System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring health data; means for analyzing the health data and calculating a daily required calorie and nutrient amount; means for generating a meal menu based on the calculated calorie and nutrient amount; means for providing store information or restaurant information for purchasing foodstuffs based on the generated menu; and means for notifying a user of the provided information and menu.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 daily health management, it is difficult for users to consciously consume the appropriate amount of calories and nutrients according to their activity level. In particular, it is cumbersome for users to understand the amount of calories and nutrients they need, plan appropriate meal menus, and then find foods and restaurants. As a result, it becomes difficult to eat a balanced diet, leading to inadequate health management. Therefore, there is a need to develop a system that suggests appropriate meal menus based on the user's health data and provides information on places to purchase ingredients and restaurants that serve them. [Means for solving the problem]

[0005] The present invention includes a means for acquiring health data and a means for analyzing the data to calculate the daily calorie and nutrient requirements. It also includes a means for generating a meal menu using generative artificial intelligence based on the calculated calories and nutrient requirements. It also includes a means for providing information on retailers selling the necessary ingredients and restaurants based on the generated menu. It also includes a means for notifying the user of this information and menu. This system, configured in this way, allows users to easily eat an appropriate diet in line with their health condition and lead a balanced diet.

[0006] "Health data" refers to the user's physical information and activity information, and specifically includes data such as heart rate, number of steps, calories burned, and sleep data.

[0007] "Means for acquiring" refers to devices and methods for collecting health data, such as methods for acquiring data using wearable devices or health management applications.

[0008] "Means for analyzing" refers to a method or device for analyzing the acquired health data and evaluating the user's activity status and health condition.

[0009] "Required calories and nutrients" refers to the amount of energy and various nutrients that a user should take in each day to maintain good health.

[0010] "Calculating means" refers to a method or device for calculating the amount of calories and nutrients a user needs based on health data.

[0011] "Means for generating" refers to generative artificial intelligence or software that generates appropriate meal menus based on the calculated calorie and nutrient content.

[0012] "Means for providing" refers to a method or device for informing the user of places where ingredients needed for the generated menu can be purchased and information about restaurants that offer the menu.

[0013] "Notification means" refers to a method or device for notifying the user of the generated menu and store information, and specifically includes the application notification function of a smartphone. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention is a system for effectively collecting and analyzing a user's health data to generate an optimal meal menu, identify ingredients and meal locations, and notify the user. This system is composed of a server, a terminal, and a user device.

[0036] Overall system flow and functions

[0037] 1. Health data collection

[0038] The terminal collects the user's health data (heart rate, steps taken, calories burned, etc.) from a wearable device (e.g., a smart watch) and sends it to a server.

[0039] Users wear wearable devices on a daily basis and continue to record their health data.

[0040] 2. Data Receipt and Analysis

[0041] The server receives the health data sent from the device and stores it in a database, which is updated in real time.

[0042] The server analyzes this data to assess the user's activity level and calories burned.

[0043] 3. Calculating calorie and nutrient needs

[0044] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[0045] Based on the calculation results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0046] 4. Generating a meal menu

[0047] The server uses generative artificial intelligence (AI) to generate an appropriate meal menu based on the calculated calorie and nutrient content.

[0048] The AI ​​learns from past data and user preferences to suggest personalized menus.

[0049] 5. Providing information about ingredients and stores

[0050] The server identifies information about the nearest store where ingredients needed for the generated menu can be purchased and information about restaurants that offer the menu.

[0051] The server uses location information services to search for the most suitable store based on the user's current location.

[0052] 6. Notice to Users

[0053] The server transmits the generated meal menu and store information to the terminal.

[0054] The device will send push notifications and display an interface to the user through the application.

[0055] Specific examples

[0056] morning

[0057] The user wakes up in the morning, puts on the wearable device, and begins their daily activities. The device continues to collect data during their commute.

[0058] The terminal continues to transmit the user's health data to the server.

[0059] The server receives and stores the data in real time and prepares it for analysis.

[0060] noon

[0061] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[0062] The server uses AI to generate healthy lunch menus, suggesting meals that balance calories and nutrients, such as chicken breast salad and brown rice.

[0063] The terminal notifies the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at the nearby supermarket A."

[0064] evening

[0065] The user stops by Supermarket A on the way home from work to buy some necessary ingredients, such as chicken breast and brown rice, or chooses to enjoy dinner at Restaurant B, which is suggested by the server.

[0066] The server provides information about nearby stores based on the user's location information and guides them to the best places to shop and eat.

[0067] night

[0068] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[0069] The device collects satisfaction feedback after the user consumes a meal to help improve future suggestions.

[0070] In this way, the system of the present invention supports users in managing their health by proposing appropriate meal plans based on their health data and supporting their implementation, while also providing a comprehensive solution for users to achieve a balanced diet without having to spend time and effort.

[0071] The processing flow will be explained below.

[0072] Step 1: Collect health data

[0073] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[0074] The device collects real-time health data such as the user's heart rate, number of steps taken, and calories burned.

[0075] The terminal periodically transmits the collected data to the server.

[0076] Step 2: Receiving and analyzing data

[0077] The server receives the health data sent from the terminal and stores it in a database.

[0078] The server analyzes the received health data and prepares to evaluate the user's activity level and calories burned.

[0079] Step 3: Calculate your calorie and nutrient needs

[0080] The server calculates the daily calorie and nutrient requirements based on the user's activity data and personal information (age, gender, weight, height).

[0081] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0082] Step 4: Generate the meal menu

[0083] The server uses generative artificial intelligence (AI) to generate an appropriate meal menu based on the calculated calorie and nutrient content.

[0084] The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[0085] Step 5: Provide ingredients and store information

[0086] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[0087] The server uses the user's current location information to search for the most suitable store.

[0088] Step 6: Notify users

[0089] The server transmits the generated meal menu and store information to the terminal.

[0090] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[0091] Step 7: Gather feedback

[0092] The user prepares and consumes a meal based on the suggested menu, or enjoys a meal at the suggested restaurant.

[0093] After the user consumes the meal, the device collects satisfaction and feedback within the app.

[0094] The server stores the collected feedback to help improve future menu suggestions.

[0095] Through these processing steps, the system of the present invention proposes an appropriate meal menu based on the user's health data and supports the user in achieving it, significantly reducing the user's effort and providing a comprehensive solution for promoting a balanced diet.

[0096] Example 1

[0097] 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."

[0098] In modern society, the number of systems that assist individuals in managing their health is increasing. However, these systems often only record the user's activity data and do not adequately provide specific dietary recommendations appropriate for the user's health condition. Furthermore, existing systems do not provide sufficient information on where to purchase ingredients or restaurants, and do not fully satisfy user convenience. Given this background, there is a need for a system that can generate specific and personalized meal menus based on the user's health data, effectively notify the user, and also provide information on where to purchase ingredients and restaurants.

[0099] 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.

[0100] In this invention, the server includes means for acquiring health data, means for analyzing the health data and calculating the daily calorie and nutrient requirements, means for generating a meal menu based on the calculated calories and nutrient amounts, means for identifying ingredients required for the generated menu, means for providing information on the nearest stores or restaurants where the identified ingredients can be purchased, and means for notifying the user of the provided information and menu. This makes it possible to make specific and personalized meal suggestions based on the user's health data and further provide information on where to purchase ingredients and restaurants, thereby improving user convenience and effectively supporting health management.

[0101] "Health data" refers to various data (e.g., heart rate, number of steps, calories burned, etc.) regarding an individual's physical condition and activity level obtained from wearable devices such as smartwatches.

[0102] "Analysis" refers to the process of using statistical methods and algorithms to evaluate and calculate a user's activity level, calories burned, etc. based on the collected health data.

[0103] "Required calories" refers to the amount of energy a user should consume per day, and is calculated based on the user's basic information (age, gender, weight, height) and activity level.

[0104] "Nutrient amount" refers to the amount of each nutrient (e.g., carbohydrates, proteins, fats, etc.) that a user should consume per day.

[0105] A "meal menu" refers to a combination of appropriate meals that a user should consume, and is generated by a generative artificial intelligence based on the required calories and nutrient amounts.

[0106] "Identification" refers to the process of specifying the ingredients required for the generated meal menu and identifying where those ingredients can be purchased and where they are served.

[0107] "Sales point" refers to a commercial facility such as a supermarket or grocery store where users can purchase ingredients.

[0108] "Eating place" refers to a restaurant, cafe, or other eating and drinking establishment that serves a specific meal menu.

[0109] "Notification" refers to the act of communicating the generated meal menu, ingredients, and store information to the user, and is usually done through push notifications on the device or the application interface.

[0110] "User" refers to an individual who uses this system to manage their own health.

[0111] This invention is a system that effectively collects and analyzes a user's health data to generate an optimal meal menu, identifies where to purchase the necessary ingredients, and identifies restaurants that serve them, and notifies the user. Specifically, this system is composed of a server, a terminal, and a user device.

[0112] System configuration, hardware, and software

[0113] 1. Health data collection

[0114] Users wear smartwatches and other wearable devices on a daily basis to record health data such as heart rate, steps taken, and calories burned.

[0115] The terminal collects health data from the smartwatch using wireless communication such as Bluetooth and transmits it to a server at regular intervals. The specific hardware used for this is a mobile device such as a smartphone or tablet.

[0116] 2. Data Receipt and Analysis

[0117] The server uses a RESTful API to receive health data in JSON format sent from the device in real time.

[0118] The server stores the received data in a database (for example, MySQL (registered trademark), PostgreSQL), and this storage process maintains data consistency through transaction control.

[0119] The server uses Python and the Pandas library to analyze the received health data and assess the user's activity level and calories burned.

[0120] 3. Calculating calorie and nutrient needs

[0121] The server retrieves personal information and health data such as the user's age, sex, weight, and height from the database.

[0122] The server uses estimation formulas such as the Harris-Benedict equation to calculate the user's daily calorie needs and the appropriate intake of each nutrient (carbohydrates, protein, and fat).

[0123] 4. Generating a meal menu

[0124] The server generates a prompt for a generative AI model (e.g., OpenAI® GPT-4®) based on the calorie and nutrient needs. The prompt looks like this:

[0125] "Based on the user's age, gender, weight, height, and activity level, they need 2500kcal, 150g protein, 300g carbohydrates, and 70g fat per day. Based on this, please suggest an appropriate lunch menu."

[0126] The server sends this prompt to a generative AI model, which then generates a meal menu for the user. The AI ​​learns the user's preferences and past data and suggests a personalized menu (e.g., chicken breast salad, brown rice, and broccoli).

[0127] 5. Providing information about ingredients and stores

[0128] The server generates a list of ingredients required for the generated menu, identifying specific ingredients such as "chicken breast, brown rice, and broccoli."

[0129] The server uses a geolocation service (e.g., Google (registered trademark) Maps API) to search for information on the nearest supermarkets and restaurants based on the user's current location.

[0130] 6. Notice to Users

[0131] The server converts the generated menu and restaurant information into a specific format and sends it to the device using a notification service such as Firebase Cloud Messaging.

[0132] The device will notify the user via push notification or the application interface, saying, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at your local supermarket."

[0133] Specific examples

[0134] Morning Processing

[0135] Users wake up in the morning, put on their smartwatch, and the device continues to collect health data as they head off to work or school.

[0136] The device continuously transmits collected health data to the server, which receives and stores the data in real time and prepares it for analysis.

[0137] Daytime processing

[0138] Based on the data collected in the morning, the server evaluates the user's calorie consumption and calculates the calorie and nutrient requirements for the day.

[0139] The server uses a generative AI model to generate a healthy lunch menu, suggesting a balanced meal such as chicken breast salad and brown rice.

[0140] The device notifies the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at your local supermarket, Supermarket A."

[0141] Processing from evening to night

[0142] The user can stop by Supermarket A on the way home from work to buy the ingredients they need, or they can have dinner at Restaurant B, which the server suggests.

[0143] The server will guide the user to the best places to shop and eat based on their current location.

[0144] Users can cook the recommended menu items at home and eat a balanced diet, or enjoy the recommended menu items at a restaurant.

[0145] The device collects satisfaction feedback from users after the meal to help improve future suggestions.

[0146] In this way, the system proposes specific and personalized meal plans based on the user's health data and supports their implementation, improving user convenience and effectively supporting health management.

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

[0148] Step 1:

[0149] The user wears the smartwatch and starts their daily activities, which then record health data such as heart rate, number of steps, and calories burned in real time. The input data are heart rate, number of steps, calories burned, etc., obtained at each timestamp.

[0150] Step 2:

[0151] The device periodically collects health data from the smartwatch using Bluetooth. The input data is the raw health data collected from the smartwatch, and the output data is JSON-formatted data containing the collected health data. Specifically, the device pairs with the smartwatch and acquires data at regular intervals.

[0152] Step 3:

[0153] The device sends the collected health data to the server at regular intervals (e.g., every hour). The input data is the health data stored on the device, and the output data is the health data in JSON format sent to the server. Specifically, the device establishes a network connection and sends the data to the server via a RESTful API.

[0154] Step 4:

[0155] The server receives health data sent from the device via a RESTful API. The input data is the JSON format health data sent from the device, and the output data is the raw data stored in a database on the server. Upon receiving the data, the server executes specific operations to store it in a database (e.g., MySQL, PostgreSQL).

[0156] Step 5:

[0157] The server analyzes the received health data using Python's Pandas library. The input data is the health data stored in the database, and the output data is the analyzed evaluation data of activity level and calories burned. Specifically, it uses Pandas to generate a data frame and calculate various statistics.

[0158] Step 6:

[0159] The server uses the user's personal information, such as age, gender, weight, and height, and analyzed health data to calculate the daily calorie and nutrient requirements using estimation formulas such as the Harris-Benedict equation. The input data is the user's basic information and analyzed data, and the output data is the calorie and nutrient requirements. Specifically, the server uses a formula to calculate the basal metabolic rate and multiply it by an activity coefficient.

[0160] Step 7:

[0161] The server generates a prompt for a generative AI model (e.g., OpenAI GPT-4). The input data is the required calories and nutrient amounts, and the output data is the generated prompt. An example of a specific prompt is, "Based on the user's age, gender, weight, height, and activity level, the required daily calories are 2500 kcal, protein 150 g, carbohydrates 300 g, and fat 70 g. Based on these conditions, please suggest an appropriate lunch menu."

[0162] Step 8:

[0163] The server sends the generated prompt to the generative AI model to generate a personalized meal menu. The input data is the prompt, and the output data is the generated meal menu (e.g., chicken breast salad, brown rice, and broccoli). Specifically, the server sends a request to the AI ​​model via an API and retrieves the generated results.

[0164] Step 9:

[0165] The server generates a list of ingredients required for the generated menu and uses the Geolocation service to search for information on the nearest sales or dining locations. The input data is the meal menu and the user's location information, and the output data is information on the nearest store (e.g., supermarket A, restaurant B). The specific operation is to obtain location information using the Google Maps API or similar and search for store information.

[0166] Step 10:

[0167] The server sends the generated menu and restaurant information to the device. The input data is the menu and restaurant information, and the output data is the information sent to the device. Specifically, the server sends the data to the device via a notification service such as Firebase Cloud Messaging.

[0168] Step 11:

[0169] The device notifies the user of the information through push notifications or the application interface. The input data is the data sent from the server, and the output data is the notification to the user. The specific operation is to display to the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at nearby Supermarket A."

[0170] Specific actions

[0171] Morning: The user puts on the smartwatch and starts their activities.

[0172] Lunch: The server calculates the required calories and nutrients and generates an appropriate meal menu using a generative AI model.

[0173] Evening: The server provides the user with information about the nearest store based on the user's current location and notifies the user via the terminal.

[0174] Evening: The user consumes the recommended menu and the device collects satisfaction feedback.

[0175] Through this specific processing flow, the system provides personalized meal suggestions and purchasing information based on the user's health data, effectively supporting health management.

[0176] (Application example 1)

[0177] 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."

[0178] In modern society, busy lifestyles make it difficult to eat a balanced diet. Choosing and cooking healthy meals, as well as purchasing ingredients, can be particularly time-consuming. The objective of this invention is to provide a system that easily suggests personalized healthy meal menus based on the user's health data and allows for quick delivery.

[0179] 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.

[0180] In this invention, the server includes a means for acquiring health data, a means for analyzing the health data and calculating the daily calorie and nutrient requirements, a means for generating a meal menu based on the calculated calories and nutrient amounts, a means for providing information on delivery stores for delivering ingredients based on the generated menu, and a means for notifying the user of the provided information and menu, thereby enabling the user to easily select and order a personalized healthy meal menu and use the optimal delivery service.

[0181] "Health data" refers to information about a user's physical activity and physical condition, such as heart rate, number of steps taken, and calories burned.

[0182] "Analysis" refers to the process of evaluating a user's activity level and calorie expenditure based on collected health data, and calculating the amount of calories and nutrients they need.

[0183] "Required calories and nutrients" refers to the amount of energy and various nutrients that should be consumed per day based on personal information such as the user's age, gender, weight, and height.

[0184] A "meal menu" refers to the specific meal content and composition suggested based on the calculated calorie and nutrient requirements.

[0185] "Delivery store information" indicates information about the nearest delivery service that delivers the ingredients and dishes required for the generated meal menu.

[0186] "Notification" refers to the act of communicating the generated meal menu and delivery store information to the user by push notification or displaying it within the application.

[0187] A "wearable device" refers to a device worn on the user's body that collects health data, and typically includes a smartwatch or fitness tracker.

[0188] "Generative artificial intelligence" is an AI that has the ability to learn from past data and user preferences to generate personalized meal menus.

[0189] The present invention is a system for effectively collecting and analyzing a user's health data to generate an optimal meal menu, identify ingredients and meal locations, and notify the user. This system is composed of a server, a terminal, and a user device.

[0190] The overall system flow is as follows: first, a wearable device (e.g., a smartwatch) collects the user's health data and sends it to a server. Next, the server analyzes the received data and calculates the daily calorie and nutrient requirements. Based on these calculations, an appropriate meal menu is generated using generative artificial intelligence (AI). Next, information about nearby delivery restaurants is identified based on the generated menu. Finally, this information is notified to the user, who can then order delivery with one touch.

[0191] Hardware and software used

[0192] Wearable devices: Smartwatches and fitness trackers used to collect health data (e.g., Apple Watch, Fitbit).

[0193] Smartphone App: This Application provides an interface with the User.

[0194] Cloud server: A server for data collection and analysis, menu generation, and store suggestion searches (e.g., AWS (registered trademark), Google Cloud Platform).

[0195] Location information service: Geopy (a Python library) is used to obtain the user's location and search for the appropriate delivery store.

[0196] Data processing and calculation

[0197] 1. Health Data Collection:

[0198] The wearable device collects health data such as the user's heart rate, number of steps taken, and calories burned, and transmits it to a server via the terminal.

[0199] 2. Data Analysis:

[0200] The server receives the collected health data, stores and updates it in real time, and prepares it for analysis.

[0201] The server also uses an analytical engine to assess activity levels and calorie expenditures, and calculates daily calorie and nutrient needs.

[0202] 3. Menu generation:

[0203] The server uses generative artificial intelligence to generate meal menus based on the calculated calorie and nutrient content.

[0204] The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[0205] 4. Store Information Identification:

[0206] The server uses a location information service to identify ingredients required for the generated menu and store information that provides them.

[0207] This allows the system to search for and provide the optimal delivery store based on the user's current location.

[0208] 5. Notifications & Order Processing:

[0209] The server sends the generated meal menu and delivery store information to the terminal and notifies the user through the application.

[0210] Users can order meals with just one touch based on the information provided.

[0211] Specific examples

[0212] Example prompt sentence:

[0213] The user's name is Yamada Taro. He is a 35-year-old man, weighs 80 kg, and is 178 cm tall. This morning, his heart rate was 72, he took 8,500 steps, and he burned 450 calories. Please suggest the best dinner menu for Yamada Taro today. Also, please provide information about the nearest delivery store where he can order this menu.

[0214] In this way, the system of the present invention supports the user's health management by proposing appropriate meal menus based on the user's health data and supporting the execution of these menus, thereby enabling the user to easily achieve a balanced diet.

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

[0216] Step 1:

[0217] Health data collection

[0218] The user puts on a wearable device and begins their daily activities. The wearable device, such as a smartwatch, then collects health data such as heart rate, steps taken, and calories burned in real time. This data is then sent to a server via the device (smartphone).

[0219] Input: Health data (heart rate, steps, calories burned)

[0220] Output: Send data to the server

[0221] Step 2:

[0222] Receiving and storing data

[0223] The server receives the health data sent from the device and stores it in a database, which keeps the user's activity information updated in real time and ready for analysis.

[0224] Input: Health data from your device

[0225] Output: Health data stored in a database

[0226] Step 3:

[0227] Data analysis

[0228] The server analyzes the health data stored in the database to assess the user's activity level and calorie expenditure, and uses an analytical engine to calculate the daily calorie and nutrient requirements.

