system

Abstract

Provide a system. The method includes: collecting health data, lifestyle, dietary, and exercise data from a user; A means for analyzing the collected data and generating a personalized meal plan based on the user's health and lifestyle habits; a means for creating and providing recipes to the user based on the generated meal plan; A means of using AR technology to guide users through cooking steps; A system including:

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] Today's general health management services struggle to provide specific and effective meal plans that meet the needs of individual users. Furthermore, when users try to cook new recipes, the steps are often difficult to understand, resulting in frequent mistakes. These challenges make it difficult to provide effective health management. [Means for solving the problem]

[0005] This invention is a system that uses AI to analyze health, lifestyle, diet, and exercise data collected from users and generate optimal meal plans for each individual user. The system automatically creates specific recipes from the generated meal plans and also uses AR technology to visually guide users through cooking steps. This allows users to efficiently prepare meals that suit their own health condition, achieving more effective health management.

[0006] "User" refers to an individual who provides health, lifestyle, diet, and exercise data to the system.

[0007] "Health Data" refers to information about a user's physical condition, such as weight, blood pressure, heart rate, and sleep patterns.

[0008] "Lifestyle habits" refers to the user's daily habits and behavioral patterns (e.g., sleep time, meal timing, drinking habits, etc.).

[0009] "Dietary Data" refers to information regarding the content and quantity of food consumed by a User.

[0010] "Exercise Data" refers to information regarding the type, intensity, duration, etc. of exercise performed by a user.

[0011] "Analysis" refers to the process of evaluating a user's health and lifestyle based on collected data and generating meaningful indicators.

[0012] "Meal Plan" refers to a combination of appropriate meals suggested to improve the User's health.

[0013] A "recipe" refers to a specific list of ingredients and instructions for cooking a specific meal plan.

[0014] "AR technology" refers to technology that uses Augmented Reality technology to provide visual guidance to users.

[0015] "Cloud Server" refers to a remote server that stores collected data and runs the analysis engine.

[0016] "Machine learning algorithm" refers to an algorithm that identifies patterns, analyzes, and makes predictions based on input data.

[0017] "Cooking instructions" refer to the specific steps taken to complete a dish according to a particular recipe. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[0040] Data collection

[0041] Users record health information such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data through a smartphone app or smartwatch. This data is sent from the device to a cloud server in real time or periodically. The data is encrypted before transmission, protecting the user's privacy.

[0042] Data analysis

[0043] The server analyzes the data it receives, comparing it with a large amount of past data stored in a database to assess the user's health status. Specifically, it uses machine learning algorithms to recognize patterns in the user's data and identify health issues such as vitamin D deficiency or lack of exercise.

[0044] Generate a meal plan

[0045] Based on the analysis results, the server generates a meal plan to improve the user's health. This plan takes into account the ingredients needed to supplement the user's nutrient deficiencies and the appropriate energy intake to balance daily calorie consumption. The meal plan includes specific ingredient selection and cooking methods.

[0046] Creating a Recipe

[0047] The server then creates a specific recipe based on the meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[0048] Cooking assistance

[0049] After receiving the recipe, when the user starts cooking, the device (e.g., smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. Specifically, it visually shows the specific steps for preparing ingredients, chopping, grilling, etc. For example, the smart glasses might instruct the user, "Place the spinach on the cutting board and use this position to chop it finely."

[0050] Specific examples

[0051] A specific user scenario is shown below.

[0052] When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep time, sleep quality), which is then sent to a cloud server.

[0053] The server receives the data and diagnoses insufficient sleep and vitamin D deficiency.

[0054] The server generates a vitamin D-rich meal plan, suggesting, for example, "grilled salmon and spinach."

[0055] The server creates a specific recipe and sends it to the user's smartphone app.

[0056] The user opens the smartphone app in the kitchen and starts cooking, while the smart glasses display specific instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0057] In this way, users can easily prepare meals that suit their own health condition and efficiently manage their daily health.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] Users use smartphone apps or smartwatches to record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), diet, and exercise data (e.g., number of steps, exercise time).

[0061] Step 2:

[0062] The device sends data from the user to the cloud server in real time or periodically, and the data is encrypted to protect the user's privacy.

[0063] Step 3:

[0064] The server stores the received data in a cloud database, which is updated whenever new data is added.

[0065] Step 4:

[0066] The server's analytics engine analyzes the stored data, assessing the user's health status based on, for example, the past week's dietary data, exercise data, and sleep patterns.

[0067] Step 5:

[0068] The server applies machine learning algorithms to recognize patterns in the user's data, which can identify health issues such as vitamin D deficiency or lack of exercise.

[0069] Step 6:

[0070] The server generates a meal plan to improve the user's health, taking into account the ingredients and energy intake required to compensate for the user's nutrient deficiencies.

[0071] Step 7:

[0072] The server then creates a specific recipe based on the generated meal plan, including a list of ingredients, cooking instructions, cooking time, and nutritional information.

[0073] Step 8:

[0074] The server sends the created recipe to the user's smartphone app, and the user receives a notification that the new recipe is ready.

[0075] Step 9:

[0076] The user receives the recipe and prepares the necessary ingredients. The user then starts cooking in the kitchen based on the recipe.

[0077] Step 10:

[0078] The device uses a smartphone app or AR-enabled device to visually guide users through cooking steps, providing specific instructions such as "wash and finely chop the spinach" or "grill the salmon over medium heat for 5 minutes."

[0079] Step 11:

[0080] The device uses AR technology to visually show the location of ingredients and how to use cooking utensils, for example, "Place the cutting board here and use this position to chop the spinach."

[0081] Step 12:

[0082] The user follows the instructions to proceed with the cooking process, and when the cooking is complete, they register the completion with the app, which will be used as feedback when generating the next meal plan.

[0083] Example 1

[0084] 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."

[0085] In modern society, busy lifestyles often make health management difficult. In particular, designing a meal plan tailored to an individual's health condition requires specialized knowledge and is not easy for the average person. Furthermore, the process of implementing a meal plan can be complicated, including cooking methods and selecting the necessary ingredients, making it difficult to implement in everyday life. To solve these issues, a system is needed that collects and analyzes users' health data and provides appropriate meal plans and recipes based on them. Furthermore, a means of supporting users in the cooking process is also required.

[0086] 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.

[0087] In this invention, the server includes means for collecting biometric information, lifestyle, dietary, and exercise data from a user, means for encrypting the collected data and transmitting it to a cloud server, means for analyzing the received data using a machine learning algorithm and generating an individualized meal plan based on the user's health condition and lifestyle, means for creating recipes based on the generated meal plan and transmitting a list of ingredients, cooking instructions, cooking time, and nutritional information to a user terminal, and means for providing cooking instructions to the user using AR technology. This allows users to easily obtain a meal plan tailored to their health condition and visually understand specific cooking instructions, enabling more efficient and practical daily health management.

[0088] "User" refers to an individual who uses the system to manage their own health.

[0089] "Biometric information" refers to data that indicates a user's health status, such as weight, blood pressure, heart rate, and sleep patterns.

[0090] "Lifestyle" refers to the habits and behaviors of a user in their daily life, such as eating and exercise patterns.

[0091] "Dietary data" refers to information about the contents and ingredients of the meals consumed by the user.

[0092] "Exercise data" refers to information about the type of exercise a user performs, the duration, calories burned, etc.

[0093] "Cloud server" refers to a remote server used to store and analyze data via the Internet.

[0094] A "machine learning algorithm" refers to a computational method for finding patterns and rules from input data and making predictions and classifications.

[0095] "Meal Plan" refers to specific meal plans and ingredient combinations designed based on the user's health and lifestyle.

[0096] A "recipe" is a document created based on a meal plan that includes a specific list of ingredients, cooking instructions, and nutritional information.

[0097] "User terminal" refers to a device, such as a smartphone or tablet, that a user uses to display information received from the system.

[0098] "AR technology" refers to a technology that overlays digital information on real-world images and provides visual instructions to users.

[0099] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[0100] Data collection

[0101] Users record various health-related information, such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data, through smartphone apps or wearable devices. This data is encrypted and sent from the device to a cloud server in real time or periodically. Common biometric information collection APIs are used to link the data. For example, data can be collected efficiently by using Apple's "HealthKit" or Google's (registered trademark) "Fit API."

[0102] Data Transmission and Encryption

[0103] The device encrypts the collected data before sending it to the cloud server. Security protocols such as SSL / TLS are used for encryption. Wireless communication technologies such as Bluetooth and Wi-Fi are used to ensure stable data transmission.

[0104] Data analysis

[0105] The server first stores the received data in a database for analysis. It then analyzes the stored data using machine learning libraries such as Python's "TENSORFLOW (registered trademark)" and "scikit-learn." By comparing it with past data, the system can accurately assess the user's health status and identify problems such as vitamin D deficiency or lack of exercise. Specifically, the data used is evaluated using statistical analysis and pattern recognition techniques.

[0106] Generate a meal plan

[0107] Based on the analyzed data, the server uses a generative AI model to generate an optimal meal plan to improve the user's health. This AI model incorporates neural networks and deep learning algorithms to select specific ingredients to supplement the user's nutrient deficiencies and devise a plan that takes into account the balance with calorie consumption. For example, if a user is determined to have a vitamin D deficiency, it can suggest grilled salmon and spinach.

[0108] Creating a Recipe

[0109] The server creates a recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app using Firebase Cloud Messaging, and the user is notified via a notification function. For example, the recipe might say, "To make grilled salmon and spinach to supplement vitamin D, you need the following ingredients and steps."

[0110] Cooking assistance

[0111] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to assist with the cooking steps. Specific steps are visually presented to support the user's cooking. For example, smart glasses may display specific instructions such as "Place the spinach on the cutting board and use this position to finely chop it," or "Grill the salmon over medium heat for 5 minutes."

[0112] Examples of prompts that can be used include:

[0113] "Take last night's sleep data and generate a vitamin D-rich meal plan and specific recipes for users who have been diagnosed with vitamin D deficiency. Also, display cooking instructions using AR technology."

[0114] This system allows users to efficiently obtain and prepare meal plans that suit their own health condition, enabling more effective health management in daily life.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] Users use smartphone apps and wearable devices to record their weight, blood pressure, heart rate, sleep patterns, diet, exercise data, and more.

[0118] Input: User health data (e.g. weight, blood pressure, heart rate)

[0119] How it works: The user records sleep data on the smartwatch and syncs the data to the smartphone app.

[0120] Output: Recorded health data is stored in a smartphone app.

[0121] Step 2:

[0122] The device encrypts the collected health data and sends it to a cloud server.

[0123] Input: User health data stored in a smartphone app

[0124] How it works: The smartphone app encrypts health data using SSL / TLS and sends it to a cloud server using wireless communication technologies (Bluetooth, Wi-Fi).

[0125] Output: The encrypted data is sent to the cloud server.

[0126] Step 3:

[0127] The server receives the data sent to the cloud server and stores it in a database.

[0128] Input: Encrypted health data sent to a cloud server

[0129] Specific operation: The server decrypts the data and saves it in a table for storage in the database.

[0130] Output: Health data stored in a database

[0131] Step 4:

[0132] The server analyzes the stored data using machine learning algorithms.

[0133] Input: Health data stored in a database

[0134] How it works: It uses Python's TensorFlow and scikit-learn to analyze input data, including statistical analysis and pattern recognition to assess health conditions such as vitamin D deficiency and physical inactivity.

[0135] Output: User's health assessment data as a result of the analysis (e.g., vitamin D deficiency)

[0136] Step 5:

[0137] The server generates a meal plan based on the analysis results to improve the user's health.

[0138] Input: User's health assessment data

[0139] How it works: It uses a generative AI model to generate meal plans that take into account the nutritional needs of the user and their calorie intake. For example, if a vitamin D deficiency is identified, it will suggest grilled salmon and spinach.

[0140] Output: Optimized meal plan for each user

[0141] Step 6:

[0142] The server creates specific recipes based on the generated meal plan and sends them to the user's terminal.

[0143] Input: meal plan

[0144] What it does: The server generates a recipe that includes a list of ingredients, cooking instructions, cooking time, and nutritional information. It then uses Firebase Cloud Messaging to send the recipe to the smartphone app.

[0145] Output: The recipe displayed on the user's terminal

[0146] Step 7:

[0147] The user receives the recipe on their smartphone app and begins cooking.

[0148] Input: Recipe displayed on smartphone app

[0149] Specific operation: The user cooks in the kitchen while checking the recipe.

[0150] Output: Cooked food

[0151] Step 8:

[0152] The device uses AR technology to guide users through specific cooking steps while cooking.

[0153] Input: Cooking instructions based on the recipe displayed on the smartphone app

[0154] Specific actions: Smart glasses or a smartphone will display cooking steps in AR, providing specific instructions (e.g., "finely chop the spinach").

[0155] Output: Visual guidance to help users create a dish with precise steps

[0156] (Application example 1)

[0157] 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."

[0158] Existing health management systems can generate meal plans based on the data collected by users, but they lack specific support for actually purchasing ingredients and cooking. Users often experience difficulties, especially when selecting ingredients and following specific cooking procedures in physical stores. For this reason, there is a need for a comprehensive support system that allows users to efficiently purchase and cook meals based on their health status in physical stores.

[0159] 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.

[0160] In this invention, the server includes means for collecting health data, lifestyle, diet, and exercise data from the user, means for analyzing the collected data and generating an individual meal plan based on the user's health condition and lifestyle, means for creating recipes based on the generated meal plan and providing them to the user, and means for using AR technology to provide guidance on ingredients to be purchased and cooking procedures in a physical store, thereby enabling the user to select appropriate ingredients and cook efficiently in the physical store.

[0161] - "Health Data" refers to information about your body, such as your weight, blood pressure, heart rate, and sleep patterns.

[0162] "Lifestyle habits" refers to the user's daily habits, such as eating habits, exercise habits, and sleeping habits.

[0163] "Dietary data" refers to information such as the ingredients a user consumes, their nutritional content, and meal timing.

[0164] "Exercise data" refers to information such as the type of exercise performed by the user, the duration of the exercise, and the calories burned.

[0165] "Means of collection" refers to the methods of obtaining data via smartphones, smartwatches, and other devices.

[0166] "Analytics Engine" refers to a computer program used to analyze collected data and identify trends and patterns.

[0167] "Meal Plan" refers to a meal suggestion generated based on the analysis results to improve the user's health.

[0168] A "recipe" refers to instructions that list specific ingredients and cooking methods based on a meal plan.

[0169] "Means for providing" refers to a method for notifying or displaying the generated recipe to the user.

[0170] "AR technology" refers to the technology of overlaying digital information onto the real-world environment.

[0171] "Brick and mortar store" refers to a physical store where users actually visit and purchase products.

[0172] "Instruction" refers to a method of instructing users to take appropriate action.

[0173] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[0174] Data collection

[0175] Users use their smartphones or smartwatches to record health information such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data. This data is sent from the device to a cloud server in real time or periodically. The data is encrypted during transmission to protect user privacy.

[0176] Data analysis

[0177] The cloud server analyzes the received data. The server compares it with a large amount of past data stored in a database to evaluate the user's health status. This analysis uses machine learning algorithms (e.g., Random Forest) to recognize patterns in the user's data and identify health issues such as vitamin D deficiency or lack of exercise.

[0178] Generate a meal plan

[0179] Based on the analysis results, the cloud server generates a meal plan to improve the user's health. This plan takes into account the ingredients needed to supplement the user's nutrient deficiencies and the appropriate energy intake to balance daily calorie consumption. The meal plan includes specific ingredient selection and cooking methods.

[0180] Creating a Recipe

[0181] The cloud server creates specific recipes based on the generated meal plan. These recipes include a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipes are sent to the user's smartphone app and notified to the user via a notification function.

[0182] In-store support

[0183] After receiving the recipe, when choosing ingredients in a physical store, users can receive guidance on which ingredients to purchase and how to cook them using AR technology on their smart glasses or smartphones. Specifically, the smart glasses will provide visual instructions such as, "Place the spinach on the cutting board and use this position to finely chop it."

[0184] Specific examples

[0185] Here are some specific user scenarios:

[0186] 1. When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep time, sleep quality), which is then sent to a cloud server.

[0187] 2. The server receives the data and diagnoses insufficient sleep and vitamin D deficiency.

[0188] 3. The server generates a meal plan rich in vitamin D, suggesting, for example, "grilled salmon and spinach."

[0189] 4. The server creates a specific recipe and sends it to the user's smartphone app.

[0190] 5. The user opens the smartphone app in the kitchen and starts cooking. The smart glasses guide the user through the cooking process, displaying specific instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0191] In this way, users can easily prepare meals that suit their health condition and efficiently manage their daily health. Below is an example of a prompt:

[0192] prompt:

[0193] "30-year-old male, weighs 70 kg, exercise inactive, vitamin D deficient. Please suggest a suitable meal plan and specific recipes."

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Step 1:

[0196] Users record health data, lifestyle habits, diet, and exercise data through their smartphones or smartwatches. This includes weight, blood pressure, heart rate, sleep patterns, dietary content, and exercise data. The collected data is encrypted and sent from the device to a cloud server. The input is the user's health data, and the output is encrypted data.

[0197] Step 2:

[0198] The server receives the transmitted data. The server stores this data in a cloud database and prepares it for analysis. The input is encrypted data, and the output is data stored in the cloud database. A cloud service such as Amazon RDS is used for database management.

[0199] Step 3:

[0200] The server analyzes the data stored in the cloud database. It uses a machine learning algorithm (e.g., Random Forest) to evaluate the user's health condition. The input is the stored user data, and the output is the analysis results. The analysis involves comparison with similar data from the past and trend analysis.

[0201] Step 4:

[0202] The server generates a meal plan based on the analysis results. This plan takes into account the ingredients needed to improve the user's health and the amount of energy intake needed to balance daily calorie expenditure. The input is the analysis results, and the output is a meal plan. Specifically, if a vitamin D deficiency is identified, the server will list foods that are rich in vitamin D.

[0203] Step 5:

[0204] The server creates a specific recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The input is the meal plan, and the output is a detailed recipe. For example, a recipe for grilled salmon and spinach is created.

[0205] Step 6:

[0206] The server sends the created recipe to the user's smartphone app. This step also notifies the user through the notification function. The input is a detailed recipe, and the output is a notification sent to the user's smartphone app. Here, a notification system such as Firebase Cloud Messaging is used.

[0207] Step 7:

[0208] When a user is choosing ingredients in a physical store, they receive guidance on which ingredients to purchase and how to cook them using AR technology on their smart glasses or smartphone. The input is the user's current location and recipe information, and the output is visual guidance information. Specifically, when a user wears the smart glasses in a physical store, they are shown a visual guide on how to place spinach on a cutting board and finely chop it.

