System

Abstract

A system is provided.SOLUTION: A system comprising: means for registering personal data of each user; means for automatically acquiring measurement data of each user in conjunction with a fitness device and a body composition meter; means for analyzing the acquired personal data and measurement data and generating an optimal training plan and meal menu; means for notifying the user of the generated training plan and meal menu; means for inputting progress data of the user; and means for reanalyzing the input progress data and modifying the training plan and meal menu.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, health problems caused by lack of exercise and unhealthy eating habits are on the rise. However, many people face the challenge of finding optimal training plans and meal menus based on their individual physical fitness levels and dietary habits. It is particularly difficult for people who are not good at exercise or who do not know specific health management methods to determine what kind of exercise and diet are appropriate. Furthermore, the time and expense of attending a gym or sports club can often be a burden. Given these circumstances, there is a need for a system that can easily provide optimal training plans and meal menus for each individual's physical fitness and health status, and support sustainable health management. [Means for solving the problem]

[0005] The present invention provides a system including a means for registering each user's personal data, a means for automatically acquiring each user's measurement data in conjunction with a fitness device and a body composition scale, a means for analyzing the acquired personal data and measurement data to generate an optimal training plan and meal menu, a means for notifying the user of the generated training plan and meal menu, a means for inputting the user's progress data, and a means for reanalyzing the input progress data and modifying the training plan and meal menu. This automatically generates an optimal training and meal plan based on each individual's current condition, allowing users to easily continue long-term health management. Furthermore, by acquiring data from the fitness device and body composition scale via the cloud, accurate data management is possible in real time, allowing plans to be timely adjusted according to the user's goals.

[0006] "Personal data" refers to information that indicates the individual characteristics and conditions of a user, such as height, weight, past sports history, body fat percentage, age, and gender.

[0007] A "fitness device" is a device that is worn to measure and record fitness-related data such as heart rate, steps taken, and calories burned.

[0008] A "body composition monitor" is a device for measuring a user's body components such as weight, body fat percentage, muscle mass, and water content.

[0009] "Measurement data" refers to information about the user's physical condition obtained from fitness devices and body composition scales.

[0010] "Analysis" refers to the process of calculation and evaluation to evaluate the user's health condition and suggest appropriate actions based on the acquired personal data and measurement data.

[0011] A "training plan" is a specific exercise menu designed based on the user's physical fitness and health goals.

[0012] A "meal menu" is a meal plan suggested based on the user's health condition and goals.

[0013] "Notification" is an action for informing the user of the contents of a training plan or meal menu.

[0014] "Progress data" is information relating to the results of a user's execution of a training plan or meal menu.

[0015] "Reanalysis" is the process of evaluating the effectiveness of existing plans and menus based on user progress data and making modifications if necessary.

[0016] "Correction" refers to the process of updating training plans and meal menus based on the analysis results.

[0017] "Cloud" refers to a group of servers for storing and processing data over the Internet. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

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

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system for efficiently managing the health of each user, and a specific embodiment for implementing this system will be described. This system registers personal data and generates and provides optimal training plans and meal menus based on that data. The system's program processing and specific examples will be explained below.

[0040] Initial registration and data entry

[0041] The user installs the smartphone app and performs the initial registration. At this time, the user enters personal data such as height, weight, past sports history, etc. The app sends this data to the server, which receives it and stores it in a database.

[0042] Integration with fitness devices and body composition monitors

[0043] Users pair their fitness device or body composition monitor with the app to complete the connection, which then uploads the data measured by the device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud.

[0044] The server periodically retrieves measurement data from the cloud and adds and updates it to the user's database, enabling accurate real-time monitoring of health conditions.

[0045] AI-based data analysis and plan generation

[0046] An AI engine installed on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. The generated plan is saved on the server and notified to the user via their device (smartphone app).

[0047] Plan notification and execution management

[0048] The device notifies the user of daily training plans and meal menus, and the user follows the training and meal plans and enters their progress into the app.

[0049] The device sends the entered progress data to the server, which then receives it and reanalyzes it with an AI engine. This analysis evaluates the effectiveness of the training plan and meal menu, and modifies the plan if necessary.

[0050] The revised plan is then sent to the user via the device, providing optimal training and dietary information based on the user's current health status.

[0051] Specific examples

[0052] For User A:

[0053] 1. User A installs the smartphone app and enters the following personal data:

[0054] Height: 170cm

[0055] Weight: 70kg

[0056] Date of Birth: January 1, 1985

[0057] Gender: Male

[0058] Past sports history: None

[0059] 2. Pair your fitness device and body composition monitor and send the measurement data to the cloud. Let's assume the initial measurement data is as follows:

[0060] Weight: 70.2 kg

[0061] Body fat percentage: 25%

[0062] 3. The AI ​​engine generates a training plan that:

[0063] Easy jogging: 20 minutes x 3 days / week

[0064] Squats: 15 reps x 2 sets / day

[0065] 4. The AI ​​engine will suggest the following meal options:

[0066] Breakfast: Protein shake

[0067] Lunch: Salad and grilled chicken

[0068] Dinner: Steamed fish and vegetables

[0069] 5. User A goes jogging and enters the progress into the smartphone app. For example, he / she jogs for 20 minutes and records that his / her weight has increased to 70.0 kg.

[0070] 6. The server receives and analyzes the progress data, fine-tunes the next plan, and notifies User A.

[0071] By repeating this cycle, users can continuously implement optimal training plans and meal menus, enabling effective health management according to individual goals.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] A user installs the smartphone app and opens it.

[0075] Step 2:

[0076] The terminal displays an initial registration screen and prompts the user to enter personal data such as height, weight, date of birth, sex, and past sports history.

[0077] Step 3:

[0078] The user enters the required information and presses the send button.

[0079] Step 4:

[0080] The terminal transmits the input personal data to the server.

[0081] Step 5:

[0082] The server receives the data and stores it in a database along with the user ID.

[0083] Step 6:

[0084] The user acquires new measurement data (e.g., weight, body fat percentage) using a fitness device and a body composition monitor.

[0085] Step 7:

[0086] Fitness devices and body composition scales upload measurement data to the cloud.

[0087] Step 8:

[0088] The server periodically retrieves measurement data from the cloud and adds / updates it to the user's database.

[0089] Step 9:

[0090] An AI engine in the server analyzes the user's personal data and measurement data.

[0091] Step 10:

[0092] Based on the analysis results, the AI ​​engine generates a training plan and meal menu tailored to the user's goals (e.g., weight loss, muscle building).

[0093] Step 11:

[0094] The server stores the generated training plan and meal menu for each user.

[0095] Step 12:

[0096] The device sends the user daily training plans and meal menu notifications.

[0097] Step 13:

[0098] The user follows the notified content and carries out training and meals.

[0099] Step 14:

[0100] Users enter their training and dietary progress into a smartphone app.

[0101] Step 15:

[0102] The terminal transmits the input progress data to the server.

[0103] Step 16:

[0104] The server receives the progress data and the AI ​​engine reanalyzes it.

[0105] Step 17:

[0106] The AI ​​engine adjusts and modifies training plans and meal menus based on progress data.

[0107] Step 18:

[0108] The server stores the modified plan and sends it back to the device.

[0109] Step 19:

[0110] The terminal notifies the user of the modified plan.

[0111] Step 20:

[0112] The user follows the new plan, resumes training and eating, and continues to enter progress.

[0113] The above is the specific processing flow of the system, which allows users to continuously implement optimal training plans and meal menus, and effectively manage their health according to their individual goals.

[0114] Example 1

[0115] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0116] In recent years, the importance of managing exercise and diet based on individual health conditions has increased. However, conventional methods require users to manage their own health, which requires time-consuming regular data entry and progress tracking. Furthermore, it is difficult to generate optimal plans for individual goals and make ongoing adjustments, making efficient health management difficult. Therefore, there is a demand for systems that enable more efficient and accurate data collection, analysis, plan generation, and progress management.

[0117] 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.

[0118] In this invention, the server includes means for registering each user's biometric information, means for working with the detection device to automatically acquire each user's measurement data, means for analyzing the acquired biometric information and measurement data to generate an optimal exercise plan and diet plan, means for notifying the user of the generated exercise plan and diet plan, means for inputting the user's progress data, and means for reanalyzing the input progress data and correcting the exercise plan and diet plan. This allows the user to understand their own health condition in real time and continuously implement an optimal exercise and diet plan.

[0119] "Biometric information" refers to data specific to each individual user, such as their height, weight, age, gender, and past sports history.

[0120] "Detection device" refers to a device for obtaining measurement data such as a user's weight, body fat percentage, heart rate, etc.

[0121] An "exercise plan" refers to a specific exercise or training program created based on the user's health status and goals.

[0122] "Diet plan" refers to a specific diet and nutrition program created based on the user's health status and goals.

[0123] "Cloud" refers to a collective term for data storage and processing services over the internet used to send and receive measurement and plan data.

[0124] "Server" refers to a computer system that receives, stores, analyzes, and reanalyzes data.

[0125] "User" refers to an individual who uses this system to manage their own health.

[0126] "Progress data" refers to data including recorded data on the exercises and dietary details performed by the user, as well as resulting changes in weight and body fat percentage.

[0127] "Reanalysis" refers to the process of reevaluating the effectiveness of your exercise and diet plans based on the progress data you enter and adjusting the plans as necessary.

[0128] This invention is a system for efficiently managing the health of each user, and in particular, for generating and providing optimal exercise and dietary plans based on each user's biological information and measurement data. This system operates by combining multiple hardware and software components.

[0129] First, the user installs a smartphone app. Through the app, the user inputs biometric information such as their height, weight, past sports history, age, and gender. This data is sent from the device to a server, which then stores the received data in a database. This allows each user's individual biometric information to be recorded and managed.

[0130] Next, users pair their fitness devices, body composition scales, and other detection devices with the app, which automatically uploads measurement data such as weight, body fat percentage, and heart rate to the cloud. The server periodically retrieves this data and adds and updates it to a database.

[0131] The server contains an AI engine equipped with a generative AI model. This AI engine analyzes the user's biometric information and measurement data to generate optimal exercise and diet plans. Specific analysis methods include clustering, regression analysis, and machine learning algorithms (using TensorFlow or PyTorch, for example). The generated plans are stored on the server and notified to the user via a smartphone app.

[0132] The user receives the notification and executes their daily exercise and diet plans. After execution, the user enters progress data (e.g., which exercises were performed for how long, and changes in weight after weighing) into the app. This progress data is sent back to the server, which reanalyzes the received data using an AI engine. As a result of the reanalysis, the user is again notified of the newly revised exercise and diet plans.

[0133] As a concrete example, User A installs a smartphone app and inputs the following biometric information:

[0134] Height: 170cm

[0135] Weight: 70kg

[0136] Date of Birth: January 1, 1985

[0137] Gender: Male

[0138] Past sports history: None

[0139] The fitness device is paired with a body composition scale, and data on a weight of 70.2 kg and a body fat percentage of 25% is sent to the cloud. The AI ​​engine then generates an exercise plan consisting of light jogging (20 minutes x 3 days per week) and squats (15 repetitions x 2 sets per day), as well as a diet plan consisting of a protein shake for breakfast, salad and grilled chicken for lunch, and steamed fish and vegetables for dinner.

[0140] User A goes jogging and records the progress on the smartphone app as "20 minutes of jogging, weight fluctuated to 70.0 kg." The server receives the progress data, and the AI ​​engine reanalyzes it to fine-tune the next plan and notify User A.

[0141] The following prompts are examples of input to a generative AI model:

[0142] "Please suggest an appropriate exercise and diet plan for a 30-year-old male who is 170cm tall, weighs 70kg, and has no prior athletic experience. The initial measurement data is 70.2kg in weight and 25% body fat."

[0143] The above is a specific embodiment for carrying out the present invention. This system enables a user to grasp their own health condition in real time and continuously implement an optimal exercise and diet plan.

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] Step 1:

[0146] A user installs a smartphone app

[0147] A user downloads and installs the app from the App Store or Google Play. After installation, the user launches the app and proceeds to the initial setup screen.

[0148] Input: None

[0149] Output: The app is installed on your smartphone.

[0150] Step 2:

[0151] The user inputs biometric information

[0152] The user enters biometric information such as height, weight, past sports history, age, and gender on the initial setup screen of the app. The user enters each piece of data into the input form and presses the "Send" button. The device sends the entered data to the server. The server stores the received data in a database.

[0153] Input: User's height, weight, past sports history, age, gender

[0154] Output: User's biometric information stored in a database

[0155] Step 3:

[0156] The user pairs the detector with the app

[0157] Users pair fitness devices and body composition monitors via Bluetooth or Wi-Fi on the app's settings screen. Once paired, measurement data is sent from the device to the cloud.

[0158] Input: Fitness devices, body composition monitors

[0159] Output: Measurement data sent to the cloud

[0160] Step 4:

[0161] The server acquires the measurement data from the cloud.

[0162] The server accesses the cloud and periodically retrieves user measurement data. The server sends a request to the cloud service using an API call and receives the response data. The server then adds or updates the obtained data to the existing user database.

[0163] Input: Measurement data obtained from the cloud

[0164] Output: Updated user database

[0165] Step 5:

[0166] The AI ​​engine on the server analyzes the data

[0167] An AI engine located on the server analyzes the user's biometric information and measurement data and executes algorithms using machine learning libraries such as Python, TensorFlow, and PyTorch.

[0168] Input: Biometric information, measurement data

[0169] Output: Analysis results (user's physical fitness level, health condition)

[0170] Step 6:

[0171] AI engine generates exercise and diet plans

[0172] Based on the analysis results, the AI ​​engine generates an individualized exercise and diet plan. The plan is then saved on a server, which then sends it to the user's device and notifies them via a smartphone app.

[0173] Input: Analysis results

[0174] Output: Exercise plan, diet plan (saved on the server and notified via the device)

[0175] Step 7:

[0176] The user executes an exercise plan and a diet plan.

[0177] The user performs daily training and meals according to the exercise and diet plans notified by the app. After completing the plan, the user enters and saves progress data (e.g., jogging time, weight fluctuations, etc.) into the app.

[0178] Input: Exercise plan, diet plan

[0179] Output: Progress data entered into the app

[0180] Step 8:

[0181] The device sends progress data to the server

[0182] The device sends the progress data entered by the user to the server via an HTTP request, and the server receives the data and stores it in a database.

[0183] Input: Progress data

[0184] Output: Progress data stored in a database

[0185] Step 9:

[0186] The server re-parses the progress data

[0187] The server reanalyzes the received progress data using an AI engine. Based on the analysis results, the exercise plan and diet plan are evaluated and revised. The revised plan is saved back on the server and notified to the user via their device.

[0188] Input: Progress data

[0189] Output: Evaluation results, revised exercise plan, diet plan

[0190] Through these processing steps, users can understand their own health status in real time and implement optimal exercise and diet plans. This system enables effective health management according to individual health goals.

[0191] (Application example 1)

[0192] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0193] Current health management systems have difficulty accurately understanding a user's health status and continuously providing optimal training plans and meal menus. There are also challenges in efficiently managing and utilizing data from fitness devices and body composition scales. Furthermore, there is no established method for utilizing generative AI models to analyze personal and progress data and provide users with optimal plans. A new health management system is needed to solve these challenges.

[0194] 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.

[0195] In this invention, the server includes means for registering personal data of each user, means for linking with a fitness device and a body composition scale and automatically acquiring measurement data of each user, means for analyzing the acquired personal data and measurement data and generating an optimal training plan and meal menu, means for notifying the user of the generated training plan and meal menu, means for inputting user progress data, means for reanalyzing the input progress data and modifying the training plan and meal menu, means for transmitting any type of progress data of each user to the server and acquiring the progress data from a cloud service, means for analyzing the personal data and progress data using a generative AI model and generating and modifying an optimal training plan and meal menu based on prompts, and means for providing the user with visual feedback of the training plan and meal menu via a head-mounted display, thereby enabling the system to grasp the user's health condition in real time, continuously provide optimal plans, and effectively manage the user's health.

[0196] A "user" is an individual who uses this system to manage their own health.

[0197] "Personal data" refers to information about a user's physical data and lifestyle, including height, weight, age, gender, and sports history.

[0198] A "fitness device" is a device used to measure a user's exercise status and biological data, and includes smartwatches and activity monitors.

[0199] A "body composition scale" is a device that measures body composition data such as weight and body fat percentage.

[0200] "Measurement data" refers to data obtained from measuring equipment such as fitness devices and body composition monitors.

[0201] "Analysis" refers to data processing to extract meaningful information from acquired personal data and measurement data.

[0202] A "training plan" is a recommended exercise schedule for a user to improve their health.

[0203] A "meal menu" is a meal content recommended for a user to maintain or improve their health.

[0204] "Notification" is an action of communicating the generated training plan and meal menu to the user.

[0205] "Progress data" is data that records the results and status of the training and diet that the user has performed.

[0206] "Reanalysis" is the process of reanalyzing progress data to find new insights and areas for improvement.

[0207] "Cloud services" are services such as data storage and computing power provided over the internet.

[0208] A "generative AI model" is a type of artificial intelligence that uses machine learning algorithms to analyze user data and generate optimal training plans and meal menus.

[0209] A "prompt" is a sentence that gives instructions to an AI model for data analysis and plan generation.

[0210] A "head-mounted display" is a wearable device that provides visual information to the user and is primarily worn on the head.

[0211] The present invention provides a system for efficiently and effectively managing a user's health. The system is configured using a smartphone, a fitness device, a body composition monitor, a server, a cloud service, a generative AI model, and a head-mounted display. Specific embodiments are described below.

[0212] Overall system configuration

[0213] 1. User registration and initial data entry

[0214] First, users install a dedicated application on their smartphone and perform initial registration. At this time, users enter their personal data (height, weight, age, gender, sports history, etc.) into the application. This data is sent to the server and stored in a database.

[0215] 2. Integration with fitness devices and body composition monitors

[0216] Users pair their fitness devices and body composition monitors with the app, which uploads data measured by the devices (e.g., weight, body fat percentage, heart rate, etc.) to the cloud service. The server retrieves this data from the cloud service and updates the user's database.

[0217] 3. AI-based data analysis and plan generation

[0218] A generative AI model stored on the server analyzes the acquired personal and measurement data. Based on the prompts, the AI ​​generates a training plan and meal menu that is optimal for the user's fitness level and health goals. The generated plan is stored on the server and notified to the user via smartphone.

[0219] 4. Plan notification and execution management

[0220] A smartphone app notifies users of daily training plans and meal menus. Users then follow the instructions and enter their progress into the app, for example, recording their jogging time or weight fluctuations.

[0221] 5. Reanalyze progress data and revise plans

[0222] The server receives the progress data sent by the user and reanalyzes it. This reanalysis evaluates the effectiveness of the training plan and meal menu, and modifies the plan if necessary. The modified plan is then notified to the user again via smartphone.

[0223] 6. Providing visual feedback

[0224] Additionally, a head-mounted display is used to provide users with visual feedback on their training plan and meal menu, helping them better understand the plan and motivating them to follow it.

[0225] Hardware and software used

[0226] Hardware: Smartphones, fitness devices, body composition monitors, head-mounted displays

[0227] Software: Dedicated smartphone app, cloud service, generative AI model

[0228] Specific examples

[0229] Initial user data entry

[0230] A user installs a smartphone app and enters the following data:

[0231] Height: 175cm

[0232] Weight: 72kg

[0233] Date of Birth: February 15, 1990

[0234] Gender: Female

[0235] Sports history: Occasional jogging

[0236] Prompt Sentence Examples

[0237] "Analyze user data and generate personalized training plans and meal menus. Personal data: Height 175cm, Weight 72kg, Date of birth 1990-02-15, Gender Female, Sports history Occasional jogging. Device data: Weight 72.5kg, Body fat 22%, Heart rate 78."