[0229] Input: Stored health data

[0230] Output: Calculated calorie and nutrient needs

[0231] Step 4:

[0232] Generate a meal menu

[0233] The server uses generative artificial intelligence to generate an appropriate meal plan based on the calculated calorie and nutrient content. The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[0234] Input: Calculated calorie and nutrient needs, user's past data, user's food preferences

[0235] Output: personalized meal menu

[0236] Step 5:

[0237] Identifying delivery store information

[0238] The server identifies nearby delivery stores that provide the ingredients needed for the generated menu. It uses location-based services (such as Geopy) to search for the optimal delivery store based on the user's current location.

[0239] Input: Food menu, user location

[0240] Output: Delivery store information

[0241] Step 6:

[0242] User Notification and Orders

[0243] The server sends the generated menu and delivery store information to the device and notifies the user through the application. The user can then place a delivery order with one touch based on the information provided. Once the order is complete, a confirmation notification is sent.

[0244] Input: Meal menu, delivery store information

[0245] Output: Push notification, delivery order

[0246] Step 7:

[0247] Gathering feedback

[0248] After a meal, users provide their satisfaction and feedback through the application, which is sent to the server and used to improve future menu suggestions.

[0249] Input: User feedback

[0250] Output: Feedback data stored on the server

[0251] 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.

[0252] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and a user device. Furthermore, it is characterized by using an emotion engine to recognize the user's emotional state and reflect it in the meal menu it generates.

[0253] Overall system flow and functions

[0254] 1. Health data collection

[0255] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[0256] The device collects health data such as the user's heart rate, number of steps taken, and calories burned in real time and sends it to a server.

[0257] 2. Collecting Emotional Data

[0258] Users use the cameras and microphones built into their smartphones and other devices.

[0259] The device detects the user's emotional state (e.g., joy, anger, sadness, stress, etc.) through voice recognition and facial expression recognition.

[0260] The terminal transmits the emotion data to the server.

[0261] 3. Data Receipt and Analysis

[0262] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[0263] The server analyzes this data to assess the user's activity level, calories burned, and emotional state.

[0264] 4. Calculating calorie and nutrient needs

[0265] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[0266] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0267] 5. Generating a meal menu

[0268] The server uses generative artificial intelligence (AI) and an emotion engine to generate appropriate meal menus based on the calculated calorie and nutrient content.

[0269] The AI ​​learns from past data and the user's food preferences to suggest personalized menus that reflect the user's emotional state.

[0270] 6. Providing information about ingredients and stores

[0271] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[0272] The server uses the user's current location information to search for the most suitable store.

[0273] 7. Notice to Users

[0274] The server transmits the generated meal menu and store information to the terminal.

[0275] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[0276] Specific examples

[0277] morning

[0278] The user wakes up in the morning, puts on the wearable device, and begins their daily activities, with the camera and microphone of their smartphone enabled.

[0279] The device continuously collects health data (e.g., heart rate, steps, calories burned) and emotional data (emotional state through voice recognition and facial expression recognition) in real time.

[0280] The server receives and stores the data in real time and prepares it for analysis.

[0281] noon

[0282] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[0283] The server uses AI and an emotion engine to generate lunch menus that reflect your emotional state while also taking into account your health status. For example, if you're feeling stressed, it will suggest meals that include foods that have a relaxing effect.

[0284] The terminal notifies the user, "For today's lunch, a relaxing salmon and avocado salad is recommended. You can purchase the ingredients at the nearby supermarket A."

[0285] evening

[0286] The user can stop by Supermarket A on the way home from work to purchase the recommended ingredients, or choose to enjoy dinner at Restaurant B, which is suggested by the server.

[0287] The server provides information about nearby stores based on the user's location information and guides them to the best places to shop and eat.

[0288] night

[0289] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[0290] After the user consumes the meal, the device collects satisfaction feedback to help improve future suggestions.

[0291] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[0292] The processing flow will be explained below.

[0293] Step 1: Collecting health and emotional data

[0294] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[0295] Users use the cameras and microphones built into their smartphones and other devices.

[0296] The device collects real-time health data such as the user's heart rate, number of steps taken, and calories burned.

[0297] The device detects the user's emotional state (e.g., joy, anger, sadness, stress, etc.) through voice recognition and facial expression recognition.

[0298] The terminal periodically transmits the collected health data and emotion data to the server.

[0299] Step 2: Receiving and storing data

[0300] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[0301] Step 3: Analyze the data

[0302] The server analyzes the stored health data and evaluates the user's activity level and calories burned.

[0303] The server analyzes the stored emotional data and assesses the user's emotional state.

[0304] Step 4: Calculate your calorie and nutrient needs

[0305] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[0306] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0307] Step 5: Generate the meal menu

[0308] The server uses generative artificial intelligence (AI) and an emotion engine to generate appropriate meal menus based on the calculated calorie and nutrient content.

[0309] The AI ​​learns from past data and the user's food preferences to suggest personalized menus that reflect the user's emotional state (e.g., a menu that includes foods that have a relaxing effect when stressed).

[0310] Step 6: Provide ingredients and store information

[0311] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[0312] The server uses the user's current location information to search for the most suitable store.

[0313] Step 7: Notify users

[0314] The server transmits the generated meal menu and store information to the terminal.

[0315] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[0316] Step 8: Gather feedback

[0317] The user prepares and consumes a meal based on the suggested menu, or enjoys a meal at the suggested restaurant.

[0318] After the user consumes the meal, the device collects satisfaction and feedback within the app.

[0319] The server stores the collected feedback to help improve future menu suggestions.

[0320] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[0321] Example 2

[0322] 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."

[0323] In today's world, achieving a balanced diet while efficiently managing one's health and emotions is a major challenge for many people. However, conventional systems have had difficulty integrating health data and emotional states to generate appropriate meal plans and provide them to users. Furthermore, they have not been able to propose specific meal plans based on the user's current emotional state. As a result, it has been impossible to provide an optimal meal plan for each individual user's health and emotional state, making effective health and emotional management difficult. Therefore, there is a need for a system that can combine and analyze health data and emotional states to generate optimal meal plans and provide them to users.

[0324] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring health data and emotional state, a means for analyzing the health data and emotional state and calculating the daily required calories and nutrient amount, and a means for generating a meal menu based on the calculated calories and nutrient amount. This makes it possible to comprehensively analyze the user's health data and emotional state and provide an optimal meal menu for each individual user. Furthermore, since a menu is proposed that takes the user's emotional state into consideration based on the acquired data, the user's health and emotional management can be performed efficiently and effectively.

[0325] "Health data" refers to physiological information obtained from wearable devices, such as a user's heart rate, number of steps taken, and calories burned.

[0326] The "emotional state" refers to the user's psychological state, such as joy, anger, sadness, or stress, and is data acquired through a voice recognition or facial expression recognition device.

[0327] "Required calories and nutrients" refers to the amount of energy a user should consume in a day, as well as the necessary vitamins, minerals, proteins, and other nutrients.

[0328] A "meal menu" is a specific meal combination or recipe that allows a user to consume the required amount of calories and nutrients per day.

[0329] "Store information" refers to related information such as the location, contact information, and opening hours of stores where ingredients necessary to create the generated meal menu can be purchased.

[0330] "Restaurant information" refers to related information such as the location, contact information, and business hours of the restaurant that offers the generated meal menu.

[0331] "Generative AI" is an AI technology that generates optimal meal plans based on a user's health data and emotional state.

[0332] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and a user device. Furthermore, it is characterized by using an emotion engine to recognize the user's emotional state and reflect it in the meal menu it generates.

[0333] Hardware and software used

[0334] Wearable devices (e.g., smartwatches): collect health data such as the user's heart rate, steps taken, and calories burned.

[0335] Speech recognizer (e.g., Google Cloud Speech-to-Text): obtains the emotional state from the user's voice.

[0336] Facial expression recognition device (e.g., Face++ API): Obtains the user's emotional state from their facial expressions.

[0337] Database system (e.g., MySQL): to store the collected health and emotion data.

[0338] Data analysis software (e.g., Python, Pandas): to analyze health and emotion data.

[0339] Machine learning model (e.g., Scikit-learn): Calculates daily calorie and nutrient requirements based on the analysis results.

[0340] Generative AI (e.g., OpenAI's GPT-3®): Generates personalized meal plans based on calorie and nutrient needs.

[0341] Map information service (e.g., Google Maps API): Provides information on the nearest stores and restaurants.

[0342] Notifications (e.g., Firebase Cloud Messaging): Notify users of suggested menu items and store availability information.

[0343] Example of system operation

[0344] morning

[0345] The user wakes up in the morning, puts on the wearable device, and begins their daily activities, with the camera and microphone of their smartphone enabled.

[0346] The device continuously collects health data (heart rate, steps, calories burned) and emotional data (emotional state through voice recognition and facial expression recognition) in real time.

[0347] The server receives the data in real time and stores it in a database for analysis.

[0348] noon

[0349] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[0350] The server uses AI and an emotion engine to generate lunch menus that reflect your emotional state while also taking into account your health status. For example, if you're feeling stressed, it will suggest meals that include foods that have a relaxing effect.

[0351] The device notifies the user, "Today's lunch is recommended as a relaxing salmon and avocado salad. You can purchase the ingredients at your local supermarket."

[0352] evening

[0353] The user can choose to stop by supermarket A on the way home from work and purchase the recommended ingredients, or to enjoy dinner at restaurant B suggested by the server.

[0354] The server uses the user's location information to provide information about nearby stores and guides them to the best places to shop or eat.

[0355] night

[0356] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[0357] The device collects satisfaction feedback after the user consumes the meal to help improve future suggestions.

[0358] Prompt Sentence Examples

[0359] "Generate a relaxing lunch menu based on the user's health data and emotional state. For example, suggest a menu suitable for when your heart rate is high and you're stressed."

[0360] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[0361] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0362] Program processing steps

[0363] Step 1:

[0364] A user puts on a wearable device (e.g., a smartwatch) and begins their daily activities, which starts collecting health data. The input is the user's biometric data (heart rate, steps, calories burned), and the output is this real-time data.

[0365] Specifically, the wearable device measures biosignals, converts them into data format, and transmits them to the terminal.

[0366] Step 2:

[0367] The terminal acquires health data from the wearable device in real time. The input is the health data sent from the wearable device, and the output is the health data in a format that is sent to the server.

[0368] Specifically, the device receives various sensor data via Bluetooth or Wi-Fi and packetizes it for transmission to the server.

[0369] Step 3:

[0370] Users turn on the camera and microphone on their smartphones to record emotional data on a daily basis. The input is the user's face and voice, and the output is raw data based on these.

[0371] Specifically, the smartphone's camera and microphone collect the user's facial expressions and voice.

[0372] Step 4:

[0373] The device acquires the user's emotional state through voice and facial recognition. The input is recorded or photographed data, and the output is data indicating the emotional state (e.g., joy, anger, sadness, stress, etc.).

[0374] Specifically, the built-in voice recognition software (e.g., Google Cloud Speech-to-Text) and facial expression recognition software (e.g., Face++ API) analyze the recorded and image data to generate emotional data.

[0375] Step 5:

[0376] The terminal transmits the collected health data and emotion data to the server. The input is the collected health data and emotion data, and the output is data packet information containing these data.

[0377] Specifically, the device creates and sends an HTTP request to send data to the server's API endpoint.

[0378] Step 6:

[0379] The server receives the health and emotion data sent from the device. The input is the data packet sent from the device, and the output is the data stored in a database (e.g., MySQL) on the server.

[0380] Specifically, the server analyzes the received data, converts it into a valid format, and stores it in a database.

[0381] Step 7:

[0382] The server analyzes the received data and evaluates the user's activity, calorie consumption, and emotional state. The input is health data and emotional data, and the output is the evaluated data (e.g., calories consumed, nutritional status, emotional state).

[0383] Specifically, a data analysis script is executed using Python and Pandas.

[0384] Step 8:

[0385] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height). The input is the health data and personal information, and the output is the calculated daily calorie and nutrient requirements.

[0386] Specifically, a machine learning model using Scikit-learn is executed to calculate individual calorie and nutrient requirements.

[0387] Step 9:

[0388] The server uses generative artificial intelligence (AI) and an emotion engine to generate an appropriate meal plan based on the calculated calorie and nutrient content. The input is the calorie needs, nutrient content, and emotion data, and the output is a personalized meal plan.

[0389] Specifically, OpenAI's GPT-3 creates prompts based on user data and generates an appropriate meal menu.

[0390] Step 10:

[0391] The server identifies the nearest store where the ingredients needed to create the generated menu can be purchased, and the restaurant that serves that menu. The input is the generated meal menu and the user's location information, and the output is the optimal store and restaurant information.

[0392] Specifically, the Google Maps API searches for the nearest store information based on the user's location information.

[0393] Step 11:

[0394] The server sends the generated meal menu and store information to the terminal. The input is the generated meal menu and store information, and the output is notification information sent to the user terminal.

[0395] Specifically, notifications are sent to the device using Firebase Cloud Messaging.

[0396] Step 12:

[0397] The terminal uses the notification function to notify the user of suggested menus and store information where purchases can be made. The input is notification information sent from the server, and the output is visual and audible notifications to the user.

[0398] Specifically, the suggestions will be displayed in the smartphone's notification center, and in some cases an audio notification will also be given.

[0399] Through this series of processing steps, the user is presented with an optimal meal menu based on their individual health data and emotional state, enabling them to manage their health and emotions on a daily basis.

[0400] (Application example 2)

[0401] 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."

[0402] In modern society, maintaining health and managing emotions are important, but balancing these two is difficult in our busy daily lives. Selecting an appropriate meal plan based on an individual's health and emotional state is particularly challenging. Furthermore, finding the optimal shopping and dining locations for a suggested meal plan can be time-consuming. This invention aims to enable users to eat healthy and emotionally satisfying meals by collecting and analyzing a user's health and emotional state data, and automatically generating and providing appropriate meal plans and information on nearby stores and restaurants.

[0403] 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.

[0404] In this invention, the server includes means for acquiring health data, means for analyzing the health data and calculating daily calorie needs and nutrient amounts, means for generating a meal menu based on the calculated calories and nutrient amounts, means for providing store information or restaurant information for purchasing ingredients based on the generated menu, means for notifying the user of the provided information and menu, means for analyzing the user's emotional state and adjusting the meal menu based on the emotional state using an emotion engine, and means for automatically processing an order with the nearest affiliated restaurant based on the meal menu. This allows users to easily obtain a meal menu that is optimal for their health and emotional state, and to efficiently purchase ingredients and eat at restaurants based on the meal menu.

[0405] "Health data" refers to data that indicates the user's physical condition, and includes physiological indicators such as heart rate, number of steps, calories burned, weight, and blood pressure.

[0406] The "emotional state" is a state that indicates the emotions and moods that the user is currently feeling, and is information that includes emotional responses such as joy, anger, sadness, stress, and relaxation.

[0407] An "emotion engine" refers to artificial intelligence and algorithms that analyze users' emotional data and adjust meal menus and other suggestions based on the results.

[0408] A "meal menu" is a series of dishes or food combinations suggested for consumption by a user, generated based on calorie and nutrient content.

[0409] "Store information" refers to information about stores where the user can purchase ingredients needed for the proposed meal menu.

[0410] "Restaurant information" refers to information about restaurants that offer the meal menu suggested by the user.

[0411] "Notification methods" refers to digital and analog methods, such as smartphone applications and emails, used to inform users about suggested meal menus and places to buy and eat.

[0412] "Generative AI" refers to a general term for AI technology that learns from a user's past data and preferences and then makes personalized suggestions based on that information.

[0413] "Affiliated restaurant" refers to a restaurant that provides services in cooperation with this system, and is a place that provides suggested menus to users.

[0414] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and the user's device. A notable feature is that it uses an emotion engine to reflect the user's emotional state in the menu. It is also possible to automatically process orders from nearby partner restaurants based on the proposed meal menu.

[0415] Program processing explanation

[0416] Hardware and software usage:

[0417] Flask: Used to provide the web server and API endpoints, through which health and emotion data is collected and analysis results are sent.

[0418] Python: A programming language used for data processing and server logic implementation. Used to analyze health and emotion data, generate meal menus, and provide location information.

[0419] Database: Store user health data, emotion data, and generated menu information in a database such as MongoDB or MySQL.

[0420] Generative AI: Uses AI models (e.g., GPT-3, BERT, etc.) to generate meal menus based on the user's health and emotional data.

[0421] Location API: Uses location services such as Google Maps API to find the nearest partner restaurant based on the user's current location.

[0422] Data processing and calculation flow:

[0423] The server uses the following means:

[0424] 1. Health data collection: Health data is collected from user devices such as smartwatches and smartphones. This data includes heart rate, steps taken, calories burned, etc.

[0425] 2. Data analysis method: Analyzes collected health data and calculates daily calorie and nutrient needs, taking into account personal information such as the user's age, gender, weight, and height.

[0426] 3. Meal menu generation: Based on the calculated calorie and nutrient content, an optimal meal menu is generated using generative artificial intelligence. Furthermore, an emotion engine is used to reflect the user's emotional state in the meal menu.

[0427] 4. Store information provision means: Based on the generated menu, information on the nearest stores and restaurants where necessary ingredients can be purchased is provided, and this information is also notified to the user.

[0428] 5. Automatic Order Processing: Automatically processes orders with the nearest partner restaurant, allowing users to try the suggested menu items without any hassle.

[0429] Examples:

[0430] Users record their daily health and emotional data using a smartwatch or smartphone. For example, assume that their heart rate is 75, their steps are 5,500, and their calories burned are 1,500 kcal. If the emotional data is detected as "stress," the server receives and analyzes this data and uses generative artificial intelligence to suggest a "salmon and avocado salad with a relaxing effect." The server also identifies the nearest partner restaurant using a location information API, and suggests "Restaurant A." The user is notified of this suggestion via a smartphone application, and the order is automatically processed.

[0431] Example prompt sentence:

[0432] "Today's user's emotional data is 'stress', and their health data is 'heart rate 75, steps taken 5500, calories burned 1500 kcal.' Based on this data, please suggest a lunch menu that will relieve stress and is under 1500 kcal."

[0433] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0434] Step 1:

[0435] Data collection

[0436] The device collects health data (heart rate, steps taken, calories burned) in real time from smartwatches and smartphones.

[0437] The device sends the collected health data to a server. It also uses the device's camera and microphone to collect the user's voice and facial expressions in real time, and sends this as emotional data to the server.

[0438] Input: User's heart rate, steps, calories burned, voice, and facial expressions.

[0439] Output: Health and emotion data sent to the server.

[0440] Step 2:

[0441] Data storage

[0442] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[0443] Input: Submitted health and emotion data.

[0444] Output: Health and emotion data stored in a database.

[0445] Specific operation: The server creates a new record in the database and records the health and emotion data.

[0446] Step 3:

[0447] Data analysis

[0448] The server analyzes the health data in the database and calculates the user's daily calorie and nutrient needs.

[0449] Based on the analysis results, the server generates meal menus for lunch, dinner, etc. that take into account the user's emotional state.

[0450] Input: Health data, personal information (age, gender, weight, height).

[0451] Output: Calculated calorie and nutrient needs, generated meal plan.

[0452] How it works: Based on health data, the server applies an energy expenditure model to calculate daily calorie needs, then uses a generative AI model to suggest meal plans.

[0453] Step 4:

[0454] Reflection of emotional state

[0455] The server uses an emotion engine to tailor the meal menu based on the user's emotional state, for example, suggesting relaxing meals to a stressed user.

[0456] Input: User emotion data (happiness, anger, sadness, stress, etc.).

[0457] Output: A tailored meal menu.

[0458] Specific operation: The server analyzes the emotional data using an emotion engine and fine-tunes the menus suggested by the generative AI model based on the results.

[0459] Step 5:

[0460] Store information provision

[0461] Based on the generated menu, the server provides information on the nearest store where the necessary ingredients can be purchased and information on restaurants that offer the menu.

[0462] Input: Generated food menu, user's current location.

[0463] Output: Retailer information, restaurant information.

[0464] Specific operation: The server uses the location information API to search for and provide appropriate store information based on the user's current location.

[0465] Step 6:

[0466] Notifications and Auto Ordering

[0467] The terminal notifies the user of the provided information and menu, and with the user's consent, automatically processes the order with the partner restaurant.

[0468] Input: Retailer information, Restaurant information, generated meal menu, User consent.

[0469] Output: Notification to the user and order information to the restaurant.

[0470] Specific operation: The smartphone application sends a notification to the user, and if the order is confirmed, the order information is automatically sent to the partner restaurant.

[0471] Example prompt sentence:

[0472] "Today's user's emotional data is 'stress', and their health data is 'heart rate 75, steps taken 5500, calories burned 1500 kcal.' Based on this data, please suggest a lunch menu that will relieve stress and is under 1500 kcal."

