field.

[0963] Input: Ingredient inventory information, budget information

[0964] Output: Inventory and budget information sent to the server

[0965] Step 6: Generate a list of ingredients needed

[0966] The server generates a list of ingredients based on the proposed recipe and the inventory and budget information entered by the user. Specifically, the server compares the information in the database, determines that "the proposed recipe requires tomatoes, but they are already in stock, so cheese and pasta are needed," and generates a shopping list.

[0967] Input: Personalized recipe, inventory information, budget information

[0968] Output: List of ingredients needed (e.g. cheese and pasta)

[0969] Step 7: Integrate with online food delivery services and automate ordering

[0970] The server connects to an online food delivery service and automatically orders the necessary ingredients. Specifically, the server calls the delivery service's API, sends the generated shopping list, and confirms the order.

[0971] Input: List of ingredients needed

[0972] Output: Order sent to online grocery delivery service

[0973] Step 8: Check delivery status

[0974] Users can check the delivery status of their groceries in real time through the app. Specifically, users open the "Delivery Status" tab in the app and view the delivery status (e.g., "Departed," "Delivering," "Delivery completed") updated in real time.

[0975] Input: Delivery status confirmation request

[0976] Output: Real-time delivery status

[0977] (Application example 1)

[0978] 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."

[0979] Conventional meal recommendation systems lack the functionality to propose optimal recipes by comprehensively considering the user's health information, preferences, ingredient availability, budget, etc. However, comprehensive and personalized meal recommendations are necessary to maintain optimal health. In addition, since systems lack the functionality to arrange ingredients by linking with food delivery services or online stores based on the proposed recipes, user convenience must also be improved.

[0980] 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.

[0981] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for generating personalized recipes using a generative artificial intelligence model based on the created health profile, means for automatically ordering ingredients needed according to the personalized recipe and delivering the ingredients to the user in cooperation with a food delivery service, means for placing an order through a food delivery service based on the suggested recipe, and means for automatically ordering missing ingredients from an online store. This allows users to easily obtain personalized recipes based on their health information and preferences, saving time and effort while enjoying a healthy diet.

[0982] "Health information" refers to data related to a user's allergies, medical history, and daily lifestyle habits.

[0983] "Preference data" refers to information such as the user's preferred dishes, ingredients, and eating style.

[0984] A "health profile" is a collection of information that indicates a user's individual health condition, created by analyzing health information and preference data.

[0985] A "generative artificial intelligence model" is an algorithm that generates personalized recipes based on collected data.

[0986] "Personalized recipes" are customized meal suggestions based on a user's health profile.

[0987] The "food delivery service" is an online service that delivers the necessary ingredients to the user based on a suggested recipe.

[0988] A "food delivery service" is a service that delivers cooked food directly from a restaurant to the user.

[0989] An "online store" is a system that provides a service for ordering and delivering food ingredients via the Internet.

[0990] "Ingredient inventory" refers to a list of ingredients currently in the user's home.

[0991] A "health management device" is a device that records and manages a user's daily lifestyle and physical activity. Examples include smartwatches and fitness trackers.

[0992] A "health management report" is a document that analyzes a user's health data and periodically provides an overview of the user's health status and suggestions for improvement.

[0993] The following system configuration and program are required to implement this invention. This system provides consistent support from the stage where the user inputs health information and preference data, to creating personalized recipes and actually arranging the ingredients.

[0994] 1. System Configuration

[0995] Hardware

[0996] Smartphone: A device that allows users to input health and preference data and receive recipe suggestions.

[0997] Smartwatch: A device for collecting data on a user's lifestyle habits.

[0998] Server: The main hardware used to analyze data, create health profiles, generate recipes, and manage ingredients and delivery. Uses cloud services (e.g., AWS, Azure).

[0999] software

[1000] Smartphone app: Has the ability to input health information and preference data, and display recipe suggestions (iOS / Android app).

[1001] Health management data collection platform: Collects data from smartwatches (e.g., HealthKit, Google® Fit).

[1002] Generative AI models: Generate personalized recipes based on collected data (e.g., OpenAI GPT-3).

[1003] Database management system: Stores and manages user health information, preference data, food inventory, budget, etc. (e.g., Firebase).

[1004] 2. System Operation

[1005] Collection of health and preference data

[1006] Users enter their own health information (allergy information, medical history, etc.) and preference data (favorite dishes, disliked ingredients, etc.) through a smartphone app. In addition, lifestyle data (e.g., amount of exercise, sleep time, etc.) is automatically collected from the smartwatch and sent to a health management data collection platform.

[1007] Generate personalized recipes

[1008] The server analyzes the collected health and preference data and uses a generative AI model to generate a personalized recipe that is optimal for the user. Below are some example prompts for the generative AI model:

[1009] Example prompt sentence:

[1010] User profile:

[1011] Allergies: Nut allergies

[1012] Preferences: Italian cuisine

[1013] Recent exercise data: Running for 45 minutes three times a week

[1014] Based on these, please suggest some low-calorie, low-salt recipes.

[1015] Food arrangement and food delivery service

[1016] The necessary ingredients are automatically ordered according to the generated recipe. Any missing ingredients are automatically ordered from the online store, and an affiliated food delivery service delivers them to the user. In addition, an order is placed with a food delivery service based on the suggested recipe, and cooked food is delivered to the user. The user can check the delivery progress in real time through the app.

[1017] Generate health management reports

[1018] The server periodically analyzes the user's health data and uses a generative AI model to create a health management report, which is provided to the user via a smartphone app and reflected in the user's next meal recommendations.

[1019] For example, a user inputs health information such as a nut allergy and preference data such as a love of Italian food, and recent exercise data is collected from the smartwatch. Based on this information, the server recommends a low-calorie, low-sodium "tomato and basil pasta" and arranges for the necessary ingredients and pre-cooked food from a food delivery service and online store. Periodically generated health management reports are reflected in the next meal recommendations, helping to maintain optimal health for the user.

[1020] In this way, users receive comprehensive and personalized meal suggestions, enabling them to maintain a healthy diet.

[1021] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1022] Step 1:

[1023] The user opens the smartphone app and enters their health information (allergies, medical history, etc.) and preference data (favorite dishes, disliked ingredients, etc.). The device collects this data within the app and sends it to the server.

[1024] Input: User's health information, preference data

[1025] Output: Send data to the server

[1026] Step 2:

[1027] The smartwatch automatically collects the user's lifestyle data (such as exercise volume and sleep time) and sends it to a health management data collection platform, which then transfers the data to a server.

[1028] Input: User's lifestyle data

[1029] Output: Send data to the server

[1030] Step 3:

[1031] The server creates a user's health profile based on their health information, preference data, and lifestyle data. Data analysis algorithms analyze this data and generate a health profile.

[1032] Input: Health information, preference data, lifestyle data

[1033] Output: Health Profile

[1034] Step 4:

[1035] The server uses the generative AI model to generate personalized recipes based on the health profile. The server inputs prompts into the generative AI model to obtain appropriate recipes.

[1036] Example prompt sentence:

[1037] User profile:

[1038] Allergies: Nut allergies

[1039] Preferences: Italian cuisine

[1040] Recent exercise data: Running for 45 minutes three times a week

[1041] Based on these, please suggest some low-calorie, low-salt recipes.

[1042] Input: Health profile, prompt text

[1043] Output: Personalized recipe

[1044] Step 5:

[1045] The server automatically orders the necessary ingredients based on the recipe. If any ingredients are missing, they are automatically ordered from the online store and delivered to the user by the food delivery service.

[1046] Input: personalized recipes, ingredient inventory information

[1047] Output: List of food orders, order request to food delivery service

[1048] Step 6:

[1049] Based on the suggested recipe, the server places an order with a food delivery service, and the prepared food is delivered to the user from a partner restaurant.

[1050] Input: Personalized Recipe

[1051] Output: Food delivery service order request, delivery status tracking information

[1052] Step 7:

[1053] The server periodically analyzes the user's health data and generates a health management report, which is provided to the user via a smartphone app.

[1054] Input: Past health data, lifestyle data

[1055] Output: Health management report, display in app

[1056] 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.

[1057] The present invention aims to provide a system that integrates a user's health information, preference data, and an emotion engine that recognizes the user's emotional state, and provides an optimal meal plan for the user. This system allows users to easily achieve an efficient, personalized meal plan that takes into account their emotional state.

[1058] System configuration

[1059] This system is broadly composed of the following five modules:

[1060] 1. Data Collection and Analysis Module

[1061] 2. Emotion Recognition Engine

[1062] 3. Recipe Generation Module

[1063] 4. Food inventory and budget management module

[1064] 5. Delivery Management Module

[1065] Data Collection and Analysis Module

[1066] Users enter their health information and preference data through a dedicated device app or web interface. For example, users can enter allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (e.g., smartwatches and fitness trackers) is also automatically collected. This data is sent to a server, where a health profile is created.

