System

Abstract

To provide a system.SOLUTION: A system includes: means for acquiring user's biological information; an AI engine for analyzing the biological information; means for making a proposal to the user in accordance with the results of the analysis in which the proposal includes a meal menu; and means for ordering the proposed meal menu in cooperation with a food delivery service.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] To manage the health and improve lifestyle habits of each individual, it is necessary to propose dietary improvements, stress relief methods, and appropriate exercise programs that are tailored to each individual. However, there is no system in place to propose these things to each individual. [Means for solving the problem]

[0005] The system works in conjunction with generative AI to provide a system that accumulates an individual's daily exercise volume and personal data. Based on this data, it will suggest dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for each individual. Furthermore, by linking with a smartwatch, it will enable detailed health management, and by linking with smart home appliances, it will help regulate indoor temperatures taking into account the individual's physical condition. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] As one embodiment of the present invention, a means for linking with generative AI is provided, which includes a database that stores the amount of exercise and personal data of each user, and an AI engine that analyzes this data and proposes dietary improvement plans, stress relief methods, and appropriate exercise menus that are suitable for each user. Specifically, the amount of exercise a user does is obtained from a smartwatch or fitness app, and personal data is obtained from user input or other linked apps. This data is stored in a database and analyzed by the AI ​​engine. Based on the analysis results, dietary improvement plans, stress relief methods, and appropriate exercise menus are proposed to the user.

[0029] "Example 2"

[0030] In another embodiment of the present invention, biometric information such as heart rate and step count acquired from a smartwatch is used as a means of linking with the smartwatch. Specifically, the biometric information acquired from the smartwatch is stored in a database and analyzed by an AI engine. Based on the analysis results, suggestions for improving dietary habits, stress relief methods, and appropriate exercise menus are proposed, taking into account the user's physical condition and exercise volume.

[0031] "Example 3"

[0032] In a further embodiment of the present invention, information such as indoor temperature and humidity obtained from smart home appliances is used as a means for linking with smart home appliances. Specifically, the information obtained from the smart home appliances is stored in a database and analyzed by an AI engine. Based on the analysis results, assistance is provided for adjusting the indoor temperature taking into account the user's physical condition. For example, if it is determined that the user has a cold, the smart thermostat is controlled to appropriately adjust the indoor temperature.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Obtain the user's exercise and personal data from their smartwatch or fitness app.

[0036] Step 2: Store the acquired data in a database.

[0037] Step 3: Analyze the accumulated data using an AI engine.

[0038] Step 4: Based on the analysis results, the system will suggest dietary improvements, stress relief methods, and appropriate exercise menus to the user.

[0039] "Example 2"

[0040] Step 1: Obtain biometric information such as heart rate and steps from the smartwatch.

[0041] Step 2: The acquired biometric information is stored in a database.

[0042] Step 3: Analyze the accumulated biometric information using an AI engine.

[0043] Step 4: Based on the analysis results, the system will suggest dietary improvements, stress relief methods, and appropriate exercise menus that take into account the user's physical condition and amount of exercise.

[0044] "Example 3"

[0045] Step 1: Obtain information such as indoor temperature and humidity from smart home appliances.

[0046] Step 2: The acquired information is stored in a database.

[0047] Step 3: Analyze the accumulated information using an AI engine.

[0048] Step 4: Based on the analysis results, the system provides assistance with adjusting the indoor temperature based on the user's physical condition. For example, if the system determines that the user has a cold, it will control the smart thermostat to adjust the indoor temperature appropriately.

[0049] Example 1

[0050] Next, a description will be given of Example 1 of Form 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."

[0051] In modern society, personal health management is an important issue. However, there is no adequate system in place to efficiently collect individual exercise amounts and personal data and then make appropriate health management suggestions based on that data. In particular, it is difficult to provide individually optimized dietary improvement suggestions, stress relief methods, and exercise menus.

[0052] 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. In this invention, the server includes a means for collecting an individual's daily exercise amount and personal data, a means for transmitting the collected data to the server and storing it in a database, a means for generating dietary improvement suggestions, stress relief methods, and appropriate exercise menus suitable for the individual using an AI engine that analyzes the stored data, and a means for notifying the generated suggestions to the individual's terminal. This makes it possible to provide health management suggestions optimized for each individual user.

[0053] "Individual daily physical activity" refers to the amount of physical activity an individual engages in on a daily basis, as measured using devices such as smartwatches and fitness apps.

[0054] "Personal data" refers to information about an individual's health and lifestyle, including diet, stress levels, and sleep patterns.

[0055] "Means of collection" refers to the methods used to collect an individual's daily exercise and personal data using smartwatches, fitness apps, and other devices and applications.

[0056] "Means of transmitting to a server and storing in a database" refers to a method of transmitting collected data to a server via the Internet and storing it in a database on the server.

[0057] An "AI engine" refers to software or algorithms that use machine learning and data analysis techniques to analyze accumulated data and generate personalized suggestions.

[0058] "Means of generation" refers to a method that uses an AI engine to create dietary improvement suggestions, stress relief methods, and appropriate exercise menus that are suitable for individuals based on the analysis results.

[0059] "Means for notifying" refers to a method for sending the generated proposal to a personal device and notifying the user.

[0060] A "smartwatch" is a wristwatch-type wearable device that has the function of measuring health data such as exercise volume and heart rate.

[0061] "Smart home appliances" refer to home appliances that are connected to the Internet and can be controlled and configured remotely.

[0062] The present invention is a system for collecting an individual's daily exercise amount and other personal data, and making health management suggestions based on the collected data. A specific embodiment of this system will be described below.

[0063] First, users measure their daily exercise volume using a smartwatch and fitness app. A typical wearable device (e.g., a wristwatch-type health management device) is used as the smartwatch. A health management application (e.g., an exercise volume measurement app or a diet management app) is used as the fitness app.

[0064] The devices (smartphones and tablets) then send the data collected from these devices and applications to a server, which stores the received data in a database. The database organizes the data for each user and allows for efficient access.

[0065] The server passes the data stored in the database to an AI engine, which uses software that uses machine learning and data analysis techniques (e.g., TENSORFLOW (registered trademark) and PyTorch). The AI ​​engine analyzes the user's exercise volume and personal data to evaluate their health condition and lifestyle habits.

[0066] Based on the analysis results, the server generates personalized suggestions for improving the user's diet, stress relief, and exercise routines.

[0067] Finally, the server sends the generated proposal to the terminal, which notifies the user and displays the proposal, allowing the user to confirm and execute the proposal.

[0068] As a concrete example, a user measures their daily exercise volume with a smartwatch and syncs the data with a fitness app. They then enter their meal details into a diet management app. This data is sent to a server and stored in a database. The AI ​​engine analyzes this data and makes suggestions to the user, such as "eat protein-rich foods for breakfast," "meditate for 10 minutes every day to relieve stress," and "do aerobic exercise three times a week."

[0069] Example prompts to input to the generative AI model:

[0070] Based on the user's exercise data and personal data, suggest appropriate dietary improvements, stress relief methods, and exercise menus.

[0071] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[0072] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0073] Step 1: Data collection

[0074] Users measure their daily exercise volume using a smartwatch and a fitness app. Specifically, the user goes jogging and the smartwatch measures their exercise volume. They also input personal data such as their diet and stress level into the fitness app. The input data includes the amount of exercise, heart rate, calorie intake, and nutritional information.

[0075] Input: User's exercise data, personal data

[0076] Output: Collected data

[0077] Step 2: Data transmission and storage

[0078] The device (smartphone or tablet) sends the collected data to a server. Specifically, it acquires data from a smartwatch or fitness app and sends it to the server via the internet. The server stores the received data in a database. The database organizes the data for each user and allows efficient access.

[0079] Input: Collected data

[0080] Output: Data stored in the database

[0081] Step 3: Data analysis

[0082] The server passes the data stored in the database to the AI ​​engine. Specifically, it extracts the user's exercise data and personal data from the database and inputs it into the AI ​​engine. The AI ​​engine then analyzes this data and evaluates the user's health condition and lifestyle habits. Machine learning algorithms and data analysis techniques are used for the analysis.

[0083] Input: Data stored in the database

[0084] Output: Analysis results

[0085] Step 4: Generate proposals

[0086] Based on the analysis results of the AI ​​engine, the server generates dietary improvement suggestions, stress relief methods, and appropriate exercise menus suitable for the user. Specifically, based on the analysis results, it creates suggestions that are optimal for the user's health condition and lifestyle. The suggestions include specific meal menus, activities to relieve stress, and the frequency and type of exercise.

[0087] Input: Analysis results

[0088] Output: Generated proposals

[0089] Step 5: Proposal Notification

[0090] The server sends the generated proposal to the device. Specifically, the proposal is notified to the user's smartphone or tablet. The device notifies the user and displays the proposal. The user can confirm the notification and carry out the proposal.

[0091] Input: Generated proposals

[0092] Output: Proposal notified to the user

[0093] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[0094] (Application example 1)

[0095] Next, a description will be given of Application Example 1 of Embodiment 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."

[0096] In modern society, it is extremely difficult for individual users to manage their own health status and find optimal training menus and meal plans. Furthermore, there is a lack of systems that can centrally manage this information and provide individually optimized recommendations. Furthermore, there is a need for a method to effectively utilize users' exercise volume and personal data and to perform analysis in conjunction with a cloud database.

[0097] 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.

[0098] In this invention, the server includes means for linking with the generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for the individual based on this data, means for saving the data in a cloud database, means for sending the data to an AI engine for analysis, means for generating training menus and meal plans based on the analysis results, and means for providing these suggestions to the user, thereby enabling the user to effectively manage their health condition and receive optimal training menus and meal plans.

[0099] "Generative AI" is an artificial intelligence system that analyzes user data and generates individually optimized suggestions.

[0100] "Individual daily exercise volume" is data that indicates the amount and type of exercise a user engages in on a daily basis.

[0101] "Personal data" refers to data that includes individual information about a user, such as age, weight, height, and health status.

[0102] A "cloud database" is a database accessible via the Internet and a system for storing user data.

[0103] The "AI engine" is the core part of the artificial intelligence that analyzes collected data and generates optimal suggestions for users.

[0104] A "training menu" is an exercise program suggested based on the user's exercise volume and personal data.

[0105] A "meal plan" is a suggested dietary improvement plan based on the user's health status and goals.

[0106] The "means for providing suggestions to the user" is a system for notifying the user of training menus and meal plans generated based on the analysis results.

[0107] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for saving the data in a cloud database, means for sending the data to an AI engine for analysis, means for generating training menus and meal plans based on the analysis results, and means for providing these suggestions to the user.

[0108] The server collects the user's exercise data from the smartwatch and fitness app. This data is stored in a cloud database. The server then sends the stored data to the AI ​​engine for analysis. The AI ​​engine generates optimal training menus and meal plans based on the user's exercise volume and personal data. The generated suggestions are provided to the user through the smartphone app.

[0109] Specifically, the server uses the following hardware and software:

[0110] Smartwatch: A device that collects a user's exercise data.

[0111] Fitness app: An application that works with a smartwatch to manage exercise data.

[0112] Cloud database: A database system for storing collected data.

[0113] AI Engine: An artificial intelligence system that analyzes data and generates optimal suggestions.

[0114] For example, when a user works out at a gym, the smartwatch collects exercise data and sends it to a cloud database via a fitness app. The server then sends this data to an AI engine, which then generates optimal training and meal plans for the user based on the analysis results. The generated suggestions are then sent to the user's smartphone app.

[0115] An example prompt might have the following format:

[0116] User ID: 12345

[0117] Exercise data: steps: 10000, calories: 500, heart_rate: 120

[0118] Personal data: age: 30, weight: 70, height: 175

[0119] By inputting this prompt into the AI ​​engine, the optimal training menu and meal plan for the user will be generated.

[0120] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0121] Step 1:

[0122] The server collects the user's exercise data from the smartwatch and fitness app. Specifically, the smartwatch measures the user's steps, calories burned, heart rate, and other data, and sends it to the server via the fitness app. The input is the exercise data from the smartwatch, and the output is the exercise data stored on the server.

[0123] Step 2:

[0124] The server stores the collected exercise data in a cloud database. Specifically, the server converts the exercise data into an appropriate format and stores it in the corresponding user's database in the cloud database. The input is the exercise data stored in the server, and the output is the exercise data stored in the cloud database.

[0125] Step 3:

[0126] The server sends the exercise data and personal data stored in the cloud database to the AI ​​engine. Specifically, it combines the user's exercise data and personal data into a single prompt and sends it to the AI ​​engine. The input is the exercise data and personal data stored in the cloud database, and the output is the prompt sent to the AI ​​engine.

[0127] Step 4:

[0128] The AI ​​engine performs data analysis based on the received prompts. Specifically, it uses a generative AI model to analyze the user's exercise data and personal data to generate optimal training menus and meal plans. The input is the prompt sent to the AI ​​engine, and the output is the generated training menu and meal plan.

[0129] Step 5:

[0130] The server sends the analysis results received from the AI ​​engine to the user's smartphone app. Specifically, it notifies the user's smartphone app of the generated training menu and meal plan. The input is the analysis results received from the AI ​​engine, and the output is the training menu and meal plan notified to the user's smartphone app.

[0131] Step 6:

[0132] The user checks and executes the training menu and meal plan provided through the smartphone app. Specifically, the user adjusts their daily training and diet based on the suggestions displayed on the smartphone app. The input is the training menu and meal plan notified to the smartphone app, and the output is the user's actual training and diet.

[0133] Example 2

[0134] Next, a description will be given of Example 2 of Form 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."

[0135] In modern society, personal health management is an important issue. However, conventional systems have difficulty obtaining individual users' biometric information in real time and providing appropriate health management suggestions based on that information. In addition, there is a lack of analytical methods to provide specific suggestions based on the user's physical condition and exercise volume. This makes it difficult for users to accurately understand their own health status and take appropriate measures.

[0136] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for linking with a generative AI, means for accumulating an individual's daily exercise amount and personal data, means for proposing dietary improvement plans, stress relief methods, and appropriate exercise menus suitable for the individual based on this data, means for linking with a wearable device for acquiring biometric information, means for storing the acquired biometric information in a database, means including an AI engine for analyzing the stored biometric information, and means for making suggestions to the user based on the analysis results. This makes it possible to acquire the user's biometric information in real time and make specific health management suggestions based on the analysis results.

[0137] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[0138] "Individual daily exercise amount" is data indicating the amount of exercise a user does on a daily basis, and includes the number of steps taken and calories burned.

[0139] "Personal data" refers to information about an individual user's health condition and lifestyle habits, including heart rate, sleep duration, and dietary habits.

[0140] "Dietary improvement suggestions" are meal contents and methods for improving eating habits that are suggested based on the user's health condition and amount of exercise.

[0141] "Stress relief" refers to specific methods or activities that reduce a user's stress level.

[0142] The "appropriate exercise menu" refers to exercise programs and exercise content suggested based on the user's health condition and exercise volume.

[0143] A "wearable device" is an electronic device that can be worn by a user and is used to obtain biometric information such as heart rate and number of steps taken.

[0144] A "database" is a system for systematically storing acquired data and for searching and analyzing it as needed.

[0145] "AI engine" refers to artificial intelligence technology that analyzes data and finds patterns and trends.

[0146] "Analysis results" are the information and conclusions obtained after data is analyzed by an AI engine.

[0147] "Suggestion" refers to specific advice or instructions given to the user based on the analysis results.

[0148] MODE FOR CARRYING OUT THE INVENTION

[0149] This invention is a system for supporting users' health management, which works in conjunction with generative AI to accumulate an individual's daily exercise volume and personal data, and makes appropriate suggestions based on that information. Specific embodiments of this system are described below.

[0150] 1. System Configuration

[0151] This system consists of three main elements: a server, a device, and a user. The server works in conjunction with generative AI and plays a central role in accumulating and analyzing data. The device is a wearable device or smartphone used by the user, which collects biometric information and receives suggestions. The user uses these devices to manage their daily health.

[0152] 2. Hardware and Software Used

[0153] The server is a high-performance computer system equipped with a database (e.g., MySQL (registered trademark) or PostgreSQL) and an AI engine (e.g., TensorFlow or PyTorch). The terminals used are wearable devices such as smartwatches and smartphones. These devices communicate with the server using wireless communication technologies such as Bluetooth and Wi-Fi.

[0154] 3. Data acquisition and storage

[0155] When a user wears a smartwatch and goes about their daily activities, biometric information such as heart rate and number of steps is collected in real time. The device then sends this data to a server, which stores it in a database. For example, after a user finishes their morning jog, the smartwatch automatically sends the data to the server.

[0156] 4. Data analysis and proposals

[0157] The server passes the biometric information stored in the database to an AI engine for analysis. Based on the analysis results, suggestions for improving diet, stress relief, and appropriate exercise menus are made, taking into account the user's physical condition and exercise volume. For example, data from the past week is obtained and input into the AI ​​model for analysis.

[0158] 5. Notification of Proposal

[0159] The server makes specific suggestions to the user based on the analysis results of the AI ​​engine. The suggestions are sent to the user's smartphone or PC. For example, a message may be sent to the user's smartphone saying, "You've taken few steps today, so we recommend walking 30 minutes in the evening."

[0160] 6. Examples and prompts

[0161] As a concrete example, consider the case where a user records the number of steps taken and heart rate for the day using a smartwatch. The server acquires this data and stores it in a database. Next, the AI ​​engine analyzes this data and makes suggestions to the user, such as "Since you took fewer steps today, I recommend walking for 30 minutes in the evening."

[0162] Example prompts to input to a generative AI model:

[0163] "Please suggest an appropriate exercise menu based on the user's heart rate and step count data."

[0164] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[0165] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0166] Step 1:

[0167] The server acquires biometric information from the device (smartwatch). Specifically, it receives data such as the user's heart rate and number of steps in real time using wireless communication technologies such as Bluetooth and Wi-Fi. The input is the biometric information sent from the smartwatch, and the output is the biometric information stored on the server. For example, after a user finishes their morning jog, the smartwatch automatically sends the data to the server.

[0168] Step 2:

[0169] The server stores the acquired biometric information in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL and issues SQL queries to the database to insert data. The input is the biometric information stored on the server, and the output is the biometric information stored in the database. For example, the following SQL query is used:

[0170] sql

[0171] INSERT INTO bio_data (user_id, heart_rate, steps, timestamp) VALUES (1, 75, 10000, '2023-10-01 08:00:00');

[0172] Step 3:

[0173] The server retrieves biometric information from the database and passes it to the AI ​​engine. Specifically, it issues an SQL query to retrieve the necessary data from the database and inputs the retrieved data into the AI ​​engine. The input is the biometric information retrieved from the database, and the output is the data input into the AI ​​engine. For example, use the following Python code:

[0174] python

[0175] import tensorflow as tf

[0176] Retrieving data from a database

[0177] data = fetch_data_from_db(user_id=1)

[0178] Inputting data into the AI ​​model

[0179] model = tf.keras.models.load_model('health_model.h5')

[0180] predictions = model.predict(data)

[0181] Step 4:

[0182] The server makes suggestions to the user based on the analysis results of the AI ​​engine. Specifically, the analysis results are generated as a text message and sent to the user's smartphone or PC. The input is the analysis results of the AI ​​engine, and the output is a suggested message sent to the user. For example, a message such as "You've taken fewer steps today, so we recommend walking 30 minutes in the evening" is generated and sent to the user's smartphone.

[0183] Step 5:

[0184] The user receives the suggestions from the server and executes them. Specifically, the user checks the suggestion message on their smartphone or PC and implements the suggested exercise and dietary improvements. The input is the suggestion message sent from the server, and the output is the user's behavior. For example, the user follows the suggestion and takes a 30-minute walk in the evening.

[0185] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[0186] (Application example 2)

[0187] Next, a description will be given of Application Example 2 of Form 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."

[0188] In modern society, personal health management is an important issue. However, there is a lack of information on appropriate dietary recommendations based on daily exercise and physical condition, stress relief methods, and exercise menus. There is also a lack of systems that allow people to easily order healthy meals. This makes it difficult for individuals to take appropriate actions according to their own health condition.

[0189] 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 linking with the generative AI, means for accumulating an individual's daily exercise amount and personal data, means for proposing dietary improvement plans, stress relief methods, and appropriate exercise menus suitable for the individual based on this data, means for analyzing biometric information and proposing meal menus based on the user's physical condition and exercise amount, and means for ordering healthy meals from a food delivery service based on the proposed meal menus. This makes it possible to suggest appropriate dietary habits according to an individual's health condition and to easily order healthy meals.

[0190] "Generative AI" is a type of artificial intelligence that analyzes data and makes suggestions and predictions based on specific purposes.

[0191] "Individual daily exercise volume" refers to data such as the energy consumed and number of steps taken by an individual through daily life and exercise activities.

[0192] "Personal data" refers to information about an individual's health and lifestyle, including heart rate, sleep duration, and dietary habits.

[0193] "Dietary improvement suggestions" are nutritionally balanced meal menus and suggested changes to eating habits based on an individual's health status and amount of exercise.

[0194] "Stress Relief" is a suggestion of specific methods or activities to reduce an individual's stress level.

[0195] An "appropriate exercise menu" is an exercise program proposed based on an individual's physical condition and exercise volume, with the aim of maintaining health and improving physical strength.

[0196] "Biometric information" refers to data about an individual's physical condition, such as heart rate, number of steps, and body temperature, obtained from devices such as smartwatches.

[0197] A "food delivery service" is a service that delivers meals ordered by a user to a specified location.

[0198] A "healthy diet" is a nutritionally balanced meal menu that is appropriate for an individual's health condition and amount of exercise.

[0199] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for suggesting dietary improvements and stress relief methods suitable for the individual based on this data, and an appropriate exercise menu, means for analyzing biometric information and suggesting a meal menu based on the user's physical condition and exercise volume, and means for ordering healthy meals from a food delivery service based on the suggested meal menu.

[0200] Hardware and Software Configuration

[0201] Hardware:

[0202] Smartwatches (e.g. Apple Watch, Fitbit)

[0203] Smartphones (e.g. iPhone (registered trademark), ANDROID (registered trademark))

[0204] software:

[0205] Python

[0206] The Requests library (for sending HTTP requests)

[0207] AI engines (e.g. TensorFlow, PyTorch)

[0208] Data processing and calculation

[0209] The server transmits biometric information (heart rate, number of steps, etc.) obtained from the smartwatch to the smartphone and stores it in a database. The stored data is analyzed by an AI engine. Based on the analysis results, suggestions for improving dietary habits, stress relief methods, and appropriate exercise menus are generated based on the user's physical condition and exercise level. Furthermore, the generated meal menus are suggested to the user, who can then order healthy meals from a food delivery service based on the menus.

[0210] Specific examples

[0211] For example, if a user has not exercised much throughout the day, the AI ​​engine will suggest low-calorie, nutritionally balanced meals, such as "grilled chicken salad" or "vegetable soup." Based on these suggestions, the user can easily order the suggested healthy meal using a food delivery service.

[0212] Prompt Sentence Examples

[0213] "If the user's heart rate is 75 and their steps are 10,000, suggest an appropriate meal plan."

[0214] In this way, a system can be realized that utilizes biometric information obtained from a smartwatch to support the user's health.