[0473] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0474] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0475] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0476] [Second embodiment]

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

[0478] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0479] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0480] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0481] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0482] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0483] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0484] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0485] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0486] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0487] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0488] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0489] The present invention is a system for effectively collecting and analyzing a user's health data to generate an optimal meal menu, identify ingredients and meal locations, and notify the user. This system is composed of a server, a terminal, and a user device.

[0490] Overall system flow and functions

[0491] 1. Health data collection

[0492] The terminal collects the user's health data (heart rate, steps taken, calories burned, etc.) from a wearable device (e.g., a smart watch) and sends it to a server.

[0493] Users wear wearable devices on a daily basis and continue to record their health data.

[0494] 2. Data Receipt and Analysis

[0495] The server receives the health data sent from the device and stores it in a database, which is updated in real time.

[0496] The server analyzes this data to assess the user's activity level and calories burned.

[0497] 3. Calculating calorie and nutrient needs

[0498] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[0499] Based on the calculation results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0500] 4. Generating a meal menu

[0501] The server uses generative artificial intelligence (AI) to generate an appropriate meal menu based on the calculated calorie and nutrient content.

[0502] The AI ​​learns from past data and user preferences to suggest personalized menus.

[0503] 5. Providing information about ingredients and stores

[0504] The server identifies information about the nearest store where ingredients needed for the generated menu can be purchased and information about restaurants that offer the menu.

[0505] The server uses location information services to search for the most suitable store based on the user's current location.

[0506] 6. Notice to Users

[0507] The server transmits the generated meal menu and store information to the terminal.

[0508] The device will send push notifications and display an interface to the user through the application.

[0509] Specific examples

[0510] morning

[0511] The user wakes up in the morning, puts on the wearable device, and begins their daily activities. The device continues to collect data during their commute.

[0512] The terminal continues to transmit the user's health data to the server.

[0513] The server receives and stores the data in real time and prepares it for analysis.

[0514] noon

[0515] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[0516] The server uses AI to generate healthy lunch menus, suggesting meals that balance calories and nutrients, such as chicken breast salad and brown rice.

[0517] The terminal notifies the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at the nearby supermarket A."

[0518] evening

[0519] The user stops by Supermarket A on the way home from work to buy some necessary ingredients, such as chicken breast and brown rice, or chooses to enjoy dinner at Restaurant B, which is suggested by the server.

[0520] The server provides information about nearby stores based on the user's location information and guides them to the best places to shop and eat.

[0521] night

[0522] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[0523] The device collects satisfaction feedback after the user consumes a meal to help improve future suggestions.

[0524] In this way, the system of the present invention supports users in managing their health by proposing appropriate meal plans based on their health data and supporting their implementation, while also providing a comprehensive solution for users to achieve a balanced diet without having to spend time and effort.

[0525] The processing flow will be explained below.

[0526] Step 1: Collect health data

[0527] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[0528] The device collects real-time health data such as the user's heart rate, number of steps taken, and calories burned.

[0529] The terminal periodically transmits the collected data to the server.

[0530] Step 2: Receiving and analyzing data

[0531] The server receives the health data sent from the terminal and stores it in a database.

[0532] The server analyzes the received health data and prepares to evaluate the user's activity level and calories burned.

[0533] Step 3: Calculate your calorie and nutrient needs

[0534] The server calculates the daily calorie and nutrient requirements based on the user's activity data and personal information (age, gender, weight, height).

[0535] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0536] Step 4: Generate the meal menu

[0537] The server uses generative artificial intelligence (AI) to generate an appropriate meal menu based on the calculated calorie and nutrient content.

[0538] The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[0539] Step 5: Provide ingredients and store information

[0540] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[0541] The server uses the user's current location information to search for the most suitable store.

[0542] Step 6: Notify users

[0543] The server transmits the generated meal menu and store information to the terminal.

[0544] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[0545] Step 7: Gather feedback

[0546] The user prepares and consumes a meal based on the suggested menu, or enjoys a meal at the suggested restaurant.

[0547] After the user consumes the meal, the device collects satisfaction and feedback within the app.

[0548] The server stores the collected feedback to help improve future menu suggestions.

[0549] Through these processing steps, the system of the present invention proposes an appropriate meal menu based on the user's health data and supports the user in achieving it, significantly reducing the user's effort and providing a comprehensive solution for promoting a balanced diet.

[0550] Example 1

[0551] 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."

[0552] In modern society, the number of systems that assist individuals in managing their health is increasing. However, these systems often only record the user's activity data and do not adequately provide specific dietary recommendations appropriate for the user's health condition. Furthermore, existing systems do not provide sufficient information on where to purchase ingredients or restaurants, and do not fully satisfy user convenience. Given this background, there is a need for a system that can generate specific and personalized meal menus based on the user's health data, effectively notify the user, and also provide information on where to purchase ingredients and restaurants.

[0553] 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.

[0554] In this invention, the server includes means for acquiring health data, means for analyzing the health data and calculating the daily calorie and nutrient requirements, means for generating a meal menu based on the calculated calories and nutrient amounts, means for identifying ingredients required for the generated menu, means for providing information on the nearest stores or restaurants where the identified ingredients can be purchased, and means for notifying the user of the provided information and menu. This makes it possible to make specific and personalized meal suggestions based on the user's health data and further provide information on where to purchase ingredients and restaurants, thereby improving user convenience and effectively supporting health management.

[0555] "Health data" refers to various data (e.g., heart rate, number of steps, calories burned, etc.) regarding an individual's physical condition and activity level obtained from wearable devices such as smartwatches.

[0556] "Analysis" refers to the process of using statistical methods and algorithms to evaluate and calculate a user's activity level, calories burned, etc. based on the collected health data.

[0557] "Required calories" refers to the amount of energy a user should consume per day, and is calculated based on the user's basic information (age, gender, weight, height) and activity level.

[0558] "Nutrient amount" refers to the amount of each nutrient (e.g., carbohydrates, proteins, fats, etc.) that a user should consume per day.

[0559] A "meal menu" refers to a combination of appropriate meals that a user should consume, and is generated by a generative artificial intelligence based on the required calories and nutrient amounts.

[0560] "Identification" refers to the process of specifying the ingredients required for the generated meal menu and identifying where those ingredients can be purchased and where they are served.

[0561] "Sales point" refers to a commercial facility such as a supermarket or grocery store where users can purchase ingredients.

[0562] "Eating place" refers to a restaurant, cafe, or other eating and drinking establishment that serves a specific meal menu.

[0563] "Notification" refers to the act of communicating the generated meal menu, ingredients, and store information to the user, and is usually done through push notifications on the device or the application interface.

[0564] "User" refers to an individual who uses this system to manage their own health.

[0565] This invention is a system that effectively collects and analyzes a user's health data to generate an optimal meal menu, identifies where to purchase the necessary ingredients, and identifies restaurants that serve them, and notifies the user. Specifically, this system is composed of a server, a terminal, and a user device.

[0566] System configuration, hardware, and software

[0567] 1. Health data collection

[0568] Users wear smartwatches and other wearable devices on a daily basis to record health data such as heart rate, steps taken, and calories burned.

[0569] The terminal collects health data from the smartwatch using wireless communication such as Bluetooth and transmits it to a server at regular intervals. The specific hardware used for this is a mobile device such as a smartphone or tablet.

[0570] 2. Data Receipt and Analysis

[0571] The server uses a RESTful API to receive health data in JSON format sent from the device in real time.

[0572] The server stores the received data in a database (e.g., MySQL, PostgreSQL), and this storage process maintains data consistency through transaction control.

[0573] The server uses Python and the Pandas library to analyze the received health data and assess the user's activity level and calories burned.

[0574] 3. Calculating calorie and nutrient needs

[0575] The server retrieves personal information and health data such as the user's age, sex, weight, and height from the database.

[0576] The server uses estimation formulas such as the Harris-Benedict equation to calculate the user's daily calorie needs and the appropriate intake of each nutrient (carbohydrates, protein, and fat).

[0577] 4. Generating a meal menu

[0578] The server generates a prompt for a generative AI model (e.g., OpenAI GPT-4) based on the calorie and nutrient needs. The prompt looks like this:

[0579] "Based on the user's age, gender, weight, height, and activity level, they need 2500kcal, 150g protein, 300g carbohydrates, and 70g fat per day. Based on this, please suggest an appropriate lunch menu."

[0580] The server sends this prompt to a generative AI model, which then generates a meal menu for the user. The AI ​​learns the user's preferences and past data and suggests a personalized menu (e.g., chicken breast salad, brown rice, and broccoli).

[0581] 5. Providing information about ingredients and stores

[0582] The server generates a list of ingredients required for the generated menu, identifying specific ingredients such as "chicken breast, brown rice, and broccoli."

[0583] The server uses a geolocation service (e.g., Google Maps API) to search for information on the nearest supermarkets and restaurants based on the user's current location.

[0584] 6. Notice to Users

[0585] The server converts the generated menu and restaurant information into a specific format and sends it to the device using a notification service such as Firebase Cloud Messaging.

[0586] The device will notify the user via push notification or the application interface, saying, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at your local supermarket."

[0587] Specific examples

[0588] Morning Processing

[0589] Users wake up in the morning, put on their smartwatch, and the device continues to collect health data as they head off to work or school.

[0590] The device continuously transmits collected health data to the server, which receives and stores the data in real time and prepares it for analysis.

[0591] Daytime processing

[0592] Based on the data collected in the morning, the server evaluates the user's calorie consumption and calculates the calorie and nutrient requirements for the day.

[0593] The server uses a generative AI model to generate a healthy lunch menu, suggesting a balanced meal such as chicken breast salad and brown rice.

[0594] The device notifies the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at your local supermarket, Supermarket A."

[0595] Processing from evening to night

[0596] The user can stop by Supermarket A on the way home from work to buy the ingredients they need, or they can have dinner at Restaurant B, which the server suggests.

[0597] The server will guide the user to the best places to shop and eat based on their current location.

[0598] Users can cook the recommended menu items at home and eat a balanced diet, or enjoy the recommended menu items at a restaurant.

[0599] The device collects satisfaction feedback from users after the meal to help improve future suggestions.

[0600] In this way, the system proposes specific and personalized meal plans based on the user's health data and supports their implementation, improving user convenience and effectively supporting health management.

[0601] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0602] Step 1:

[0603] The user wears the smartwatch and starts their daily activities, which then record health data such as heart rate, number of steps, and calories burned in real time. The input data are heart rate, number of steps, calories burned, etc., obtained at each timestamp.

[0604] Step 2:

[0605] The device periodically collects health data from the smartwatch using Bluetooth. The input data is the raw health data collected from the smartwatch, and the output data is JSON-formatted data containing the collected health data. Specifically, the device pairs with the smartwatch and acquires data at regular intervals.

[0606] Step 3:

[0607] The device sends the collected health data to the server at regular intervals (e.g., every hour). The input data is the health data stored on the device, and the output data is the health data in JSON format sent to the server. Specifically, the device establishes a network connection and sends the data to the server via a RESTful API.

[0608] Step 4:

[0609] The server receives health data sent from the device via a RESTful API. The input data is the JSON format health data sent from the device, and the output data is the raw data stored in a database on the server. Upon receiving the data, the server executes specific operations to store it in a database (e.g., MySQL, PostgreSQL).

[0610] Step 5:

[0611] The server analyzes the received health data using Python's Pandas library. The input data is the health data stored in the database, and the output data is the analyzed evaluation data of activity level and calories burned. Specifically, it uses Pandas to generate a data frame and calculate various statistics.

[0612] Step 6:

[0613] The server uses the user's personal information, such as age, gender, weight, and height, and analyzed health data to calculate the daily calorie and nutrient requirements using estimation formulas such as the Harris-Benedict equation. The input data is the user's basic information and analyzed data, and the output data is the calorie and nutrient requirements. Specifically, the server uses a formula to calculate the basal metabolic rate and multiply it by an activity coefficient.

[0614] Step 7:

[0615] The server generates a prompt for a generative AI model (e.g., OpenAI GPT-4). The input data is the required calories and nutrient amounts, and the output data is the generated prompt. An example of a specific prompt is, "Based on the user's age, gender, weight, height, and activity level, the required daily calories are 2500 kcal, protein 150 g, carbohydrates 300 g, and fat 70 g. Based on these conditions, please suggest an appropriate lunch menu."

[0616] Step 8:

[0617] The server sends the generated prompt to the generative AI model to generate a personalized meal menu. The input data is the prompt, and the output data is the generated meal menu (e.g., chicken breast salad, brown rice, and broccoli). Specifically, the server sends a request to the AI ​​model via an API and retrieves the generated results.

[0618] Step 9:

[0619] The server generates a list of ingredients required for the generated menu and uses the Geolocation service to search for information on the nearest sales or dining locations. The input data is the meal menu and the user's location information, and the output data is information on the nearest store (e.g., supermarket A, restaurant B). The specific operation is to obtain location information using the Google Maps API or similar and search for store information.

[0620] Step 10:

[0621] The server sends the generated menu and restaurant information to the device. The input data is the menu and restaurant information, and the output data is the information sent to the device. Specifically, the server sends the data to the device via a notification service such as Firebase Cloud Messaging.

[0622] Step 11:

[0623] The device notifies the user of the information through push notifications or the application interface. The input data is the data sent from the server, and the output data is the notification to the user. The specific operation is to display to the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at nearby Supermarket A."

[0624] Specific actions

[0625] Morning: The user puts on the smartwatch and starts their activities.

[0626] Lunch: The server calculates the required calories and nutrients and generates an appropriate meal menu using a generative AI model.

[0627] Evening: The server provides the user with information about the nearest store based on the user's current location and notifies the user via the terminal.

[0628] Evening: The user consumes the recommended menu and the device collects satisfaction feedback.

[0629] Through this specific processing flow, the system provides personalized meal suggestions and purchasing information based on the user's health data, effectively supporting health management.

[0630] (Application example 1)

[0631] 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."

[0632] In modern society, busy lifestyles make it difficult to eat a balanced diet. Choosing and cooking healthy meals, as well as purchasing ingredients, can be particularly time-consuming. The objective of this invention is to provide a system that easily suggests personalized healthy meal menus based on the user's health data and allows for quick delivery.

[0633] 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.

[0634] In this invention, the server includes a means for acquiring health data, a means for analyzing the health data and calculating the daily calorie and nutrient requirements, a means for generating a meal menu based on the calculated calories and nutrient amounts, a means for providing information on delivery stores for delivering ingredients based on the generated menu, and a means for notifying the user of the provided information and menu, thereby enabling the user to easily select and order a personalized healthy meal menu and use the optimal delivery service.

[0635] "Health data" refers to information about a user's physical activity and physical condition, such as heart rate, number of steps taken, and calories burned.

[0636] "Analysis" refers to the process of evaluating a user's activity level and calorie expenditure based on collected health data, and calculating the amount of calories and nutrients they need.

[0637] "Required calories and nutrients" refers to the amount of energy and various nutrients that should be consumed per day based on personal information such as the user's age, gender, weight, and height.

[0638] A "meal menu" refers to the specific meal content and composition suggested based on the calculated calorie and nutrient requirements.

[0639] "Delivery store information" indicates information about the nearest delivery service that delivers the ingredients and dishes required for the generated meal menu.

[0640] "Notification" refers to the act of communicating the generated meal menu and delivery store information to the user by push notification or displaying it within the application.

[0641] A "wearable device" refers to a device worn on the user's body that collects health data, and typically includes a smartwatch or fitness tracker.

[0642] "Generative artificial intelligence" is an AI that has the ability to learn from past data and user preferences to generate personalized meal menus.

[0643] The present invention is a system for effectively collecting and analyzing a user's health data to generate an optimal meal menu, identify ingredients and meal locations, and notify the user. This system is composed of a server, a terminal, and a user device.

[0644] The overall system flow is as follows: first, a wearable device (e.g., a smartwatch) collects the user's health data and sends it to a server. Next, the server analyzes the received data and calculates the daily calorie and nutrient requirements. Based on these calculations, an appropriate meal menu is generated using generative artificial intelligence (AI). Next, information about nearby delivery restaurants is identified based on the generated menu. Finally, this information is notified to the user, who can then order delivery with one touch.

[0645] Hardware and software used

[0646] Wearable devices: Smartwatches and fitness trackers used to collect health data (e.g., Apple Watch, Fitbit).

[0647] Smartphone App: This Application provides an interface with the User.

[0648] Cloud server: A server for collecting data, analyzing it, generating menus, and searching for suggested restaurants (e.g., AWS, Google Cloud Platform).

[0649] Location information service: Geopy (a Python library) is used to obtain the user's location and search for the appropriate delivery store.

[0650] Data processing and calculation

[0651] 1. Health Data Collection:

[0652] The wearable device collects health data such as the user's heart rate, number of steps taken, and calories burned, and transmits it to a server via the terminal.

[0653] 2. Data Analysis:

[0654] The server receives the collected health data, stores and updates it in real time, and prepares it for analysis.

[0655] The server also uses an analytical engine to assess activity levels and calorie expenditures, and calculates daily calorie and nutrient needs.

[0656] 3. Menu generation:

[0657] The server uses generative artificial intelligence to generate meal menus based on the calculated calorie and nutrient content.

[0658] The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[0659] 4. Store Information Identification:

[0660] The server uses a location information service to identify ingredients required for the generated menu and store information that provides them.

[0661] This allows the system to search for and provide the optimal delivery store based on the user's current location.

[0662] 5. Notifications & Order Processing:

[0663] The server sends the generated meal menu and delivery store information to the terminal and notifies the user through the application.

[0664] Users can order meals with just one touch based on the information provided.

[0665] Specific examples

[0666] Example prompt sentence:

[0667] The user's name is Yamada Taro. He is a 35-year-old man, weighs 80 kg, and is 178 cm tall. This morning, his heart rate was 72, he took 8,500 steps, and he burned 450 calories. Please suggest the best dinner menu for Yamada Taro today. Also, please provide information about the nearest delivery store where he can order this menu.

[0668] In this way, the system of the present invention supports the user's health management by proposing appropriate meal menus based on the user's health data and supporting the execution of these menus, thereby enabling the user to easily achieve a balanced diet.

[0669] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0670] Step 1:

[0671] Health data collection

[0672] The user puts on a wearable device and begins their daily activities. The wearable device, such as a smartwatch, then collects health data such as heart rate, steps taken, and calories burned in real time. This data is then sent to a server via the device (smartphone).

[0673] Input: Health data (heart rate, steps, calories burned)

[0674] Output: Send data to the server

[0675] Step 2:

[0676] Receiving and storing data

[0677] The server receives the health data sent from the device and stores it in a database, which keeps the user's activity information updated in real time and ready for analysis.

[0678] Input: Health data from your device

[0679] Output: Health data stored in a database

[0680] Step 3:

[0681] Data analysis

[0682] The server analyzes the health data stored in the database to assess the user's activity level and calorie expenditure, and uses an analytical engine to calculate the daily calorie and nutrient requirements.

[0683] Input: Stored health data

[0684] Output: Calculated calorie and nutrient needs

[0685] Step 4:

[0686] Generate a meal menu

[0687] The server uses generative artificial intelligence to generate an appropriate meal plan based on the calculated calorie and nutrient content. The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[0688] Input: Calculated calorie and nutrient needs, user's past data, user's food preferences

[0689] Output: personalized meal menu

[0690] Step 5:

[0691] Identifying delivery store information

[0692] The server identifies nearby delivery stores that provide the ingredients needed for the generated menu. It uses location-based services (such as Geopy) to search for the optimal delivery store based on the user's current location.

[0693] Input: Food menu, user location

[0694] Output: Delivery store information

[0695] Step 6:

[0696] User Notification and Orders

[0697] The server sends the generated menu and delivery store information to the device and notifies the user through the application. The user can then place a delivery order with one touch based on the information provided. Once the order is complete, a confirmation notification is sent.

[0698] Input: Meal menu, delivery store information

[0699] Output: Push notification, delivery order

[0700] Step 7:

[0701] Gathering feedback

[0702] After a meal, users provide their satisfaction and feedback through the application, which is sent to the server and used to improve future menu suggestions.

[0703] Input: User feedback

[0704] Output: Feedback data stored on the server

[0705] 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.

[0706] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and a user device. Furthermore, it is characterized by using an emotion engine to recognize the user's emotional state and reflect it in the meal menu it generates.

[0707] Overall system flow and functions

[0708] 1. Health data collection

[0709] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[0710] The device collects health data such as the user's heart rate, number of steps taken, and calories burned in real time and sends it to a server.

[0711] 2. Collecting Emotional Data

[0712] Users use the cameras and microphones built into their smartphones and other devices.

[0713] The device detects the user's emotional state (e.g., joy, anger, sadness, stress, etc.) through voice recognition and facial expression recognition.

[0714] The terminal transmits the emotion data to the server.