[1067] Specific examples

[1068] The user opens the device app and enters their allergy information (nut allergy) and favorite food (Italian food). The health management device automatically sends information to the server, such as running for 45 minutes three times a week.

[1069] Emotion Recognition Engine

[1070] The emotion recognition engine analyzes the user's current emotional state using technology that recognizes emotions from the user's tone of voice, facial expressions, text messages, etc. This data is combined with health and preference data to deliver more accurate and personalized meal suggestions.

[1071] Specific examples

[1072] The user types "I'm tired today" into the device. The emotion recognition engine analyzes the user's tone of voice and text content and detects that they are feeling very tired.

[1073] Recipe Generation Module

[1074] The server uses a generative artificial intelligence model to generate personalized recipes based on data from the health profile and emotion recognition engine, and the generated recipes are optimized to take into account the user's health status, emotional state, and preferences.

[1075] Specific examples

[1076] For users who feel extremely tired, the server uses a generative AI model to suggest "nutritious chicken soup that can be made in a short time."

[1077] Food inventory and budget management module

[1078] The server acquires the user's food inventory data and creates a list of necessary ingredients. It also optimizes food purchasing plans based on budget information. This allows for efficient management of necessary ingredients and eliminates waste.

[1079] Specific examples

[1080] The user inputs inventory information (two tomatoes, one bunch of basil) into the terminal, and the server lists the ingredients that are in short supply (chicken, soup base) and creates a purchasing list within the budget.

[1081] Delivery Management Module

[1082] The server connects to an online food delivery service and automatically orders the necessary ingredients. Once the order is confirmed, the ingredients are delivered to the user's specified address. This eliminates the need for the user to spend time shopping for ingredients.

[1083] Specific examples

[1084] The server automatically places an order with a food delivery service, which then delivers the food to the user's home. The user can check the delivery status in real time via their device.

[1085] In this way, the system provides personalized meal plans that take into account not only the user's health and preference data, but also their emotional state, making it easy for users to achieve a healthy and emotionally appropriate diet.

[1086] The processing flow will be explained below.

[1087] Step 1:

[1088] Users open a dedicated device app or web interface and enter their health information (e.g., allergies, dietary restrictions) and preference data (e.g., favorite dishes, disliked ingredients).

[1089] Step 2:

[1090] The device collects the health and preference data entered and transmits it in real time to a server, which then creates a health profile for the user based on the data received.

[1091] Step 3:

[1092] A health management device (e.g., a smartwatch) automatically collects and transmits user lifestyle data (e.g., exercise volume, sleep patterns) to a server, which then imports the data and updates the user's health profile.

[1093] Step 4:

[1094] The user inputs their emotional state (e.g., "I'm tired today") into the device. The emotion recognition engine analyzes the text, tone of voice, facial expressions, etc. to recognize the user's current emotional state.

[1095] Step 5:

[1096] The server integrates the health information, preference data, lifestyle data, and emotional state data to create a comprehensive health profile for the user.

[1097] Step 6:

[1098] The server uses a generative artificial intelligence model to generate personalized recipes based on the health profile created, adjusting the difficulty and cooking time of the recipe depending on the emotional state.

[1099] Step 7:

[1100] The server sends the generated recipe to the user's device, which displays the recipe details (e.g., ingredients, cooking instructions, and nutritional information) to the user.

[1101] Step 8:

[1102] The server checks the user's food inventory data by having the user input the inventory information via the terminal. The user inputs the ingredients they have on hand (e.g., 2 tomatoes, 1 bunch of basil).

[1103] Step 9:

[1104] The server compares the recipe with the inventory of ingredients, lists the ingredients that are in short supply, and optimizes the purchasing plan for the ingredients based on the user's budget information.

[1105] Step 10:

[1106] The server connects to an online food delivery service to automatically order the necessary ingredients, which then receives the order and delivers the ingredients to the specified address.

[1107] Step 11:

[1108] Users can check the delivery status in real time through their device, and once the ingredients arrive, they can start cooking based on the suggested recipe and enjoy a healthy meal that suits their mood.

[1109] This detailed processing step allows the user to execute an efficient and personalized meal plan through the system.

[1110] Example 2

[1111] 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."

[1112] In modern society, many users find it difficult to create personalized meal plans due to their busy lifestyles. Furthermore, there is a lack of systems that provide appropriate meal plans that take into account emotional states in addition to health information and preference data. Furthermore, there is a need for a system that can not only suggest recipes based on a user's health information and preference data, but also suggest meals according to their emotional state. This necessitates a system that can provide comprehensive support, including food inventory management and budget management.

[1113] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's health information and preference data, means for analyzing the health information and preference data to create a health profile, means for recognizing an emotional state, means for generating a personalized recipe using a generative artificial intelligence model based on the health profile and the emotional state, and means for automatically ordering ingredients required according to the personalized recipe and delivering the ingredients to the user in cooperation with a delivery service. This allows the user to easily create an optimal meal plan based on their health information, preference data, and emotional state.

[1114] "Health information" refers to information such as the user's physical condition, medical history, allergy information, and daily health data.

[1115] "Preference data" refers to data that includes information about a user's favorite foods and dishes, disliked foods, and allergies.

[1116] "Health Profile" refers to a comprehensive health record generated based on a User's health information and preference data.

[1117] "Emotional state" refers to information that indicates the user's current emotional or mental state.

[1118] A "generative AI model" refers to an AI algorithm or model that is optimized for a specific purpose (in this case, generating recipes) based on collected data.

[1119] "Personalized recipes" refer to cooking recipes that are individually optimized based on the user's health information, preference data, and emotional state.

[1120] "Delivery Service" refers to an external service that delivers the necessary ingredients to the address specified by the user.

[1121] "Lifestyle data" refers to activity data in the user's daily life and data collected from health management devices.

[1122] "Inventory information" refers to information about the remaining amount and type of ingredients that the user owns.

[1123] This invention is a system that proposes personalized meal plans by comprehensively analyzing a user's health information, preference data, and emotional state. This system mainly consists of three entities: a server, a terminal, and the user.

[1124] 1. Data collection and input

[1125] Users enter health information (e.g., allergy information) and preference data (e.g., favorite foods) through a device app or web interface. This data is sent by the device to the server. In addition, lifestyle data (e.g., exercise frequency) is automatically collected from the user's health management devices (e.g., smartwatches, fitness trackers) and sent to the server. The server then creates a comprehensive health profile.

[1126] 2. Recognizing emotional states

[1127] The user inputs their emotional state through the device, for example, by entering text such as "I feel tired today." The device then sends this data to the server, which uses an emotion recognition engine to analyze the user's emotional state from the input text. The analysis results are added to the health profile.

[1128] 3. Recipe Generation

[1129] The server uses a generative AI model to generate personalized recipes based on the user's health profile and emotional state. This process takes into account the user's health profile, preference data, and emotional state. For example, a user who is feeling very tired might be recommended a nutritious chicken soup that can be made quickly.

[1130] 4. Inventory and budget management

[1131] The user inputs the inventory information of ingredients at home through the device. The device then sends this inventory information to the server, which then creates a list of ingredients needed for a personalized recipe. The server then takes into account the user's budget information and optimizes the shopping list.

[1132] 5. Automatic food delivery

[1133] The server connects to an online delivery service and automatically orders the necessary ingredients. The order information is sent to the delivery service, and the ingredients are delivered to the user's address. The user can check the delivery status in real time from their device.

[1134] Specific examples

[1135] The user uses a device app to input their allergy information and favorite dishes. The health management device sends the user's exercise frequency data to the server, which creates a health profile based on this information. The user enters "I feel tired today" into the device, and the server analyzes the level of fatigue using an emotion recognition engine. The server uses a generative AI model to generate and recommend a "nutritious chicken soup that can be made in a short time." The user enters inventory information, and the server lists ingredients that are in short supply and creates a shopping list within a budget. The server orders the necessary ingredients from an online delivery service, the delivery is made, and the user checks the delivery status on their device.

[1136] An example prompt might look like this:

[1137] "Generate recipes that are effective for fatigue recovery based on the user's health information and preference data."

[1138] In this way, the present invention realizes a system that provides a personalized meal plan that comprehensively takes into account a user's health information, preference data, and emotional state, thereby enabling the user to efficiently achieve a healthy and individualized diet.

[1139] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1140] Step 1: Enter and collect user data

[1141] Users input health information and preference data through a device app or web interface. For example, this includes inputting allergy information (nut allergy) or favorite cuisine (Italian food). This input data is sent by the device to the server. In parallel, lifestyle data (e.g., running for 45 minutes three times a week) is automatically collected from the user's health management device (e.g., smartwatch, fitness tracker) and sent to the server. The server then creates a health profile based on the input and collected data.