[0215] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0216] Step 1:

[0217] The smartwatch acquires the user's biometric information (heart rate, number of steps, etc.). The input is the user's biometric information, and the output is the acquired biometric information. Specifically, the smartwatch's sensors measure the user's heart rate and number of steps in real time.

[0218] Step 2:

[0219] The smartwatch transmits the acquired biometric information to a smartphone. The input is the biometric information acquired from the smartwatch, and the output is the biometric information transmitted to the smartphone. Specifically, the data is transferred to the smartphone using a communication method such as Bluetooth or Wi-Fi.

[0220] Step 3:

[0221] The smartphone sends the received biometric information to the server. The input is the biometric information sent to the smartphone, and the output is the biometric information sent to the server. Specifically, the smartphone application sends the data to the server using an HTTP request.

[0222] Step 4:

[0223] The server stores the received biometric information in a database. The input is the biometric information sent to the server, and the output is the biometric information stored in the database. Specifically, the server program saves the data in the database.

[0224] Step 5:

[0225] The server uses an AI engine to analyze the biometric information stored in the database. The input is the biometric information stored in the database, and the output is the analysis results. Specifically, the AI ​​engine analyzes the biometric information and generates a meal menu based on the user's physical condition and exercise level.

[0226] Step 6:

[0227] The server then proposes a meal menu suitable for the user based on the analysis results. The input is the analysis results of the AI ​​engine, and the output is the proposed meal menu. Specifically, the server sends the generated meal menu to the smartphone.

[0228] Step 7:

[0229] The smartphone displays the proposed meal menu to the user. The input is the meal menu sent from the server, and the output is the meal menu displayed to the user. In concrete terms, the smartphone application displays the meal menu on the screen.

[0230] Step 8:

[0231] A user orders a meal using a food delivery service based on a suggested meal menu. The input is the meal menu selected by the user, and the output is an order to the food delivery service. Specifically, the user selects a meal and confirms the order through a smartphone application.

[0232] Step 9:

[0233] A food delivery service delivers healthy meals to users. The input is the user's order, and the output is the meal delivered to the user. The specific behavior is that the food delivery service receives the order and delivers the meal to the specified location.

[0234] Example 3

[0235] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0236] Conventional health management systems accumulate an individual's daily exercise volume and personal data, and can suggest dietary improvements, stress relief methods, and appropriate exercise menus, but they do not adequately address the issue of adjusting the indoor environment. Furthermore, there is a lack of means to link with smart home appliances and adjust the indoor environment to take the user's physical condition into consideration, making it difficult to provide comprehensive support for the user's health management.

[0237] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0238] In this invention, the server includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for saving environmental data acquired from smart home appliances in a database, means for analyzing the saved data with an AI engine, and means for adjusting the indoor environment taking the user's physical condition into consideration based on the analysis results. This makes it possible to comprehensively manage the user's physical condition and adjust the indoor environment appropriately.

[0239] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[0240] "Individual daily exercise volume" refers to the amount of exercise an individual engages in on a daily basis, and includes data such as the number of steps taken and calories burned.

[0241] "Personal data" is specific information about an individual, including data about their health, diet, stress levels, etc.

[0242] "Smart appliances" are home appliances that are connected to the Internet and can be controlled remotely.

[0243] "Environmental data" refers to information about the environment, such as indoor temperature and humidity.

[0244] A "database" is a system for efficiently storing, managing, and retrieving data.

[0245] An "AI engine" is software or hardware for running artificial intelligence algorithms.

[0246] "Analysis" is the process of examining data in detail to find meaning and patterns.

[0247] "Indoor environment" refers to the environmental conditions of a living space, such as indoor temperature, humidity, and air quality.

[0248] "Adjustment" refers to changing a setting or state to suit specific conditions or goals.

[0249] MODE FOR CARRYING OUT THE INVENTION

[0250] This invention is a system that works in conjunction with generative AI to accumulate an individual's daily exercise volume and personal data, and based on this data, suggests dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for that individual. It also includes a function that stores environmental data acquired from smart home appliances in a database, analyzes the stored data with an AI engine, and adjusts the indoor environment based on the analysis results, taking into account the user's physical condition.

[0251] 1. Program Generation

[0252] The server generates a program for linking with smart home appliances. This program stores information such as indoor temperature and humidity obtained from the smart appliances in a database and analyzes it using an AI engine.

[0253] 2. Program processing explanation

[0254] The server collects indoor temperature and humidity data from smart home appliances such as smart thermostats and smart hygrometers. This data is stored in a database such as MySQL. The server then analyzes the stored data using an AI engine implemented in Python (e.g., TensorFlow or PyTorch). Based on the analysis results, it makes recommendations for indoor temperature control that take the user's physical condition into account.

[0255] For example, if the server determines that the user has a cold, it will control the smart thermostat to adjust the room temperature appropriately. This control is performed through the API of the smart home appliance.

[0256] 3. Examples and prompts

[0257] As a specific example, the process when it is determined that the user has a cold will be described.

[0258] 1. The server retrieves the current room temperature from the smart thermostat.

[0259] 2. The server obtains the current indoor humidity from the smart hygrometer.

[0260] 3. Store this data in a MySQL database.

[0261] 4. The server analyzes the stored data using an AI engine (e.g., TensorFlow) and determines that the user has a cold.

[0262] 5. The server sets the room temperature to an appropriate value (e.g., 22 degrees) through the smart thermostat's API.

[0263] Example prompts to input to a generative AI model:

[0264] "If the user has a cold, explain the steps to properly regulate the indoor temperature."

[0265] By inputting this prompt into a generative AI model, detailed steps based on the specific example above will be generated.

[0266] In this way, the server can comprehensively manage the user's physical condition and adjust the indoor environment appropriately. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0267] Step 1:

[0268] The server retrieves data from smart home appliances. Specifically, it sends a GET request to the API of the smart thermostat or smart hygrometer to retrieve indoor temperature and humidity data. The input is the API endpoint of the smart home appliance, and the output is the retrieved indoor temperature and humidity data.

[0269] Step 2:

[0270] The server saves the acquired data in a database. Specifically, it connects to a MySQL database and inserts the data into a table using the INSERT statement. The input is the acquired indoor temperature and humidity data, and the output is the data saved in the database.

[0271] Step 3:

[0272] The server analyzes the stored data using an AI engine. Specifically, it executes a Python script to read the data and inputs it into a machine learning model such as TensorFlow or PyTorch to obtain the analysis results. The input is the data stored in the database, and the output is the analysis results.

[0273] Step 4:

[0274] The server determines the action to take based on the analysis results. Specifically, if the AI ​​engine receives the analysis results and determines that the user has a cold, the room temperature needs to be adjusted appropriately. The input is the analysis results, and the output is the determined action.

[0275] Step 5:

[0276] The server controls smart appliances. Specifically, it sends a POST request to the smart thermostat's API to set the indoor temperature. The input is the determined action, and the output is the set indoor temperature.

[0277] In this way, the server can comprehensively manage the user's physical condition and adjust the indoor environment appropriately.

[0278] (Application example 3)

[0279] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0280] Conventional health management systems can suggest dietary improvements, stress relief methods, and appropriate exercise menus based on an individual's exercise volume and personal data, but they do not address indoor or store environment adjustments. This makes it difficult to comprehensively manage users' physical condition and comfort. Furthermore, since the environment is not adjusted to maximize customer comfort in physical stores, it is difficult to improve customer satisfaction.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0282] In this invention, the server includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for accumulating environmental data acquired from smart home appliances in a database and analyzing it with an AI engine, means for adjusting the room temperature taking the user's physical condition into consideration based on the analysis results, and means for acquiring environmental data within a physical store and adjusting the environment to maximize customer comfort. This makes it possible to comprehensively manage the user's physical condition and comfort and improve customer satisfaction even in physical stores.

[0283] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[0284] "Individual daily exercise volume" refers to the amount of exercise an individual engages in on a daily basis, and includes data such as the number of steps taken and calories burned.

[0285] "Personal data" refers to specific information about an individual, including their health, lifestyle, preferences, etc.

[0286] "Dietary improvement suggestions" are dietary improvement methods suggested based on an individual's health condition and lifestyle habits.

[0287] "Stress relief" refers to specific methods and suggestions for reducing personal stress.

[0288] An "appropriate exercise menu" is an exercise program suggested based on an individual's health condition and physical ability.

[0289] "Smart home appliances" are home appliances that are connected to the Internet and can be remotely controlled or automatically controlled.

[0290] "Environmental data" refers to information about the indoor environment, such as temperature, humidity, and illuminance.

[0291] A "database" is a system for efficiently storing, managing, and searching data.

[0292] An "AI engine" is software or hardware used to run artificial intelligence algorithms.

[0293] "Analysis results" refer to the conclusions or recommendations reached after analyzing the data.

[0294] "Indoor temperature control" refers to control to keep the indoor temperature within an appropriate range.

[0295] A "brick and mortar store" is a commercial establishment or retail outlet that exists in a physical location.

[0296] "Customer comfort" refers to the comfort and satisfaction that customers feel in the store.

[0297] "Environmental adjustment" refers to the appropriate control of environmental factors such as temperature, humidity, and lighting.

[0298] A system for implementing the present invention has the following configuration.

[0299] System configuration

[0300] 1. Means of collaboration with generative AI

[0301] The server works in conjunction with a generative AI to generate new information and suggestions based on an individual's daily exercise volume and other personal data. The generative AI is implemented using machine learning libraries such as Python and TensorFlow.

[0302] 2. A means of accumulating an individual's daily exercise volume and personal data

[0303] The server stores the exercise volume and personal data acquired from the smartwatch or smartphone in a database using a relational database such as MySQL or PostgreSQL.

[0304] 3. A means of suggesting dietary improvements, stress relief methods, and appropriate exercise menus

[0305] Based on the accumulated data, the server will propose dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for each individual. These suggestions are generated by generative AI and sent to the user's smartphone or smartwatch.

[0306] 4. A means of storing environmental data obtained from smart home appliances in a database and analyzing it with an AI engine

[0307] The server collects environmental data from smart home appliances (such as thermometers and hygrometers) and stores it in a database, then analyzes it using an AI engine implemented using machine learning libraries such as Scikit-learn and Keras.

[0308] 5. A method for adjusting the indoor temperature based on the analysis results and taking into account the user's physical condition

[0309] Based on the analysis results, the server adjusts the room temperature taking into account the user's physical condition. Specifically, it controls the smart thermostat to set the room temperature appropriately. Control is performed using HTTP requests.

[0310] 6. A means of acquiring environmental data within physical stores and adjusting the environment to maximize customer comfort

[0311] The server collects environmental data such as temperature and humidity in the physical store and adjusts the environment to maximize customer comfort, thereby improving customer satisfaction.

[0312] Program processing explanation

[0313] The server collects data from smart home appliances, smartwatches, and smartphones and stores it in a database. The data is retrieved using the Python Requests library. The accumulated data is analyzed by an AI engine, and appropriate suggestions and environmental adjustments are made based on the analysis results.

[0314] For example, if the temperature in the store is 25 degrees and the humidity is 60%, and past data indicates that a customer has a cold, the room temperature will be set to 22 degrees, providing a comfortable environment for the customer.

[0315] Prompt Sentence Examples

[0316] Create a Python program that uses temperature and humidity data obtained from smart appliances to adjust the room temperature based on the customer's physical condition. Train a linear regression model using past data and analyze current data to set the room temperature to 22°C if the customer has a cold, and 24°C otherwise.

[0317] In this way, it is possible to comprehensively manage the user's physical condition and comfort, and improve customer satisfaction even in physical stores.

[0318] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0319] Step 1:

[0320] The server acquires data from smart home appliances, smartwatches, and smartphones. Specifically, it uses the Python Requests library to acquire temperature, humidity, exercise volume, personal data, etc. from each device's API. The input is data from each device, and the output is a JSON-formatted response of the acquired data.

[0321] Step 2:

[0322] The server accumulates the acquired data in a database. Specifically, it saves the data in a relational database such as MySQL or PostgreSQL. The input is the JSON format data acquired in step 1, and the output is the records saved in the database.

[0323] Step 3:

[0324] Based on the accumulated data, the server uses generative AI to generate personalized suggestions for improving dietary habits, stress relief methods, and appropriate exercise menus. Specifically, it trains models using machine learning libraries such as TensorFlow and Keras to generate suggestions. The input is the data accumulated in the database, and the output is the generated suggestions.

[0325] Step 4:

[0326] The server notifies the user of the generated proposal via push notification or email. The input is the proposal generated in step 3, and the output is a notification displayed on the user's device.

[0327] Step 5:

[0328] The server accumulates environmental data acquired from smart home appliances in a database and analyzes it using an AI engine. Specifically, it analyzes the data using machine learning libraries such as Scikit-learn and Keras. The input is the environmental data acquired from the smart home appliances, and the output is the analysis results.

[0329] Step 6:

[0330] Based on the analysis results, the server adjusts the room temperature taking into account the user's physical condition. Specifically, it controls the smart thermostat to set the room temperature appropriately. The input is the analysis result obtained in step 5, and the output is the temperature setting of the smart thermostat.

[0331] Step 7:

[0332] The server collects environmental data from the physical store and adjusts the environment to maximize customer comfort. Specifically, it collects data such as temperature and humidity within the store, analyzes it with an AI engine, and adjusts the environment appropriately. The input is the environmental data from the physical store, and the output is the setting value for the environmental adjustment.

[0333] Step 8:

[0334] The server notifies the physical store management system of the results of the environmental adjustment. Specifically, it sends the environmental adjustment setting values ​​using an HTTP request. The input is the environmental adjustment setting value obtained in step 7, and the output is the setting value reflected in the physical store management system.

[0335] 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.

[0336] "Example 1"

[0337] One embodiment of the present invention provides a system including a means for linking with generative AI, a means for accumulating an individual's daily exercise volume and personal data, a means for suggesting dietary improvements, stress relief methods, and appropriate exercise menus based on the data, and an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's emotions based on facial expressions, tone of voice, and social media posts. For example, if it determines that the user is feeling stressed, the system plays music to promote relaxation and suggests relaxation exercises.

[0338] "Example 2"

[0339] In another embodiment of the present invention, a system is provided that further includes a means for linking with a smartwatch. Biometric information such as heart rate and step count is acquired from the smartwatch, and this information is combined with emotional information obtained from the emotion engine to enable more detailed health management. For example, if the system determines that the user is feeling stressed and has a high heart rate, it will provide instructions such as relaxation exercises and deep breathing.

[0340] "Example 3"

[0341] In yet another embodiment of the present invention, a system is provided that further includes a means for linking with smart home appliances. Information such as indoor temperature and humidity is acquired from the smart home appliances, and this information is combined with emotion information obtained from the emotion engine to provide a more comfortable living environment. For example, if it is determined that the user is irritated and the indoor temperature is high, an instruction to control the air conditioner to lower the indoor temperature is issued.

[0342] The processing flow of each embodiment will be described below.

[0343] "Example 1"

[0344] Step 1: Collect the user's daily exercise volume and personal data.

[0345] Step 2: The emotion engine analyzes the user's emotions based on their facial expressions, tone of voice, and the content of their social media posts.

[0346] Step 3: Based on the analysis results, we will propose dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for each individual.

[0347] Step 4: For example, if the user is determined to be stressed, relaxation music may be played or relaxation exercises may be suggested.

[0348] "Example 2"

[0349] Step 1: Obtain biometric information such as heart rate and steps from the smartwatch.

[0350] Step 2: The emotion engine analyzes the user's emotions.

[0351] Step 3: The acquired biometric information and emotional information are combined to provide more detailed health management. Step 4: For example, if the user is determined to be feeling stressed and their heart rate is high, instructions such as relaxation exercises and deep breathing will be displayed.

[0352] "Example 3"

[0353] Step 1: Obtain information such as indoor temperature and humidity from smart home appliances.

[0354] Step 2: The emotion engine analyzes the user's emotions.

[0355] Step 3: Combine the acquired indoor environmental information with emotional information to provide a more comfortable living environment.

[0356] Step 4: For example, if it is determined that the user is irritated and the indoor temperature is high, an instruction is given to control the air conditioner to lower the indoor temperature.

[0357] Example 1

[0358] Next, a description will be given of Example 1 of Form 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."

[0359] In modern society, individual health management is an important issue, but it is difficult to effectively utilize an individual's exercise volume and personal data to perform appropriate health management. In addition, there is a lack of health suggestions that take into account the user's emotional state, and stress management is not being adequately implemented. This has led to a demand for a system that can comprehensively improve the user's health condition.

[0360] 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.

[0361] In this invention, the server includes a means for linking with the generative AI, a means for accumulating the amount of daily exercise and personal data of an individual, a means for suggesting dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for the individual based on this data, a means including an emotion engine for analyzing the user's emotions, and a means for suggesting relaxation based on the emotion analysis results, thereby enabling comprehensive and individually tailored health management for each user.

[0362] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[0363] "Amount of exercise" is data that indicates the amount of physical activity an individual engages in each day, and includes the number of steps taken, heart rate, calories burned, and so on.

[0364] "Personal data" refers to data related to an individual's health and lifestyle, including diet, sleep, weight, etc.

[0365] A "database" is a system for efficiently managing and storing collected data.

[0366] An "AI engine" is an artificial intelligence that analyzes collected data and generates suggestions suited to individual users.

[0367] The "emotion engine" is a system for analyzing emotions from a user's facial expressions, tone of voice, and the content of their social media posts.

[0368] "Suggestions to promote relaxation" refers to suggestions such as playing relaxation music or doing relaxation exercises when the user is feeling stressed.

[0369] A "smartwatch" is a wearable device for collecting exercise data.

[0370] "Smart home appliances" are household electrical appliances that are connected to the Internet and can be remotely controlled or automatically controlled.

[0371] MODE FOR CARRYING OUT THE INVENTION

[0372] The present invention is a system that comprehensively supports the health management of individual users. A specific embodiment of this system will be described below.

[0373] Data collection

[0374] Users collect exercise data using smartwatches and fitness apps. Smartwatches record data such as the number of steps taken, heart rate, and calories burned. Users also manually enter personal data such as diet and sleep time, or the data is automatically obtained from other connected apps.

[0375] Data transmission

[0376] The device sends the collected exercise data and personal data to a server, where the data is encrypted and securely transmitted using the SSL / TLS protocol.

[0377] Data accumulation

[0378] The server stores the received data in a database, which has a separate table for each user and is indexed for efficient data access.

[0379] Data analysis

[0380] The server sends the data to the AI ​​engine, which then uses machine learning algorithms to analyze the user's exercise patterns and dietary habits and generate optimal improvement recommendations.

[0381] Emotion analysis

[0382] The server analyzes the user's emotions using an emotion engine, which uses facial expression recognition technology and voice analysis technology to determine whether the user is feeling stressed.

[0383] Proposal Generation

[0384] The server combines the analysis results of the AI ​​engine and the emotion engine to generate optimal suggestions for the user. For example, if the user is feeling stressed, it will suggest playing relaxation music or doing relaxation exercises.

[0385] Providing suggestions

[0386] The device displays the suggestions sent from the server to the user. The user can then confirm and implement the suggested dietary improvements and exercise menu. For example, an exercise menu such as "Aim for 10,000 steps a day" may be displayed.

[0387] Specific examples

[0388] Example 1: Collecting and analyzing momentum data

[0389] Users wear a smartwatch every day to record their exercise. The smartwatch collects data such as the number of steps taken, heart rate, and calories burned. This data is sent to a server via a fitness app and stored in a database. The server uses an AI engine to analyze this data and suggests exercise regimens to users, such as "Aim for 10,000 steps a day."

[0390] Example 2: Emotion analysis and relaxation suggestions

[0391] If a user posts on social media, "I'm so tired today," the server uses the emotion engine to determine that the user is feeling stressed. Based on this, the device will make suggestions to the user, such as, "Would you like to play some relaxation music?"

[0392] Prompt Sentence Examples

[0393] "Design a system that uses the user's exercise data and personal data to suggest dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for each individual user. Use an emotion engine to analyze the user's emotions and make suggestions to encourage relaxation if the user is feeling stressed."

[0394] In this way, the system collects and analyzes user data and makes suggestions based on individual needs, supporting health management.

[0395] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0396] Step 1:

[0397] Users collect exercise data using a smartwatch and a fitness app. The smartwatch records data such as the number of steps taken, heart rate, and calories burned. This data is automatically sent to the fitness app. The input is the exercise data from the smartwatch, and the output is the exercise data accumulated in the fitness app.

[0398] Step 2:

[0399] Users can manually input personal data such as dietary habits and sleep duration, or automatically obtain it from other linked apps. The input is data manually entered by the user or data obtained from linked apps, and the output is personal data accumulated in the fitness app.

[0400] Step 3:

[0401] The device sends the collected exercise data and personal data to the server. The data is encrypted and transmitted securely using the SSL / TLS protocol. The input is the data stored in the fitness app, and the output is the encrypted data sent to the server.

[0402] Step 4:

[0403] The server stores the received data in a database with a separate table for each user, indexed for efficient data access. The input is the encrypted data sent to the server, and the output is the data stored in the database.

[0404] Step 5:

[0405] The server sends the data to the AI ​​engine and begins analysis. The AI ​​engine uses machine learning algorithms to analyze the user's exercise patterns and dietary habits and generate optimal improvement suggestions. The input is the data stored in the database, and the output is the improvement suggestions generated by the AI ​​engine.

[0406] Step 6:

[0407] The server uses an emotion engine to analyze the user's emotions. The emotion engine uses facial expression recognition and voice analysis technologies to determine whether the user is feeling stressed. Inputs include the user's facial expressions, tone of voice, and social media posts, and the output is the user's emotional state analyzed by the emotion engine.

[0408] Step 7:

[0409] The server integrates the analysis results of the AI ​​engine and emotion engine to generate optimal suggestions for the user. For example, if the user is feeling stressed, it will play relaxation music or suggest relaxation exercises. The input is the analysis results of the AI ​​engine and emotion engine, and the output is the suggestions for the user.

[0410] Step 8:

[0411] The terminal displays the suggestions sent from the server to the user. The user can confirm and implement the suggested dietary improvements and exercise menus. The input is the suggestions sent from the server, and the output is the suggestions displayed to the user.

[0412] (Application example 1)

[0413] Next, a description will be given of Application Example 1 of Embodiment 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."

[0414] In modern society, health and stress management for individual users are important issues. However, conventional systems have had difficulty effectively utilizing the user's exercise volume and personal data to propose individually optimized dietary improvements, stress relief methods, and appropriate exercise menus. There has also been a lack of systems that can recognize the user's emotional state in real time and respond appropriately based on that. Furthermore, there have been insufficient concrete means for linking these suggestions to actual behavior, such as linking with meal delivery services.

[0415] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for linking with the generative AI, means for accumulating an individual's daily exercise amount and personal data, means for proposing dietary improvement plans, stress relief methods, and appropriate exercise menus that are suitable for the individual based on this data, means including an emotion engine that recognizes the user's emotions, and means for proposing optimal meal menus for the user and providing delivery services. This makes it possible to make individually optimized suggestions based on the user's health condition and emotional state, and to link them to specific actions.

[0416] "Generative AI" is a type of artificial intelligence that analyzes data and provides users with optimal suggestions and products.