[0715] 3. Data Receipt and Analysis

[0716] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[0717] The server analyzes this data to assess the user's activity level, calories burned, and emotional state.

[0718] 4. Calculating calorie and nutrient needs

[0719] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[0720] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0721] 5. Generating a meal menu

[0722] The server uses generative artificial intelligence (AI) and an emotion engine to generate appropriate meal menus based on the calculated calorie and nutrient content.

[0723] The AI ​​learns from past data and the user's food preferences to suggest personalized menus that reflect the user's emotional state.

[0724] 6. Providing information about ingredients and stores

[0725] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[0726] The server uses the user's current location information to search for the most suitable store.

[0727] 7. Notice to Users

[0728] The server transmits the generated meal menu and store information to the terminal.

[0729] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[0730] Specific examples

[0731] morning

[0732] The user wakes up in the morning, puts on the wearable device, and begins their daily activities, with the camera and microphone of their smartphone enabled.

[0733] The device continuously collects health data (e.g., heart rate, steps, calories burned) and emotional data (emotional state through voice recognition and facial expression recognition) in real time.

[0734] The server receives and stores the data in real time and prepares it for analysis.

[0735] noon

[0736] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[0737] The server uses AI and an emotion engine to generate lunch menus that reflect your emotional state while also taking into account your health status. For example, if you're feeling stressed, it will suggest meals that include foods that have a relaxing effect.

[0738] The terminal notifies the user, "For today's lunch, a relaxing salmon and avocado salad is recommended. You can purchase the ingredients at the nearby supermarket A."

[0739] evening

[0740] The user can stop by Supermarket A on the way home from work to purchase the recommended ingredients, or choose to enjoy dinner at Restaurant B, which is suggested by the server.

[0741] The server provides information about nearby stores based on the user's location information and guides them to the best places to shop and eat.

[0742] night

[0743] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[0744] After the user consumes the meal, the device collects satisfaction feedback to help improve future suggestions.

[0745] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[0746] The processing flow will be explained below.

[0747] Step 1: Collecting health and emotional data

[0748] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[0749] Users use the cameras and microphones built into their smartphones and other devices.

[0750] The device collects real-time health data such as the user's heart rate, number of steps taken, and calories burned.

[0751] The device detects the user's emotional state (e.g., joy, anger, sadness, stress, etc.) through voice recognition and facial expression recognition.

[0752] The terminal periodically transmits the collected health data and emotion data to the server.

[0753] Step 2: Receiving and storing data

[0754] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[0755] Step 3: Analyze the data

[0756] The server analyzes the stored health data and evaluates the user's activity level and calories burned.

[0757] The server analyzes the stored emotional data and assesses the user's emotional state.

[0758] Step 4: Calculate your calorie and nutrient needs

[0759] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[0760] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0761] Step 5: Generate the meal menu

[0762] The server uses generative artificial intelligence (AI) and an emotion engine to generate appropriate meal menus based on the calculated calorie and nutrient content.

[0763] The AI ​​learns from past data and the user's food preferences to suggest personalized menus that reflect the user's emotional state (e.g., a menu that includes foods that have a relaxing effect when stressed).

[0764] Step 6: Provide ingredients and store information

[0765] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[0766] The server uses the user's current location information to search for the most suitable store.

[0767] Step 7: Notify users

[0768] The server transmits the generated meal menu and store information to the terminal.

[0769] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[0770] Step 8: Gather feedback

[0771] The user prepares and consumes a meal based on the suggested menu, or enjoys a meal at the suggested restaurant.

[0772] After the user consumes the meal, the device collects satisfaction and feedback within the app.

[0773] The server stores the collected feedback to help improve future menu suggestions.

[0774] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[0775] Example 2

[0776] 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."

[0777] In today's world, achieving a balanced diet while efficiently managing one's health and emotions is a major challenge for many people. However, conventional systems have had difficulty integrating health data and emotional states to generate appropriate meal plans and provide them to users. Furthermore, they have not been able to propose specific meal plans based on the user's current emotional state. As a result, it has been impossible to provide an optimal meal plan for each individual user's health and emotional state, making effective health and emotional management difficult. Therefore, there is a need for a system that can combine and analyze health data and emotional states to generate optimal meal plans and provide them to users.

[0778] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring health data and emotional state, a means for analyzing the health data and emotional state and calculating the daily required calories and nutrient amount, and a means for generating a meal menu based on the calculated calories and nutrient amount. This makes it possible to comprehensively analyze the user's health data and emotional state and provide an optimal meal menu for each individual user. Furthermore, since a menu is proposed that takes the user's emotional state into consideration based on the acquired data, the user's health and emotional management can be performed efficiently and effectively.

[0779] "Health data" refers to physiological information obtained from wearable devices, such as a user's heart rate, number of steps taken, and calories burned.

[0780] The "emotional state" refers to the user's psychological state, such as joy, anger, sadness, or stress, and is data acquired through a voice recognition or facial expression recognition device.

[0781] "Required calories and nutrients" refers to the amount of energy a user should consume in a day, as well as the necessary vitamins, minerals, proteins, and other nutrients.

[0782] A "meal menu" is a specific meal combination or recipe that allows a user to consume the required amount of calories and nutrients per day.

[0783] "Store information" refers to related information such as the location, contact information, and opening hours of stores where ingredients necessary to create the generated meal menu can be purchased.

[0784] "Restaurant information" refers to related information such as the location, contact information, and business hours of the restaurant that offers the generated meal menu.

[0785] "Generative AI" is an AI technology that generates optimal meal plans based on a user's health data and emotional state.

[0786] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and a user device. Furthermore, it is characterized by using an emotion engine to recognize the user's emotional state and reflect it in the meal menu it generates.

[0787] Hardware and software used

[0788] Wearable devices (e.g., smartwatches): collect health data such as the user's heart rate, steps taken, and calories burned.

[0789] Speech recognizer (e.g., Google Cloud Speech-to-Text): obtains the emotional state from the user's voice.

[0790] Facial expression recognition device (e.g., Face++ API): Obtains the user's emotional state from their facial expressions.

[0791] Database system (e.g., MySQL): to store the collected health and emotion data.

[0792] Data analysis software (e.g., Python, Pandas): to analyze health and emotion data.

[0793] Machine learning model (e.g., Scikit-learn): Calculates daily calorie and nutrient requirements based on the analysis results.

[0794] Generative AI (e.g., OpenAI's GPT-3): Generates personalized meal plans based on calorie and nutrient needs.

[0795] Map information service (e.g., Google Maps API): Provides information on the nearest stores and restaurants.

[0796] Notifications (e.g., Firebase Cloud Messaging): Notify users of suggested menu items and store availability information.

[0797] Example of system operation

[0798] morning

[0799] The user wakes up in the morning, puts on the wearable device, and begins their daily activities, with the camera and microphone of their smartphone enabled.

[0800] The device continuously collects health data (heart rate, steps, calories burned) and emotional data (emotional state through voice recognition and facial expression recognition) in real time.

[0801] The server receives the data in real time and stores it in a database for analysis.

[0802] noon

[0803] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[0804] The server uses AI and an emotion engine to generate lunch menus that reflect your emotional state while also taking into account your health status. For example, if you're feeling stressed, it will suggest meals that include foods that have a relaxing effect.

[0805] The device notifies the user, "Today's lunch is recommended as a relaxing salmon and avocado salad. You can purchase the ingredients at your local supermarket."

[0806] evening

[0807] The user can choose to stop by supermarket A on the way home from work and purchase the recommended ingredients, or to enjoy dinner at restaurant B suggested by the server.

[0808] The server uses the user's location information to provide information about nearby stores and guides them to the best places to shop or eat.

[0809] night

[0810] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[0811] The device collects satisfaction feedback after the user consumes the meal to help improve future suggestions.

[0812] Prompt Sentence Examples

[0813] "Generate a relaxing lunch menu based on the user's health data and emotional state. For example, suggest a menu suitable for when your heart rate is high and you're stressed."

[0814] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[0815] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0816] Program processing steps

[0817] Step 1:

[0818] A user puts on a wearable device (e.g., a smartwatch) and begins their daily activities, which starts collecting health data. The input is the user's biometric data (heart rate, steps, calories burned), and the output is this real-time data.

[0819] Specifically, the wearable device measures biosignals, converts them into data format, and transmits them to the terminal.

[0820] Step 2:

[0821] The terminal acquires health data from the wearable device in real time. The input is the health data sent from the wearable device, and the output is the health data in a format that is sent to the server.

[0822] Specifically, the device receives various sensor data via Bluetooth or Wi-Fi and packetizes it for transmission to the server.

[0823] Step 3:

[0824] Users turn on the camera and microphone on their smartphones to record emotional data on a daily basis. The input is the user's face and voice, and the output is raw data based on these.

[0825] Specifically, the smartphone's camera and microphone collect the user's facial expressions and voice.

[0826] Step 4:

[0827] The device acquires the user's emotional state through voice and facial recognition. The input is recorded or photographed data, and the output is data indicating the emotional state (e.g., joy, anger, sadness, stress, etc.).

[0828] Specifically, the built-in voice recognition software (e.g., Google Cloud Speech-to-Text) and facial expression recognition software (e.g., Face++ API) analyze the recorded and image data to generate emotional data.

[0829] Step 5:

[0830] The terminal transmits the collected health data and emotion data to the server. The input is the collected health data and emotion data, and the output is data packet information containing these data.

[0831] Specifically, the device creates and sends an HTTP request to send data to the server's API endpoint.

[0832] Step 6:

[0833] The server receives the health and emotion data sent from the device. The input is the data packet sent from the device, and the output is the data stored in a database (e.g., MySQL) on the server.

[0834] Specifically, the server analyzes the received data, converts it into a valid format, and stores it in a database.

[0835] Step 7:

[0836] The server analyzes the received data and evaluates the user's activity, calorie consumption, and emotional state. The input is health data and emotional data, and the output is the evaluated data (e.g., calories consumed, nutritional status, emotional state).

[0837] Specifically, a data analysis script is executed using Python and Pandas.

[0838] Step 8:

[0839] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height). The input is the health data and personal information, and the output is the calculated daily calorie and nutrient requirements.

[0840] Specifically, a machine learning model using Scikit-learn is executed to calculate individual calorie and nutrient requirements.

[0841] Step 9:

[0842] The server uses generative artificial intelligence (AI) and an emotion engine to generate an appropriate meal plan based on the calculated calorie and nutrient content. The input is the calorie needs, nutrient content, and emotion data, and the output is a personalized meal plan.

[0843] Specifically, OpenAI's GPT-3 creates prompts based on user data and generates an appropriate meal menu.

[0844] Step 10:

[0845] The server identifies the nearest store where the ingredients needed to create the generated menu can be purchased, and the restaurant that serves that menu. The input is the generated meal menu and the user's location information, and the output is the optimal store and restaurant information.

[0846] Specifically, the Google Maps API searches for the nearest store information based on the user's location information.

[0847] Step 11:

[0848] The server sends the generated meal menu and store information to the terminal. The input is the generated meal menu and store information, and the output is notification information sent to the user terminal.

[0849] Specifically, notifications are sent to the device using Firebase Cloud Messaging.

[0850] Step 12:

[0851] The terminal uses the notification function to notify the user of suggested menus and store information where purchases can be made. The input is notification information sent from the server, and the output is visual and audible notifications to the user.

[0852] Specifically, the suggestions will be displayed in the smartphone's notification center, and in some cases an audio notification will also be given.

[0853] Through this series of processing steps, the user is presented with an optimal meal menu based on their individual health data and emotional state, enabling them to manage their health and emotions on a daily basis.

[0854] (Application example 2)

[0855] 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."

[0856] In modern society, maintaining health and managing emotions are important, but balancing these two is difficult in our busy daily lives. Selecting an appropriate meal plan based on an individual's health and emotional state is particularly challenging. Furthermore, finding the optimal shopping and dining locations for a suggested meal plan can be time-consuming. This invention aims to enable users to eat healthy and emotionally satisfying meals by collecting and analyzing a user's health and emotional state data, and automatically generating and providing appropriate meal plans and information on nearby stores and restaurants.

[0857] 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.

[0858] In this invention, the server includes means for acquiring health data, means for analyzing the health data and calculating daily calorie needs and nutrient amounts, means for generating a meal menu based on the calculated calories and nutrient amounts, means for providing store information or restaurant information for purchasing ingredients based on the generated menu, means for notifying the user of the provided information and menu, means for analyzing the user's emotional state and adjusting the meal menu based on the emotional state using an emotion engine, and means for automatically processing an order with the nearest affiliated restaurant based on the meal menu. This allows users to easily obtain a meal menu that is optimal for their health and emotional state, and to efficiently purchase ingredients and eat at restaurants based on the meal menu.

[0859] "Health data" refers to data that indicates the user's physical condition, and includes physiological indicators such as heart rate, number of steps, calories burned, weight, and blood pressure.

[0860] The "emotional state" is a state that indicates the emotions and moods that the user is currently feeling, and is information that includes emotional responses such as joy, anger, sadness, stress, and relaxation.

[0861] An "emotion engine" refers to artificial intelligence and algorithms that analyze users' emotional data and adjust meal menus and other suggestions based on the results.

[0862] A "meal menu" is a series of dishes or food combinations suggested for consumption by a user, generated based on calorie and nutrient content.

[0863] "Store information" refers to information about stores where the user can purchase ingredients needed for the proposed meal menu.

[0864] "Restaurant information" refers to information about restaurants that offer the meal menu suggested by the user.

[0865] "Notification methods" refers to digital and analog methods, such as smartphone applications and emails, used to inform users about suggested meal menus and places to buy and eat.

[0866] "Generative AI" refers to a general term for AI technology that learns from a user's past data and preferences and then makes personalized suggestions based on that information.

[0867] "Affiliated restaurant" refers to a restaurant that provides services in cooperation with this system, and is a place that provides suggested menus to users.

[0868] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and the user's device. A notable feature is that it uses an emotion engine to reflect the user's emotional state in the menu. It is also possible to automatically process orders from nearby partner restaurants based on the proposed meal menu.

[0869] Program processing explanation

[0870] Hardware and software usage:

[0871] Flask: Used to provide the web server and API endpoints, through which health and emotion data is collected and analysis results are sent.

[0872] Python: A programming language used for data processing and server logic implementation. Used to analyze health and emotion data, generate meal menus, and provide location information.

[0873] Database: Store user health data, emotion data, and generated menu information in a database such as MongoDB or MySQL.

[0874] Generative AI: Uses AI models (e.g., GPT-3, BERT, etc.) to generate meal menus based on the user's health and emotional data.

[0875] Location API: Uses location services such as Google Maps API to find the nearest partner restaurant based on the user's current location.

[0876] Data processing and calculation flow:

[0877] The server uses the following means:

[0878] 1. Health data collection: Health data is collected from user devices such as smartwatches and smartphones. This data includes heart rate, steps taken, calories burned, etc.

[0879] 2. Data analysis method: Analyzes collected health data and calculates daily calorie and nutrient needs, taking into account personal information such as the user's age, gender, weight, and height.

[0880] 3. Meal menu generation: Based on the calculated calorie and nutrient content, an optimal meal menu is generated using generative artificial intelligence. Furthermore, an emotion engine is used to reflect the user's emotional state in the meal menu.

[0881] 4. Store information provision means: Based on the generated menu, information on the nearest stores and restaurants where necessary ingredients can be purchased is provided, and this information is also notified to the user.

[0882] 5. Automatic Order Processing: Automatically processes orders with the nearest partner restaurant, allowing users to try the suggested menu items without any hassle.

[0883] Examples:

[0884] Users record their daily health and emotional data using a smartwatch or smartphone. For example, assume that their heart rate is 75, their steps are 5,500, and their calories burned are 1,500 kcal. If the emotional data is detected as "stress," the server receives and analyzes this data and uses generative artificial intelligence to suggest a "salmon and avocado salad with a relaxing effect." The server also identifies the nearest partner restaurant using a location information API, and suggests "Restaurant A." The user is notified of this suggestion via a smartphone application, and the order is automatically processed.

[0885] Example prompt sentence:

[0886] "Today's user's emotional data is 'stress', and their health data is 'heart rate 75, steps taken 5500, calories burned 1500 kcal.' Based on this data, please suggest a lunch menu that will relieve stress and is under 1500 kcal."

[0887] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0888] Step 1:

[0889] Data collection

[0890] The device collects health data (heart rate, steps taken, calories burned) in real time from smartwatches and smartphones.

[0891] The device sends the collected health data to a server. It also uses the device's camera and microphone to collect the user's voice and facial expressions in real time, and sends this as emotional data to the server.

[0892] Input: User's heart rate, steps, calories burned, voice, and facial expressions.

[0893] Output: Health and emotion data sent to the server.

[0894] Step 2:

[0895] Data storage

[0896] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[0897] Input: Submitted health and emotion data.

[0898] Output: Health and emotion data stored in a database.

[0899] Specific operation: The server creates a new record in the database and records the health and emotion data.

[0900] Step 3:

[0901] Data analysis

[0902] The server analyzes the health data in the database and calculates the user's daily calorie and nutrient needs.

[0903] Based on the analysis results, the server generates meal menus for lunch, dinner, etc. that take into account the user's emotional state.

[0904] Input: Health data, personal information (age, gender, weight, height).

[0905] Output: Calculated calorie and nutrient needs, generated meal plan.

[0906] How it works: Based on health data, the server applies an energy expenditure model to calculate daily calorie needs, then uses a generative AI model to suggest meal plans.

[0907] Step 4:

[0908] Reflection of emotional state

[0909] The server uses an emotion engine to tailor the meal menu based on the user's emotional state, for example, suggesting relaxing meals to a stressed user.

[0910] Input: User emotion data (happiness, anger, sadness, stress, etc.).

[0911] Output: A tailored meal menu.

[0912] Specific operation: The server analyzes the emotional data using an emotion engine and fine-tunes the menus suggested by the generative AI model based on the results.

[0913] Step 5:

[0914] Store information provision

[0915] Based on the generated menu, the server provides information on the nearest store where the necessary ingredients can be purchased and information on restaurants that offer the menu.

[0916] Input: Generated food menu, user's current location.

[0917] Output: Retailer information, restaurant information.

[0918] Specific operation: The server uses the location information API to search for and provide appropriate store information based on the user's current location.

[0919] Step 6:

[0920] Notifications and Auto Ordering

[0921] The terminal notifies the user of the provided information and menu, and with the user's consent, automatically processes the order with the partner restaurant.

[0922] Input: Retailer information, Restaurant information, generated meal menu, User consent.

[0923] Output: Notification to the user and order information to the restaurant.

[0924] Specific operation: The smartphone application sends a notification to the user, and if the order is confirmed, the order information is automatically sent to the partner restaurant.

[0925] Example prompt sentence:

[0926] "Today's user's emotional data is 'stress', and their health data is 'heart rate 75, steps taken 5500, calories burned 1500 kcal.' Based on this data, please suggest a lunch menu that will relieve stress and is under 1500 kcal."

[0927] 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.

[0928] 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.

[0929] 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.

[0930] [Third embodiment]

[0931] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0932] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0933] 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).

[0934] 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.

[0935] 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.

[0936] 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).

[0937] 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. 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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."

[0943] The present invention is a system for effectively collecting and analyzing a user's health data to generate an optimal meal menu, identify ingredients and meal locations, and notify the user. This system is composed of a server, a terminal, and a user device.

[0944] Overall system flow and functions

[0945] 1. Health data collection

[0946] The terminal collects the user's health data (heart rate, steps taken, calories burned, etc.) from a wearable device (e.g., a smart watch) and sends it to a server.

[0947] Users wear wearable devices on a daily basis and continue to record their health data.

[0948] 2. Data Receipt and Analysis

[0949] The server receives the health data sent from the device and stores it in a database, which is updated in real time.

[0950] The server analyzes this data to assess the user's activity level and calories burned.

[0951] 3. Calculating calorie and nutrient needs

[0952] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[0953] Based on the calculation results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0954] 4. Generating a meal menu

[0955] The server uses generative artificial intelligence (AI) to generate an appropriate meal menu based on the calculated calorie and nutrient content.

[0956] The AI ​​learns from past data and user preferences to suggest personalized menus.

[0957] 5. Providing information about ingredients and stores

[0958] The server identifies information about the nearest store where ingredients needed for the generated menu can be purchased and information about restaurants that offer the menu.

[0959] The server uses location information services to search for the most suitable store based on the user's current location.

[0960] 6. Notice to Users

[0961] The server transmits the generated meal menu and store information to the terminal.

[0962] The device will send push notifications and display an interface to the user through the application.

[0963] Specific examples

[0964] morning

[0965] The user wakes up in the morning, puts on the wearable device, and begins their daily activities. The device continues to collect data during their commute.

[0966] The terminal continues to transmit the user's health data to the server.

[0967] The server receives and stores the data in real time and prepares it for analysis.