[1142] input:

[1143] Health and preference data (user input)

[1144] Lifestyle data (health management device)

[1145] output:

[1146] Health profile (created on the server)

[1147] Step 2: Recognizing your emotional state

[1148] The user inputs their current emotional state through the device. For example, this includes inputting the text "I feel tired today." This emotional data is sent by the device to the server. The server uses an emotion recognition engine to analyze the text data and detect the input emotional state. The server adds this emotional data to a health profile to build more accurate user information.

[1149] input:

[1150] Emotional state text data (user input)

[1151] output:

[1152] Updated health profile (stored on server)

[1153] Step 3: Generate a personalized recipe

[1154] The server acquires the user's health profile and emotional state data and uses a generative AI model to generate personalized recipes. For example, based on the user's health profile and emotional data indicating "high fatigue," it generates a recipe such as "a quick and nutritious chicken soup." The generated recipe is then sent from the server to the user's device.

[1155] input:

[1156] User health profile and emotional state data

[1157] output:

[1158] Personalized recipes (sent to your device)

[1159] Step 4: Manage your food inventory and budget

[1160] The user inputs the inventory information of ingredients they currently own through their device. For example, this includes inputting "two tomatoes, one bunch of basil." The device then sends this inventory information to the server, which then creates a list of ingredients needed for the recipe. The server then optimizes the shopping list by taking into account the user's budget information.

[1161] input:

[1162] Ingredient inventory information (user input)

[1163] Budget information (user settings)

[1164] output:

[1165] Optimized shopping list (created on the server)

[1166] Step 5: Automated delivery of ingredients

[1167] The server connects with an online delivery service and automatically orders the necessary ingredients based on the optimized shopping list. The order information is sent to the delivery service, and the ingredients are delivered to the user's specified address. The user can check the delivery status in real time via their device.

[1168] input:

[1169] Optimized shopping list (created on the server)

[1170] output:

[1171] Delivery status (check on device)

[1172] Through these steps, users can easily achieve a personalized meal plan based on health information, preference data and emotional state.

[1173] (Application example 2)

[1174] 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."

[1175] In today's busy lifestyles, it is challenging to appropriately design and efficiently provide meal plans that take into account individual health conditions, preferences, and even emotional states. Conventional meal plan delivery systems offer personalized meals based on health information and preference data, but they fail to fully enhance user satisfaction because they do not take into account the user's emotional state. In addition, insufficient food inventory and budget management led to problems of waste. Furthermore, few systems offer integrated food delivery services, making it difficult for users to receive ingredients without hassle.

[1176] 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.

[1177] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for analyzing the user's emotional state from voice and facial expression data, means for generating a personalized recipe using a generative artificial intelligence model based on the created health profile and emotional state, and means for automatically ordering ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with a food delivery service. This allows the user to easily receive a meal plan that reflects the user's health information, preference data, and emotional state, and to receive appropriate ingredients without hassle while minimizing food waste.

[1178] "User's health information" refers to data relating to the user's physical and physiological condition, including, for example, fitness data obtained from health management devices, medical history, and allergy information.

[1179] "Preference data" is data that indicates the types of food and drink that a user likes, cooking methods, or preferences for particular dishes.

[1180] "Voice data" refers to data that is the analysis target of audio signals such as the user's speaking voice, and is used to estimate the emotional state.

[1181] "Facial expression data" is data obtained by image analysis of the user's facial expression, and is used to determine the emotional state.

[1182] A "health profile" is a data set that comprehensively represents a user's health status, generated based on the user's health information and preference data.

[1183] "Emotional state" is data that indicates the user's current psychological and emotional state, and is obtained by analyzing voice, facial expressions, etc.

[1184] A "generative artificial intelligence model" is a model developed using machine learning technology that has the ability to generate optimal results based on specific conditions and data.

[1185] A "personalized recipe" is a recipe that includes meal instructions and ingredient lists optimized for a user based on individual data such as the user's health information, preference data, and emotional state.

[1186] "Ingredients" refers to the specific foods and seasonings used in cooking based on a recipe.

[1187] "Automatic ordering" is the process by which the system orders the required items from the supplier without requiring user confirmation or manual intervention.

[1188] "Food delivery service" refers to a service that specializes in delivering ordered food ingredients to a location specified by the user.

[1189] The following system is used as an embodiment of this invention. The system allows users to input and manage health information, preference data, and emotional state using a device such as a smartphone. A server collects and analyzes this data and provides the user with a personalized meal plan. The hardware and software used, as well as the processing involved, are described in detail below.

[1190] Hardware and software used

[1191] Hardware:

[1192] Smartphone (iOS / Android)

[1193] Health management devices (e.g., smart watches, fitness trackers)

[1194] software:

[1195] Server side: Django (backend framework), PostgreSQL (database)

[1196] Machine learning models: TensorFlow (for emotion recognition and generative AI models)

[1197] API integration: RESTful API

[1198] What the program does

[1199] 1. User Information Input and Data Collection:

[1200] Users use a smartphone app to input their health information and preference data. Lifestyle data (e.g., number of steps, heart rate, and sleep time) is automatically sent from the health management device to the server. In addition, the user's emotional state is recognized by analyzing voice and facial expression data. Voice recognition and facial expression recognition are performed using TensorFlow models.

[1201] 2. Data analysis and health profile creation:

[1202] The server analyzes the received health information, preference data, and emotional state to create a health profile, using the Django backend framework to manage the data and storing it in PostgreSQL.

[1203] 3. Generate personalized recipes:

[1204] Based on the created health profile and emotional state, the server generates personalized recipes using a generative AI model, which is fed prompts using OpenAI's API, such as:

[1205] Example: "Generate the best recipes for a user whose emotional state is fatigue and whose health data is 3 times weekly exercise. Preferences: Italian food, nut allergy."

[1206] 4. Automated food ordering and delivery:

[1207] The server creates a list of ingredients based on the generated recipe and automatically places an order with the delivery service. A RESTful API is used to connect with the delivery service. Users can check the status of their order and delivery via their smartphone.

[1208] Examples:

[1209] When a user types "I feel tired today" into a smartphone app, the smartphone's microphone recognizes the user's voice, and a TensorFlow model analyzes the audio data to estimate the emotional state as "fatigue." The fitness tracker automatically sends data on the user's 45-minute runs three times a week. Based on this data, the server uses OpenAI's generative AI model to generate a recipe for "nutritious chicken soup that can be made quickly." The server then lists any missing ingredients (chicken, soup base) and automatically places an order with a delivery service. Once the order is confirmed, the ingredients are delivered to the user's address.

[1210] This allows users to easily receive personalized meal plans that reflect their health information, preference data, and emotional state, while also ensuring they receive the right ingredients while minimizing food waste.

[1211] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1212] Step 1:

[1213] User Data Input and Collection

[1214] Users can use their smartphones to input their health and preference data. For example, they can enter allergy information, favorite foods, and emotional state in text. The devices also automatically collect lifestyle data from health management devices (smartwatches, fitness trackers, etc.) and send it to the server.

[1215] Input: Health information, preference data, lifestyle data

[1216] Output: Send data to the server

[1217] Step 2:

[1218] Recognition of emotional states

[1219] When a user inputs a voice request or text into their smartphone, the device's microphone and camera are used to collect voice and facial expression data. The collected data is then sent to a server, where an emotion recognition engine using TensorFlow analyzes the user's emotional state.

[1220] Input: Voice data, facial expression data

[1221] Output: Emotional state (happy, sad, tired, etc.)

[1222] Step 3:

[1223] Data analysis and health profile creation

[1224] The server analyzes the collected health information, preference data, and emotional state to create a health profile. The data is managed using the Django framework and stored in PostgreSQL.

[1225] Input: Health information, preference data, emotional state

[1226] Output: Health Profile

[1227] Step 4:

[1228] Generate personalized recipes

[1229] The server generates personalized recipes using a generative AI model based on the health profile and emotional state by providing the following prompts to the model using OpenAI's API:

[1230] "Generate the best recipes for a user whose emotional state is fatigue and whose health data is 3 times weekly exercise. Preferences: Italian food, nut allergy"

[1231] Input: Health profile, emotional state

[1232] Output: personalized recipe

[1233] Step 5:

[1234] Ingredient inventory and budget management

[1235] The server obtains the user's food inventory data and lists the ingredients that are in short supply. It also optimizes the purchasing plan based on the user's budget information. It constantly compares the user's inventory data with their budget data to generate an appropriate list.

[1236] Input: Food inventory data, budget information

[1237] Output: Ingredients shopping list

[1238] Step 6:

[1239] Automated food ordering and delivery integration

[1240] Based on the generated recipe, the server automatically orders any missing ingredients from a food delivery service and delivers them to the user's address. The server connects with the delivery service using a RESTful API. Users can check the order status and delivery status in real time from their smartphones.