[0417] "Individual daily exercise" is data that indicates the amount of exercise a user engages in on a daily basis, and is obtained from a smartwatch or fitness app.

[0418] "Personal data" refers to data that includes personal information such as a user's age, gender, and allergy information.

[0419] "Dietary improvement suggestions" are ways to improve your diet that are suggested based on the user's health condition and personal data.

[0420] "Stress relief" refers to specific methods or suggestions for reducing the user's stress level.

[0421] An "appropriate exercise menu" is an exercise program suggested based on the user's exercise volume and health condition.

[0422] The "emotion engine" is a system that analyzes emotions from a user's facial expressions, tone of voice, and the content of their social media posts.

[0423] A "delivery service" is a service in which users order suggested meal menus and have them delivered to their homes or other locations.

[0424] A system for implementing the present invention has the following configuration.

[0425] System configuration

[0426] 1. Data collection methods:

[0427] Hardware: Smartwatches, smartphones

[0428] Software: Fitness app, personal data management app, sentiment analysis engine

[0429] Processing details: Collects user exercise data from smartwatches and fitness apps, and collects personal information from personal data management apps. Also, uses an emotion analysis engine to obtain emotional data from the user's facial expressions, tone of voice, and social media posts.

[0430] 2. Data storage means:

[0431] Hardware: Server

[0432] Software: Database Management System

[0433] Processing details: Collected exercise data, personal data, and emotional data are stored in a database on the server.

[0434] 3. Data analysis methods:

[0435] Hardware: Server

[0436] Software: Generative AI engine

[0437] Processing details: The accumulated data is analyzed using a generative AI engine to generate optimal dietary improvement suggestions, stress relief methods, and appropriate exercise menus for the user.

[0438] 4. Proposal method:

[0439] Hardware: Smartphones, tablets

[0440] Software: Proposed application

[0441] Processing content: Based on the analysis results of the generative AI engine, the system suggests dietary improvements, stress relief methods, and appropriate exercise menus to the user.

[0442] 5. Delivery Service Method:

[0443] Hardware: Servers, smartphones

[0444] Software: Delivery Service API

[0445] What it does: Allows users to order suggested meals with one click and delivers the meal through a delivery service.

[0446] Specific examples

[0447] For example, consider a case where User A is a 30-year-old male with a moderate amount of exercise and a high stress level. The system obtains exercise data from the user's smartwatch, and collects age, gender, and allergy information from a personal data management app. Using a sentiment analysis engine, the system detects high stress levels from the user's social media posts.

[0448] This data is stored in a database on a server and analyzed by a generative AI engine. As a result of the analysis, "chicken salad and brown rice" is recommended as the optimal meal menu for User A. In addition, because User A's stress level is high, "herbal tea and yoga" is also recommended as a relaxation menu.

[0449] User A can check the suggested meal menu through the suggestion application on his smartphone and place an order using the delivery service with one click.

[0450] Prompt Sentence Examples

[0451] "Collect the user's exercise data, personal data, and emotional data, analyze it with a generative AI engine, and suggest the optimal meal menu. If the user is feeling stressed, suggest a relaxation menu as well."

[0452] In this way, it is possible to provide personalized recommendations based on the user's health and emotional state, which can then be linked to specific actions.

[0453] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0454] Step 1:

[0455] A user exercises using a smartwatch or fitness app. The smartwatch or fitness app collects data on the user's exercise, including the number of steps taken, heart rate, calories burned, etc. The collected data is sent to a smartphone.

[0456] Input: User's exercise data (number of steps, heart rate, calories burned, etc.)

[0457] Output: Exercise data sent to a smartphone

[0458] Step 2:

[0459] The smartphone collects personal information of the user through a personal data management app, including age, gender, allergy information, etc. The collected data is sent to a server.

[0460] Input: User's personal data (age, gender, allergy information, etc.)

[0461] Output: Personal data sent to the server

[0462] Step 3:

[0463] The emotion analysis engine collects emotional data from the user's facial expressions, tone of voice, and social media posts. The emotion analysis engine analyzes this data and determines the user's emotional state (e.g., stress level). The determination result is sent to the server.

[0464] Input: User facial expression data, tone of voice data, SNS post content

[0465] Output: Emotional data (e.g. stress level) sent to the server

[0466] Step 4:

[0467] The server stores the exercise data, personal data, and emotional data in a database, forming a comprehensive data set on the user's health and emotional state.

[0468] Input: Exercise data, personal data, emotion data

[0469] Output: A comprehensive dataset stored in a database

[0470] Step 5:

[0471] The server analyzes the accumulated data using a generative AI engine, which generates optimal dietary improvement suggestions, stress relief methods, and appropriate exercise menus for the user. The analysis results are sent to the proposal application.

[0472] Input: Comprehensive dataset stored in a database

[0473] Output: Analysis results sent to the proposal application (dietary improvement suggestions, stress relief methods, exercise menu)

[0474] Step 6:

[0475] Based on the analysis results of the generative AI engine, the suggestion application will suggest dietary improvements, stress relief methods, and appropriate exercise menus to the user. The user can check the suggestions on their smartphone or tablet.

[0476] Input: Analysis results sent to the proposal application

[0477] Output: Dietary improvement suggestions, stress relief methods, and exercise menus presented to the user

[0478] Step 7:

[0479] The user can view the suggested meal menu through the suggestion application and place an order using the delivery service with one click. The suggestion application then calls the delivery service API to process the order.

[0480] Input: User order operation

[0481] Output: Order data sent to the delivery service API

[0482] Step 8:

[0483] The delivery service receives an order from a user and delivers the specified meal menu to the user's home, etc. The user can receive the suggested meal menu.

[0484] Input: Order data sent to the delivery service API

[0485] Output: The meal menu delivered to the user

[0486] Example 2

[0487] Next, a description will be given of Example 2 of Form 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."

[0488] In modern society, personal health management is an important issue. However, conventional health management systems are unable to fully utilize the biometric and emotional information of individual users, making it difficult to provide individually optimized advice. In addition, there is a lack of means to connect with devices such as smartwatches and smart home appliances, making it impossible to achieve comprehensive health management based on the user's physical and emotional state. This can prevent users from receiving appropriate health management, potentially delaying improvements in their health.

[0489] 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.

[0490] In this invention, the server includes means for linking with the generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for acquiring biometric information from the smartwatch, means for storing the biometric information in a database, an AI engine for analyzing the biometric information, an emotion engine for acquiring emotional information, means for combining and analyzing the biometric information and emotional information, and means for providing appropriate advice to the user based on the analysis results. This makes it possible to comprehensively analyze the user's biometric information and emotional information and provide individually optimized health management advice.

[0491] "Generative AI" refers to AI that has the ability to generate new information and predictions based on data.

[0492] "Individual daily exercise volume" refers to data that indicates the amount of exercise a user engages in on a daily basis.

[0493] "Personal data" refers to all information about an individual user, including data such as health status and lifestyle habits.

[0494] A "smartwatch" is a wearable device that has the ability to acquire and transmit a user's biometric information in real time.

[0495] "Biometric information" refers to data related to the user's physical condition, such as heart rate and number of steps taken.

[0496] A "database" is a system for storing and managing acquired data.

[0497] An "artificial intelligence engine" is an artificial intelligence that analyzes data and detects patterns and anomalies.

[0498] An "emotion engine" is a system for analyzing a user's emotional state and acquiring emotional information.

[0499] "Analysis results" refers to the results of analyzing data obtained by an artificial intelligence engine or emotion engine.

[0500] "Advice" refers to suggestions or instructions regarding health management provided to users based on the analysis results.

[0501] This invention is a system that supports users' health management, and uses hardware and software such as generative artificial intelligence, a smart watch, a database, an artificial intelligence engine, and an emotion engine.

[0502] The server works in conjunction with generative AI to accumulate an individual's daily exercise volume and other personal data. Specifically, it acquires biometric information such as heart rate and number of steps from the smartwatch and stores this data in a database. The server then sends the stored data to an AI engine for analysis. The AI ​​engine compares it with past data to detect outliers and analyze trends.

[0503] The server then acquires the user's emotional information using an emotion engine, which analyzes the user's emotions from facial expressions, voice data, etc. The server then analyzes this biometric information and emotional information in combination to comprehensively evaluate the user's health condition.

[0504] Based on the analysis results, the server will provide appropriate advice to the user, such as relaxation exercises or deep breathing prompts, via notifications on the smartphone or smartwatch.

[0505] As a concrete example, consider the case where a user is wearing a smartwatch. The smartwatch detects that the user's heart rate is higher than normal. The device sends this information to a server. The server stores the received heart rate data in a database and sends it to an artificial intelligence engine for analysis. The artificial intelligence engine compares it with the user's past data and determines that stress is causing the elevated heart rate. The emotion engine similarly detects that the user is feeling stressed. Based on this information, the server instructs the user to take deep breaths.

[0506] Example prompt sentence:

[0507] If the user's heart rate is higher than normal, determine if they are experiencing stress and suggest appropriate measures.

[0508] In this way, a system is realized in which the server, terminal, and user work together to manage the user's health.

[0509] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0510] Step 1:

[0511] The device collects biometric information such as heart rate and number of steps in real time from the smartwatch worn by the user.

[0512] Input: Biometric information from smartwatch (heart rate, steps, etc.)

[0513] Output: Acquired biometric information

[0514] Specific operation: The smartwatch detects the user's heart rate as 80 bpm and sends the data to the device.

[0515] Step 2:

[0516] The terminal transmits the acquired biometric information to the server.

[0517] Input: Biometric information stored on the device

[0518] Output: Biometric information sent to the server

[0519] Specific operation: The device sends the heart rate data received from the smartwatch to the server.

[0520] Step 3:

[0521] The server stores the received biometric information in a database.

[0522] Input: Biometric information sent to the server

[0523] Output: Biometric information stored in a database

[0524] Specific operation: The server stores the heart rate data of 80 bpm in the database.

[0525] Step 4:

[0526] The server sends the biometric information stored in the database to an artificial intelligence engine for analysis.

[0527] Input: Biometric information stored in the database

[0528] Output: Analysis results by the AI ​​engine

[0529] Specific operation: The server sends heart rate data to an artificial intelligence engine, which compares it with past data to detect abnormalities.

[0530] Step 5:

[0531] The server acquires the user's emotion information using an emotion engine.

[0532] Input: User's facial expressions and voice data

[0533] Output: Emotion information from the emotion engine

[0534] Specific operation: The server sends the user's facial expression data to the emotion engine and determines that the user is feeling stressed.

[0535] Step 6:

[0536] The server combines and analyzes the biometric information analyzed by the artificial intelligence engine and the emotional information acquired by the emotion engine.

[0537] Input: Analysis results of the AI ​​engine, emotional information of the emotion engine

[0538] Output: Comprehensive analysis results

[0539] Specific operation: The server combines abnormal heart rate values ​​with stress levels to determine that the user is in a high stress state.

[0540] Step 7:

[0541] The server provides appropriate advice to the user based on the analysis results.

[0542] Input: Comprehensive analysis results

[0543] Output: Advice notice to user

[0544] Specific operation: The server sends a notification to the smartphone instructing the user to take a deep breath.

[0545] In this way, a system is realized in which the server, terminal, and user work together to manage the user's health.

[0546] (Application example 2)

[0547] Next, a description will be given of Application Example 2 of Form 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."

[0548] Conventional health management systems were unable to fully utilize the user's biometric and emotional information, and were unable to suggest optimal meal menus for each individual user or to link with food delivery services. Furthermore, detailed health management based on the user's physical condition and emotional state was difficult, and suggestions for stress relief and appropriate exercise menus were limited.

[0549] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0550] In this invention, the server includes means for linking with the generative AI, means for accumulating an individual's daily exercise volume and personal data, means for suggesting dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for the individual based on this data, means for analyzing biometric and emotional information obtained from the smartwatch and suggesting meal menus based on the user's physical condition and emotional state, and means for ordering the suggested meal menus in collaboration with a food delivery service. This enables individual health management based on the user's physical condition and emotional state, and the provision of healthy meals through food delivery services.

[0551] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[0552] "Individual daily exercise amount" is data indicating the amount of exercise a user performs in their daily life.

[0553] "Personal data" refers to information about an individual user, including their health status, lifestyle habits, etc.

[0554] "Dietary improvement suggestions" are suggestions for meal menus and changes to eating habits that take into account the user's health condition.

[0555] "Stress relief" refers to methods and suggestions for reducing stress for users.

[0556] An "appropriate exercise menu" is an exercise program suggested based on the user's health condition and athletic ability.

[0557] A "smartwatch" is a wearable device that collects a user's biometric information.

[0558] "Biometric information" refers to data related to the user's physical condition, such as heart rate, number of steps, and sleep data.

[0559] "Emotion information" is data that indicates the user's emotional state.

[0560] A "food delivery service" is a service that delivers meals ordered by users.

[0561] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for suggesting dietary improvements and stress relief methods suitable for the individual based on this data, and an appropriate exercise menu, means for analyzing biometric and emotional information obtained from the smartwatch and suggesting a meal menu based on the user's physical condition and emotional state, and means for linking with a food delivery service to order the suggested meal menu.

[0562] Hardware and software used

[0563] Hardware:

[0564] Smartwatches (e.g. Apple Watch, Fitbit)

[0565] Smartphone (e.g. iPhone, Android device)

[0566] software:

[0567] Python

[0568] The Requests library (for sending HTTP requests)

[0569] Dummy API (food delivery service)

[0570] Data processing and calculation

[0571] The server acquires biometric information such as heart rate, step count, and sleep data from the smartwatch. This data is sent to the server via the user's smartphone. Furthermore, the server analyzes the user's emotional state using an emotion engine to acquire emotional information.

[0572] The server analyzes the acquired biometric and emotional information using an AI engine and suggests meal plans based on the user's physical condition and emotional state. These suggestions are then sent to the user's smartphone.

[0573] The user checks the suggested meal menu and places an order through a food delivery service. The server uses the food delivery service's API to send the order and provide the user with a healthy meal.

[0574] Specific examples

[0575] For example, if User A's heart rate is 80, the number of steps taken is 5,000, the amount of sleep is 6 hours, and the emotional state is stressed, the AI ​​engine will suggest a "relaxation menu." This suggestion will be notified to the user's smartphone, and the user can order the relaxation menu through a food delivery service.

[0576] Prompt Sentence Examples

[0577] "If a user's heart rate is 80, they have taken 5,000 steps, slept 6 hours, and their emotional state is stressed, what meal plan would you suggest?"

[0578] In this way, it becomes possible to provide personalized health management based on the user's physical and emotional state, and healthy meals through food delivery services.

[0579] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0580] Step 1:

[0581] The server acquires biometric information from the smartwatch. Specifically, the heart rate, number of steps, sleep data, etc. measured by the smartwatch are sent to the server via the user's smartphone. The input is the biometric data from the smartwatch, and the output is the biometric data stored on the server.

[0582] Step 2:

[0583] The server acquires the user's emotional information using the emotion engine. Specifically, it receives data indicating the user's emotional state from the user's smartphone and analyzes it using the emotion engine. The input is data indicating the user's emotional state, and the output is the analyzed emotional information.

[0584] Step 3:

[0585] The server analyzes the acquired biometric and emotional information using an AI engine. Specifically, it integrates heart rate, step count, sleep data, and emotional information to generate a meal menu based on the user's physical condition and emotional state. The input is biometric and emotional information, and the output is a suggested meal menu.

[0586] Step 4:

[0587] The server notifies the user's smartphone of the proposed meal menu. Specifically, it sends the generated meal menu to the user's smartphone and displays a notification. The input is the proposed meal menu, and the output is the notification displayed on the user's smartphone.

[0588] Step 5:

[0589] The user uses a smartphone to check the proposed meal menu and place an order through a food delivery service. Specifically, the user operates the smartphone application to select a proposed menu and confirm the order. The input is the operation to check the proposed meal menu, and the output is the order information.

[0590] Step 6:

[0591] The server sends the order using the food delivery service's API. Specifically, it sends the user's order information to the food delivery service's API and confirms the order. The input is the user's order information, and the output is the order data sent to the food delivery service.

[0592] Step 7:

[0593] A food delivery service receives orders and delivers meals to users. Specifically, it prepares meals based on the received order information and delivers them to the specified address. The input is the order data sent from the server, and the output is the meal delivered to the user.

[0594] Example 3

[0595] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0596] In modern society, personal health management and maintaining a comfortable living environment are important issues. However, existing systems do not adequately adjust the environment based on an individual's physical condition and emotional state. Furthermore, there is a lack of systems that effectively utilize an individual's daily exercise volume and personal data to improve diet, relieve stress, and suggest appropriate exercise menus. This makes it difficult to provide personal health management and a comfortable living environment.

[0597] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0598] In this invention, the server includes a means for linking with the generative AI, a means for accumulating an individual's daily exercise volume and personal data, a means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, a means for accumulating environmental data acquired from smart home appliances, an AI engine for analyzing the accumulated environmental data, and a means for controlling the smart home appliances based on the analysis results. This enables appropriate environmental adjustments based on the individual's physical condition and emotional state, thereby realizing personal health management and providing a comfortable living environment.

[0599] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[0600] "Individual daily exercise volume" refers to the amount of exercise an individual engages in on a daily basis, and includes data such as the number of steps taken and calories burned.

[0601] "Personal data" is specific information relating to an individual, including information about their health, diet, emotional state, etc.

[0602] "Dietary Improvement Suggestions" are suggested changes to dietary content and eating habits based on an individual's health status and goals.

[0603] "Stress relief" refers to specific methods and suggestions for reducing personal stress.

[0604] An "appropriate exercise menu" refers to the exercise content and schedule suggested based on an individual's health condition and goals.

[0605] "Smart home appliances" are household electrical appliances that are connected to the Internet and can be remotely or automatically controlled.

[0606] "Environmental data" refers to information related to living environments, such as indoor temperature and humidity.

[0607] An "AI engine" is a core technology in artificial intelligence that analyzes data and generates results tailored to specific purposes.

[0608] "Analysis results" are the conclusions and suggestions reached by the AI ​​engine after analyzing the data.

[0609] "Control means" refers to a method or device for operating smart home appliances based on the analysis results.

[0610] MODE FOR CARRYING OUT THE INVENTION

[0611] The present invention is a system for managing personal health and providing a comfortable living environment. A specific embodiment of this system will be described below.

[0612] server

[0613] The server acquires environmental data such as indoor temperature and humidity from smart home appliances. This data is acquired using devices such as smart thermostats and smart hygrometers. The server stores this data in a database and analyzes it using an AI engine (e.g., TensorFlow or PyTorch). Based on the analysis results, the server generates control instructions for the smart home appliances and sends them to the smart home appliances.

[0614] Specifically, the server calls the smart thermostat's API and sends instructions for setting the temperature. For example, if the server determines that the user has a cold, it sends the instruction to the smart thermostat to "set the room temperature to 22 degrees."

[0615] Terminal

[0616] The device receives information such as indoor temperature and humidity from smart home appliances and sends it to the server. After receiving the analysis results from the server, the device displays appropriate indoor temperature adjustment suggestions to the user. For example, if the device determines that the user has a cold, it will display a message such as, "Would you like to set the indoor temperature to 22 degrees?"

[0617] User

[0618] Users can maintain a comfortable indoor environment using a system that connects with smart home appliances. If the user has a cold, the system will automatically adjust the indoor temperature to provide a comfortable environment tailored to the user's physical condition. If the system determines that the user is irritated, it will control the air conditioner to lower the indoor temperature.

[0619] Examples and prompts

[0620] Specific examples

[0621] 1. If the user has a cold, the server controls the smart thermostat to set the room temperature to 22 degrees.

[0622] 2. If the user is determined to be irritated and the room temperature is 28 degrees, the server will control the air conditioner to lower the room temperature to 24 degrees.

[0623] Prompt Sentence Examples

[0624] "If the user has a cold, explain how to control the smart thermostat based on the analysis results of the AI ​​engine to adjust the indoor temperature appropriately."

[0625] "Please explain the operation of the system that controls the air conditioner to lower the room temperature when it is determined that the user is irritated." The flow of the identification process in the third embodiment will be described with reference to FIG. 21.

[0626] Step 1: Data Acquisition

[0627] The server obtains environmental data such as indoor temperature and humidity from smart home appliances. Specifically, the server calls the API of the smart thermostat or smart hygrometer to obtain the latest temperature and humidity data. The input is the environmental data from the smart home appliances, and the output is the obtained temperature and humidity data.

[0628] Step 2: Data accumulation

[0629] The server accumulates the acquired environmental data in a database. Specifically, the server saves the acquired temperature and humidity data in the database along with a timestamp. The input is the acquired temperature and humidity data, and the output is the data saved in the database.

[0630] Step 3: Data analysis

[0631] The server analyzes the accumulated data using an AI engine. Specifically, the server uses an AI model (e.g., TensorFlow or PyTorch) to predict the user's physical and emotional state. The input is environmental data stored in a database, and the output is the analysis results.

[0632] Step 4: Control instruction generation

[0633] The server generates control instructions for smart home appliances based on the analysis results. Specifically, if the server determines that the user has a cold, it generates an instruction to the smart thermostat to "set the room temperature to 22 degrees." The input is the analysis result, and the output is the generated control instruction.

[0634] Step 5: Sending control instructions

[0635] The server sends the generated control instructions to the smart appliances. Specifically, the server calls the smart thermostat's API to send temperature setting instructions. The input is the generated control instructions, and the output is the instructions sent to the smart appliances.

[0636] Step 6: Receiving Data

[0637] The terminal receives information such as indoor temperature and humidity from smart home appliances and sends it to the server. Specifically, the terminal calls the API of the smart home appliance to obtain the latest temperature and humidity data and send it to the server. The input is environmental data from the smart home appliance, and the output is data sent to the server.

[0638] Step 7: Receive analysis results

[0639] The terminal receives the analysis results from the server. Specifically, the terminal calls the server's API to obtain the analysis results. The input is the analysis results from the server, and the output is the display of the analysis results on the terminal.

[0640] Step 8: User Notification

[0641] The terminal notifies the user based on the analysis results. Specifically, the terminal displays a message to the user such as "Do you want to set the room temperature to 22 degrees?" The input is the analysis result from the server, and the output is a notification to the user.

[0642] Step 9: System Usage

[0643] A user uses a system that connects with smart home appliances. Specifically, the user logs in to the system using a smartphone or tablet. The input is the user's login information, and the output is the start of use of the system.

[0644] Step 10: Provide data

[0645] The user provides the system with data about their physical and emotional state. Specifically, the user inputs information such as "I have a cold" or "I'm irritated" through the application. The input is data about the user's physical and emotional state, and the output is data provided to the system.

[0646] Step 11: Check notifications

[0647] The user checks the notification from the device. Specifically, the user checks the message displayed on the device, such as "Do you want to set the room temperature to 22 degrees?", and approves it if necessary. The input is the notification from the device, and the output is the user's confirmation and approval.

[0648] Step 12: Adjusting the environment

[0649] The user adjusts the indoor environment based on the system's suggestions. Specifically, the user changes the settings of the smart thermostat according to instructions on the device. The input is the system's suggestions, and the output is the adjustment of the indoor environment.