[0209] Through these steps, users can efficiently cook meals that are optimal for their health condition and manage their health.

[0210] 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.

[0211] This invention is a system that collects and analyzes a user's health data, lifestyle habits, diet, exercise data, and emotional data using AI and an emotion engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[0212] Data collection

[0213] Users record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary habits, and exercise data (e.g., number of steps, exercise time) through smartphone apps or smartwatches. Furthermore, the system analyzes the user's facial expressions and voice to record their current emotional state (e.g., joy, sadness, stress, etc.) as emotional data.

[0214] The device sends this data to a cloud server in real time, and the data is encrypted to protect user privacy.

[0215] Data analysis

[0216] The server stores the received data in a cloud database and analyzes it, for example, assessing the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data.

[0217] The server-based analytics engine uses a combination of machine learning and sentiment analysis algorithms to recognize patterns in a user's data, such as vitamin D deficiency or lack of exercise, as well as emotional fluctuations (e.g., periods of increased stress).

[0218] Generate a meal plan

[0219] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if a user is feeling stressed, a meal plan that includes ingredients that have a relaxing effect will be suggested.

[0220] Creating a Recipe

[0221] The server creates specific recipes based on the generated meal plan. These recipes include a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipes are sent to the user's smartphone app and notified to the user via a notification function.

[0222] Cooking assistance

[0223] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. The emotion engine customizes the cooking instruction method according to the user's emotional state. For example, if the user is stressed, more friendly and slower instructions are provided.

[0224] Specific examples

[0225] A specific user scenario is shown below.

[0226] When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration, sleep quality) and sends it to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data.

[0227] The server receives the data and detects low sleep, vitamin D deficiency, and increased stress.

[0228] The server generates a relaxing meal plan high in vitamin D, suggesting, for example, "grilled salmon and spinach."

[0229] The server creates a specific recipe and sends it to the user's smartphone app.

[0230] The user opens the smartphone app in the kitchen and starts cooking. Using the emotion engine, the smart glasses guide the cooking process by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0231] In this way, users can easily prepare meals that suit their health and emotional state, and efficiently manage their daily health.

[0232] The processing flow will be explained below.

[0233] Step 1:

[0234] Users use smartphone apps or smartwatches to record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary habits, and exercise data (e.g., number of steps, exercise time).Furthermore, the smartphone's camera and voice recognition functions are used to record emotional data from facial expressions and voice.

[0235] Step 2:

[0236] The device sends data from the user to the cloud server in real time or periodically, and the data is encrypted to protect the user's privacy.

[0237] Step 3:

[0238] The server stores the received data in a cloud database, which is updated whenever new data is added.

[0239] Step 4:

[0240] The server's analysis engine analyzes health data, lifestyle data, dietary data, exercise data, and emotional data. For example, it evaluates the user's health and emotional state based on data from the past week.

[0241] Step 5:

[0242] The server uses a combination of machine learning and sentiment analysis algorithms to recognize patterns in a user's data, identifying, for example, vitamin D deficiency, lack of exercise, and even emotional fluctuations (times when stress levels are high).

[0243] Step 6:

[0244] The server generates a personalized meal plan based on the user's emotional data, taking into account the user's health and emotional state. The meal plan includes ingredients that improve the user's emotional state while taking into account nutritional balance. For example, if the user is feeling stressed, a meal plan that includes ingredients with a relaxing effect will be suggested.

[0245] Step 7:

[0246] The server then creates specific recipes based on the generated meal plan, including a list of ingredients, cooking instructions, cooking time, and nutritional information.

[0247] Step 8:

[0248] The server sends the created recipe to the user's smartphone app, where the user receives a notification and can check the recipe within the app.

[0249] Step 9:

[0250] After the user checks the recipe, they use the shopping list to prepare the ingredients they need, and then they start cooking according to the recipe.

[0251] Step 10:

[0252] The device (smartphone or smart glasses) uses AR technology to visually guide the user through the cooking process. An emotion engine customizes the instruction method based on the user's emotional state. For example, if the user is stressed, the instructions will be more helpful and clear.

[0253] Step 11:

[0254] The device displays cooking instructions in real time, directing the user to take specific actions, such as "wash and finely chop the spinach" or "grill the salmon over medium heat for 5 minutes."

[0255] Step 12:

[0256] The user follows the instructions on the device to proceed with the cooking process. When cooking is complete, the user registers the completion in the app, which uses the information as feedback for the next meal plan generation. This feedback is used for more accurate analysis and suggestions.

[0257] Example 2

[0258] 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."

[0259] Conventional health management systems only collect users' health and lifestyle data and provide personalized meal plans and cooking instructions, but they do not take into account the user's emotional state, limiting their ability to improve mental health. Furthermore, when it comes to providing cooking instructions, they only provide general instructions and are unable to flexibly adapt to the user's emotional state. Therefore, there is a need for a comprehensive health management system that takes into account not only the user's health status but also their emotional state.

[0260] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0261] In this invention, the server includes means for collecting health data, lifestyle habits, diet, exercise data, and emotion data from the user, means for transmitting the collected data to the cloud server in real time, and means for storing the collected data in the cloud database, thereby enabling the provision of personalized meal plans and flexible cooking instructions based on the analysis results using machine learning algorithms and emotion analysis algorithms.

[0262] "Health data" refers to numerical data relating to the user's physical condition, such as weight, blood pressure, and heart rate.

[0263] "Lifestyle" refers to data about a user's daily life behaviors and habits, such as their sleep patterns, stress levels, diet, and exercise habits.

[0264] "Emotional data" refers to data about the user's emotional state obtained by analyzing their facial expressions and voice.

[0265] A "cloud server" is a server that provides data storage and computing resources over the Internet for storing and analyzing collected data.

[0266] A "machine learning algorithm" is an algorithm that automatically learns patterns and trends from collected data and makes predictions and classifications.

[0267] An "emotion analysis algorithm" is an algorithm that analyzes a user's facial expressions and voice data to identify the user's emotional state.

[0268] A "meal plan" is a plan that suggests the optimal meal menu for a user, taking into account the user's health and emotional state.

[0269] A "recipe" is a document that provides specific cooking instructions, ingredients, and nutritional information based on a meal plan.

[0270] "AR technology" stands for augmented reality technology, which displays virtual information overlaid on images of the real world.

[0271] The "emotion engine" is a system that analyzes the user's emotional state and responds accordingly.

[0272] MODE FOR CARRYING OUT THE INVENTION

[0273] This invention is a system that collects a user's health data, lifestyle habits, dietary data, exercise data, and emotional data, and analyzes them using AI and an emotional engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[0274] Hardware and software used

[0275] The main hardware components of this system are a smartphone, a smartwatch, a cloud server, and smart glasses, while the software components include a smartphone app, machine learning algorithms, emotion analysis algorithms, an emotion engine, and AR technology.

[0276] Data collection

[0277] Through a smartphone app or smartwatch, users record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary details, and exercise data (e.g., number of steps, exercise time). In addition, the device analyzes the user's facial expressions and voice to record their current emotional state (e.g., joy, sadness, stress, etc.) as emotional data. The device then transmits this data to a cloud server in real time. The transmitted data is encrypted to protect the user's privacy.

[0278] Example: When a user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration, sleep quality) and sends it to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data.

[0279] Data analysis

[0280] The server stores the received data in a cloud database and analyzes it. For example, it evaluates the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data. The server's analysis engine uses machine learning and emotion analysis algorithms to recognize patterns in the user's data. For example, it can identify vitamin D deficiency, lack of exercise, and even emotional fluctuations (e.g., periods of increased stress).

[0281] Example: A server receives data and detects low sleep, vitamin D deficiency, and increased stress.

[0282] Generate a meal plan

[0283] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if the user is feeling stressed, a meal plan that includes ingredients with a relaxing effect will be suggested.

[0284] Example: A server generates a relaxing meal plan high in vitamin D, suggesting, for example, "grilled salmon and spinach."

[0285] Creating a Recipe

[0286] The server creates a specific recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[0287] Example: The server creates a specific recipe and sends it to the user's smartphone app.

[0288] Cooking assistance

[0289] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. The emotion engine customizes the cooking instruction method according to the user's emotional state. For example, if the user is stressed, more friendly and slower instructions are provided.

[0290] Example: A user opens a smartphone app in the kitchen and starts cooking. The smart glasses, based on the emotion engine, will proceed with the cooking by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0291] Prompt Sentence Examples

[0292] "Please suggest the best breakfast for you based on your sleep time and mood data from yesterday."

[0293] These prompts can be fed into a generative AI model to get specific meal suggestions based on the user's data.

[0294] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0295] Step 1:

[0296] Users record health data, lifestyle habits, diet, exercise data, and emotional data through a smartphone app or smartwatch. The input is the user's physical data (e.g., weight, blood pressure, heart rate) and lifestyle data (e.g., sleep patterns, stress levels), and the output is the initial data stored in the app. Specifically, the user enters their weight into the app, and the smartwatch automatically measures their sleep time, among other things.

[0297] Step 2:

[0298] The device sends the collected data to a cloud server in real time. The input is encrypted user data, and the output is data stored on the cloud server. Specifically, the smartphone encrypts the data using the HTTPS protocol and sends it to the cloud server.

[0299] Step 3:

[0300] The server stores the received data in a cloud database. The input is the user data sent to the cloud server, and the output is the data stored in the cloud database. Specifically, the server writes the data to a specific table in the database.

[0301] Step 4:

[0302] The server analyzes the data using machine learning algorithms and sentiment analysis algorithms. The input is data stored in the cloud database, and the output is the analysis results (e.g., health assessment, emotional state assessment). Specifically, the algorithm retrieves data from the database for the past week and performs calculations to detect vitamin D deficiency and increased stress.

[0303] Step 5:

[0304] The server evaluates the user's health and emotional state based on the analysis results. The input is the analyzed data pattern, and the output is an evaluation report. Specifically, the server generates evaluation results such as "Vitamin D deficiency" or "High stress level."

[0305] Step 6:

[0306] The server generates a personalized meal plan based on the user's health and emotional state. The input is the assessment report, and the output is a specific meal plan. Specifically, the server generates a "relaxing meal plan rich in vitamin D" and suggests "grilled salmon and spinach."

[0307] Step 7:

[0308] The server creates a specific recipe based on the generated meal plan. The input is the meal plan, and the output is detailed recipe information. Specifically, the server generates a document containing cooking instructions such as "Wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0309] Step 8:

[0310] The server sends the recipe to the user's smartphone app. The input is the created recipe, and the output is the recipe displayed on the user's smartphone. Specifically, the server sends the recipe information to the app and displays a notification saying, "A new recipe has arrived."

[0311] Step 9:

[0312] A user opens a smartphone app and starts cooking according to a recipe. The input is the recipe displayed on the smartphone app, and the output is the user starting to cook. Specifically, the user taps the "View Recipe" button to start cooking.

[0313] Step 10:

[0314] The device uses AR technology to guide the user through cooking steps. The input is recipe information and the user's current cooking status, and the output is the cooking steps displayed using AR technology. Specifically, the smart glasses display instructions such as "Wash and chop the spinach" and "Next, grill the salmon over medium heat," guiding the user.

[0315] In this way, users can obtain optimal meal plans and specific cooking procedures, enabling efficient health management that takes into account their physical and emotional states.

[0316] (Application example 2)

[0317] 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."

[0318] While conventional health management systems have the ability to collect and analyze users' health data, lifestyle habits, dietary and exercise data, none of them take emotional data into account. As a result, they were unable to propose optimal meal plans that reflected the user's mental state. Furthermore, there was also the problem that instructions for actually cooking the proposed meal plans were not provided according to individual circumstances, such as when the user was feeling stressed.

[0319] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data, lifestyle habits, dietary habits, exercise data, and emotional data from the user, means for analyzing the collected data and generating an individualized meal plan based on the user's health condition, lifestyle habits, and emotional state, means for creating recipes based on the generated meal plan, selecting corresponding menus from affiliated restaurants and providing them to the user, and means for providing cooking instructions to the user using AR technology. This makes it possible to generate and implement a more personalized meal plan based on the user's health condition and emotional state.

[0320] "Health Data" refers to information about a user's physical health, such as their weight, blood pressure, and heart rate.

[0321] "Lifestyle" refers to information including a user's daily behavioral patterns, stress levels, sleep patterns, etc.

[0322] "Dietary data" refers to information about the contents and nutritional components of meals consumed by the user.

[0323] "Exercise data" refers to information such as the user's number of steps, exercise time, and calories burned.

[0324] "Emotional data" is information about the emotional state of a user that is analyzed from their facial expressions and voice.

[0325] A "cloud server" is a remote server that stores and processes data over the Internet.

[0326] An "analytics engine" is a system that includes programs and algorithms for analyzing collected data.

[0327] A "machine learning algorithm" is an algorithm that learns patterns from data and makes predictions and classifications.

[0328] An "emotion analysis algorithm" is an algorithm for analyzing a user's emotional state from their facial expressions and voice.

[0329] A "meal plan" is a combination of meals that are optimally recommended based on the user's health and emotional state.

[0330] "Corresponding menu" refers to the dishes offered by partner restaurants that match the generated meal plan.

[0331] "Partner restaurants" refer to restaurants that provide meals that correspond to the meal plans proposed to users.

[0332] "AR technology" is an abbreviation for augmented reality technology, which is a technology that displays computer-generated information overlaid on real-world visual information.

[0333] This invention is a system that collects and analyzes a user's health data, lifestyle habits, diet, and exercise data, as well as emotional data, using AI and an emotional engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[0334] Data collection

[0335] Users use smartphone apps or wearable devices to collect health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary content, exercise data (e.g., number of steps, exercise time), and emotional data (emotional state is recorded by analyzing facial expressions and voice). The devices send this data to a cloud server in real time, and the data is encrypted to protect the user's privacy.

[0336] Data analysis

[0337] The server stores the received data in a cloud database and analyzes it. For example, it evaluates the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data. The server's analysis engine uses a combination of machine learning and emotion analysis algorithms to recognize the user's data patterns and identify vitamin D deficiency, lack of exercise, and emotional fluctuations (e.g., periods of increased stress).

[0338] Generate a meal plan

[0339] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if a user is feeling stressed, a meal plan that includes ingredients that have a relaxing effect will be suggested.

[0340] Creating a Recipe

[0341] The server then creates a specific recipe based on the meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[0342] Cooking assistance

[0343] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. An emotion engine customizes the cooking instruction method according to the user's emotional state; for example, a user feeling stressed will be provided with gentler, more relaxed instructions.

[0344] As a concrete example, when a user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration and quality) and sends this to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data. The cloud server receives the data and detects that the user is getting less sleep, has a vitamin D deficiency, and is experiencing increased stress. The server then suggests "grilled salmon and spinach" as a meal plan that is rich in vitamin D and has a relaxing effect, and sends the specific recipe to the smartphone app. When the user opens the smartphone app in the kitchen and begins cooking, the smart glasses guide the user through the cooking process by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0345] An example of an input prompt for a generative AI model is:

[0346] "User health data: weight 70kg, blood pressure 120 / 80, heart rate 75

[0347] Lifestyle: 6 hours of sleep, high stress level

[0348] Current diet: Vitamin D deficiency

[0349] Exercise data: 3000 steps

[0350] Emotional data: Emotional state "Stress"

[0351] Suggest the best meal plan for this user."

[0352] It is written as follows.

[0353] This allows users to easily prepare meals that suit their own health and emotional state, enabling them to efficiently manage their daily health.

[0354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0355] Step 1:

[0356] Users collect health data, lifestyle habits, dietary details, exercise data, and emotional data.

[0357] Users use smartphone apps or wearable devices to record health data such as weight, blood pressure, and heart rate, lifestyle habits such as sleep patterns and stress levels, dietary intake, and exercise data such as number of steps and exercise time. Furthermore, the smartphone's camera and microphone are used to analyze facial expressions and voice to record emotional states. This data is sent from the device to a cloud server in real time.

[0358] Input: Health data, lifestyle data, dietary data, exercise data, emotional data

[0359] Output: Sending encrypted data to cloud server

[0360] Step 2:

[0361] The server stores the data in a cloud database and analyzes it using machine learning and sentiment analysis algorithms.

[0362] The server stores the collected data in a cloud database, and the analytics engine uses a combination of machine learning and sentiment analysis algorithms to assess the user's health and emotional state, recognizing patterns in the data and identifying periods of increased stress, such as vitamin D deficiency, lack of exercise, and increased stress.

[0363] Input: Encrypted data sent to the cloud server

[0364] Output: Health status evaluation results, emotional status evaluation results

[0365] Step 3:

[0366] Based on the analysis results, the server generates a personalized meal plan that takes into account the user's health and emotional state.

[0367] Based on the results of the health and emotional assessment, the server generates a meal plan to improve nutritional balance and mental state. For example, a segment experiencing stress will be suggested a menu containing ingredients that have a relaxing effect.

[0368] Input: Health status assessment results, emotional status assessment results

[0369] Output: User-optimized meal plan

[0370] Step 4:

[0371] Based on the meal plan generated by the server, the server creates specific recipes and selects corresponding menus from partner restaurants.

[0372] The server creates recipes based on the meal plan, including specific ingredient lists, cooking instructions, cooking times, and nutritional information, and selects menu items from partner restaurants that correspond to the recipes, allowing users to order the suggested dishes immediately.

[0373] Enter: meal plan

[0374] Output: Specific recipes and menus from partner restaurants

[0375] Step 5:

[0376] The server sends the created recipe and the corresponding menu to the user's smartphone app and notifies them.

[0377] The server then sends the created recipe and menu information from partner restaurants to the user's smartphone app, where the user receives a notification and can check the meal details.

[0378] Input: Specific recipes, menus from partner restaurants

[0379] Output: Notification to the user's smartphone app

[0380] Step 6:

[0381] When the user starts cooking, the device uses AR technology to guide them through the cooking process.

[0382] When a user opens the smartphone app and starts cooking, the smartphone, smart glasses, or other device uses AR technology to guide the user through the cooking process. An emotion engine analyzes the user's emotional state, and if the user is feeling stressed, friendly, slow-paced instructions are displayed.

[0383] Input: User's emotional state, specific recipe

[0384] Output: Customized cooking instructions for the user

[0385] Specific prompt examples:

[0386] "User health data: weight 70kg, blood pressure 120 / 80, heart rate 75

[0387] Lifestyle: 6 hours of sleep, high stress level

[0388] Current diet: Vitamin D deficiency

[0389] Exercise data: 3000 steps

[0390] Emotional data: Emotional state "Stress"

[0391] Suggest the best meal plan for this user."

[0392] 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.

[0393] 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.

[0394] 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.

[0395] [Second embodiment]

[0396] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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."

[0408] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[0409] Data collection

[0410] Users record health information such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data through a smartphone app or smartwatch. This data is sent from the device to a cloud server in real time or periodically. The data is encrypted before transmission, protecting the user's privacy.