[1241] Input: Personalized recipes, grocery shopping lists

[1242] Output: Delivered ingredients, user confirmation information

[1243] In this way, the system can provide personalized meal plans and efficiently deliver the necessary ingredients to users, taking into consideration their health information, preference data, and emotional state.

[1244] 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.

[1245] 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.

[1246] 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.

[1247] [Fourth embodiment]

[1248] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1249] 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.

[1250] 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).

[1251] 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.

[1252] 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.

[1253] 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).

[1254] 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.

[1255] 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.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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."

[1261] The present invention is a system that generates individual recipes based on a user's health information and preferences, and provides meal plans that take into account ingredient availability and budget. The system also works in conjunction with a health management device to suggest meals to maintain the user's optimal health.

[1262] System configuration

[1263] This system is broadly composed of the following four modules:

[1264] 1. Data Collection and Analysis Module

[1265] 2. Recipe Generation Module

[1266] 3. Food inventory and budget management module

[1267] 4. Delivery Management Module

[1268] Data Collection and Analysis Module

[1269] Users can input their health information and dietary preferences through a dedicated device app or web interface. For example, users can input allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (such as smartwatches and fitness trackers) is also automatically collected, and the user's health profile is updated in real time.

[1270] Specific examples

[1271] The user opens the device app and enters their allergy information (e.g., nut allergy) and favorite food (e.g., Italian food). The health management device also automatically sends information to the server, such as running for 45 minutes three times a week.

[1272] Recipe Generation Module

[1273] The server uses information from the data collection and analysis module to create a user's health profile and uses a generative artificial intelligence model to generate personalized recipes that are optimized not only based on the user's health status, but also on their food availability and budget.

[1274] Specific examples

[1275] The server takes into account the user's allergy information and exercise level, and uses a generative AI model to suggest a low-salt, calorie-controlled "tomato and basil pasta."

[1276] Food inventory and budget management module

[1277] Users can manage their food inventory, and the server generates a list of ingredients based on this information. It also optimizes food purchasing plans taking into account the user's budget. This allows for efficient management of necessary ingredients and eliminates waste.

[1278] Specific examples

[1279] The user inputs a list of ingredients they have at home into the app (e.g., two tomatoes, one bunch of basil). The server determines what ingredients are missing based on the proposed recipe and creates a shopping list within the user's budget.

[1280] Delivery Management Module

[1281] The server connects to an online food delivery service and automatically orders the necessary ingredients. Once the order is confirmed, the ingredients are delivered to the user's specified address. This eliminates the need for the user to spend time shopping for ingredients.

[1282] Specific examples

[1283] Based on the recipe proposed by the server, any missing ingredients (e.g., cheese, pasta) are automatically ordered from a food delivery service. The delivery service delivers the ingredients to the user's home, and the user can check the delivery status in real time on the app.

[1284] This system allows users to easily access personalized recipes based on health information and preferences, saving time and effort while enjoying a healthy diet.

[1285] The processing flow will be explained below.

[1286] Step 1:

[1287] Users open a dedicated terminal app or web interface and enter their health and preference data, such as allergies (e.g., nut allergies), dietary restrictions (e.g., low salt), favorite dishes (e.g., Italian food), and disliked ingredients (e.g., celery).

[1288] Step 2:

[1289] The device collects the entered health information and preference data and sends it to a server, which stores the received data in a database and prepares it for creating the user's health profile.

[1290] Step 3:

[1291] Health management AI automatically collects lifestyle data (e.g., exercise volume, sleep patterns) from the user's health management device (e.g., smartwatch) and sends it to a server. The server receives this data and updates the user's health profile.

[1292] Step 4:

[1293] The server analyzes the collected health information, preference data, and lifestyle data to create a health profile for the user, which assesses the user's overall health and includes appropriate dietary restrictions and nutritional balance.

[1294] Step 5:

[1295] The server uses the health profile to activate a generative artificial intelligence model (generative AI model) that generates personalized recipes for the user. The generative AI model takes into account the user's preferences, health status, and dietary restrictions to suggest the most suitable recipes.

[1296] Step 6:

[1297] The server provides the generated recipe as feedback to the user. The device displays the recipe details (e.g., ingredients, cooking instructions, and nutritional information) to the user. The user reviews the suggested recipes and selects one.

[1298] Step 7:

[1299] To obtain the user's food inventory data, the server asks the user to input the inventory information via the terminal. The user inputs the ingredients they have on hand (e.g., two tomatoes, one bunch of basil) into the terminal.

[1300] Step 8:

[1301] The server compares the user's inventory information with the recipe's ingredient list to identify the ingredients needed. Any missing ingredients (e.g., cheese, pasta) are listed.

[1302] Step 9:

[1303] The server checks the user's budget information and automatically adds the necessary ingredients to the order list within that budget. Once the order list is complete, the order data is sent to the online food delivery service.

[1304] Step 10:

[1305] The online food delivery service receives the order and delivers the food to the user's specified address, allowing the user to track the delivery status in real time via their device.

[1306] Step 11:

[1307] Users can then use the delivered ingredients to prepare healthy meals based on personalized recipe suggestions. Users can then follow the recipes to enjoy healthy and delicious meals.

[1308] In this way, users can utilize the system to efficiently create personalized meal plans.

[1309] Example 1

[1310] 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."

[1311] In modern life, maintaining a balance between health management and diet is extremely important, but planning meals and managing ingredients to suit each individual's health condition and preferences can be time-consuming. Furthermore, finding the time to purchase the right ingredients and prepare meals in a busy daily life can be difficult. Furthermore, efficiently preparing meals within a budget while reducing food waste is a challenging task. Conventional systems manage a user's health information, preferences, inventory information, budget, etc. separately, making it difficult to provide an integrated meal plan.

[1312] 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.

[1313] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for automatically collecting lifestyle habit data from a health management device to update the health profile in real time, means for generating personalized recipes using a generative AI model based on the created health profile, means for the user to input ingredient inventory and budget using a terminal app, means for generating a required ingredient list according to the personalized recipe, means for automatically ordering ingredients based on the required ingredient list and delivering the ingredients to the user in cooperation with a food delivery service, and means for providing a terminal app for checking delivery status in real time. This enables efficient and personalized meal planning based on the user's health information and preferences, significantly saving time and effort while maintaining a healthy diet.

[1314] "User" refers to a person who uses the system to input health information and preference data to manage and optimize their diet.

[1315] "Device app" refers to software that users use to input health information, preference data, food inventory, budget information, etc.

[1316] "Health management device" refers to a device (e.g., a smartwatch or fitness tracker) that monitors a user's lifestyle data and automatically transmits that data to a server.

[1317] "Server" refers to the central system that receives and analyzes user-submitted data, generates and updates health profiles, and generates personalized recipes.

[1318] The "data collection means" refers to a method for inputting the user's health information and preference data and automatically collecting lifestyle habit data from the health management device.

[1319] "Health profile" refers to an individual health information profile generated by integrating a user's health condition, preferences, lifestyle data, etc.

[1320] "Generative AI Model" refers to the artificial intelligence model used to generate personalized recipes based on a health profile.

[1321] "Personalized recipes" refer to cooking recipes optimized for a user, generated by a generative AI model based on the user's health profile.

[1322] "Ingredient inventory management means" refers to a method by which a user inputs and manages inventory information of ingredients on hand.

[1323] "Budget input means" refers to a method by which a user inputs budget information for purchasing ingredients.

[1324] "Ingredient list generator" refers to a method for generating a list of ingredients required based on a personalized recipe.

[1325] "Online grocery delivery service" refers to an external delivery service used to deliver required groceries to a user.

[1326] "Delivery status confirmation means" refers to a method used by a user to check the delivery status of ingredients in real time.

[1327] The present invention is a system that generates individual recipes based on a user's health information and preferences, and provides meal plans that take into account ingredient availability and budget. This system works in conjunction with a health management device to suggest meals that will help the user maintain optimal health. The detailed configuration and operation of the system are described below.

[1328] System configuration

[1329] This system is broadly composed of the following modules:

[1330] 1. Data Collection and Analysis Module

[1331] 2. Recipe Generation Module

[1332] 3. Food inventory and budget management module

[1333] 4. Delivery Management Module

[1334] Data Collection and Analysis Module

[1335] Users enter their health information and dietary preferences through a dedicated device app or web interface. For example, users can enter allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (e.g., smartwatches and fitness trackers) is also automatically collected and sent to the server. This allows the user's health profile to be updated in real time.

[1336] Examples:

[1337] The user opens the device app and enters information such as "I have a nut allergy" and "I like Italian food." The health management device also automatically sends information such as "I run for 45 minutes three times a week" to the server.

[1338] Recipe Generation Module

[1339] The server creates a user's health profile based on information obtained from the data collection and analysis module. Based on the health profile, a generative AI model is used to generate personalized recipes. These recipes are optimized taking into account the user's health status, ingredient availability, and budget.