[0650] (Application example 3)

[0651] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0652] Conventional systems do not adequately adjust the environment according to the physical condition and emotions of individuals, and there are a lack of concrete measures to improve customer comfort, especially in brick-and-mortar stores. Furthermore, there is no established method for effectively utilizing data obtained from smart home appliances and smart devices. This makes it difficult to provide an optimal environment according to the physical condition and emotions of customers.

[0653] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0654] In this invention, the server includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for accumulating information such as indoor temperature and humidity obtained from smart home appliances in a database and analyzing it with an AI engine, means for adjusting the indoor environment based on the analysis results according to the user's physical condition and emotions, means for optimizing the environment of a physical store according to the customer's physical condition and emotions, and means for being installed on a smartphone or smart glasses and operating in conjunction with the smart home appliances in the store, thereby making it possible to provide an optimal environment according to the customer's physical condition and emotions.

[0655] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[0656] "Individual daily exercise volume" refers to the amount of exercise an individual performs on a daily basis.

[0657] "Personal data" refers to specific information about an individual, including their health status and lifestyle habits.

[0658] "Dietary Improvement Suggestions" refers to dietary suggestions to improve an individual's health.

[0659] "Stress management" refers to methods used by individuals to reduce stress.

[0660] An "appropriate exercise menu" refers to an exercise program that is tailored to an individual's health condition and goals.

[0661] "Smart appliances" refer to home appliances that are connected to the Internet and can be controlled remotely.

[0662] "Indoor temperature" refers to the temperature of the air inside a building.

[0663] "Humidity" refers to the amount of water vapor contained in the air.

[0664] A "database" refers to a system for systematically organizing and storing data.

[0665] An "AI engine" refers to software or hardware used to run artificial intelligence algorithms.

[0666] "Analysis" refers to the act of examining data in detail to find meaning and patterns.

[0667] "User's physical condition" refers to the user's health condition.

[0668] "Emotion" refers to the user's psychological state.

[0669] "Adjusting the indoor environment" refers to changing environmental factors such as the temperature and humidity inside the room.

[0670] "Brick and Mortar Store" means a retail store located in a physical location.

[0671] "Customer" refers to a person who purchases goods or services.

[0672] A "smartphone" refers to a mobile phone that can connect to the Internet and use applications.

[0673] "Smart glasses" refers to a glasses-type device that can display information and connect to the Internet.

[0674] "Smart device" refers to an electronic device that is connected to the Internet and can be controlled remotely.

[0675] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for suggesting dietary improvements and stress relief methods suitable for the individual based on this data, and an appropriate exercise menu, means for accumulating information such as indoor temperature and humidity obtained from smart home appliances in a database and analyzing it using an AI engine, means for adjusting the indoor environment according to the user's physical condition and emotions based on the analysis results, means for optimizing the environment of a physical store according to the customer's physical condition and emotions, and means that is installed on a smartphone or smart glasses and operates in conjunction with smart home appliances in the store.

[0676] The server collects data from smart home appliances and smart devices and stores it in a database. Specifically, it collects information such as indoor temperature, humidity, lighting brightness, heart rate, body temperature, and emotional information. This data is obtained using a REST API.

[0677] The server then analyzes the collected data using an AI engine, which uses software such as TensorFlow and PyTorch. Based on the analysis results, the system calculates optimal environmental settings based on the user's physical condition and emotions. For example, if the user is irritated or has a cold, the system will control the air conditioner and humidifier to provide an appropriate indoor environment.

[0678] Furthermore, the server controls smart home appliances based on the analysis results, issuing instructions such as switching the air conditioner to cooling mode, turning on the humidifier, and adjusting the brightness of the lights, making it possible to provide an optimal environment according to the user's physical condition and emotions.

[0679] For example, if a customer is judged to be irritated in a physical store and the indoor temperature is high, the air conditioner can be controlled to lower the temperature, thus providing a more comfortable shopping experience for the customer.

[0680] An example prompt is, "If a customer is frustrated, how can I control the air conditioning to lower the room temperature?"

[0681] This system makes it possible to provide an optimal environment according to an individual's physical condition and emotions, thereby improving customer comfort, especially in physical stores.

[0682] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0683] Step 1:

[0684] The server collects data from smart home appliances and smart devices. Specifically, it obtains information such as indoor temperature, humidity, lighting brightness, heart rate, body temperature, and emotional information using a REST API. The input is sensor data from smart home appliances and smart devices, and the output is to store this data in a database.

[0685] Step 2:

[0686] The server stores the collected data in a database. The database stores the data acquired from each device in chronological order. The input is the sensor data acquired in step 1, and the output is the data stored in the database.

[0687] Step 3:

[0688] The server analyzes the data stored in the database using an AI engine. The AI ​​engine uses software such as TensorFlow and PyTorch. The input is sensor data stored in the database, and the output is the analysis results based on the user's physical condition and emotions. Specifically, the data is input into an AI model, which estimates the user's physical condition and emotional state.

[0689] Step 4:

[0690] The server calculates the optimal environmental settings based on the analysis results, taking into account the user's physical condition and emotions. The input is the analysis results obtained in step 3, and the output is instructions for the optimal environmental settings. For example, if the user is irritated or has a cold, the server calculates the settings to control the air conditioner or humidifier.

[0691] Step 5:

[0692] The server sends instructions for optimal environmental settings to the smart appliances. The input is the environmental setting instructions calculated in step 4, and the output is the actions of the smart appliances. Specific actions include switching the air conditioner to cooling mode, turning on the humidifier, adjusting the brightness of the lights, etc.

[0693] Step 6:

[0694] Users can check the system's operating status through their smartphones or smart glasses. The input is an environmental setting instruction from the server, and the output is feedback to the user. Specifically, notifications are displayed on the smartphone or smart glasses to inform the user of the current indoor environment and adjustments.

[0695] In this way, it becomes possible to provide an optimal environment according to the physical condition and emotions of each individual, thereby improving customer comfort, especially in brick-and-mortar stores.

[0696] 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.

[0697] 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> ) 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.

[0698] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0699] 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.

[0700] [Second embodiment]

[0701] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0702] 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.

[0703] 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).

[0704] 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.

[0705] 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.

[0706] 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).

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0713] "Example 1"

[0714] As one embodiment of the present invention, a means for linking with generative AI is provided, which includes a database that stores the amount of exercise and personal data of each user, and an AI engine that analyzes this data and proposes dietary improvement plans, stress relief methods, and appropriate exercise menus that are suitable for each user. Specifically, the amount of exercise a user does is obtained from a smartwatch or fitness app, and personal data is obtained from user input or other linked apps. This data is stored in a database and analyzed by the AI ​​engine. Based on the analysis results, dietary improvement plans, stress relief methods, and appropriate exercise menus are proposed to the user.

[0715] "Example 2"

[0716] In another embodiment of the present invention, biometric information such as heart rate and step count acquired from a smartwatch is used as a means of linking with the smartwatch. Specifically, the biometric information acquired from the smartwatch is stored in a database and analyzed by an AI engine. Based on the analysis results, suggestions for improving dietary habits, stress relief methods, and appropriate exercise menus are proposed, taking into account the user's physical condition and exercise volume.

[0717] "Example 3"

[0718] In a further embodiment of the present invention, information such as indoor temperature and humidity obtained from smart home appliances is used as a means for linking with smart home appliances. Specifically, the information obtained from the smart home appliances is stored in a database and analyzed by an AI engine. Based on the analysis results, assistance is provided for adjusting the indoor temperature taking into account the user's physical condition. For example, if it is determined that the user has a cold, the smart thermostat is controlled to appropriately adjust the indoor temperature.

[0719] The processing flow of each embodiment will be described below.

[0720] "Example 1"

[0721] Step 1: Obtain the user's exercise and personal data from their smartwatch or fitness app.

[0722] Step 2: Store the acquired data in a database.

[0723] Step 3: Analyze the accumulated data using an AI engine.

[0724] Step 4: Based on the analysis results, the system will suggest dietary improvements, stress relief methods, and appropriate exercise menus to the user.

[0725] "Example 2"

[0726] Step 1: Obtain biometric information such as heart rate and steps from the smartwatch.

[0727] Step 2: The acquired biometric information is stored in a database.

[0728] Step 3: Analyze the accumulated biometric information using an AI engine.

[0729] Step 4: Based on the analysis results, the system will suggest dietary improvements, stress relief methods, and appropriate exercise menus that take into account the user's physical condition and amount of exercise.

[0730] "Example 3"

[0731] Step 1: Obtain information such as indoor temperature and humidity from smart home appliances.

[0732] Step 2: The acquired information is stored in a database.

[0733] Step 3: Analyze the accumulated information using an AI engine.

[0734] Step 4: Based on the analysis results, the system provides assistance with adjusting the indoor temperature based on the user's physical condition. For example, if the system determines that the user has a cold, it will control the smart thermostat to adjust the indoor temperature appropriately.

[0735] Example 1

[0736] Next, a description will be given of Example 1 of Form 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."

[0737] In modern society, personal health management is an important issue. However, there is no adequate system in place to efficiently collect individual exercise amounts and personal data and then make appropriate health management suggestions based on that data. In particular, it is difficult to provide individually optimized dietary improvement suggestions, stress relief methods, and exercise menus.

[0738] 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. In this invention, the server includes a means for collecting an individual's daily exercise amount and personal data, a means for transmitting the collected data to the server and storing it in a database, a means for generating dietary improvement suggestions, stress relief methods, and appropriate exercise menus suitable for the individual using an AI engine that analyzes the stored data, and a means for notifying the generated suggestions to the individual's terminal. This makes it possible to provide health management suggestions optimized for each individual user.

[0739] "Individual daily physical activity" refers to the amount of physical activity an individual engages in on a daily basis, as measured using devices such as smartwatches and fitness apps.

[0740] "Personal data" refers to information about an individual's health and lifestyle, including diet, stress levels, and sleep patterns.

[0741] "Means of collection" refers to the methods used to collect an individual's daily exercise and personal data using smartwatches, fitness apps, and other devices and applications.

[0742] "Means of transmitting to a server and storing in a database" refers to a method of transmitting collected data to a server via the Internet and storing it in a database on the server.

[0743] An "AI engine" refers to software or algorithms that use machine learning and data analysis techniques to analyze accumulated data and generate personalized suggestions.

[0744] "Means of generation" refers to a method that uses an AI engine to create dietary improvement suggestions, stress relief methods, and appropriate exercise menus that are suitable for individuals based on the analysis results.

[0745] "Means for notifying" refers to a method for sending the generated proposal to a personal device and notifying the user.

[0746] A "smartwatch" is a wristwatch-type wearable device that has the function of measuring health data such as exercise volume and heart rate.

[0747] "Smart home appliances" refer to home appliances that are connected to the Internet and can be controlled and configured remotely.

[0748] The present invention is a system for collecting an individual's daily exercise amount and other personal data, and making health management suggestions based on the collected data. A specific embodiment of this system will be described below.

[0749] First, users measure their daily exercise volume using a smartwatch and fitness app. A typical wearable device (e.g., a wristwatch-type health management device) is used as the smartwatch. A health management application (e.g., an exercise volume measurement app or a diet management app) is used as the fitness app.

[0750] The devices (smartphones and tablets) then send the data collected from these devices and applications to a server, which stores the received data in a database. The database organizes the data for each user and allows for efficient access.

[0751] The server passes the data stored in the database to an AI engine, which uses software that employs machine learning and data analysis techniques (e.g., TensorFlow, PyTorch). The AI ​​engine analyzes the user's exercise volume and personal data to evaluate their health condition and lifestyle habits.

[0752] Based on the analysis results, the server generates personalized suggestions for improving the user's diet, stress relief, and exercise routines.

[0753] Finally, the server sends the generated proposal to the terminal, which notifies the user and displays the proposal, allowing the user to confirm and execute the proposal.

[0754] As a concrete example, a user measures their daily exercise volume with a smartwatch and syncs the data with a fitness app. They then enter their meal details into a diet management app. This data is sent to a server and stored in a database. The AI ​​engine analyzes this data and makes suggestions to the user, such as "eat protein-rich foods for breakfast," "meditate for 10 minutes every day to relieve stress," and "do aerobic exercise three times a week."

[0755] Example prompts to input to the generative AI model:

[0756] Based on the user's exercise data and personal data, suggest appropriate dietary improvements, stress relief methods, and exercise menus.

[0757] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[0758] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0759] Step 1: Data collection

[0760] Users measure their daily exercise volume using a smartwatch and a fitness app. Specifically, the user goes jogging and the smartwatch measures their exercise volume. They also input personal data such as their diet and stress level into the fitness app. The input data includes the amount of exercise, heart rate, calorie intake, and nutritional information.

[0761] Input: User's exercise data, personal data

[0762] Output: Collected data

[0763] Step 2: Data transmission and storage

[0764] The device (smartphone or tablet) sends the collected data to a server. Specifically, it acquires data from a smartwatch or fitness app and sends it to the server via the internet. The server stores the received data in a database. The database organizes the data for each user and allows efficient access.

[0765] Input: Collected data

[0766] Output: Data stored in the database

[0767] Step 3: Data analysis

[0768] The server passes the data stored in the database to the AI ​​engine. Specifically, it extracts the user's exercise data and personal data from the database and inputs it into the AI ​​engine. The AI ​​engine then analyzes this data and evaluates the user's health condition and lifestyle habits. Machine learning algorithms and data analysis techniques are used for the analysis.

[0769] Input: Data stored in the database

[0770] Output: Analysis results

[0771] Step 4: Generate proposals

[0772] Based on the analysis results of the AI ​​engine, the server generates dietary improvement suggestions, stress relief methods, and appropriate exercise menus suitable for the user. Specifically, based on the analysis results, it creates suggestions that are optimal for the user's health condition and lifestyle. The suggestions include specific meal menus, activities to relieve stress, and the frequency and type of exercise.

[0773] Input: Analysis results

[0774] Output: Generated proposals

[0775] Step 5: Proposal Notification

[0776] The server sends the generated proposal to the device. Specifically, the proposal is notified to the user's smartphone or tablet. The device notifies the user and displays the proposal. The user can confirm the notification and carry out the proposal.

[0777] Input: Generated proposals

[0778] Output: Proposal notified to the user

[0779] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[0780] (Application example 1)

[0781] Next, a description will be given of Application Example 1 of Form 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."

[0782] In modern society, it is extremely difficult for individual users to manage their own health status and find optimal training menus and meal plans. Furthermore, there is a lack of systems that can centrally manage this information and provide individually optimized recommendations. Furthermore, there is a need for a method to effectively utilize users' exercise volume and personal data and to perform analysis in conjunction with a cloud database.

[0783] 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.

[0784] In this invention, the server includes means for linking with the generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for the individual based on this data, means for saving the data in a cloud database, means for sending the data to an AI engine for analysis, means for generating training menus and meal plans based on the analysis results, and means for providing these suggestions to the user, thereby enabling the user to effectively manage their health condition and receive optimal training menus and meal plans.

[0785] "Generative AI" is an artificial intelligence system that analyzes user data and generates individually optimized suggestions.

[0786] "Individual daily exercise volume" is data that indicates the amount and type of exercise a user engages in on a daily basis.

[0787] "Personal data" refers to data that includes individual information about a user, such as age, weight, height, and health status.

[0788] A "cloud database" is a database accessible via the Internet and a system for storing user data.

[0789] The "AI engine" is the core part of the artificial intelligence that analyzes collected data and generates optimal suggestions for users.

[0790] A "training menu" is an exercise program suggested based on the user's exercise volume and personal data.

[0791] A "meal plan" is a suggested dietary improvement plan based on the user's health status and goals.

[0792] The "means for providing suggestions to the user" is a system for notifying the user of training menus and meal plans generated based on the analysis results.

[0793] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for saving the data in a cloud database, means for sending the data to an AI engine for analysis, means for generating training menus and meal plans based on the analysis results, and means for providing these suggestions to the user.

[0794] The server collects the user's exercise data from the smartwatch and fitness app. This data is stored in a cloud database. The server then sends the stored data to the AI ​​engine for analysis. The AI ​​engine generates optimal training menus and meal plans based on the user's exercise volume and personal data. The generated suggestions are provided to the user through the smartphone app.

[0795] Specifically, the server uses the following hardware and software:

[0796] Smartwatch: A device that collects a user's exercise data.

[0797] Fitness app: An application that works with a smartwatch to manage exercise data.

[0798] Cloud database: A database system for storing collected data.

[0799] AI Engine: An artificial intelligence system that analyzes data and generates optimal suggestions.

[0800] For example, when a user works out at a gym, the smartwatch collects exercise data and sends it to a cloud database via a fitness app. The server then sends this data to an AI engine, which then generates optimal training and meal plans for the user based on the analysis results. The generated suggestions are then sent to the user's smartphone app.

[0801] An example prompt might have the following format:

[0802] User ID: 12345

[0803] Exercise data: steps: 10000, calories: 500, heart_rate: 120

[0804] Personal data: age: 30, weight: 70, height: 175

[0805] By inputting this prompt into the AI ​​engine, the optimal training menu and meal plan for the user will be generated.

[0806] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0807] Step 1:

[0808] The server collects the user's exercise data from the smartwatch and fitness app. Specifically, the smartwatch measures the user's steps, calories burned, heart rate, and other data, and sends it to the server via the fitness app. The input is the exercise data from the smartwatch, and the output is the exercise data stored on the server.

[0809] Step 2:

[0810] The server stores the collected exercise data in a cloud database. Specifically, the server converts the exercise data into an appropriate format and stores it in the corresponding user's database in the cloud database. The input is the exercise data stored in the server, and the output is the exercise data stored in the cloud database.

[0811] Step 3:

[0812] The server sends the exercise data and personal data stored in the cloud database to the AI ​​engine. Specifically, it combines the user's exercise data and personal data into a single prompt and sends it to the AI ​​engine. The input is the exercise data and personal data stored in the cloud database, and the output is the prompt sent to the AI ​​engine.

[0813] Step 4:

[0814] The AI ​​engine performs data analysis based on the received prompts. Specifically, it uses a generative AI model to analyze the user's exercise data and personal data to generate optimal training menus and meal plans. The input is the prompt sent to the AI ​​engine, and the output is the generated training menu and meal plan.

[0815] Step 5:

[0816] The server sends the analysis results received from the AI ​​engine to the user's smartphone app. Specifically, it notifies the user's smartphone app of the generated training menu and meal plan. The input is the analysis results received from the AI ​​engine, and the output is the training menu and meal plan notified to the user's smartphone app.

[0817] Step 6:

[0818] The user checks and executes the training menu and meal plan provided through the smartphone app. Specifically, the user adjusts their daily training and diet based on the suggestions displayed on the smartphone app. The input is the training menu and meal plan notified to the smartphone app, and the output is the user's actual training and diet.

[0819] Example 2

[0820] Next, a description will be given of Example 2 of Form 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."

[0821] In modern society, personal health management is an important issue. However, conventional systems have difficulty obtaining individual users' biometric information in real time and providing appropriate health management suggestions based on that information. In addition, there is a lack of analytical methods to provide specific suggestions based on the user's physical condition and exercise volume. This makes it difficult for users to accurately understand their own health status and take appropriate measures.

[0822] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for linking with a generative AI, means for accumulating an individual's daily exercise amount and personal data, means for proposing dietary improvement plans, stress relief methods, and appropriate exercise menus suitable for the individual based on this data, means for linking with a wearable device for acquiring biometric information, means for storing the acquired biometric information in a database, means including an AI engine for analyzing the stored biometric information, and means for making suggestions to the user based on the analysis results. This makes it possible to acquire the user's biometric information in real time and make specific health management suggestions based on the analysis results.

[0823] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[0824] "Individual daily exercise amount" is data indicating the amount of exercise a user does on a daily basis, and includes the number of steps taken and calories burned.

[0825] "Personal data" refers to information about an individual user's health condition and lifestyle habits, including heart rate, sleep duration, and dietary habits.

[0826] "Dietary improvement suggestions" are meal contents and methods for improving eating habits that are suggested based on the user's health condition and amount of exercise.

[0827] "Stress relief" refers to specific methods or activities that reduce a user's stress level.

[0828] The "appropriate exercise menu" refers to exercise programs and exercise content suggested based on the user's health condition and exercise volume.

[0829] A "wearable device" is an electronic device that can be worn by a user and is used to obtain biometric information such as heart rate and number of steps taken.

[0830] A "database" is a system for systematically storing acquired data and for searching and analyzing it as needed.

[0831] "AI engine" refers to artificial intelligence technology that analyzes data and finds patterns and trends.

[0832] "Analysis results" are the information and conclusions obtained after data is analyzed by an AI engine.

[0833] "Suggestion" refers to specific advice or instructions given to the user based on the analysis results.

[0834] MODE FOR CARRYING OUT THE INVENTION

[0835] This invention is a system for supporting users' health management, which works in conjunction with generative AI to accumulate an individual's daily exercise volume and personal data, and makes appropriate suggestions based on that information. Specific embodiments of this system are described below.

[0836] 1. System Configuration

[0837] This system consists of three main elements: a server, a device, and a user. The server works in conjunction with generative AI and plays a central role in accumulating and analyzing data. The device is a wearable device or smartphone used by the user, which collects biometric information and receives suggestions. The user uses these devices to manage their daily health.

[0838] 2. Hardware and Software Used

[0839] The server is a high-performance computer system equipped with a database (e.g., MySQL or PostgreSQL) and an AI engine (e.g., TensorFlow or PyTorch). Wearable devices such as smartwatches and smartphones are used as terminals. These devices communicate with the server using wireless communication technologies such as Bluetooth and Wi-Fi.

[0840] 3. Data acquisition and storage

[0841] When a user wears a smartwatch and goes about their daily activities, biometric information such as heart rate and number of steps is collected in real time. The device then sends this data to a server, which stores it in a database. For example, after a user finishes their morning jog, the smartwatch automatically sends the data to the server.

[0842] 4. Data analysis and proposals

[0843] The server passes the biometric information stored in the database to an AI engine for analysis. Based on the analysis results, suggestions for improving diet, stress relief, and appropriate exercise menus are made, taking into account the user's physical condition and exercise volume. For example, data from the past week is obtained and input into the AI ​​model for analysis.

[0844] 5. Notification of Proposal

[0845] The server makes specific suggestions to the user based on the analysis results of the AI ​​engine. The suggestions are sent to the user's smartphone or PC. For example, a message may be sent to the user's smartphone saying, "You've taken few steps today, so we recommend walking 30 minutes in the evening."

[0846] 6. Examples and prompts

[0847] As a concrete example, consider the case where a user records the number of steps taken and heart rate for the day using a smartwatch. The server acquires this data and stores it in a database. Next, the AI ​​engine analyzes this data and makes suggestions to the user, such as "Since you took fewer steps today, I recommend walking for 30 minutes in the evening."

[0848] Example prompts to input to the generative AI model:

[0849] "Please suggest an appropriate exercise menu based on the user's heart rate and step count data."

[0850] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[0851] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0852] Step 1:

[0853] The server acquires biometric information from the device (smartwatch). Specifically, it receives data such as the user's heart rate and number of steps in real time using wireless communication technologies such as Bluetooth and Wi-Fi. The input is the biometric information sent from the smartwatch, and the output is the biometric information stored on the server. For example, after a user finishes their morning jog, the smartwatch automatically sends the data to the server.