[0411] Data analysis

[0412] The server analyzes the data it receives, comparing it with a large amount of past data stored in a database to assess the user's health status. Specifically, it uses machine learning algorithms to recognize patterns in the user's data and identify health issues such as vitamin D deficiency or lack of exercise.

[0413] Generate a meal plan

[0414] Based on the analysis results, the server generates a meal plan to improve the user's health. This plan takes into account the ingredients needed to supplement the user's nutrient deficiencies and the appropriate energy intake to balance daily calorie consumption. The meal plan includes specific ingredient selection and cooking methods.

[0415] Creating a Recipe

[0416] The server then creates a specific recipe based on the meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[0417] Cooking assistance

[0418] After receiving the recipe, when the user starts cooking, the device (e.g., smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. Specifically, it visually shows the specific steps for preparing ingredients, chopping, grilling, etc. For example, the smart glasses might instruct the user, "Place the spinach on the cutting board and use this position to chop it finely."

[0419] Specific examples

[0420] A specific user scenario is shown below.

[0421] When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep time, sleep quality), which is then sent to a cloud server.

[0422] The server receives the data and diagnoses insufficient sleep and vitamin D deficiency.

[0423] The server generates a vitamin D-rich meal plan, suggesting, for example, "grilled salmon and spinach."

[0424] The server creates a specific recipe and sends it to the user's smartphone app.

[0425] The user opens the smartphone app in the kitchen and starts cooking, while the smart glasses display specific instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0426] In this way, users can easily prepare meals that suit their own health condition and efficiently manage their daily health.

[0427] The processing flow will be explained below.

[0428] Step 1:

[0429] Users use smartphone apps or smartwatches to record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), diet, and exercise data (e.g., number of steps, exercise time).

[0430] Step 2:

[0431] The device sends data from the user to the cloud server in real time or periodically, and the data is encrypted to protect the user's privacy.

[0432] Step 3:

[0433] The server stores the received data in a cloud database, which is updated whenever new data is added.

[0434] Step 4:

[0435] The server's analytics engine analyzes the stored data, assessing the user's health status based on, for example, the past week's dietary data, exercise data, and sleep patterns.

[0436] Step 5:

[0437] The server applies machine learning algorithms to recognize patterns in the user's data, which can identify health issues such as vitamin D deficiency or lack of exercise.

[0438] Step 6:

[0439] The server generates a meal plan to improve the user's health, taking into account the ingredients and energy intake required to compensate for the user's nutrient deficiencies.

[0440] Step 7:

[0441] The server then creates a specific recipe based on the generated meal plan, including a list of ingredients, cooking instructions, cooking time, and nutritional information.

[0442] Step 8:

[0443] The server sends the created recipe to the user's smartphone app, and the user receives a notification that the new recipe is ready.

[0444] Step 9:

[0445] The user receives the recipe and prepares the necessary ingredients. The user then starts cooking in the kitchen based on the recipe.

[0446] Step 10:

[0447] The device uses a smartphone app or AR-enabled device to visually guide users through cooking steps, providing specific instructions such as "wash and finely chop the spinach" or "grill the salmon over medium heat for 5 minutes."

[0448] Step 11:

[0449] The device uses AR technology to visually show the location of ingredients and how to use cooking utensils, for example, "Place the cutting board here and use this position to chop the spinach."

[0450] Step 12:

[0451] The user follows the instructions to proceed with the cooking process, and when the cooking is complete, they register the completion with the app, which will be used as feedback when generating the next meal plan.

[0452] Example 1

[0453] 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."

[0454] In modern society, busy lifestyles often make health management difficult. In particular, designing a meal plan tailored to an individual's health condition requires specialized knowledge and is not easy for the average person. Furthermore, the process of implementing a meal plan can be complicated, including cooking methods and selecting the necessary ingredients, making it difficult to implement in everyday life. To solve these issues, a system is needed that collects and analyzes users' health data and provides appropriate meal plans and recipes based on them. Furthermore, a means of supporting users in the cooking process is also required.

[0455] 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.

[0456] In this invention, the server includes means for collecting biometric information, lifestyle, dietary, and exercise data from a user, means for encrypting the collected data and transmitting it to a cloud server, means for analyzing the received data using a machine learning algorithm and generating an individualized meal plan based on the user's health condition and lifestyle, means for creating recipes based on the generated meal plan and transmitting a list of ingredients, cooking instructions, cooking time, and nutritional information to a user terminal, and means for providing cooking instructions to the user using AR technology. This allows users to easily obtain a meal plan tailored to their health condition and visually understand specific cooking instructions, enabling more efficient and practical daily health management.

[0457] "User" refers to an individual who uses the system to manage their own health.

[0458] "Biometric information" refers to data that indicates a user's health status, such as weight, blood pressure, heart rate, and sleep patterns.

[0459] "Lifestyle" refers to the habits and behaviors of a user in their daily life, such as eating and exercise patterns.

[0460] "Dietary data" refers to information about the contents and ingredients of the meals consumed by the user.

[0461] "Exercise data" refers to information about the type of exercise a user performs, the duration, calories burned, etc.

[0462] "Cloud server" refers to a remote server used to store and analyze data via the Internet.

[0463] A "machine learning algorithm" refers to a computational method for finding patterns and rules from input data and making predictions and classifications.

[0464] "Meal Plan" refers to specific meal plans and ingredient combinations designed based on the user's health and lifestyle.

[0465] A "recipe" is a document created based on a meal plan that includes a specific list of ingredients, cooking instructions, and nutritional information.

[0466] "User terminal" refers to a device, such as a smartphone or tablet, that a user uses to display information received from the system.

[0467] "AR technology" refers to a technology that overlays digital information on real-world images and provides visual instructions to users.

[0468] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[0469] Data collection

[0470] Users record various health-related information, such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data, through smartphone apps or wearable devices. This data is encrypted and sent from the device to a cloud server in real time or periodically. Common biometric information collection APIs are used to link the data. For example, Apple's "HealthKit" or Google's "Fit API" can be used to efficiently collect data.

[0471] Data Transmission and Encryption

[0472] The device encrypts the collected data before sending it to the cloud server. Security protocols such as SSL / TLS are used for encryption. Wireless communication technologies such as Bluetooth and Wi-Fi are used to ensure stable data transmission.

[0473] Data analysis

[0474] The server first stores the received data in a database for analysis. It then analyzes the stored data using machine learning libraries such as Python's "TensorFlow" and "scikit-learn." By comparing it with past data, the system can accurately assess the user's health status and identify problems such as vitamin D deficiency or lack of exercise. Specifically, the data used is evaluated using statistical analysis and pattern recognition techniques.

[0475] Generate a meal plan

[0476] Based on the analyzed data, the server uses a generative AI model to generate an optimal meal plan to improve the user's health. This AI model incorporates neural networks and deep learning algorithms to select specific ingredients to supplement the user's nutrient deficiencies and devise a plan that takes into account the balance with calorie consumption. For example, if a user is determined to have a vitamin D deficiency, it can suggest grilled salmon and spinach.

[0477] Creating a Recipe

[0478] The server creates a recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app using Firebase Cloud Messaging, and the user is notified via a notification function. For example, the recipe might say, "To make grilled salmon and spinach to supplement vitamin D, you need the following ingredients and steps."

[0479] Cooking assistance

[0480] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to assist with the cooking steps. Specific steps are visually presented to support the user's cooking. For example, smart glasses may display specific instructions such as "Place the spinach on the cutting board and use this position to finely chop it," or "Grill the salmon over medium heat for 5 minutes."

[0481] Examples of prompts that can be used include:

[0482] "Take last night's sleep data and generate a vitamin D-rich meal plan and specific recipes for users who have been diagnosed with vitamin D deficiency. Also, display cooking instructions using AR technology."

[0483] This system allows users to efficiently obtain and prepare meal plans that suit their own health condition, enabling more effective health management in daily life.

[0484] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0485] Step 1:

[0486] Users use smartphone apps and wearable devices to record their weight, blood pressure, heart rate, sleep patterns, diet, exercise data, and more.

[0487] Input: User health data (e.g. weight, blood pressure, heart rate)

[0488] How it works: The user records sleep data on the smartwatch and syncs the data to the smartphone app.

[0489] Output: Recorded health data is stored in a smartphone app.

[0490] Step 2:

[0491] The device encrypts the collected health data and sends it to a cloud server.

[0492] Input: User health data stored in a smartphone app

[0493] How it works: The smartphone app encrypts health data using SSL / TLS and sends it to a cloud server using wireless communication technologies (Bluetooth, Wi-Fi).

[0494] Output: The encrypted data is sent to the cloud server.

[0495] Step 3:

[0496] The server receives the data sent to the cloud server and stores it in a database.

[0497] Input: Encrypted health data sent to a cloud server

[0498] Specific operation: The server decrypts the data and saves it in a table for storage in the database.

[0499] Output: Health data stored in a database

[0500] Step 4:

[0501] The server analyzes the stored data using machine learning algorithms.

[0502] Input: Health data stored in a database

[0503] How it works: It uses Python's TensorFlow and scikit-learn to analyze input data, including statistical analysis and pattern recognition to assess health conditions such as vitamin D deficiency and physical inactivity.

[0504] Output: User's health assessment data as a result of the analysis (e.g., vitamin D deficiency)

[0505] Step 5:

[0506] The server generates a meal plan based on the analysis results to improve the user's health.

[0507] Input: User's health assessment data

[0508] How it works: It uses a generative AI model to generate meal plans that take into account the nutritional needs of the user and their calorie intake. For example, if a vitamin D deficiency is identified, it will suggest grilled salmon and spinach.

[0509] Output: Optimized meal plan for each user

[0510] Step 6:

[0511] The server creates specific recipes based on the generated meal plan and sends them to the user's terminal.

[0512] Input: meal plan

[0513] What it does: The server generates a recipe that includes a list of ingredients, cooking instructions, cooking time, and nutritional information. It then uses Firebase Cloud Messaging to send the recipe to the smartphone app.

[0514] Output: The recipe displayed on the user's terminal

[0515] Step 7:

[0516] The user receives the recipe on their smartphone app and begins cooking.

[0517] Input: Recipe displayed on smartphone app

[0518] Specific operation: The user cooks in the kitchen while checking the recipe.

[0519] Output: Cooked food

[0520] Step 8:

[0521] The device uses AR technology to guide users through specific cooking steps while cooking.

[0522] Input: Cooking instructions based on the recipe displayed on the smartphone app

[0523] Specific actions: Smart glasses or a smartphone will display cooking steps in AR, providing specific instructions (e.g., "finely chop the spinach").

[0524] Output: Visual guidance to help users create a dish with precise steps

[0525] (Application example 1)

[0526] 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."

[0527] Existing health management systems can generate meal plans based on the data collected by users, but they lack specific support for actually purchasing ingredients and cooking. Users often experience difficulties, especially when selecting ingredients and following specific cooking procedures in physical stores. For this reason, there is a need for a comprehensive support system that allows users to efficiently purchase and cook meals based on their health status in physical stores.

[0528] 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.

[0529] In this invention, the server includes means for collecting health data, lifestyle, diet, and exercise data from the user, means for analyzing the collected data and generating an individual meal plan based on the user's health condition and lifestyle, means for creating recipes based on the generated meal plan and providing them to the user, and means for using AR technology to provide guidance on ingredients to be purchased and cooking procedures in a physical store, thereby enabling the user to select appropriate ingredients and cook efficiently in the physical store.

[0530] - "Health Data" refers to information about your body, such as your weight, blood pressure, heart rate, and sleep patterns.

[0531] "Lifestyle habits" refers to the user's daily habits, such as eating habits, exercise habits, and sleeping habits.

[0532] "Dietary data" refers to information such as the ingredients a user consumes, their nutritional content, and meal timing.

[0533] "Exercise data" refers to information such as the type of exercise performed by the user, the duration of the exercise, and the calories burned.

[0534] "Means of collection" refers to the methods of obtaining data via smartphones, smartwatches, and other devices.

[0535] "Analytics Engine" refers to a computer program used to analyze collected data and identify trends and patterns.

[0536] "Meal Plan" refers to a meal suggestion generated based on the analysis results to improve the user's health.

[0537] A "recipe" refers to instructions that list specific ingredients and cooking methods based on a meal plan.

[0538] "Means for providing" refers to a method for notifying or displaying the generated recipe to the user.

[0539] "AR technology" refers to the technology of overlaying digital information onto the real-world environment.

[0540] "Brick and mortar store" refers to a physical store where users actually visit and purchase products.

[0541] "Instruction" refers to a method of instructing users to take appropriate action.

[0542] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[0543] Data collection

[0544] Users use their smartphones or smartwatches to record health information such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data. This data is sent from the device to a cloud server in real time or periodically. The data is encrypted during transmission to protect user privacy.

[0545] Data analysis

[0546] The cloud server analyzes the received data. The server compares it with a large amount of past data stored in a database to evaluate the user's health status. This analysis uses machine learning algorithms (e.g., Random Forest) to recognize patterns in the user's data and identify health issues such as vitamin D deficiency or lack of exercise.

[0547] Generate a meal plan

[0548] Based on the analysis results, the cloud server generates a meal plan to improve the user's health. This plan takes into account the ingredients needed to supplement the user's nutrient deficiencies and the appropriate energy intake to balance daily calorie consumption. The meal plan includes specific ingredient selection and cooking methods.

[0549] Creating a Recipe

[0550] The cloud server creates specific recipes based on the generated meal plan. These recipes include a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipes are sent to the user's smartphone app and notified to the user via a notification function.

[0551] In-store support

[0552] After receiving the recipe, when choosing ingredients in a physical store, users can receive guidance on which ingredients to purchase and how to cook them using AR technology on their smart glasses or smartphones. Specifically, the smart glasses will provide visual instructions such as, "Place the spinach on the cutting board and use this position to finely chop it."

[0553] Specific examples

[0554] Here are some specific user scenarios:

[0555] 1. When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep time, sleep quality), which is then sent to a cloud server.

[0556] 2. The server receives the data and diagnoses insufficient sleep and vitamin D deficiency.

[0557] 3. The server generates a meal plan rich in vitamin D, suggesting, for example, "grilled salmon and spinach."

[0558] 4. The server creates a specific recipe and sends it to the user's smartphone app.

[0559] 5. The user opens the smartphone app in the kitchen and starts cooking. The smart glasses guide the user through the cooking process, displaying specific instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0560] In this way, users can easily prepare meals that suit their health condition and efficiently manage their daily health. Below is an example of a prompt:

[0561] prompt:

[0562] "30-year-old male, weighs 70 kg, exercise inactive, vitamin D deficient. Please suggest a suitable meal plan and specific recipes."

[0563] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0564] Step 1:

[0565] Users record health data, lifestyle habits, diet, and exercise data through their smartphones or smartwatches. This includes weight, blood pressure, heart rate, sleep patterns, dietary content, and exercise data. The collected data is encrypted and sent from the device to a cloud server. The input is the user's health data, and the output is encrypted data.

[0566] Step 2:

[0567] The server receives the transmitted data. The server stores this data in a cloud database and prepares it for analysis. The input is encrypted data, and the output is data stored in the cloud database. A cloud service such as Amazon RDS is used for database management.

[0568] Step 3:

[0569] The server analyzes the data stored in the cloud database. It uses a machine learning algorithm (e.g., Random Forest) to evaluate the user's health condition. The input is the stored user data, and the output is the analysis results. The analysis involves comparison with similar data from the past and trend analysis.

[0570] Step 4:

[0571] The server generates a meal plan based on the analysis results. This plan takes into account the ingredients needed to improve the user's health and the amount of energy intake needed to balance daily calorie expenditure. The input is the analysis results, and the output is a meal plan. Specifically, if a vitamin D deficiency is identified, the server will list foods that are rich in vitamin D.

[0572] Step 5:

[0573] The server creates a specific recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The input is the meal plan, and the output is a detailed recipe. For example, a recipe for grilled salmon and spinach is created.

[0574] Step 6:

[0575] The server sends the created recipe to the user's smartphone app. This step also notifies the user through the notification function. The input is a detailed recipe, and the output is a notification sent to the user's smartphone app. Here, a notification system such as Firebase Cloud Messaging is used.

[0576] Step 7:

[0577] When a user is choosing ingredients in a physical store, they receive guidance on which ingredients to purchase and how to cook them using AR technology on their smart glasses or smartphone. The input is the user's current location and recipe information, and the output is visual guidance information. Specifically, when a user wears the smart glasses in a physical store, they are shown a visual guide on how to place spinach on a cutting board and finely chop it.

[0578] Through these steps, users can efficiently cook meals that are optimal for their health condition and manage their health.

[0579] 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.

[0580] This invention is a system that collects and analyzes a user's health data, lifestyle habits, diet, exercise data, and emotional data using AI and an emotion engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[0581] Data collection

[0582] Users record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary habits, and exercise data (e.g., number of steps, exercise time) through smartphone apps or smartwatches. Furthermore, the system analyzes the user's facial expressions and voice to record their current emotional state (e.g., joy, sadness, stress, etc.) as emotional data.

[0583] The device sends this data to a cloud server in real time, and the data is encrypted to protect user privacy.

[0584] Data analysis

[0585] The server stores the received data in a cloud database and analyzes it, for example, assessing the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data.

[0586] The server-based analytics engine uses a combination of machine learning and sentiment analysis algorithms to recognize patterns in a user's data, such as vitamin D deficiency or lack of exercise, as well as emotional fluctuations (e.g., periods of increased stress).

[0587] Generate a meal plan

[0588] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if a user is feeling stressed, a meal plan that includes ingredients that have a relaxing effect will be suggested.

[0589] Creating a Recipe

[0590] The server creates specific recipes based on the generated meal plan. These recipes include a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipes are sent to the user's smartphone app and notified to the user via a notification function.

[0591] Cooking assistance

[0592] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. The emotion engine customizes the cooking instruction method according to the user's emotional state. For example, if the user is stressed, more friendly and slower instructions are provided.

[0593] Specific examples

[0594] A specific user scenario is shown below.

[0595] When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration, sleep quality) and sends it to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data.

[0596] The server receives the data and detects low sleep, vitamin D deficiency, and increased stress.

[0597] The server generates a relaxing meal plan high in vitamin D, suggesting, for example, "grilled salmon and spinach."

[0598] The server creates a specific recipe and sends it to the user's smartphone app.

[0599] The user opens the smartphone app in the kitchen and starts cooking. Using the emotion engine, the smart glasses guide the cooking process by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0600] In this way, users can easily prepare meals that suit their health and emotional state, and efficiently manage their daily health.

[0601] The processing flow will be explained below.