[1340] Examples:

[1341] The server takes into account the user's information about their nut allergy and running three times a week, and uses a generative AI model to suggest a low-salt, calorie-controlled tomato and basil pasta.

[1342] Example prompt for a generative AI model:

[1343] "Suggest low-sodium, calorie-controlled recipes for someone who has a nut allergy, loves Italian food, and runs three times a week."

[1344] Food inventory and budget management module

[1345] The user inputs their current inventory and budget information using a terminal app. The server uses this information to generate a list of ingredients needed for the proposed recipe. This provides an optimal food purchasing plan that takes into account the user's inventory and budget.

[1346] Examples:

[1347] The user enters inventory information such as "two tomatoes" and "one bunch of basil" and a budget of "5,000 yen" for purchasing ingredients into the app. The server checks the proposed recipe and adds the missing items "cheese" and "pasta" to the shopping list.

[1348] Delivery Management Module

[1349] The server connects to an online food delivery service and automatically orders the necessary ingredients. Users can also check the delivery status of their ingredients in real time through the device app. This means that users can receive the ingredients they need simply by waiting at home.

[1350] Examples:

[1351] The server automatically orders "cheese" and "pasta" from a food delivery service, which then delivers them to the user's home. The user can open the "Delivery Status" tab in the device app and check the delivery status (e.g., "Delivering" or "Delivery Complete") in real time.

[1352] In this way, users can create efficient and personalized meal plans based on their health information and preferences, saving them significant time and effort while maintaining a healthy diet.

[1353] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1354] Step 1: Enter your health information and preferences

[1355] Users enter their health information and dietary preferences using a dedicated device app or web interface. Specifically, a user opens the app on their smartphone, enters information such as "I have a nut allergy" or "I like Italian food" into a form, and then presses the submit button.

[1356] Input: Allergy information, favorite dishes, disliked dishes

[1357] Output: The entered health information and preference data is sent to the server.

[1358] Step 2: Automatically collect data from health management devices

[1359] The device monitors the user's lifestyle data and automatically transmits it to a server at regular intervals. Specifically, the smartwatch sends data such as heart rate and exercise volume to the server every hour via Bluetooth or Wi-Fi.

[1360] Input: Lifestyle data such as heart rate and exercise amount

[1361] Output: Lifestyle data is sent to the server

[1362] Step 3: Creating a user's health profile

[1363] The server integrates the user's health information and preference data with the lifestyle data sent from the device to generate a health profile for the user, and then updates the user's information in the database to reflect their health status in real time.

[1364] Input: Health information, preference data, lifestyle data

[1365] Output: Creation and updating of a unified health profile

[1366] Step 4: Generate a personalized recipe

[1367] The server uses the generative AI model to generate personalized recipes based on the user's health profile. Specifically, the server inputs data such as "nut allergy" and "running three times a week" as prompts into the generative AI model, and generates appropriate recipes.

[1368] Input: Health profile, prompt (e.g., Suggest low-sodium, calorie-controlled recipes for someone who has a nut allergy, loves Italian food, and runs three times a week)

[1369] Output: A personalized recipe (e.g., pasta with tomatoes and basil)

[1370] Step 5: Enter your food inventory and budget

[1371] The user uses the device app to input the inventory and budget information for ingredients at home. Specifically, the user opens the app's inventory management screen, inputs items such as "2 tomatoes" and "1 bunch of basil," and enters "5,000 yen" in the budget field.

[1372] Input: Ingredient inventory information, budget information

[1373] Output: Inventory and budget information sent to the server

[1374] Step 6: Generate a list of ingredients needed

[1375] The server generates a list of ingredients based on the proposed recipe and the inventory and budget information entered by the user. Specifically, the server compares the information in the database, determines that "the proposed recipe requires tomatoes, but they are already in stock, so cheese and pasta are needed," and generates a shopping list.

[1376] Input: Personalized recipe, inventory information, budget information

[1377] Output: List of ingredients needed (e.g. cheese and pasta)

[1378] Step 7: Integrate with online food delivery services and automate ordering

[1379] The server connects to an online food delivery service and automatically orders the necessary ingredients. Specifically, the server calls the delivery service's API, sends the generated shopping list, and confirms the order.

[1380] Input: List of ingredients needed

[1381] Output: Order sent to online grocery delivery service

[1382] Step 8: Check delivery status

[1383] Users can check the delivery status of their groceries in real time through the app. Specifically, users open the "Delivery Status" tab in the app and view the delivery status (e.g., "Departed," "Delivering," "Delivery completed") updated in real time.

[1384] Input: Delivery status confirmation request

[1385] Output: Real-time delivery status

[1386] (Application example 1)

[1387] 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."

[1388] Conventional meal recommendation systems lack the functionality to propose optimal recipes by comprehensively considering the user's health information, preferences, ingredient availability, budget, etc. However, comprehensive and personalized meal recommendations are necessary to maintain optimal health. In addition, since systems lack the functionality to arrange ingredients by linking with food delivery services or online stores based on the proposed recipes, user convenience must also be improved.

[1389] 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.

[1390] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for generating personalized recipes using a generative artificial intelligence model based on the created health profile, means for automatically ordering ingredients needed according to the personalized recipe and delivering the ingredients to the user in cooperation with a food delivery service, means for placing an order through a food delivery service based on the suggested recipe, and means for automatically ordering missing ingredients from an online store. This allows users to easily obtain personalized recipes based on their health information and preferences, saving time and effort while enjoying a healthy diet.

[1391] "Health information" refers to data related to a user's allergies, medical history, and daily lifestyle habits.

[1392] "Preference data" refers to information such as the user's preferred dishes, ingredients, and eating style.

[1393] A "health profile" is a collection of information that indicates a user's individual health condition, created by analyzing health information and preference data.

[1394] A "generative artificial intelligence model" is an algorithm that generates personalized recipes based on collected data.

[1395] "Personalized recipes" are customized meal suggestions based on a user's health profile.

[1396] The "food delivery service" is an online service that delivers the necessary ingredients to the user based on a suggested recipe.

[1397] A "food delivery service" is a service that delivers cooked food directly from a restaurant to the user.

[1398] An "online store" is a system that provides a service for ordering and delivering food ingredients via the Internet.

[1399] "Ingredient inventory" refers to a list of ingredients currently in the user's home.

[1400] A "health management device" is a device that records and manages a user's daily lifestyle and physical activity. Examples include smartwatches and fitness trackers.

[1401] A "health management report" is a document that analyzes a user's health data and periodically provides an overview of the user's health status and suggestions for improvement.

[1402] The following system configuration and program are required to implement this invention. This system provides consistent support from the stage where the user inputs health information and preference data, to creating personalized recipes and actually arranging the ingredients.

[1403] 1. System Configuration

[1404] Hardware

[1405] Smartphone: A device that allows users to input health and preference data and receive recipe suggestions.

[1406] Smartwatch: A device for collecting data on a user's lifestyle habits.

[1407] Server: The main hardware used to analyze data, create health profiles, generate recipes, and manage ingredients and delivery. Uses cloud services (e.g., AWS, Azure).

[1408] software

[1409] Smartphone app: Has the ability to input health information and preference data, and display recipe suggestions (iOS / Android app).

[1410] Health management data collection platform: Collects data from smartwatches (e.g., HealthKit, Google Fit).

[1411] Generative AI models: Generate personalized recipes based on collected data (e.g., OpenAI GPT-3).

[1412] Database management system: Stores and manages user health information, preference data, food inventory, budget, etc. (e.g., Firebase).

[1413] 2. System Operation

[1414] Collection of health and preference data

[1415] Users enter their own health information (allergy information, medical history, etc.) and preference data (favorite dishes, disliked ingredients, etc.) through a smartphone app. In addition, lifestyle data (e.g., amount of exercise, sleep time, etc.) is automatically collected from the smartwatch and sent to a health management data collection platform.

[1416] Generate personalized recipes

[1417] The server analyzes the collected health and preference data and uses a generative AI model to generate a personalized recipe that is optimal for the user. Below are some example prompts for the generative AI model:

[1418] Example prompt sentence:

[1419] User profile:

[1420] Allergies: Nut allergies

[1421] Preferences: Italian cuisine

[1422] Recent exercise data: Running for 45 minutes three times a week

[1423] Based on these, please suggest some low-calorie, low-salt recipes.

[1424] Food arrangement and food delivery service

[1425] The necessary ingredients are automatically ordered according to the generated recipe. Any missing ingredients are automatically ordered from the online store, and an affiliated food delivery service delivers them to the user. In addition, an order is placed with a food delivery service based on the suggested recipe, and cooked food is delivered to the user. The user can check the delivery progress in real time through the app.

[1426] Generate health management reports

[1427] The server periodically analyzes the user's health data and uses a generative AI model to create a health management report, which is provided to the user via a smartphone app and reflected in the user's next meal recommendations.