[0854] Step 2:

[0855] The server stores the acquired biometric information in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL and issues SQL queries to the database to insert data. The input is the biometric information stored on the server, and the output is the biometric information stored in the database. For example, the following SQL query is used:

[0856] sql

[0857] INSERT INTO bio_data (user_id, heart_rate, steps, timestamp) VALUES (1, 75, 10000, '2023-10-01 08:00:00');

[0858] Step 3:

[0859] The server retrieves biometric information from the database and passes it to the AI ​​engine. Specifically, it issues an SQL query to retrieve the necessary data from the database and inputs the retrieved data into the AI ​​engine. The input is the biometric information retrieved from the database, and the output is the data input into the AI ​​engine. For example, use the following Python code:

[0860] python

[0861] import tensorflow as tf

[0862] Retrieving data from a database

[0863] data = fetch_data_from_db(user_id=1)

[0864] Inputting data into the AI ​​model

[0865] model = tf.keras.models.load_model('health_model.h5')

[0866] predictions = model.predict(data)

[0867] Step 4:

[0868] The server makes suggestions to the user based on the analysis results of the AI ​​engine. Specifically, the analysis results are generated as a text message and sent to the user's smartphone or PC. The input is the analysis results of the AI ​​engine, and the output is a suggested message sent to the user. For example, a message such as "You've taken fewer steps today, so we recommend walking 30 minutes in the evening" is generated and sent to the user's smartphone.

[0869] Step 5:

[0870] The user receives the suggestions from the server and executes them. Specifically, the user checks the suggestion message on their smartphone or PC and implements the suggested exercise and dietary improvements. The input is the suggestion message sent from the server, and the output is the user's behavior. For example, the user follows the suggestion and takes a 30-minute walk in the evening.

[0871] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[0872] (Application example 2)

[0873] Next, a description will be given of Application Example 2 of Form 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."

[0874] In modern society, personal health management is an important issue. However, there is a lack of information on appropriate dietary recommendations based on daily exercise and physical condition, stress relief methods, and exercise menus. There is also a lack of systems that allow people to easily order healthy meals. This makes it difficult for individuals to take appropriate actions according to their own health condition.

[0875] 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 linking with the generative AI, means for accumulating an individual's daily exercise amount and personal data, means for proposing dietary improvement plans, stress relief methods, and appropriate exercise menus suitable for the individual based on this data, means for analyzing biometric information and proposing meal menus based on the user's physical condition and exercise amount, and means for ordering healthy meals from a food delivery service based on the proposed meal menus. This makes it possible to suggest appropriate dietary habits according to an individual's health condition and to easily order healthy meals.

[0876] "Generative AI" is a type of artificial intelligence that analyzes data and makes suggestions and predictions based on specific purposes.

[0877] "Individual daily exercise volume" refers to data such as the energy consumed and number of steps taken by an individual through daily life and exercise activities.

[0878] "Personal data" refers to information about an individual's health and lifestyle, including heart rate, sleep duration, and dietary habits.

[0879] "Dietary improvement suggestions" are nutritionally balanced meal menus and suggested changes to eating habits based on an individual's health status and amount of exercise.

[0880] "Stress Relief" is a suggestion of specific methods or activities to reduce an individual's stress level.

[0881] An "appropriate exercise menu" is an exercise program proposed based on an individual's physical condition and exercise volume, with the aim of maintaining health and improving physical strength.

[0882] "Biometric information" refers to data about an individual's physical condition, such as heart rate, number of steps, and body temperature, obtained from devices such as smartwatches.

[0883] A "food delivery service" is a service that delivers meals ordered by a user to a specified location.

[0884] A "healthy diet" is a nutritionally balanced meal menu that is appropriate for an individual's health condition and amount of exercise.

[0885] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for suggesting dietary improvements and stress relief methods suitable for the individual based on this data, and an appropriate exercise menu, means for analyzing biometric information and suggesting a meal menu based on the user's physical condition and exercise volume, and means for ordering healthy meals from a food delivery service based on the suggested meal menu.

[0886] Hardware and Software Configuration

[0887] Hardware:

[0888] Smartwatches (e.g. Apple Watch, Fitbit)

[0889] Smartphone (e.g. iPhone, Android)

[0890] software:

[0891] Python

[0892] The Requests library (for sending HTTP requests)

[0893] AI engines (e.g. TensorFlow, PyTorch)

[0894] Data processing and calculation

[0895] The server transmits biometric information (heart rate, number of steps, etc.) obtained from the smartwatch to the smartphone and stores it in a database. The stored data is analyzed by an AI engine. Based on the analysis results, suggestions for improving dietary habits, stress relief methods, and appropriate exercise menus are generated based on the user's physical condition and exercise level. Furthermore, the generated meal menus are suggested to the user, who can then order healthy meals from a food delivery service based on the menus.

[0896] Specific examples

[0897] For example, if a user has not exercised much throughout the day, the AI ​​engine will suggest low-calorie, nutritionally balanced meals, such as "grilled chicken salad" or "vegetable soup." Based on these suggestions, the user can easily order the suggested healthy meal using a food delivery service.

[0898] Prompt Sentence Examples

[0899] "If the user's heart rate is 75 and their steps are 10,000, suggest an appropriate meal plan."

[0900] In this way, a system can be realized that utilizes biometric information obtained from a smartwatch to support the user's health.

[0901] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0902] Step 1:

[0903] The smartwatch acquires the user's biometric information (heart rate, number of steps, etc.). The input is the user's biometric information, and the output is the acquired biometric information. Specifically, the smartwatch's sensors measure the user's heart rate and number of steps in real time.

[0904] Step 2:

[0905] The smartwatch transmits the acquired biometric information to a smartphone. The input is the biometric information acquired from the smartwatch, and the output is the biometric information transmitted to the smartphone. Specifically, the data is transferred to the smartphone using a communication method such as Bluetooth or Wi-Fi.

[0906] Step 3:

[0907] The smartphone sends the received biometric information to the server. The input is the biometric information sent to the smartphone, and the output is the biometric information sent to the server. Specifically, the smartphone application sends the data to the server using an HTTP request.

[0908] Step 4:

[0909] The server stores the received biometric information in a database. The input is the biometric information sent to the server, and the output is the biometric information stored in the database. Specifically, the server program saves the data in the database.

[0910] Step 5:

[0911] The server uses an AI engine to analyze the biometric information stored in the database. The input is the biometric information stored in the database, and the output is the analysis results. Specifically, the AI ​​engine analyzes the biometric information and generates a meal menu based on the user's physical condition and exercise level.

[0912] Step 6:

[0913] The server then proposes a meal menu suitable for the user based on the analysis results. The input is the analysis results of the AI ​​engine, and the output is the proposed meal menu. Specifically, the server sends the generated meal menu to the smartphone.

[0914] Step 7:

[0915] The smartphone displays the proposed meal menu to the user. The input is the meal menu sent from the server, and the output is the meal menu displayed to the user. In concrete terms, the smartphone application displays the meal menu on the screen.

[0916] Step 8:

[0917] A user orders a meal using a food delivery service based on a suggested meal menu. The input is the meal menu selected by the user, and the output is an order to the food delivery service. Specifically, the user selects a meal and confirms the order through a smartphone application.

[0918] Step 9:

[0919] A food delivery service delivers healthy meals to users. The input is the user's order, and the output is the meal delivered to the user. The specific behavior is that the food delivery service receives the order and delivers the meal to the specified location.

[0920] Example 3

[0921] Next, a description will be given of Example 3 of Form Example 3. 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."

[0922] Conventional health management systems accumulate an individual's daily exercise volume and personal data, and can suggest dietary improvements, stress relief methods, and appropriate exercise menus, but they do not adequately address the issue of adjusting the indoor environment. Furthermore, there is a lack of means to link with smart home appliances and adjust the indoor environment to take the user's physical condition into consideration, making it difficult to provide comprehensive support for the user's health management.

[0923] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0924] In this invention, the server includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for saving environmental data acquired from smart home appliances in a database, means for analyzing the saved data with an AI engine, and means for adjusting the indoor environment taking the user's physical condition into consideration based on the analysis results. This makes it possible to comprehensively manage the user's physical condition and adjust the indoor environment appropriately.

[0925] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[0926] "Individual daily exercise volume" refers to the amount of exercise an individual engages in on a daily basis, and includes data such as the number of steps taken and calories burned.

[0927] "Personal data" is specific information about an individual, including data about their health, diet, stress levels, etc.

[0928] "Smart appliances" are home appliances that are connected to the Internet and can be controlled remotely.

[0929] "Environmental data" refers to information about the environment, such as indoor temperature and humidity.

[0930] A "database" is a system for efficiently storing, managing, and retrieving data.

[0931] An "AI engine" is software or hardware for running artificial intelligence algorithms.

[0932] "Analysis" is the process of examining data in detail to find meaning and patterns.

[0933] "Indoor environment" refers to the environmental conditions of a living space, such as indoor temperature, humidity, and air quality.

[0934] "Adjustment" refers to changing a setting or state to suit specific conditions or goals.

[0935] MODE FOR CARRYING OUT THE INVENTION

[0936] This invention is a system that works in conjunction with generative AI to accumulate an individual's daily exercise volume and personal data, and based on this data, suggests dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for that individual. It also includes a function that stores environmental data acquired from smart home appliances in a database, analyzes the stored data with an AI engine, and adjusts the indoor environment based on the analysis results, taking into account the user's physical condition.

[0937] 1. Program Generation

[0938] The server generates a program for linking with smart home appliances. This program stores information such as indoor temperature and humidity obtained from the smart appliances in a database and analyzes it using an AI engine.

[0939] 2. Program processing explanation

[0940] The server collects indoor temperature and humidity data from smart home appliances such as smart thermostats and smart hygrometers. This data is stored in a database such as MySQL. The server then analyzes the stored data using an AI engine implemented in Python (e.g., TensorFlow or PyTorch). Based on the analysis results, it makes recommendations for indoor temperature control that take the user's physical condition into account.

[0941] For example, if the server determines that the user has a cold, it will control the smart thermostat to adjust the room temperature appropriately. This control is performed through the API of the smart home appliance.

[0942] 3. Examples and prompts

[0943] As a specific example, the process when it is determined that the user has a cold will be described.

[0944] 1. The server retrieves the current room temperature from the smart thermostat.

[0945] 2. The server obtains the current indoor humidity from the smart hygrometer.

[0946] 3. Store this data in a MySQL database.

[0947] 4. The server analyzes the stored data using an AI engine (e.g., TensorFlow) and determines that the user has a cold.

[0948] 5. The server sets the room temperature to an appropriate value (e.g., 22 degrees) through the smart thermostat's API.

[0949] Example prompts to input to the generative AI model:

[0950] "If the user has a cold, explain the steps to properly regulate the indoor temperature."

[0951] By inputting this prompt into a generative AI model, detailed steps based on the specific example above will be generated.

[0952] In this way, the server can comprehensively manage the user's physical condition and adjust the indoor environment appropriately. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0953] Step 1:

[0954] The server retrieves data from smart home appliances. Specifically, it sends a GET request to the API of the smart thermostat or smart hygrometer to retrieve indoor temperature and humidity data. The input is the API endpoint of the smart home appliance, and the output is the retrieved indoor temperature and humidity data.

[0955] Step 2:

[0956] The server saves the acquired data in a database. Specifically, it connects to a MySQL database and inserts the data into a table using the INSERT statement. The input is the acquired indoor temperature and humidity data, and the output is the data saved in the database.

[0957] Step 3:

[0958] The server analyzes the stored data using an AI engine. Specifically, it executes a Python script to read the data and inputs it into a machine learning model such as TensorFlow or PyTorch to obtain the analysis results. The input is the data stored in the database, and the output is the analysis results.

[0959] Step 4:

[0960] The server determines the action to take based on the analysis results. Specifically, if the AI ​​engine receives the analysis results and determines that the user has a cold, the room temperature needs to be adjusted appropriately. The input is the analysis results, and the output is the determined action.

[0961] Step 5:

[0962] The server controls smart appliances. Specifically, it sends a POST request to the smart thermostat's API to set the indoor temperature. The input is the determined action, and the output is the set indoor temperature.

[0963] In this way, the server can comprehensively manage the user's physical condition and adjust the indoor environment appropriately.

[0964] (Application example 3)

[0965] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0966] Conventional health management systems can suggest dietary improvements, stress relief methods, and appropriate exercise menus based on an individual's exercise volume and personal data, but they do not address indoor or store environment adjustments. This makes it difficult to comprehensively manage users' physical condition and comfort. Furthermore, since the environment is not adjusted to maximize customer comfort in physical stores, it is difficult to improve customer satisfaction.

[0967] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0968] In this invention, the server includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for accumulating environmental data acquired from smart home appliances in a database and analyzing it with an AI engine, means for adjusting the room temperature taking the user's physical condition into consideration based on the analysis results, and means for acquiring environmental data within a physical store and adjusting the environment to maximize customer comfort. This makes it possible to comprehensively manage the user's physical condition and comfort and improve customer satisfaction even in physical stores.

[0969] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[0970] "Individual daily exercise volume" refers to the amount of exercise an individual engages in on a daily basis, and includes data such as the number of steps taken and calories burned.

[0971] "Personal data" refers to specific information about an individual, including their health, lifestyle, preferences, etc.

[0972] "Dietary improvement suggestions" are dietary improvement methods suggested based on an individual's health condition and lifestyle habits.

[0973] "Stress relief" refers to specific methods and suggestions for reducing personal stress.

[0974] An "appropriate exercise menu" is an exercise program suggested based on an individual's health condition and physical ability.

[0975] "Smart home appliances" are home appliances that are connected to the Internet and can be remotely controlled or automatically controlled.

[0976] "Environmental data" refers to information about the indoor environment, such as temperature, humidity, and illuminance.

[0977] A "database" is a system for efficiently storing, managing, and searching data.

[0978] An "AI engine" is software or hardware used to run artificial intelligence algorithms.

[0979] "Analysis results" refer to the conclusions or recommendations reached after analyzing the data.

[0980] "Indoor temperature control" refers to control to keep the indoor temperature within an appropriate range.

[0981] A "brick and mortar store" is a commercial establishment or retail outlet that exists in a physical location.

[0982] "Customer comfort" refers to the comfort and satisfaction that customers feel in the store.

[0983] "Environmental adjustment" refers to the appropriate control of environmental factors such as temperature, humidity, and lighting.

[0984] A system for implementing the present invention has the following configuration.

[0985] System configuration

[0986] 1. Means of collaboration with generative AI

[0987] The server works in conjunction with generative AI to generate new information and suggestions based on an individual's daily exercise volume and other personal data. The generative AI is implemented using machine learning libraries such as Python and TensorFlow.

[0988] 2. A means of accumulating an individual's daily exercise volume and personal data

[0989] The server stores the exercise volume and personal data acquired from the smartwatch or smartphone in a database using a relational database such as MySQL or PostgreSQL.

[0990] 3. A means of suggesting dietary improvements, stress relief methods, and appropriate exercise menus

[0991] Based on the accumulated data, the server will propose dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for each individual. These suggestions are generated by generative AI and sent to the user's smartphone or smartwatch.

[0992] 4. A means of storing environmental data obtained from smart home appliances in a database and analyzing it with an AI engine

[0993] The server collects environmental data from smart home appliances (such as thermometers and hygrometers) and stores it in a database, then analyzes it using an AI engine implemented using machine learning libraries such as Scikit-learn and Keras.

[0994] 5. A method for adjusting the indoor temperature based on the analysis results and taking into account the user's physical condition

[0995] Based on the analysis results, the server adjusts the room temperature taking into account the user's physical condition. Specifically, it controls the smart thermostat to set the room temperature appropriately. Control is performed using HTTP requests.

[0996] 6. A means of acquiring environmental data within physical stores and adjusting the environment to maximize customer comfort

[0997] The server collects environmental data such as temperature and humidity in the physical store and adjusts the environment to maximize customer comfort, thereby improving customer satisfaction.

[0998] Program processing explanation

[0999] The server collects data from smart home appliances, smartwatches, and smartphones and stores it in a database. The data is retrieved using the Python Requests library. The accumulated data is analyzed by an AI engine, and appropriate suggestions and environmental adjustments are made based on the analysis results.

[1000] For example, if the temperature in the store is 25 degrees and the humidity is 60%, and past data indicates that a customer has a cold, the room temperature will be set to 22 degrees, providing a comfortable environment for the customer.

[1001] Prompt Sentence Examples

[1002] Create a Python program that uses temperature and humidity data obtained from smart appliances to adjust the room temperature based on the customer's physical condition. Train a linear regression model using past data and analyze current data to set the room temperature to 22°C if the customer has a cold, and 24°C otherwise.

[1003] In this way, it is possible to comprehensively manage the user's physical condition and comfort, and improve customer satisfaction even in physical stores.

[1004] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1005] Step 1:

[1006] The server acquires data from smart home appliances, smartwatches, and smartphones. Specifically, it uses the Python Requests library to acquire temperature, humidity, exercise volume, personal data, etc. from each device's API. The input is data from each device, and the output is a JSON-formatted response of the acquired data.

[1007] Step 2:

[1008] The server accumulates the acquired data in a database. Specifically, it saves the data in a relational database such as MySQL or PostgreSQL. The input is the JSON format data acquired in step 1, and the output is the records saved in the database.

[1009] Step 3:

[1010] Based on the accumulated data, the server uses generative AI to generate personalized suggestions for improving dietary habits, stress relief methods, and appropriate exercise menus. Specifically, it trains models using machine learning libraries such as TensorFlow and Keras to generate suggestions. The input is the data accumulated in the database, and the output is the generated suggestions.

[1011] Step 4:

[1012] The server notifies the user of the generated proposal via push notification or email. The input is the proposal generated in step 3, and the output is a notification displayed on the user's device.

[1013] Step 5:

[1014] The server accumulates environmental data acquired from smart home appliances in a database and analyzes it using an AI engine. Specifically, it analyzes the data using machine learning libraries such as Scikit-learn and Keras. The input is the environmental data acquired from the smart home appliances, and the output is the analysis results.

[1015] Step 6:

[1016] Based on the analysis results, the server adjusts the room temperature taking into account the user's physical condition. Specifically, it controls the smart thermostat to set the room temperature appropriately. The input is the analysis result obtained in step 5, and the output is the temperature setting of the smart thermostat.

[1017] Step 7:

[1018] The server collects environmental data from the physical store and adjusts the environment to maximize customer comfort. Specifically, it collects data such as temperature and humidity within the store, analyzes it with an AI engine, and adjusts the environment appropriately. The input is the environmental data from the physical store, and the output is the setting value for the environmental adjustment.

[1019] Step 8:

[1020] The server notifies the physical store management system of the results of the environmental adjustment. Specifically, it sends the environmental adjustment setting values ​​using an HTTP request. The input is the environmental adjustment setting value obtained in step 7, and the output is the setting value reflected in the physical store management system.

[1021] 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.

[1022] "Example 1"

[1023] One embodiment of the present invention provides a system including a means for linking with generative AI, a means for accumulating an individual's daily exercise volume and personal data, a means for suggesting dietary improvements, stress relief methods, and appropriate exercise menus based on the data, and an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's emotions based on facial expressions, tone of voice, and social media posts. For example, if it determines that the user is feeling stressed, the system plays music to promote relaxation and suggests relaxation exercises.

[1024] "Example 2"

[1025] In another embodiment of the present invention, a system is provided that further includes a means for linking with a smartwatch. Biometric information such as heart rate and step count is acquired from the smartwatch, and this information is combined with emotional information obtained from the emotion engine to enable more detailed health management. For example, if the system determines that the user is feeling stressed and has a high heart rate, it will provide instructions such as relaxation exercises and deep breathing.

[1026] "Example 3"

[1027] In yet another embodiment of the present invention, a system is provided that further includes a means for linking with smart home appliances. Information such as indoor temperature and humidity is acquired from the smart home appliances, and this information is combined with emotion information obtained from the emotion engine to provide a more comfortable living environment. For example, if it is determined that the user is irritated and the indoor temperature is high, an instruction to control the air conditioner to lower the indoor temperature is issued.

[1028] The processing flow of each embodiment will be described below.

[1029] "Example 1"

[1030] Step 1: Collect the user's daily exercise volume and personal data.

[1031] Step 2: The emotion engine analyzes the user's emotions based on their facial expressions, tone of voice, and the content of their social media posts.

[1032] Step 3: Based on the analysis results, we will propose dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for each individual.

[1033] Step 4: For example, if the user is determined to be stressed, relaxation music may be played or relaxation exercises may be suggested.

[1034] "Example 2"

[1035] Step 1: Obtain biometric information such as heart rate and steps from the smartwatch.

[1036] Step 2: The emotion engine analyzes the user's emotions.

[1037] Step 3: The acquired biometric information and emotional information are combined to provide more detailed health management. Step 4: For example, if the user is determined to be feeling stressed and their heart rate is high, instructions such as relaxation exercises and deep breathing will be displayed.

[1038] "Example 3"

[1039] Step 1: Obtain information such as indoor temperature and humidity from smart home appliances.

[1040] Step 2: The emotion engine analyzes the user's emotions.

[1041] Step 3: Combine the acquired indoor environmental information with emotional information to provide a more comfortable living environment.

[1042] Step 4: For example, if it is determined that the user is irritated and the indoor temperature is high, an instruction is given to control the air conditioner to lower the indoor temperature.

[1043] Example 1

[1044] Next, a description will be given of Example 1 of Form 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."

[1045] In modern society, individual health management is an important issue, but it is difficult to effectively utilize an individual's exercise volume and personal data to perform appropriate health management. In addition, there is a lack of health suggestions that take into account the user's emotional state, and stress management is not being adequately implemented. This has led to a demand for a system that can comprehensively improve the user's health condition.

[1046] 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.

[1047] In this invention, the server includes a means for linking with the generative AI, a means for accumulating the amount of daily exercise and personal data of an individual, a means for suggesting dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for the individual based on this data, a means including an emotion engine for analyzing the user's emotions, and a means for suggesting relaxation based on the emotion analysis results, thereby enabling comprehensive and individually tailored health management for each user.

[1048] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[1049] "Amount of exercise" is data that indicates the amount of physical activity an individual engages in each day, and includes the number of steps taken, heart rate, calories burned, and so on.

[1050] "Personal data" refers to data related to an individual's health and lifestyle, including diet, sleep, weight, etc.

[1051] A "database" is a system for efficiently managing and storing collected data.

[1052] An "AI engine" is an artificial intelligence that analyzes collected data and generates suggestions suited to individual users.

[1053] The "emotion engine" is a system for analyzing emotions from a user's facial expressions, tone of voice, and the content of their social media posts.

[1054] "Suggestions to promote relaxation" refers to suggestions such as playing relaxation music or doing relaxation exercises when the user is feeling stressed.

[1055] A "smartwatch" is a wearable device for collecting exercise data.

[1056] "Smart home appliances" are household electrical appliances that are connected to the Internet and can be remotely controlled or automatically controlled.

[1057] MODE FOR CARRYING OUT THE INVENTION

[1058] The present invention is a system that comprehensively supports the health management of individual users. A specific embodiment of this system will be described below.