[0238] This invention makes it possible to grasp the user's health condition in real time and provide optimal training plans and meal menus at all times, thereby enabling the user to effectively manage their health.

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1:

[0241] The user installs the smartphone app, performs initial registration, and enters personal data.

[0242] Specific operation: The user uses a smartphone to enter personal data such as height, weight, age, gender, and sports history, and sends it to the server.

[0243] Input: Personal data (height, weight, age, gender, sports history)

[0244] Output: Personal data of the user registered on the server

[0245] Step 2:

[0246] The user pairs the fitness device and body composition scale with the app to obtain measurement data.

[0247] Specific operation: The user pairs the fitness device and body composition scale with their smartphone and uploads the data to the cloud service. The server retrieves the measurement data from the cloud and stores it in a database.

[0248] Input: Measurement data (weight, body fat percentage, heart rate)

[0249] Output: Measurement data stored on the server

[0250] Step 3:

[0251] The server analyzes personal data and measurement data to generate optimal training plans and meal menus.

[0252] How it works: The generative AI model on the server analyzes personal and measurement data. Based on prompts, the AI ​​generates optimal training plans and meal menus.

[0253] Input: Personal data, measurement data, prompt text

[0254] Output: Generated training plan and meal menu

[0255] Step 4:

[0256] The generated training plan and meal menu are notified to the user.

[0257] Specific operation: The server sends the generated training plan and meal menu to the smartphone app and notifies the user.

[0258] Input: Generated training plan and meal menu

[0259] Output: Training plan and meal menu notified to the user

[0260] Step 5:

[0261] The user follows a training plan and meal menu and enters progress data.

[0262] Specific operation: The user enters training and diet progress (e.g., jogging time, weight fluctuations) into the smartphone app and sends it to the server.

[0263] Input: Progress data (training time, weight fluctuations, etc.)

[0264] Output: Progress data stored on the server

[0265] Step 6:

[0266] The server reanalyzes the progress data and adjusts the training plan and meal menu.

[0267] What it does: The server's generative AI model reanalyzes the progress data and modifies the training plan and meal menu as needed. The modified plan is then notified to the user again.

[0268] Input: Progress data, current training plan and meal menu

[0269] Output: Modified training plan and meal menu

[0270] Step 7:

[0271] A head-mounted display is used to provide the user with visual feedback of their training plan and meal menu.

[0272] Specific operation: The server generates a training plan and sends visual feedback of the meal menu to the head-mounted display. The user receives the feedback using the head-mounted display.

[0273] Input: Training plan and meal menu

[0274] Output: Visual feedback provided to the user

[0275] 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.

[0276] The present invention combines a system that manages each user's personal data and measurement data and proposes optimal training plans and meal menus with an emotion engine that recognizes the user's emotions. This system can support effective health management while taking into account the user's health and emotional state. The system's program processing and specific examples are explained below.

[0277] Initial registration and data entry

[0278] The user installs the smartphone app and performs the initial registration. At this time, the user enters personal data such as height, weight, past sports history, etc. The app sends this data to the server, which receives it and stores it in a database.

[0279] Integration with fitness devices and body composition monitors

[0280] Users pair their fitness device or body composition monitor with the app to complete the connection, which then uploads the data measured by the device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud.

[0281] The server periodically retrieves measurement data from the cloud and adds and updates it to the user's database, enabling accurate real-time monitoring of health conditions.

[0282] AI and emotion engine for data analysis and plan generation

[0283] An AI engine located on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. At the same time, an emotion engine acquires and analyzes the user's emotional data and adjusts the plan based on their emotional state (e.g., stress, fatigue). The generated plan is stored on the server and notified to the user via their device (smartphone app).

[0284] Plan notification and execution management

[0285] The device notifies the user of daily training plans and meal menus, and the user follows the training and meal plans and enters their progress and emotional state into the app.

[0286] The device sends the entered progress and emotional data to the server. The server receives the progress and emotional data, which is then reanalyzed by the AI ​​engine and emotion engine. This analysis evaluates the effectiveness of the training plan and meal menu, and the plan is revised as necessary. The revised plan is then notified to the user again via the device. This ensures that optimal training and meals are always provided based on the user's latest health and emotional state.

[0287] Specific examples

[0288] For User B:

[0289] 1. User B installs the smartphone app and enters the following personal data:

[0290] Height: 165cm

[0291] Weight: 60kg

[0292] Date of Birth: June 15, 1990

[0293] Gender: Female

[0294] Past sports experience: 3 years of yoga experience

[0295] 2. Pair your fitness device and body composition monitor and send the measurement data to the cloud. Let's assume the initial measurement data is as follows:

[0296] Weight: 60.5 kg

[0297] Body fat percentage: 22%

[0298] 3. The AI ​​engine generates a training plan that:

[0299] Yoga: 30 minutes x 3 days / week

[0300] Walking: 20 minutes x 3 days / week

[0301] 4. The AI ​​engine will suggest the following meal options:

[0302] Breakfast: Fruit smoothie

[0303] Lunch: Salad bowl

[0304] Dinner: Grilled chicken and vegetables

[0305] 5. User B performs training and diet and enters their progress and emotional state into the smartphone app. For example, on a day when they feel high stress, they record their emotional state as "high stress."

[0306] 6. The server receives and analyzes the progress data and emotional data, fine-tunes the next plan, and notifies User B. On days when stress is high, adjustments may be made, such as increasing the number of yoga sessions to promote relaxation.

[0307] By repeating this cycle, users can continuously implement optimal training plans and meal menus, and manage their health, including their emotional state, enabling effective health management tailored to individual goals.

[0308] The processing flow will be explained below.

[0309] Step 1:

[0310] A user installs the smartphone app and opens it.

[0311] Step 2:

[0312] The terminal displays an initial registration screen and prompts the user to enter personal data such as height, weight, date of birth, sex, and past sports history.

[0313] Step 3:

[0314] The user enters the required information and presses the send button.

[0315] Step 4:

[0316] The terminal transmits the input personal data to the server.

[0317] Step 5:

[0318] The server receives the data and stores it in a database along with the user ID.

[0319] Step 6:

[0320] The user acquires new measurement data (e.g., weight, body fat percentage) using a fitness device and a body composition monitor.

[0321] Step 7:

[0322] Fitness devices and body composition scales upload measurement data to the cloud.

[0323] Step 8:

[0324] The server periodically retrieves measurement data from the cloud and adds / updates it to the user's database.

[0325] Step 9:

[0326] An AI engine in the server analyzes the user's personal data and measurement data.

[0327] Step 10:

[0328] Based on the analysis results, the AI ​​engine generates a training plan and meal menu tailored to the user's goals (e.g., weight loss, muscle building).

[0329] Step 11:

[0330] The emotion engine acquires and analyzes the user's emotion data and evaluates their emotional state (e.g., stress, fatigue).

[0331] Step 12:

[0332] The AI ​​engine and emotion engine work together to modify training plans and meal menus taking into account the user's emotional state.

[0333] Step 13:

[0334] The server stores the generated training plan and meal menu for each user.

[0335] Step 14:

[0336] The device sends the user daily training plans and meal menu notifications.

[0337] Step 15:

[0338] The user follows the notified content and carries out training and meals.

[0339] Step 16:

[0340] Users enter their training and diet progress and emotional state into a smartphone app.

[0341] Step 17:

[0342] The terminal transmits the input progress data and emotion data to the server.

[0343] Step 18:

[0344] The server receives the progress data and emotion data, which are then reanalyzed by the AI ​​engine and emotion engine.

[0345] Step 19:

[0346] The AI ​​engine adjusts and modifies training plans and meal menus based on progress data.

[0347] Step 20:

[0348] The emotion engine re-evaluates the emotion data and makes modifications that take into account the user's emotional state.

[0349] Step 21:

[0350] The server stores the modified plan and sends it back to the device.

[0351] Step 22:

[0352] The terminal notifies the user of the modified plan.

[0353] Step 23:

[0354] The user follows the new plan, again training and eating, and continually inputs their progress and emotional state.

[0355] The above is the specific processing flow of the system. This allows users to continuously implement optimal training plans and meal menus, and effectively manage their health according to their individual goals. Furthermore, by taking the user's emotional state into consideration, more appropriate and sustainable health management can be achieved.

[0356] Example 2

[0357] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0358] Conventional health management systems often provide uniform exercise programs and meal menus without considering the personal data or emotional state of each individual user. As a result, plans tailored to the user's health and emotional state are not provided, making effective health management difficult. In addition, there are few ways to effectively integrate and analyze data from fitness devices and body composition monitors, making it difficult to adjust training plans and meal menus in real time.

[0359] 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.

[0360] In this invention, the server includes means for registering personal data of each user, means for automatically acquiring measurement data of each user in conjunction with the measurement device and body composition monitor, means for analyzing the acquired personal data and measurement data to generate an optimal exercise program and meal menu, and means for analyzing the user's emotional data and adjusting the exercise program and meal menu based on the user's emotional state. This allows for the provision of an exercise program and meal menu optimized for each individual user, enabling effective health management that takes into account the user's emotional state.

[0361] "Personal data" refers to basic information about an individual user, such as the user's height, weight, date of birth, gender, and past sports history.

[0362] "Measuring device" is a general term for equipment used to measure a user's health status, such as weight, body fat percentage, and heart rate.

[0363] A "body composition scale" is a device that measures data related to a user's body composition, such as body fat percentage and muscle mass.

[0364] "Emotion data" is information related to emotions such as the user's stress level or fatigue.

[0365] An "exercise program" is a training plan designed based on the user's health and goals.

[0366] A "meal menu" is a meal plan suggested based on the user's health management and fitness goals.

[0367] The "notification means" is a means for transmitting information such as the generated exercise program and meal menu to the user.

[0368] A "server" is a central processing unit that stores and analyzes data through cloud services.

[0369] "Analysis methods" refer to algorithms and software that derive optimal exercise programs and meal menus based on acquired personal data and measurement data.

[0370] The "modification means" is a means for readjusting the exercise program and meal menu based on the user's progress data and emotional data.

[0371] The present invention combines a system that manages each user's personal data and measurement data and suggests optimal exercise programs and meal plans with an emotion engine that recognizes the user's emotions. This system can support effective health management while taking into account the user's health and emotional state.

[0372] Initial registration and data entry

[0373] First, the user installs the smartphone app and performs initial registration within the app. At this time, the user enters personal data such as height, weight, date of birth, gender, and past sports history. The entered data is sent from the device to the server, which then stores it in a database.

[0374] Integration with fitness devices and body composition monitors

[0375] When users pair their fitness devices or body composition monitors with a smartphone app, the data measured by these devices (such as weight, body fat percentage, heart rate, etc.) is automatically uploaded to the cloud. The server periodically retrieves new measurement data from the cloud and adds and updates it to the user's database. This collaboration allows users to accurately understand their health status in real time.

[0376] AI and emotion engine for data analysis and plan generation

[0377] The AI ​​engine located on the server analyzes the acquired personal and measurement data using programs such as Python. Based on this analysis, the AI ​​generates an optimal exercise program and meal menu based on the user's physical fitness level and goals. At the same time, the emotion engine analyzes the user's emotional data (e.g., stress, fatigue) and adjusts the plan based on that emotional state. The generated plan is stored on the server and notified to the user via their device.

[0378] Plan notification and execution management

[0379] The device notifies the user of their daily exercise program and meal menu. The user then exercises and eats according to the notified information, and enters their progress and emotional state into the app. For example, on a day when they feel high stress, they record their emotional state as "high stress." The device then sends the entered progress and emotional data to the server. The server receives this data, and the AI ​​engine and emotion engine reanalyze it. Based on the results of this analysis, the effectiveness of the exercise program and meal menu is evaluated, and the plan is revised if necessary. The revised plan is then notified to the user again via the device.

[0380] Specific examples

[0381] User B's case

[0382] 1. User B installs the smartphone app and enters the following personal data:

[0383] Height: 165cm

[0384] Weight: 60kg

[0385] Date of Birth: June 15, 1990

[0386] Gender: Female

[0387] Past sports experience: 3 years of yoga experience

[0388] 2. User B pairs the fitness device and body composition monitor and sends the measurement data to the cloud. The initial measurement data is as follows:

[0389] Weight: 60.5 kg

[0390] Body fat percentage: 22%

[0391] 3. The AI ​​engine generates the following exercise program:

[0392] Yoga: 30 minutes x 3 days / week

[0393] Walking: 20 minutes x 3 days / week

[0394] 4. The AI ​​engine will suggest the following meal options:

[0395] Breakfast: Fruit smoothie

[0396] Lunch: Salad bowl

[0397] Dinner: Grilled chicken and vegetables

[0398] 5. User B exercises and eats, and enters their progress and emotional state into the smartphone app. For example, on a day when they feel stressed, they record their emotional state as "high stress."

[0399] 6. The server receives and analyzes the progress data and emotional data, fine-tunes the next plan, and notifies User B. For example, on days when stress is high, adjustments may be made such as increasing the number of yoga plans to promote relaxation.

[0400] By repeating this cycle, users can continuously implement optimal exercise programs and meal menus, and manage their health, including their emotional state.

[0401] Example input to a generative AI model

[0402] Example prompt sentence:

[0403] "Enter the user's personal data (height, weight, sports history, etc.) and measurement data (weight, body fat percentage, heart rate, etc.) and use the emotion engine to analyze the user's emotional state. Generate appropriate exercise program and meal menu suggestions."

[0404] By inputting this prompt into a generative AI model, specific suggestions can be obtained, including exercise programs and meal menus optimized for each individual user.

[0405] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0406] Step 1:

[0407] The user installs the smartphone app and launches it. The user selects the "New Registration" option within the app and enters personal data (e.g., height, weight, date of birth, gender, past sports history, etc.). The entered data is sent from the device to the server. The specific input data is height 165 cm, weight 60 kg, date of birth June 15, 1990, gender female, past sports history 3 years of yoga experience. The server stores the received data in a database. The output is a state in which the user's personal data has been registered in the database.

[0408] Step 2:

[0409] The user pairs the measurement device and body composition monitor with a smartphone app. This uploads data measured by the measurement device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud. The server periodically retrieves new measurement data from the cloud. The specific input data is the initial measurement results: weight 60.5 kg, body fat percentage 22%, heart rate 75 bpm. The output is the latest measurement data added and updated to the user's database.

[0410] Step 3:

[0411] The acquired personal and measurement data is analyzed using an AI engine located on a server. Specifically, a Python program is used to generate an optimal exercise program and meal menu based on the user's physical fitness level and goals. Specific input data includes height 165 cm, weight 60.5 kg, body fat percentage 22%, heart rate 75 bpm, and three years of yoga experience. The output is the generated exercise program (30 minutes of yoga three days per week, 20 minutes of walking three days per week) and meal menu (breakfast: fruit smoothie, lunch: salad bowl, dinner: grilled chicken and vegetables).

[0412] Step 4:

[0413] The server uses the emotion engine to analyze the user's emotional data (e.g., stress level, fatigue level) and adjusts the exercise program and meal menu. The specific input data is the emotional data entered by the user into the app. For example, a high-stress day is recorded as "high stress." The output is an exercise program and meal menu adjusted based on the emotional state. For example, on a high-stress day, yoga sessions to promote relaxation are increased.

[0414] Step 5:

[0415] The device notifies the user of their daily exercise program and meal menu. Specifically, a smartphone app uses push notifications to notify the user of today's exercise program (e.g., 30 minutes of yoga, 20 minutes of walking) and meal menu (e.g., fruit smoothie, salad bowl, grilled chicken and vegetables). The input data are the exercise program and meal menu stored on the server. The output is the state in which the user has received the notification.

[0416] Step 6:

[0417] The user follows the notified exercise program and meal menu and inputs their progress and emotional state into the smartphone app. Specific input data includes the completion of today's yoga session (e.g., "30 minutes of yoga completed") and their emotional state (e.g., "high stress"). The output is the state in which the input progress data and emotional data are recorded in the app.

[0418] Step 7:

[0419] The device encrypts the input progress data and emotion data in real time and sends it to the server. The input data is the progress data and emotion data entered by the user into the app. The output is the state in which this data is stored on the server.

[0420] Step 8:

[0421] The AI ​​engine and emotion engine reanalyze the progress and emotion data received by the server. The specific input data is the user's progress and emotion data. The output is a revised exercise program and meal menu.

[0422] Step 9:

[0423] The server then makes any necessary modifications to the exercise program and meal menu based on the analysis results. Specific input data are the reanalyzed exercise program and meal menu modifications. The output is the modified exercise program and meal menu.

[0424] Step 10:

[0425] The server saves the revised exercise program and meal menu in the database again and notifies the user via the terminal. The input data is the revised exercise program and meal menu. The output is a state in which the user has received the latest exercise program and meal menu.

[0426] Through these steps, users can continuously receive optimal exercise programs and meal menus based on their individual health and emotional state.

[0427] (Application example 2)

[0428] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0429] Many conventional health management systems propose training plans and meal menus based on the user's personal and fitness data. However, these systems do not take the user's emotional state into account, and the proposed plans and menus are not necessarily optimal for the user's current situation. Furthermore, even if a healthy meal menu is proposed, it is difficult to quickly prepare the ingredients, making it difficult to apply the system to real situations.

[0430] 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.

[0431] In this invention, the server includes means for registering personal data of each user, means for linking with the fitness device and body composition scale to automatically acquire measurement data of each user, means for analyzing the acquired personal data and measurement data to generate an optimal training plan and meal menu, means for notifying the user of the generated training plan and meal menu, means for inputting progress data and emotional data of the user, means for reanalyzing the input progress data and emotional data to modify the training plan and meal menu, and means for generating an optimal meal menu based on the emotional data and providing ingredients based on the meal menu in cooperation with a delivery service. This enables more precise health management based on the user's health and emotional state, and further enables the ingredients for the suggested meal menu to be quickly obtained, making it possible to implement the menu in line with reality.

[0432] "Personal data" refers to information about an individual, such as the user's height, weight, age, sex, and past sports activities.

[0433] A "fitness device" is a device for acquiring fitness-related data such as a user's weight, heart rate, and number of steps.

[0434] A "body composition scale" is a device that measures not only weight but also body composition such as body fat percentage, muscle mass, and water content.

[0435] "Measurement data" refers to data relating to the user's health condition obtained using a fitness device and a body composition monitor.

[0436] A "training plan" is a workout or exercise plan generated based on the user's health status and goals.

[0437] A "meal menu" is a daily meal plan suggested based on the user's health condition and nutritional needs.

[0438] "Emotion data" is data that indicates the user's mental state, such as stress or fatigue.

[0439] A "delivery service" is a service that delivers goods, especially ingredients and food, to a specific location.

[0440] This invention combines a system that manages a user's personal data and measurement data and proposes optimal training plans and meal menus with an emotion engine. This system is designed to support effective health management based on the user's health and emotional state.

[0441] Initial registration and data entry

[0442] First, users install the smartphone app and perform initial registration, entering personal data such as height, weight, and past sports history. The device then sends this data to the server and stores it in a database.

[0443] Integration with fitness devices and body composition monitors

[0444] Users pair the app with fitness devices or body composition scales and periodically upload measurement data to the cloud. The server retrieves the measurement data from the cloud and updates the database, enabling accurate understanding of health status in real time.

[0445] AI and emotion engine for data analysis and plan generation

[0446] The AI ​​engine located on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. At the same time, the emotion engine acquires and analyzes the user's emotional data and adjusts the plan based on their emotional state (e.g., stress, fatigue). The generated plan is stored on the server and notified to the user via their device.

[0447] Plan notification and execution management

[0448] The device notifies the user of daily training plans and meal menus. The user then follows the training and meal plans and enters their progress and emotional state into the app. The device then sends the entered progress and emotional data to the server. The server receives the progress and emotional data, and the AI ​​engine and emotion engine reanalyze them to evaluate the effectiveness of the training plan and meal menu, and modify the plan as necessary.