[1428] For example, a user inputs health information such as a nut allergy and preference data such as a love of Italian food, and recent exercise data is collected from the smartwatch. Based on this information, the server recommends a low-calorie, low-sodium "tomato and basil pasta" and arranges for the necessary ingredients and pre-cooked food from a food delivery service and online store. Periodically generated health management reports are reflected in the next meal recommendations, helping to maintain optimal health for the user.

[1429] In this way, users receive comprehensive and personalized meal suggestions, enabling them to maintain a healthy diet.

[1430] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1431] Step 1:

[1432] The user opens the smartphone app and enters their health information (allergies, medical history, etc.) and preference data (favorite dishes, disliked ingredients, etc.). The device collects this data within the app and sends it to the server.

[1433] Input: User's health information, preference data

[1434] Output: Send data to the server

[1435] Step 2:

[1436] The smartwatch automatically collects the user's lifestyle data (such as exercise volume and sleep time) and sends it to a health management data collection platform, which then transfers the data to a server.

[1437] Input: User's lifestyle data

[1438] Output: Send data to the server

[1439] Step 3:

[1440] The server creates a user's health profile based on their health information, preference data, and lifestyle data. Data analysis algorithms analyze this data and generate a health profile.

[1441] Input: Health information, preference data, lifestyle data

[1442] Output: Health Profile

[1443] Step 4:

[1444] The server uses the generative AI model to generate personalized recipes based on the health profile. The server inputs prompts into the generative AI model to obtain appropriate recipes.

[1445] Example prompt sentence:

[1446] User profile:

[1447] Allergies: Nut allergies

[1448] Preferences: Italian cuisine

[1449] Recent exercise data: Running for 45 minutes three times a week

[1450] Based on these, please suggest some low-calorie, low-salt recipes.

[1451] Input: Health profile, prompt text

[1452] Output: Personalized recipe

[1453] Step 5:

[1454] The server automatically orders the necessary ingredients based on the recipe. If any ingredients are missing, they are automatically ordered from the online store and delivered to the user by the food delivery service.

[1455] Input: personalized recipes, ingredient inventory information

[1456] Output: List of food orders, order request to food delivery service

[1457] Step 6:

[1458] Based on the suggested recipe, the server places an order with a food delivery service, and the prepared food is delivered to the user from a partner restaurant.

[1459] Input: Personalized Recipe

[1460] Output: Food delivery service order request, delivery status tracking information

[1461] Step 7:

[1462] The server periodically analyzes the user's health data and generates a health management report, which is provided to the user via a smartphone app.

[1463] Input: Past health data, lifestyle data

[1464] Output: Health management report, display in app

[1465] 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.

[1466] The present invention aims to provide a system that integrates a user's health information, preference data, and an emotion engine that recognizes the user's emotional state, and provides an optimal meal plan for the user. This system allows users to easily achieve an efficient, personalized meal plan that takes into account their emotional state.

[1467] System configuration

[1468] This system is broadly composed of the following five modules:

[1469] 1. Data Collection and Analysis Module

[1470] 2. Emotion Recognition Engine

[1471] 3. Recipe Generation Module

[1472] 4. Food inventory and budget management module

[1473] 5. Delivery Management Module

[1474] Data Collection and Analysis Module

[1475] Users enter their health information and preference data through a dedicated device app or web interface. For example, users can enter allergy information, favorite dishes, and disliked dishes. Lifestyle data from health management devices (e.g., smartwatches and fitness trackers) is also automatically collected. This data is sent to a server, where a health profile is created.

[1476] Specific examples

[1477] The user opens the device app and enters their allergy information (nut allergy) and favorite food (Italian food). The health management device automatically sends information to the server, such as running for 45 minutes three times a week.

[1478] Emotion Recognition Engine

[1479] The emotion recognition engine analyzes the user's current emotional state using technology that recognizes emotions from the user's tone of voice, facial expressions, text messages, etc. This data is combined with health and preference data to deliver more accurate and personalized meal suggestions.

[1480] Specific examples

[1481] The user types "I'm tired today" into the device. The emotion recognition engine analyzes the user's tone of voice and text content and detects that they are feeling very tired.

[1482] Recipe Generation Module

[1483] The server uses a generative artificial intelligence model to generate personalized recipes based on data from the health profile and emotion recognition engine, and the generated recipes are optimized to take into account the user's health status, emotional state, and preferences.

[1484] Specific examples

[1485] For users who feel extremely tired, the server uses a generative AI model to suggest "nutritious chicken soup that can be made in a short time."

[1486] Food inventory and budget management module

[1487] The server acquires the user's food inventory data and creates a list of necessary ingredients. It also optimizes food purchasing plans based on budget information. This allows for efficient management of necessary ingredients and eliminates waste.

[1488] Specific examples

[1489] The user inputs inventory information (two tomatoes, one bunch of basil) into the terminal, and the server lists the ingredients that are in short supply (chicken, soup base) and creates a purchasing list within the budget.

[1490] Delivery Management Module

[1491] The server connects to an online food delivery service and automatically orders the necessary ingredients. Once the order is confirmed, the ingredients are delivered to the user's specified address. This eliminates the need for the user to spend time shopping for ingredients.

[1492] Specific examples

[1493] The server automatically places an order with a food delivery service, which then delivers the food to the user's home. The user can check the delivery status in real time via their device.

[1494] In this way, the system provides personalized meal plans that take into account not only the user's health and preference data, but also their emotional state, making it easy for users to achieve a healthy and emotionally appropriate diet.

[1495] The processing flow will be explained below.

[1496] Step 1:

[1497] Users open a dedicated device app or web interface and enter their health information (e.g., allergies, dietary restrictions) and preference data (e.g., favorite dishes, disliked ingredients).

[1498] Step 2:

[1499] The device collects the health and preference data entered and transmits it in real time to a server, which then creates a health profile for the user based on the data received.

[1500] Step 3:

[1501] A health management device (e.g., a smartwatch) automatically collects and transmits user lifestyle data (e.g., exercise volume, sleep patterns) to a server, which then imports the data and updates the user's health profile.

[1502] Step 4:

[1503] The user inputs their emotional state (e.g., "I'm tired today") into the device. The emotion recognition engine analyzes the text, tone of voice, facial expressions, etc. to recognize the user's current emotional state.

[1504] Step 5:

[1505] The server integrates the health information, preference data, lifestyle data, and emotional state data to create a comprehensive health profile for the user.

[1506] Step 6:

[1507] The server uses a generative artificial intelligence model to generate personalized recipes based on the health profile created, adjusting the difficulty and cooking time of the recipe depending on the emotional state.

[1508] Step 7:

[1509] The server sends the generated recipe to the user's device, which displays the recipe details (e.g., ingredients, cooking instructions, and nutritional information) to the user.

[1510] Step 8:

[1511] The server checks the user's food inventory data by having the user input the inventory information via the terminal. The user inputs the ingredients they have on hand (e.g., 2 tomatoes, 1 bunch of basil).

[1512] Step 9:

[1513] The server compares the recipe with the inventory of ingredients, lists the ingredients that are in short supply, and optimizes the purchasing plan for the ingredients based on the user's budget information.

[1514] Step 10:

[1515] The server connects to an online food delivery service to automatically order the necessary ingredients, which then receives the order and delivers the ingredients to the specified address.

[1516] Step 11:

[1517] Users can check the delivery status in real time through their device, and once the ingredients arrive, they can start cooking based on the suggested recipe and enjoy a healthy meal that suits their mood.

[1518] This detailed processing step allows the user to execute an efficient and personalized meal plan through the system.

[1519] Example 2

[1520] 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."

[1521] In modern society, many users find it difficult to create personalized meal plans due to their busy lifestyles. Furthermore, there is a lack of systems that provide appropriate meal plans that take into account emotional states in addition to health information and preference data. Furthermore, there is a need for a system that can not only suggest recipes based on a user's health information and preference data, but also suggest meals according to their emotional state. This necessitates a system that can provide comprehensive support, including food inventory management and budget management.

[1522] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's health information and preference data, means for analyzing the health information and preference data to create a health profile, means for recognizing an emotional state, means for generating a personalized recipe using a generative artificial intelligence model based on the health profile and the emotional state, and means for automatically ordering ingredients required according to the personalized recipe and delivering the ingredients to the user in cooperation with a delivery service. This allows the user to easily create an optimal meal plan based on their health information, preference data, and emotional state.

[1523] "Health information" refers to information such as the user's physical condition, medical history, allergy information, and daily health data.

[1524] "Preference data" refers to data that includes information about a user's favorite foods and dishes, disliked foods, and allergies.

[1525] "Health Profile" refers to a comprehensive health record generated based on a User's health information and preference data.

[1526] "Emotional state" refers to information that indicates the user's current emotional or mental state.

[1527] A "generative AI model" refers to an AI algorithm or model that is optimized for a specific purpose (in this case, generating recipes) based on collected data.