[1059] Data collection

[1060] Users collect exercise data using smartwatches and fitness apps. Smartwatches record data such as the number of steps taken, heart rate, and calories burned. Users also manually enter personal data such as diet and sleep time, or the data is automatically obtained from other connected apps.

[1061] Data transmission

[1062] The device sends the collected exercise data and personal data to a server, where the data is encrypted and securely transmitted using the SSL / TLS protocol.

[1063] Data accumulation

[1064] The server stores the received data in a database, which has a separate table for each user and is indexed for efficient data access.

[1065] Data analysis

[1066] The server sends the data to the AI ​​engine, which then uses machine learning algorithms to analyze the user's exercise patterns and dietary habits and generate optimal improvement recommendations.

[1067] Emotion analysis

[1068] The server analyzes the user's emotions using an emotion engine, which uses facial expression recognition technology and voice analysis technology to determine whether the user is feeling stressed.

[1069] Proposal Generation

[1070] The server combines the analysis results of the AI ​​engine and the emotion engine to generate optimal suggestions for the user. For example, if the user is feeling stressed, it will suggest playing relaxation music or doing relaxation exercises.

[1071] Providing suggestions

[1072] The device displays the suggestions sent from the server to the user. The user can then confirm and implement the suggested dietary improvements and exercise menu. For example, an exercise menu such as "Aim for 10,000 steps a day" may be displayed.

[1073] Specific examples

[1074] Example 1: Collecting and analyzing momentum data

[1075] Users wear a smartwatch every day to record their exercise. The smartwatch collects data such as the number of steps taken, heart rate, and calories burned. This data is sent to a server via a fitness app and stored in a database. The server uses an AI engine to analyze this data and suggests exercise regimens to users, such as "Aim for 10,000 steps a day."

[1076] Example 2: Emotion analysis and relaxation suggestions

[1077] If a user posts on social media, "I'm so tired today," the server uses the emotion engine to determine that the user is feeling stressed. Based on this, the device will make suggestions to the user, such as, "Would you like to play some relaxation music?"

[1078] Prompt Sentence Examples

[1079] "Design a system that uses the user's exercise data and personal data to suggest dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for each individual user. Use an emotion engine to analyze the user's emotions and make suggestions to encourage relaxation if the user is feeling stressed."

[1080] In this way, the system collects and analyzes user data and makes suggestions based on individual needs, supporting health management.

[1081] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1082] Step 1:

[1083] Users collect exercise data using a smartwatch and a fitness app. The smartwatch records data such as the number of steps taken, heart rate, and calories burned. This data is automatically sent to the fitness app. The input is the exercise data from the smartwatch, and the output is the exercise data accumulated in the fitness app.

[1084] Step 2:

[1085] Users can manually input personal data such as dietary habits and sleep duration, or automatically obtain it from other linked apps. The input is data manually entered by the user or data obtained from linked apps, and the output is personal data accumulated in the fitness app.

[1086] Step 3:

[1087] The device sends the collected exercise data and personal data to the server. The data is encrypted and transmitted securely using the SSL / TLS protocol. The input is the data stored in the fitness app, and the output is the encrypted data sent to the server.

[1088] Step 4:

[1089] The server stores the received data in a database with a separate table for each user, indexed for efficient data access. The input is the encrypted data sent to the server, and the output is the data stored in the database.

[1090] Step 5:

[1091] The server sends the data to the AI ​​engine and begins analysis. The AI ​​engine uses machine learning algorithms to analyze the user's exercise patterns and dietary habits and generate optimal improvement suggestions. The input is the data stored in the database, and the output is the improvement suggestions generated by the AI ​​engine.

[1092] Step 6:

[1093] The server uses an emotion engine to analyze the user's emotions. The emotion engine uses facial expression recognition and voice analysis technologies to determine whether the user is feeling stressed. Inputs include the user's facial expressions, tone of voice, and social media posts, and the output is the user's emotional state analyzed by the emotion engine.

[1094] Step 7:

[1095] The server integrates the analysis results of the AI ​​engine and emotion engine to generate optimal suggestions for the user. For example, if the user is feeling stressed, it will play relaxation music or suggest relaxation exercises. The input is the analysis results of the AI ​​engine and emotion engine, and the output is the suggestions for the user.

[1096] Step 8:

[1097] The terminal displays the suggestions sent from the server to the user. The user can confirm and implement the suggested dietary improvements and exercise menus. The input is the suggestions sent from the server, and the output is the suggestions displayed to the user.

[1098] (Application example 1)

[1099] Next, a description will be given of Application Example 1 of Form 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."

[1100] In modern society, health and stress management for individual users are important issues. However, conventional systems have had difficulty effectively utilizing the user's exercise volume and personal data to propose individually optimized dietary improvements, stress relief methods, and appropriate exercise menus. There has also been a lack of systems that can recognize the user's emotional state in real time and respond appropriately based on that. Furthermore, there have been insufficient concrete means for linking these suggestions to actual behavior, such as linking with meal delivery services.

[1101] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for linking with the generative AI, means for accumulating an individual's daily exercise amount and personal data, means for proposing dietary improvement plans, stress relief methods, and appropriate exercise menus that are suitable for the individual based on this data, means including an emotion engine that recognizes the user's emotions, and means for proposing optimal meal menus for the user and providing delivery services. This makes it possible to make individually optimized suggestions based on the user's health condition and emotional state, and to link them to specific actions.

[1102] "Generative AI" is a type of artificial intelligence that analyzes data and provides users with optimal suggestions and products.

[1103] "Individual daily exercise" is data that indicates the amount of exercise a user engages in on a daily basis, and is obtained from a smartwatch or fitness app.

[1104] "Personal data" refers to data that includes personal information such as a user's age, gender, and allergy information.

[1105] "Dietary improvement suggestions" are ways to improve your diet that are suggested based on the user's health condition and personal data.

[1106] "Stress relief" refers to specific methods or suggestions for reducing the user's stress level.

[1107] An "appropriate exercise menu" is an exercise program suggested based on the user's exercise volume and health condition.

[1108] The "emotion engine" is a system that analyzes emotions from a user's facial expressions, tone of voice, and the content of their social media posts.

[1109] A "delivery service" is a service in which users order suggested meal menus and have them delivered to their homes or other locations.

[1110] A system for implementing the present invention has the following configuration.

[1111] System configuration

[1112] 1. Data collection methods:

[1113] Hardware: Smartwatches, smartphones

[1114] Software: Fitness app, personal data management app, sentiment analysis engine

[1115] Processing details: Collects user exercise data from smartwatches and fitness apps, and collects personal information from personal data management apps. Also, uses an emotion analysis engine to obtain emotional data from the user's facial expressions, tone of voice, and social media posts.

[1116] 2. Data storage means:

[1117] Hardware: Server

[1118] Software: Database Management System

[1119] Processing details: Collected exercise data, personal data, and emotional data are stored in a database on the server.

[1120] 3. Data analysis methods:

[1121] Hardware: Server

[1122] Software: Generative AI engine

[1123] Processing details: The accumulated data is analyzed using a generative AI engine to generate optimal dietary improvement suggestions, stress relief methods, and appropriate exercise menus for the user.

[1124] 4. Proposal method:

[1125] Hardware: Smartphones, tablets

[1126] Software: Proposed application

[1127] Processing content: Based on the analysis results of the generative AI engine, the system suggests dietary improvements, stress relief methods, and appropriate exercise menus to the user.

[1128] 5. Delivery Service Method:

[1129] Hardware: Servers, smartphones

[1130] Software: Delivery Service API

[1131] What it does: Allows users to order suggested meals with one click and delivers the meal through a delivery service.

[1132] Specific examples

[1133] For example, consider a case where User A is a 30-year-old male with a moderate amount of exercise and a high stress level. The system obtains exercise data from the user's smartwatch, and collects age, gender, and allergy information from a personal data management app. Using a sentiment analysis engine, the system detects high stress levels from the user's social media posts.

[1134] This data is stored in a database on a server and analyzed by a generative AI engine. As a result of the analysis, "chicken salad and brown rice" is recommended as the optimal meal menu for User A. In addition, because User A's stress level is high, "herbal tea and yoga" is also recommended as a relaxation menu.

[1135] User A can check the suggested meal menu through the suggestion application on his smartphone and order using the delivery service with one click.

[1136] Prompt Sentence Examples

[1137] "Collect the user's exercise data, personal data, and emotional data, analyze it with a generative AI engine, and suggest the optimal meal menu. If the user is feeling stressed, suggest a relaxation menu as well."

[1138] In this way, it is possible to provide personalized recommendations based on the user's health and emotional state, which can then be linked to specific actions.

[1139] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1140] Step 1:

[1141] A user exercises using a smartwatch or fitness app. The smartwatch or fitness app collects data on the user's exercise, including the number of steps taken, heart rate, calories burned, etc. The collected data is sent to a smartphone.

[1142] Input: User's exercise data (number of steps, heart rate, calories burned, etc.)

[1143] Output: Exercise data sent to a smartphone

[1144] Step 2:

[1145] The smartphone collects personal information of the user through a personal data management app, including age, gender, allergy information, etc. The collected data is sent to a server.

[1146] Input: User's personal data (age, gender, allergy information, etc.)

[1147] Output: Personal data sent to the server

[1148] Step 3:

[1149] The emotion analysis engine collects emotional data from the user's facial expressions, tone of voice, and social media posts. The emotion analysis engine analyzes this data and determines the user's emotional state (e.g., stress level). The determination result is sent to the server.

[1150] Input: User facial expression data, tone of voice data, SNS post content

[1151] Output: Emotional data (e.g. stress level) sent to the server

[1152] Step 4:

[1153] The server stores the exercise data, personal data, and emotional data in a database, forming a comprehensive data set on the user's health and emotional state.

[1154] Input: Exercise data, personal data, emotion data

[1155] Output: A comprehensive dataset stored in a database

[1156] Step 5:

[1157] The server analyzes the accumulated data using a generative AI engine, which generates optimal dietary improvement suggestions, stress relief methods, and appropriate exercise menus for the user. The analysis results are sent to the proposal application.

[1158] Input: Comprehensive dataset stored in a database

[1159] Output: Analysis results sent to the proposal application (dietary improvement suggestions, stress relief methods, exercise menu)

[1160] Step 6:

[1161] Based on the analysis results of the generative AI engine, the suggestion application will suggest dietary improvements, stress relief methods, and appropriate exercise menus to the user. The user can check the suggestions on their smartphone or tablet.

[1162] Input: Analysis results sent to the proposal application

[1163] Output: Dietary improvement suggestions, stress relief methods, and exercise menus presented to the user

[1164] Step 7:

[1165] The user can view the suggested meal menu through the suggestion application and place an order using the delivery service with one click. The suggestion application then calls the delivery service API to process the order.

[1166] Input: User order operation

[1167] Output: Order data sent to the delivery service API

[1168] Step 8:

[1169] The delivery service receives an order from a user and delivers the specified meal menu to the user's home, etc. The user can receive the suggested meal menu.

[1170] Input: Order data sent to the delivery service API

[1171] Output: The meal menu delivered to the user

[1172] Example 2

[1173] Next, a description will be given of Example 2 of Form 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."

[1174] In modern society, personal health management is an important issue. However, conventional health management systems are unable to fully utilize the biometric and emotional information of individual users, making it difficult to provide individually optimized advice. In addition, there is a lack of means to connect with devices such as smartwatches and smart home appliances, making it impossible to achieve comprehensive health management based on the user's physical and emotional state. This can prevent users from receiving appropriate health management, potentially delaying improvements in their health.

[1175] 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.

[1176] In this invention, the server includes means for linking with the generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for acquiring biometric information from the smartwatch, means for storing the biometric information in a database, an AI engine for analyzing the biometric information, an emotion engine for acquiring emotional information, means for combining and analyzing the biometric information and emotional information, and means for providing appropriate advice to the user based on the analysis results. This makes it possible to comprehensively analyze the user's biometric information and emotional information and provide individually optimized health management advice.

[1177] "Generative AI" refers to AI that has the ability to generate new information and predictions based on data.

[1178] "Individual daily exercise volume" refers to data that indicates the amount of exercise a user engages in on a daily basis.

[1179] "Personal data" refers to all information about an individual user, including data such as health status and lifestyle habits.

[1180] A "smartwatch" is a wearable device that has the ability to acquire and transmit a user's biometric information in real time.

[1181] "Biometric information" refers to data related to the user's physical condition, such as heart rate and number of steps taken.

[1182] A "database" is a system for storing and managing acquired data.

[1183] An "artificial intelligence engine" is an artificial intelligence that analyzes data and detects patterns and anomalies.

[1184] An "emotion engine" is a system for analyzing a user's emotional state and acquiring emotional information.

[1185] "Analysis results" refers to the results of analyzing data obtained by an artificial intelligence engine or emotion engine.

[1186] "Advice" refers to suggestions or instructions regarding health management provided to users based on the analysis results.

[1187] This invention is a system that supports users' health management, and uses hardware and software such as generative artificial intelligence, a smart watch, a database, an artificial intelligence engine, and an emotion engine.

[1188] The server works in conjunction with generative AI to accumulate an individual's daily exercise volume and other personal data. Specifically, it acquires biometric information such as heart rate and number of steps from the smartwatch and stores this data in a database. The server then sends the stored data to an AI engine for analysis. The AI ​​engine compares it with past data to detect outliers and analyze trends.

[1189] The server then acquires the user's emotional information using an emotion engine, which analyzes the user's emotions from facial expressions, voice data, etc. The server then combines and analyzes this biometric information and emotional information to comprehensively evaluate the user's health condition.

[1190] Based on the analysis results, the server will provide appropriate advice to the user, such as relaxation exercises or deep breathing prompts, via notifications on the smartphone or smartwatch.

[1191] As a concrete example, consider the case where a user is wearing a smartwatch. The smartwatch detects that the user's heart rate is higher than normal. The device sends this information to a server. The server stores the received heart rate data in a database and sends it to an artificial intelligence engine for analysis. The artificial intelligence engine compares it with the user's past data and determines that stress is causing the elevated heart rate. The emotion engine similarly detects that the user is feeling stressed. Based on this information, the server instructs the user to take deep breaths.

[1192] Example prompt sentence:

[1193] If the user's heart rate is higher than normal, determine if they are experiencing stress and suggest appropriate measures.

[1194] In this way, a system is realized in which the server, terminal, and user work together to manage the user's health.

[1195] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1196] Step 1:

[1197] The device collects biometric information such as heart rate and number of steps in real time from the smartwatch worn by the user.

[1198] Input: Biometric information from smartwatch (heart rate, steps, etc.)

[1199] Output: Acquired biometric information

[1200] Specific operation: The smartwatch detects the user's heart rate as 80 bpm and sends the data to the device.

[1201] Step 2:

[1202] The terminal transmits the acquired biometric information to the server.

[1203] Input: Biometric information stored on the device

[1204] Output: Biometric information sent to the server

[1205] Specific operation: The device sends the heart rate data received from the smartwatch to the server.

[1206] Step 3:

[1207] The server stores the received biometric information in a database.

[1208] Input: Biometric information sent to the server

[1209] Output: Biometric information stored in a database

[1210] Specific operation: The server stores the heart rate data of 80 bpm in the database.

[1211] Step 4:

[1212] The server sends the biometric information stored in the database to an artificial intelligence engine for analysis.

[1213] Input: Biometric information stored in the database

[1214] Output: Analysis results by the AI ​​engine

[1215] Specific operation: The server sends heart rate data to an artificial intelligence engine, which compares it with past data to detect abnormalities.

[1216] Step 5:

[1217] The server acquires the user's emotion information using an emotion engine.

[1218] Input: User's facial expressions and voice data

[1219] Output: Emotion information from the emotion engine

[1220] Specific operation: The server sends the user's facial expression data to the emotion engine and determines that the user is feeling stressed.

[1221] Step 6:

[1222] The server combines and analyzes the biometric information analyzed by the artificial intelligence engine and the emotional information acquired by the emotion engine.

[1223] Input: Analysis results of the AI ​​engine, emotional information of the emotion engine

[1224] Output: Comprehensive analysis results

[1225] Specific operation: The server combines abnormal heart rate values ​​with stress levels to determine that the user is in a high stress state.

[1226] Step 7:

[1227] The server provides appropriate advice to the user based on the analysis results.

[1228] Input: Comprehensive analysis results

[1229] Output: Advice notice to user

[1230] Specific operation: The server notifies the smartphone of the user's instruction to take a deep breath.

[1231] In this way, a system is realized in which the server, terminal, and user work together to manage the user's health.

[1232] (Application example 2)

[1233] Next, a description will be given of Application Example 2 of Form 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."

[1234] Conventional health management systems were unable to fully utilize the user's biometric and emotional information, and were unable to suggest optimal meal menus for each individual user or to link with food delivery services. Furthermore, detailed health management based on the user's physical condition and emotional state was difficult, and suggestions for stress relief and appropriate exercise menus were limited.

[1235] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1236] In this invention, the server includes means for linking with the generative AI, means for accumulating an individual's daily exercise volume and personal data, means for suggesting dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for the individual based on this data, means for analyzing biometric and emotional information obtained from the smartwatch and suggesting meal menus based on the user's physical condition and emotional state, and means for ordering the suggested meal menus in collaboration with a food delivery service. This enables individual health management based on the user's physical condition and emotional state, and the provision of healthy meals through food delivery services.

[1237] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[1238] "Individual daily exercise amount" is data indicating the amount of exercise a user performs in their daily life.

[1239] "Personal data" refers to information about an individual user, including their health status, lifestyle habits, etc.

[1240] "Dietary improvement suggestions" are suggestions for meal menus and changes to eating habits that take into account the user's health condition.

[1241] "Stress relief" refers to methods and suggestions for reducing stress for users.

[1242] An "appropriate exercise menu" is an exercise program suggested based on the user's health condition and athletic ability.

[1243] A "smartwatch" is a wearable device that collects a user's biometric information.

[1244] "Biometric information" refers to data related to the user's physical condition, such as heart rate, number of steps, and sleep data.

[1245] "Emotion information" is data that indicates the user's emotional state.

[1246] A "food delivery service" is a service that delivers meals ordered by users.

[1247] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for suggesting dietary improvements and stress relief methods suitable for the individual based on this data, and an appropriate exercise menu, means for analyzing biometric and emotional information obtained from the smartwatch and suggesting a meal menu based on the user's physical condition and emotional state, and means for linking with a food delivery service to order the suggested meal menu.

[1248] Hardware and software used

[1249] Hardware:

[1250] Smartwatches (e.g. Apple Watch, Fitbit)

[1251] Smartphone (e.g. iPhone, Android device)

[1252] software:

[1253] Python

[1254] The Requests library (for sending HTTP requests)

[1255] Dummy API (food delivery service)

[1256] Data processing and calculation

[1257] The server acquires biometric information such as heart rate, step count, and sleep data from the smartwatch. This data is sent to the server via the user's smartphone. Furthermore, the server analyzes the user's emotional state using an emotion engine to acquire emotional information.

[1258] The server analyzes the acquired biometric and emotional information using an AI engine and suggests meal plans based on the user's physical condition and emotional state. These suggestions are then sent to the user's smartphone.

[1259] The user checks the suggested meal menu and places an order through a food delivery service. The server uses the food delivery service's API to send the order and provide the user with a healthy meal.

[1260] Specific examples

[1261] For example, if User A's heart rate is 80, the number of steps taken is 5,000, the amount of sleep is 6 hours, and the emotional state is stressed, the AI ​​engine will suggest a "relaxation menu." This suggestion will be notified to the user's smartphone, and the user can order the relaxation menu through a food delivery service.

[1262] Prompt Sentence Examples

[1263] "If a user's heart rate is 80, they have taken 5,000 steps, slept 6 hours, and their emotional state is stressed, what meal plan would you suggest?"

[1264] In this way, it becomes possible to provide personalized health management based on the user's physical and emotional state, and healthy meals through food delivery services.

[1265] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1266] Step 1:

[1267] The server acquires biometric information from the smartwatch. Specifically, the heart rate, number of steps, sleep data, etc. measured by the smartwatch are sent to the server via the user's smartphone. The input is the biometric data from the smartwatch, and the output is the biometric data stored on the server.

[1268] Step 2:

[1269] The server acquires the user's emotional information using the emotion engine. Specifically, it receives data indicating the user's emotional state from the user's smartphone and analyzes it using the emotion engine. The input is data indicating the user's emotional state, and the output is the analyzed emotional information.

[1270] Step 3:

[1271] The server analyzes the acquired biometric and emotional information using an AI engine. Specifically, it integrates heart rate, step count, sleep data, and emotional information to generate a meal menu based on the user's physical condition and emotional state. The input is biometric and emotional information, and the output is a suggested meal menu.

[1272] Step 4:

[1273] The server notifies the user's smartphone of the proposed meal menu. Specifically, it sends the generated meal menu to the user's smartphone and displays a notification. The input is the proposed meal menu, and the output is the notification displayed on the user's smartphone.

[1274] Step 5:

[1275] The user uses a smartphone to check the proposed meal menu and place an order through a food delivery service. Specifically, the user operates the smartphone application to select a proposed menu and confirm the order. The input is the operation to check the proposed meal menu, and the output is the order information.

[1276] Step 6:

[1277] The server sends the order using the food delivery service's API. Specifically, it sends the user's order information to the food delivery service's API and confirms the order. The input is the user's order information, and the output is the order data sent to the food delivery service.

[1278] Step 7:

[1279] A food delivery service receives orders and delivers meals to users. Specifically, it prepares meals based on the received order information and delivers them to the specified address. The input is the order data sent from the server, and the output is the meal delivered to the user.

[1280] Example 3

[1281] Next, a description will be given of Example 3 of Form Example 3. 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."

[1282] In modern society, personal health management and maintaining a comfortable living environment are important issues. However, existing systems do not adequately adjust the environment based on an individual's physical condition and emotional state. Furthermore, there is a lack of systems that effectively utilize an individual's daily exercise volume and personal data to improve diet, relieve stress, and suggest appropriate exercise menus. This makes it difficult to provide personal health management and a comfortable living environment.

[1283] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1284] In this invention, the server includes a means for linking with the generative AI, a means for accumulating an individual's daily exercise volume and personal data, a means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, a means for accumulating environmental data acquired from smart home appliances, an AI engine for analyzing the accumulated environmental data, and a means for controlling the smart home appliances based on the analysis results. This enables appropriate environmental adjustments based on the individual's physical condition and emotional state, thereby realizing personal health management and providing a comfortable living environment.

[1285] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[1286] "Individual daily exercise volume" refers to the amount of exercise an individual engages in on a daily basis, and includes data such as the number of steps taken and calories burned.

[1287] "Personal data" is specific information relating to an individual, including information about their health, diet, emotional state, etc.

[1288] "Dietary Improvement Suggestions" are suggested changes to dietary content and eating habits based on an individual's health status and goals.

[1289] "Stress relief" refers to specific methods and suggestions for reducing personal stress.

[1290] An "appropriate exercise menu" refers to the exercise content and schedule suggested based on an individual's health condition and goals.

[1291] "Smart home appliances" are household electrical appliances that are connected to the Internet and can be remotely or automatically controlled.

[1292] "Environmental data" refers to information related to living environments, such as indoor temperature and humidity.

[1293] An "AI engine" is a core technology in artificial intelligence that analyzes data and generates results tailored to specific purposes.