[0449] Collaboration with food delivery companies

[0450] Based on the emotion data, the server generates an optimal meal menu and provides ingredients based on the menu in cooperation with a delivery service. Users can quickly obtain ingredients for the suggested meal menu, reducing the effort required for preparation and cooking.

[0451] Specific examples

[0452] User B's case

[0453] User B installs the smartphone app and enters personal data such as height 165 cm, weight 60 kg, and three years of yoga experience. The fitness device and body composition scale are paired, and the measurement data is sent to the server. The AI ​​engine generates a training plan of 30 minutes of yoga three days per week and 20 minutes of walking three days per week, as well as meal menus such as "fruit smoothie," "salad bowl," and "grilled chicken and vegetables." User B records his or her emotional state, entering, for example, "high stress," and the server adjusts the menu to one with a relaxing effect and provides the ingredients via a delivery service.

[0454] Example prompts for generative AI models

[0455] "I'm a 165cm, 60kg woman with three years of yoga experience. I've been feeling a bit stressed lately. Please use this data to suggest the best training plan and meal menu, and provide the ingredients via a delivery service."

[0456] This allows users to always practice optimal training and diet based on their latest health and emotional state, realizing sustainable health management.

[0457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0458] Step 1:

[0459] The user installs the smartphone app and performs initial registration. The personal data entered (height, weight, past sports history, etc.) is sent from the device to the server and stored in a database. This registration allows the system to grasp the user's basic information.

[0460] Step 2:

[0461] Users pair their fitness devices or body composition scales with the app. The paired devices periodically upload measurement data (weight, body fat percentage, heart rate, etc.) to the cloud, where the server collects and stores the data. This allows users to accurately understand their health status in real time.

[0462] Step 3:

[0463] The AI ​​engine located on the server analyzes the acquired personal data and measurement data. For example, based on data such as height, weight, and heart rate, it generates a training plan and meal menu according to the user's physical fitness level and goals. The input for this process is the personal data and measurement data, and the output is a training plan and meal menu.

[0464] Step 4:

[0465] At the same time, the emotion engine acquires and analyzes the user's emotional data. The input emotional data could be information such as "high stress," and the emotion engine uses this information to analyze the user's emotional state and output an appropriate result. The emotion analysis results obtained here are used to adjust the training plan and meal menu.

[0466] Step 5:

[0467] The server notifies the generated training plan and meal menu to the terminal, and the terminal notifies the user of the daily training and meal menu, allowing the user to understand the tasks to be performed.

[0468] Step 6:

[0469] The user follows the training and meal menu provided and enters their progress and emotional state into the app. The progress and emotional data entered through this operation is then sent from the device to the server.

[0470] Step 7:

[0471] The server re-analyzes the received progress and emotion data. The AI ​​engine and emotion engine use this data to evaluate the effectiveness of the training plan and meal menu and modify the plan as necessary. The input of this re-analysis is the progress and emotion data, and the output is the modified plan.

[0472] Step 8:

[0473] Based on the emotion data, the server generates an optimal meal menu. The server then provides ingredients based on the generated menu in cooperation with a delivery service. The delivery service delivers the ingredients to the user, allowing the user to quickly complete the suggested menu.

[0474] 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.

[0475] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0476] 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.

[0477] [Second embodiment]

[0478] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0479] 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.

[0480] 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).

[0481] 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.

[0482] 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.

[0483] 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).

[0484] 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.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0490] The present invention is a system for efficiently managing the health of each user, and a specific embodiment for implementing this system will be described. This system registers personal data and generates and provides optimal training plans and meal menus based on that data. The system's program processing and specific examples will be explained below.

[0491] Initial registration and data entry

[0492] The user installs the smartphone app and performs the initial registration. At this time, the user enters personal data such as height, weight, past sports history, etc. The app sends this data to the server, which receives it and stores it in a database.

[0493] Integration with fitness devices and body composition monitors

[0494] Users pair their fitness device or body composition monitor with the app to complete the connection, which then uploads the data measured by the device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud.

[0495] The server periodically retrieves measurement data from the cloud and adds and updates it to the user's database, enabling accurate real-time monitoring of health conditions.

[0496] AI-based data analysis and plan generation

[0497] An AI engine installed on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. The generated plan is saved on the server and notified to the user via their device (smartphone app).

[0498] Plan notification and execution management

[0499] The device notifies the user of daily training plans and meal menus, and the user follows the training and meal plans and enters their progress into the app.

[0500] The device sends the entered progress data to the server, which then receives it and reanalyzes it with an AI engine. This analysis evaluates the effectiveness of the training plan and meal menu, and modifies the plan if necessary.

[0501] The revised plan is then sent to the user via the device, providing optimal training and dietary information based on the user's current health status.

[0502] Specific examples

[0503] For User A:

[0504] 1. User A installs the smartphone app and enters the following personal data:

[0505] Height: 170cm

[0506] Weight: 70kg

[0507] Date of Birth: January 1, 1985

[0508] Gender: Male

[0509] Past sports history: None

[0510] 2. Pair your fitness device and body composition monitor and send the measurement data to the cloud. Let's assume the initial measurement data is as follows:

[0511] Weight: 70.2 kg

[0512] Body fat percentage: 25%

[0513] 3. The AI ​​engine generates a training plan that:

[0514] Easy jogging: 20 minutes x 3 days / week

[0515] Squats: 15 reps x 2 sets / day

[0516] 4. The AI ​​engine will suggest the following meal options:

[0517] Breakfast: Protein shake

[0518] Lunch: Salad and grilled chicken

[0519] Dinner: Steamed fish and vegetables

[0520] 5. User A goes jogging and enters the progress into the smartphone app. For example, he / she jogs for 20 minutes and records that his / her weight has increased to 70.0 kg.

[0521] 6. The server receives and analyzes the progress data, fine-tunes the next plan, and notifies User A.

[0522] By repeating this cycle, users can continuously implement optimal training plans and meal menus, enabling effective health management according to individual goals.

[0523] The processing flow will be explained below.

[0524] Step 1:

[0525] A user installs the smartphone app and opens it.

[0526] Step 2:

[0527] The terminal displays an initial registration screen and prompts the user to enter personal data such as height, weight, date of birth, sex, and past sports history.

[0528] Step 3:

[0529] The user enters the required information and presses the send button.

[0530] Step 4:

[0531] The terminal transmits the input personal data to the server.

[0532] Step 5:

[0533] The server receives the data and stores it in a database along with the user ID.

[0534] Step 6:

[0535] The user acquires new measurement data (e.g., weight, body fat percentage) using a fitness device and a body composition monitor.

[0536] Step 7:

[0537] Fitness devices and body composition scales upload measurement data to the cloud.

[0538] Step 8:

[0539] The server periodically retrieves measurement data from the cloud and adds / updates it to the user's database.

[0540] Step 9:

[0541] An AI engine in the server analyzes the user's personal data and measurement data.

[0542] Step 10:

[0543] Based on the analysis results, the AI ​​engine generates a training plan and meal menu tailored to the user's goals (e.g., weight loss, muscle building).

[0544] Step 11:

[0545] The server stores the generated training plan and meal menu for each user.

[0546] Step 12:

[0547] The device sends the user daily training plans and meal menu notifications.

[0548] Step 13:

[0549] The user follows the notified content and carries out training and meals.

[0550] Step 14:

[0551] Users enter their training and dietary progress into a smartphone app.

[0552] Step 15:

[0553] The terminal transmits the input progress data to the server.

[0554] Step 16:

[0555] The server receives the progress data and the AI ​​engine reanalyzes it.

[0556] Step 17:

[0557] The AI ​​engine adjusts and modifies training plans and meal menus based on progress data.

[0558] Step 18:

[0559] The server stores the modified plan and sends it back to the device.

[0560] Step 19:

[0561] The terminal notifies the user of the modified plan.

[0562] Step 20:

[0563] The user follows the new plan, resumes training and eating, and continues to enter progress.

[0564] The above is the specific processing flow of the system, which allows users to continuously implement optimal training plans and meal menus, and effectively manage their health according to their individual goals.

[0565] Example 1

[0566] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0567] In recent years, the importance of managing exercise and diet based on individual health conditions has increased. However, conventional methods require users to manage their own health, which requires time-consuming regular data entry and progress tracking. Furthermore, it is difficult to generate optimal plans for individual goals and make ongoing adjustments, making efficient health management difficult. Therefore, there is a demand for systems that enable more efficient and accurate data collection, analysis, plan generation, and progress management.

[0568] 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.

[0569] In this invention, the server includes means for registering each user's biometric information, means for working with the detection device to automatically acquire each user's measurement data, means for analyzing the acquired biometric information and measurement data to generate an optimal exercise plan and diet plan, means for notifying the user of the generated exercise plan and diet plan, means for inputting the user's progress data, and means for reanalyzing the input progress data and correcting the exercise plan and diet plan. This allows the user to understand their own health condition in real time and continuously implement an optimal exercise and diet plan.

[0570] "Biometric information" refers to data specific to each individual user, such as their height, weight, age, gender, and past sports history.

[0571] "Detection device" refers to a device for obtaining measurement data such as a user's weight, body fat percentage, heart rate, etc.

[0572] An "exercise plan" refers to a specific exercise or training program created based on the user's health status and goals.

[0573] "Diet plan" refers to a specific diet and nutrition program created based on the user's health status and goals.

[0574] "Cloud" refers to a collective term for data storage and processing services over the internet used to send and receive measurement and plan data.

[0575] "Server" refers to a computer system that receives, stores, analyzes, and reanalyzes data.

[0576] "User" refers to an individual who uses this system to manage their own health.

[0577] "Progress data" refers to data including recorded data on the exercises and dietary details performed by the user, as well as resulting changes in weight and body fat percentage.

[0578] "Reanalysis" refers to the process of reevaluating the effectiveness of your exercise and diet plans based on the progress data you enter and adjusting the plans as necessary.

[0579] This invention is a system for efficiently managing the health of each user, and in particular, for generating and providing optimal exercise and dietary plans based on each user's biological information and measurement data. This system operates by combining multiple hardware and software components.

[0580] First, the user installs a smartphone app. Through the app, the user inputs biometric information such as their height, weight, past sports history, age, and gender. This data is sent from the device to a server, which then stores the received data in a database. This allows each user's individual biometric information to be recorded and managed.

[0581] Next, users pair their fitness devices, body composition scales, and other detection devices with the app, which automatically uploads measurement data such as weight, body fat percentage, and heart rate to the cloud. The server periodically retrieves this data and adds and updates it to a database.

[0582] The server contains an AI engine equipped with a generative AI model. This AI engine analyzes the user's biometric information and measurement data to generate optimal exercise and diet plans. Specific analysis methods include clustering, regression analysis, and machine learning algorithms (using TensorFlow or PyTorch, for example). The generated plans are stored on the server and notified to the user via a smartphone app.

[0583] The user receives the notification and executes their daily exercise and diet plans. After execution, the user enters progress data (e.g., which exercises were performed for how long, and changes in weight after weighing) into the app. This progress data is sent back to the server, which reanalyzes the received data using an AI engine. As a result of the reanalysis, the user is again notified of the newly revised exercise and diet plans.

[0584] As a concrete example, User A installs a smartphone app and inputs the following biometric information:

[0585] Height: 170cm

[0586] Weight: 70kg

[0587] Date of Birth: January 1, 1985

[0588] Gender: Male

[0589] Past sports history: None

[0590] The fitness device is paired with a body composition scale, and data on a weight of 70.2 kg and a body fat percentage of 25% is sent to the cloud. The AI ​​engine then generates an exercise plan consisting of light jogging (20 minutes x 3 days per week) and squats (15 repetitions x 2 sets per day), as well as a diet plan consisting of a protein shake for breakfast, salad and grilled chicken for lunch, and steamed fish and vegetables for dinner.

[0591] User A goes jogging and records the progress on the smartphone app as "20 minutes of jogging, weight fluctuated to 70.0 kg." The server receives the progress data, and the AI ​​engine reanalyzes it to fine-tune the next plan and notify User A.

[0592] The following prompts are examples of input to a generative AI model:

[0593] "Please suggest an appropriate exercise and diet plan for a 30-year-old male who is 170cm tall, weighs 70kg, and has no prior athletic experience. The initial measurement data is 70.2kg in weight and 25% body fat."

[0594] The above is a specific embodiment for carrying out the present invention. This system enables a user to grasp their own health condition in real time and continuously implement an optimal exercise and diet plan.

[0595] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0596] Step 1:

[0597] A user installs a smartphone app

[0598] A user downloads and installs the app from the App Store or Google Play. After installation, the user launches the app and proceeds to the initial setup screen.

[0599] Input: None

[0600] Output: The app is installed on your smartphone.

[0601] Step 2:

[0602] The user inputs biometric information

[0603] The user enters biometric information such as height, weight, past sports history, age, and gender on the initial setup screen of the app. The user enters each piece of data into the input form and presses the "Send" button. The device sends the entered data to the server. The server stores the received data in a database.

[0604] Input: User's height, weight, past sports history, age, gender

[0605] Output: User's biometric information stored in a database

[0606] Step 3:

[0607] The user pairs the detector with the app

[0608] Users pair fitness devices and body composition monitors via Bluetooth or Wi-Fi on the app's settings screen. Once paired, measurement data is sent from the device to the cloud.

[0609] Input: Fitness devices, body composition monitors

[0610] Output: Measurement data sent to the cloud

[0611] Step 4:

[0612] The server acquires the measurement data from the cloud.

[0613] The server accesses the cloud and periodically retrieves user measurement data. The server sends a request to the cloud service using an API call and receives the response data. The server then adds or updates the obtained data to the existing user database.

[0614] Input: Measurement data obtained from the cloud

[0615] Output: Updated user database

[0616] Step 5:

[0617] The AI ​​engine on the server analyzes the data

[0618] An AI engine located on the server analyzes the user's biometric information and measurement data and executes algorithms using machine learning libraries such as Python, TensorFlow, and PyTorch.

[0619] Input: Biometric information, measurement data

[0620] Output: Analysis results (user's physical fitness level, health condition)

[0621] Step 6:

[0622] AI engine generates exercise and diet plans

[0623] Based on the analysis results, the AI ​​engine generates an individualized exercise and diet plan. The plan is then saved on a server, which then sends it to the user's device and notifies them via a smartphone app.

[0624] Input: Analysis results

[0625] Output: Exercise plan, diet plan (saved on the server and notified via the device)

[0626] Step 7:

[0627] The user executes an exercise plan and a diet plan.

[0628] The user performs daily training and meals according to the exercise and diet plans notified by the app. After completing the plan, the user enters and saves progress data (e.g., jogging time, weight fluctuations, etc.) into the app.

[0629] Input: Exercise plan, diet plan

[0630] Output: Progress data entered into the app

[0631] Step 8:

[0632] The device sends progress data to the server

[0633] The device sends the progress data entered by the user to the server via an HTTP request, and the server receives the data and stores it in a database.

[0634] Input: Progress data

[0635] Output: Progress data stored in a database

[0636] Step 9:

[0637] The server re-parses the progress data

[0638] The server reanalyzes the received progress data using an AI engine. Based on the analysis results, the exercise plan and diet plan are evaluated and revised. The revised plan is saved back on the server and notified to the user via their device.

[0639] Input: Progress data

[0640] Output: Evaluation results, revised exercise plan, diet plan

[0641] Through these processing steps, users can understand their own health status in real time and implement optimal exercise and diet plans. This system enables effective health management according to individual health goals.

[0642] (Application example 1)

[0643] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0644] Current health management systems have difficulty accurately understanding a user's health status and continuously providing optimal training plans and meal menus. There are also challenges in efficiently managing and utilizing data from fitness devices and body composition scales. Furthermore, there is no established method for utilizing generative AI models to analyze personal and progress data and provide users with optimal plans. A new health management system is needed to solve these challenges.

[0645] 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.

[0646] In this invention, the server includes means for registering personal data of each user, means for linking with a fitness device and a body composition scale and automatically acquiring measurement data of each user, means for analyzing the acquired personal data and measurement data and generating an optimal training plan and meal menu, means for notifying the user of the generated training plan and meal menu, means for inputting user progress data, means for reanalyzing the input progress data and modifying the training plan and meal menu, means for transmitting any type of progress data of each user to the server and acquiring the progress data from a cloud service, means for analyzing the personal data and progress data using a generative AI model and generating and modifying an optimal training plan and meal menu based on prompts, and means for providing the user with visual feedback of the training plan and meal menu via a head-mounted display, thereby enabling the system to grasp the user's health condition in real time, continuously provide optimal plans, and effectively manage the user's health.

[0647] A "user" is an individual who uses this system to manage their own health.

[0648] "Personal data" refers to information about a user's physical data and lifestyle, including height, weight, age, gender, and sports history.

[0649] A "fitness device" is a device used to measure a user's exercise status and biological data, and includes smartwatches and activity monitors.

[0650] A "body composition scale" is a device that measures body composition data such as weight and body fat percentage.

[0651] "Measurement data" refers to data obtained from measuring equipment such as fitness devices and body composition monitors.

[0652] "Analysis" refers to data processing to extract meaningful information from acquired personal data and measurement data.

[0653] A "training plan" is a recommended exercise schedule for a user to improve their health.

[0654] A "meal menu" is a meal content recommended for a user to maintain or improve their health.

[0655] "Notification" is an action of communicating the generated training plan and meal menu to the user.

[0656] "Progress data" is data that records the results and status of the training and diet that the user has performed.

[0657] "Reanalysis" is the process of reanalyzing progress data to find new insights and areas for improvement.

[0658] "Cloud services" are services such as data storage and computing power provided over the internet.

[0659] A "generative AI model" is a type of artificial intelligence that uses machine learning algorithms to analyze user data and generate optimal training plans and meal menus.

[0660] A "prompt" is a sentence that gives instructions to an AI model for data analysis and plan generation.

[0661] A "head-mounted display" is a wearable device that provides visual information to the user and is primarily worn on the head.

[0662] The present invention provides a system for efficiently and effectively managing a user's health. The system is configured using a smartphone, a fitness device, a body composition monitor, a server, a cloud service, a generative AI model, and a head-mounted display. Specific embodiments are described below.

[0663] Overall system configuration

[0664] 1. User registration and initial data entry

[0665] First, users install a dedicated application on their smartphone and perform initial registration. At this time, users enter their personal data (height, weight, age, gender, sports history, etc.) into the application. This data is sent to the server and stored in a database.

[0666] 2. Integration with fitness devices and body composition monitors

[0667] Users pair their fitness devices and body composition monitors with the app, which uploads data measured by the devices (e.g., weight, body fat percentage, heart rate, etc.) to the cloud service. The server retrieves this data from the cloud service and updates the user's database.

[0668] 3. AI-based data analysis and plan generation

[0669] A generative AI model stored on the server analyzes the acquired personal and measurement data. Based on the prompts, the AI ​​generates a training plan and meal menu that is optimal for the user's fitness level and health goals. The generated plan is stored on the server and notified to the user via smartphone.

[0670] 4. Plan notification and execution management

[0671] A smartphone app notifies users of daily training plans and meal menus. Users then follow the instructions and enter their progress into the app, for example, recording their jogging time or weight fluctuations.

[0672] 5. Reanalyze progress data and revise plans

[0673] The server receives the progress data sent by the user and reanalyzes it. This reanalysis evaluates the effectiveness of the training plan and meal menu, and modifies the plan if necessary. The modified plan is then notified to the user again via smartphone.

[0674] 6. Providing visual feedback

[0675] Additionally, a head-mounted display is used to provide users with visual feedback on their training plan and meal menu, helping them better understand the plan and motivating them to follow it.