[0602] Step 1:

[0603] Users use smartphone apps or smartwatches to record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary habits, and exercise data (e.g., number of steps, exercise time).Furthermore, the smartphone's camera and voice recognition functions are used to record emotional data from facial expressions and voice.

[0604] Step 2:

[0605] The device sends data from the user to the cloud server in real time or periodically, and the data is encrypted to protect the user's privacy.

[0606] Step 3:

[0607] The server stores the received data in a cloud database, which is updated whenever new data is added.

[0608] Step 4:

[0609] The server's analysis engine analyzes health data, lifestyle data, dietary data, exercise data, and emotional data. For example, it evaluates the user's health and emotional state based on data from the past week.

[0610] Step 5:

[0611] The server uses a combination of machine learning and sentiment analysis algorithms to recognize patterns in a user's data, identifying, for example, vitamin D deficiency, lack of exercise, and even emotional fluctuations (times when stress levels are high).

[0612] Step 6:

[0613] The server generates a personalized meal plan based on the user's emotional data, taking into account the user's health and emotional state. The meal plan includes ingredients that improve the user's emotional state while taking into account nutritional balance. For example, if the user is feeling stressed, a meal plan that includes ingredients with a relaxing effect will be suggested.

[0614] Step 7:

[0615] The server then creates specific recipes based on the generated meal plan, including a list of ingredients, cooking instructions, cooking time, and nutritional information.

[0616] Step 8:

[0617] The server sends the created recipe to the user's smartphone app, where the user receives a notification and can check the recipe within the app.

[0618] Step 9:

[0619] After the user checks the recipe, they use the shopping list to prepare the ingredients they need, and then they start cooking according to the recipe.

[0620] Step 10:

[0621] The device (smartphone or smart glasses) uses AR technology to visually guide the user through the cooking process. An emotion engine customizes the instruction method based on the user's emotional state. For example, if the user is stressed, the instructions will be more helpful and clear.

[0622] Step 11:

[0623] The device displays cooking instructions in real time, directing the user to take specific actions, such as "wash and finely chop the spinach" or "grill the salmon over medium heat for 5 minutes."

[0624] Step 12:

[0625] The user follows the instructions on the device to proceed with the cooking process. When cooking is complete, the user registers the completion in the app, which uses the information as feedback for the next meal plan generation. This feedback is used for more accurate analysis and suggestions.

[0626] Example 2

[0627] 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."

[0628] Conventional health management systems only collect users' health and lifestyle data and provide personalized meal plans and cooking instructions, but they do not take into account the user's emotional state, limiting their ability to improve mental health. Furthermore, when it comes to providing cooking instructions, they only provide general instructions and are unable to flexibly adapt to the user's emotional state. Therefore, there is a need for a comprehensive health management system that takes into account not only the user's health status but also their emotional state.

[0629] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0630] In this invention, the server includes means for collecting health data, lifestyle habits, diet, exercise data, and emotion data from the user, means for transmitting the collected data to the cloud server in real time, and means for storing the collected data in the cloud database, thereby enabling the provision of personalized meal plans and flexible cooking instructions based on the analysis results using machine learning algorithms and emotion analysis algorithms.

[0631] "Health data" refers to numerical data relating to the user's physical condition, such as weight, blood pressure, and heart rate.

[0632] "Lifestyle" refers to data about a user's daily life behaviors and habits, such as their sleep patterns, stress levels, diet, and exercise habits.

[0633] "Emotional data" refers to data about the user's emotional state obtained by analyzing their facial expressions and voice.

[0634] A "cloud server" is a server that provides data storage and computing resources over the Internet for storing and analyzing collected data.

[0635] A "machine learning algorithm" is an algorithm that automatically learns patterns and trends from collected data and makes predictions and classifications.

[0636] An "emotion analysis algorithm" is an algorithm that analyzes a user's facial expressions and voice data to identify the user's emotional state.

[0637] A "meal plan" is a plan that suggests the optimal meal menu for a user, taking into account the user's health and emotional state.

[0638] A "recipe" is a document that provides specific cooking instructions, ingredients, and nutritional information based on a meal plan.

[0639] "AR technology" stands for augmented reality technology, which displays virtual information overlaid on images of the real world.

[0640] The "emotion engine" is a system that analyzes the user's emotional state and responds accordingly.

[0641] MODE FOR CARRYING OUT THE INVENTION

[0642] This invention is a system that collects a user's health data, lifestyle habits, dietary data, exercise data, and emotional data, and analyzes them using AI and an emotional engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[0643] Hardware and software used

[0644] The main hardware components of this system are a smartphone, a smartwatch, a cloud server, and smart glasses, while the software components include a smartphone app, machine learning algorithms, emotion analysis algorithms, an emotion engine, and AR technology.

[0645] Data collection

[0646] Through a smartphone app or smartwatch, users record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary details, and exercise data (e.g., number of steps, exercise time). In addition, the device analyzes the user's facial expressions and voice to record their current emotional state (e.g., joy, sadness, stress, etc.) as emotional data. The device then transmits this data to a cloud server in real time. The transmitted data is encrypted to protect the user's privacy.

[0647] Example: When a user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration, sleep quality) and sends it to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data.

[0648] Data analysis

[0649] The server stores the received data in a cloud database and analyzes it. For example, it evaluates the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data. The server's analysis engine uses machine learning and emotion analysis algorithms to recognize patterns in the user's data. For example, it can identify vitamin D deficiency, lack of exercise, and even emotional fluctuations (e.g., periods of increased stress).

[0650] Example: A server receives data and detects low sleep, vitamin D deficiency, and increased stress.

[0651] Generate a meal plan

[0652] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if the user is feeling stressed, a meal plan that includes ingredients with a relaxing effect will be suggested.

[0653] Example: A server generates a relaxing meal plan high in vitamin D, suggesting, for example, "grilled salmon and spinach."

[0654] Creating a Recipe

[0655] The server creates a specific recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[0656] Example: The server creates a specific recipe and sends it to the user's smartphone app.

[0657] Cooking assistance

[0658] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. The emotion engine customizes the cooking instruction method according to the user's emotional state. For example, if the user is stressed, more friendly and slower instructions are provided.

[0659] Example: A user opens a smartphone app in the kitchen and starts cooking. The smart glasses, based on the emotion engine, will proceed with the cooking by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0660] Prompt Sentence Examples

[0661] "Please suggest the best breakfast for you based on your sleep time and mood data from yesterday."

[0662] These prompts can be fed into a generative AI model to get specific meal suggestions based on the user's data.

[0663] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0664] Step 1:

[0665] Users record health data, lifestyle habits, diet, exercise data, and emotional data through a smartphone app or smartwatch. The input is the user's physical data (e.g., weight, blood pressure, heart rate) and lifestyle data (e.g., sleep patterns, stress levels), and the output is the initial data stored in the app. Specifically, the user enters their weight into the app, and the smartwatch automatically measures their sleep time, among other things.

[0666] Step 2:

[0667] The device sends the collected data to a cloud server in real time. The input is encrypted user data, and the output is data stored on the cloud server. Specifically, the smartphone encrypts the data using the HTTPS protocol and sends it to the cloud server.

[0668] Step 3:

[0669] The server stores the received data in a cloud database. The input is the user data sent to the cloud server, and the output is the data stored in the cloud database. Specifically, the server writes the data to a specific table in the database.

[0670] Step 4:

[0671] The server analyzes the data using machine learning algorithms and sentiment analysis algorithms. The input is data stored in the cloud database, and the output is the analysis results (e.g., health assessment, emotional state assessment). Specifically, the algorithm retrieves data from the database for the past week and performs calculations to detect vitamin D deficiency and increased stress.

[0672] Step 5:

[0673] The server evaluates the user's health and emotional state based on the analysis results. The input is the analyzed data pattern, and the output is an evaluation report. Specifically, the server generates evaluation results such as "Vitamin D deficiency" or "High stress level."

[0674] Step 6:

[0675] The server generates a personalized meal plan based on the user's health and emotional state. The input is the assessment report, and the output is a specific meal plan. Specifically, the server generates a "relaxing meal plan rich in vitamin D" and suggests "grilled salmon and spinach."

[0676] Step 7:

[0677] The server creates a specific recipe based on the generated meal plan. The input is the meal plan, and the output is detailed recipe information. Specifically, the server generates a document containing cooking instructions such as "Wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0678] Step 8:

[0679] The server sends the recipe to the user's smartphone app. The input is the created recipe, and the output is the recipe displayed on the user's smartphone. Specifically, the server sends the recipe information to the app and displays a notification saying, "A new recipe has arrived."

[0680] Step 9:

[0681] A user opens a smartphone app and starts cooking according to a recipe. The input is the recipe displayed on the smartphone app, and the output is the user starting to cook. Specifically, the user taps the "View Recipe" button to start cooking.

[0682] Step 10:

[0683] The device uses AR technology to guide the user through cooking steps. The input is recipe information and the user's current cooking status, and the output is the cooking steps displayed using AR technology. Specifically, the smart glasses display instructions such as "Wash and chop the spinach" and "Next, grill the salmon over medium heat," guiding the user.

[0684] In this way, users can obtain optimal meal plans and specific cooking procedures, enabling efficient health management that takes into account their physical and emotional states.

[0685] (Application example 2)

[0686] 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."

[0687] While conventional health management systems have the ability to collect and analyze users' health data, lifestyle habits, dietary and exercise data, none of them take emotional data into account. As a result, they were unable to propose optimal meal plans that reflected the user's mental state. Furthermore, there was also the problem that instructions for actually cooking the proposed meal plans were not provided according to individual circumstances, such as when the user was feeling stressed.

[0688] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data, lifestyle habits, dietary habits, exercise data, and emotional data from the user, means for analyzing the collected data and generating an individualized meal plan based on the user's health condition, lifestyle habits, and emotional state, means for creating recipes based on the generated meal plan, selecting corresponding menus from affiliated restaurants and providing them to the user, and means for providing cooking instructions to the user using AR technology. This makes it possible to generate and implement a more personalized meal plan based on the user's health condition and emotional state.

[0689] "Health Data" refers to information about a user's physical health, such as their weight, blood pressure, and heart rate.

[0690] "Lifestyle" refers to information including a user's daily behavioral patterns, stress levels, sleep patterns, etc.

[0691] "Dietary data" refers to information about the contents and nutritional components of meals consumed by the user.

[0692] "Exercise data" refers to information such as the user's number of steps, exercise time, and calories burned.

[0693] "Emotional data" is information about the emotional state of a user that is analyzed from their facial expressions and voice.

[0694] A "cloud server" is a remote server that stores and processes data over the Internet.

[0695] An "analytics engine" is a system that includes programs and algorithms for analyzing collected data.

[0696] A "machine learning algorithm" is an algorithm that learns patterns from data and makes predictions and classifications.

[0697] An "emotion analysis algorithm" is an algorithm for analyzing a user's emotional state from their facial expressions and voice.

[0698] A "meal plan" is a combination of meals that are optimally recommended based on the user's health and emotional state.

[0699] "Corresponding menu" refers to the dishes offered by partner restaurants that match the generated meal plan.

[0700] "Partner restaurants" refer to restaurants that provide meals that correspond to the meal plans proposed to users.

[0701] "AR technology" is an abbreviation for augmented reality technology, which is a technology that displays computer-generated information overlaid on real-world visual information.

[0702] This invention is a system that collects and analyzes a user's health data, lifestyle habits, diet, and exercise data, as well as emotional data, using AI and an emotional engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[0703] Data collection

[0704] Users use smartphone apps or wearable devices to collect health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary content, exercise data (e.g., number of steps, exercise time), and emotional data (emotional state is recorded by analyzing facial expressions and voice). The devices send this data to a cloud server in real time, and the data is encrypted to protect the user's privacy.

[0705] Data analysis

[0706] The server stores the received data in a cloud database and analyzes it. For example, it evaluates the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data. The server's analysis engine uses a combination of machine learning and emotion analysis algorithms to recognize the user's data patterns and identify vitamin D deficiency, lack of exercise, and emotional fluctuations (e.g., periods of increased stress).

[0707] Generate a meal plan

[0708] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if a user is feeling stressed, a meal plan that includes ingredients that have a relaxing effect will be suggested.

[0709] Creating a Recipe

[0710] The server then creates a specific recipe based on the meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[0711] Cooking assistance

[0712] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. An emotion engine customizes the cooking instruction method according to the user's emotional state; for example, a user feeling stressed will be provided with gentler, more relaxed instructions.

[0713] As a concrete example, when a user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration and quality) and sends this to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data. The cloud server receives the data and detects that the user is getting less sleep, has a vitamin D deficiency, and is experiencing increased stress. The server then suggests "grilled salmon and spinach" as a meal plan that is rich in vitamin D and has a relaxing effect, and sends the specific recipe to the smartphone app. When the user opens the smartphone app in the kitchen and begins cooking, the smart glasses guide the user through the cooking process by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0714] An example of an input prompt for a generative AI model is:

[0715] "User health data: weight 70kg, blood pressure 120 / 80, heart rate 75

[0716] Lifestyle: 6 hours of sleep, high stress level

[0717] Current diet: Vitamin D deficiency

[0718] Exercise data: 3000 steps

[0719] Emotional data: Emotional state "Stress"

[0720] Suggest the best meal plan for this user."

[0721] It is written as follows.

[0722] This allows users to easily prepare meals that suit their own health and emotional state, enabling them to efficiently manage their daily health.

[0723] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0724] Step 1:

[0725] Users collect health data, lifestyle habits, dietary details, exercise data, and emotional data.

[0726] Users use smartphone apps or wearable devices to record health data such as weight, blood pressure, and heart rate, lifestyle habits such as sleep patterns and stress levels, dietary intake, and exercise data such as number of steps and exercise time. Furthermore, the smartphone's camera and microphone are used to analyze facial expressions and voice to record emotional states. This data is sent from the device to a cloud server in real time.

[0727] Input: Health data, lifestyle data, dietary data, exercise data, emotional data

[0728] Output: Sending encrypted data to cloud server

[0729] Step 2:

[0730] The server stores the data in a cloud database and analyzes it using machine learning and sentiment analysis algorithms.

[0731] The server stores the collected data in a cloud database, and the analytics engine uses a combination of machine learning and sentiment analysis algorithms to assess the user's health and emotional state, recognizing patterns in the data and identifying periods of increased stress, such as vitamin D deficiency, lack of exercise, and increased stress.

[0732] Input: Encrypted data sent to the cloud server

[0733] Output: Health status evaluation results, emotional status evaluation results

[0734] Step 3:

[0735] Based on the analysis results, the server generates a personalized meal plan that takes into account the user's health and emotional state.

[0736] Based on the results of the health and emotional assessment, the server generates a meal plan to improve nutritional balance and mental state. For example, a segment experiencing stress will be suggested a menu containing ingredients that have a relaxing effect.

[0737] Input: Health status assessment results, emotional status assessment results

[0738] Output: User-optimized meal plan

[0739] Step 4:

[0740] Based on the meal plan generated by the server, the server creates specific recipes and selects corresponding menus from partner restaurants.

[0741] The server creates recipes based on the meal plan, including specific ingredient lists, cooking instructions, cooking times, and nutritional information, and selects menu items from partner restaurants that correspond to the recipes, allowing users to order the suggested dishes immediately.

[0742] Enter: meal plan

[0743] Output: Specific recipes and menus from partner restaurants

[0744] Step 5:

[0745] The server sends the created recipe and the corresponding menu to the user's smartphone app and notifies them.

[0746] The server then sends the created recipe and menu information from partner restaurants to the user's smartphone app, where the user receives a notification and can check the meal details.

[0747] Input: Specific recipes, menus from partner restaurants

[0748] Output: Notification to the user's smartphone app

[0749] Step 6:

[0750] When the user starts cooking, the device uses AR technology to guide them through the cooking process.

[0751] When a user opens the smartphone app and starts cooking, the smartphone, smart glasses, or other device uses AR technology to guide the user through the cooking process. An emotion engine analyzes the user's emotional state, and if the user is feeling stressed, friendly, slow-paced instructions are displayed.

[0752] Input: User's emotional state, specific recipe

[0753] Output: Customized cooking instructions for the user

[0754] Specific prompt examples:

[0755] "User health data: weight 70kg, blood pressure 120 / 80, heart rate 75

[0756] Lifestyle: 6 hours of sleep, high stress level

[0757] Current diet: Vitamin D deficiency

[0758] Exercise data: 3000 steps

[0759] Emotional data: Emotional state "Stress"

[0760] Suggest the best meal plan for this user."

[0761] 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.

[0762] 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.

[0763] 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.

[0764] [Third embodiment]

[0765] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0766] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0767] 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).

[0768] 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.

[0769] 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.

[0770] 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).

[0771] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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."

[0777] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[0778] Data collection

[0779] Users record health information such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data through a smartphone app or smartwatch. This data is sent from the device to a cloud server in real time or periodically. The data is encrypted before transmission, protecting the user's privacy.

[0780] Data analysis

[0781] The server analyzes the data it receives, comparing it with a large amount of past data stored in a database to assess the user's health status. Specifically, it uses machine learning algorithms to recognize patterns in the user's data and identify health issues such as vitamin D deficiency or lack of exercise.

[0782] Generate a meal plan

[0783] Based on the analysis results, the server generates a meal plan to improve the user's health. This plan takes into account the ingredients needed to supplement the user's nutrient deficiencies and the appropriate energy intake to balance daily calorie consumption. The meal plan includes specific ingredient selection and cooking methods.

[0784] Creating a Recipe

[0785] The server then creates a specific recipe based on the meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[0786] Cooking assistance

[0787] After receiving the recipe, when the user starts cooking, the device (e.g., smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. Specifically, it visually shows the specific steps for preparing ingredients, chopping, grilling, etc. For example, the smart glasses might instruct the user, "Place the spinach on the cutting board and use this position to chop it finely."

[0788] Specific examples

[0789] A specific user scenario is shown below.

[0790] When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep time, sleep quality), which is then sent to a cloud server.

[0791] The server receives the data and diagnoses insufficient sleep and vitamin D deficiency.

[0792] The server generates a vitamin D-rich meal plan, suggesting, for example, "grilled salmon and spinach."

[0793] The server creates a specific recipe and sends it to the user's smartphone app.

[0794] The user opens the smartphone app in the kitchen and starts cooking, while the smart glasses display specific instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0795] In this way, users can easily prepare meals that suit their own health condition and efficiently manage their daily health.

[0796] The processing flow will be explained below.

[0797] Step 1:

[0798] Users use smartphone apps or smartwatches to record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), diet, and exercise data (e.g., number of steps, exercise time).

[0799] Step 2:

[0800] The device sends data from the user to the cloud server in real time or periodically, and the data is encrypted to protect the user's privacy.

[0801] Step 3:

[0802] The server stores the received data in a cloud database, which is updated whenever new data is added.