[1294] "Analysis results" are the conclusions and suggestions reached by the AI ​​engine after analyzing the data.

[1295] "Control means" refers to a method or device for operating smart home appliances based on the analysis results.

[1296] MODE FOR CARRYING OUT THE INVENTION

[1297] The present invention is a system for managing personal health and providing a comfortable living environment. A specific embodiment of this system will be described below.

[1298] server

[1299] The server acquires environmental data such as indoor temperature and humidity from smart home appliances. This data is acquired using devices such as smart thermostats and smart hygrometers. The server stores this data in a database and analyzes it using an AI engine (e.g., TensorFlow or PyTorch). Based on the analysis results, the server generates control instructions for the smart home appliances and sends them to the smart home appliances.

[1300] Specifically, the server calls the smart thermostat's API and sends instructions for setting the temperature. For example, if the server determines that the user has a cold, it sends the instruction to the smart thermostat to "set the room temperature to 22 degrees."

[1301] Terminal

[1302] The device receives information such as indoor temperature and humidity from smart home appliances and sends it to the server. After receiving the analysis results from the server, the device displays appropriate indoor temperature adjustment suggestions to the user. For example, if the device determines that the user has a cold, it will display a message such as, "Would you like to set the indoor temperature to 22 degrees?"

[1303] User

[1304] Users can maintain a comfortable indoor environment using a system that connects with smart home appliances. If the user has a cold, the system will automatically adjust the indoor temperature to provide a comfortable environment tailored to the user's physical condition. If the system determines that the user is irritated, it will control the air conditioner to lower the indoor temperature.

[1305] Examples and prompts

[1306] Specific examples

[1307] 1. If the user has a cold, the server controls the smart thermostat to set the room temperature to 22 degrees.

[1308] 2. If the user is determined to be irritated and the room temperature is 28 degrees, the server will control the air conditioner to lower the room temperature to 24 degrees.

[1309] Prompt Sentence Examples

[1310] "If the user has a cold, explain how to control the smart thermostat based on the analysis results of the AI ​​engine to adjust the indoor temperature appropriately."

[1311] "Please explain the operation of the system that controls the air conditioner to lower the room temperature when it is determined that the user is irritated." The flow of the identification process in the third embodiment will be described with reference to FIG. 21.

[1312] Step 1: Data Acquisition

[1313] The server obtains environmental data such as indoor temperature and humidity from smart home appliances. Specifically, the server calls the API of the smart thermostat or smart hygrometer to obtain the latest temperature and humidity data. The input is the environmental data from the smart home appliances, and the output is the obtained temperature and humidity data.

[1314] Step 2: Data accumulation

[1315] The server accumulates the acquired environmental data in a database. Specifically, the server saves the acquired temperature and humidity data in the database along with a timestamp. The input is the acquired temperature and humidity data, and the output is the data saved in the database.

[1316] Step 3: Data analysis

[1317] The server analyzes the accumulated data using an AI engine. Specifically, the server uses an AI model (e.g., TensorFlow or PyTorch) to predict the user's physical and emotional state. The input is environmental data stored in a database, and the output is the analysis results.

[1318] Step 4: Generate control instructions

[1319] The server generates control instructions for smart home appliances based on the analysis results. Specifically, if the server determines that the user has a cold, it generates an instruction to the smart thermostat to "set the room temperature to 22 degrees." The input is the analysis result, and the output is the generated control instruction.

[1320] Step 5: Sending control instructions

[1321] The server sends the generated control instructions to the smart appliances. Specifically, the server calls the smart thermostat's API to send temperature setting instructions. The input is the generated control instructions, and the output is the instructions sent to the smart appliances.

[1322] Step 6: Receiving Data

[1323] The terminal receives information such as indoor temperature and humidity from smart home appliances and sends it to the server. Specifically, the terminal calls the API of the smart home appliance to obtain the latest temperature and humidity data and send it to the server. The input is environmental data from the smart home appliance, and the output is data sent to the server.

[1324] Step 7: Receive analysis results

[1325] The terminal receives the analysis results from the server. Specifically, the terminal calls the server's API to obtain the analysis results. The input is the analysis results from the server, and the output is the display of the analysis results on the terminal.

[1326] Step 8: User Notification

[1327] The terminal notifies the user based on the analysis results. Specifically, the terminal displays a message to the user such as "Do you want to set the room temperature to 22 degrees?" The input is the analysis result from the server, and the output is a notification to the user.

[1328] Step 9: System Usage

[1329] A user uses a system that connects with smart home appliances. Specifically, the user logs in to the system using a smartphone or tablet. The input is the user's login information, and the output is the start of use of the system.

[1330] Step 10: Provide data

[1331] The user provides the system with data about their physical and emotional state. Specifically, the user inputs information such as "I have a cold" or "I'm irritated" through the application. The input is data about the user's physical and emotional state, and the output is data provided to the system.

[1332] Step 11: Check notifications

[1333] The user checks the notification from the device. Specifically, the user checks the message displayed on the device, such as "Do you want to set the room temperature to 22 degrees?", and approves it if necessary. The input is the notification from the device, and the output is the user's confirmation and approval.

[1334] Step 12: Adjusting the environment

[1335] The user adjusts the indoor environment based on the system's suggestions. Specifically, the user changes the settings of the smart thermostat according to instructions on the device. The input is the system's suggestions, and the output is the adjustment of the indoor environment.

[1336] (Application example 3)

[1337] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1338] Conventional systems do not adequately adjust the environment according to the physical condition and emotions of individuals, and there are a lack of concrete measures to improve customer comfort, especially in brick-and-mortar stores. Furthermore, there is no established method for effectively utilizing data obtained from smart home appliances and smart devices. This makes it difficult to provide an optimal environment according to the physical condition and emotions of customers.

[1339] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1340] In this invention, the server includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for accumulating information such as indoor temperature and humidity obtained from smart home appliances in a database and analyzing it with an AI engine, means for adjusting the indoor environment based on the analysis results according to the user's physical condition and emotions, means for optimizing the environment of a physical store according to the customer's physical condition and emotions, and means for being installed on a smartphone or smart glasses and operating in conjunction with the smart home appliances in the store, thereby making it possible to provide an optimal environment according to the customer's physical condition and emotions.

[1341] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[1342] "Individual daily exercise volume" refers to the amount of exercise an individual performs on a daily basis.

[1343] "Personal data" refers to specific information about an individual, including their health status and lifestyle habits.

[1344] "Dietary Improvement Suggestions" refers to dietary suggestions to improve an individual's health.

[1345] "Stress management" refers to methods used by individuals to reduce stress.

[1346] An "appropriate exercise menu" refers to an exercise program that is tailored to an individual's health condition and goals.

[1347] "Smart appliances" refer to home appliances that are connected to the Internet and can be controlled remotely.

[1348] "Indoor temperature" refers to the temperature of the air inside a building.

[1349] "Humidity" refers to the amount of water vapor contained in the air.

[1350] A "database" refers to a system for systematically organizing and storing data.

[1351] An "AI engine" refers to software or hardware used to run artificial intelligence algorithms.

[1352] "Analysis" refers to the act of examining data in detail to find meaning and patterns.

[1353] "User's physical condition" refers to the user's health condition.

[1354] "Emotion" refers to the user's psychological state.

[1355] "Adjusting the indoor environment" refers to changing environmental factors such as the temperature and humidity inside the room.

[1356] "Brick and Mortar Store" means a retail store located in a physical location.

[1357] "Customer" refers to a person who purchases goods or services.

[1358] A "smartphone" refers to a mobile phone that can connect to the Internet and use applications.

[1359] "Smart glasses" refers to a glasses-type device that can display information and connect to the Internet.

[1360] "Smart device" refers to an electronic device that is connected to the Internet and can be controlled remotely.

[1361] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for suggesting dietary improvements and stress relief methods suitable for the individual based on this data, and an appropriate exercise menu, means for accumulating information such as indoor temperature and humidity obtained from smart home appliances in a database and analyzing it using an AI engine, means for adjusting the indoor environment according to the user's physical condition and emotions based on the analysis results, means for optimizing the environment of a physical store according to the customer's physical condition and emotions, and means that is installed on a smartphone or smart glasses and operates in conjunction with smart home appliances in the store.

[1362] The server collects data from smart home appliances and smart devices and stores it in a database. Specifically, it collects information such as indoor temperature, humidity, lighting brightness, heart rate, body temperature, and emotional information. This data is obtained using a REST API.

[1363] The server then analyzes the collected data using an AI engine, which uses software such as TensorFlow and PyTorch. Based on the analysis results, the system calculates optimal environmental settings based on the user's physical condition and emotions. For example, if the user is irritated or has a cold, the system will control the air conditioner and humidifier to provide an appropriate indoor environment.

[1364] Furthermore, the server controls smart home appliances based on the analysis results, issuing instructions such as switching the air conditioner to cooling mode, turning on the humidifier, and adjusting the brightness of the lights, making it possible to provide an optimal environment according to the user's physical condition and emotions.

[1365] For example, if a customer is judged to be irritated in a physical store and the indoor temperature is high, the air conditioner can be controlled to lower the temperature, thus providing a more comfortable shopping experience for the customer.

[1366] An example prompt is, "If a customer is frustrated, how can I control the air conditioning to lower the room temperature?"

[1367] This system makes it possible to provide an optimal environment according to an individual's physical condition and emotions, thereby improving customer comfort, especially in physical stores.

[1368] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1369] Step 1:

[1370] The server collects data from smart home appliances and smart devices. Specifically, it obtains information such as indoor temperature, humidity, lighting brightness, heart rate, body temperature, and emotional information using a REST API. The input is sensor data from smart home appliances and smart devices, and the output is to store this data in a database.

[1371] Step 2:

[1372] The server stores the collected data in a database. The database stores the data acquired from each device in chronological order. The input is the sensor data acquired in step 1, and the output is the data stored in the database.

[1373] Step 3:

[1374] The server analyzes the data stored in the database using an AI engine. The AI ​​engine uses software such as TensorFlow and PyTorch. The input is sensor data stored in the database, and the output is the analysis results based on the user's physical condition and emotions. Specifically, the data is input into an AI model, which estimates the user's physical condition and emotional state.

[1375] Step 4:

[1376] The server calculates the optimal environmental settings based on the analysis results, taking into account the user's physical condition and emotions. The input is the analysis results obtained in step 3, and the output is instructions for the optimal environmental settings. For example, if the user is irritated or has a cold, the server calculates the settings to control the air conditioner or humidifier.

[1377] Step 5:

[1378] The server sends instructions for optimal environmental settings to the smart appliances. The input is the environmental setting instructions calculated in step 4, and the output is the actions of the smart appliances. Specific actions include switching the air conditioner to cooling mode, turning on the humidifier, adjusting the brightness of the lights, etc.

[1379] Step 6:

[1380] Users can check the system's operating status through their smartphones or smart glasses. The input is an environmental setting instruction from the server, and the output is feedback to the user. Specifically, notifications are displayed on the smartphone or smart glasses to inform the user of the current indoor environment and adjustments.

[1381] In this way, it becomes possible to provide an optimal environment according to the physical condition and emotions of each individual, thereby improving customer comfort, especially in brick-and-mortar stores.

[1382] 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.

[1383] 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> ) 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.

[1384] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1385] 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.

[1386] [Third embodiment]

[1387] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1388] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1389] 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.

[1390] The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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).

[1391] 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.

[1392] 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.

[1393] 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).

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1400] "Example 1"

[1401] As one embodiment of the present invention, a means for linking with generative AI is provided, which includes a database that stores the amount of exercise and personal data of each user, and an AI engine that analyzes this data and proposes dietary improvement plans, stress relief methods, and appropriate exercise menus that are suitable for each user. Specifically, the amount of exercise a user does is obtained from a smartwatch or fitness app, and personal data is obtained from user input or other linked apps. This data is stored in a database and analyzed by the AI ​​engine. Based on the analysis results, dietary improvement plans, stress relief methods, and appropriate exercise menus are proposed to the user.

[1402] "Example 2"

[1403] In another embodiment of the present invention, biometric information such as heart rate and step count acquired from a smartwatch is used as a means of linking with the smartwatch. Specifically, the biometric information acquired from the smartwatch is stored in a database and analyzed by an AI engine. Based on the analysis results, suggestions for improving dietary habits, stress relief methods, and appropriate exercise menus are proposed, taking into account the user's physical condition and exercise volume.

[1404] "Example 3"

[1405] In a further embodiment of the present invention, information such as indoor temperature and humidity obtained from smart home appliances is used as a means for linking with smart home appliances. Specifically, the information obtained from the smart home appliances is stored in a database and analyzed by an AI engine. Based on the analysis results, assistance is provided for adjusting the indoor temperature taking into account the user's physical condition. For example, if it is determined that the user has a cold, the smart thermostat is controlled to appropriately adjust the indoor temperature.

[1406] The processing flow of each embodiment will be described below.

[1407] "Example 1"

[1408] Step 1: Obtain the user's exercise and personal data from their smartwatch or fitness app.

[1409] Step 2: Store the acquired data in a database.

[1410] Step 3: Analyze the accumulated data using an AI engine.

[1411] Step 4: Based on the analysis results, the system will suggest dietary improvements, stress relief methods, and appropriate exercise menus to the user.

[1412] "Example 2"

[1413] Step 1: Obtain biometric information such as heart rate and steps from the smartwatch.

[1414] Step 2: The acquired biometric information is stored in a database.

[1415] Step 3: Analyze the accumulated biometric information using an AI engine.

[1416] Step 4: Based on the analysis results, the system will suggest dietary improvements, stress relief methods, and appropriate exercise menus that take into account the user's physical condition and amount of exercise.

[1417] "Example 3"

[1418] Step 1: Obtain information such as indoor temperature and humidity from smart home appliances.

[1419] Step 2: The acquired information is stored in a database.

[1420] Step 3: Analyze the accumulated information using an AI engine.

[1421] Step 4: Based on the analysis results, the system provides assistance with adjusting the indoor temperature based on the user's physical condition. For example, if the system determines that the user has a cold, it will control the smart thermostat to adjust the indoor temperature appropriately.

[1422] Example 1

[1423] Next, a description will be given of Example 1 of Form 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."

[1424] In modern society, personal health management is an important issue. However, there is no adequate system in place to efficiently collect individual exercise amounts and personal data and then make appropriate health management suggestions based on that data. In particular, it is difficult to provide individually optimized dietary improvement suggestions, stress relief methods, and exercise menus.

[1425] 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. In this invention, the server includes a means for collecting an individual's daily exercise amount and personal data, a means for transmitting the collected data to the server and storing it in a database, a means for generating dietary improvement suggestions, stress relief methods, and appropriate exercise menus suitable for the individual using an AI engine that analyzes the stored data, and a means for notifying the generated suggestions to the individual's terminal. This makes it possible to provide health management suggestions optimized for each individual user.

[1426] "Individual daily physical activity" refers to the amount of physical activity an individual engages in on a daily basis, as measured using devices such as smartwatches and fitness apps.

[1427] "Personal data" refers to information about an individual's health and lifestyle, including diet, stress levels, and sleep patterns.

[1428] "Means of collection" refers to the methods used to collect an individual's daily exercise and personal data using smartwatches, fitness apps, and other devices and applications.

[1429] "Means of transmitting to a server and storing in a database" refers to a method of transmitting collected data to a server via the Internet and storing it in a database on the server.

[1430] An "AI engine" refers to software or algorithms that use machine learning and data analysis techniques to analyze accumulated data and generate personalized suggestions.

[1431] "Means of generation" refers to a method that uses an AI engine to create dietary improvement suggestions, stress relief methods, and appropriate exercise menus that are suitable for individuals based on the analysis results.

[1432] "Means for notifying" refers to a method for sending the generated proposal to a personal device and notifying the user.

[1433] A "smartwatch" is a wristwatch-type wearable device that has the function of measuring health data such as exercise volume and heart rate.

[1434] "Smart home appliances" refer to home appliances that are connected to the Internet and can be controlled and configured remotely.

[1435] The present invention is a system for collecting an individual's daily exercise amount and other personal data, and making health management suggestions based on the collected data. A specific embodiment of this system will be described below.

[1436] First, users measure their daily exercise volume using a smartwatch and fitness app. A typical wearable device (e.g., a wristwatch-type health management device) is used as the smartwatch. A health management application (e.g., an exercise volume measurement app or a diet management app) is used as the fitness app.

[1437] The devices (smartphones and tablets) then send the data collected from these devices and applications to a server, which stores the received data in a database. The database organizes the data for each user and allows for efficient access.

[1438] The server passes the data stored in the database to an AI engine, which uses software that employs machine learning and data analysis techniques (e.g., TensorFlow, PyTorch). The AI ​​engine analyzes the user's exercise volume and personal data to evaluate their health condition and lifestyle habits.

[1439] Based on the analysis results, the server generates personalized suggestions for improving the user's diet, stress relief, and exercise routines.

[1440] Finally, the server sends the generated proposal to the terminal, which notifies the user and displays the proposal, allowing the user to confirm and execute the proposal.

[1441] As a concrete example, a user measures their daily exercise volume with a smartwatch and syncs the data with a fitness app. They then enter their meal details into a diet management app. This data is sent to a server and stored in a database. The AI ​​engine analyzes this data and makes suggestions to the user, such as "eat protein-rich foods for breakfast," "meditate for 10 minutes every day to relieve stress," and "do aerobic exercise three times a week."

[1442] Example prompts to input to the generative AI model:

[1443] Based on the user's exercise data and personal data, suggest appropriate dietary improvements, stress relief methods, and exercise menus.

[1444] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[1445] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1446] Step 1: Data collection

[1447] Users measure their daily exercise volume using a smartwatch and a fitness app. Specifically, the user goes jogging and the smartwatch measures their exercise volume. They also input personal data such as their diet and stress level into the fitness app. The input data includes the amount of exercise, heart rate, calorie intake, and nutritional information.

[1448] Input: User's exercise data, personal data

[1449] Output: Collected data

[1450] Step 2: Data transmission and storage

[1451] The device (smartphone or tablet) sends the collected data to a server. Specifically, it acquires data from a smartwatch or fitness app and sends it to the server via the internet. The server stores the received data in a database. The database organizes the data for each user and allows efficient access.

[1452] Input: Collected data

[1453] Output: Data stored in the database

[1454] Step 3: Data analysis

[1455] The server passes the data stored in the database to the AI ​​engine. Specifically, it extracts the user's exercise data and personal data from the database and inputs it into the AI ​​engine. The AI ​​engine then analyzes this data and evaluates the user's health condition and lifestyle habits. Machine learning algorithms and data analysis techniques are used for the analysis.

[1456] Input: Data stored in the database

[1457] Output: Analysis results

[1458] Step 4: Generate proposals

[1459] Based on the analysis results of the AI ​​engine, the server generates dietary improvement suggestions, stress relief methods, and appropriate exercise menus suitable for the user. Specifically, based on the analysis results, it creates suggestions that are optimal for the user's health condition and lifestyle. The suggestions include specific meal menus, activities to relieve stress, and the frequency and type of exercise.

[1460] Input: Analysis results

[1461] Output: Generated proposals

[1462] Step 5: Proposal Notification

[1463] The server sends the generated proposal to the device. Specifically, the proposal is notified to the user's smartphone or tablet. The device notifies the user and displays the proposal. The user can confirm the notification and carry out the proposal.

[1464] Input: Generated proposals

[1465] Output: Proposal notified to the user

[1466] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[1467] (Application example 1)

[1468] Next, a description will be given of Application Example 1 of Form 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."

[1469] In modern society, it is extremely difficult for individual users to manage their own health status and find optimal training menus and meal plans. Furthermore, there is a lack of systems that can centrally manage this information and provide individually optimized recommendations. Furthermore, there is a need for a method to effectively utilize users' exercise volume and personal data and to perform analysis in conjunction with a cloud database.

[1470] 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.

[1471] In this invention, the server includes means for linking with the generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for the individual based on this data, means for saving the data in a cloud database, means for sending the data to an AI engine for analysis, means for generating training menus and meal plans based on the analysis results, and means for providing these suggestions to the user, thereby enabling the user to effectively manage their health condition and receive optimal training menus and meal plans.

[1472] "Generative AI" is an artificial intelligence system that analyzes user data and generates individually optimized suggestions.

[1473] "Individual daily exercise volume" is data that indicates the amount and type of exercise a user engages in on a daily basis.

[1474] "Personal data" refers to data that includes individual information about a user, such as age, weight, height, and health status.

[1475] A "cloud database" is a database accessible via the Internet and a system for storing user data.

[1476] The "AI engine" is the core part of the artificial intelligence that analyzes collected data and generates optimal suggestions for users.

[1477] A "training menu" is an exercise program suggested based on the user's exercise volume and personal data.

[1478] A "meal plan" is a suggested dietary improvement plan based on the user's health status and goals.

[1479] The "means for providing suggestions to the user" is a system for notifying the user of training menus and meal plans generated based on the analysis results.

[1480] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for saving the data in a cloud database, means for sending the data to an AI engine for analysis, means for generating training menus and meal plans based on the analysis results, and means for providing these suggestions to the user.

[1481] The server collects the user's exercise data from the smartwatch and fitness app. This data is stored in a cloud database. The server then sends the stored data to the AI ​​engine for analysis. The AI ​​engine generates optimal training menus and meal plans based on the user's exercise volume and personal data. The generated suggestions are provided to the user through the smartphone app.

[1482] Specifically, the server uses the following hardware and software:

[1483] Smartwatch: A device that collects a user's exercise data.

[1484] Fitness app: An application that works with a smartwatch to manage exercise data.

[1485] Cloud database: A database system for storing collected data.

[1486] AI Engine: An artificial intelligence system that analyzes data and generates optimal suggestions.

[1487] For example, when a user works out at a gym, the smartwatch collects exercise data and sends it to a cloud database via a fitness app. The server then sends this data to an AI engine, which then generates optimal training and meal plans for the user based on the analysis results. The generated suggestions are then sent to the user's smartphone app.

[1488] An example prompt might have the following format:

[1489] User ID: 12345

[1490] Exercise data: steps: 10000, calories: 500, heart_rate: 120

[1491] Personal data: age: 30, weight: 70, height: 175

[1492] By inputting this prompt into the AI ​​engine, the optimal training menu and meal plan for the user will be generated.

[1493] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1494] Step 1:

[1495] The server collects the user's exercise data from the smartwatch and fitness app. Specifically, the smartwatch measures the user's steps, calories burned, heart rate, and other data, and sends it to the server via the fitness app. The input is the exercise data from the smartwatch, and the output is the exercise data stored on the server.

[1496] Step 2:

[1497] The server stores the collected exercise data in a cloud database. Specifically, the server converts the exercise data into an appropriate format and stores it in the corresponding user's database in the cloud database. The input is the exercise data stored in the server, and the output is the exercise data stored in the cloud database.

[1498] Step 3:

[1499] The server sends the exercise data and personal data stored in the cloud database to the AI ​​engine. Specifically, it combines the user's exercise data and personal data into a single prompt and sends it to the AI ​​engine. The input is the exercise data and personal data stored in the cloud database, and the output is the prompt sent to the AI ​​engine.