[0676] Hardware and software used

[0677] Hardware: Smartphones, fitness devices, body composition monitors, head-mounted displays

[0678] Software: Dedicated smartphone app, cloud service, generative AI model

[0679] Specific examples

[0680] Initial user data entry

[0681] A user installs a smartphone app and enters the following data:

[0682] Height: 175cm

[0683] Weight: 72kg

[0684] Date of Birth: February 15, 1990

[0685] Gender: Female

[0686] Sports history: Occasional jogging

[0687] Prompt Sentence Examples

[0688] "Analyze user data and generate personalized training plans and meal menus. Personal data: Height 175cm, Weight 72kg, Date of birth 1990-02-15, Gender Female, Sports history Occasional jogging. Device data: Weight 72.5kg, Body fat 22%, Heart rate 78."

[0689] This invention makes it possible to grasp the user's health condition in real time and provide optimal training plans and meal menus at all times, thereby enabling the user to effectively manage their health.

[0690] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0691] Step 1:

[0692] The user installs the smartphone app, performs initial registration, and enters personal data.

[0693] Specific operation: The user uses a smartphone to enter personal data such as height, weight, age, gender, and sports history, and sends it to the server.

[0694] Input: Personal data (height, weight, age, gender, sports history)

[0695] Output: Personal data of the user registered on the server

[0696] Step 2:

[0697] The user pairs the fitness device and body composition scale with the app to obtain measurement data.

[0698] Specific operation: The user pairs the fitness device and body composition scale with their smartphone and uploads the data to the cloud service. The server retrieves the measurement data from the cloud and stores it in a database.

[0699] Input: Measurement data (weight, body fat percentage, heart rate)

[0700] Output: Measurement data stored on the server

[0701] Step 3:

[0702] The server analyzes personal data and measurement data to generate optimal training plans and meal menus.

[0703] How it works: The generative AI model on the server analyzes personal and measurement data. Based on prompts, the AI ​​generates optimal training plans and meal menus.

[0704] Input: Personal data, measurement data, prompt text

[0705] Output: Generated training plan and meal menu

[0706] Step 4:

[0707] The generated training plan and meal menu are notified to the user.

[0708] Specific operation: The server sends the generated training plan and meal menu to the smartphone app and notifies the user.

[0709] Input: Generated training plan and meal menu

[0710] Output: Training plan and meal menu notified to the user

[0711] Step 5:

[0712] The user follows a training plan and meal menu and enters progress data.

[0713] Specific operation: The user enters training and diet progress (e.g., jogging time, weight fluctuations) into the smartphone app and sends it to the server.

[0714] Input: Progress data (training time, weight fluctuations, etc.)

[0715] Output: Progress data stored on the server

[0716] Step 6:

[0717] The server reanalyzes the progress data and adjusts the training plan and meal menu.

[0718] What it does: The server's generative AI model reanalyzes the progress data and modifies the training plan and meal menu as needed. The modified plan is then notified to the user again.

[0719] Input: Progress data, current training plan and meal menu

[0720] Output: Modified training plan and meal menu

[0721] Step 7:

[0722] A head-mounted display is used to provide the user with visual feedback of their training plan and meal menu.

[0723] Specific operation: The server generates a training plan and sends visual feedback of the meal menu to the head-mounted display. The user receives the feedback using the head-mounted display.

[0724] Input: Training plan and meal menu

[0725] Output: Visual feedback provided to the user

[0726] 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.

[0727] The present invention combines a system that manages each user's personal data and measurement data and proposes optimal training plans and meal menus with an emotion engine that recognizes the user's emotions. This system can support effective health management while taking into account the user's health and emotional state. The system's program processing and specific examples are explained below.

[0728] Initial registration and data entry

[0729] The user installs the smartphone app and performs the initial registration. At this time, the user enters personal data such as height, weight, past sports history, etc. The app sends this data to the server, which receives it and stores it in a database.

[0730] Integration with fitness devices and body composition monitors

[0731] Users pair their fitness device or body composition monitor with the app to complete the connection, which then uploads the data measured by the device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud.

[0732] The server periodically retrieves measurement data from the cloud and adds and updates it to the user's database, enabling accurate real-time monitoring of health conditions.

[0733] AI and emotion engine for data analysis and plan generation

[0734] An AI engine located on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. At the same time, an emotion engine acquires and analyzes the user's emotional data and adjusts the plan based on their emotional state (e.g., stress, fatigue). The generated plan is stored on the server and notified to the user via their device (smartphone app).

[0735] Plan notification and execution management

[0736] The device notifies the user of daily training plans and meal menus, and the user follows the training and meal plans and enters their progress and emotional state into the app.

[0737] The device sends the entered progress and emotional data to the server. The server receives the progress and emotional data, which is then reanalyzed by the AI ​​engine and emotion engine. This analysis evaluates the effectiveness of the training plan and meal menu, and the plan is revised as necessary. The revised plan is then notified to the user again via the device. This ensures that optimal training and meals are always provided based on the user's latest health and emotional state.

[0738] Specific examples

[0739] For User B:

[0740] 1. User B installs the smartphone app and enters the following personal data:

[0741] Height: 165cm

[0742] Weight: 60kg

[0743] Date of Birth: June 15, 1990

[0744] Gender: Female

[0745] Past sports experience: 3 years of yoga experience

[0746] 2. Pair your fitness device and body composition monitor and send the measurement data to the cloud. Let's assume the initial measurement data is as follows:

[0747] Weight: 60.5 kg

[0748] Body fat percentage: 22%

[0749] 3. The AI ​​engine generates a training plan that:

[0750] Yoga: 30 minutes x 3 days / week

[0751] Walking: 20 minutes x 3 days / week

[0752] 4. The AI ​​engine will suggest the following meal options:

[0753] Breakfast: Fruit smoothie

[0754] Lunch: Salad bowl

[0755] Dinner: Grilled chicken and vegetables

[0756] 5. User B performs training and diet and enters their progress and emotional state into the smartphone app. For example, on a day when they feel high stress, they record their emotional state as "high stress."

[0757] 6. The server receives and analyzes the progress data and emotional data, fine-tunes the next plan, and notifies User B. On days when stress is high, adjustments may be made, such as increasing the number of yoga sessions to promote relaxation.

[0758] By repeating this cycle, users can continuously implement optimal training plans and meal menus, and manage their health, including their emotional state, enabling effective health management tailored to individual goals.

[0759] The processing flow will be explained below.

[0760] Step 1:

[0761] A user installs the smartphone app and opens it.

[0762] Step 2:

[0763] The terminal displays an initial registration screen and prompts the user to enter personal data such as height, weight, date of birth, sex, and past sports history.

[0764] Step 3:

[0765] The user enters the required information and presses the send button.

[0766] Step 4:

[0767] The terminal transmits the input personal data to the server.

[0768] Step 5:

[0769] The server receives the data and stores it in a database along with the user ID.

[0770] Step 6:

[0771] The user acquires new measurement data (e.g., weight, body fat percentage) using a fitness device and a body composition monitor.

[0772] Step 7:

[0773] Fitness devices and body composition scales upload measurement data to the cloud.

[0774] Step 8:

[0775] The server periodically retrieves measurement data from the cloud and adds / updates it to the user's database.

[0776] Step 9:

[0777] An AI engine in the server analyzes the user's personal data and measurement data.

[0778] Step 10:

[0779] Based on the analysis results, the AI ​​engine generates a training plan and meal menu tailored to the user's goals (e.g., weight loss, muscle building).

[0780] Step 11:

[0781] The emotion engine acquires and analyzes the user's emotion data and evaluates their emotional state (e.g., stress, fatigue).

[0782] Step 12:

[0783] The AI ​​engine and emotion engine work together to modify training plans and meal menus taking into account the user's emotional state.

[0784] Step 13:

[0785] The server stores the generated training plan and meal menu for each user.

[0786] Step 14:

[0787] The device sends the user daily training plans and meal menu notifications.

[0788] Step 15:

[0789] The user follows the notified content and carries out training and meals.

[0790] Step 16:

[0791] Users enter their training and diet progress and emotional state into a smartphone app.

[0792] Step 17:

[0793] The terminal transmits the input progress data and emotion data to the server.

[0794] Step 18:

[0795] The server receives the progress data and emotion data, which are then reanalyzed by the AI ​​engine and emotion engine.

[0796] Step 19:

[0797] The AI ​​engine adjusts and modifies training plans and meal menus based on progress data.

[0798] Step 20:

[0799] The emotion engine re-evaluates the emotion data and makes modifications that take into account the user's emotional state.

[0800] Step 21:

[0801] The server stores the modified plan and sends it back to the device.

[0802] Step 22:

[0803] The terminal notifies the user of the modified plan.

[0804] Step 23:

[0805] The user follows the new plan, again training and eating, and continually inputs their progress and emotional state.

[0806] The above is the specific processing flow of the system. This allows users to continuously implement optimal training plans and meal menus, and effectively manage their health according to their individual goals. Furthermore, by taking the user's emotional state into consideration, more appropriate and sustainable health management can be achieved.

[0807] Example 2

[0808] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0809] Conventional health management systems often provide uniform exercise programs and meal menus without considering the personal data or emotional state of each individual user. As a result, plans tailored to the user's health and emotional state are not provided, making effective health management difficult. In addition, there are few ways to effectively integrate and analyze data from fitness devices and body composition monitors, making it difficult to adjust training plans and meal menus in real time.

[0810] 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.

[0811] In this invention, the server includes means for registering personal data of each user, means for automatically acquiring measurement data of each user in conjunction with the measurement device and body composition monitor, means for analyzing the acquired personal data and measurement data to generate an optimal exercise program and meal menu, and means for analyzing the user's emotional data and adjusting the exercise program and meal menu based on the user's emotional state. This allows for the provision of an exercise program and meal menu optimized for each individual user, enabling effective health management that takes into account the user's emotional state.

[0812] "Personal data" refers to basic information about an individual user, such as the user's height, weight, date of birth, gender, and past sports history.

[0813] "Measuring device" is a general term for equipment used to measure a user's health status, such as weight, body fat percentage, and heart rate.

[0814] A "body composition scale" is a device that measures data related to a user's body composition, such as body fat percentage and muscle mass.

[0815] "Emotion data" is information related to emotions such as the user's stress level or fatigue.

[0816] An "exercise program" is a training plan designed based on the user's health and goals.

[0817] A "meal menu" is a meal plan suggested based on the user's health management and fitness goals.

[0818] The "notification means" is a means for transmitting information such as the generated exercise program and meal menu to the user.

[0819] A "server" is a central processing unit that stores and analyzes data through cloud services.

[0820] "Analysis methods" refer to algorithms and software that derive optimal exercise programs and meal menus based on acquired personal data and measurement data.

[0821] The "modification means" is a means for readjusting the exercise program and meal menu based on the user's progress data and emotional data.

[0822] The present invention combines a system that manages each user's personal data and measurement data and suggests optimal exercise programs and meal plans with an emotion engine that recognizes the user's emotions. This system can support effective health management while taking into account the user's health and emotional state.

[0823] Initial registration and data entry

[0824] First, the user installs the smartphone app and performs initial registration within the app. At this time, the user enters personal data such as height, weight, date of birth, gender, and past sports history. The entered data is sent from the device to the server, which then stores it in a database.

[0825] Integration with fitness devices and body composition monitors

[0826] When users pair their fitness devices or body composition monitors with a smartphone app, the data measured by these devices (such as weight, body fat percentage, heart rate, etc.) is automatically uploaded to the cloud. The server periodically retrieves new measurement data from the cloud and adds and updates it to the user's database. This collaboration allows users to accurately understand their health status in real time.

[0827] AI and emotion engine for data analysis and plan generation

[0828] The AI ​​engine located on the server analyzes the acquired personal and measurement data using programs such as Python. Based on this analysis, the AI ​​generates an optimal exercise program and meal menu based on the user's physical fitness level and goals. At the same time, the emotion engine analyzes the user's emotional data (e.g., stress, fatigue) and adjusts the plan based on that emotional state. The generated plan is stored on the server and notified to the user via their device.

[0829] Plan notification and execution management

[0830] The device notifies the user of their daily exercise program and meal menu. The user then exercises and eats according to the notified information, and enters their progress and emotional state into the app. For example, on a day when they feel high stress, they record their emotional state as "high stress." The device then sends the entered progress and emotional data to the server. The server receives this data, and the AI ​​engine and emotion engine reanalyze it. Based on the results of this analysis, the effectiveness of the exercise program and meal menu is evaluated, and the plan is revised if necessary. The revised plan is then notified to the user again via the device.

[0831] Specific examples

[0832] User B's case

[0833] 1. User B installs the smartphone app and enters the following personal data:

[0834] Height: 165cm

[0835] Weight: 60kg

[0836] Date of Birth: June 15, 1990

[0837] Gender: Female

[0838] Past sports experience: 3 years of yoga experience

[0839] 2. User B pairs the fitness device and body composition monitor and sends the measurement data to the cloud. The initial measurement data is as follows:

[0840] Weight: 60.5 kg

[0841] Body fat percentage: 22%

[0842] 3. The AI ​​engine generates the following exercise program:

[0843] Yoga: 30 minutes x 3 days / week

[0844] Walking: 20 minutes x 3 days / week

[0845] 4. The AI ​​engine will suggest the following meal options:

[0846] Breakfast: Fruit smoothie

[0847] Lunch: Salad bowl

[0848] Dinner: Grilled chicken and vegetables

[0849] 5. User B exercises and eats, and enters their progress and emotional state into the smartphone app. For example, on a day when they feel stressed, they record their emotional state as "high stress."

[0850] 6. The server receives and analyzes the progress data and emotional data, fine-tunes the next plan, and notifies User B. For example, on days when stress is high, adjustments may be made such as increasing the number of yoga plans to promote relaxation.

[0851] By repeating this cycle, users can continuously implement optimal exercise programs and meal menus, and manage their health, including their emotional state.

[0852] Example input to a generative AI model

[0853] Example prompt sentence:

[0854] "Enter the user's personal data (height, weight, sports history, etc.) and measurement data (weight, body fat percentage, heart rate, etc.) and use the emotion engine to analyze the user's emotional state. Generate appropriate exercise program and meal menu suggestions."

[0855] By inputting this prompt into a generative AI model, specific suggestions can be obtained, including exercise programs and meal menus optimized for each individual user.

[0856] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0857] Step 1:

[0858] The user installs the smartphone app and launches it. The user selects the "New Registration" option within the app and enters personal data (e.g., height, weight, date of birth, gender, past sports history, etc.). The entered data is sent from the device to the server. The specific input data is height 165 cm, weight 60 kg, date of birth June 15, 1990, gender female, past sports history 3 years of yoga experience. The server stores the received data in a database. The output is a state in which the user's personal data has been registered in the database.

[0859] Step 2:

[0860] The user pairs the measurement device and body composition monitor with a smartphone app. This uploads data measured by the measurement device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud. The server periodically retrieves new measurement data from the cloud. The specific input data is the initial measurement results: weight 60.5 kg, body fat percentage 22%, heart rate 75 bpm. The output is the latest measurement data added and updated to the user's database.

[0861] Step 3:

[0862] The acquired personal and measurement data is analyzed using an AI engine located on a server. Specifically, a Python program is used to generate an optimal exercise program and meal menu based on the user's physical fitness level and goals. Specific input data includes height 165 cm, weight 60.5 kg, body fat percentage 22%, heart rate 75 bpm, and three years of yoga experience. The output is the generated exercise program (30 minutes of yoga three days per week, 20 minutes of walking three days per week) and meal menu (breakfast: fruit smoothie, lunch: salad bowl, dinner: grilled chicken and vegetables).

[0863] Step 4:

[0864] The server uses the emotion engine to analyze the user's emotional data (e.g., stress level, fatigue level) and adjusts the exercise program and meal menu. The specific input data is the emotional data entered by the user into the app. For example, a high-stress day is recorded as "high stress." The output is an exercise program and meal menu adjusted based on the emotional state. For example, on a high-stress day, yoga sessions to promote relaxation are increased.

[0865] Step 5:

[0866] The device notifies the user of their daily exercise program and meal menu. Specifically, a smartphone app uses push notifications to notify the user of today's exercise program (e.g., 30 minutes of yoga, 20 minutes of walking) and meal menu (e.g., fruit smoothie, salad bowl, grilled chicken and vegetables). The input data are the exercise program and meal menu stored on the server. The output is the state in which the user has received the notification.

[0867] Step 6:

[0868] The user follows the notified exercise program and meal menu and inputs their progress and emotional state into the smartphone app. Specific input data includes the completion of today's yoga session (e.g., "30 minutes of yoga completed") and their emotional state (e.g., "high stress"). The output is the state in which the input progress data and emotional data are recorded in the app.

[0869] Step 7:

[0870] The device encrypts the input progress data and emotion data in real time and sends it to the server. The input data is the progress data and emotion data entered by the user into the app. The output is the state in which this data is stored on the server.

[0871] Step 8:

[0872] The AI ​​engine and emotion engine reanalyze the progress and emotion data received by the server. The specific input data is the user's progress and emotion data. The output is a revised exercise program and meal menu.

[0873] Step 9:

[0874] The server then makes any necessary modifications to the exercise program and meal menu based on the analysis results. Specific input data are the reanalyzed exercise program and meal menu modifications. The output is the modified exercise program and meal menu.

[0875] Step 10:

[0876] The server saves the revised exercise program and meal menu in the database again and notifies the user via the terminal. The input data is the revised exercise program and meal menu. The output is a state in which the user has received the latest exercise program and meal menu.

[0877] Through these steps, users can continuously receive optimal exercise programs and meal menus based on their individual health and emotional state.

[0878] (Application example 2)

[0879] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0880] Many conventional health management systems propose training plans and meal menus based on the user's personal and fitness data. However, these systems do not take the user's emotional state into account, and the proposed plans and menus are not necessarily optimal for the user's current situation. Furthermore, even if a healthy meal menu is proposed, it is difficult to quickly prepare the ingredients, making it difficult to apply the system to real situations.

[0881] 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.

[0882] In this invention, the server includes means for registering personal data of each user, means for linking with the fitness device and body composition scale to automatically acquire measurement data of each user, means for analyzing the acquired personal data and measurement data to generate an optimal training plan and meal menu, means for notifying the user of the generated training plan and meal menu, means for inputting progress data and emotional data of the user, means for reanalyzing the input progress data and emotional data to modify the training plan and meal menu, and means for generating an optimal meal menu based on the emotional data and providing ingredients based on the meal menu in cooperation with a delivery service. This enables more precise health management based on the user's health and emotional state, and further enables the ingredients for the suggested meal menu to be quickly obtained, making it possible to implement the menu in line with reality.

[0883] "Personal data" refers to information about an individual, such as the user's height, weight, age, sex, and past sports activities.

[0884] A "fitness device" is a device for acquiring fitness-related data such as a user's weight, heart rate, and number of steps.

[0885] A "body composition scale" is a device that measures not only weight but also body composition such as body fat percentage, muscle mass, and water content.

[0886] "Measurement data" refers to data relating to the user's health condition obtained using a fitness device and a body composition monitor.

[0887] A "training plan" is a workout or exercise plan generated based on the user's health status and goals.

[0888] A "meal menu" is a daily meal plan suggested based on the user's health condition and nutritional needs.

[0889] "Emotion data" is data that indicates the user's mental state, such as stress or fatigue.

[0890] A "delivery service" is a service that delivers goods, especially ingredients and food, to a specific location.

[0891] This invention combines a system that manages a user's personal data and measurement data and proposes optimal training plans and meal menus with an emotion engine. This system is designed to support effective health management based on the user's health and emotional state.

[0892] Initial registration and data entry

[0893] First, users install the smartphone app and perform initial registration, entering personal data such as height, weight, and past sports history. The device then sends this data to the server and stores it in a database.

[0894] Integration with fitness devices and body composition monitors

[0895] Users pair the app with fitness devices or body composition scales and periodically upload measurement data to the cloud. The server retrieves the measurement data from the cloud and updates the database, enabling accurate understanding of health status in real time.

[0896] AI and emotion engine for data analysis and plan generation

[0897] The AI ​​engine located on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. At the same time, the emotion engine acquires and analyzes the user's emotional data and adjusts the plan based on their emotional state (e.g., stress, fatigue). The generated plan is stored on the server and notified to the user via their device.