[0803] Step 4:

[0804] The server's analytics engine analyzes the stored data, assessing the user's health status based on, for example, the past week's dietary data, exercise data, and sleep patterns.

[0805] Step 5:

[0806] The server applies machine learning algorithms to recognize patterns in the user's data, which can identify health issues such as vitamin D deficiency or lack of exercise.

[0807] Step 6:

[0808] The server generates a meal plan to improve the user's health, taking into account the ingredients and energy intake required to compensate for the user's nutrient deficiencies.

[0809] Step 7:

[0810] The server then creates a specific recipe based on the generated meal plan, including a list of ingredients, cooking instructions, cooking time, and nutritional information.

[0811] Step 8:

[0812] The server sends the created recipe to the user's smartphone app, and the user receives a notification that the new recipe is ready.

[0813] Step 9:

[0814] The user receives the recipe and prepares the necessary ingredients. The user then starts cooking in the kitchen based on the recipe.

[0815] Step 10:

[0816] The device uses a smartphone app or AR-enabled device to visually guide users through cooking steps, providing specific instructions such as "wash and finely chop the spinach" or "grill the salmon over medium heat for 5 minutes."

[0817] Step 11:

[0818] The device uses AR technology to visually show the location of ingredients and how to use cooking utensils, for example, "Place the cutting board here and use this position to chop the spinach."

[0819] Step 12:

[0820] The user follows the instructions to proceed with the cooking process, and when the cooking is complete, they register the completion with the app, which will be used as feedback when generating the next meal plan.

[0821] Example 1

[0822] 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."

[0823] In modern society, busy lifestyles often make health management difficult. In particular, designing a meal plan tailored to an individual's health condition requires specialized knowledge and is not easy for the average person. Furthermore, the process of implementing a meal plan can be complicated, including cooking methods and selecting the necessary ingredients, making it difficult to implement in everyday life. To solve these issues, a system is needed that collects and analyzes users' health data and provides appropriate meal plans and recipes based on them. Furthermore, a means of supporting users in the cooking process is also required.

[0824] 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.

[0825] In this invention, the server includes means for collecting biometric information, lifestyle, dietary, and exercise data from a user, means for encrypting the collected data and transmitting it to a cloud server, means for analyzing the received data using a machine learning algorithm and generating an individualized meal plan based on the user's health condition and lifestyle, means for creating recipes based on the generated meal plan and transmitting a list of ingredients, cooking instructions, cooking time, and nutritional information to a user terminal, and means for providing cooking instructions to the user using AR technology. This allows users to easily obtain a meal plan tailored to their health condition and visually understand specific cooking instructions, enabling more efficient and practical daily health management.

[0826] "User" refers to an individual who uses the system to manage their own health.

[0827] "Biometric information" refers to data that indicates a user's health status, such as weight, blood pressure, heart rate, and sleep patterns.

[0828] "Lifestyle" refers to the habits and behaviors of a user in their daily life, such as eating and exercise patterns.

[0829] "Dietary data" refers to information about the contents and ingredients of the meals consumed by the user.

[0830] "Exercise data" refers to information about the type of exercise a user performs, the duration, calories burned, etc.

[0831] "Cloud server" refers to a remote server used to store and analyze data via the Internet.

[0832] A "machine learning algorithm" refers to a computational method for finding patterns and rules from input data and making predictions and classifications.

[0833] "Meal Plan" refers to specific meal plans and ingredient combinations designed based on the user's health and lifestyle.

[0834] A "recipe" is a document created based on a meal plan that includes a specific list of ingredients, cooking instructions, and nutritional information.

[0835] "User terminal" refers to a device, such as a smartphone or tablet, that a user uses to display information received from the system.

[0836] "AR technology" refers to a technology that overlays digital information on real-world images and provides visual instructions to users.

[0837] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[0838] Data collection

[0839] Users record various health-related information, such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data, through smartphone apps or wearable devices. This data is encrypted and sent from the device to a cloud server in real time or periodically. Common biometric information collection APIs are used to link the data. For example, Apple's "HealthKit" or Google's "Fit API" can be used to efficiently collect data.

[0840] Data Transmission and Encryption

[0841] The device encrypts the collected data before sending it to the cloud server. Security protocols such as SSL / TLS are used for encryption. Wireless communication technologies such as Bluetooth and Wi-Fi are used to ensure stable data transmission.

[0842] Data analysis

[0843] The server first stores the received data in a database for analysis. It then analyzes the stored data using machine learning libraries such as Python's "TensorFlow" and "scikit-learn." By comparing it with past data, the system can accurately assess the user's health status and identify problems such as vitamin D deficiency or lack of exercise. Specifically, the data used is evaluated using statistical analysis and pattern recognition techniques.

[0844] Generate a meal plan

[0845] Based on the analyzed data, the server uses a generative AI model to generate an optimal meal plan to improve the user's health. This AI model incorporates neural networks and deep learning algorithms to select specific ingredients to supplement the user's nutrient deficiencies and devise a plan that takes into account the balance with calorie consumption. For example, if a user is determined to have a vitamin D deficiency, it can suggest grilled salmon and spinach.

[0846] Creating a Recipe

[0847] The server creates a recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app using Firebase Cloud Messaging, and the user is notified via a notification function. For example, the recipe might say, "To make grilled salmon and spinach to supplement vitamin D, you need the following ingredients and steps."

[0848] Cooking assistance

[0849] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to assist with the cooking steps. Specific steps are visually presented to support the user's cooking. For example, smart glasses may display specific instructions such as "Place the spinach on the cutting board and use this position to finely chop it," or "Grill the salmon over medium heat for 5 minutes."

[0850] Examples of prompts that can be used include:

[0851] "Take last night's sleep data and generate a vitamin D-rich meal plan and specific recipes for users who have been diagnosed with vitamin D deficiency. Also, display cooking instructions using AR technology."

[0852] This system allows users to efficiently obtain and prepare meal plans that suit their own health condition, enabling more effective health management in daily life.

[0853] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0854] Step 1:

[0855] Users use smartphone apps and wearable devices to record their weight, blood pressure, heart rate, sleep patterns, diet, exercise data, and more.

[0856] Input: User health data (e.g. weight, blood pressure, heart rate)

[0857] How it works: The user records sleep data on the smartwatch and syncs the data to the smartphone app.

[0858] Output: Recorded health data is stored in a smartphone app.

[0859] Step 2:

[0860] The device encrypts the collected health data and sends it to a cloud server.

[0861] Input: User health data stored in a smartphone app

[0862] How it works: The smartphone app encrypts health data using SSL / TLS and sends it to a cloud server using wireless communication technologies (Bluetooth, Wi-Fi).

[0863] Output: The encrypted data is sent to the cloud server.

[0864] Step 3:

[0865] The server receives the data sent to the cloud server and stores it in a database.

[0866] Input: Encrypted health data sent to a cloud server

[0867] Specific operation: The server decrypts the data and saves it in a table for storage in the database.

[0868] Output: Health data stored in a database

[0869] Step 4:

[0870] The server analyzes the stored data using machine learning algorithms.

[0871] Input: Health data stored in a database

[0872] How it works: It uses Python's TensorFlow and scikit-learn to analyze input data, including statistical analysis and pattern recognition to assess health conditions such as vitamin D deficiency and physical inactivity.

[0873] Output: User's health assessment data as a result of the analysis (e.g., vitamin D deficiency)

[0874] Step 5:

[0875] The server generates a meal plan based on the analysis results to improve the user's health.

[0876] Input: User's health assessment data

[0877] How it works: It uses a generative AI model to generate meal plans that take into account the nutritional needs of the user and their calorie intake. For example, if a vitamin D deficiency is identified, it will suggest grilled salmon and spinach.

[0878] Output: Optimized meal plan for each user

[0879] Step 6:

[0880] The server creates specific recipes based on the generated meal plan and sends them to the user's terminal.

[0881] Input: meal plan

[0882] What it does: The server generates a recipe that includes a list of ingredients, cooking instructions, cooking time, and nutritional information. It then uses Firebase Cloud Messaging to send the recipe to the smartphone app.

[0883] Output: The recipe displayed on the user's terminal

[0884] Step 7:

[0885] The user receives the recipe on their smartphone app and begins cooking.

[0886] Input: Recipe displayed on smartphone app

[0887] Specific operation: The user cooks in the kitchen while checking the recipe.

[0888] Output: Cooked food

[0889] Step 8:

[0890] The device uses AR technology to guide users through specific cooking steps while cooking.

[0891] Input: Cooking instructions based on the recipe displayed on the smartphone app

[0892] Specific actions: Smart glasses or a smartphone will display cooking steps in AR, providing specific instructions (e.g., "finely chop the spinach").

[0893] Output: Visual guidance to help users create a dish with precise steps

[0894] (Application example 1)

[0895] 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."

[0896] Existing health management systems can generate meal plans based on the data collected by users, but they lack specific support for actually purchasing ingredients and cooking. Users often experience difficulties, especially when selecting ingredients and following specific cooking procedures in physical stores. For this reason, there is a need for a comprehensive support system that allows users to efficiently purchase and cook meals based on their health status in physical stores.

[0897] 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.

[0898] In this invention, the server includes means for collecting health data, lifestyle, diet, and exercise data from the user, means for analyzing the collected data and generating an individual meal plan based on the user's health condition and lifestyle, means for creating recipes based on the generated meal plan and providing them to the user, and means for using AR technology to provide guidance on ingredients to be purchased and cooking procedures in a physical store, thereby enabling the user to select appropriate ingredients and cook efficiently in the physical store.

[0899] - "Health Data" refers to information about your body, such as your weight, blood pressure, heart rate, and sleep patterns.

[0900] "Lifestyle habits" refers to the user's daily habits, such as eating habits, exercise habits, and sleeping habits.

[0901] "Dietary data" refers to information such as the ingredients a user consumes, their nutritional content, and meal timing.

[0902] "Exercise data" refers to information such as the type of exercise performed by the user, the duration of the exercise, and the calories burned.

[0903] "Means of collection" refers to the methods of obtaining data via smartphones, smartwatches, and other devices.

[0904] "Analytics Engine" refers to a computer program used to analyze collected data and identify trends and patterns.

[0905] "Meal Plan" refers to a meal suggestion generated based on the analysis results to improve the user's health.

[0906] A "recipe" refers to instructions that list specific ingredients and cooking methods based on a meal plan.

[0907] "Means for providing" refers to a method for notifying or displaying the generated recipe to the user.

[0908] "AR technology" refers to the technology of overlaying digital information onto the real-world environment.

[0909] "Brick and mortar store" refers to a physical store where users actually visit and purchase products.

[0910] "Instruction" refers to a method of instructing users to take appropriate action.

[0911] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[0912] Data collection

[0913] Users use their smartphones or smartwatches to record health information such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data. This data is sent from the device to a cloud server in real time or periodically. The data is encrypted during transmission to protect user privacy.

[0914] Data analysis

[0915] The cloud server analyzes the received data. The server compares it with a large amount of past data stored in a database to evaluate the user's health status. This analysis uses machine learning algorithms (e.g., Random Forest) to recognize patterns in the user's data and identify health issues such as vitamin D deficiency or lack of exercise.

[0916] Generate a meal plan

[0917] Based on the analysis results, the cloud server generates a meal plan to improve the user's health. This plan takes into account the ingredients needed to supplement the user's nutrient deficiencies and the appropriate energy intake to balance daily calorie consumption. The meal plan includes specific ingredient selection and cooking methods.

[0918] Creating a Recipe

[0919] The cloud server creates specific recipes based on the generated meal plan. These recipes include a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipes are sent to the user's smartphone app and notified to the user via a notification function.

[0920] In-store support

[0921] After receiving the recipe, when choosing ingredients in a physical store, users can receive guidance on which ingredients to purchase and how to cook them using AR technology on their smart glasses or smartphones. Specifically, the smart glasses will provide visual instructions such as, "Place the spinach on the cutting board and use this position to finely chop it."

[0922] Specific examples

[0923] Here are some specific user scenarios:

[0924] 1. When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep time, sleep quality), which is then sent to a cloud server.

[0925] 2. The server receives the data and diagnoses insufficient sleep and vitamin D deficiency.

[0926] 3. The server generates a meal plan rich in vitamin D, suggesting, for example, "grilled salmon and spinach."

[0927] 4. The server creates a specific recipe and sends it to the user's smartphone app.

[0928] 5. The user opens the smartphone app in the kitchen and starts cooking. The smart glasses guide the user through the cooking process, displaying specific instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0929] In this way, users can easily prepare meals that suit their health condition and efficiently manage their daily health. Below is an example of a prompt:

[0930] prompt:

[0931] "30-year-old male, weighs 70 kg, exercise inactive, vitamin D deficient. Please suggest a suitable meal plan and specific recipes."

[0932] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0933] Step 1:

[0934] Users record health data, lifestyle habits, diet, and exercise data through their smartphones or smartwatches. This includes weight, blood pressure, heart rate, sleep patterns, dietary content, and exercise data. The collected data is encrypted and sent from the device to a cloud server. The input is the user's health data, and the output is encrypted data.

[0935] Step 2:

[0936] The server receives the transmitted data. The server stores this data in a cloud database and prepares it for analysis. The input is encrypted data, and the output is data stored in the cloud database. A cloud service such as Amazon RDS is used for database management.

[0937] Step 3:

[0938] The server analyzes the data stored in the cloud database. It uses a machine learning algorithm (e.g., Random Forest) to evaluate the user's health condition. The input is the stored user data, and the output is the analysis results. The analysis involves comparison with similar data from the past and trend analysis.

[0939] Step 4:

[0940] The server generates a meal plan based on the analysis results. This plan takes into account the ingredients needed to improve the user's health and the amount of energy intake needed to balance daily calorie expenditure. The input is the analysis results, and the output is a meal plan. Specifically, if a vitamin D deficiency is identified, the server will list foods that are rich in vitamin D.

[0941] Step 5:

[0942] The server creates a specific recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The input is the meal plan, and the output is a detailed recipe. For example, a recipe for grilled salmon and spinach is created.

[0943] Step 6:

[0944] The server sends the created recipe to the user's smartphone app. This step also notifies the user through the notification function. The input is a detailed recipe, and the output is a notification sent to the user's smartphone app. Here, a notification system such as Firebase Cloud Messaging is used.

[0945] Step 7:

[0946] When a user is choosing ingredients in a physical store, they receive guidance on which ingredients to purchase and how to cook them using AR technology on their smart glasses or smartphone. The input is the user's current location and recipe information, and the output is visual guidance information. Specifically, when a user wears the smart glasses in a physical store, they are shown a visual guide on how to place spinach on a cutting board and finely chop it.

[0947] Through these steps, users can efficiently cook meals that are optimal for their health condition and manage their health.

[0948] 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.

[0949] This invention is a system that collects and analyzes a user's health data, lifestyle habits, diet, exercise data, and emotional data using AI and an emotion engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[0950] Data collection

[0951] Users record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary habits, and exercise data (e.g., number of steps, exercise time) through smartphone apps or smartwatches. Furthermore, the system analyzes the user's facial expressions and voice to record their current emotional state (e.g., joy, sadness, stress, etc.) as emotional data.

[0952] The device sends this data to a cloud server in real time, and the data is encrypted to protect user privacy.

[0953] Data analysis

[0954] The server stores the received data in a cloud database and analyzes it, for example, assessing the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data.

[0955] The server-based analytics engine uses a combination of machine learning and sentiment analysis algorithms to recognize patterns in a user's data, such as vitamin D deficiency or lack of exercise, as well as emotional fluctuations (e.g., periods of increased stress).

[0956] Generate a meal plan

[0957] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if a user is feeling stressed, a meal plan that includes ingredients that have a relaxing effect will be suggested.

[0958] Creating a Recipe

[0959] The server creates specific recipes based on the generated meal plan. These recipes include a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipes are sent to the user's smartphone app and notified to the user via a notification function.

[0960] Cooking assistance

[0961] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. The emotion engine customizes the cooking instruction method according to the user's emotional state. For example, if the user is stressed, more friendly and slower instructions are provided.

[0962] Specific examples

[0963] A specific user scenario is shown below.

[0964] When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration, sleep quality) and sends it to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data.

[0965] The server receives the data and detects low sleep, vitamin D deficiency, and increased stress.

[0966] The server generates a relaxing meal plan high in vitamin D, suggesting, for example, "grilled salmon and spinach."

[0967] The server creates a specific recipe and sends it to the user's smartphone app.

[0968] The user opens the smartphone app in the kitchen and starts cooking. Using the emotion engine, the smart glasses guide the cooking process by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[0969] In this way, users can easily prepare meals that suit their health and emotional state, and efficiently manage their daily health.

[0970] The processing flow will be explained below.

[0971] Step 1:

[0972] Users use smartphone apps or smartwatches to record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary habits, and exercise data (e.g., number of steps, exercise time).Furthermore, the smartphone's camera and voice recognition functions are used to record emotional data from facial expressions and voice.

[0973] Step 2:

[0974] The device sends data from the user to the cloud server in real time or periodically, and the data is encrypted to protect the user's privacy.

[0975] Step 3:

[0976] The server stores the received data in a cloud database, which is updated whenever new data is added.

[0977] Step 4:

[0978] The server's analysis engine analyzes health data, lifestyle data, dietary data, exercise data, and emotional data. For example, it evaluates the user's health and emotional state based on data from the past week.

[0979] Step 5:

[0980] The server uses a combination of machine learning and sentiment analysis algorithms to recognize patterns in a user's data, identifying, for example, vitamin D deficiency, lack of exercise, and even emotional fluctuations (times when stress levels are high).

[0981] Step 6:

[0982] The server generates a personalized meal plan based on the user's emotional data, taking into account the user's health and emotional state. The meal plan includes ingredients that improve the user's emotional state while taking into account nutritional balance. For example, if the user is feeling stressed, a meal plan that includes ingredients with a relaxing effect will be suggested.

[0983] Step 7:

[0984] The server then creates specific recipes based on the generated meal plan, including a list of ingredients, cooking instructions, cooking time, and nutritional information.

[0985] Step 8:

[0986] The server sends the created recipe to the user's smartphone app, where the user receives a notification and can check the recipe within the app.

[0987] Step 9:

[0988] After the user checks the recipe, they use the shopping list to prepare the ingredients they need, and then they start cooking according to the recipe.

[0989] Step 10:

[0990] The device (smartphone or smart glasses) uses AR technology to visually guide the user through the cooking process. An emotion engine customizes the instruction method based on the user's emotional state. For example, if the user is stressed, the instructions will be more helpful and clear.

[0991] Step 11:

[0992] The device displays cooking instructions in real time, directing the user to take specific actions, such as "wash and finely chop the spinach" or "grill the salmon over medium heat for 5 minutes."