[1500] Step 4:

[1501] The AI ​​engine performs data analysis based on the received prompts. Specifically, it uses a generative AI model to analyze the user's exercise data and personal data to generate optimal training menus and meal plans. The input is the prompt sent to the AI ​​engine, and the output is the generated training menu and meal plan.

[1502] Step 5:

[1503] The server sends the analysis results received from the AI ​​engine to the user's smartphone app. Specifically, it notifies the user's smartphone app of the generated training menu and meal plan. The input is the analysis results received from the AI ​​engine, and the output is the training menu and meal plan notified to the user's smartphone app.

[1504] Step 6:

[1505] The user checks and executes the training menu and meal plan provided through the smartphone app. Specifically, the user adjusts their daily training and diet based on the suggestions displayed on the smartphone app. The input is the training menu and meal plan notified to the smartphone app, and the output is the user's actual training and diet.

[1506] Example 2

[1507] Next, a description will be given of Example 2 of Form 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."

[1508] In modern society, personal health management is an important issue. However, conventional systems have difficulty obtaining individual users' biometric information in real time and providing appropriate health management suggestions based on that information. In addition, there is a lack of analytical methods to provide specific suggestions based on the user's physical condition and exercise volume. This makes it difficult for users to accurately understand their own health status and take appropriate measures.

[1509] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for linking with a generative AI, means for accumulating an individual's daily exercise amount and personal data, means for proposing dietary improvement plans, stress relief methods, and appropriate exercise menus suitable for the individual based on this data, means for linking with a wearable device for acquiring biometric information, means for storing the acquired biometric information in a database, means including an AI engine for analyzing the stored biometric information, and means for making suggestions to the user based on the analysis results. This makes it possible to acquire the user's biometric information in real time and make specific health management suggestions based on the analysis results.

[1510] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[1511] "Individual daily exercise amount" is data indicating the amount of exercise a user does on a daily basis, and includes the number of steps taken and calories burned.

[1512] "Personal data" refers to information about an individual user's health condition and lifestyle habits, including heart rate, sleep duration, and dietary habits.

[1513] "Dietary improvement suggestions" are meal contents and methods for improving eating habits that are suggested based on the user's health condition and amount of exercise.

[1514] "Stress relief" refers to specific methods or activities that reduce a user's stress level.

[1515] The "appropriate exercise menu" refers to exercise programs and exercise content suggested based on the user's health condition and exercise volume.

[1516] A "wearable device" is an electronic device that can be worn by a user and is used to obtain biometric information such as heart rate and number of steps taken.

[1517] A "database" is a system for systematically storing acquired data and for searching and analyzing it as needed.

[1518] "AI engine" refers to artificial intelligence technology that analyzes data and finds patterns and trends.

[1519] "Analysis results" are the information and conclusions obtained after data is analyzed by an AI engine.

[1520] "Suggestion" refers to specific advice or instructions given to the user based on the analysis results.

[1521] MODE FOR CARRYING OUT THE INVENTION

[1522] This invention is a system for supporting users' health management, which works in conjunction with generative AI to accumulate an individual's daily exercise volume and personal data, and makes appropriate suggestions based on that information. Specific embodiments of this system are described below.

[1523] 1. System Configuration

[1524] This system consists of three main elements: a server, a device, and a user. The server works in conjunction with generative AI and plays a central role in accumulating and analyzing data. The device is a wearable device or smartphone used by the user, which collects biometric information and receives suggestions. The user uses these devices to manage their daily health.

[1525] 2. Hardware and Software Used

[1526] The server is a high-performance computer system equipped with a database (e.g., MySQL or PostgreSQL) and an AI engine (e.g., TensorFlow or PyTorch). Wearable devices such as smartwatches and smartphones are used as terminals. These devices communicate with the server using wireless communication technologies such as Bluetooth and Wi-Fi.

[1527] 3. Data acquisition and storage

[1528] When a user wears a smartwatch and goes about their daily activities, biometric information such as heart rate and number of steps is collected in real time. The device then sends this data to a server, which stores it in a database. For example, after a user finishes their morning jog, the smartwatch automatically sends the data to the server.

[1529] 4. Data analysis and proposals

[1530] The server passes the biometric information stored in the database to an AI engine for analysis. Based on the analysis results, suggestions for improving diet, stress relief, and appropriate exercise menus are made, taking into account the user's physical condition and exercise volume. For example, data from the past week is obtained and input into the AI ​​model for analysis.

[1531] 5. Notification of Proposal

[1532] The server makes specific suggestions to the user based on the analysis results of the AI ​​engine. The suggestions are sent to the user's smartphone or PC. For example, a message may be sent to the user's smartphone saying, "You've taken few steps today, so we recommend walking 30 minutes in the evening."

[1533] 6. Examples and prompts

[1534] As a concrete example, consider the case where a user records the number of steps taken and heart rate for the day using a smartwatch. The server acquires this data and stores it in a database. Next, the AI ​​engine analyzes this data and makes suggestions to the user, such as "Since you took fewer steps today, I recommend walking for 30 minutes in the evening."

[1535] Example prompts to input to the generative AI model:

[1536] "Please suggest an appropriate exercise menu based on the user's heart rate and step count data."

[1537] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[1538] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1539] Step 1:

[1540] The server acquires biometric information from the device (smartwatch). Specifically, it receives data such as the user's heart rate and number of steps in real time using wireless communication technologies such as Bluetooth and Wi-Fi. The input is the biometric information sent from the smartwatch, and the output is the biometric information stored on the server. For example, after a user finishes their morning jog, the smartwatch automatically sends the data to the server.

[1541] Step 2:

[1542] The server stores the acquired biometric information in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL and issues SQL queries to the database to insert data. The input is the biometric information stored on the server, and the output is the biometric information stored in the database. For example, the following SQL query is used:

[1543] sql

[1544] INSERT INTO bio_data (user_id, heart_rate, steps, timestamp) VALUES (1, 75, 10000, '2023-10-01 08:00:00');

[1545] Step 3:

[1546] The server retrieves biometric information from the database and passes it to the AI ​​engine. Specifically, it issues an SQL query to retrieve the necessary data from the database and inputs the retrieved data into the AI ​​engine. The input is the biometric information retrieved from the database, and the output is the data input into the AI ​​engine. For example, use the following Python code:

[1547] python

[1548] import tensorflow as tf

[1549] Retrieving data from a database

[1550] data = fetch_data_from_db(user_id=1)

[1551] Inputting data into the AI ​​model

[1552] model = tf.keras.models.load_model('health_model.h5')

[1553] predictions = model.predict(data)

[1554] Step 4:

[1555] The server makes suggestions to the user based on the analysis results of the AI ​​engine. Specifically, the analysis results are generated as a text message and sent to the user's smartphone or PC. The input is the analysis results of the AI ​​engine, and the output is a suggested message sent to the user. For example, a message such as "You've taken fewer steps today, so we recommend walking 30 minutes in the evening" is generated and sent to the user's smartphone.

[1556] Step 5:

[1557] The user receives the suggestions from the server and executes them. Specifically, the user checks the suggestion message on their smartphone or PC and implements the suggested exercise and dietary improvements. The input is the suggestion message sent from the server, and the output is the user's behavior. For example, the user follows the suggestion and takes a 30-minute walk in the evening.

[1558] In this way, a system is realized in which the server, terminal, and user work together to support the user's health management.

[1559] (Application example 2)

[1560] Next, a description will be given of Application Example 2 of Form 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."

[1561] In modern society, personal health management is an important issue. However, there is a lack of information on appropriate dietary recommendations based on daily exercise and physical condition, stress relief methods, and exercise menus. There is also a lack of systems that allow people to easily order healthy meals. This makes it difficult for individuals to take appropriate actions according to their own health condition.

[1562] 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 linking with the generative AI, means for accumulating an individual's daily exercise amount and personal data, means for proposing dietary improvement plans, stress relief methods, and appropriate exercise menus suitable for the individual based on this data, means for analyzing biometric information and proposing meal menus based on the user's physical condition and exercise amount, and means for ordering healthy meals from a food delivery service based on the proposed meal menus. This makes it possible to suggest appropriate dietary habits according to an individual's health condition and to easily order healthy meals.

[1563] "Generative AI" is a type of artificial intelligence that analyzes data and makes suggestions and predictions based on specific purposes.

[1564] "Individual daily exercise volume" refers to data such as the energy consumed and number of steps taken by an individual through daily life and exercise activities.

[1565] "Personal data" refers to information about an individual's health and lifestyle, including heart rate, sleep duration, and dietary habits.

[1566] "Dietary improvement suggestions" are nutritionally balanced meal menus and suggested changes to eating habits based on an individual's health status and amount of exercise.

[1567] "Stress Relief" is a suggestion of specific methods or activities to reduce an individual's stress level.

[1568] An "appropriate exercise menu" is an exercise program proposed based on an individual's physical condition and exercise volume, with the aim of maintaining health and improving physical strength.

[1569] "Biometric information" refers to data about an individual's physical condition, such as heart rate, number of steps, and body temperature, obtained from devices such as smartwatches.

[1570] A "food delivery service" is a service that delivers meals ordered by a user to a specified location.

[1571] A "healthy diet" is a nutritionally balanced meal menu that is appropriate for an individual's health condition and amount of exercise.

[1572] A system for implementing this invention includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for suggesting dietary improvements and stress relief methods suitable for the individual based on this data, and an appropriate exercise menu, means for analyzing biometric information and suggesting a meal menu based on the user's physical condition and exercise volume, and means for ordering healthy meals from a food delivery service based on the suggested meal menu.

[1573] Hardware and Software Configuration

[1574] Hardware:

[1575] Smartwatches (e.g. Apple Watch, Fitbit)

[1576] Smartphone (e.g. iPhone, Android)

[1577] software:

[1578] Python

[1579] The Requests library (for sending HTTP requests)

[1580] AI engines (e.g. TensorFlow, PyTorch)

[1581] Data processing and calculation

[1582] The server transmits biometric information (heart rate, number of steps, etc.) obtained from the smartwatch to the smartphone and stores it in a database. The stored data is analyzed by an AI engine. Based on the analysis results, suggestions for improving dietary habits, stress relief methods, and appropriate exercise menus are generated based on the user's physical condition and exercise level. Furthermore, the generated meal menus are suggested to the user, who can then order healthy meals from a food delivery service based on the menus.

[1583] Specific examples

[1584] For example, if a user has not exercised much throughout the day, the AI ​​engine will suggest low-calorie, nutritionally balanced meals, such as "grilled chicken salad" or "vegetable soup." Based on these suggestions, the user can easily order the suggested healthy meal using a food delivery service.

[1585] Prompt Sentence Examples

[1586] "If the user's heart rate is 75 and their steps are 10,000, suggest an appropriate meal plan."

[1587] In this way, a system can be realized that utilizes biometric information obtained from a smartwatch to support the user's health.

[1588] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1589] Step 1:

[1590] The smartwatch acquires the user's biometric information (heart rate, number of steps, etc.). The input is the user's biometric information, and the output is the acquired biometric information. Specifically, the smartwatch's sensors measure the user's heart rate and number of steps in real time.

[1591] Step 2:

[1592] The smartwatch transmits the acquired biometric information to a smartphone. The input is the biometric information acquired from the smartwatch, and the output is the biometric information transmitted to the smartphone. Specifically, the data is transferred to the smartphone using a communication method such as Bluetooth or Wi-Fi.

[1593] Step 3:

[1594] The smartphone sends the received biometric information to the server. The input is the biometric information sent to the smartphone, and the output is the biometric information sent to the server. Specifically, the smartphone application sends the data to the server using an HTTP request.

[1595] Step 4:

[1596] The server stores the received biometric information in a database. The input is the biometric information sent to the server, and the output is the biometric information stored in the database. Specifically, the server program saves the data in the database.

[1597] Step 5:

[1598] The server uses an AI engine to analyze the biometric information stored in the database. The input is the biometric information stored in the database, and the output is the analysis results. Specifically, the AI ​​engine analyzes the biometric information and generates a meal menu based on the user's physical condition and exercise level.

[1599] Step 6:

[1600] The server then proposes a meal menu suitable for the user based on the analysis results. The input is the analysis results of the AI ​​engine, and the output is the proposed meal menu. Specifically, the server sends the generated meal menu to the smartphone.

[1601] Step 7:

[1602] The smartphone displays the proposed meal menu to the user. The input is the meal menu sent from the server, and the output is the meal menu displayed to the user. In concrete terms, the smartphone application displays the meal menu on the screen.

[1603] Step 8:

[1604] A user orders a meal using a food delivery service based on a suggested meal menu. The input is the meal menu selected by the user, and the output is an order to the food delivery service. Specifically, the user selects a meal and confirms the order through a smartphone application.

[1605] Step 9:

[1606] A food delivery service delivers healthy meals to users. The input is the user's order, and the output is the meal delivered to the user. The specific behavior is that the food delivery service receives the order and delivers the meal to the specified location.

[1607] Example 3

[1608] Next, a third embodiment of the third embodiment will be described. 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."

[1609] Conventional health management systems accumulate an individual's daily exercise volume and personal data, and can suggest dietary improvements, stress relief methods, and appropriate exercise menus, but they do not adequately address the issue of adjusting the indoor environment. Furthermore, there is a lack of means to link with smart home appliances and adjust the indoor environment to take the user's physical condition into consideration, making it difficult to provide comprehensive support for the user's health management.

[1610] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1611] In this invention, the server includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for saving environmental data acquired from smart home appliances in a database, means for analyzing the saved data with an AI engine, and means for adjusting the indoor environment taking the user's physical condition into consideration based on the analysis results. This makes it possible to comprehensively manage the user's physical condition and adjust the indoor environment appropriately.

[1612] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[1613] "Individual daily exercise volume" refers to the amount of exercise an individual engages in on a daily basis, and includes data such as the number of steps taken and calories burned.

[1614] "Personal data" is specific information about an individual, including data about their health, diet, stress levels, etc.

[1615] "Smart appliances" are home appliances that are connected to the Internet and can be controlled remotely.

[1616] "Environmental data" refers to information about the environment, such as indoor temperature and humidity.

[1617] A "database" is a system for efficiently storing, managing, and retrieving data.

[1618] An "AI engine" is software or hardware for running artificial intelligence algorithms.

[1619] "Analysis" is the process of examining data in detail to find meaning and patterns.

[1620] "Indoor environment" refers to the environmental conditions of a living space, such as indoor temperature, humidity, and air quality.

[1621] "Adjustment" refers to changing a setting or state to suit specific conditions or goals.

[1622] MODE FOR CARRYING OUT THE INVENTION

[1623] This invention is a system that works in conjunction with generative AI to accumulate an individual's daily exercise volume and personal data, and based on this data, suggests dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for that individual. It also includes a function that stores environmental data acquired from smart home appliances in a database, analyzes the stored data with an AI engine, and adjusts the indoor environment based on the analysis results, taking into account the user's physical condition.

[1624] 1. Program Generation

[1625] The server generates a program for linking with smart home appliances. This program stores information such as indoor temperature and humidity obtained from the smart appliances in a database and analyzes it using an AI engine.

[1626] 2. Program processing explanation

[1627] The server collects indoor temperature and humidity data from smart home appliances such as smart thermostats and smart hygrometers. This data is stored in a database such as MySQL. The server then analyzes the stored data using an AI engine implemented in Python (e.g., TensorFlow or PyTorch). Based on the analysis results, it makes recommendations for indoor temperature control that take the user's physical condition into account.

[1628] For example, if the server determines that the user has a cold, it will control the smart thermostat to adjust the room temperature appropriately. This control is performed through the API of the smart home appliance.

[1629] 3. Examples and prompts

[1630] As a specific example, the process when it is determined that the user has a cold will be described.

[1631] 1. The server retrieves the current room temperature from the smart thermostat.

[1632] 2. The server obtains the current indoor humidity from the smart hygrometer.

[1633] 3. Store this data in a MySQL database.

[1634] 4. The server analyzes the stored data using an AI engine (e.g., TensorFlow) and determines that the user has a cold.

[1635] 5. The server sets the room temperature to an appropriate value (e.g., 22 degrees) through the smart thermostat's API.

[1636] Example prompts to input to the generative AI model:

[1637] "If the user has a cold, explain the steps to properly regulate the indoor temperature."

[1638] By inputting this prompt into a generative AI model, detailed steps based on the specific example above will be generated.

[1639] In this way, the server can comprehensively manage the user's physical condition and adjust the indoor environment appropriately. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1640] Step 1:

[1641] The server retrieves data from smart home appliances. Specifically, it sends a GET request to the API of the smart thermostat or smart hygrometer to retrieve indoor temperature and humidity data. The input is the API endpoint of the smart home appliance, and the output is the retrieved indoor temperature and humidity data.

[1642] Step 2:

[1643] The server saves the acquired data in a database. Specifically, it connects to a MySQL database and inserts the data into a table using the INSERT statement. The input is the acquired indoor temperature and humidity data, and the output is the data saved in the database.

[1644] Step 3:

[1645] The server analyzes the stored data using an AI engine. Specifically, it executes a Python script to read the data and inputs it into a machine learning model such as TensorFlow or PyTorch to obtain the analysis results. The input is the data stored in the database, and the output is the analysis results.

[1646] Step 4:

[1647] The server determines the action to take based on the analysis results. Specifically, if the AI ​​engine receives the analysis results and determines that the user has a cold, the room temperature needs to be adjusted appropriately. The input is the analysis results, and the output is the determined action.

[1648] Step 5:

[1649] The server controls smart appliances. Specifically, it sends a POST request to the smart thermostat's API to set the indoor temperature. The input is the determined action, and the output is the set indoor temperature.

[1650] In this way, the server can comprehensively manage the user's physical condition and adjust the indoor environment appropriately.

[1651] (Application example 3)

[1652] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1653] Conventional health management systems can suggest dietary improvements, stress relief methods, and appropriate exercise menus based on an individual's exercise volume and personal data, but they do not address indoor or store environment adjustments. This makes it difficult to comprehensively manage users' physical condition and comfort. Furthermore, since the environment is not adjusted to maximize customer comfort in physical stores, it is difficult to improve customer satisfaction.

[1654] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1655] In this invention, the server includes means for linking with generative AI, means for accumulating an individual's daily exercise volume and personal data, means for proposing dietary improvements, stress relief methods, and appropriate exercise menus suited to the individual based on this data, means for accumulating environmental data acquired from smart home appliances in a database and analyzing it with an AI engine, means for adjusting the room temperature taking the user's physical condition into consideration based on the analysis results, and means for acquiring environmental data within a physical store and adjusting the environment to maximize customer comfort. This makes it possible to comprehensively manage the user's physical condition and comfort and improve customer satisfaction even in physical stores.

[1656] "Generative AI" refers to artificial intelligence that generates new information and suggestions based on data.

[1657] "Individual daily exercise volume" refers to the amount of exercise an individual engages in on a daily basis, and includes data such as the number of steps taken and calories burned.

[1658] "Personal data" refers to specific information about an individual, including their health, lifestyle, preferences, etc.

[1659] "Dietary improvement suggestions" are dietary improvement methods suggested based on an individual's health condition and lifestyle habits.

[1660] "Stress relief" refers to specific methods and suggestions for reducing personal stress.

[1661] An "appropriate exercise menu" is an exercise program suggested based on an individual's health condition and physical ability.

[1662] "Smart home appliances" are home appliances that are connected to the Internet and can be remotely controlled or automatically controlled.

[1663] "Environmental data" refers to information about the indoor environment, such as temperature, humidity, and illuminance.

[1664] A "database" is a system for efficiently storing, managing, and searching data.

[1665] An "AI engine" is software or hardware used to run artificial intelligence algorithms.

[1666] "Analysis results" refer to the conclusions or recommendations reached after analyzing the data.

[1667] "Indoor temperature control" refers to control to keep the indoor temperature within an appropriate range.

[1668] A "brick and mortar store" is a commercial establishment or retail outlet that exists in a physical location.

[1669] "Customer comfort" refers to the comfort and satisfaction that customers feel in the store.

[1670] "Environmental adjustment" refers to the appropriate control of environmental factors such as temperature, humidity, and lighting.

[1671] A system for implementing the present invention has the following configuration.

[1672] System configuration

[1673] 1. Means of collaboration with generative AI

[1674] The server works in conjunction with generative AI to generate new information and suggestions based on an individual's daily exercise volume and other personal data. The generative AI is implemented using machine learning libraries such as Python and TensorFlow.

[1675] 2. A means of accumulating an individual's daily exercise volume and personal data

[1676] The server stores the exercise volume and personal data acquired from the smartwatch or smartphone in a database using a relational database such as MySQL or PostgreSQL.

[1677] 3. A means of suggesting dietary improvements, stress relief methods, and appropriate exercise menus

[1678] Based on the accumulated data, the server will propose dietary improvements, stress relief methods, and appropriate exercise menus that are suitable for each individual. These suggestions are generated by generative AI and sent to the user's smartphone or smartwatch.

[1679] 4. A means of storing environmental data obtained from smart home appliances in a database and analyzing it with an AI engine

[1680] The server collects environmental data from smart home appliances (such as thermometers and hygrometers) and stores it in a database, then analyzes it using an AI engine implemented using machine learning libraries such as Scikit-learn and Keras.

[1681] 5. A method for adjusting the indoor temperature based on the analysis results and taking into account the user's physical condition

[1682] Based on the analysis results, the server adjusts the room temperature taking into account the user's physical condition. Specifically, it controls the smart thermostat to set the room temperature appropriately. Control is performed using HTTP requests.

[1683] 6. A means of acquiring environmental data within physical stores and adjusting the environment to maximize customer comfort

[1684] The server collects environmental data such as temperature and humidity in the physical store and adjusts the environment to maximize customer comfort, thereby improving customer satisfaction.

[1685] Program processing explanation

[1686] The server collects data from smart home appliances, smartwatches, and smartphones and stores it in a database. The data is retrieved using the Python Requests library. The accumulated data is analyzed by an AI engine, and appropriate suggestions and environmental adjustments are made based on the analysis results.

[1687] For example, if the temperature in the store is 25 degrees and the humidity is 60%, and past data indicates that a customer has a cold, the room temperature will be set to 22 degrees, providing a comfortable environment for the customer.

[1688] Prompt Sentence Examples

[1689] Create a Python program that uses temperature and humidity data obtained from smart appliances to adjust the room temperature based on the customer's physical condition. Train a linear regression model using past data and analyze current data to set the room temperature to 22°C if the customer has a cold, and 24°C otherwise.

[1690] In this way, it is possible to comprehensively manage the user's physical condition and comfort, and improve customer satisfaction even in physical stores.

[1691] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1692] Step 1:

[1693] The server acquires data from smart home ap...

Claims

1. A means for acquiring biometric information of a user; An AI engine that analyzes the biometric information; A means for making a suggestion to the user based on the result of the analysis, the suggestion including suggesting a meal menu; and a means for ordering the suggested meal menu in cooperation with a food delivery service. system.

2. A means for acquiring biometric information of a user; An AI engine that analyzes the biometric information; A means for making a suggestion to the user based on the result of the analysis, the suggestion including suggesting an exercise menu; A means for adjusting an indoor environment in cooperation with smart home appliances; system.

3. The method further includes: The system according to claim 1 or 2, wherein the AI ​​engine analyzes the biometric information and the emotional information in combination.

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