[0898] Plan notification and execution management

[0899] The device notifies the user of daily training plans and meal menus. The user then follows the training and meal plans and enters their progress and emotional state into the app. The device then sends the entered progress and emotional data to the server. The server receives the progress and emotional data, and the AI ​​engine and emotion engine reanalyze them to evaluate the effectiveness of the training plan and meal menu, and modify the plan as necessary.

[0900] Collaboration with food delivery companies

[0901] Based on the emotion data, the server generates an optimal meal menu and provides ingredients based on the menu in cooperation with a delivery service. Users can quickly obtain ingredients for the suggested meal menu, reducing the effort required for preparation and cooking.

[0902] Specific examples

[0903] User B's case

[0904] User B installs the smartphone app and enters personal data such as height 165 cm, weight 60 kg, and three years of yoga experience. The fitness device and body composition scale are paired, and the measurement data is sent to the server. The AI ​​engine generates a training plan of 30 minutes of yoga three days per week and 20 minutes of walking three days per week, as well as meal menus such as "fruit smoothie," "salad bowl," and "grilled chicken and vegetables." User B records his or her emotional state, entering, for example, "high stress," and the server adjusts the menu to one with a relaxing effect and provides the ingredients via a delivery service.

[0905] Example prompts for generative AI models

[0906] "I'm a 165cm, 60kg woman with three years of yoga experience. I've been feeling a bit stressed lately. Please use this data to suggest the best training plan and meal menu, and provide the ingredients via a delivery service."

[0907] This allows users to always practice optimal training and diet based on their latest health and emotional state, realizing sustainable health management.

[0908] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0909] Step 1:

[0910] The user installs the smartphone app and performs initial registration. The personal data entered (height, weight, past sports history, etc.) is sent from the device to the server and stored in a database. This registration allows the system to grasp the user's basic information.

[0911] Step 2:

[0912] Users pair their fitness devices or body composition scales with the app. The paired devices periodically upload measurement data (weight, body fat percentage, heart rate, etc.) to the cloud, where the server collects and stores the data. This allows users to accurately understand their health status in real time.

[0913] Step 3:

[0914] The AI ​​engine located on the server analyzes the acquired personal data and measurement data. For example, based on data such as height, weight, and heart rate, it generates a training plan and meal menu according to the user's physical fitness level and goals. The input for this process is the personal data and measurement data, and the output is a training plan and meal menu.

[0915] Step 4:

[0916] At the same time, the emotion engine acquires and analyzes the user's emotional data. The input emotional data could be information such as "high stress," and the emotion engine uses this information to analyze the user's emotional state and output an appropriate result. The emotion analysis results obtained here are used to adjust the training plan and meal menu.

[0917] Step 5:

[0918] The server notifies the generated training plan and meal menu to the terminal, and the terminal notifies the user of the daily training and meal menu, allowing the user to understand the tasks to be performed.

[0919] Step 6:

[0920] The user follows the training and meal menu provided and enters their progress and emotional state into the app. The progress and emotional data entered through this operation is then sent from the device to the server.

[0921] Step 7:

[0922] The server re-analyzes the received progress and emotion data. The AI ​​engine and emotion engine use this data to evaluate the effectiveness of the training plan and meal menu and modify the plan as necessary. The input of this re-analysis is the progress and emotion data, and the output is the modified plan.

[0923] Step 8:

[0924] Based on the emotion data, the server generates an optimal meal menu. The server then provides ingredients based on the generated menu in cooperation with a delivery service. The delivery service delivers the ingredients to the user, allowing the user to quickly complete the suggested menu.

[0925] 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.

[0926] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0927] 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.

[0928] [Third embodiment]

[0929] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0930] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0931] 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).

[0932] 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.

[0933] 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.

[0934] 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).

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0941] The present invention is a system for efficiently managing the health of each user, and a specific embodiment for implementing this system will be described. This system registers personal data and generates and provides optimal training plans and meal menus based on that data. The system's program processing and specific examples will be explained below.

[0942] Initial registration and data entry

[0943] The user installs the smartphone app and performs the initial registration. At this time, the user enters personal data such as height, weight, past sports history, etc. The app sends this data to the server, which receives it and stores it in a database.

[0944] Integration with fitness devices and body composition monitors

[0945] Users pair their fitness device or body composition monitor with the app to complete the connection, which then uploads the data measured by the device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud.

[0946] The server periodically retrieves measurement data from the cloud and adds and updates it to the user's database, enabling accurate real-time monitoring of health conditions.

[0947] AI-based data analysis and plan generation

[0948] An AI engine installed on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. The generated plan is saved on the server and notified to the user via their device (smartphone app).

[0949] Plan notification and execution management

[0950] The device notifies the user of daily training plans and meal menus, and the user follows the training and meal plans and enters their progress into the app.

[0951] The device sends the entered progress data to the server, which then receives it and reanalyzes it with an AI engine. This analysis evaluates the effectiveness of the training plan and meal menu, and modifies the plan if necessary.

[0952] The revised plan is then sent to the user via the device, providing optimal training and dietary information based on the user's current health status.

[0953] Specific examples

[0954] For User A:

[0955] 1. User A installs the smartphone app and enters the following personal data:

[0956] Height: 170cm

[0957] Weight: 70kg

[0958] Date of Birth: January 1, 1985

[0959] Gender: Male

[0960] Past sports history: None

[0961] 2. Pair your fitness device and body composition monitor and send the measurement data to the cloud. Let's assume the initial measurement data is as follows:

[0962] Weight: 70.2 kg

[0963] Body fat percentage: 25%

[0964] 3. The AI ​​engine generates a training plan that:

[0965] Easy jogging: 20 minutes x 3 days / week

[0966] Squats: 15 reps x 2 sets / day

[0967] 4. The AI ​​engine will suggest the following meal options:

[0968] Breakfast: Protein shake

[0969] Lunch: Salad and grilled chicken

[0970] Dinner: Steamed fish and vegetables

[0971] 5. User A goes jogging and enters the progress into the smartphone app. For example, he / she jogs for 20 minutes and records that his / her weight has increased to 70.0 kg.

[0972] 6. The server receives and analyzes the progress data, fine-tunes the next plan, and notifies User A.

[0973] By repeating this cycle, users can continuously implement optimal training plans and meal menus, enabling effective health management according to individual goals.

[0974] The processing flow will be explained below.

[0975] Step 1:

[0976] A user installs the smartphone app and opens it.

[0977] Step 2:

[0978] The terminal displays an initial registration screen and prompts the user to enter personal data such as height, weight, date of birth, sex, and past sports history.

[0979] Step 3:

[0980] The user enters the required information and presses the send button.

[0981] Step 4:

[0982] The terminal transmits the input personal data to the server.

[0983] Step 5:

[0984] The server receives the data and stores it in a database along with the user ID.

[0985] Step 6:

[0986] The user acquires new measurement data (e.g., weight, body fat percentage) using a fitness device and a body composition monitor.

[0987] Step 7:

[0988] Fitness devices and body composition scales upload measurement data to the cloud.

[0989] Step 8:

[0990] The server periodically retrieves measurement data from the cloud and adds / updates it to the user's database.

[0991] Step 9:

[0992] An AI engine in the server analyzes the user's personal data and measurement data.

[0993] Step 10:

[0994] Based on the analysis results, the AI ​​engine generates a training plan and meal menu tailored to the user's goals (e.g., weight loss, muscle building).

[0995] Step 11:

[0996] The server stores the generated training plan and meal menu for each user.

[0997] Step 12:

[0998] The device sends the user daily training plans and meal menu notifications.

[0999] Step 13:

[1000] The user follows the notified content and carries out training and meals.

[1001] Step 14:

[1002] Users enter their training and dietary progress into a smartphone app.

[1003] Step 15:

[1004] The terminal transmits the input progress data to the server.

[1005] Step 16:

[1006] The server receives the progress data and the AI ​​engine reanalyzes it.

[1007] Step 17:

[1008] The AI ​​engine adjusts and modifies training plans and meal menus based on progress data.

[1009] Step 18:

[1010] The server stores the modified plan and sends it back to the device.

[1011] Step 19:

[1012] The terminal notifies the user of the modified plan.

[1013] Step 20:

[1014] The user follows the new plan, resumes training and eating, and continues to enter progress.

[1015] The above is the specific processing flow of the system, which allows users to continuously implement optimal training plans and meal menus, and effectively manage their health according to their individual goals.

[1016] Example 1

[1017] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1018] In recent years, the importance of managing exercise and diet based on individual health conditions has increased. However, conventional methods require users to manage their own health, which requires time-consuming regular data entry and progress tracking. Furthermore, it is difficult to generate optimal plans for individual goals and make ongoing adjustments, making efficient health management difficult. Therefore, there is a demand for systems that enable more efficient and accurate data collection, analysis, plan generation, and progress management.

[1019] 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.

[1020] In this invention, the server includes means for registering each user's biometric information, means for working with the detection device to automatically acquire each user's measurement data, means for analyzing the acquired biometric information and measurement data to generate an optimal exercise plan and diet plan, means for notifying the user of the generated exercise plan and diet plan, means for inputting the user's progress data, and means for reanalyzing the input progress data and correcting the exercise plan and diet plan. This allows the user to understand their own health condition in real time and continuously implement an optimal exercise and diet plan.

[1021] "Biometric information" refers to data specific to each individual user, such as their height, weight, age, gender, and past sports history.

[1022] "Detection device" refers to a device for obtaining measurement data such as a user's weight, body fat percentage, heart rate, etc.

[1023] An "exercise plan" refers to a specific exercise or training program created based on the user's health status and goals.

[1024] "Diet plan" refers to a specific diet and nutrition program created based on the user's health status and goals.

[1025] "Cloud" refers to a collective term for data storage and processing services over the internet used to send and receive measurement and plan data.

[1026] "Server" refers to a computer system that receives, stores, analyzes, and reanalyzes data.

[1027] "User" refers to an individual who uses this system to manage their own health.

[1028] "Progress data" refers to data including recorded data on the exercises and dietary details performed by the user, as well as resulting changes in weight and body fat percentage.

[1029] "Reanalysis" refers to the process of reevaluating the effectiveness of your exercise and diet plans based on the progress data you enter and adjusting the plans as necessary.

[1030] This invention is a system for efficiently managing the health of each user, and in particular, for generating and providing optimal exercise and dietary plans based on each user's biological information and measurement data. This system operates by combining multiple hardware and software components.

[1031] First, the user installs a smartphone app. Through the app, the user inputs biometric information such as their height, weight, past sports history, age, and gender. This data is sent from the device to a server, which then stores the received data in a database. This allows each user's individual biometric information to be recorded and managed.

[1032] Next, users pair their fitness devices, body composition scales, and other detection devices with the app, which automatically uploads measurement data such as weight, body fat percentage, and heart rate to the cloud. The server periodically retrieves this data and adds and updates it to a database.

[1033] The server contains an AI engine equipped with a generative AI model. This AI engine analyzes the user's biometric information and measurement data to generate optimal exercise and diet plans. Specific analysis methods include clustering, regression analysis, and machine learning algorithms (using TensorFlow or PyTorch, for example). The generated plans are stored on the server and notified to the user via a smartphone app.

[1034] The user receives the notification and executes their daily exercise and diet plans. After execution, the user enters progress data (e.g., which exercises were performed for how long, and changes in weight after weighing) into the app. This progress data is sent back to the server, which reanalyzes the received data using an AI engine. As a result of the reanalysis, the user is again notified of the newly revised exercise and diet plans.

[1035] As a concrete example, User A installs a smartphone app and inputs the following biometric information:

[1036] Height: 170cm

[1037] Weight: 70kg

[1038] Date of Birth: January 1, 1985

[1039] Gender: Male

[1040] Past sports history: None

[1041] The fitness device is paired with a body composition scale, and data on a weight of 70.2 kg and a body fat percentage of 25% is sent to the cloud. The AI ​​engine then generates an exercise plan consisting of light jogging (20 minutes x 3 days per week) and squats (15 repetitions x 2 sets per day), as well as a diet plan consisting of a protein shake for breakfast, salad and grilled chicken for lunch, and steamed fish and vegetables for dinner.

[1042] User A goes jogging and records the progress on the smartphone app as "20 minutes of jogging, weight fluctuated to 70.0 kg." The server receives the progress data, and the AI ​​engine reanalyzes it to fine-tune the next plan and notify User A.

[1043] The following prompts are examples of input to a generative AI model:

[1044] "Please suggest an appropriate exercise and diet plan for a 30-year-old male who is 170cm tall, weighs 70kg, and has no prior athletic experience. The initial measurement data is 70.2kg in weight and 25% body fat."

[1045] The above is a specific embodiment for carrying out the present invention. This system enables a user to grasp their own health condition in real time and continuously implement an optimal exercise and diet plan.

[1046] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1047] Step 1:

[1048] A user installs a smartphone app

[1049] A user downloads and installs the app from the App Store or Google Play. After installation, the user launches the app and proceeds to the initial setup screen.

[1050] Input: None

[1051] Output: The app is installed on your smartphone.

[1052] Step 2:

[1053] The user inputs biometric information

[1054] The user enters biometric information such as height, weight, past sports history, age, and gender on the initial setup screen of the app. The user enters each piece of data into the input form and presses the "Send" button. The device sends the entered data to the server. The server stores the received data in a database.

[1055] Input: User's height, weight, past sports history, age, gender

[1056] Output: User's biometric information stored in a database

[1057] Step 3:

[1058] The user pairs the detector with the app

[1059] Users pair fitness devices and body composition monitors via Bluetooth or Wi-Fi on the app's settings screen. Once paired, measurement data is sent from the device to the cloud.

[1060] Input: Fitness devices, body composition monitors

[1061] Output: Measurement data sent to the cloud

[1062] Step 4:

[1063] The server acquires the measurement data from the cloud.

[1064] The server accesses the cloud and periodically retrieves user measurement data. The server sends a request to the cloud service using an API call and receives the response data. The server then adds or updates the obtained data to the existing user database.

[1065] Input: Measurement data obtained from the cloud

[1066] Output: Updated user database

[1067] Step 5:

[1068] The AI ​​engine on the server analyzes the data

[1069] An AI engine located on the server analyzes the user's biometric information and measurement data and executes algorithms using machine learning libraries such as Python, TensorFlow, and PyTorch.

[1070] Input: Biometric information, measurement data

[1071] Output: Analysis results (user's physical fitness level, health condition)

[1072] Step 6:

[1073] AI engine generates exercise and diet plans

[1074] Based on the analysis results, the AI ​​engine generates an individualized exercise and diet plan. The plan is then saved on a server, which then sends it to the user's device and notifies them via a smartphone app.

[1075] Input: Analysis results

[1076] Output: Exercise plan, diet plan (saved on the server and notified via the device)

[1077] Step 7:

[1078] The user executes an exercise plan and a diet plan.

[1079] The user performs daily training and meals according to the exercise and diet plans notified by the app. After completing the plan, the user enters and saves progress data (e.g., jogging time, weight fluctuations, etc.) into the app.

[1080] Input: Exercise plan, diet plan

[1081] Output: Progress data entered into the app

[1082] Step 8:

[1083] The device sends progress data to the server

[1084] The device sends the progress data entered by the user to the server via an HTTP request, and the server receives the data and stores it in a database.

[1085] Input: Progress data

[1086] Output: Progress data stored in a database

[1087] Step 9:

[1088] The server re-parses the progress data

[1089] The server reanalyzes the received progress data using an AI engine. Based on the analysis results, the exercise plan and diet plan are evaluated and revised. The revised plan is saved back on the server and notified to the user via their device.

[1090] Input: Progress data

[1091] Output: Evaluation results, revised exercise plan, diet plan

[1092] Through these processing steps, users can understand their own health status in real time and implement optimal exercise and diet plans. This system enables effective health management according to individual health goals.

[1093] (Application example 1)

[1094] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1095] Current health management systems have difficulty accurately understanding a user's health status and continuously providing optimal training plans and meal menus. There are also challenges in efficiently managing and utilizing data from fitness devices and body composition scales. Furthermore, there is no established method for utilizing generative AI models to analyze personal and progress data and provide users with optimal plans. A new health management system is needed to solve these challenges.

[1096] 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.

[1097] In this invention, the server includes means for registering personal data of each user, means for linking with a fitness device and a body composition scale and automatically acquiring measurement data of each user, means for analyzing the acquired personal data and measurement data and generating an optimal training plan and meal menu, means for notifying the user of the generated training plan and meal menu, means for inputting user progress data, means for reanalyzing the input progress data and modifying the training plan and meal menu, means for transmitting any type of progress data of each user to the server and acquiring the progress data from a cloud service, means for analyzing the personal data and progress data using a generative AI model and generating and modifying an optimal training plan and meal menu based on prompts, and means for providing the user with visual feedback of the training plan and meal menu via a head-mounted display, thereby enabling the system to grasp the user's health condition in real time, continuously provide optimal plans, and effectively manage the user's health.

[1098] A "user" is an individual who uses this system to manage their own health.

[1099] "Personal data" refers to information about a user's physical data and lifestyle, including height, weight, age, gender, and sports history.

[1100] A "fitness device" is a device used to measure a user's exercise status and biological data, and includes smartwatches and activity monitors.

[1101] A "body composition scale" is a device that measures body composition data such as weight and body fat percentage.

[1102] "Measurement data" refers to data obtained from measuring equipment such as fitness devices and body composition monitors.

[1103] "Analysis" refers to data processing to extract meaningful information from acquired personal data and measurement data.

[1104] A "training plan" is a recommended exercise schedule for a user to improve their health.

[1105] A "meal menu" is a meal content recommended for a user to maintain or improve their health.

[1106] "Notification" is an action of communicating the generated training plan and meal menu to the user.

[1107] "Progress data" is data that records the results and status of the training and diet that the user has performed.

[1108] "Reanalysis" is the process of reanalyzing progress data to find new insights and areas for improvement.

[1109] "Cloud services" are services such as data storage and computing power provided over the internet.

[1110] A "generative AI model" is a type of artificial intelligence that uses machine learning algorithms to analyze user data and generate optimal training plans and meal menus.

[1111] A "prompt" is a sentence that gives instructions to an AI model for data analysis and plan generation.

[1112] A "head-mounted display" is a wearable device that provides visual information to the user and is primarily worn on the head.

[1113] The present invention provides a system for efficiently and effectively managing a user's health. The system is configured using a smartphone, a fitness device, a body composition monitor, a server, a cloud service, a generative AI model, and a head-mounted display. Specific embodiments are described below.

[1114] Overall system configuration

[1115] 1. User registration and initial data entry

[1116] First, users install a dedicated application on their smartphone and perform initial registration. At this time, users enter their personal data (height, weight, age, gender, sports history, etc.) into the application. This data is sent to the server and stored in a database.

[1117] 2. Integration with fitness devices and body composition monitors

[1118] Users pair their fitness devices and body composition monitors with the app, which uploads data measured by the devices (e.g., weight, body fat percentage, heart rate, etc.) to the cloud service. The server retrieves this data from the cloud service and updates the user's database.

[1119] 3. AI-based data analysis and plan generation

[1120] A generative AI model stored on the server analyzes the acquired personal and measurement data. Based on the prompts, the AI ​​generates a training plan and meal menu that is optimal for the user's fitness level and health goals. The generated plan is stored on the server and notified to the user via smartphone.

[1121] 4. Plan notification and execution management

[1122] A smartphone app notifies users of daily training plans and meal menus. Users then follow the instructions and enter their progress into the app, for example, recording their jogging time or weight fluctuations.

[1123] 5. Reanalyze progress data and revise plans

[1124] The server receives the progress data sent by the user and reanalyzes it. This reanalysis evaluates the effectiveness of the training plan and meal menu, and modifies the plan if necessary. The modified plan is then notified to the user again via smartphone.