[0993] Step 12:

[0994] The user follows the instructions on the device to proceed with the cooking process. When cooking is complete, the user registers the completion in the app, which uses the information as feedback for the next meal plan generation. This feedback is used for more accurate analysis and suggestions.

[0995] Example 2

[0996] 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."

[0997] Conventional health management systems only collect users' health and lifestyle data and provide personalized meal plans and cooking instructions, but they do not take into account the user's emotional state, limiting their ability to improve mental health. Furthermore, when it comes to providing cooking instructions, they only provide general instructions and are unable to flexibly adapt to the user's emotional state. Therefore, there is a need for a comprehensive health management system that takes into account not only the user's health status but also their emotional state.

[0998] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0999] In this invention, the server includes means for collecting health data, lifestyle habits, diet, exercise data, and emotion data from the user, means for transmitting the collected data to the cloud server in real time, and means for storing the collected data in the cloud database, thereby enabling the provision of personalized meal plans and flexible cooking instructions based on the analysis results using machine learning algorithms and emotion analysis algorithms.

[1000] "Health data" refers to numerical data relating to the user's physical condition, such as weight, blood pressure, and heart rate.

[1001] "Lifestyle" refers to data about a user's daily life behaviors and habits, such as their sleep patterns, stress levels, diet, and exercise habits.

[1002] "Emotional data" refers to data about the user's emotional state obtained by analyzing their facial expressions and voice.

[1003] A "cloud server" is a server that provides data storage and computing resources over the Internet for storing and analyzing collected data.

[1004] A "machine learning algorithm" is an algorithm that automatically learns patterns and trends from collected data and makes predictions and classifications.

[1005] An "emotion analysis algorithm" is an algorithm that analyzes a user's facial expressions and voice data to identify the user's emotional state.

[1006] A "meal plan" is a plan that suggests the optimal meal menu for a user, taking into account the user's health and emotional state.

[1007] A "recipe" is a document that provides specific cooking instructions, ingredients, and nutritional information based on a meal plan.

[1008] "AR technology" stands for augmented reality technology, which displays virtual information overlaid on images of the real world.

[1009] The "emotion engine" is a system that analyzes the user's emotional state and responds accordingly.

[1010] MODE FOR CARRYING OUT THE INVENTION

[1011] This invention is a system that collects a user's health data, lifestyle habits, dietary data, exercise data, and emotional data, and analyzes them using AI and an emotional engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[1012] Hardware and software used

[1013] The main hardware components of this system are a smartphone, a smartwatch, a cloud server, and smart glasses, while the software components include a smartphone app, machine learning algorithms, emotion analysis algorithms, an emotion engine, and AR technology.

[1014] Data collection

[1015] Through a smartphone app or smartwatch, users record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary details, and exercise data (e.g., number of steps, exercise time). In addition, the device analyzes the user's facial expressions and voice to record their current emotional state (e.g., joy, sadness, stress, etc.) as emotional data. The device then transmits this data to a cloud server in real time. The transmitted data is encrypted to protect the user's privacy.

[1016] Example: When a user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration, sleep quality) and sends it to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data.

[1017] Data analysis

[1018] The server stores the received data in a cloud database and analyzes it. For example, it evaluates the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data. The server's analysis engine uses machine learning and emotion analysis algorithms to recognize patterns in the user's data. For example, it can identify vitamin D deficiency, lack of exercise, and even emotional fluctuations (e.g., periods of increased stress).

[1019] Example: A server receives data and detects low sleep, vitamin D deficiency, and increased stress.

[1020] Generate a meal plan

[1021] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if the user is feeling stressed, a meal plan that includes ingredients with a relaxing effect will be suggested.

[1022] Example: A server generates a relaxing meal plan high in vitamin D, suggesting, for example, "grilled salmon and spinach."

[1023] Creating a Recipe

[1024] The server creates a specific recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[1025] Example: The server creates a specific recipe and sends it to the user's smartphone app.

[1026] Cooking assistance

[1027] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. The emotion engine customizes the cooking instruction method according to the user's emotional state. For example, if the user is stressed, more friendly and slower instructions are provided.

[1028] Example: A user opens a smartphone app in the kitchen and starts cooking. The smart glasses, based on the emotion engine, will proceed with the cooking by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[1029] Prompt Sentence Examples

[1030] "Please suggest the best breakfast for you based on your sleep time and mood data from yesterday."

[1031] These prompts can be fed into a generative AI model to get specific meal suggestions based on the user's data.

[1032] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1033] Step 1:

[1034] Users record health data, lifestyle habits, diet, exercise data, and emotional data through a smartphone app or smartwatch. The input is the user's physical data (e.g., weight, blood pressure, heart rate) and lifestyle data (e.g., sleep patterns, stress levels), and the output is the initial data stored in the app. Specifically, the user enters their weight into the app, and the smartwatch automatically measures their sleep time, among other things.

[1035] Step 2:

[1036] The device sends the collected data to a cloud server in real time. The input is encrypted user data, and the output is data stored on the cloud server. Specifically, the smartphone encrypts the data using the HTTPS protocol and sends it to the cloud server.

[1037] Step 3:

[1038] The server stores the received data in a cloud database. The input is the user data sent to the cloud server, and the output is the data stored in the cloud database. Specifically, the server writes the data to a specific table in the database.

[1039] Step 4:

[1040] The server analyzes the data using machine learning algorithms and sentiment analysis algorithms. The input is data stored in the cloud database, and the output is the analysis results (e.g., health assessment, emotional state assessment). Specifically, the algorithm retrieves data from the database for the past week and performs calculations to detect vitamin D deficiency and increased stress.

[1041] Step 5:

[1042] The server evaluates the user's health and emotional state based on the analysis results. The input is the analyzed data pattern, and the output is an evaluation report. Specifically, the server generates evaluation results such as "Vitamin D deficiency" or "High stress level."

[1043] Step 6:

[1044] The server generates a personalized meal plan based on the user's health and emotional state. The input is the assessment report, and the output is a specific meal plan. Specifically, the server generates a "relaxing meal plan rich in vitamin D" and suggests "grilled salmon and spinach."

[1045] Step 7:

[1046] The server creates a specific recipe based on the generated meal plan. The input is the meal plan, and the output is detailed recipe information. Specifically, the server generates a document containing cooking instructions such as "Wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[1047] Step 8:

[1048] The server sends the recipe to the user's smartphone app. The input is the created recipe, and the output is the recipe displayed on the user's smartphone. Specifically, the server sends the recipe information to the app and displays a notification saying, "A new recipe has arrived."

[1049] Step 9:

[1050] A user opens a smartphone app and starts cooking according to a recipe. The input is the recipe displayed on the smartphone app, and the output is the user starting to cook. Specifically, the user taps the "View Recipe" button to start cooking.

[1051] Step 10:

[1052] The device uses AR technology to guide the user through cooking steps. The input is recipe information and the user's current cooking status, and the output is the cooking steps displayed using AR technology. Specifically, the smart glasses display instructions such as "Wash and chop the spinach" and "Next, grill the salmon over medium heat," guiding the user.

[1053] In this way, users can obtain optimal meal plans and specific cooking procedures, enabling efficient health management that takes into account their physical and emotional states.

[1054] (Application example 2)

[1055] 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."

[1056] While conventional health management systems have the ability to collect and analyze users' health data, lifestyle habits, dietary and exercise data, none of them take emotional data into account. As a result, they were unable to propose optimal meal plans that reflected the user's mental state. Furthermore, there was also the problem that instructions for actually cooking the proposed meal plans were not provided according to individual circumstances, such as when the user was feeling stressed.

[1057] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data, lifestyle habits, dietary habits, exercise data, and emotional data from the user, means for analyzing the collected data and generating an individualized meal plan based on the user's health condition, lifestyle habits, and emotional state, means for creating recipes based on the generated meal plan, selecting corresponding menus from affiliated restaurants and providing them to the user, and means for providing cooking instructions to the user using AR technology. This makes it possible to generate and implement a more personalized meal plan based on the user's health condition and emotional state.

[1058] "Health Data" refers to information about a user's physical health, such as their weight, blood pressure, and heart rate.

[1059] "Lifestyle" refers to information including a user's daily behavioral patterns, stress levels, sleep patterns, etc.

[1060] "Dietary data" refers to information about the contents and nutritional components of meals consumed by the user.

[1061] "Exercise data" refers to information such as the user's number of steps, exercise time, and calories burned.

[1062] "Emotional data" is information about the emotional state of a user that is analyzed from their facial expressions and voice.

[1063] A "cloud server" is a remote server that stores and processes data over the Internet.

[1064] An "analytics engine" is a system that includes programs and algorithms for analyzing collected data.

[1065] A "machine learning algorithm" is an algorithm that learns patterns from data and makes predictions and classifications.

[1066] An "emotion analysis algorithm" is an algorithm for analyzing a user's emotional state from their facial expressions and voice.

[1067] A "meal plan" is a combination of meals that are optimally recommended based on the user's health and emotional state.

[1068] "Corresponding menu" refers to the dishes offered by partner restaurants that match the generated meal plan.

[1069] "Partner restaurants" refer to restaurants that provide meals that correspond to the meal plans proposed to users.

[1070] "AR technology" is an abbreviation for augmented reality technology, which is a technology that displays computer-generated information overlaid on real-world visual information.

[1071] This invention is a system that collects and analyzes a user's health data, lifestyle habits, diet, and exercise data, as well as emotional data, using AI and an emotional engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[1072] Data collection

[1073] Users use smartphone apps or wearable devices to collect health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary content, exercise data (e.g., number of steps, exercise time), and emotional data (emotional state is recorded by analyzing facial expressions and voice). The devices send this data to a cloud server in real time, and the data is encrypted to protect the user's privacy.

[1074] Data analysis

[1075] The server stores the received data in a cloud database and analyzes it. For example, it evaluates the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data. The server's analysis engine uses a combination of machine learning and emotion analysis algorithms to recognize the user's data patterns and identify vitamin D deficiency, lack of exercise, and emotional fluctuations (e.g., periods of increased stress).

[1076] Generate a meal plan

[1077] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if a user is feeling stressed, a meal plan that includes ingredients that have a relaxing effect will be suggested.

[1078] Creating a Recipe

[1079] The server then creates a specific recipe based on the meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[1080] Cooking assistance

[1081] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. An emotion engine customizes the cooking instruction method according to the user's emotional state; for example, a user feeling stressed will be provided with gentler, more relaxed instructions.

[1082] As a concrete example, when a user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration and quality) and sends this to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data. The cloud server receives the data and detects that the user is getting less sleep, has a vitamin D deficiency, and is experiencing increased stress. The server then suggests "grilled salmon and spinach" as a meal plan that is rich in vitamin D and has a relaxing effect, and sends the specific recipe to the smartphone app. When the user opens the smartphone app in the kitchen and begins cooking, the smart glasses guide the user through the cooking process by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[1083] An example of an input prompt for a generative AI model is:

[1084] "User health data: weight 70kg, blood pressure 120 / 80, heart rate 75

[1085] Lifestyle: 6 hours of sleep, high stress level

[1086] Current diet: Vitamin D deficiency

[1087] Exercise data: 3000 steps

[1088] Emotional data: Emotional state "Stress"

[1089] Suggest the best meal plan for this user."

[1090] It is written as follows.

[1091] This allows users to easily prepare meals that suit their own health and emotional state, enabling them to efficiently manage their daily health.

[1092] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1093] Step 1:

[1094] Users collect health data, lifestyle habits, dietary details, exercise data, and emotional data.

[1095] Users use smartphone apps or wearable devices to record health data such as weight, blood pressure, and heart rate, lifestyle habits such as sleep patterns and stress levels, dietary intake, and exercise data such as number of steps and exercise time. Furthermore, the smartphone's camera and microphone are used to analyze facial expressions and voice to record emotional states. This data is sent from the device to a cloud server in real time.

[1096] Input: Health data, lifestyle data, dietary data, exercise data, emotional data

[1097] Output: Sending encrypted data to cloud server

[1098] Step 2:

[1099] The server stores the data in a cloud database and analyzes it using machine learning and sentiment analysis algorithms.

[1100] The server stores the collected data in a cloud database, and the analytics engine uses a combination of machine learning and sentiment analysis algorithms to assess the user's health and emotional state, recognizing patterns in the data and identifying periods of increased stress, such as vitamin D deficiency, lack of exercise, and increased stress.

[1101] Input: Encrypted data sent to the cloud server

[1102] Output: Health status evaluation results, emotional status evaluation results

[1103] Step 3:

[1104] Based on the analysis results, the server generates a personalized meal plan that takes into account the user's health and emotional state.

[1105] Based on the results of the health and emotional assessment, the server generates a meal plan to improve nutritional balance and mental state. For example, a segment experiencing stress will be suggested a menu containing ingredients that have a relaxing effect.

[1106] Input: Health status assessment results, emotional status assessment results

[1107] Output: User-optimized meal plan

[1108] Step 4:

[1109] Based on the meal plan generated by the server, the server creates specific recipes and selects corresponding menus from partner restaurants.

[1110] The server creates recipes based on the meal plan, including specific ingredient lists, cooking instructions, cooking times, and nutritional information, and selects menu items from partner restaurants that correspond to the recipes, allowing users to order the suggested dishes immediately.

[1111] Enter: meal plan

[1112] Output: Specific recipes and menus from partner restaurants

[1113] Step 5:

[1114] The server sends the created recipe and the corresponding menu to the user's smartphone app and notifies them.

[1115] The server then sends the created recipe and menu information from partner restaurants to the user's smartphone app, where the user receives a notification and can check the meal details.

[1116] Input: Specific recipes, menus from partner restaurants

[1117] Output: Notification to the user's smartphone app

[1118] Step 6:

[1119] When the user starts cooking, the device uses AR technology to guide them through the cooking process.

[1120] When a user opens the smartphone app and starts cooking, the smartphone, smart glasses, or other device uses AR technology to guide the user through the cooking process. An emotion engine analyzes the user's emotional state, and if the user is feeling stressed, friendly, slow-paced instructions are displayed.

[1121] Input: User's emotional state, specific recipe

[1122] Output: Customized cooking instructions for the user

[1123] Specific prompt examples:

[1124] "User health data: weight 70kg, blood pressure 120 / 80, heart rate 75

[1125] Lifestyle: 6 hours of sleep, high stress level

[1126] Current diet: Vitamin D deficiency

[1127] Exercise data: 3000 steps

[1128] Emotional data: Emotional state "Stress"

[1129] Suggest the best meal plan for this user."

[1130] 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.

[1131] 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.

[1132] 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.

[1133] [Fourth embodiment]

[1134] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1135] 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.

[1136] 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).

[1137] 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.

[1138] 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.

[1139] 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).

[1140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1141] 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.

[1142] 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.

[1143] 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.

[1144] 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.

[1145] 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.

[1146] 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."

[1147] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[1148] Data collection

[1149] Users record health information such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data through a smartphone app or smartwatch. This data is sent from the device to a cloud server in real time or periodically. The data is encrypted before transmission, protecting the user's privacy.

[1150] Data analysis

[1151] The server analyzes the data it receives, comparing it with a large amount of past data stored in a database to assess the user's health status. Specifically, it uses machine learning algorithms to recognize patterns in the user's data and identify health issues such as vitamin D deficiency or lack of exercise.

[1152] Generate a meal plan

[1153] Based on the analysis results, the server generates a meal plan to improve the user's health. This plan takes into account the ingredients needed to supplement the user's nutrient deficiencies and the appropriate energy intake to balance daily calorie consumption. The meal plan includes specific ingredient selection and cooking methods.

[1154] Creating a Recipe

[1155] The server then creates a specific recipe based on the meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[1156] Cooking assistance

[1157] After receiving the recipe, when the user starts cooking, the device (e.g., smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. Specifically, it visually shows the specific steps for preparing ingredients, chopping, grilling, etc. For example, the smart glasses might instruct the user, "Place the spinach on the cutting board and use this position to chop it finely."

[1158] Specific examples

[1159] A specific user scenario is shown below.

[1160] When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep time, sleep quality), which is then sent to a cloud server.

[1161] The server receives the data and diagnoses insufficient sleep and vitamin D deficiency.

[1162] The server generates a vitamin D-rich meal plan, suggesting, for example, "grilled salmon and spinach."

[1163] The server creates a specific recipe and sends it to the user's smartphone app.

[1164] The user opens the smartphone app in the kitchen and starts cooking, while the smart glasses display specific instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[1165] In this way, users can easily prepare meals that suit their own health condition and efficiently manage their daily health.

[1166] The processing flow will be explained below.

[1167] Step 1:

[1168] Users use smartphone apps or smartwatches to record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), diet, and exercise data (e.g., number of steps, exercise time).

[1169] Step 2:

[1170] The device sends data from the user to the cloud server in real time or periodically, and the data is encrypted to protect the user's privacy.

[1171] Step 3:

[1172] The server stores the received data in a cloud database, which is updated whenever new data is added.

[1173] Step 4:

[1174] The server's analytics engine analyzes the stored data, assessing the user's health status based on, for example, the past week's dietary data, exercise data, and sleep patterns.

[1175] Step 5:

[1176] The server applies machine learning algorithms to recognize patterns in the user's data, which can identify health issues such as vitamin D deficiency or lack of exercise.

[1177] Step 6:

[1178] The server generates a meal plan to improve the user's health, taking into account the ingredients and energy intake required to compensate for the user's nutrient deficiencies.

[1179] Step 7:

[1180] The server then creates a specific recipe based on the generated meal plan, including a list of ingredients, cooking instructions, cooking time, and nutritional information.

[1181] Step 8:

[1182] The server sends the created recipe to the user's smartphone app, and the user receives a notification that the new recipe is ready.

[1183] Step 9:

[1184] The user receives the recipe and prepares the necessary ingredients. The user then starts cooking in the kitchen based on the recipe.

[1185] Step 10:

[1186] The device uses a smartphone app or AR-enabled device to visually guide users through cooking steps, providing specific instructions such as "wash and finely chop the spinach" or "grill the salmon over medium heat for 5 minutes."

[1187] Step 11:

[1188] The device uses AR technology to visually show the location of ingredients and how to use cooking utensils, for example, "Place the cutting board here and use this position to chop the spinach."

[1189] Step 12:

[1190] The user follows the instructions to proceed with the cooking process, and when the cooking is complete, they register the completion with the app, which will be used as feedback when generating the next meal plan.

[1191] Example 1

[1192] 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."

[1193] In modern society, busy lifestyles often make health management difficult. In particular, designing a meal plan tailored to an individual's health condition requires specialized knowledge and is not easy for the average person. Furthermore, the process of implementing a meal plan can be complicated, including cooking methods and selecting the necessary ingredients, making it difficult to implement in everyday life. To solve these issues, a system is needed that collects and analyzes users' health data and provides appropriate meal plans and recipes based on them. Furthermore, a means of supporting users in the cooking process is also required.