[0968] noon

[0969] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[0970] The server uses AI to generate healthy lunch menus, suggesting meals that balance calories and nutrients, such as chicken breast salad and brown rice.

[0971] The terminal notifies the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at the nearby supermarket A."

[0972] evening

[0973] The user stops by Supermarket A on the way home from work to buy some necessary ingredients, such as chicken breast and brown rice, or chooses to enjoy dinner at Restaurant B, which is suggested by the server.

[0974] The server provides information about nearby stores based on the user's location information and guides them to the best places to shop and eat.

[0975] night

[0976] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[0977] The device collects satisfaction feedback after the user consumes a meal to help improve future suggestions.

[0978] In this way, the system of the present invention supports users in managing their health by proposing appropriate meal plans based on their health data and supporting their implementation, while also providing a comprehensive solution for users to achieve a balanced diet without having to spend time and effort.

[0979] The processing flow will be explained below.

[0980] Step 1: Collect health data

[0981] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[0982] The device collects real-time health data such as the user's heart rate, number of steps taken, and calories burned.

[0983] The terminal periodically transmits the collected data to the server.

[0984] Step 2: Receiving and analyzing data

[0985] The server receives the health data sent from the terminal and stores it in a database.

[0986] The server analyzes the received health data and prepares to evaluate the user's activity level and calories burned.

[0987] Step 3: Calculate your calorie and nutrient needs

[0988] The server calculates the daily calorie and nutrient requirements based on the user's activity data and personal information (age, gender, weight, height).

[0989] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[0990] Step 4: Generate the meal menu

[0991] The server uses generative artificial intelligence (AI) to generate an appropriate meal menu based on the calculated calorie and nutrient content.

[0992] The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[0993] Step 5: Provide ingredients and store information

[0994] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[0995] The server uses the user's current location information to search for the most suitable store.

[0996] Step 6: Notify users

[0997] The server transmits the generated meal menu and store information to the terminal.

[0998] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[0999] Step 7: Gather feedback

[1000] The user prepares and consumes a meal based on the suggested menu, or enjoys a meal at the suggested restaurant.

[1001] After the user consumes the meal, the device collects satisfaction and feedback within the app.

[1002] The server stores the collected feedback to help improve future menu suggestions.

[1003] Through these processing steps, the system of the present invention proposes an appropriate meal menu based on the user's health data and supports the user in achieving it, significantly reducing the user's effort and providing a comprehensive solution for promoting a balanced diet.

[1004] Example 1

[1005] 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."

[1006] In modern society, the number of systems that assist individuals in managing their health is increasing. However, these systems often only record the user's activity data and do not adequately provide specific dietary recommendations appropriate for the user's health condition. Furthermore, existing systems do not provide sufficient information on where to purchase ingredients or restaurants, and do not fully satisfy user convenience. Given this background, there is a need for a system that can generate specific and personalized meal menus based on the user's health data, effectively notify the user, and also provide information on where to purchase ingredients and restaurants.

[1007] 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.

[1008] In this invention, the server includes means for acquiring health data, means for analyzing the health data and calculating the daily calorie and nutrient requirements, means for generating a meal menu based on the calculated calories and nutrient amounts, means for identifying ingredients required for the generated menu, means for providing information on the nearest stores or restaurants where the identified ingredients can be purchased, and means for notifying the user of the provided information and menu. This makes it possible to make specific and personalized meal suggestions based on the user's health data and further provide information on where to purchase ingredients and restaurants, thereby improving user convenience and effectively supporting health management.

[1009] "Health data" refers to various data (e.g., heart rate, number of steps, calories burned, etc.) regarding an individual's physical condition and activity level obtained from wearable devices such as smartwatches.

[1010] "Analysis" refers to the process of using statistical methods and algorithms to evaluate and calculate a user's activity level, calories burned, etc. based on the collected health data.

[1011] "Required calories" refers to the amount of energy a user should consume per day, and is calculated based on the user's basic information (age, gender, weight, height) and activity level.

[1012] "Nutrient amount" refers to the amount of each nutrient (e.g., carbohydrates, proteins, fats, etc.) that a user should consume per day.

[1013] A "meal menu" refers to a combination of appropriate meals that a user should consume, and is generated by a generative artificial intelligence based on the required calories and nutrient amounts.

[1014] "Identification" refers to the process of specifying the ingredients required for the generated meal menu and identifying where those ingredients can be purchased and where they are served.

[1015] "Sales point" refers to a commercial facility such as a supermarket or grocery store where users can purchase ingredients.

[1016] "Eating place" refers to a restaurant, cafe, or other eating and drinking establishment that serves a specific meal menu.

[1017] "Notification" refers to the act of communicating the generated meal menu, ingredients, and store information to the user, and is usually done through push notifications on the device or the application interface.

[1018] "User" refers to an individual who uses this system to manage their own health.

[1019] This invention is a system that effectively collects and analyzes a user's health data to generate an optimal meal menu, identifies where to purchase the necessary ingredients, and identifies restaurants that serve them, and notifies the user. Specifically, this system is composed of a server, a terminal, and a user device.

[1020] System configuration, hardware, and software

[1021] 1. Health data collection

[1022] Users wear smartwatches and other wearable devices on a daily basis to record health data such as heart rate, steps taken, and calories burned.

[1023] The terminal collects health data from the smartwatch using wireless communication such as Bluetooth and transmits it to a server at regular intervals. The specific hardware used for this is a mobile device such as a smartphone or tablet.

[1024] 2. Data Receipt and Analysis

[1025] The server uses a RESTful API to receive health data in JSON format sent from the device in real time.

[1026] The server stores the received data in a database (e.g., MySQL, PostgreSQL), and this storage process maintains data consistency through transaction control.

[1027] The server uses Python and the Pandas library to analyze the received health data and assess the user's activity level and calories burned.

[1028] 3. Calculating calorie and nutrient needs

[1029] The server retrieves personal information and health data such as the user's age, sex, weight, and height from the database.

[1030] The server uses estimation formulas such as the Harris-Benedict equation to calculate the user's daily calorie needs and the appropriate intake of each nutrient (carbohydrates, protein, and fat).

[1031] 4. Generating a meal menu

[1032] The server generates a prompt for a generative AI model (e.g., OpenAI GPT-4) based on the calorie and nutrient needs. The prompt looks like this:

[1033] "Based on the user's age, gender, weight, height, and activity level, they need 2500kcal, 150g protein, 300g carbohydrates, and 70g fat per day. Based on this, please suggest an appropriate lunch menu."

[1034] The server sends this prompt to a generative AI model, which then generates a meal menu for the user. The AI ​​learns the user's preferences and past data and suggests a personalized menu (e.g., chicken breast salad, brown rice, and broccoli).

[1035] 5. Providing information about ingredients and stores

[1036] The server generates a list of ingredients required for the generated menu, identifying specific ingredients such as "chicken breast, brown rice, and broccoli."

[1037] The server uses a geolocation service (e.g., Google Maps API) to search for information on the nearest supermarkets and restaurants based on the user's current location.

[1038] 6. Notice to Users

[1039] The server converts the generated menu and restaurant information into a specific format and sends it to the device using a notification service such as Firebase Cloud Messaging.

[1040] The device will notify the user via push notification or the application interface, saying, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at your local supermarket."

[1041] Specific examples

[1042] Morning Processing

[1043] Users wake up in the morning, put on their smartwatch, and the device continues to collect health data as they head off to work or school.

[1044] The device continuously transmits collected health data to the server, which receives and stores the data in real time and prepares it for analysis.

[1045] Daytime processing

[1046] Based on the data collected in the morning, the server evaluates the user's calorie consumption and calculates the calorie and nutrient requirements for the day.

[1047] The server uses a generative AI model to generate a healthy lunch menu, suggesting a balanced meal such as chicken breast salad and brown rice.

[1048] The device notifies the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at your local supermarket, Supermarket A."

[1049] Processing from evening to night

[1050] The user can stop by Supermarket A on the way home from work to buy the ingredients they need, or they can have dinner at Restaurant B, which the server suggests.

[1051] The server will guide the user to the best places to shop and eat based on their current location.

[1052] Users can cook the recommended menu items at home and eat a balanced diet, or enjoy the recommended menu items at a restaurant.

[1053] The device collects satisfaction feedback from users after the meal to help improve future suggestions.

[1054] In this way, the system proposes specific and personalized meal plans based on the user's health data and supports their implementation, improving user convenience and effectively supporting health management.

[1055] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1056] Step 1:

[1057] The user wears the smartwatch and starts their daily activities, which then record health data such as heart rate, number of steps, and calories burned in real time. The input data are heart rate, number of steps, calories burned, etc., obtained at each timestamp.

[1058] Step 2:

[1059] The device periodically collects health data from the smartwatch using Bluetooth. The input data is the raw health data collected from the smartwatch, and the output data is JSON-formatted data containing the collected health data. Specifically, the device pairs with the smartwatch and acquires data at regular intervals.

[1060] Step 3:

[1061] The device sends the collected health data to the server at regular intervals (e.g., every hour). The input data is the health data stored on the device, and the output data is the health data in JSON format sent to the server. Specifically, the device establishes a network connection and sends the data to the server via a RESTful API.

[1062] Step 4:

[1063] The server receives health data sent from the device via a RESTful API. The input data is the JSON format health data sent from the device, and the output data is the raw data stored in a database on the server. Upon receiving the data, the server executes specific operations to store it in a database (e.g., MySQL, PostgreSQL).

[1064] Step 5:

[1065] The server analyzes the received health data using Python's Pandas library. The input data is the health data stored in the database, and the output data is the analyzed evaluation data of activity level and calories burned. Specifically, it uses Pandas to generate a data frame and calculate various statistics.

[1066] Step 6:

[1067] The server uses the user's personal information, such as age, gender, weight, and height, and analyzed health data to calculate the daily calorie and nutrient requirements using estimation formulas such as the Harris-Benedict equation. The input data is the user's basic information and analyzed data, and the output data is the calorie and nutrient requirements. Specifically, the server uses a formula to calculate the basal metabolic rate and multiply it by an activity coefficient.

[1068] Step 7:

[1069] The server generates a prompt for a generative AI model (e.g., OpenAI GPT-4). The input data is the required calories and nutrient amounts, and the output data is the generated prompt. An example of a specific prompt is, "Based on the user's age, gender, weight, height, and activity level, the required daily calories are 2500 kcal, protein 150 g, carbohydrates 300 g, and fat 70 g. Based on these conditions, please suggest an appropriate lunch menu."

[1070] Step 8:

[1071] The server sends the generated prompt to the generative AI model to generate a personalized meal menu. The input data is the prompt, and the output data is the generated meal menu (e.g., chicken breast salad, brown rice, and broccoli). Specifically, the server sends a request to the AI ​​model via an API and retrieves the generated results.

[1072] Step 9:

[1073] The server generates a list of ingredients required for the generated menu and uses the Geolocation service to search for information on the nearest sales or dining locations. The input data is the meal menu and the user's location information, and the output data is information on the nearest store (e.g., supermarket A, restaurant B). The specific operation is to obtain location information using the Google Maps API or similar and search for store information.

[1074] Step 10:

[1075] The server sends the generated menu and restaurant information to the device. The input data is the menu and restaurant information, and the output data is the information sent to the device. Specifically, the server sends the data to the device via a notification service such as Firebase Cloud Messaging.

[1076] Step 11:

[1077] The device notifies the user of the information through push notifications or the application interface. The input data is the data sent from the server, and the output data is the notification to the user. The specific operation is to display to the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at nearby Supermarket A."

[1078] Specific actions

[1079] Morning: The user puts on the smartwatch and starts their activities.

[1080] Lunch: The server calculates the required calories and nutrients and generates an appropriate meal menu using a generative AI model.

[1081] Evening: The server provides the user with information about the nearest store based on the user's current location and notifies the user via the terminal.

[1082] Evening: The user consumes the recommended menu and the device collects satisfaction feedback.

[1083] Through this specific processing flow, the system provides personalized meal suggestions and purchasing information based on the user's health data, effectively supporting health management.

[1084] (Application example 1)

[1085] 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."

[1086] In modern society, busy lifestyles make it difficult to eat a balanced diet. Choosing and cooking healthy meals, as well as purchasing ingredients, can be particularly time-consuming. The objective of this invention is to provide a system that easily suggests personalized healthy meal menus based on the user's health data and allows for quick delivery.

[1087] 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.

[1088] In this invention, the server includes a means for acquiring health data, a means for analyzing the health data and calculating the daily calorie and nutrient requirements, a means for generating a meal menu based on the calculated calories and nutrient amounts, a means for providing information on delivery stores for delivering ingredients based on the generated menu, and a means for notifying the user of the provided information and menu, thereby enabling the user to easily select and order a personalized healthy meal menu and use the optimal delivery service.

[1089] "Health data" refers to information about a user's physical activity and physical condition, such as heart rate, number of steps taken, and calories burned.

[1090] "Analysis" refers to the process of evaluating a user's activity level and calorie expenditure based on collected health data, and calculating the amount of calories and nutrients they need.

[1091] "Required calories and nutrients" refers to the amount of energy and various nutrients that should be consumed per day based on personal information such as the user's age, gender, weight, and height.

[1092] A "meal menu" refers to the specific meal content and composition suggested based on the calculated calorie and nutrient requirements.

[1093] "Delivery store information" indicates information about the nearest delivery service that delivers the ingredients and dishes required for the generated meal menu.

[1094] "Notification" refers to the act of communicating the generated meal menu and delivery store information to the user by push notification or displaying it within the application.

[1095] A "wearable device" refers to a device worn on the user's body that collects health data, and typically includes a smartwatch or fitness tracker.

[1096] "Generative artificial intelligence" is an AI that has the ability to learn from past data and user preferences to generate personalized meal menus.

[1097] The present invention is a system for effectively collecting and analyzing a user's health data to generate an optimal meal menu, identify ingredients and meal locations, and notify the user. This system is composed of a server, a terminal, and a user device.

[1098] The overall system flow is as follows: first, a wearable device (e.g., a smartwatch) collects the user's health data and sends it to a server. Next, the server analyzes the received data and calculates the daily calorie and nutrient requirements. Based on these calculations, an appropriate meal menu is generated using generative artificial intelligence (AI). Next, information about nearby delivery restaurants is identified based on the generated menu. Finally, this information is notified to the user, who can then order delivery with one touch.

[1099] Hardware and software used

[1100] Wearable devices: Smartwatches and fitness trackers used to collect health data (e.g., Apple Watch, Fitbit).

[1101] Smartphone App: This Application provides an interface with the User.

[1102] Cloud server: A server for collecting data, analyzing it, generating menus, and searching for suggested restaurants (e.g., AWS, Google Cloud Platform).

[1103] Location information service: Geopy (a Python library) is used to obtain the user's location and search for the appropriate delivery store.

[1104] Data processing and calculation

[1105] 1. Health Data Collection:

[1106] The wearable device collects health data such as the user's heart rate, number of steps taken, and calories burned, and transmits it to a server via the terminal.

[1107] 2. Data Analysis:

[1108] The server receives the collected health data, stores and updates it in real time, and prepares it for analysis.

[1109] The server also uses an analytical engine to assess activity levels and calorie expenditures, and calculates daily calorie and nutrient needs.

[1110] 3. Menu generation:

[1111] The server uses generative artificial intelligence to generate meal menus based on the calculated calorie and nutrient content.

[1112] The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[1113] 4. Store Information Identification:

[1114] The server uses a location information service to identify ingredients required for the generated menu and store information that provides them.

[1115] This allows the system to search for and provide the optimal delivery store based on the user's current location.

[1116] 5. Notifications & Order Processing:

[1117] The server sends the generated meal menu and delivery store information to the terminal and notifies the user through the application.

[1118] Users can order meals with just one touch based on the information provided.

[1119] Specific examples

[1120] Example prompt sentence:

[1121] The user's name is Yamada Taro. He is a 35-year-old man, weighs 80 kg, and is 178 cm tall. This morning, his heart rate was 72, he took 8,500 steps, and he burned 450 calories. Please suggest the best dinner menu for Yamada Taro today. Also, please provide information about the nearest delivery store where he can order this menu.

[1122] In this way, the system of the present invention supports the user's health management by proposing appropriate meal menus based on the user's health data and supporting the execution of these menus, thereby enabling the user to easily achieve a balanced diet.

[1123] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1124] Step 1:

[1125] Health data collection

[1126] The user puts on a wearable device and begins their daily activities. The wearable device, such as a smartwatch, then collects health data such as heart rate, steps taken, and calories burned in real time. This data is then sent to a server via the device (smartphone).

[1127] Input: Health data (heart rate, steps, calories burned)

[1128] Output: Send data to the server

[1129] Step 2:

[1130] Receiving and storing data

[1131] The server receives the health data sent from the device and stores it in a database, which keeps the user's activity information updated in real time and ready for analysis.

[1132] Input: Health data from your device

[1133] Output: Health data stored in a database

[1134] Step 3:

[1135] Data analysis

[1136] The server analyzes the health data stored in the database to assess the user's activity level and calorie expenditure, and uses an analytical engine to calculate the daily calorie and nutrient requirements.

[1137] Input: Stored health data

[1138] Output: Calculated calorie and nutrient needs

[1139] Step 4:

[1140] Generate a meal menu

[1141] The server uses generative artificial intelligence to generate an appropriate meal plan based on the calculated calorie and nutrient content. The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[1142] Input: Calculated calorie and nutrient needs, user's past data, user's food preferences

[1143] Output: personalized meal menu

[1144] Step 5:

[1145] Identifying delivery store information

[1146] The server identifies nearby delivery stores that provide the ingredients needed for the generated menu. It uses location-based services (such as Geopy) to search for the optimal delivery store based on the user's current location.

[1147] Input: Food menu, user location

[1148] Output: Delivery store information

[1149] Step 6:

[1150] User Notification and Orders

[1151] The server sends the generated menu and delivery store information to the device and notifies the user through the application. The user can then place a delivery order with one touch based on the information provided. Once the order is complete, a confirmation notification is sent.

[1152] Input: Meal menu, delivery store information

[1153] Output: Push notification, delivery order

[1154] Step 7:

[1155] Gathering feedback

[1156] After a meal, users provide their satisfaction and feedback through the application, which is sent to the server and used to improve future menu suggestions.

[1157] Input: User feedback

[1158] Output: Feedback data stored on the server

[1159] 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.

[1160] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and a user device. Furthermore, it is characterized by using an emotion engine to recognize the user's emotional state and reflect it in the meal menu it generates.

[1161] Overall system flow and functions

[1162] 1. Health data collection

[1163] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[1164] The device collects health data such as the user's heart rate, number of steps taken, and calories burned in real time and sends it to a server.

[1165] 2. Collecting Emotional Data

[1166] Users use the cameras and microphones built into their smartphones and other devices.

[1167] The device detects the user's emotional state (e.g., joy, anger, sadness, stress, etc.) through voice recognition and facial expression recognition.

[1168] The terminal transmits the emotion data to the server.

[1169] 3. Data Receipt and Analysis

[1170] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[1171] The server analyzes this data to assess the user's activity level, calories burned, and emotional state.

[1172] 4. Calculating calorie and nutrient needs

[1173] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[1174] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[1175] 5. Generating a meal menu

[1176] The server uses generative artificial intelligence (AI) and an emotion engine to generate appropriate meal menus based on the calculated calorie and nutrient content.

[1177] The AI ​​learns from past data and the user's food preferences to suggest personalized menus that reflect the user's emotional state.

[1178] 6. Providing information about ingredients and stores

[1179] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[1180] The server uses the user's current location information to search for the most suitable store.

[1181] 7. Notice to Users

[1182] The server transmits the generated meal menu and store information to the terminal.

[1183] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[1184] Specific examples

[1185] morning

[1186] The user wakes up in the morning, puts on the wearable device, and begins their daily activities, with the camera and microphone of their smartphone enabled.

[1187] The device continuously collects health data (e.g., heart rate, steps, calories burned) and emotional data (emotional state through voice recognition and facial expression recognition) in real time.

[1188] The server receives and stores the data in real time and prepares it for analysis.

[1189] noon

[1190] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[1191] The server uses AI and an emotion engine to generate lunch menus that reflect your emotional state while also taking into account your health status. For example, if you're feeling stressed, it will suggest meals that include foods that have a relaxing effect.

[1192] The terminal notifies the user, "For today's lunch, a relaxing salmon and avocado salad is recommended. You can purchase the ingredients at the nearby supermarket A."

[1193] evening

[1194] The user can stop by Supermarket A on the way home from work to purchase the recommended ingredients, or choose to enjoy dinner at Restaurant B, which is suggested by the server.

[1195] The server provides information about nearby stores based on the user's location information and guides them to the best places to shop and eat.

[1196] night

[1197] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[1198] After the user consumes the meal, the device collects satisfaction feedback to help improve future suggestions.

[1199] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[1200] The processing flow will be explained below.