[1125] 6. Providing visual feedback

[1126] Additionally, a head-mounted display is used to provide users with visual feedback on their training plan and meal menu, helping them better understand the plan and motivating them to follow it.

[1127] Hardware and software used

[1128] Hardware: Smartphones, fitness devices, body composition monitors, head-mounted displays

[1129] Software: Dedicated smartphone app, cloud service, generative AI model

[1130] Specific examples

[1131] Initial user data entry

[1132] A user installs a smartphone app and enters the following data:

[1133] Height: 175cm

[1134] Weight: 72kg

[1135] Date of Birth: February 15, 1990

[1136] Gender: Female

[1137] Sports history: Occasional jogging

[1138] Prompt Sentence Examples

[1139] "Analyze user data and generate personalized training plans and meal menus. Personal data: Height 175cm, Weight 72kg, Date of birth 1990-02-15, Gender Female, Sports history Occasional jogging. Device data: Weight 72.5kg, Body fat 22%, Heart rate 78."

[1140] This invention makes it possible to grasp the user's health condition in real time and provide optimal training plans and meal menus at all times, thereby enabling the user to effectively manage their health.

[1141] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1142] Step 1:

[1143] The user installs the smartphone app, performs initial registration, and enters personal data.

[1144] Specific operation: The user uses a smartphone to enter personal data such as height, weight, age, gender, and sports history, and sends it to the server.

[1145] Input: Personal data (height, weight, age, gender, sports history)

[1146] Output: Personal data of the user registered on the server

[1147] Step 2:

[1148] The user pairs the fitness device and body composition scale with the app to obtain measurement data.

[1149] Specific operation: The user pairs the fitness device and body composition scale with their smartphone and uploads the data to the cloud service. The server retrieves the measurement data from the cloud and stores it in a database.

[1150] Input: Measurement data (weight, body fat percentage, heart rate)

[1151] Output: Measurement data stored on the server

[1152] Step 3:

[1153] The server analyzes personal data and measurement data to generate optimal training plans and meal menus.

[1154] How it works: The generative AI model on the server analyzes personal and measurement data. Based on prompts, the AI ​​generates optimal training plans and meal menus.

[1155] Input: Personal data, measurement data, prompt text

[1156] Output: Generated training plan and meal menu

[1157] Step 4:

[1158] The generated training plan and meal menu are notified to the user.

[1159] Specific operation: The server sends the generated training plan and meal menu to the smartphone app and notifies the user.

[1160] Input: Generated training plan and meal menu

[1161] Output: Training plan and meal menu notified to the user

[1162] Step 5:

[1163] The user follows a training plan and meal menu and enters progress data.

[1164] Specific operation: The user enters training and diet progress (e.g., jogging time, weight fluctuations) into the smartphone app and sends it to the server.

[1165] Input: Progress data (training time, weight fluctuations, etc.)

[1166] Output: Progress data stored on the server

[1167] Step 6:

[1168] The server reanalyzes the progress data and adjusts the training plan and meal menu.

[1169] What it does: The server's generative AI model reanalyzes the progress data and modifies the training plan and meal menu as needed. The modified plan is then notified to the user again.

[1170] Input: Progress data, current training plan and meal menu

[1171] Output: Modified training plan and meal menu

[1172] Step 7:

[1173] A head-mounted display is used to provide the user with visual feedback of their training plan and meal menu.

[1174] Specific operation: The server generates a training plan and sends visual feedback of the meal menu to the head-mounted display. The user receives the feedback using the head-mounted display.

[1175] Input: Training plan and meal menu

[1176] Output: Visual feedback provided to the user

[1177] 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.

[1178] The present invention combines a system that manages each user's personal data and measurement data and proposes optimal training plans and meal menus with an emotion engine that recognizes the user's emotions. This system can support effective health management while taking into account the user's health and emotional state. The system's program processing and specific examples are explained below.

[1179] Initial registration and data entry

[1180] The user installs the smartphone app and performs the initial registration. At this time, the user enters personal data such as height, weight, past sports history, etc. The app sends this data to the server, which receives it and stores it in a database.

[1181] Integration with fitness devices and body composition monitors

[1182] Users pair their fitness device or body composition monitor with the app to complete the connection, which then uploads the data measured by the device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud.

[1183] The server periodically retrieves measurement data from the cloud and adds and updates it to the user's database, enabling accurate real-time monitoring of health conditions.

[1184] AI and emotion engine for data analysis and plan generation

[1185] An AI engine located on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. At the same time, an emotion engine acquires and analyzes the user's emotional data and adjusts the plan based on their emotional state (e.g., stress, fatigue). The generated plan is stored on the server and notified to the user via their device (smartphone app).

[1186] Plan notification and execution management

[1187] The device notifies the user of daily training plans and meal menus, and the user follows the training and meal plans and enters their progress and emotional state into the app.

[1188] The device sends the entered progress and emotional data to the server. The server receives the progress and emotional data, which is then reanalyzed by the AI ​​engine and emotion engine. This analysis evaluates the effectiveness of the training plan and meal menu, and the plan is revised as necessary. The revised plan is then notified to the user again via the device. This ensures that optimal training and meals are always provided based on the user's latest health and emotional state.

[1189] Specific examples

[1190] For User B:

[1191] 1. User B installs the smartphone app and enters the following personal data:

[1192] Height: 165cm

[1193] Weight: 60kg

[1194] Date of Birth: June 15, 1990

[1195] Gender: Female

[1196] Past sports experience: 3 years of yoga experience

[1197] 2. Pair your fitness device and body composition monitor and send the measurement data to the cloud. Let's assume the initial measurement data is as follows:

[1198] Weight: 60.5 kg

[1199] Body fat percentage: 22%

[1200] 3. The AI ​​engine generates a training plan that:

[1201] Yoga: 30 minutes x 3 days / week

[1202] Walking: 20 minutes x 3 days / week

[1203] 4. The AI ​​engine will suggest the following meal options:

[1204] Breakfast: Fruit smoothie

[1205] Lunch: Salad bowl

[1206] Dinner: Grilled chicken and vegetables

[1207] 5. User B performs training and diet and enters their progress and emotional state into the smartphone app. For example, on a day when they feel high stress, they record their emotional state as "high stress."

[1208] 6. The server receives and analyzes the progress data and emotional data, fine-tunes the next plan, and notifies User B. On days when stress is high, adjustments may be made, such as increasing the number of yoga sessions to promote relaxation.

[1209] By repeating this cycle, users can continuously implement optimal training plans and meal menus, and manage their health, including their emotional state, enabling effective health management tailored to individual goals.

[1210] The processing flow will be explained below.

[1211] Step 1:

[1212] A user installs the smartphone app and opens it.

[1213] Step 2:

[1214] The terminal displays an initial registration screen and prompts the user to enter personal data such as height, weight, date of birth, sex, and past sports history.

[1215] Step 3:

[1216] The user enters the required information and presses the send button.

[1217] Step 4:

[1218] The terminal transmits the input personal data to the server.

[1219] Step 5:

[1220] The server receives the data and stores it in a database along with the user ID.

[1221] Step 6:

[1222] The user acquires new measurement data (e.g., weight, body fat percentage) using a fitness device and a body composition monitor.

[1223] Step 7:

[1224] Fitness devices and body composition scales upload measurement data to the cloud.

[1225] Step 8:

[1226] The server periodically retrieves measurement data from the cloud and adds / updates it to the user's database.

[1227] Step 9:

[1228] An AI engine in the server analyzes the user's personal data and measurement data.

[1229] Step 10:

[1230] Based on the analysis results, the AI ​​engine generates a training plan and meal menu tailored to the user's goals (e.g., weight loss, muscle building).

[1231] Step 11:

[1232] The emotion engine acquires and analyzes the user's emotion data and evaluates their emotional state (e.g., stress, fatigue).

[1233] Step 12:

[1234] The AI ​​engine and emotion engine work together to modify training plans and meal menus taking into account the user's emotional state.

[1235] Step 13:

[1236] The server stores the generated training plan and meal menu for each user.

[1237] Step 14:

[1238] The device sends the user daily training plans and meal menu notifications.

[1239] Step 15:

[1240] The user follows the notified content and carries out training and meals.

[1241] Step 16:

[1242] Users enter their training and diet progress and emotional state into a smartphone app.

[1243] Step 17:

[1244] The terminal transmits the input progress data and emotion data to the server.

[1245] Step 18:

[1246] The server receives the progress data and emotion data, which are then reanalyzed by the AI ​​engine and emotion engine.

[1247] Step 19:

[1248] The AI ​​engine adjusts and modifies training plans and meal menus based on progress data.

[1249] Step 20:

[1250] The emotion engine re-evaluates the emotion data and makes modifications that take into account the user's emotional state.

[1251] Step 21:

[1252] The server stores the modified plan and sends it back to the device.

[1253] Step 22:

[1254] The terminal notifies the user of the modified plan.

[1255] Step 23:

[1256] The user follows the new plan, again training and eating, and continually inputs their progress and emotional state.

[1257] The above is the specific processing flow of the system. This allows users to continuously implement optimal training plans and meal menus, and effectively manage their health according to their individual goals. Furthermore, by taking the user's emotional state into consideration, more appropriate and sustainable health management can be achieved.

[1258] Example 2

[1259] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1260] Conventional health management systems often provide uniform exercise programs and meal menus without considering the personal data or emotional state of each individual user. As a result, plans tailored to the user's health and emotional state are not provided, making effective health management difficult. In addition, there are few ways to effectively integrate and analyze data from fitness devices and body composition monitors, making it difficult to adjust training plans and meal menus in real time.

[1261] 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.

[1262] In this invention, the server includes means for registering personal data of each user, means for automatically acquiring measurement data of each user in conjunction with the measurement device and body composition monitor, means for analyzing the acquired personal data and measurement data to generate an optimal exercise program and meal menu, and means for analyzing the user's emotional data and adjusting the exercise program and meal menu based on the user's emotional state. This allows for the provision of an exercise program and meal menu optimized for each individual user, enabling effective health management that takes into account the user's emotional state.

[1263] "Personal data" refers to basic information about an individual user, such as the user's height, weight, date of birth, gender, and past sports history.

[1264] "Measuring device" is a general term for equipment used to measure a user's health status, such as weight, body fat percentage, and heart rate.

[1265] A "body composition scale" is a device that measures data related to a user's body composition, such as body fat percentage and muscle mass.

[1266] "Emotion data" is information related to emotions such as the user's stress level or fatigue.

[1267] An "exercise program" is a training plan designed based on the user's health and goals.

[1268] A "meal menu" is a meal plan suggested based on the user's health management and fitness goals.

[1269] The "notification means" is a means for transmitting information such as the generated exercise program and meal menu to the user.

[1270] A "server" is a central processing unit that stores and analyzes data through cloud services.

[1271] "Analysis methods" refer to algorithms and software that derive optimal exercise programs and meal menus based on acquired personal data and measurement data.

[1272] The "modification means" is a means for readjusting the exercise program and meal menu based on the user's progress data and emotional data.

[1273] The present invention combines a system that manages each user's personal data and measurement data and suggests optimal exercise programs and meal plans with an emotion engine that recognizes the user's emotions. This system can support effective health management while taking into account the user's health and emotional state.

[1274] Initial registration and data entry

[1275] First, the user installs the smartphone app and performs initial registration within the app. At this time, the user enters personal data such as height, weight, date of birth, gender, and past sports history. The entered data is sent from the device to the server, which then stores it in a database.

[1276] Integration with fitness devices and body composition monitors

[1277] When users pair their fitness devices or body composition monitors with a smartphone app, the data measured by these devices (such as weight, body fat percentage, heart rate, etc.) is automatically uploaded to the cloud. The server periodically retrieves new measurement data from the cloud and adds and updates it to the user's database. This collaboration allows users to accurately understand their health status in real time.

[1278] AI and emotion engine for data analysis and plan generation

[1279] The AI ​​engine located on the server analyzes the acquired personal and measurement data using programs such as Python. Based on this analysis, the AI ​​generates an optimal exercise program and meal menu based on the user's physical fitness level and goals. At the same time, the emotion engine analyzes the user's emotional data (e.g., stress, fatigue) and adjusts the plan based on that emotional state. The generated plan is stored on the server and notified to the user via their device.

[1280] Plan notification and execution management

[1281] The device notifies the user of their daily exercise program and meal menu. The user then exercises and eats according to the notified information, and enters their progress and emotional state into the app. For example, on a day when they feel high stress, they record their emotional state as "high stress." The device then sends the entered progress and emotional data to the server. The server receives this data, and the AI ​​engine and emotion engine reanalyze it. Based on the results of this analysis, the effectiveness of the exercise program and meal menu is evaluated, and the plan is revised if necessary. The revised plan is then notified to the user again via the device.

[1282] Specific examples

[1283] User B's case

[1284] 1. User B installs the smartphone app and enters the following personal data:

[1285] Height: 165cm

[1286] Weight: 60kg

[1287] Date of Birth: June 15, 1990

[1288] Gender: Female

[1289] Past sports experience: 3 years of yoga experience

[1290] 2. User B pairs the fitness device and body composition monitor and sends the measurement data to the cloud. The initial measurement data is as follows:

[1291] Weight: 60.5 kg

[1292] Body fat percentage: 22%

[1293] 3. The AI ​​engine generates the following exercise program:

[1294] Yoga: 30 minutes x 3 days / week

[1295] Walking: 20 minutes x 3 days / week

[1296] 4. The AI ​​engine will suggest the following meal options:

[1297] Breakfast: Fruit smoothie

[1298] Lunch: Salad bowl

[1299] Dinner: Grilled chicken and vegetables

[1300] 5. User B exercises and eats, and enters their progress and emotional state into the smartphone app. For example, on a day when they feel stressed, they record their emotional state as "high stress."

[1301] 6. The server receives and analyzes the progress data and emotional data, fine-tunes the next plan, and notifies User B. For example, on days when stress is high, adjustments may be made such as increasing the number of yoga plans to promote relaxation.

[1302] By repeating this cycle, users can continuously implement optimal exercise programs and meal menus, and manage their health, including their emotional state.

[1303] Example input to a generative AI model

[1304] Example prompt sentence:

[1305] "Enter the user's personal data (height, weight, sports history, etc.) and measurement data (weight, body fat percentage, heart rate, etc.) and use the emotion engine to analyze the user's emotional state. Generate appropriate exercise program and meal menu suggestions."

[1306] By inputting this prompt into a generative AI model, specific suggestions can be obtained, including exercise programs and meal menus optimized for each individual user.

[1307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1308] Step 1:

[1309] The user installs the smartphone app and launches it. The user selects the "New Registration" option within the app and enters personal data (e.g., height, weight, date of birth, gender, past sports history, etc.). The entered data is sent from the device to the server. The specific input data is height 165 cm, weight 60 kg, date of birth June 15, 1990, gender female, past sports history 3 years of yoga experience. The server stores the received data in a database. The output is a state in which the user's personal data has been registered in the database.

[1310] Step 2:

[1311] The user pairs the measurement device and body composition monitor with a smartphone app. This uploads data measured by the measurement device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud. The server periodically retrieves new measurement data from the cloud. The specific input data is the initial measurement results: weight 60.5 kg, body fat percentage 22%, heart rate 75 bpm. The output is the latest measurement data added and updated to the user's database.

[1312] Step 3:

[1313] The acquired personal and measurement data is analyzed using an AI engine located on a server. Specifically, a Python program is used to generate an optimal exercise program and meal menu based on the user's physical fitness level and goals. Specific input data includes height 165 cm, weight 60.5 kg, body fat percentage 22%, heart rate 75 bpm, and three years of yoga experience. The output is the generated exercise program (30 minutes of yoga three days per week, 20 minutes of walking three days per week) and meal menu (breakfast: fruit smoothie, lunch: salad bowl, dinner: grilled chicken and vegetables).

[1314] Step 4:

[1315] The server uses the emotion engine to analyze the user's emotional data (e.g., stress level, fatigue level) and adjusts the exercise program and meal menu. The specific input data is the emotional data entered by the user into the app. For example, a high-stress day is recorded as "high stress." The output is an exercise program and meal menu adjusted based on the emotional state. For example, on a high-stress day, yoga sessions to promote relaxation are increased.

[1316] Step 5:

[1317] The device notifies the user of their daily exercise program and meal menu. Specifically, a smartphone app uses push notifications to notify the user of today's exercise program (e.g., 30 minutes of yoga, 20 minutes of walking) and meal menu (e.g., fruit smoothie, salad bowl, grilled chicken and vegetables). The input data are the exercise program and meal menu stored on the server. The output is the state in which the user has received the notification.

[1318] Step 6:

[1319] The user follows the notified exercise program and meal menu and inputs their progress and emotional state into the smartphone app. Specific input data includes the completion of today's yoga session (e.g., "30 minutes of yoga completed") and their emotional state (e.g., "high stress"). The output is the state in which the input progress data and emotional data are recorded in the app.

[1320] Step 7:

[1321] The device encrypts the input progress data and emotion data in real time and sends it to the server. The input data is the progress data and emotion data entered by the user into the app. The output is the state in which this data is stored on the server.

[1322] Step 8:

[1323] The AI ​​engine and emotion engine reanalyze the progress and emotion data received by the server. The specific input data is the user's progress and emotion data. The output is a revised exercise program and meal menu.

[1324] Step 9:

[1325] The server then makes any necessary modifications to the exercise program and meal menu based on the analysis results. Specific input data are the reanalyzed exercise program and meal menu modifications. The output is the modified exercise program and meal menu.

[1326] Step 10:

[1327] The server saves the revised exercise program and meal menu in the database again and notifies the user via the terminal. The input data is the revised exercise program and meal menu. The output is a state in which the user has received the latest exercise program and meal menu.

[1328] Through these steps, users can continuously receive optimal exercise programs and meal menus based on their individual health and emotional state.

[1329] (Application example 2)

[1330] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1331] Many conventional health management systems propose training plans and meal menus based on the user's personal and fitness data. However, these systems do not take the user's emotional state into account, and the proposed plans and menus are not necessarily optimal for the user's current situation. Furthermore, even if a healthy meal menu is proposed, it is difficult to quickly prepare the ingredients, making it difficult to apply the system to real situations.

[1332] 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.

[1333] In this invention, the server includes means for registering personal data of each user, means for linking with the fitness device and body composition scale to automatically acquire measurement data of each user, means for analyzing the acquired personal data and measurement data to generate an optimal training plan and meal menu, means for notifying the user of the generated training plan and meal menu, means for inputting progress data and emotional data of the user, means for reanalyzing the input progress data and emotional data to modify the training plan and meal menu, and means for generating an optimal meal menu based on the emotional data and providing ingredients based on the meal menu in cooperation with a delivery service. This enables more precise health management based on the user's health and emotional state, and further enables the ingredients for the suggested meal menu to be quickly obtained, making it possible to implement the menu in line with reality.

[1334] "Personal data" refers to information about an individual, such as the user's height, weight, age, sex, and past sports activities.

[1335] A "fitness device" is a device for acquiring fitness-related data such as a user's weight, heart rate, and number of steps.

[1336] A "body composition scale" is a device that measures not only weight but also body composition such as body fat percentage, muscle mass, and water content.

[1337] "Measurement data" refers to data relating to the user's health condition obtained using a fitness device and a body composition monitor.

[1338] A "training plan" is a workout or exercise plan generated based on the user's health status and goals.

[1339] A "meal menu" is a daily meal plan suggested based on the user's health condition and nutritional needs.

[1340] "Emotion data" is data that indicates the user's mental state, such as stress or fatigue.

[1341] A "delivery service" is a service that delivers goods, especially ingredients and food, to a specific location.

[1342] This invention combines a system that manages a user's personal data and measurement data and proposes optimal training plans and meal menus with an emotion engine. This system is designed to support effective health management based on the user's health and emotional state.