[1528] "Personalized recipes" refer to cooking recipes that are individually optimized based on the user's health information, preference data, and emotional state.

[1529] "Delivery Service" refers to an external service that delivers the necessary ingredients to the address specified by the user.

[1530] "Lifestyle data" refers to activity data in the user's daily life and data collected from health management devices.

[1531] "Inventory information" refers to information about the remaining amount and type of ingredients that the user owns.

[1532] This invention is a system that proposes personalized meal plans by comprehensively analyzing a user's health information, preference data, and emotional state. This system mainly consists of three entities: a server, a terminal, and the user.

[1533] 1. Data collection and input

[1534] Users enter health information (e.g., allergy information) and preference data (e.g., favorite foods) through a device app or web interface. This data is sent by the device to the server. In addition, lifestyle data (e.g., exercise frequency) is automatically collected from the user's health management devices (e.g., smartwatches, fitness trackers) and sent to the server. The server then creates a comprehensive health profile.

[1535] 2. Recognizing emotional states

[1536] The user inputs their emotional state through the device, for example, by entering text such as "I feel tired today." The device then sends this data to the server, which uses an emotion recognition engine to analyze the user's emotional state from the input text. The analysis results are added to the health profile.

[1537] 3. Recipe Generation

[1538] The server uses a generative AI model to generate personalized recipes based on the user's health profile and emotional state. This process takes into account the user's health profile, preference data, and emotional state. For example, a user who is feeling very tired might be recommended a nutritious chicken soup that can be made quickly.

[1539] 4. Inventory and budget management

[1540] The user inputs the inventory information of ingredients at home through the device. The device then sends this inventory information to the server, which then creates a list of ingredients needed for a personalized recipe. The server then takes into account the user's budget information and optimizes the shopping list.

[1541] 5. Automatic food delivery

[1542] The server connects to an online delivery service and automatically orders the necessary ingredients. The order information is sent to the delivery service, and the ingredients are delivered to the user's address. The user can check the delivery status in real time from their device.

[1543] Specific examples

[1544] The user uses a device app to input their allergy information and favorite dishes. The health management device sends the user's exercise frequency data to the server, which creates a health profile based on this information. The user enters "I feel tired today" into the device, and the server analyzes the level of fatigue using an emotion recognition engine. The server uses a generative AI model to generate and recommend a "nutritious chicken soup that can be made in a short time." The user enters inventory information, and the server lists ingredients that are in short supply and creates a shopping list within a budget. The server orders the necessary ingredients from an online delivery service, the delivery is made, and the user checks the delivery status on their device.

[1545] An example prompt might look like this:

[1546] "Generate recipes that are effective for fatigue recovery based on the user's health information and preference data."

[1547] In this way, the present invention realizes a system that provides a personalized meal plan that comprehensively takes into account a user's health information, preference data, and emotional state, thereby enabling the user to efficiently achieve a healthy and individualized diet.

[1548] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1549] Step 1: Enter and collect user data

[1550] Users input health information and preference data through a device app or web interface. For example, this includes inputting allergy information (nut allergy) or favorite cuisine (Italian food). This input data is sent by the device to the server. In parallel, lifestyle data (e.g., running for 45 minutes three times a week) is automatically collected from the user's health management device (e.g., smartwatch, fitness tracker) and sent to the server. The server then creates a health profile based on the input and collected data.

[1551] input:

[1552] Health and preference data (user input)

[1553] Lifestyle data (health management device)

[1554] output:

[1555] Health profile (created on the server)

[1556] Step 2: Recognizing your emotional state

[1557] The user inputs their current emotional state through the device. For example, this includes inputting the text "I feel tired today." This emotional data is sent by the device to the server. The server uses an emotion recognition engine to analyze the text data and detect the input emotional state. The server adds this emotional data to a health profile to build more accurate user information.

[1558] input:

[1559] Emotional state text data (user input)

[1560] output:

[1561] Updated health profile (stored on server)

[1562] Step 3: Generate a personalized recipe

[1563] The server acquires the user's health profile and emotional state data and uses a generative AI model to generate personalized recipes. For example, based on the user's health profile and emotional data indicating "high fatigue," it generates a recipe such as "a quick and nutritious chicken soup." The generated recipe is then sent from the server to the user's device.

[1564] input:

[1565] User health profile and emotional state data

[1566] output:

[1567] Personalized recipes (sent to your device)

[1568] Step 4: Manage your food inventory and budget

[1569] The user inputs the inventory information of ingredients they currently own through their device. For example, this includes inputting "two tomatoes, one bunch of basil." The device then sends this inventory information to the server, which then creates a list of ingredients needed for the recipe. The server then optimizes the shopping list by taking into account the user's budget information.

[1570] input:

[1571] Ingredient inventory information (user input)

[1572] Budget information (user settings)

[1573] output:

[1574] Optimized shopping list (created on the server)

[1575] Step 5: Automated delivery of ingredients

[1576] The server connects with an online delivery service and automatically orders the necessary ingredients based on the optimized shopping list. The order information is sent to the delivery service, and the ingredients are delivered to the user's specified address. The user can check the delivery status in real time via their device.

[1577] input:

[1578] Optimized shopping list (created on the server)

[1579] output:

[1580] Delivery status (check on device)

[1581] Through these steps, users can easily achieve a personalized meal plan based on health information, preference data and emotional state.

[1582] (Application example 2)

[1583] 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."

[1584] In today's busy lifestyles, it is challenging to appropriately design and efficiently provide meal plans that take into account individual health conditions, preferences, and even emotional states. Conventional meal plan delivery systems offer personalized meals based on health information and preference data, but they fail to fully enhance user satisfaction because they do not take into account the user's emotional state. In addition, insufficient food inventory and budget management led to problems of waste. Furthermore, few systems offer integrated food delivery services, making it difficult for users to receive ingredients without hassle.

[1585] 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.

[1586] In this invention, the server includes means for inputting a user's health information and preference data, means for analyzing the user's health information and preference data to create a health profile, means for analyzing the user's emotional state from voice and facial expression data, means for generating a personalized recipe using a generative artificial intelligence model based on the created health profile and emotional state, and means for automatically ordering ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with a food delivery service. This allows the user to easily receive a meal plan that reflects the user's health information, preference data, and emotional state, and to receive appropriate ingredients without hassle while minimizing food waste.

[1587] "User's health information" refers to data relating to the user's physical and physiological condition, including, for example, fitness data obtained from health management devices, medical history, and allergy information.

[1588] "Preference data" is data that indicates the types of food and drink that a user likes, cooking methods, or preferences for particular dishes.

[1589] "Voice data" refers to data that is the analysis target of audio signals such as the user's speaking voice, and is used to estimate the emotional state.

[1590] "Facial expression data" is data obtained by image analysis of the user's facial expression, and is used to determine the emotional state.

[1591] A "health profile" is a data set that comprehensively represents a user's health status, generated based on the user's health information and preference data.

[1592] "Emotional state" is data that indicates the user's current psychological and emotional state, and is obtained by analyzing voice, facial expressions, etc.

[1593] A "generative artificial intelligence model" is a model developed using machine learning technology that has the ability to generate optimal results based on specific conditions and data.

[1594] A "personalized recipe" is a recipe that includes meal instructions and ingredient lists optimized for a user based on individual data such as the user's health information, preference data, and emotional state.

[1595] "Ingredients" refers to the specific foods and seasonings used in cooking based on a recipe.

[1596] "Automatic ordering" is the process by which the system orders the required items from the supplier without requiring user confirmation or manual intervention.

[1597] "Food delivery service" refers to a service that specializes in delivering ordered food ingredients to a location specified by the user.

[1598] The following system is used as an embodiment of this invention. The system allows users to input and manage health information, preference data, and emotional state using a device such as a smartphone. A server collects and analyzes this data and provides the user with a personalized meal plan. The hardware and software used, as well as the processing involved, are described in detail below.

[1599] Hardware and software used

[1600] Hardware:

[1601] Smartphone (iOS / Android)

[1602] Health management devices (e.g., smart watches, fitness trackers)

[1603] software:

[1604] Server side: Django (backend framework), PostgreSQL (database)

[1605] Machine learning models: TensorFlow (for emotion recognition and generative AI models)

[1606] API integration: RESTful API

[1607] What the program does

[1608] 1. User Information Input and Data Collection:

[1609] Users use a smartphone app to input their health information and preference data. Lifestyle data (e.g., number of steps, heart rate, and sleep time) is automatically sent from the health management device to the server. In addition, the user's emotional state is recognized by analyzing voice and facial expression data. Voice recognition and facial expression recognition are performed using TensorFlow models.

[1610] 2. Data analysis and health profile creation:

[1611] The server analyzes the received health information, preference data, and emotional state to create a health profile, using the Django backend framework to manage the data and storing it in PostgreSQL.