[1201] Step 1: Collecting health and emotional data

[1202] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[1203] Users use the cameras and microphones built into their smartphones and other devices.

[1204] The device collects real-time health data such as the user's heart rate, number of steps taken, and calories burned.

[1205] The device detects the user's emotional state (e.g., joy, anger, sadness, stress, etc.) through voice recognition and facial expression recognition.

[1206] The terminal periodically transmits the collected health data and emotion data to the server.

[1207] Step 2: Receiving and storing data

[1208] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[1209] Step 3: Analyze the data

[1210] The server analyzes the stored health data and evaluates the user's activity level and calories burned.

[1211] The server analyzes the stored emotional data and assesses the user's emotional state.

[1212] Step 4: Calculate your calorie and nutrient needs

[1213] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[1214] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[1215] Step 5: Generate the meal menu

[1216] The server uses generative artificial intelligence (AI) and an emotion engine to generate appropriate meal menus based on the calculated calorie and nutrient content.

[1217] The AI ​​learns from past data and the user's food preferences to suggest personalized menus that reflect the user's emotional state (e.g., a menu that includes foods that have a relaxing effect when stressed).

[1218] Step 6: Provide ingredients and store information

[1219] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[1220] The server uses the user's current location information to search for the most suitable store.

[1221] Step 7: Notify users

[1222] The server transmits the generated meal menu and store information to the terminal.

[1223] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[1224] Step 8: Gather feedback

[1225] The user prepares and consumes a meal based on the suggested menu, or enjoys a meal at the suggested restaurant.

[1226] After the user consumes the meal, the device collects satisfaction and feedback within the app.

[1227] The server stores the collected feedback to help improve future menu suggestions.

[1228] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[1229] Example 2

[1230] 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."

[1231] In today's world, achieving a balanced diet while efficiently managing one's health and emotions is a major challenge for many people. However, conventional systems have had difficulty integrating health data and emotional states to generate appropriate meal plans and provide them to users. Furthermore, they have not been able to propose specific meal plans based on the user's current emotional state. As a result, it has been impossible to provide an optimal meal plan for each individual user's health and emotional state, making effective health and emotional management difficult. Therefore, there is a need for a system that can combine and analyze health data and emotional states to generate optimal meal plans and provide them to users.

[1232] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring health data and emotional state, a means for analyzing the health data and emotional state and calculating the daily required calories and nutrient amount, and a means for generating a meal menu based on the calculated calories and nutrient amount. This makes it possible to comprehensively analyze the user's health data and emotional state and provide an optimal meal menu for each individual user. Furthermore, since a menu is proposed that takes the user's emotional state into consideration based on the acquired data, the user's health and emotional management can be performed efficiently and effectively.

[1233] "Health data" refers to physiological information obtained from wearable devices, such as a user's heart rate, number of steps taken, and calories burned.

[1234] The "emotional state" refers to the user's psychological state, such as joy, anger, sadness, or stress, and is data acquired through a voice recognition or facial expression recognition device.

[1235] "Required calories and nutrients" refers to the amount of energy a user should consume in a day, as well as the necessary vitamins, minerals, proteins, and other nutrients.

[1236] A "meal menu" is a specific meal combination or recipe that allows a user to consume the required amount of calories and nutrients per day.

[1237] "Store information" refers to related information such as the location, contact information, and opening hours of stores where ingredients necessary to create the generated meal menu can be purchased.

[1238] "Restaurant information" refers to related information such as the location, contact information, and business hours of the restaurant that offers the generated meal menu.

[1239] "Generative AI" is an AI technology that generates optimal meal plans based on a user's health data and emotional state.

[1240] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and a user device. Furthermore, it is characterized by using an emotion engine to recognize the user's emotional state and reflect it in the meal menu it generates.

[1241] Hardware and software used

[1242] Wearable devices (e.g., smartwatches): collect health data such as the user's heart rate, steps taken, and calories burned.

[1243] Speech recognizer (e.g., Google Cloud Speech-to-Text): obtains the emotional state from the user's voice.

[1244] Facial expression recognition device (e.g., Face++ API): Obtains the user's emotional state from their facial expressions.

[1245] Database system (e.g., MySQL): to store the collected health and emotion data.

[1246] Data analysis software (e.g., Python, Pandas): to analyze health and emotion data.

[1247] Machine learning model (e.g., Scikit-learn): Calculates daily calorie and nutrient requirements based on the analysis results.

[1248] Generative AI (e.g., OpenAI's GPT-3): Generates personalized meal plans based on calorie and nutrient needs.

[1249] Map information service (e.g., Google Maps API): Provides information on the nearest stores and restaurants.

[1250] Notifications (e.g., Firebase Cloud Messaging): Notify users of suggested menu items and store availability information.

[1251] Example of system operation

[1252] morning

[1253] The user wakes up in the morning, puts on the wearable device, and begins their daily activities, with the camera and microphone of their smartphone enabled.

[1254] The device continuously collects health data (heart rate, steps, calories burned) and emotional data (emotional state through voice recognition and facial expression recognition) in real time.

[1255] The server receives the data in real time and stores it in a database for analysis.

[1256] noon

[1257] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[1258] The server uses AI and an emotion engine to generate lunch menus that reflect your emotional state while also taking into account your health status. For example, if you're feeling stressed, it will suggest meals that include foods that have a relaxing effect.

[1259] The device notifies the user, "Today's lunch is recommended as a relaxing salmon and avocado salad. You can purchase the ingredients at your local supermarket."

[1260] evening

[1261] The user can choose to stop by supermarket A on the way home from work and purchase the recommended ingredients, or to enjoy dinner at restaurant B suggested by the server.

[1262] The server uses the user's location information to provide information about nearby stores and guides them to the best places to shop or eat.

[1263] night

[1264] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[1265] The device collects satisfaction feedback after the user consumes the meal to help improve future suggestions.

[1266] Prompt Sentence Examples

[1267] "Generate a relaxing lunch menu based on the user's health data and emotional state. For example, suggest a menu suitable for when your heart rate is high and you're stressed."

[1268] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[1269] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1270] Program processing steps

[1271] Step 1:

[1272] A user puts on a wearable device (e.g., a smartwatch) and begins their daily activities, which starts collecting health data. The input is the user's biometric data (heart rate, steps, calories burned), and the output is this real-time data.

[1273] Specifically, the wearable device measures biosignals, converts them into data format, and transmits them to the terminal.

[1274] Step 2:

[1275] The terminal acquires health data from the wearable device in real time. The input is the health data sent from the wearable device, and the output is the health data in a format that is sent to the server.

[1276] Specifically, the device receives various sensor data via Bluetooth or Wi-Fi and packetizes it for transmission to the server.

[1277] Step 3:

[1278] Users turn on the camera and microphone on their smartphones to record emotional data on a daily basis. The input is the user's face and voice, and the output is raw data based on these.

[1279] Specifically, the smartphone's camera and microphone collect the user's facial expressions and voice.

[1280] Step 4:

[1281] The device acquires the user's emotional state through voice and facial recognition. The input is recorded or photographed data, and the output is data indicating the emotional state (e.g., joy, anger, sadness, stress, etc.).

[1282] Specifically, the built-in voice recognition software (e.g., Google Cloud Speech-to-Text) and facial expression recognition software (e.g., Face++ API) analyze the recorded and image data to generate emotional data.

[1283] Step 5:

[1284] The terminal transmits the collected health data and emotion data to the server. The input is the collected health data and emotion data, and the output is data packet information containing these data.

[1285] Specifically, the device creates and sends an HTTP request to send data to the server's API endpoint.

[1286] Step 6:

[1287] The server receives the health and emotion data sent from the device. The input is the data packet sent from the device, and the output is the data stored in a database (e.g., MySQL) on the server.

[1288] Specifically, the server analyzes the received data, converts it into a valid format, and stores it in a database.

[1289] Step 7:

[1290] The server analyzes the received data and evaluates the user's activity, calorie consumption, and emotional state. The input is health data and emotional data, and the output is the evaluated data (e.g., calories consumed, nutritional status, emotional state).

[1291] Specifically, a data analysis script is executed using Python and Pandas.

[1292] Step 8:

[1293] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height). The input is the health data and personal information, and the output is the calculated daily calorie and nutrient requirements.

[1294] Specifically, a machine learning model using Scikit-learn is executed to calculate individual calorie and nutrient requirements.

[1295] Step 9:

[1296] The server uses generative artificial intelligence (AI) and an emotion engine to generate an appropriate meal plan based on the calculated calorie and nutrient content. The input is the calorie needs, nutrient content, and emotion data, and the output is a personalized meal plan.

[1297] Specifically, OpenAI's GPT-3 creates prompts based on user data and generates an appropriate meal menu.

[1298] Step 10:

[1299] The server identifies the nearest store where the ingredients needed to create the generated menu can be purchased, and the restaurant that serves that menu. The input is the generated meal menu and the user's location information, and the output is the optimal store and restaurant information.

[1300] Specifically, the Google Maps API searches for the nearest store information based on the user's location information.

[1301] Step 11:

[1302] The server sends the generated meal menu and store information to the terminal. The input is the generated meal menu and store information, and the output is notification information sent to the user terminal.

[1303] Specifically, notifications are sent to the device using Firebase Cloud Messaging.

[1304] Step 12:

[1305] The terminal uses the notification function to notify the user of suggested menus and store information where purchases can be made. The input is notification information sent from the server, and the output is visual and audible notifications to the user.

[1306] Specifically, the suggestions will be displayed in the smartphone's notification center, and in some cases an audio notification will also be given.

[1307] Through this series of processing steps, the user is presented with an optimal meal menu based on their individual health data and emotional state, enabling them to manage their health and emotions on a daily basis.

[1308] (Application example 2)

[1309] 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."

[1310] In modern society, maintaining health and managing emotions are important, but balancing these two is difficult in our busy daily lives. Selecting an appropriate meal plan based on an individual's health and emotional state is particularly challenging. Furthermore, finding the optimal shopping and dining locations for a suggested meal plan can be time-consuming. This invention aims to enable users to eat healthy and emotionally satisfying meals by collecting and analyzing a user's health and emotional state data, and automatically generating and providing appropriate meal plans and information on nearby stores and restaurants.

[1311] 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.

[1312] In this invention, the server includes means for acquiring health data, means for analyzing the health data and calculating daily calorie needs and nutrient amounts, means for generating a meal menu based on the calculated calories and nutrient amounts, means for providing store information or restaurant information for purchasing ingredients based on the generated menu, means for notifying the user of the provided information and menu, means for analyzing the user's emotional state and adjusting the meal menu based on the emotional state using an emotion engine, and means for automatically processing an order with the nearest affiliated restaurant based on the meal menu. This allows users to easily obtain a meal menu that is optimal for their health and emotional state, and to efficiently purchase ingredients and eat at restaurants based on the meal menu.

[1313] "Health data" refers to data that indicates the user's physical condition, and includes physiological indicators such as heart rate, number of steps, calories burned, weight, and blood pressure.

[1314] The "emotional state" is a state that indicates the emotions and moods that the user is currently feeling, and is information that includes emotional responses such as joy, anger, sadness, stress, and relaxation.

[1315] An "emotion engine" refers to artificial intelligence and algorithms that analyze users' emotional data and adjust meal menus and other suggestions based on the results.

[1316] A "meal menu" is a series of dishes or food combinations suggested for consumption by a user, generated based on calorie and nutrient content.

[1317] "Store information" refers to information about stores where the user can purchase ingredients needed for the proposed meal menu.

[1318] "Restaurant information" refers to information about restaurants that offer the meal menu suggested by the user.

[1319] "Notification methods" refers to digital and analog methods, such as smartphone applications and emails, used to inform users about suggested meal menus and places to buy and eat.

[1320] "Generative AI" refers to a general term for AI technology that learns from a user's past data and preferences and then makes personalized suggestions based on that information.

[1321] "Affiliated restaurant" refers to a restaurant that provides services in cooperation with this system, and is a place that provides suggested menus to users.

[1322] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and the user's device. A notable feature is that it uses an emotion engine to reflect the user's emotional state in the menu. It is also possible to automatically process orders from nearby partner restaurants based on the proposed meal menu.

[1323] Program processing explanation

[1324] Hardware and software usage:

[1325] Flask: Used to provide the web server and API endpoints, through which health and emotion data is collected and analysis results are sent.

[1326] Python: A programming language used for data processing and server logic implementation. Used to analyze health and emotion data, generate meal menus, and provide location information.

[1327] Database: Store user health data, emotion data, and generated menu information in a database such as MongoDB or MySQL.

[1328] Generative AI: Uses AI models (e.g., GPT-3, BERT, etc.) to generate meal menus based on the user's health and emotional data.

[1329] Location API: Uses location services such as Google Maps API to find the nearest partner restaurant based on the user's current location.

[1330] Data processing and calculation flow:

[1331] The server uses the following means:

[1332] 1. Health data collection: Health data is collected from user devices such as smartwatches and smartphones. This data includes heart rate, steps taken, calories burned, etc.

[1333] 2. Data analysis method: Analyzes collected health data and calculates daily calorie and nutrient needs, taking into account personal information such as the user's age, gender, weight, and height.

[1334] 3. Meal menu generation: Based on the calculated calorie and nutrient content, an optimal meal menu is generated using generative artificial intelligence. Furthermore, an emotion engine is used to reflect the user's emotional state in the meal menu.

[1335] 4. Store information provision means: Based on the generated menu, information on the nearest stores and restaurants where necessary ingredients can be purchased is provided, and this information is also notified to the user.

[1336] 5. Automatic Order Processing: Automatically processes orders with the nearest partner restaurant, allowing users to try the suggested menu items without any hassle.

[1337] Examples:

[1338] Users record their daily health and emotional data using a smartwatch or smartphone. For example, assume that their heart rate is 75, their steps are 5,500, and their calories burned are 1,500 kcal. If the emotional data is detected as "stress," the server receives and analyzes this data and uses generative artificial intelligence to suggest a "salmon and avocado salad with a relaxing effect." The server also identifies the nearest partner restaurant using a location information API, and suggests "Restaurant A." The user is notified of this suggestion via a smartphone application, and the order is automatically processed.

[1339] Example prompt sentence:

[1340] "Today's user's emotional data is 'stress', and their health data is 'heart rate 75, steps taken 5500, calories burned 1500 kcal.' Based on this data, please suggest a lunch menu that will relieve stress and is under 1500 kcal."

[1341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1342] Step 1:

[1343] Data collection

[1344] The device collects health data (heart rate, steps taken, calories burned) in real time from smartwatches and smartphones.

[1345] The device sends the collected health data to a server. It also uses the device's camera and microphone to collect the user's voice and facial expressions in real time, and sends this as emotional data to the server.

[1346] Input: User's heart rate, steps, calories burned, voice, and facial expressions.

[1347] Output: Health and emotion data sent to the server.

[1348] Step 2:

[1349] Data storage

[1350] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[1351] Input: Submitted health and emotion data.

[1352] Output: Health and emotion data stored in a database.

[1353] Specific operation: The server creates a new record in the database and records the health and emotion data.

[1354] Step 3:

[1355] Data analysis

[1356] The server analyzes the health data in the database and calculates the user's daily calorie and nutrient needs.

[1357] Based on the analysis results, the server generates meal menus for lunch, dinner, etc. that take into account the user's emotional state.

[1358] Input: Health data, personal information (age, gender, weight, height).

[1359] Output: Calculated calorie and nutrient needs, generated meal plan.

[1360] How it works: Based on health data, the server applies an energy expenditure model to calculate daily calorie needs, then uses a generative AI model to suggest meal plans.

[1361] Step 4:

[1362] Reflection of emotional state

[1363] The server uses an emotion engine to tailor the meal menu based on the user's emotional state, for example, suggesting relaxing meals to a stressed user.

[1364] Input: User emotion data (happiness, anger, sadness, stress, etc.).

[1365] Output: A tailored meal menu.

[1366] Specific operation: The server analyzes the emotional data using an emotion engine and fine-tunes the menus suggested by the generative AI model based on the results.

[1367] Step 5:

[1368] Store information provision

[1369] Based on the generated menu, the server provides information on the nearest store where the necessary ingredients can be purchased and information on restaurants that offer the menu.

[1370] Input: Generated food menu, user's current location.

[1371] Output: Retailer information, restaurant information.

[1372] Specific operation: The server uses the location information API to search for and provide appropriate store information based on the user's current location.

[1373] Step 6:

[1374] Notifications and Auto Ordering

[1375] The terminal notifies the user of the provided information and menu, and with the user's consent, automatically processes the order with the partner restaurant.

[1376] Input: Retailer information, Restaurant information, generated meal menu, User consent.

[1377] Output: Notification to the user and order information to the restaurant.

[1378] Specific operation: The smartphone application sends a notification to the user, and if the order is confirmed, the order information is automatically sent to the partner restaurant.

[1379] Example prompt sentence:

[1380] "Today's user's emotional data is 'stress', and their health data is 'heart rate 75, steps taken 5500, calories burned 1500 kcal.' Based on this data, please suggest a lunch menu that will relieve stress and is under 1500 kcal."

[1381] 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.

[1382] 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.

[1383] 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.

[1384] [Fourth embodiment]

[1385] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1386] 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.

[1387] 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).

[1388] 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.

[1389] 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.

[1390] 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).

[1391] 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. 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.

[1392] 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.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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."

[1398] The present invention is a system for effectively collecting and analyzing a user's health data to generate an optimal meal menu, identify ingredients and meal locations, and notify the user. This system is composed of a server, a terminal, and a user device.

[1399] Overall system flow and functions

[1400] 1. Health data collection

[1401] The terminal collects the user's health data (heart rate, steps taken, calories burned, etc.) from a wearable device (e.g., a smart watch) and sends it to a server.

[1402] Users wear wearable devices on a daily basis and continue to record their health data.

[1403] 2. Data Receipt and Analysis

[1404] The server receives the health data sent from the device and stores it in a database, which is updated in real time.

[1405] The server analyzes this data to assess the user's activity level and calories burned.

[1406] 3. Calculating calorie and nutrient needs

[1407] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[1408] Based on the calculation results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[1409] 4. Generating a meal menu

[1410] The server uses generative artificial intelligence (AI) to generate an appropriate meal menu based on the calculated calorie and nutrient content.

[1411] The AI ​​learns from past data and user preferences to suggest personalized menus.

[1412] 5. Providing information about ingredients and stores

[1413] The server identifies information about the nearest store where ingredients needed for the generated menu can be purchased and information about restaurants that offer the menu.

[1414] The server uses location information services to search for the most suitable store based on the user's current location.

[1415] 6. Notice to Users

[1416] The server transmits the generated meal menu and store information to the terminal.

[1417] The device will send push notifications and display an interface to the user through the application.

[1418] Specific examples

[1419] morning

[1420] The user wakes up in the morning, puts on the wearable device, and begins their daily activities. The device continues to collect data during their commute.

[1421] The terminal continues to transmit the user's health data to the server.

[1422] The server receives and stores the data in real time and prepares it for analysis.

[1423] noon

[1424] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[1425] The server uses AI to generate healthy lunch menus, suggesting meals that balance calories and nutrients, such as chicken breast salad and brown rice.

[1426] The terminal notifies the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at the nearby supermarket A."

[1427] evening

[1428] The user stops by Supermarket A on the way home from work to buy some necessary ingredients, such as chicken breast and brown rice, or chooses to enjoy dinner at Restaurant B, which is suggested by the server.

[1429] The server provides information about nearby stores based on the user's location information and guides them to the best places to shop and eat.

[1430] night

[1431] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[1432] The device collects satisfaction feedback after the user consumes a meal to help improve future suggestions.

[1433] In this way, the system of the present invention supports users in managing their health by proposing appropriate meal plans based on their health data and supporting their implementation, while also providing a comprehensive solution for users to achieve a balanced diet without having to spend time and effort.

[1434] The processing flow will be explained below.

[1435] Step 1: Collect health data

[1436] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[1437] The device collects real-time health data such as the user's heart rate, number of steps taken, and calories burned.

[1438] The terminal periodically transmits the collected data to the server.

[1439] Step 2: Receiving and analyzing data

[1440] The server receives the health data sent from the terminal and stores it in a database.

[1441] The server analyzes the received health data and prepares to evaluate the user's activity level and calories burned.

[1442] Step 3: Calculate your calorie and nutrient needs

[1443] The server calculates the daily calorie and nutrient requirements based on the user's activity data and personal information (age, gender, weight, height).

[1444] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[1445] Step 4: Generate the meal menu

[1446] The server uses generative artificial intelligence (AI) to generate an appropriate meal menu based on the calculated calorie and nutrient content.

[1447] The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[1448] Step 5: Provide ingredients and store information

[1449] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[1450] The server uses the user's current location information to search for the most suitable store.

[1451] Step 6: Notify users

[1452] The server transmits the generated meal menu and store information to the terminal.