[1343] Initial registration and data entry

[1344] First, users install the smartphone app and perform initial registration, entering personal data such as height, weight, and past sports history. The device then sends this data to the server and stores it in a database.

[1345] Integration with fitness devices and body composition monitors

[1346] Users pair the app with fitness devices or body composition scales and periodically upload measurement data to the cloud. The server retrieves the measurement data from the cloud and updates the database, enabling accurate understanding of health status in real time.

[1347] AI and emotion engine for data analysis and plan generation

[1348] The AI ​​engine located on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. At the same time, the emotion engine acquires and analyzes the user's emotional data and adjusts the plan based on their emotional state (e.g., stress, fatigue). The generated plan is stored on the server and notified to the user via their device.

[1349] Plan notification and execution management

[1350] The device notifies the user of daily training plans and meal menus. The user then follows the training and meal plans and enters their progress and emotional state into the app. The device then sends the entered progress and emotional data to the server. The server receives the progress and emotional data, and the AI ​​engine and emotion engine reanalyze them to evaluate the effectiveness of the training plan and meal menu, and modify the plan as necessary.

[1351] Collaboration with food delivery companies

[1352] Based on the emotion data, the server generates an optimal meal menu and provides ingredients based on the menu in cooperation with a delivery service. Users can quickly obtain ingredients for the suggested meal menu, reducing the effort required for preparation and cooking.

[1353] Specific examples

[1354] User B's case

[1355] User B installs the smartphone app and enters personal data such as height 165 cm, weight 60 kg, and three years of yoga experience. The fitness device and body composition scale are paired, and the measurement data is sent to the server. The AI ​​engine generates a training plan of 30 minutes of yoga three days per week and 20 minutes of walking three days per week, as well as meal menus such as "fruit smoothie," "salad bowl," and "grilled chicken and vegetables." User B records his or her emotional state, entering, for example, "high stress," and the server adjusts the menu to one with a relaxing effect and provides the ingredients via a delivery service.

[1356] Example prompts for generative AI models

[1357] "I'm a 165cm, 60kg woman with three years of yoga experience. I've been feeling a bit stressed lately. Please use this data to suggest the best training plan and meal menu, and provide the ingredients via a delivery service."

[1358] This allows users to always practice optimal training and diet based on their latest health and emotional state, realizing sustainable health management.

[1359] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1360] Step 1:

[1361] The user installs the smartphone app and performs initial registration. The personal data entered (height, weight, past sports history, etc.) is sent from the device to the server and stored in a database. This registration allows the system to grasp the user's basic information.

[1362] Step 2:

[1363] Users pair their fitness devices or body composition scales with the app. The paired devices periodically upload measurement data (weight, body fat percentage, heart rate, etc.) to the cloud, where the server collects and stores the data. This allows users to accurately understand their health status in real time.

[1364] Step 3:

[1365] The AI ​​engine located on the server analyzes the acquired personal data and measurement data. For example, based on data such as height, weight, and heart rate, it generates a training plan and meal menu according to the user's physical fitness level and goals. The input for this process is the personal data and measurement data, and the output is a training plan and meal menu.

[1366] Step 4:

[1367] At the same time, the emotion engine acquires and analyzes the user's emotional data. The input emotional data could be information such as "high stress," and the emotion engine uses this information to analyze the user's emotional state and output an appropriate result. The emotion analysis results obtained here are used to adjust the training plan and meal menu.

[1368] Step 5:

[1369] The server notifies the generated training plan and meal menu to the terminal, and the terminal notifies the user of the daily training and meal menu, allowing the user to understand the tasks to be performed.

[1370] Step 6:

[1371] The user follows the training and meal menu provided and enters their progress and emotional state into the app. The progress and emotional data entered through this operation is then sent from the device to the server.

[1372] Step 7:

[1373] The server re-analyzes the received progress and emotion data. The AI ​​engine and emotion engine use this data to evaluate the effectiveness of the training plan and meal menu and modify the plan as necessary. The input of this re-analysis is the progress and emotion data, and the output is the modified plan.

[1374] Step 8:

[1375] Based on the emotion data, the server generates an optimal meal menu. The server then provides ingredients based on the generated menu in cooperation with a delivery service. The delivery service delivers the ingredients to the user, allowing the user to quickly complete the suggested menu.

[1376] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1377] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1378] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1379] [Fourth embodiment]

[1380] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1381] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1382] 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).

[1383] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1384] 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.

[1385] 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).

[1386] 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.

[1387] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1388] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1389] 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.

[1390] 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.

[1391] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1392] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1393] The present invention is a system for efficiently managing the health of each user, and a specific embodiment for implementing this system will be described. This system registers personal data and generates and provides optimal training plans and meal menus based on that data. The system's program processing and specific examples will be explained below.

[1394] Initial registration and data entry

[1395] The user installs the smartphone app and performs the initial registration. At this time, the user enters personal data such as height, weight, past sports history, etc. The app sends this data to the server, which receives it and stores it in a database.

[1396] Integration with fitness devices and body composition monitors

[1397] Users pair their fitness device or body composition monitor with the app to complete the connection, which then uploads the data measured by the device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud.

[1398] The server periodically retrieves measurement data from the cloud and adds and updates it to the user's database, enabling accurate real-time monitoring of health conditions.

[1399] AI-based data analysis and plan generation

[1400] An AI engine installed on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. The generated plan is saved on the server and notified to the user via their device (smartphone app).

[1401] Plan notification and execution management

[1402] The device notifies the user of daily training plans and meal menus, and the user follows the training and meal plans and enters their progress into the app.

[1403] The device sends the entered progress data to the server, which then receives it and reanalyzes it with an AI engine. This analysis evaluates the effectiveness of the training plan and meal menu, and modifies the plan if necessary.

[1404] The revised plan is then sent to the user via the device, providing optimal training and dietary information based on the user's current health status.

[1405] Specific examples

[1406] For User A:

[1407] 1. User A installs the smartphone app and enters the following personal data:

[1408] Height: 170cm

[1409] Weight: 70kg

[1410] Date of Birth: January 1, 1985

[1411] Gender: Male

[1412] Past sports history: None

[1413] 2. Pair your fitness device and body composition monitor and send the measurement data to the cloud. Let's assume the initial measurement data is as follows:

[1414] Weight: 70.2 kg

[1415] Body fat percentage: 25%

[1416] 3. The AI ​​engine generates a training plan that:

[1417] Easy jogging: 20 minutes x 3 days / week

[1418] Squats: 15 reps x 2 sets / day

[1419] 4. The AI ​​engine will suggest the following meal options:

[1420] Breakfast: Protein shake

[1421] Lunch: Salad and grilled chicken

[1422] Dinner: Steamed fish and vegetables

[1423] 5. User A goes jogging and enters the progress into the smartphone app. For example, he / she jogs for 20 minutes and records that his / her weight has increased to 70.0 kg.

[1424] 6. The server receives and analyzes the progress data, fine-tunes the next plan, and notifies User A.

[1425] By repeating this cycle, users can continuously implement optimal training plans and meal menus, enabling effective health management according to individual goals.

[1426] The processing flow will be explained below.

[1427] Step 1:

[1428] A user installs the smartphone app and opens it.

[1429] Step 2:

[1430] The terminal displays an initial registration screen and prompts the user to enter personal data such as height, weight, date of birth, sex, and past sports history.

[1431] Step 3:

[1432] The user enters the required information and presses the send button.

[1433] Step 4:

[1434] The terminal transmits the input personal data to the server.

[1435] Step 5:

[1436] The server receives the data and stores it in a database along with the user ID.

[1437] Step 6:

[1438] The user acquires new measurement data (e.g., weight, body fat percentage) using a fitness device and a body composition monitor.

[1439] Step 7:

[1440] Fitness devices and body composition scales upload measurement data to the cloud.

[1441] Step 8:

[1442] The server periodically retrieves measurement data from the cloud and adds / updates it to the user's database.

[1443] Step 9:

[1444] An AI engine in the server analyzes the user's personal data and measurement data.

[1445] Step 10:

[1446] Based on the analysis results, the AI ​​engine generates a training plan and meal menu tailored to the user's goals (e.g., weight loss, muscle building).

[1447] Step 11:

[1448] The server stores the generated training plan and meal menu for each user.

[1449] Step 12:

[1450] The device sends the user daily training plans and meal menu notifications.

[1451] Step 13:

[1452] The user follows the notified content and carries out training and meals.

[1453] Step 14:

[1454] Users enter their training and dietary progress into a smartphone app.

[1455] Step 15:

[1456] The terminal transmits the input progress data to the server.

[1457] Step 16:

[1458] The server receives the progress data and the AI ​​engine reanalyzes it.

[1459] Step 17:

[1460] The AI ​​engine adjusts and modifies training plans and meal menus based on progress data.

[1461] Step 18:

[1462] The server stores the modified plan and sends it back to the device.

[1463] Step 19:

[1464] The terminal notifies the user of the modified plan.

[1465] Step 20:

[1466] The user follows the new plan, resumes training and eating, and continues to enter progress.

[1467] The above is the specific processing flow of the system, which allows users to continuously implement optimal training plans and meal menus, and effectively manage their health according to their individual goals.

[1468] Example 1

[1469] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1470] In recent years, the importance of managing exercise and diet based on individual health conditions has increased. However, conventional methods require users to manage their own health, which requires time-consuming regular data entry and progress tracking. Furthermore, it is difficult to generate optimal plans for individual goals and make ongoing adjustments, making efficient health management difficult. Therefore, there is a demand for systems that enable more efficient and accurate data collection, analysis, plan generation, and progress management.

[1471] 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.

[1472] In this invention, the server includes means for registering each user's biometric information, means for working with the detection device to automatically acquire each user's measurement data, means for analyzing the acquired biometric information and measurement data to generate an optimal exercise plan and diet plan, means for notifying the user of the generated exercise plan and diet plan, means for inputting the user's progress data, and means for reanalyzing the input progress data and correcting the exercise plan and diet plan. This allows the user to understand their own health condition in real time and continuously implement an optimal exercise and diet plan.

[1473] "Biometric information" refers to data specific to each individual user, such as their height, weight, age, gender, and past sports history.

[1474] "Detection device" refers to a device for obtaining measurement data such as a user's weight, body fat percentage, heart rate, etc.

[1475] An "exercise plan" refers to a specific exercise or training program created based on the user's health status and goals.

[1476] "Diet plan" refers to a specific diet and nutrition program created based on the user's health status and goals.

[1477] "Cloud" refers to a collective term for data storage and processing services over the internet used to send and receive measurement and plan data.

[1478] "Server" refers to a computer system that receives, stores, analyzes, and reanalyzes data.

[1479] "User" refers to an individual who uses this system to manage their own health.

[1480] "Progress data" refers to data including recorded data on the exercises and dietary details performed by the user, as well as resulting changes in weight and body fat percentage.

[1481] "Reanalysis" refers to the process of reevaluating the effectiveness of your exercise and diet plans based on the progress data you enter and adjusting the plans as necessary.

[1482] This invention is a system for efficiently managing the health of each user, and in particular, for generating and providing optimal exercise and dietary plans based on each user's biological information and measurement data. This system operates by combining multiple hardware and software components.

[1483] First, the user installs a smartphone app. Through the app, the user inputs biometric information such as their height, weight, past sports history, age, and gender. This data is sent from the device to a server, which then stores the received data in a database. This allows each user's individual biometric information to be recorded and managed.

[1484] Next, users pair their fitness devices, body composition scales, and other detection devices with the app, which automatically uploads measurement data such as weight, body fat percentage, and heart rate to the cloud. The server periodically retrieves this data and adds and updates it to a database.

[1485] The server contains an AI engine equipped with a generative AI model. This AI engine analyzes the user's biometric information and measurement data to generate optimal exercise and diet plans. Specific analysis methods include clustering, regression analysis, and machine learning algorithms (using TensorFlow or PyTorch, for example). The generated plans are stored on the server and notified to the user via a smartphone app.

[1486] The user receives the notification and executes their daily exercise and diet plans. After execution, the user enters progress data (e.g., which exercises were performed for how long, and changes in weight after weighing) into the app. This progress data is sent back to the server, which reanalyzes the received data using an AI engine. As a result of the reanalysis, the user is again notified of the newly revised exercise and diet plans.

[1487] As a concrete example, User A installs a smartphone app and inputs the following biometric information:

[1488] Height: 170cm

[1489] Weight: 70kg

[1490] Date of Birth: January 1, 1985

[1491] Gender: Male

[1492] Past sports history: None

[1493] The fitness device is paired with a body composition scale, and data on a weight of 70.2 kg and a body fat percentage of 25% is sent to the cloud. The AI ​​engine then generates an exercise plan consisting of light jogging (20 minutes x 3 days per week) and squats (15 repetitions x 2 sets per day), as well as a diet plan consisting of a protein shake for breakfast, salad and grilled chicken for lunch, and steamed fish and vegetables for dinner.

[1494] User A goes jogging and records the progress on the smartphone app as "20 minutes of jogging, weight fluctuated to 70.0 kg." The server receives the progress data, and the AI ​​engine reanalyzes it to fine-tune the next plan and notify User A.

[1495] The following prompts are examples of input to a generative AI model:

[1496] "Please suggest an appropriate exercise and diet plan for a 30-year-old male who is 170cm tall, weighs 70kg, and has no prior athletic experience. The initial measurement data is 70.2kg in weight and 25% body fat."

[1497] The above is a specific embodiment for carrying out the present invention. This system enables a user to grasp their own health condition in real time and continuously implement an optimal exercise and diet plan.

[1498] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1499] Step 1:

[1500] A user installs a smartphone app

[1501] A user downloads and installs the app from the App Store or Google Play. After installation, the user launches the app and proceeds to the initial setup screen.

[1502] Input: None

[1503] Output: The app is installed on your smartphone.

[1504] Step 2:

[1505] The user inputs biometric information

[1506] The user enters biometric information such as height, weight, past sports history, age, and gender on the initial setup screen of the app. The user enters each piece of data into the input form and presses the "Send" button. The device sends the entered data to the server. The server stores the received data in a database.

[1507] Input: User's height, weight, past sports history, age, gender

[1508] Output: User's biometric information stored in a database

[1509] Step 3:

[1510] The user pairs the detector with the app

[1511] Users pair fitness devices and body composition monitors via Bluetooth or Wi-Fi on the app's settings screen. Once paired, measurement data is sent from the device to the cloud.

[1512] Input: Fitness devices, body composition monitors

[1513] Output: Measurement data sent to the cloud

[1514] Step 4:

[1515] The server acquires the measurement data from the cloud.

[1516] The server accesses the cloud and periodically retrieves user measurement data. The server sends a request to the cloud service using an API call and receives the response data. The server then adds or updates the obtained data to the existing user database.

[1517] Input: Measurement data obtained from the cloud

[1518] Output: Updated user database

[1519] Step 5:

[1520] The AI ​​engine on the server analyzes the data

[1521] An AI engine located on the server analyzes the user's biometric information and measurement data and executes algorithms using machine learning libraries such as Python, TensorFlow, and PyTorch.

[1522] Input: Biometric information, measurement data

[1523] Output: Analysis results (user's physical fitness level, health condition)

[1524] Step 6:

[1525] AI engine generates exercise and diet plans

[1526] Based on the analysis results, the AI ​​engine generates an individualized exercise and diet plan. The plan is then saved on a server, which then sends it to the user's device and notifies them via a smartphone app.

[1527] Input: Analysis results

[1528] Output: Exercise plan, diet plan (saved on the server and notified via the device)

[1529] Step 7:

[1530] The user executes an exercise plan and a diet plan.

[1531] The user performs daily training and meals according to the exercise and diet plans notified by the app. After completing the plan, the user enters and saves progress data (e.g., jogging time, weight fluctuations, etc.) into the app.

[1532] Input: Exercise plan, diet plan

[1533] Output: Progress data entered into the app

[1534] Step 8:

[1535] The device sends progress data to the server

[1536] The device sends the progress data entered by the user to the server via an HTTP request, and the server receives the data and stores it in a database.

[1537] Input: Progress data

[1538] Output: Progress data stored in a database

[1539] Step 9:

[1540] The server re-parses the progress data

[1541] The server reanalyzes the received progress data using an AI engine. Based on the analysis results, the exercise plan and diet plan are evaluated and revised. The revised plan is saved back on the server and notified to the user via their device.

[1542] Input: Progress data

[1543] Output: Evaluation results, revised exercise plan, diet plan

[1544] Through these processing steps, users can understand their own health status in real time and implement optimal exercise and diet plans. This system enables effective health management according to individual health goals.

[1545] (Application example 1)

[1546] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1547] Current health management systems have difficulty accurately understanding a user's health status and continuously providing optimal training plans and meal menus. There are also challenges in efficiently managing and utilizing data from fitness devices and body composition scales. Furthermore, there is no established method for utilizing generative AI models to analyze personal and progress data and provide users with optimal plans. A new health management system is needed to solve these challenges.

[1548] 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.

[1549] In this invention, the server includes means for registering personal data of each user, means for linking with a fitness device and a body composition scale and automatically acquiring measurement data of each user, means for analyzing the acquired personal data and measurement data and generating an optimal training plan and meal menu, means for notifying the user of the generated training plan and meal menu, means for inputting user progress data, means for reanalyzing the input progress data and modifying the training plan and meal menu, means for transmitting any type of progress data of each user to the server and acquiring the progress data from a cloud service, means for analyzing the personal data and progress data using a generative AI model and generating and modifying an optimal training plan and meal menu based on prompts, and means for providing the user with visual feedback of the training plan and meal menu via a head-mounted display, thereby enabling the system to grasp the user's health condition in real time, continuously provide optimal plans, and effectively manage the user's health.

[1550] A "user" is an individual who uses this system to manage their own health.

[1551] "Personal data" refers to information about a user's physical data and lifestyle, including height, weight, age, gender, and sports history.

[1552] A "fitness device" is a device used to measure a user's exercise status and biological data, and includes smartwatches and activity monitors.

[1553] A "body composition scale" is a device that measures body composition data such as weight and body fat percentage.

[1554] "Measurement data" refers to data obtained from measuring equipment such as fitness devices and body composition monitors.

[1555] "Analysis" refers to data processing to extract meaningful information from acquired personal data and measurement data.

[1556] A "training plan" is a recommended exercise schedule for a user to improve their health.

[1557] A "meal menu" is a meal content recommended for a user to maintain or improve their health.

[1558] "Notification" is an action of communicating the generated training plan and meal menu to the user.

[1559] "Progress data" is data that records the results and status of the training and diet that the user has performed.

[1560] "Reanalysis" is the process of reanalyzing progress data to find new insights and areas for improvement.

[1561] "Cloud services" are services such as data storage and computing power provided over the internet.

[1562] A "generative AI model" is a type of artificial intelligence that uses machine learning algorithms to analyze user data and generate optimal training plans and meal menus.

[1563] A "prompt" is a sentence that gives instructions to an AI model for data analysis and plan generation.

[1564] A "head-mounted display" is a wearable device that provides visual information to the user and is primarily worn on the head.

[1565] The present invention provides a system for efficiently and effectively managing a user's health. The system is configured using a smartphone, a fitness device, a body composition monitor, a server, a cloud service, a generative AI model, and a head-mounted display. Specific embodiments are described below.

[1566] Overall system configuration

[1567] 1. User registration and initial data entry

[1568] First, users install a dedicated application on their smartphone and perform initial registration. At this time, users enter their personal data (height, weight, age, gender, sports history, etc.) into the application. This data is sent to the server and stored in a database.

[1569] 2. Integration with fitness devices and body composition monitors

[1570] Users pair their fitness devices and body composition monitors with the app, which uploads data measured by the devices (e.g., weight, body fat percentage, heart rate, etc.) to the cloud service. The server retrieves this data from the cloud service and updates the user's database.