[1194] 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.

[1195] In this invention, the server includes means for collecting biometric information, lifestyle, dietary, and exercise data from a user, means for encrypting the collected data and transmitting it to a cloud server, means for analyzing the received data using a machine learning algorithm and generating an individualized meal plan based on the user's health condition and lifestyle, means for creating recipes based on the generated meal plan and transmitting a list of ingredients, cooking instructions, cooking time, and nutritional information to a user terminal, and means for providing cooking instructions to the user using AR technology. This allows users to easily obtain a meal plan tailored to their health condition and visually understand specific cooking instructions, enabling more efficient and practical daily health management.

[1196] "User" refers to an individual who uses the system to manage their own health.

[1197] "Biometric information" refers to data that indicates a user's health status, such as weight, blood pressure, heart rate, and sleep patterns.

[1198] "Lifestyle" refers to the habits and behaviors of a user in their daily life, such as eating and exercise patterns.

[1199] "Dietary data" refers to information about the contents and ingredients of the meals consumed by the user.

[1200] "Exercise data" refers to information about the type of exercise a user performs, the duration, calories burned, etc.

[1201] "Cloud server" refers to a remote server used to store and analyze data via the Internet.

[1202] A "machine learning algorithm" refers to a computational method for finding patterns and rules from input data and making predictions and classifications.

[1203] "Meal Plan" refers to specific meal plans and ingredient combinations designed based on the user's health and lifestyle.

[1204] A "recipe" is a document created based on a meal plan that includes a specific list of ingredients, cooking instructions, and nutritional information.

[1205] "User terminal" refers to a device, such as a smartphone or tablet, that a user uses to display information received from the system.

[1206] "AR technology" refers to a technology that overlays digital information on real-world images and provides visual instructions to users.

[1207] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[1208] Data collection

[1209] Users record various health-related information, such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data, through smartphone apps or wearable devices. This data is encrypted and sent from the device to a cloud server in real time or periodically. Common biometric information collection APIs are used to link the data. For example, Apple's "HealthKit" or Google's "Fit API" can be used to efficiently collect data.

[1210] Data Transmission and Encryption

[1211] The device encrypts the collected data before sending it to the cloud server. Security protocols such as SSL / TLS are used for encryption. Wireless communication technologies such as Bluetooth and Wi-Fi are used to ensure stable data transmission.

[1212] Data analysis

[1213] The server first stores the received data in a database for analysis. It then analyzes the stored data using machine learning libraries such as Python's "TensorFlow" and "scikit-learn." By comparing it with past data, the system can accurately assess the user's health status and identify problems such as vitamin D deficiency or lack of exercise. Specifically, the data used is evaluated using statistical analysis and pattern recognition techniques.

[1214] Generate a meal plan

[1215] Based on the analyzed data, the server uses a generative AI model to generate an optimal meal plan to improve the user's health. This AI model incorporates neural networks and deep learning algorithms to select specific ingredients to supplement the user's nutrient deficiencies and devise a plan that takes into account the balance with calorie consumption. For example, if a user is determined to have a vitamin D deficiency, it can suggest grilled salmon and spinach.

[1216] Creating a Recipe

[1217] The server creates a recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app using Firebase Cloud Messaging, and the user is notified via a notification function. For example, the recipe might say, "To make grilled salmon and spinach to supplement vitamin D, you need the following ingredients and steps."

[1218] Cooking assistance

[1219] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to assist with the cooking steps. Specific steps are visually presented to support the user's cooking. For example, smart glasses may display specific instructions such as "Place the spinach on the cutting board and use this position to finely chop it," or "Grill the salmon over medium heat for 5 minutes."

[1220] Examples of prompts that can be used include:

[1221] "Take last night's sleep data and generate a vitamin D-rich meal plan and specific recipes for users who have been diagnosed with vitamin D deficiency. Also, display cooking instructions using AR technology."

[1222] This system allows users to efficiently obtain and prepare meal plans that suit their own health condition, enabling more effective health management in daily life.

[1223] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1224] Step 1:

[1225] Users use smartphone apps and wearable devices to record their weight, blood pressure, heart rate, sleep patterns, diet, exercise data, and more.

[1226] Input: User health data (e.g. weight, blood pressure, heart rate)

[1227] How it works: The user records sleep data on the smartwatch and syncs the data to the smartphone app.

[1228] Output: Recorded health data is stored in a smartphone app.

[1229] Step 2:

[1230] The device encrypts the collected health data and sends it to a cloud server.

[1231] Input: User health data stored in a smartphone app

[1232] How it works: The smartphone app encrypts health data using SSL / TLS and sends it to a cloud server using wireless communication technologies (Bluetooth, Wi-Fi).

[1233] Output: The encrypted data is sent to the cloud server.

[1234] Step 3:

[1235] The server receives the data sent to the cloud server and stores it in a database.

[1236] Input: Encrypted health data sent to a cloud server

[1237] Specific operation: The server decrypts the data and saves it in a table for storage in the database.

[1238] Output: Health data stored in a database

[1239] Step 4:

[1240] The server analyzes the stored data using machine learning algorithms.

[1241] Input: Health data stored in a database

[1242] How it works: It uses Python's TensorFlow and scikit-learn to analyze input data, including statistical analysis and pattern recognition to assess health conditions such as vitamin D deficiency and physical inactivity.

[1243] Output: User's health assessment data as a result of the analysis (e.g., vitamin D deficiency)

[1244] Step 5:

[1245] The server generates a meal plan based on the analysis results to improve the user's health.

[1246] Input: User's health assessment data

[1247] How it works: It uses a generative AI model to generate meal plans that take into account the nutritional needs of the user and their calorie intake. For example, if a vitamin D deficiency is identified, it will suggest grilled salmon and spinach.

[1248] Output: Optimized meal plan for each user

[1249] Step 6:

[1250] The server creates specific recipes based on the generated meal plan and sends them to the user's terminal.

[1251] Input: meal plan

[1252] What it does: The server generates a recipe that includes a list of ingredients, cooking instructions, cooking time, and nutritional information. It then uses Firebase Cloud Messaging to send the recipe to the smartphone app.

[1253] Output: The recipe displayed on the user's terminal

[1254] Step 7:

[1255] The user receives the recipe on their smartphone app and begins cooking.

[1256] Input: Recipe displayed on smartphone app

[1257] Specific operation: The user cooks in the kitchen while checking the recipe.

[1258] Output: Cooked food

[1259] Step 8:

[1260] The device uses AR technology to guide users through specific cooking steps while cooking.

[1261] Input: Cooking instructions based on the recipe displayed on the smartphone app

[1262] Specific actions: Smart glasses or a smartphone will display cooking steps in AR, providing specific instructions (e.g., "finely chop the spinach").

[1263] Output: Visual guidance to help users create a dish with precise steps

[1264] (Application example 1)

[1265] 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."

[1266] Existing health management systems can generate meal plans based on the data collected by users, but they lack specific support for actually purchasing ingredients and cooking. Users often experience difficulties, especially when selecting ingredients and following specific cooking procedures in physical stores. For this reason, there is a need for a comprehensive support system that allows users to efficiently purchase and cook meals based on their health status in physical stores.

[1267] 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.

[1268] In this invention, the server includes means for collecting health data, lifestyle, diet, and exercise data from the user, means for analyzing the collected data and generating an individual meal plan based on the user's health condition and lifestyle, means for creating recipes based on the generated meal plan and providing them to the user, and means for using AR technology to provide guidance on ingredients to be purchased and cooking procedures in a physical store, thereby enabling the user to select appropriate ingredients and cook efficiently in the physical store.

[1269] - "Health Data" refers to information about your body, such as your weight, blood pressure, heart rate, and sleep patterns.

[1270] "Lifestyle habits" refers to the user's daily habits, such as eating habits, exercise habits, and sleeping habits.

[1271] "Dietary data" refers to information such as the ingredients a user consumes, their nutritional content, and meal timing.

[1272] "Exercise data" refers to information such as the type of exercise performed by the user, the duration of the exercise, and the calories burned.

[1273] "Means of collection" refers to the methods of obtaining data via smartphones, smartwatches, and other devices.

[1274] "Analytics Engine" refers to a computer program used to analyze collected data and identify trends and patterns.

[1275] "Meal Plan" refers to a meal suggestion generated based on the analysis results to improve the user's health.

[1276] A "recipe" refers to instructions that list specific ingredients and cooking methods based on a meal plan.

[1277] "Means for providing" refers to a method for notifying or displaying the generated recipe to the user.

[1278] "AR technology" refers to the technology of overlaying digital information onto the real-world environment.

[1279] "Brick and mortar store" refers to a physical store where users actually visit and purchase products.

[1280] "Instruction" refers to a method of instructing users to take appropriate action.

[1281] This invention is a system that uses AI to analyze health data, lifestyle habits, diet, and exercise data collected from users and generate optimal meal plans for each individual user. Specific embodiments of this system are described below.

[1282] Data collection

[1283] Users use their smartphones or smartwatches to record health information such as weight, blood pressure, heart rate, sleep patterns, dietary habits, and exercise data. This data is sent from the device to a cloud server in real time or periodically. The data is encrypted during transmission to protect user privacy.

[1284] Data analysis

[1285] The cloud server analyzes the received data. The server compares it with a large amount of past data stored in a database to evaluate the user's health status. This analysis uses machine learning algorithms (e.g., Random Forest) to recognize patterns in the user's data and identify health issues such as vitamin D deficiency or lack of exercise.

[1286] Generate a meal plan

[1287] Based on the analysis results, the cloud server generates a meal plan to improve the user's health. This plan takes into account the ingredients needed to supplement the user's nutrient deficiencies and the appropriate energy intake to balance daily calorie consumption. The meal plan includes specific ingredient selection and cooking methods.

[1288] Creating a Recipe

[1289] The cloud server creates specific recipes based on the generated meal plan. These recipes include a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipes are sent to the user's smartphone app and notified to the user via a notification function.

[1290] In-store support

[1291] After receiving the recipe, when choosing ingredients in a physical store, users can receive guidance on which ingredients to purchase and how to cook them using AR technology on their smart glasses or smartphones. Specifically, the smart glasses will provide visual instructions such as, "Place the spinach on the cutting board and use this position to finely chop it."

[1292] Specific examples

[1293] Here are some specific user scenarios:

[1294] 1. When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep time, sleep quality), which is then sent to a cloud server.

[1295] 2. The server receives the data and diagnoses insufficient sleep and vitamin D deficiency.

[1296] 3. The server generates a meal plan rich in vitamin D, suggesting, for example, "grilled salmon and spinach."

[1297] 4. The server creates a specific recipe and sends it to the user's smartphone app.

[1298] 5. The user opens the smartphone app in the kitchen and starts cooking. The smart glasses guide the user through the cooking process, displaying specific instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[1299] In this way, users can easily prepare meals that suit their health condition and efficiently manage their daily health. Below is an example of a prompt:

[1300] prompt:

[1301] "30-year-old male, weighs 70 kg, exercise inactive, vitamin D deficient. Please suggest a suitable meal plan and specific recipes."

[1302] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1303] Step 1:

[1304] Users record health data, lifestyle habits, diet, and exercise data through their smartphones or smartwatches. This includes weight, blood pressure, heart rate, sleep patterns, dietary content, and exercise data. The collected data is encrypted and sent from the device to a cloud server. The input is the user's health data, and the output is encrypted data.

[1305] Step 2:

[1306] The server receives the transmitted data. The server stores this data in a cloud database and prepares it for analysis. The input is encrypted data, and the output is data stored in the cloud database. A cloud service such as Amazon RDS is used for database management.

[1307] Step 3:

[1308] The server analyzes the data stored in the cloud database. It uses a machine learning algorithm (e.g., Random Forest) to evaluate the user's health condition. The input is the stored user data, and the output is the analysis results. The analysis involves comparison with similar data from the past and trend analysis.

[1309] Step 4:

[1310] The server generates a meal plan based on the analysis results. This plan takes into account the ingredients needed to improve the user's health and the amount of energy intake needed to balance daily calorie expenditure. The input is the analysis results, and the output is a meal plan. Specifically, if a vitamin D deficiency is identified, the server will list foods that are rich in vitamin D.

[1311] Step 5:

[1312] The server creates a specific recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The input is the meal plan, and the output is a detailed recipe. For example, a recipe for grilled salmon and spinach is created.

[1313] Step 6:

[1314] The server sends the created recipe to the user's smartphone app. This step also notifies the user through the notification function. The input is a detailed recipe, and the output is a notification sent to the user's smartphone app. Here, a notification system such as Firebase Cloud Messaging is used.

[1315] Step 7:

[1316] When a user is choosing ingredients in a physical store, they receive guidance on which ingredients to purchase and how to cook them using AR technology on their smart glasses or smartphone. The input is the user's current location and recipe information, and the output is visual guidance information. Specifically, when a user wears the smart glasses in a physical store, they are shown a visual guide on how to place spinach on a cutting board and finely chop it.

[1317] Through these steps, users can efficiently cook meals that are optimal for their health condition and manage their health.

[1318] 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.

[1319] This invention is a system that collects and analyzes a user's health data, lifestyle habits, diet, exercise data, and emotional data using AI and an emotion engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[1320] Data collection

[1321] Users record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary habits, and exercise data (e.g., number of steps, exercise time) through smartphone apps or smartwatches. Furthermore, the system analyzes the user's facial expressions and voice to record their current emotional state (e.g., joy, sadness, stress, etc.) as emotional data.

[1322] The device sends this data to a cloud server in real time, and the data is encrypted to protect user privacy.

[1323] Data analysis

[1324] The server stores the received data in a cloud database and analyzes it, for example, assessing the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data.

[1325] The server-based analytics engine uses a combination of machine learning and sentiment analysis algorithms to recognize patterns in a user's data, such as vitamin D deficiency or lack of exercise, as well as emotional fluctuations (e.g., periods of increased stress).

[1326] Generate a meal plan

[1327] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if a user is feeling stressed, a meal plan that includes ingredients that have a relaxing effect will be suggested.

[1328] Creating a Recipe

[1329] The server creates specific recipes based on the generated meal plan. These recipes include a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipes are sent to the user's smartphone app and notified to the user via a notification function.

[1330] Cooking assistance

[1331] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. The emotion engine customizes the cooking instruction method according to the user's emotional state. For example, if the user is stressed, more friendly and slower instructions are provided.

[1332] Specific examples

[1333] A specific user scenario is shown below.

[1334] When the user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration, sleep quality) and sends it to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data.

[1335] The server receives the data and detects low sleep, vitamin D deficiency, and increased stress.

[1336] The server generates a relaxing meal plan high in vitamin D, suggesting, for example, "grilled salmon and spinach."

[1337] The server creates a specific recipe and sends it to the user's smartphone app.

[1338] The user opens the smartphone app in the kitchen and starts cooking. Using the emotion engine, the smart glasses guide the cooking process by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[1339] In this way, users can easily prepare meals that suit their health and emotional state, and efficiently manage their daily health.

[1340] The processing flow will be explained below.

[1341] Step 1:

[1342] Users use smartphone apps or smartwatches to record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary habits, and exercise data (e.g., number of steps, exercise time).Furthermore, the smartphone's camera and voice recognition functions are used to record emotional data from facial expressions and voice.

[1343] Step 2:

[1344] The device sends data from the user to the cloud server in real time or periodically, and the data is encrypted to protect the user's privacy.

[1345] Step 3:

[1346] The server stores the received data in a cloud database, which is updated whenever new data is added.

[1347] Step 4:

[1348] The server's analysis engine analyzes health data, lifestyle data, dietary data, exercise data, and emotional data. For example, it evaluates the user's health and emotional state based on data from the past week.

[1349] Step 5:

[1350] The server uses a combination of machine learning and sentiment analysis algorithms to recognize patterns in a user's data, identifying, for example, vitamin D deficiency, lack of exercise, and even emotional fluctuations (times when stress levels are high).

[1351] Step 6:

[1352] The server generates a personalized meal plan based on the user's emotional data, taking into account the user's health and emotional state. The meal plan includes ingredients that improve the user's emotional state while taking into account nutritional balance. For example, if the user is feeling stressed, a meal plan that includes ingredients with a relaxing effect will be suggested.

[1353] Step 7:

[1354] The server then creates specific recipes based on the generated meal plan, including a list of ingredients, cooking instructions, cooking time, and nutritional information.

[1355] Step 8:

[1356] The server sends the created recipe to the user's smartphone app, where the user receives a notification and can check the recipe within the app.

[1357] Step 9:

[1358] After the user checks the recipe, they use the shopping list to prepare the ingredients they need, and then they start cooking according to the recipe.

[1359] Step 10:

[1360] The device (smartphone or smart glasses) uses AR technology to visually guide the user through the cooking process. An emotion engine customizes the instruction method based on the user's emotional state. For example, if the user is stressed, the instructions will be more helpful and clear.

[1361] Step 11:

[1362] The device displays cooking instructions in real time, directing the user to take specific actions, such as "wash and finely chop the spinach" or "grill the salmon over medium heat for 5 minutes."

[1363] Step 12:

[1364] The user follows the instructions on the device to proceed with the cooking process. When cooking is complete, the user registers the completion in the app, which uses the information as feedback for the next meal plan generation. This feedback is used for more accurate analysis and suggestions.

[1365] Example 2

[1366] 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."

[1367] Conventional health management systems only collect users' health and lifestyle data and provide personalized meal plans and cooking instructions, but they do not take into account the user's emotional state, limiting their ability to improve mental health. Furthermore, when it comes to providing cooking instructions, they only provide general instructions and are unable to flexibly adapt to the user's emotional state. Therefore, there is a need for a comprehensive health management system that takes into account not only the user's health status but also their emotional state.

[1368] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1369] In this invention, the server includes means for collecting health data, lifestyle habits, diet, exercise data, and emotion data from the user, means for transmitting the collected data to the cloud server in real time, and means for storing the collected data in the cloud database, thereby enabling the provision of personalized meal plans and flexible cooking instructions based on the analysis results using machine learning algorithms and emotion analysis algorithms.

[1370] "Health data" refers to numerical data relating to the user's physical condition, such as weight, blood pressure, and heart rate.

[1371] "Lifestyle" refers to data about a user's daily life behaviors and habits, such as their sleep patterns, stress levels, diet, and exercise habits.

[1372] "Emotional data" refers to data about the user's emotional state obtained by analyzing their facial expressions and voice.

[1373] A "cloud server" is a server that provides data storage and computing resources over the Internet for storing and analyzing collected data.

[1374] A "machine learning algorithm" is an algorithm that automatically learns patterns and trends from collected data and makes predictions and classifications.

[1375] An "emotion analysis algorithm" is an algorithm that analyzes a user's facial expressions and voice data to identify the user's emotional state.