[1453] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[1454] Step 7: Gather feedback

[1455] The user prepares and consumes a meal based on the suggested menu, or enjoys a meal at the suggested restaurant.

[1456] After the user consumes the meal, the device collects satisfaction and feedback within the app.

[1457] The server stores the collected feedback to help improve future menu suggestions.

[1458] Through these processing steps, the system of the present invention proposes an appropriate meal menu based on the user's health data and supports the user in achieving it, significantly reducing the user's effort and providing a comprehensive solution for promoting a balanced diet.

[1459] Example 1

[1460] 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."

[1461] In modern society, the number of systems that assist individuals in managing their health is increasing. However, these systems often only record the user's activity data and do not adequately provide specific dietary recommendations appropriate for the user's health condition. Furthermore, existing systems do not provide sufficient information on where to purchase ingredients or restaurants, and do not fully satisfy user convenience. Given this background, there is a need for a system that can generate specific and personalized meal menus based on the user's health data, effectively notify the user, and also provide information on where to purchase ingredients and restaurants.

[1462] 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.

[1463] In this invention, the server includes means for acquiring health data, means for analyzing the health data and calculating the daily calorie and nutrient requirements, means for generating a meal menu based on the calculated calories and nutrient amounts, means for identifying ingredients required for the generated menu, means for providing information on the nearest stores or restaurants where the identified ingredients can be purchased, and means for notifying the user of the provided information and menu. This makes it possible to make specific and personalized meal suggestions based on the user's health data and further provide information on where to purchase ingredients and restaurants, thereby improving user convenience and effectively supporting health management.

[1464] "Health data" refers to various data (e.g., heart rate, number of steps, calories burned, etc.) regarding an individual's physical condition and activity level obtained from wearable devices such as smartwatches.

[1465] "Analysis" refers to the process of using statistical methods and algorithms to evaluate and calculate a user's activity level, calories burned, etc. based on the collected health data.

[1466] "Required calories" refers to the amount of energy a user should consume per day, and is calculated based on the user's basic information (age, gender, weight, height) and activity level.

[1467] "Nutrient amount" refers to the amount of each nutrient (e.g., carbohydrates, proteins, fats, etc.) that a user should consume per day.

[1468] A "meal menu" refers to a combination of appropriate meals that a user should consume, and is generated by a generative artificial intelligence based on the required calories and nutrient amounts.

[1469] "Identification" refers to the process of specifying the ingredients required for the generated meal menu and identifying where those ingredients can be purchased and where they are served.

[1470] "Sales point" refers to a commercial facility such as a supermarket or grocery store where users can purchase ingredients.

[1471] "Eating place" refers to a restaurant, cafe, or other eating and drinking establishment that serves a specific meal menu.

[1472] "Notification" refers to the act of communicating the generated meal menu, ingredients, and store information to the user, and is usually done through push notifications on the device or the application interface.

[1473] "User" refers to an individual who uses this system to manage their own health.

[1474] This invention is a system that effectively collects and analyzes a user's health data to generate an optimal meal menu, identifies where to purchase the necessary ingredients, and identifies restaurants that serve them, and notifies the user. Specifically, this system is composed of a server, a terminal, and a user device.

[1475] System configuration, hardware, and software

[1476] 1. Health data collection

[1477] Users wear smartwatches and other wearable devices on a daily basis to record health data such as heart rate, steps taken, and calories burned.

[1478] The terminal collects health data from the smartwatch using wireless communication such as Bluetooth and transmits it to a server at regular intervals. The specific hardware used for this is a mobile device such as a smartphone or tablet.

[1479] 2. Data Receipt and Analysis

[1480] The server uses a RESTful API to receive health data in JSON format sent from the device in real time.

[1481] The server stores the received data in a database (e.g., MySQL, PostgreSQL), and this storage process maintains data consistency through transaction control.

[1482] The server uses Python and the Pandas library to analyze the received health data and assess the user's activity level and calories burned.

[1483] 3. Calculating calorie and nutrient needs

[1484] The server retrieves personal information and health data such as the user's age, sex, weight, and height from the database.

[1485] The server uses estimation formulas such as the Harris-Benedict equation to calculate the user's daily calorie needs and the appropriate intake of each nutrient (carbohydrates, protein, and fat).

[1486] 4. Generating a meal menu

[1487] The server generates a prompt for a generative AI model (e.g., OpenAI GPT-4) based on the calorie and nutrient needs. The prompt looks like this:

[1488] "Based on the user's age, gender, weight, height, and activity level, they need 2500kcal, 150g protein, 300g carbohydrates, and 70g fat per day. Based on this, please suggest an appropriate lunch menu."

[1489] The server sends this prompt to a generative AI model, which then generates a meal menu for the user. The AI ​​learns the user's preferences and past data and suggests a personalized menu (e.g., chicken breast salad, brown rice, and broccoli).

[1490] 5. Providing information about ingredients and stores

[1491] The server generates a list of ingredients required for the generated menu, identifying specific ingredients such as "chicken breast, brown rice, and broccoli."

[1492] The server uses a geolocation service (e.g., Google Maps API) to search for information on the nearest supermarkets and restaurants based on the user's current location.

[1493] 6. Notice to Users

[1494] The server converts the generated menu and restaurant information into a specific format and sends it to the device using a notification service such as Firebase Cloud Messaging.

[1495] The device will notify the user via push notification or the application interface, saying, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at your local supermarket."

[1496] Specific examples

[1497] Morning Processing

[1498] Users wake up in the morning, put on their smartwatch, and the device continues to collect health data as they head off to work or school.

[1499] The device continuously transmits collected health data to the server, which receives and stores the data in real time and prepares it for analysis.

[1500] Daytime processing

[1501] Based on the data collected in the morning, the server evaluates the user's calorie consumption and calculates the calorie and nutrient requirements for the day.

[1502] The server uses a generative AI model to generate a healthy lunch menu, suggesting a balanced meal such as chicken breast salad and brown rice.

[1503] The device notifies the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at your local supermarket, Supermarket A."

[1504] Processing from evening to night

[1505] The user can stop by Supermarket A on the way home from work to buy the ingredients they need, or they can have dinner at Restaurant B, which the server suggests.

[1506] The server will guide the user to the best places to shop and eat based on their current location.

[1507] Users can cook the recommended menu items at home and eat a balanced diet, or enjoy the recommended menu items at a restaurant.

[1508] The device collects satisfaction feedback from users after the meal to help improve future suggestions.

[1509] In this way, the system proposes specific and personalized meal plans based on the user's health data and supports their implementation, improving user convenience and effectively supporting health management.

[1510] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1511] Step 1:

[1512] The user wears the smartwatch and starts their daily activities, which then record health data such as heart rate, number of steps, and calories burned in real time. The input data are heart rate, number of steps, calories burned, etc., obtained at each timestamp.

[1513] Step 2:

[1514] The device periodically collects health data from the smartwatch using Bluetooth. The input data is the raw health data collected from the smartwatch, and the output data is JSON-formatted data containing the collected health data. Specifically, the device pairs with the smartwatch and acquires data at regular intervals.

[1515] Step 3:

[1516] The device sends the collected health data to the server at regular intervals (e.g., every hour). The input data is the health data stored on the device, and the output data is the health data in JSON format sent to the server. Specifically, the device establishes a network connection and sends the data to the server via a RESTful API.

[1517] Step 4:

[1518] The server receives health data sent from the device via a RESTful API. The input data is the JSON format health data sent from the device, and the output data is the raw data stored in a database on the server. Upon receiving the data, the server executes specific operations to store it in a database (e.g., MySQL, PostgreSQL).

[1519] Step 5:

[1520] The server analyzes the received health data using Python's Pandas library. The input data is the health data stored in the database, and the output data is the analyzed evaluation data of activity level and calories burned. Specifically, it uses Pandas to generate a data frame and calculate various statistics.

[1521] Step 6:

[1522] The server uses the user's personal information, such as age, gender, weight, and height, and analyzed health data to calculate the daily calorie and nutrient requirements using estimation formulas such as the Harris-Benedict equation. The input data is the user's basic information and analyzed data, and the output data is the calorie and nutrient requirements. Specifically, the server uses a formula to calculate the basal metabolic rate and multiply it by an activity coefficient.

[1523] Step 7:

[1524] The server generates a prompt for a generative AI model (e.g., OpenAI GPT-4). The input data is the required calories and nutrient amounts, and the output data is the generated prompt. An example of a specific prompt is, "Based on the user's age, gender, weight, height, and activity level, the required daily calories are 2500 kcal, protein 150 g, carbohydrates 300 g, and fat 70 g. Based on these conditions, please suggest an appropriate lunch menu."

[1525] Step 8:

[1526] The server sends the generated prompt to the generative AI model to generate a personalized meal menu. The input data is the prompt, and the output data is the generated meal menu (e.g., chicken breast salad, brown rice, and broccoli). Specifically, the server sends a request to the AI ​​model via an API and retrieves the generated results.

[1527] Step 9:

[1528] The server generates a list of ingredients required for the generated menu and uses the Geolocation service to search for information on the nearest sales or dining locations. The input data is the meal menu and the user's location information, and the output data is information on the nearest store (e.g., supermarket A, restaurant B). The specific operation is to obtain location information using the Google Maps API or similar and search for store information.

[1529] Step 10:

[1530] The server sends the generated menu and restaurant information to the device. The input data is the menu and restaurant information, and the output data is the information sent to the device. Specifically, the server sends the data to the device via a notification service such as Firebase Cloud Messaging.

[1531] Step 11:

[1532] The device notifies the user of the information through push notifications or the application interface. The input data is the data sent from the server, and the output data is the notification to the user. The specific operation is to display to the user, "Today's lunch is recommended to be chicken breast salad and brown rice. You can purchase the ingredients at nearby Supermarket A."

[1533] Specific actions

[1534] Morning: The user puts on the smartwatch and starts their activities.

[1535] Lunch: The server calculates the required calories and nutrients and generates an appropriate meal menu using a generative AI model.

[1536] Evening: The server provides the user with information about the nearest store based on the user's current location and notifies the user via the terminal.

[1537] Evening: The user consumes the recommended menu and the device collects satisfaction feedback.

[1538] Through this specific processing flow, the system provides personalized meal suggestions and purchasing information based on the user's health data, effectively supporting health management.

[1539] (Application example 1)

[1540] 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."

[1541] In modern society, busy lifestyles make it difficult to eat a balanced diet. Choosing and cooking healthy meals, as well as purchasing ingredients, can be particularly time-consuming. The objective of this invention is to provide a system that easily suggests personalized healthy meal menus based on the user's health data and allows for quick delivery.

[1542] 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.

[1543] In this invention, the server includes a means for acquiring health data, a means for analyzing the health data and calculating the daily calorie and nutrient requirements, a means for generating a meal menu based on the calculated calories and nutrient amounts, a means for providing information on delivery stores for delivering ingredients based on the generated menu, and a means for notifying the user of the provided information and menu, thereby enabling the user to easily select and order a personalized healthy meal menu and use the optimal delivery service.

[1544] "Health data" refers to information about a user's physical activity and physical condition, such as heart rate, number of steps taken, and calories burned.

[1545] "Analysis" refers to the process of evaluating a user's activity level and calorie expenditure based on collected health data, and calculating the amount of calories and nutrients they need.

[1546] "Required calories and nutrients" refers to the amount of energy and various nutrients that should be consumed per day based on personal information such as the user's age, gender, weight, and height.

[1547] A "meal menu" refers to the specific meal content and composition suggested based on the calculated calorie and nutrient requirements.

[1548] "Delivery store information" indicates information about the nearest delivery service that delivers the ingredients and dishes required for the generated meal menu.

[1549] "Notification" refers to the act of communicating the generated meal menu and delivery store information to the user by push notification or displaying it within the application.

[1550] A "wearable device" refers to a device worn on the user's body that collects health data, and typically includes a smartwatch or fitness tracker.

[1551] "Generative artificial intelligence" is an AI that has the ability to learn from past data and user preferences to generate personalized meal menus.

[1552] The present invention is a system for effectively collecting and analyzing a user's health data to generate an optimal meal menu, identify ingredients and meal locations, and notify the user. This system is composed of a server, a terminal, and a user device.

[1553] The overall system flow is as follows: first, a wearable device (e.g., a smartwatch) collects the user's health data and sends it to a server. Next, the server analyzes the received data and calculates the daily calorie and nutrient requirements. Based on these calculations, an appropriate meal menu is generated using generative artificial intelligence (AI). Next, information about nearby delivery restaurants is identified based on the generated menu. Finally, this information is notified to the user, who can then order delivery with one touch.

[1554] Hardware and software used

[1555] Wearable devices: Smartwatches and fitness trackers used to collect health data (e.g., Apple Watch, Fitbit).

[1556] Smartphone App: This Application provides an interface with the User.

[1557] Cloud server: A server for collecting data, analyzing it, generating menus, and searching for suggested restaurants (e.g., AWS, Google Cloud Platform).

[1558] Location information service: Geopy (a Python library) is used to obtain the user's location and search for the appropriate delivery store.

[1559] Data processing and calculation

[1560] 1. Health Data Collection:

[1561] The wearable device collects health data such as the user's heart rate, number of steps taken, and calories burned, and transmits it to a server via the terminal.

[1562] 2. Data Analysis:

[1563] The server receives the collected health data, stores and updates it in real time, and prepares it for analysis.

[1564] The server also uses an analytical engine to assess activity levels and calorie expenditures, and calculates daily calorie and nutrient needs.

[1565] 3. Menu generation:

[1566] The server uses generative artificial intelligence to generate meal menus based on the calculated calorie and nutrient content.

[1567] The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[1568] 4. Store Information Identification:

[1569] The server uses a location information service to identify ingredients required for the generated menu and store information that provides them.

[1570] This allows the system to search for and provide the optimal delivery store based on the user's current location.

[1571] 5. Notifications & Order Processing:

[1572] The server sends the generated meal menu and delivery store information to the terminal and notifies the user through the application.

[1573] Users can order meals with just one touch based on the information provided.

[1574] Specific examples

[1575] Example prompt sentence:

[1576] The user's name is Yamada Taro. He is a 35-year-old man, weighs 80 kg, and is 178 cm tall. This morning, his heart rate was 72, he took 8,500 steps, and he burned 450 calories. Please suggest the best dinner menu for Yamada Taro today. Also, please provide information about the nearest delivery store where he can order this menu.

[1577] In this way, the system of the present invention supports the user's health management by proposing appropriate meal menus based on the user's health data and supporting the execution of these menus, thereby enabling the user to easily achieve a balanced diet.

[1578] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1579] Step 1:

[1580] Health data collection

[1581] The user puts on a wearable device and begins their daily activities. The wearable device, such as a smartwatch, then collects health data such as heart rate, steps taken, and calories burned in real time. This data is then sent to a server via the device (smartphone).

[1582] Input: Health data (heart rate, steps, calories burned)

[1583] Output: Send data to the server

[1584] Step 2:

[1585] Receiving and storing data

[1586] The server receives the health data sent from the device and stores it in a database, which keeps the user's activity information updated in real time and ready for analysis.

[1587] Input: Health data from your device

[1588] Output: Health data stored in a database

[1589] Step 3:

[1590] Data analysis

[1591] The server analyzes the health data stored in the database to assess the user's activity level and calorie expenditure, and uses an analytical engine to calculate the daily calorie and nutrient requirements.

[1592] Input: Stored health data

[1593] Output: Calculated calorie and nutrient needs

[1594] Step 4:

[1595] Generate a meal menu

[1596] The server uses generative artificial intelligence to generate an appropriate meal plan based on the calculated calorie and nutrient content. The AI ​​learns from past data and the user's food preferences to suggest personalized menus.

[1597] Input: Calculated calorie and nutrient needs, user's past data, user's food preferences

[1598] Output: personalized meal menu

[1599] Step 5:

[1600] Identifying delivery store information

[1601] The server identifies nearby delivery stores that provide the ingredients needed for the generated menu. It uses location-based services (such as Geopy) to search for the optimal delivery store based on the user's current location.

[1602] Input: Food menu, user location

[1603] Output: Delivery store information

[1604] Step 6:

[1605] User Notification and Orders

[1606] The server sends the generated menu and delivery store information to the device and notifies the user through the application. The user can then place a delivery order with one touch based on the information provided. Once the order is complete, a confirmation notification is sent.

[1607] Input: Meal menu, delivery store information

[1608] Output: Push notification, delivery order

[1609] Step 7:

[1610] Gathering feedback

[1611] After a meal, users provide their satisfaction and feedback through the application, which is sent to the server and used to improve future menu suggestions.

[1612] Input: User feedback

[1613] Output: Feedback data stored on the server

[1614] 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.

[1615] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and a user device. Furthermore, it is characterized by using an emotion engine to recognize the user's emotional state and reflect it in the meal menu it generates.

[1616] Overall system flow and functions

[1617] 1. Health data collection

[1618] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[1619] The device collects health data such as the user's heart rate, number of steps taken, and calories burned in real time and sends it to a server.

[1620] 2. Collecting Emotional Data

[1621] Users use the cameras and microphones built into their smartphones and other devices.

[1622] The device detects the user's emotional state (e.g., joy, anger, sadness, stress, etc.) through voice recognition and facial expression recognition.

[1623] The terminal transmits the emotion data to the server.

[1624] 3. Data Receipt and Analysis

[1625] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[1626] The server analyzes this data to assess the user's activity level, calories burned, and emotional state.

[1627] 4. Calculating calorie and nutrient needs

[1628] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[1629] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[1630] 5. Generating a meal menu

[1631] The server uses generative artificial intelligence (AI) and an emotion engine to generate appropriate meal menus based on the calculated calorie and nutrient content.

[1632] The AI ​​learns from past data and the user's food preferences to suggest personalized menus that reflect the user's emotional state.

[1633] 6. Providing information about ingredients and stores

[1634] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[1635] The server uses the user's current location information to search for the most suitable store.

[1636] 7. Notice to Users

[1637] The server transmits the generated meal menu and store information to the terminal.

[1638] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[1639] Specific examples

[1640] morning

[1641] The user wakes up in the morning, puts on the wearable device, and begins their daily activities, with the camera and microphone of their smartphone enabled.

[1642] The device continuously collects health data (e.g., heart rate, steps, calories burned) and emotional data (emotional state through voice recognition and facial expression recognition) in real time.

[1643] The server receives and stores the data in real time and prepares it for analysis.

[1644] noon

[1645] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[1646] The server uses AI and an emotion engine to generate lunch menus that reflect your emotional state while also taking into account your health status. For example, if you're feeling stressed, it will suggest meals that include foods that have a relaxing effect.

[1647] The terminal notifies the user, "For today's lunch, a relaxing salmon and avocado salad is recommended. You can purchase the ingredients at the nearby supermarket A."

[1648] evening

[1649] The user can stop by Supermarket A on the way home from work to purchase the recommended ingredients, or choose to enjoy dinner at Restaurant B, which is suggested by the server.

[1650] The server provides information about nearby stores based on the user's location information and guides them to the best places to shop and eat.

[1651] night

[1652] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[1653] After the user consumes the meal, the device collects satisfaction feedback to help improve future suggestions.

[1654] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[1655] The processing flow will be explained below.

[1656] Step 1: Collecting health and emotional data

[1657] Users wear a wearable device (e.g., a smart watch) on a daily basis and begin their activities.

[1658] Users use the cameras and microphones built into their smartphones and other devices.

[1659] The device collects real-time health data such as the user's heart rate, number of steps taken, and calories burned.

[1660] The device detects the user's emotional state (e.g., joy, anger, sadness, stress, etc.) through voice recognition and facial expression recognition.

[1661] The terminal periodically transmits the collected health data and emotion data to the server.

[1662] Step 2: Receiving and storing data

[1663] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[1664] Step 3: Analyze the data

[1665] The server analyzes the stored health data and evaluates the user's activity level and calories burned.

[1666] The server analyzes the stored emotional data and assesses the user's emotional state.

[1667] Step 4: Calculate your calorie and nutrient needs

[1668] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height).

[1669] Based on the calculated results, the server determines the amount of energy and nutrient balance that the user should consume that day.

[1670] Step 5: Generate the meal menu

[1671] The server uses generative artificial intelligence (AI) and an emotion engine to generate appropriate meal menus based on the calculated calorie and nutrient content.

[1672] The AI ​​learns from past data and the user's food preferences to suggest personalized menus that reflect the user's emotional state (e.g., a menu that includes foods that have a relaxing effect when stressed).

[1673] Step 6: Provide ingredients and store information

[1674] The server identifies information about the nearest store where ingredients necessary to create the created menu can be purchased and information about restaurants that offer the menu.

[1675] The server uses the user's current location information to search for the most suitable store.

[1676] Step 7: Notify users

[1677] The server transmits the generated meal menu and store information to the terminal.

[1678] The device uses a notification function to notify the user of suggested menu items and store information where the items can be purchased.