[1612] 3. Generate personalized recipes:

[1613] Based on the created health profile and emotional state, the server generates personalized recipes using a generative AI model, which is fed prompts using OpenAI's API, such as:

[1614] Example: "Generate the best recipes for a user whose emotional state is fatigue and whose health data is 3 times weekly exercise. Preferences: Italian food, nut allergy."

[1615] 4. Automated food ordering and delivery:

[1616] The server creates a list of ingredients based on the generated recipe and automatically places an order with the delivery service. A RESTful API is used to connect with the delivery service. Users can check the status of their order and delivery via their smartphone.

[1617] Examples:

[1618] When a user types "I feel tired today" into a smartphone app, the smartphone's microphone recognizes the user's voice, and a TensorFlow model analyzes the audio data to estimate the emotional state as "fatigue." The fitness tracker automatically sends data on the user's 45-minute runs three times a week. Based on this data, the server uses OpenAI's generative AI model to generate a recipe for "nutritious chicken soup that can be made quickly." The server then lists any missing ingredients (chicken, soup base) and automatically places an order with a delivery service. Once the order is confirmed, the ingredients are delivered to the user's address.

[1619] This allows users to easily receive personalized meal plans that reflect their health information, preference data, and emotional state, while also ensuring they receive the right ingredients while minimizing food waste.

[1620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1621] Step 1:

[1622] User Data Input and Collection

[1623] Users can use their smartphones to input their health and preference data. For example, they can enter allergy information, favorite foods, and emotional state in text. The devices also automatically collect lifestyle data from health management devices (smartwatches, fitness trackers, etc.) and send it to the server.

[1624] Input: Health information, preference data, lifestyle data

[1625] Output: Send data to the server

[1626] Step 2:

[1627] Recognition of emotional states

[1628] When a user inputs a voice request or text into their smartphone, the device's microphone and camera are used to collect voice and facial expression data. The collected data is then sent to a server, where an emotion recognition engine using TensorFlow analyzes the user's emotional state.

[1629] Input: Voice data, facial expression data

[1630] Output: Emotional state (happy, sad, tired, etc.)

[1631] Step 3:

[1632] Data analysis and health profile creation

[1633] The server analyzes the collected health information, preference data, and emotional state to create a health profile. The data is managed using the Django framework and stored in PostgreSQL.

[1634] Input: Health information, preference data, emotional state

[1635] Output: Health Profile

[1636] Step 4:

[1637] Generate personalized recipes

[1638] The server generates personalized recipes using a generative AI model based on the health profile and emotional state by providing the following prompts to the model using OpenAI's API:

[1639] "Generate the best recipes for a user whose emotional state is fatigue and whose health data is 3 times weekly exercise. Preferences: Italian food, nut allergy"

[1640] Input: Health profile, emotional state

[1641] Output: personalized recipe

[1642] Step 5:

[1643] Ingredient inventory and budget management

[1644] The server obtains the user's food inventory data and lists the ingredients that are in short supply. It also optimizes the purchasing plan based on the user's budget information. It constantly compares the user's inventory data with their budget data to generate an appropriate list.

[1645] Input: Food inventory data, budget information

[1646] Output: Ingredients shopping list

[1647] Step 6:

[1648] Automated food ordering and delivery integration

[1649] Based on the generated recipe, the server automatically orders any missing ingredients from a food delivery service and delivers them to the user's address. The server connects with the delivery service using a RESTful API. Users can check the order status and delivery status in real time from their smartphones.

[1650] Input: Personalized recipes, grocery shopping lists

[1651] Output: Delivered ingredients, user confirmation information

[1652] In this way, the system can provide personalized meal plans and efficiently deliver the necessary ingredients to users, taking into consideration their health information, preference data, and emotional state.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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).

[1660] 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.

[1661] 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."

[1662] 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.

[1663] 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).

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] The following is further disclosed regarding the above embodiment.

[1675] (Claim 1)

[1676] a means for inputting user health information and preference data;

[1677] means for analyzing said user's health information and preference data to create a health profile;

[1678] means for generating personalized recipes using a generative artificial intelligence model based on the created health profile;

[1679] a means for automatically ordering ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with an ingredient delivery service;

[1680] A system including:

[1681] (Claim 2)

[1682] A means for managing a user's ingredient inventory and optimizing recipes based on the inventory information and the user's budget;

[1683] The system of claim 1 further comprising:

[1684] (Claim 3)

[1685] means for automatically collecting lifestyle data from a user's health management device and analyzing the lifestyle data to update a health profile;

[1686] The system of claim 1 further comprising:

[1687] "Example 1"

[1688] (Claim 1)

[1689] a means for inputting user health information and preference data;

[1690] means for analyzing said user's health information and preference data to create a health profile;

[1691] means for automatically collecting lifestyle data from health management devices to update the health profile in real time;

[1692] a means for generating personalized recipes using a generative AI model based on the created health profile;

[1693] A means for users to input food inventory and budget using a terminal app;

[1694] means for generating a list of ingredients required according to said personalized recipe;

[1695] a means for automatically ordering ingredients based on the list of necessary ingredients and delivering the ingredients to the user in cooperation with an ingredient delivery service;

[1696] A means of providing a terminal app for checking delivery status in real time;

[1697] A system including:

[1698] (Claim 2)

[1699] A means for managing a user's ingredient inventory and optimizing recipes based on the inventory information and the user's budget;

[1700] The system of claim 1 further comprising:

[1701] (Claim 3)

[1702] means for automatically collecting lifestyle data from a user's health management device and analyzing the lifestyle data to update a health profile;

[1703] The system of claim 1 further comprising:

[1704] "Application Example 1"

[1705] (Claim 1)

[1706] a means for inputting user health information and preference data;

[1707] means for analyzing said user's health information and preference data to create a health profile;

[1708] means for generating personalized recipes using a generative artificial intelligence model based on the created health profile;

[1709] a means for automatically ordering ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with an ingredient delivery service;

[1710] a means for placing an order with a food delivery service based on the suggested recipe;

[1711] A way to automatically order missing ingredients from the online store,

[1712] A system including:

[1713] (Claim 2)

[1714] A means for managing a user's ingredient inventory and optimizing recipes based on the inventory information and the user's budget;

[1715] means for analyzing the health data and generating periodic health management reports;

[1716] The system of claim 1 further comprising:

[1717] (Claim 3)

[1718] means for automatically collecting lifestyle data from a user's health management device and analyzing the lifestyle data to update a health profile;

[1719] a means for placing an order with a food delivery service based on the suggested recipe;

[1720] The system of claim 1 further comprising:

[1721] "Example 2: Combining Emotion Engines"

[1722] (Claim 1)

[1723] a means for inputting user health information and preference data;

[1724] means for analyzing said user's health information and preference data to create a health profile;

[1725] means for generating personalized recipes using a generative artificial intelligence model based on the created health profile and emotional state;

[1726] means for automatically ordering ingredients required according to the personalized recipe and coordinating with a delivery service to deliver the ingredients to the user;

[1727] A system including:

[1728] (Claim 2)

[1729] A means for managing a user's ingredient inventory and optimizing recipes based on the inventory information and the user's budget;

[1730] means for tailoring a recipe based on said health profile and emotional state;

[1731] The system of claim 1 further comprising:

[1732] (Claim 3)

[1733] means for automatically collecting lifestyle data from a user's health management device and analyzing the lifestyle data and emotional state data to update a health profile;

[1734] The system of claim 1 further comprising:

[1735] "Application example 2 when combining emotion engines"

[1736] (Claim 1)

[1737] a means for inputting user health information and preference data;

[1738] means for analyzing said user's health information and preference data to create a health profile;

[1739] A means for analyzing the emotional state of a user from voice and facial expression data;

[1740] means for generating personalized recipes using a generative artificial intelligence model based on the created health profile and emotional state;

[1741] a means for automatically ordering ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with an ingredient delivery service;

[1742] A system including:

[1743] (Claim 2)

[1744] A means for managing a user's ingredient inventory and optimizing recipes based on the inventory information and the user's budget;

[1745] The system of claim 1 further comprising:

[1746] (Claim 3)

[1747] means for automatically collecting lifestyle data from a user's health management device and analyzing the lifestyle data to update a health profile;

[1748] The system of claim 1 further comprising: [Explanation of symbols]

[1749] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting user health information and preference data; means for analyzing said user's health information and preference data to create a health profile; means for generating personalized recipes using a generative artificial intelligence model based on the created health profile; a means for automatically ordering ingredients according to the personalized recipe and delivering the ingredients to the user in cooperation with an ingredient delivery service; A system including:

2. A means for managing a user's ingredient inventory and optimizing recipes based on the inventory information and the user's budget; The system of claim 1 further comprising:

3. means for automatically collecting lifestyle data from a user's health management device and analyzing the lifestyle data to update a health profile; The system of claim 1 further comprising:

Images