[1376] A "meal plan" is a plan that suggests the optimal meal menu for a user, taking into account the user's health and emotional state.

[1377] A "recipe" is a document that provides specific cooking instructions, ingredients, and nutritional information based on a meal plan.

[1378] "AR technology" stands for augmented reality technology, which displays virtual information overlaid on images of the real world.

[1379] The "emotion engine" is a system that analyzes the user's emotional state and responds accordingly.

[1380] MODE FOR CARRYING OUT THE INVENTION

[1381] This invention is a system that collects a user's health data, lifestyle habits, dietary data, exercise data, and emotional data, and analyzes them using AI and an emotional engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[1382] Hardware and software used

[1383] The main hardware components of this system are a smartphone, a smartwatch, a cloud server, and smart glasses, while the software components include a smartphone app, machine learning algorithms, emotion analysis algorithms, an emotion engine, and AR technology.

[1384] Data collection

[1385] Through a smartphone app or smartwatch, users record health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary details, and exercise data (e.g., number of steps, exercise time). In addition, the device analyzes the user's facial expressions and voice to record their current emotional state (e.g., joy, sadness, stress, etc.) as emotional data. The device then transmits this data to a cloud server in real time. The transmitted data is encrypted to protect the user's privacy.

[1386] Example: When a user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration, sleep quality) and sends it to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data.

[1387] Data analysis

[1388] The server stores the received data in a cloud database and analyzes it. For example, it evaluates the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data. The server's analysis engine uses machine learning and emotion analysis algorithms to recognize patterns in the user's data. For example, it can identify vitamin D deficiency, lack of exercise, and even emotional fluctuations (e.g., periods of increased stress).

[1389] Example: A server receives data and detects low sleep, vitamin D deficiency, and increased stress.

[1390] Generate a meal plan

[1391] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if the user is feeling stressed, a meal plan that includes ingredients with a relaxing effect will be suggested.

[1392] Example: A server generates a relaxing meal plan high in vitamin D, suggesting, for example, "grilled salmon and spinach."

[1393] Creating a Recipe

[1394] The server creates a specific recipe based on the generated meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[1395] Example: The server creates a specific recipe and sends it to the user's smartphone app.

[1396] Cooking assistance

[1397] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. The emotion engine customizes the cooking instruction method according to the user's emotional state. For example, if the user is stressed, more friendly and slower instructions are provided.

[1398] Example: A user opens a smartphone app in the kitchen and starts cooking. The smart glasses, based on the emotion engine, will proceed with the cooking by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[1399] Prompt Sentence Examples

[1400] "Please suggest the best breakfast for you based on your sleep time and mood data from yesterday."

[1401] These prompts can be fed into a generative AI model to get specific meal suggestions based on the user's data.

[1402] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1403] Step 1:

[1404] Users record health data, lifestyle habits, diet, exercise data, and emotional data through a smartphone app or smartwatch. The input is the user's physical data (e.g., weight, blood pressure, heart rate) and lifestyle data (e.g., sleep patterns, stress levels), and the output is the initial data stored in the app. Specifically, the user enters their weight into the app, and the smartwatch automatically measures their sleep time, among other things.

[1405] Step 2:

[1406] The device sends the collected data to a cloud server in real time. The input is encrypted user data, and the output is data stored on the cloud server. Specifically, the smartphone encrypts the data using the HTTPS protocol and sends it to the cloud server.

[1407] Step 3:

[1408] The server stores the received data in a cloud database. The input is the user data sent to the cloud server, and the output is the data stored in the cloud database. Specifically, the server writes the data to a specific table in the database.

[1409] Step 4:

[1410] The server analyzes the data using machine learning algorithms and sentiment analysis algorithms. The input is data stored in the cloud database, and the output is the analysis results (e.g., health assessment, emotional state assessment). Specifically, the algorithm retrieves data from the database for the past week and performs calculations to detect vitamin D deficiency and increased stress.

[1411] Step 5:

[1412] The server evaluates the user's health and emotional state based on the analysis results. The input is the analyzed data pattern, and the output is an evaluation report. Specifically, the server generates evaluation results such as "Vitamin D deficiency" or "High stress level."

[1413] Step 6:

[1414] The server generates a personalized meal plan based on the user's health and emotional state. The input is the assessment report, and the output is a specific meal plan. Specifically, the server generates a "relaxing meal plan rich in vitamin D" and suggests "grilled salmon and spinach."

[1415] Step 7:

[1416] The server creates a specific recipe based on the generated meal plan. The input is the meal plan, and the output is detailed recipe information. Specifically, the server generates a document containing cooking instructions such as "Wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[1417] Step 8:

[1418] The server sends the recipe to the user's smartphone app. The input is the created recipe, and the output is the recipe displayed on the user's smartphone. Specifically, the server sends the recipe information to the app and displays a notification saying, "A new recipe has arrived."

[1419] Step 9:

[1420] A user opens a smartphone app and starts cooking according to a recipe. The input is the recipe displayed on the smartphone app, and the output is the user starting to cook. Specifically, the user taps the "View Recipe" button to start cooking.

[1421] Step 10:

[1422] The device uses AR technology to guide the user through cooking steps. The input is recipe information and the user's current cooking status, and the output is the cooking steps displayed using AR technology. Specifically, the smart glasses display instructions such as "Wash and chop the spinach" and "Next, grill the salmon over medium heat," guiding the user.

[1423] In this way, users can obtain optimal meal plans and specific cooking procedures, enabling efficient health management that takes into account their physical and emotional states.

[1424] (Application example 2)

[1425] 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."

[1426] While conventional health management systems have the ability to collect and analyze users' health data, lifestyle habits, dietary and exercise data, none of them take emotional data into account. As a result, they were unable to propose optimal meal plans that reflected the user's mental state. Furthermore, there was also the problem that instructions for actually cooking the proposed meal plans were not provided according to individual circumstances, such as when the user was feeling stressed.

[1427] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data, lifestyle habits, dietary habits, exercise data, and emotional data from the user, means for analyzing the collected data and generating an individualized meal plan based on the user's health condition, lifestyle habits, and emotional state, means for creating recipes based on the generated meal plan, selecting corresponding menus from affiliated restaurants and providing them to the user, and means for providing cooking instructions to the user using AR technology. This makes it possible to generate and implement a more personalized meal plan based on the user's health condition and emotional state.

[1428] "Health Data" refers to information about a user's physical health, such as their weight, blood pressure, and heart rate.

[1429] "Lifestyle" refers to information including a user's daily behavioral patterns, stress levels, sleep patterns, etc.

[1430] "Dietary data" refers to information about the contents and nutritional components of meals consumed by the user.

[1431] "Exercise data" refers to information such as the user's number of steps, exercise time, and calories burned.

[1432] "Emotional data" is information about the emotional state of a user that is analyzed from their facial expressions and voice.

[1433] A "cloud server" is a remote server that stores and processes data over the Internet.

[1434] An "analytics engine" is a system that includes programs and algorithms for analyzing collected data.

[1435] A "machine learning algorithm" is an algorithm that learns patterns from data and makes predictions and classifications.

[1436] An "emotion analysis algorithm" is an algorithm for analyzing a user's emotional state from their facial expressions and voice.

[1437] A "meal plan" is a combination of meals that are optimally recommended based on the user's health and emotional state.

[1438] "Corresponding menu" refers to the dishes offered by partner restaurants that match the generated meal plan.

[1439] "Partner restaurants" refer to restaurants that provide meals that correspond to the meal plans proposed to users.

[1440] "AR technology" is an abbreviation for augmented reality technology, which is a technology that displays computer-generated information overlaid on real-world visual information.

[1441] This invention is a system that collects and analyzes a user's health data, lifestyle habits, diet, and exercise data, as well as emotional data, using AI and an emotional engine to provide optimal meal plans and cooking procedures for each individual user. Specific embodiments of this system are described below.

[1442] Data collection

[1443] Users use smartphone apps or wearable devices to collect health data (e.g., weight, blood pressure, heart rate), lifestyle habits (e.g., sleep patterns, stress levels), dietary content, exercise data (e.g., number of steps, exercise time), and emotional data (emotional state is recorded by analyzing facial expressions and voice). The devices send this data to a cloud server in real time, and the data is encrypted to protect the user's privacy.

[1444] Data analysis

[1445] The server stores the received data in a cloud database and analyzes it. For example, it evaluates the user's health and emotional state based on the past week's dietary data, exercise data, sleep patterns, and emotional data. The server's analysis engine uses a combination of machine learning and emotion analysis algorithms to recognize the user's data patterns and identify vitamin D deficiency, lack of exercise, and emotional fluctuations (e.g., periods of increased stress).

[1446] Generate a meal plan

[1447] Based on the analysis results, the server generates a meal plan that takes into account the user's health and emotional state. This provides a meal plan that not only improves nutritional balance but also improves the user's mental state. For example, if a user is feeling stressed, a meal plan that includes ingredients that have a relaxing effect will be suggested.

[1448] Creating a Recipe

[1449] The server then creates a specific recipe based on the meal plan. This recipe includes a list of ingredients, cooking instructions, cooking time, and nutritional information. The created recipe is sent to the user's smartphone app and notified to the user via a notification function.

[1450] Cooking assistance

[1451] When a user receives a recipe and begins cooking, the device (e.g., a smartphone or smart glasses) uses AR technology to guide the user through the cooking steps. An emotion engine customizes the cooking instruction method according to the user's emotional state; for example, a user feeling stressed will be provided with gentler, more relaxed instructions.

[1452] As a concrete example, when a user wakes up in the morning, the smartwatch records the previous night's sleep data (sleep duration and quality) and sends this to a cloud server. In addition, the smartphone camera analyzes the user's facial expressions and records "stress" as emotional data. The cloud server receives the data and detects that the user is getting less sleep, has a vitamin D deficiency, and is experiencing increased stress. The server then suggests "grilled salmon and spinach" as a meal plan that is rich in vitamin D and has a relaxing effect, and sends the specific recipe to the smartphone app. When the user opens the smartphone app in the kitchen and begins cooking, the smart glasses guide the user through the cooking process by displaying specific and helpful instructions such as "First, wash and chop the spinach" and "Grill the salmon over medium heat for 5 minutes."

[1453] An example of an input prompt for a generative AI model is:

[1454] "User health data: weight 70kg, blood pressure 120 / 80, heart rate 75

[1455] Lifestyle: 6 hours of sleep, high stress level

[1456] Current diet: Vitamin D deficiency

[1457] Exercise data: 3000 steps

[1458] Emotional data: Emotional state "Stress"

[1459] Suggest the best meal plan for this user."

[1460] It is written as follows.

[1461] This allows users to easily prepare meals that suit their own health and emotional state, enabling them to efficiently manage their daily health.

[1462] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1463] Step 1:

[1464] Users collect health data, lifestyle habits, dietary details, exercise data, and emotional data.

[1465] Users use smartphone apps or wearable devices to record health data such as weight, blood pressure, and heart rate, lifestyle habits such as sleep patterns and stress levels, dietary intake, and exercise data such as number of steps and exercise time. Furthermore, the smartphone's camera and microphone are used to analyze facial expressions and voice to record emotional states. This data is sent from the device to a cloud server in real time.

[1466] Input: Health data, lifestyle data, dietary data, exercise data, emotional data

[1467] Output: Sending encrypted data to cloud server

[1468] Step 2:

[1469] The server stores the data in a cloud database and analyzes it using machine learning and sentiment analysis algorithms.

[1470] The server stores the collected data in a cloud database, and the analytics engine uses a combination of machine learning and sentiment analysis algorithms to assess the user's health and emotional state, recognizing patterns in the data and identifying periods of increased stress, such as vitamin D deficiency, lack of exercise, and increased stress.

[1471] Input: Encrypted data sent to the cloud server

[1472] Output: Health status evaluation results, emotional status evaluation results

[1473] Step 3:

[1474] Based on the analysis results, the server generates a personalized meal plan that takes into account the user's health and emotional state.

[1475] Based on the results of the health and emotional assessment, the server generates a meal plan to improve nutritional balance and mental state. For example, a segment experiencing stress will be suggested a menu containing ingredients that have a relaxing effect.

[1476] Input: Health status assessment results, emotional status assessment results

[1477] Output: User-optimized meal plan

[1478] Step 4:

[1479] Based on the meal plan generated by the server, the server creates specific recipes and selects corresponding menus from partner restaurants.

[1480] The server creates recipes based on the meal plan, including specific ingredient lists, cooking instructions, cooking times, and nutritional information, and selects menu items from partner restaurants that correspond to the recipes, allowing users to order the suggested dishes immediately.

[1481] Enter: meal plan

[1482] Output: Specific recipes and menus from partner restaurants

[1483] Step 5:

[1484] The server sends the created recipe and the corresponding menu to the user's smartphone app and notifies them.

[1485] The server then sends the created recipe and menu information from partner restaurants to the user's smartphone app, where the user receives a notification and can check the meal details.

[1486] Input: Specific recipes, menus from partner restaurants

[1487] Output: Notification to the user's smartphone app

[1488] Step 6:

[1489] When the user starts cooking, the device uses AR technology to guide them through the cooking process.

[1490] When a user opens the smartphone app and starts cooking, the smartphone, smart glasses, or other device uses AR technology to guide the user through the cooking process. An emotion engine analyzes the user's emotional state, and if the user is feeling stressed, friendly, slow-paced instructions are displayed.

[1491] Input: User's emotional state, specific recipe

[1492] Output: Customized cooking instructions for the user

[1493] Specific prompt examples:

[1494] "User health data: weight 70kg, blood pressure 120 / 80, heart rate 75

[1495] Lifestyle: 6 hours of sleep, high stress level

[1496] Current diet: Vitamin D deficiency

[1497] Exercise data: 3000 steps

[1498] Emotional data: Emotional state "Stress"

[1499] Suggest the best meal plan for this user."

[1500] 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.

[1501] 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.

[1502] 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 robot 414.

[1503] 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.

[1504] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1505] 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.

[1506] 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).

[1507] 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.

[1508] 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."

[1509] 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.

[1510] 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).

[1511] 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.

[1512] 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.

[1513] 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.

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] 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.

[1521] The following is further disclosed regarding the above embodiment.

[1522] (Claim 1)

[1523] A means of collecting health data, lifestyle, dietary and exercise data from users;

[1524] A means for analyzing the collected data and generating a personalized meal plan based on the user's health and lifestyle habits;

[1525] a means for creating and providing recipes to the user based on the generated meal plan;

[1526] A means of using AR technology to guide users through cooking steps;

[1527] A system including:

[1528] (Claim 2)

[1529] The system according to claim 1, further comprising means for storing the collected data in a cloud server and analyzing the data using an analysis engine.

[1530] (Claim 3)

[1531] 10. The system of claim 1, wherein a machine learning algorithm is used to generate a meal plan for a user based on the collected data.

[1532] "Example 1"

[1533] (Claim 1)

[1534] A means of collecting biometric, lifestyle, dietary and exercise data from users;

[1535] A means for encrypting the collected data and transmitting it to a cloud server;

[1536] means for analyzing the received data using machine learning algorithms to generate a personalized meal plan based on the user's health and lifestyle habits;

[1537] a means for creating a recipe based on the generated meal plan and transmitting a list of ingredients, cooking instructions, cooking time, and nutritional information to a user terminal;

[1538] A means of using AR technology to guide users through cooking steps;

[1539] A system including:

[1540] (Claim 2)

[1541] The system according to claim 1, further comprising means for storing the collected data in a cloud server and analyzing the data using an analysis engine.

[1542] (Claim 3)

[1543] 10. The system of claim 1, wherein a machine learning algorithm is used to generate a meal plan for a user based on the collected data.

[1544] "Application Example 1"

[1545] (Claim 1)

[1546] A means of collecting health data, lifestyle, dietary and exercise data from users;

[1547] A means for analyzing the collected data and generating a personalized meal plan based on the user's health and lifestyle habits;

[1548] a means for creating and providing recipes to the user based on the generated meal plan;

[1549] A method to use AR technology to guide users in physical stores on ingredients they should purchase and cooking procedures, and

[1550] A system including:

[1551] (Claim 2)

[1552] The system according to claim 1, further comprising means for storing the collected data in a cloud server and analyzing the data using an analysis engine.

[1553] (Claim 3)

[1554] 10. The system of claim 1, wherein a machine learning algorithm is used to generate a meal plan for a user based on the collected data.

[1555] "Example 2: Combining Emotion Engines"

[1556] (Claim 1)

[1557] a means for collecting health data, lifestyle, dietary, exercise data and emotional data from users;

[1558] means for transmitting the collected data to a cloud server in real time;

[1559] a means for storing the collected data in a cloud database;

[1560] a means for analyzing the data using machine learning algorithms and emotion analysis algorithms on a cloud server to assess the user's health and emotional state;

[1561] A means for generating a personalized meal plan based on the analysis results, taking into account the user's health and emotional state;

[1562] A means for creating recipes based on the generated meal plan and providing them to the user's device;

[1563] A means of using AR technology to guide users through cooking steps;

[1564] A system including:

[1565] (Claim 2)

[1566] 2. The system according to claim 1, further comprising means for encrypting the collected data using a secure communication protocol and transmitting the data to a cloud server.

[1567] (Claim 3)

[1568] 2. The system according to claim 1, further comprising means for analyzing the user's facial expressions and voice as emotional data and recording the emotional state.

[1569] (Claim 4)

[1570] 10. The system according to claim 1, further comprising an emotion engine that customizes the cooking instruction method according to the user's emotional state.

[1571] "Application example 2 when combining emotion engines"

[1572] (Claim 1)

[1573] A means of collecting health data, lifestyle habits, diet, exercise data, and emotional data from users;

[1574] A means for analyzing the collected data and generating a personalized meal plan based on the user's health, lifestyle, and emotional state; and

[1575] A means for creating recipes based on the generated meal plan, selecting corresponding menus from partner restaurants, and providing them to the user;

[1576] A means of using AR technology to guide users through cooking steps;

[1577] A system including:

[1578] (Claim 2)

[1579] The system according to claim 1, further comprising means for storing the collected data in a cloud server and analyzing the data using an analysis engine.

[1580] (Claim 3)

[1581] 10. The system of claim 1, wherein machine learning algorithms and sentiment analysis algorithms are used to generate a meal plan for a user based on the collected data. [Explanation of symbols]

[1582] 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 of collecting health data, lifestyle, dietary and exercise data from users; A means for analyzing the collected data and generating a personalized meal plan based on the user's health and lifestyle habits; a means for creating and providing recipes to the user based on the generated meal plan; A means of using AR technology to guide users through cooking steps; A system including:

2. The system according to claim 1, further comprising means for storing the collected data in a cloud server and analyzing the data using an analysis engine.

3. 10. The system of claim 1, wherein a machine learning algorithm is used to generate a meal plan for a user based on the collected data.

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