[1679] Step 8: Gather feedback

[1680] The user prepares and consumes a meal based on the suggested menu, or enjoys a meal at the suggested restaurant.

[1681] After the user consumes the meal, the device collects satisfaction and feedback within the app.

[1682] The server stores the collected feedback to help improve future menu suggestions.

[1683] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[1684] Example 2

[1685] 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."

[1686] In today's world, achieving a balanced diet while efficiently managing one's health and emotions is a major challenge for many people. However, conventional systems have had difficulty integrating health data and emotional states to generate appropriate meal plans and provide them to users. Furthermore, they have not been able to propose specific meal plans based on the user's current emotional state. As a result, it has been impossible to provide an optimal meal plan for each individual user's health and emotional state, making effective health and emotional management difficult. Therefore, there is a need for a system that can combine and analyze health data and emotional states to generate optimal meal plans and provide them to users.

[1687] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring health data and emotional state, a means for analyzing the health data and emotional state and calculating the daily required calories and nutrient amount, and a means for generating a meal menu based on the calculated calories and nutrient amount. This makes it possible to comprehensively analyze the user's health data and emotional state and provide an optimal meal menu for each individual user. Furthermore, since a menu is proposed that takes the user's emotional state into consideration based on the acquired data, the user's health and emotional management can be performed efficiently and effectively.

[1688] "Health data" refers to physiological information obtained from wearable devices, such as a user's heart rate, number of steps taken, and calories burned.

[1689] The "emotional state" refers to the user's psychological state, such as joy, anger, sadness, or stress, and is data acquired through a voice recognition or facial expression recognition device.

[1690] "Required calories and nutrients" refers to the amount of energy a user should consume in a day, as well as the necessary vitamins, minerals, proteins, and other nutrients.

[1691] A "meal menu" is a specific meal combination or recipe that allows a user to consume the required amount of calories and nutrients per day.

[1692] "Store information" refers to related information such as the location, contact information, and opening hours of stores where ingredients necessary to create the generated meal menu can be purchased.

[1693] "Restaurant information" refers to related information such as the location, contact information, and business hours of the restaurant that offers the generated meal menu.

[1694] "Generative AI" is an AI technology that generates optimal meal plans based on a user's health data and emotional state.

[1695] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and a user device. Furthermore, it is characterized by using an emotion engine to recognize the user's emotional state and reflect it in the meal menu it generates.

[1696] Hardware and software used

[1697] Wearable devices (e.g., smartwatches): collect health data such as the user's heart rate, steps taken, and calories burned.

[1698] Speech recognizer (e.g., Google Cloud Speech-to-Text): obtains the emotional state from the user's voice.

[1699] Facial expression recognition device (e.g., Face++ API): Obtains the user's emotional state from their facial expressions.

[1700] Database system (e.g., MySQL): to store the collected health and emotion data.

[1701] Data analysis software (e.g., Python, Pandas): to analyze health and emotion data.

[1702] Machine learning model (e.g., Scikit-learn): Calculates daily calorie and nutrient requirements based on the analysis results.

[1703] Generative AI (e.g., OpenAI's GPT-3): Generates personalized meal plans based on calorie and nutrient needs.

[1704] Map information service (e.g., Google Maps API): Provides information on the nearest stores and restaurants.

[1705] Notifications (e.g., Firebase Cloud Messaging): Notify users of suggested menu items and store availability information.

[1706] Example of system operation

[1707] morning

[1708] The user wakes up in the morning, puts on the wearable device, and begins their daily activities, with the camera and microphone of their smartphone enabled.

[1709] The device continuously collects health data (heart rate, steps, calories burned) and emotional data (emotional state through voice recognition and facial expression recognition) in real time.

[1710] The server receives the data in real time and stores it in a database for analysis.

[1711] noon

[1712] Based on the morning data, the server calculates the user's calorie consumption and calculates the amount of calories and nutrients required for that day.

[1713] The server uses AI and an emotion engine to generate lunch menus that reflect your emotional state while also taking into account your health status. For example, if you're feeling stressed, it will suggest meals that include foods that have a relaxing effect.

[1714] The device notifies the user, "Today's lunch is recommended as a relaxing salmon and avocado salad. You can purchase the ingredients at your local supermarket."

[1715] evening

[1716] The user can choose to stop by supermarket A on the way home from work and purchase the recommended ingredients, or to enjoy dinner at restaurant B suggested by the server.

[1717] The server uses the user's location information to provide information about nearby stores and guides them to the best places to shop or eat.

[1718] night

[1719] Users can cook the recommended meals at home and eat a balanced diet, or enjoy the recommended meals at a restaurant.

[1720] The device collects satisfaction feedback after the user consumes the meal to help improve future suggestions.

[1721] Prompt Sentence Examples

[1722] "Generate a relaxing lunch menu based on the user's health data and emotional state. For example, suggest a menu suitable for when your heart rate is high and you're stressed."

[1723] In this way, the system of the present invention proposes an appropriate meal plan based on the user's health data and emotional state, and supports the user in carrying out the plan, thereby providing a comprehensive solution for achieving a balanced diet and emotional management without the user having to do any effort.

[1724] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1725] Program processing steps

[1726] Step 1:

[1727] A user puts on a wearable device (e.g., a smartwatch) and begins their daily activities, which starts collecting health data. The input is the user's biometric data (heart rate, steps, calories burned), and the output is this real-time data.

[1728] Specifically, the wearable device measures biosignals, converts them into data format, and transmits them to the terminal.

[1729] Step 2:

[1730] The terminal acquires health data from the wearable device in real time. The input is the health data sent from the wearable device, and the output is the health data in a format that is sent to the server.

[1731] Specifically, the device receives various sensor data via Bluetooth or Wi-Fi and packetizes it for transmission to the server.

[1732] Step 3:

[1733] Users turn on the camera and microphone on their smartphones to record emotional data on a daily basis. The input is the user's face and voice, and the output is raw data based on these.

[1734] Specifically, the smartphone's camera and microphone collect the user's facial expressions and voice.

[1735] Step 4:

[1736] The device acquires the user's emotional state through voice and facial recognition. The input is recorded or photographed data, and the output is data indicating the emotional state (e.g., joy, anger, sadness, stress, etc.).

[1737] Specifically, the built-in voice recognition software (e.g., Google Cloud Speech-to-Text) and facial expression recognition software (e.g., Face++ API) analyze the recorded and image data to generate emotional data.

[1738] Step 5:

[1739] The terminal transmits the collected health data and emotion data to the server. The input is the collected health data and emotion data, and the output is data packet information containing these data.

[1740] Specifically, the device creates and sends an HTTP request to send data to the server's API endpoint.

[1741] Step 6:

[1742] The server receives the health and emotion data sent from the device. The input is the data packet sent from the device, and the output is the data stored in a database (e.g., MySQL) on the server.

[1743] Specifically, the server analyzes the received data, converts it into a valid format, and stores it in a database.

[1744] Step 7:

[1745] The server analyzes the received data and evaluates the user's activity, calorie consumption, and emotional state. The input is health data and emotional data, and the output is the evaluated data (e.g., calories consumed, nutritional status, emotional state).

[1746] Specifically, a data analysis script is executed using Python and Pandas.

[1747] Step 8:

[1748] The server calculates the daily calorie and nutrient requirements based on the user's health data and personal information (age, gender, weight, height). The input is the health data and personal information, and the output is the calculated daily calorie and nutrient requirements.

[1749] Specifically, a machine learning model using Scikit-learn is executed to calculate individual calorie and nutrient requirements.

[1750] Step 9:

[1751] The server uses generative artificial intelligence (AI) and an emotion engine to generate an appropriate meal plan based on the calculated calorie and nutrient content. The input is the calorie needs, nutrient content, and emotion data, and the output is a personalized meal plan.

[1752] Specifically, OpenAI's GPT-3 creates prompts based on user data and generates an appropriate meal menu.

[1753] Step 10:

[1754] The server identifies the nearest store where the ingredients needed to create the generated menu can be purchased, and the restaurant that serves that menu. The input is the generated meal menu and the user's location information, and the output is the optimal store and restaurant information.

[1755] Specifically, the Google Maps API searches for the nearest store information based on the user's location information.

[1756] Step 11:

[1757] The server sends the generated meal menu and store information to the terminal. The input is the generated meal menu and store information, and the output is notification information sent to the user terminal.

[1758] Specifically, notifications are sent to the device using Firebase Cloud Messaging.

[1759] Step 12:

[1760] The terminal uses the notification function to notify the user of suggested menus and store information where purchases can be made. The input is notification information sent from the server, and the output is visual and audible notifications to the user.

[1761] Specifically, the suggestions will be displayed in the smartphone's notification center, and in some cases an audio notification will also be given.

[1762] Through this series of processing steps, the user is presented with an optimal meal menu based on their individual health data and emotional state, enabling them to manage their health and emotions on a daily basis.

[1763] (Application example 2)

[1764] 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."

[1765] In modern society, maintaining health and managing emotions are important, but balancing these two is difficult in our busy daily lives. Selecting an appropriate meal plan based on an individual's health and emotional state is particularly challenging. Furthermore, finding the optimal shopping and dining locations for a suggested meal plan can be time-consuming. This invention aims to enable users to eat healthy and emotionally satisfying meals by collecting and analyzing a user's health and emotional state data, and automatically generating and providing appropriate meal plans and information on nearby stores and restaurants.

[1766] 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.

[1767] In this invention, the server includes means for acquiring health data, means for analyzing the health data and calculating daily calorie needs and nutrient amounts, means for generating a meal menu based on the calculated calories and nutrient amounts, means for providing store information or restaurant information for purchasing ingredients based on the generated menu, means for notifying the user of the provided information and menu, means for analyzing the user's emotional state and adjusting the meal menu based on the emotional state using an emotion engine, and means for automatically processing an order with the nearest affiliated restaurant based on the meal menu. This allows users to easily obtain a meal menu that is optimal for their health and emotional state, and to efficiently purchase ingredients and eat at restaurants based on the meal menu.

[1768] "Health data" refers to data that indicates the user's physical condition, and includes physiological indicators such as heart rate, number of steps, calories burned, weight, and blood pressure.

[1769] The "emotional state" is a state that indicates the emotions and moods that the user is currently feeling, and is information that includes emotional responses such as joy, anger, sadness, stress, and relaxation.

[1770] An "emotion engine" refers to artificial intelligence and algorithms that analyze users' emotional data and adjust meal menus and other suggestions based on the results.

[1771] A "meal menu" is a series of dishes or food combinations suggested for consumption by a user, generated based on calorie and nutrient content.

[1772] "Store information" refers to information about stores where the user can purchase ingredients needed for the proposed meal menu.

[1773] "Restaurant information" refers to information about restaurants that offer the meal menu suggested by the user.

[1774] "Notification methods" refers to digital and analog methods, such as smartphone applications and emails, used to inform users about suggested meal menus and places to buy and eat.

[1775] "Generative AI" refers to a general term for AI technology that learns from a user's past data and preferences and then makes personalized suggestions based on that information.

[1776] "Affiliated restaurant" refers to a restaurant that provides services in cooperation with this system, and is a place that provides suggested menus to users.

[1777] This invention is a system that collects and analyzes a user's health data and emotional state, generates an optimal meal menu based on this data, provides restaurant information, and notifies the user. This system consists of a server, a terminal, and the user's device. A notable feature is that it uses an emotion engine to reflect the user's emotional state in the menu. It is also possible to automatically process orders from nearby partner restaurants based on the proposed meal menu.

[1778] Program processing explanation

[1779] Hardware and software usage:

[1780] Flask: Used to provide the web server and API endpoints, through which health and emotion data is collected and analysis results are sent.

[1781] Python: A programming language used for data processing and server logic implementation. Used to analyze health and emotion data, generate meal menus, and provide location information.

[1782] Database: Store user health data, emotion data, and generated menu information in a database such as MongoDB or MySQL.

[1783] Generative AI: Uses AI models (e.g., GPT-3, BERT, etc.) to generate meal menus based on the user's health and emotional data.

[1784] Location API: Uses location services such as Google Maps API to find the nearest partner restaurant based on the user's current location.

[1785] Data processing and calculation flow:

[1786] The server uses the following means:

[1787] 1. Health data collection: Health data is collected from user devices such as smartwatches and smartphones. This data includes heart rate, steps taken, calories burned, etc.

[1788] 2. Data analysis method: Analyzes collected health data and calculates daily calorie and nutrient needs, taking into account personal information such as the user's age, gender, weight, and height.

[1789] 3. Meal menu generation: Based on the calculated calorie and nutrient content, an optimal meal menu is generated using generative artificial intelligence. Furthermore, an emotion engine is used to reflect the user's emotional state in the meal menu.

[1790] 4. Store information provision means: Based on the generated menu, information on the nearest stores and restaurants where necessary ingredients can be purchased is provided, and this information is also notified to the user.

[1791] 5. Automatic Order Processing: Automatically processes orders with the nearest partner restaurant, allowing users to try the suggested menu items without any hassle.

[1792] Examples:

[1793] Users record their daily health and emotional data using a smartwatch or smartphone. For example, assume that their heart rate is 75, their steps are 5,500, and their calories burned are 1,500 kcal. If the emotional data is detected as "stress," the server receives and analyzes this data and uses generative artificial intelligence to suggest a "salmon and avocado salad with a relaxing effect." The server also identifies the nearest partner restaurant using a location information API, and suggests "Restaurant A." The user is notified of this suggestion via a smartphone application, and the order is automatically processed.

[1794] Example prompt sentence:

[1795] "Today's user's emotional data is 'stress', and their health data is 'heart rate 75, steps taken 5500, calories burned 1500 kcal.' Based on this data, please suggest a lunch menu that will relieve stress and is under 1500 kcal."

[1796] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1797] Step 1:

[1798] Data collection

[1799] The device collects health data (heart rate, steps taken, calories burned) in real time from smartwatches and smartphones.

[1800] The device sends the collected health data to a server. It also uses the device's camera and microphone to collect the user's voice and facial expressions in real time, and sends this as emotional data to the server.

[1801] Input: User's heart rate, steps, calories burned, voice, and facial expressions.

[1802] Output: Health and emotion data sent to the server.

[1803] Step 2:

[1804] Data storage

[1805] The server receives the health data and emotion data sent from the terminal and stores them in a database.

[1806] Input: Submitted health and emotion data.

[1807] Output: Health and emotion data stored in a database.

[1808] Specific operation: The server creates a new record in the database and records the health and emotion data.

[1809] Step 3:

[1810] Data analysis

[1811] The server analyzes the health data in the database and calculates the user's daily calorie and nutrient needs.

[1812] Based on the analysis results, the server generates meal menus for lunch, dinner, etc. that take into account the user's emotional state.

[1813] Input: Health data, personal information (age, gender, weight, height).

[1814] Output: Calculated calorie and nutrient needs, generated meal plan.

[1815] How it works: Based on health data, the server applies an energy expenditure model to calculate daily calorie needs, then uses a generative AI model to suggest meal plans.

[1816] Step 4:

[1817] Reflection of emotional state

[1818] The server uses an emotion engine to tailor the meal menu based on the user's emotional state, for example, suggesting relaxing meals to a stressed user.

[1819] Input: User emotion data (happiness, anger, sadness, stress, etc.).

[1820] Output: A tailored meal menu.

[1821] Specific operation: The server analyzes the emotional data using an emotion engine and fine-tunes the menus suggested by the generative AI model based on the results.

[1822] Step 5:

[1823] Store information provision

[1824] Based on the generated menu, the server provides information on the nearest store where the necessary ingredients can be purchased and information on restaurants that offer the menu.

[1825] Input: Generated food menu, user's current location.

[1826] Output: Retailer information, restaurant information.

[1827] Specific operation: The server uses the location information API to search for and provide appropriate store information based on the user's current location.

[1828] Step 6:

[1829] Notifications and Auto Ordering

[1830] The terminal notifies the user of the provided information and menu, and with the user's consent, automatically processes the order with the partner restaurant.

[1831] Input: Retailer information, Restaurant information, generated meal menu, User consent.

[1832] Output: Notification to the user and order information to the restaurant.

[1833] Specific operation: The smartphone application sends a notification to the user, and if the order is confirmed, the order information is automatically sent to the partner restaurant.

[1834] Example prompt sentence:

[1835] "Today's user's emotional data is 'stress', and their health data is 'heart rate 75, steps taken 5500, calories burned 1500 kcal.' Based on this data, please suggest a lunch menu that will relieve stress and is under 1500 kcal."

[1836] 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.

[1837] 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.

[1838] 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.

[1839] 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.

[1840] FIG. 9 illustrates 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 behaviors 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.

[1841] 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.

[1842] 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).

[1843] 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.

[1844] 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."

[1845] 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.

[1846] 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).

[1847] 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.

[1848] 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.

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] 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.

[1856] 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.

[1857] The following is further disclosed regarding the above embodiment.

[1858] (Claim 1)

[1859] a means for acquiring health data;

[1860] A means for analyzing the health data and calculating the daily calorie and nutrient requirements;

[1861] A means for generating a meal menu based on the calculated calorie and nutrient amounts;

[1862] A means for providing store information or restaurant information for purchasing ingredients based on the generated menu;

[1863] means for notifying a user of the provided information and menu;

[1864] A system including:

[1865] (Claim 2)

[1866] 10. The system of claim 1, wherein the health data is obtained from a wearable device.

[1867] (Claim 3)

[1868] The system according to claim 1, characterized in that the meal menu is generated by generative artificial intelligence.

[1869]

[1870] "Example 1"

[1871] (Claim 1)

[1872] a means for acquiring health data;

[1873] A means for analyzing the health data and calculating the daily calorie and nutrient requirements;

[1874] A means for generating a meal menu based on the calculated calorie and nutrient amounts;

[1875] A means for identifying ingredients required for the generated menu;

[1876] A means for providing information on the nearest sales or eating place where the identified food material can be purchased;

[1877] means for notifying a user of the provided information and menu;

[1878] A system including:

[1879] (Claim 2)

[1880] 10. The system of claim 1, wherein the health data is obtained from a wearable device.

[1881] (Claim 3)

[1882] The system according to claim 1, characterized in that the meal menu is generated by generative artificial intelligence.

[1883] "Application Example 1"

[1884] (Claim 1)

[1885] a means for acquiring health data;

[1886] A means for analyzing the health data and calculating the daily calorie and nutrient requirements;

[1887] A means for generating a meal menu based on the calculated calorie and nutrient amounts;

[1888] a means for providing information on delivery stores for delivering ingredients based on the generated menu;

[1889] means for notifying a user of the provided information and menu;

[1890] A system including:

[1891] (Claim 2)

[1892] 10. The system of claim 1, wherein the health data is obtained from a wearable device.

[1893] (Claim 3)

[1894] The system according to claim 1, characterized in that the meal menu is generated by generative artificial intelligence.

[1895] "Example 2: Combining Emotion Engines"

[1896] (Claim 1)

[1897] a means for acquiring health data and emotional state;

[1898] means for analyzing said health data and emotional state to calculate daily calorie and nutrient requirements;

[1899] A means for generating a meal menu based on the calculated calorie and nutrient amounts;

[1900] A means for providing store information or restaurant information for purchasing ingredients based on the generated menu;

[1901] means for notifying a user of the provided information and menu;

[1902] A system including:

[1903] (Claim 2)

[1904] 2. The system of claim 1, wherein the health data and emotional state are obtained from a wearable device and a voice and facial expression recognition device.

[1905] (Claim 3)

[1906] 2. The system of claim 1, wherein the meal menu is generated by a generative artificial intelligence and reflects the user's emotional state.

[1907] "Application example 2 when combining emotion engines"

[1908] (Claim 1)

[1909] a means for acquiring health data;

[1910] A means for analyzing the health data and calculating the daily calorie and nutrient requirements;

[1911] A means for generating a meal menu based on the calculated calorie and nutrient amounts;

[1912] A means for providing store information or restaurant information for purchasing ingredients based on the generated menu;

[1913] means for notifying a user of the provided information and menu;

[1914] means for analyzing the emotional state of the user and adjusting the meal menu based on the emotional state using an emotion engine;

[1915] A means for automatically processing an order with the nearest affiliated restaurant based on the meal menu;

[1916] A system including:

[1917] (Claim 2)

[1918] 10. The system of claim 1, wherein the health data is obtained from a wearable device.

[1919] (Claim 3)

[1920] The system according to claim 1, characterized in that the meal menu is generated by generative artificial intelligence. [Explanation of symbols]

[1921] 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 acquiring health data; A means for analyzing the health data and calculating the daily calorie and nutrient requirements; A means for generating a meal menu based on the calculated calorie and nutrient amounts; A means for providing store information or restaurant information for purchasing ingredients based on the generated menu; means for notifying a user of the provided information and menu; A system including:

2. 10. The system of claim 1, wherein the health data is obtained from a wearable device.

3. The system according to claim 1, wherein the meal menu is generated by generative artificial intelligence.

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