[1571] 3. AI-based data analysis and plan generation

[1572] A generative AI model stored on the server analyzes the acquired personal and measurement data. Based on the prompts, the AI ​​generates a training plan and meal menu that is optimal for the user's fitness level and health goals. The generated plan is stored on the server and notified to the user via smartphone.

[1573] 4. Plan notification and execution management

[1574] A smartphone app notifies users of daily training plans and meal menus. Users then follow the instructions and enter their progress into the app, for example, recording their jogging time or weight fluctuations.

[1575] 5. Reanalyze progress data and revise plans

[1576] The server receives the progress data sent by the user and reanalyzes it. This reanalysis evaluates the effectiveness of the training plan and meal menu, and modifies the plan if necessary. The modified plan is then notified to the user again via smartphone.

[1577] 6. Providing visual feedback

[1578] Additionally, a head-mounted display is used to provide users with visual feedback on their training plan and meal menu, helping them better understand the plan and motivating them to follow it.

[1579] Hardware and software used

[1580] Hardware: Smartphones, fitness devices, body composition monitors, head-mounted displays

[1581] Software: Dedicated smartphone app, cloud service, generative AI model

[1582] Specific examples

[1583] Initial user data entry

[1584] A user installs a smartphone app and enters the following data:

[1585] Height: 175cm

[1586] Weight: 72kg

[1587] Date of Birth: February 15, 1990

[1588] Gender: Female

[1589] Sports history: Occasional jogging

[1590] Prompt Sentence Examples

[1591] "Analyze user data and generate personalized training plans and meal menus. Personal data: Height 175cm, Weight 72kg, Date of birth 1990-02-15, Gender Female, Sports history Occasional jogging. Device data: Weight 72.5kg, Body fat 22%, Heart rate 78."

[1592] This invention makes it possible to grasp the user's health condition in real time and provide optimal training plans and meal menus at all times, thereby enabling the user to effectively manage their health.

[1593] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1594] Step 1:

[1595] The user installs the smartphone app, performs initial registration, and enters personal data.

[1596] Specific operation: The user uses a smartphone to enter personal data such as height, weight, age, gender, and sports history, and sends it to the server.

[1597] Input: Personal data (height, weight, age, gender, sports history)

[1598] Output: Personal data of the user registered on the server

[1599] Step 2:

[1600] The user pairs the fitness device and body composition scale with the app to obtain measurement data.

[1601] Specific operation: The user pairs the fitness device and body composition scale with their smartphone and uploads the data to the cloud service. The server retrieves the measurement data from the cloud and stores it in a database.

[1602] Input: Measurement data (weight, body fat percentage, heart rate)

[1603] Output: Measurement data stored on the server

[1604] Step 3:

[1605] The server analyzes personal data and measurement data to generate optimal training plans and meal menus.

[1606] How it works: The generative AI model on the server analyzes personal and measurement data. Based on prompts, the AI ​​generates optimal training plans and meal menus.

[1607] Input: Personal data, measurement data, prompt text

[1608] Output: Generated training plan and meal menu

[1609] Step 4:

[1610] The generated training plan and meal menu are notified to the user.

[1611] Specific operation: The server sends the generated training plan and meal menu to the smartphone app and notifies the user.

[1612] Input: Generated training plan and meal menu

[1613] Output: Training plan and meal menu notified to the user

[1614] Step 5:

[1615] The user follows a training plan and meal menu and enters progress data.

[1616] Specific operation: The user enters training and diet progress (e.g., jogging time, weight fluctuations) into the smartphone app and sends it to the server.

[1617] Input: Progress data (training time, weight fluctuations, etc.)

[1618] Output: Progress data stored on the server

[1619] Step 6:

[1620] The server reanalyzes the progress data and adjusts the training plan and meal menu.

[1621] What it does: The server's generative AI model reanalyzes the progress data and modifies the training plan and meal menu as needed. The modified plan is then notified to the user again.

[1622] Input: Progress data, current training plan and meal menu

[1623] Output: Modified training plan and meal menu

[1624] Step 7:

[1625] A head-mounted display is used to provide the user with visual feedback of their training plan and meal menu.

[1626] Specific operation: The server generates a training plan and sends visual feedback of the meal menu to the head-mounted display. The user receives the feedback using the head-mounted display.

[1627] Input: Training plan and meal menu

[1628] Output: Visual feedback provided to the user

[1629] 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.

[1630] The present invention combines a system that manages each user's personal data and measurement data and proposes optimal training plans and meal menus with an emotion engine that recognizes the user's emotions. This system can support effective health management while taking into account the user's health and emotional state. The system's program processing and specific examples are explained below.

[1631] Initial registration and data entry

[1632] The user installs the smartphone app and performs the initial registration. At this time, the user enters personal data such as height, weight, past sports history, etc. The app sends this data to the server, which receives it and stores it in a database.

[1633] Integration with fitness devices and body composition monitors

[1634] Users pair their fitness device or body composition monitor with the app to complete the connection, which then uploads the data measured by the device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud.

[1635] The server periodically retrieves measurement data from the cloud and adds and updates it to the user's database, enabling accurate real-time monitoring of health conditions.

[1636] AI and emotion engine for data analysis and plan generation

[1637] An AI engine located on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. At the same time, an emotion engine acquires and analyzes the user's emotional data and adjusts the plan based on their emotional state (e.g., stress, fatigue). The generated plan is stored on the server and notified to the user via their device (smartphone app).

[1638] Plan notification and execution management

[1639] The device notifies the user of daily training plans and meal menus, and the user follows the training and meal plans and enters their progress and emotional state into the app.

[1640] The device sends the entered progress and emotional data to the server. The server receives the progress and emotional data, which is then reanalyzed by the AI ​​engine and emotion engine. This analysis evaluates the effectiveness of the training plan and meal menu, and the plan is revised as necessary. The revised plan is then notified to the user again via the device. This ensures that optimal training and meals are always provided based on the user's latest health and emotional state.

[1641] Specific examples

[1642] For User B:

[1643] 1. User B installs the smartphone app and enters the following personal data:

[1644] Height: 165cm

[1645] Weight: 60kg

[1646] Date of Birth: June 15, 1990

[1647] Gender: Female

[1648] Past sports experience: 3 years of yoga experience

[1649] 2. Pair your fitness device and body composition monitor and send the measurement data to the cloud. Let's assume the initial measurement data is as follows:

[1650] Weight: 60.5 kg

[1651] Body fat percentage: 22%

[1652] 3. The AI ​​engine generates a training plan that:

[1653] Yoga: 30 minutes x 3 days / week

[1654] Walking: 20 minutes x 3 days / week

[1655] 4. The AI ​​engine will suggest the following meal options:

[1656] Breakfast: Fruit smoothie

[1657] Lunch: Salad bowl

[1658] Dinner: Grilled chicken and vegetables

[1659] 5. User B performs training and diet and enters their progress and emotional state into the smartphone app. For example, on a day when they feel high stress, they record their emotional state as "high stress."

[1660] 6. The server receives and analyzes the progress data and emotional data, fine-tunes the next plan, and notifies User B. On days when stress is high, adjustments may be made, such as increasing the number of yoga sessions to promote relaxation.

[1661] By repeating this cycle, users can continuously implement optimal training plans and meal menus, and manage their health, including their emotional state, enabling effective health management tailored to individual goals.

[1662] The processing flow will be explained below.

[1663] Step 1:

[1664] A user installs the smartphone app and opens it.

[1665] Step 2:

[1666] The terminal displays an initial registration screen and prompts the user to enter personal data such as height, weight, date of birth, sex, and past sports history.

[1667] Step 3:

[1668] The user enters the required information and presses the send button.

[1669] Step 4:

[1670] The terminal transmits the input personal data to the server.

[1671] Step 5:

[1672] The server receives the data and stores it in a database along with the user ID.

[1673] Step 6:

[1674] The user acquires new measurement data (e.g., weight, body fat percentage) using a fitness device and a body composition monitor.

[1675] Step 7:

[1676] Fitness devices and body composition scales upload measurement data to the cloud.

[1677] Step 8:

[1678] The server periodically retrieves measurement data from the cloud and adds / updates it to the user's database.

[1679] Step 9:

[1680] An AI engine in the server analyzes the user's personal data and measurement data.

[1681] Step 10:

[1682] Based on the analysis results, the AI ​​engine generates a training plan and meal menu tailored to the user's goals (e.g., weight loss, muscle building).

[1683] Step 11:

[1684] The emotion engine acquires and analyzes the user's emotion data and evaluates their emotional state (e.g., stress, fatigue).

[1685] Step 12:

[1686] The AI ​​engine and emotion engine work together to modify training plans and meal menus taking into account the user's emotional state.

[1687] Step 13:

[1688] The server stores the generated training plan and meal menu for each user.

[1689] Step 14:

[1690] The device sends the user daily training plans and meal menu notifications.

[1691] Step 15:

[1692] The user follows the notified content and carries out training and meals.

[1693] Step 16:

[1694] Users enter their training and diet progress and emotional state into a smartphone app.

[1695] Step 17:

[1696] The terminal transmits the input progress data and emotion data to the server.

[1697] Step 18:

[1698] The server receives the progress data and emotion data, which are then reanalyzed by the AI ​​engine and emotion engine.

[1699] Step 19:

[1700] The AI ​​engine adjusts and modifies training plans and meal menus based on progress data.

[1701] Step 20:

[1702] The emotion engine re-evaluates the emotion data and makes modifications that take into account the user's emotional state.

[1703] Step 21:

[1704] The server stores the modified plan and sends it back to the device.

[1705] Step 22:

[1706] The terminal notifies the user of the modified plan.

[1707] Step 23:

[1708] The user follows the new plan, again training and eating, and continually inputs their progress and emotional state.

[1709] The above is the specific processing flow of the system. This allows users to continuously implement optimal training plans and meal menus, and effectively manage their health according to their individual goals. Furthermore, by taking the user's emotional state into consideration, more appropriate and sustainable health management can be achieved.

[1710] Example 2

[1711] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1712] Conventional health management systems often provide uniform exercise programs and meal menus without considering the personal data or emotional state of each individual user. As a result, plans tailored to the user's health and emotional state are not provided, making effective health management difficult. In addition, there are few ways to effectively integrate and analyze data from fitness devices and body composition monitors, making it difficult to adjust training plans and meal menus in real time.

[1713] 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.

[1714] In this invention, the server includes means for registering personal data of each user, means for automatically acquiring measurement data of each user in conjunction with the measurement device and body composition monitor, means for analyzing the acquired personal data and measurement data to generate an optimal exercise program and meal menu, and means for analyzing the user's emotional data and adjusting the exercise program and meal menu based on the user's emotional state. This allows for the provision of an exercise program and meal menu optimized for each individual user, enabling effective health management that takes into account the user's emotional state.

[1715] "Personal data" refers to basic information about an individual user, such as the user's height, weight, date of birth, gender, and past sports history.

[1716] "Measuring device" is a general term for equipment used to measure a user's health status, such as weight, body fat percentage, and heart rate.

[1717] A "body composition scale" is a device that measures data related to a user's body composition, such as body fat percentage and muscle mass.

[1718] "Emotion data" is information related to emotions such as the user's stress level or fatigue.

[1719] An "exercise program" is a training plan designed based on the user's health and goals.

[1720] A "meal menu" is a meal plan suggested based on the user's health management and fitness goals.

[1721] The "notification means" is a means for transmitting information such as the generated exercise program and meal menu to the user.

[1722] A "server" is a central processing unit that stores and analyzes data through cloud services.

[1723] "Analysis methods" refer to algorithms and software that derive optimal exercise programs and meal menus based on acquired personal data and measurement data.

[1724] The "modification means" is a means for readjusting the exercise program and meal menu based on the user's progress data and emotional data.

[1725] The present invention combines a system that manages each user's personal data and measurement data and suggests optimal exercise programs and meal plans with an emotion engine that recognizes the user's emotions. This system can support effective health management while taking into account the user's health and emotional state.

[1726] Initial registration and data entry

[1727] First, the user installs the smartphone app and performs initial registration within the app. At this time, the user enters personal data such as height, weight, date of birth, gender, and past sports history. The entered data is sent from the device to the server, which then stores it in a database.

[1728] Integration with fitness devices and body composition monitors

[1729] When users pair their fitness devices or body composition monitors with a smartphone app, the data measured by these devices (such as weight, body fat percentage, heart rate, etc.) is automatically uploaded to the cloud. The server periodically retrieves new measurement data from the cloud and adds and updates it to the user's database. This collaboration allows users to accurately understand their health status in real time.

[1730] AI and emotion engine for data analysis and plan generation

[1731] The AI ​​engine located on the server analyzes the acquired personal and measurement data using programs such as Python. Based on this analysis, the AI ​​generates an optimal exercise program and meal menu based on the user's physical fitness level and goals. At the same time, the emotion engine analyzes the user's emotional data (e.g., stress, fatigue) and adjusts the plan based on that emotional state. The generated plan is stored on the server and notified to the user via their device.

[1732] Plan notification and execution management

[1733] The device notifies the user of their daily exercise program and meal menu. The user then exercises and eats according to the notified information, and enters their progress and emotional state into the app. For example, on a day when they feel high stress, they record their emotional state as "high stress." The device then sends the entered progress and emotional data to the server. The server receives this data, and the AI ​​engine and emotion engine reanalyze it. Based on the results of this analysis, the effectiveness of the exercise program and meal menu is evaluated, and the plan is revised if necessary. The revised plan is then notified to the user again via the device.

[1734] Specific examples

[1735] User B's case

[1736] 1. User B installs the smartphone app and enters the following personal data:

[1737] Height: 165cm

[1738] Weight: 60kg

[1739] Date of Birth: June 15, 1990

[1740] Gender: Female

[1741] Past sports experience: 3 years of yoga experience

[1742] 2. User B pairs the fitness device and body composition monitor and sends the measurement data to the cloud. The initial measurement data is as follows:

[1743] Weight: 60.5 kg

[1744] Body fat percentage: 22%

[1745] 3. The AI ​​engine generates the following exercise program:

[1746] Yoga: 30 minutes x 3 days / week

[1747] Walking: 20 minutes x 3 days / week

[1748] 4. The AI ​​engine will suggest the following meal options:

[1749] Breakfast: Fruit smoothie

[1750] Lunch: Salad bowl

[1751] Dinner: Grilled chicken and vegetables

[1752] 5. User B exercises and eats, and enters their progress and emotional state into the smartphone app. For example, on a day when they feel stressed, they record their emotional state as "high stress."

[1753] 6. The server receives and analyzes the progress data and emotional data, fine-tunes the next plan, and notifies User B. For example, on days when stress is high, adjustments may be made such as increasing the number of yoga plans to promote relaxation.

[1754] By repeating this cycle, users can continuously implement optimal exercise programs and meal menus, and manage their health, including their emotional state.

[1755] Example input to a generative AI model

[1756] Example prompt sentence:

[1757] "Enter the user's personal data (height, weight, sports history, etc.) and measurement data (weight, body fat percentage, heart rate, etc.) and use the emotion engine to analyze the user's emotional state. Generate appropriate exercise program and meal menu suggestions."

[1758] By inputting this prompt into a generative AI model, specific suggestions can be obtained, including exercise programs and meal menus optimized for each individual user.

[1759] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1760] Step 1:

[1761] The user installs the smartphone app and launches it. The user selects the "New Registration" option within the app and enters personal data (e.g., height, weight, date of birth, gender, past sports history, etc.). The entered data is sent from the device to the server. The specific input data is height 165 cm, weight 60 kg, date of birth June 15, 1990, gender female, past sports history 3 years of yoga experience. The server stores the received data in a database. The output is a state in which the user's personal data has been registered in the database.

[1762] Step 2:

[1763] The user pairs the measurement device and body composition monitor with a smartphone app. This uploads data measured by the measurement device (e.g., weight, body fat percentage, heart rate, etc.) to the cloud. The server periodically retrieves new measurement data from the cloud. The specific input data is the initial measurement results: weight 60.5 kg, body fat percentage 22%, heart rate 75 bpm. The output is the latest measurement data added and updated to the user's database.

[1764] Step 3:

[1765] The acquired personal and measurement data is analyzed using an AI engine located on a server. Specifically, a Python program is used to generate an optimal exercise program and meal menu based on the user's physical fitness level and goals. Specific input data includes height 165 cm, weight 60.5 kg, body fat percentage 22%, heart rate 75 bpm, and three years of yoga experience. The output is the generated exercise program (30 minutes of yoga three days per week, 20 minutes of walking three days per week) and meal menu (breakfast: fruit smoothie, lunch: salad bowl, dinner: grilled chicken and vegetables).

[1766] Step 4:

[1767] The server uses the emotion engine to analyze the user's emotional data (e.g., stress level, fatigue level) and adjusts the exercise program and meal menu. The specific input data is the emotional data entered by the user into the app. For example, a high-stress day is recorded as "high stress." The output is an exercise program and meal menu adjusted based on the emotional state. For example, on a high-stress day, yoga sessions to promote relaxation are increased.

[1768] Step 5:

[1769] The device notifies the user of their daily exercise program and meal menu. Specifically, a smartphone app uses push notifications to notify the user of today's exercise program (e.g., 30 minutes of yoga, 20 minutes of walking) and meal menu (e.g., fruit smoothie, salad bowl, grilled chicken and vegetables). The input data are the exercise program and meal menu stored on the server. The output is the state in which the user has received the notification.

[1770] Step 6:

[1771] The user follows the notified exercise program and meal menu and inputs their progress and emotional state into the smartphone app. Specific input data includes the completion of today's yoga session (e.g., "30 minutes of yoga completed") and their emotional state (e.g., "high stress"). The output is the state in which the input progress data and emotional data are recorded in the app.

[1772] Step 7:

[1773] The device encrypts the input progress data and emotion data in real time and sends it to the server. The input data is the progress data and emotion data entered by the user into the app. The output is the state in which this data is stored on the server.

[1774] Step 8:

[1775] The AI ​​engine and emotion engine reanalyze the progress and emotion data received by the server. The specific input data is the user's progress and emotion data. The output is a revised exercise program and meal menu.

[1776] Step 9:

[1777] The server then makes any necessary modifications to the exercise program and meal menu based on the analysis results. Specific input data are the reanalyzed exercise program and meal menu modifications. The output is the modified exercise program and meal menu.

[1778] Step 10:

[1779] The server saves the revised exercise program and meal menu in the database again and notifies the user via the terminal. The input data is the revised exercise program and meal menu. The output is a state in which the user has received the latest exercise program and meal menu.

[1780] Through these steps, users can continuously receive optimal exercise programs and meal menus based on their individual health and emotional state.

[1781] (Application example 2)

[1782] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1783] Many conventional health management systems propose training plans and meal menus based on the user's personal and fitness data. However, these systems do not take the user's emotional state into account, and the proposed plans and menus are not necessarily optimal for the user's current situation. Furthermore, even if a healthy meal menu is proposed, it is difficult to quickly prepare the ingredients, making it difficult to apply the system to real situations.

[1784] 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.

[1785] In this invention, the server includes means for registering personal data of each user, means for linking with the fitness device and body composition scale to automatically acquire measurement data of each user, means for analyzing the acquired personal data and measurement data to generate an optimal training plan and meal menu, means for notifying the user of the generated training plan and meal menu, means for inputting progress data and emotional data of the user, means for reanalyzing the input progress data and emotional data to modify the training plan and meal menu, and means for generating an optimal meal menu based on the emotional data and providing ingredients based on the meal menu in cooperation with a delivery service. This enables more precise health management based on the user's health and emotional state, and further enables the ingredients for the suggested meal menu to be quickly obtained, making it possible to implement the menu in line with reality.

[1786] "Personal data" refers to information about an individual, such as the user's height, weight, age, sex, and past sports activities.

[1787] A "fitness device" is a device for acquiring fitness-related data such as a user's weight, heart rate, and number of steps.

[1788] A "body composition scale" is a device that measures not only weight but also body composition such as body fat percentage, muscle mass, and water content.

[1789] "Measurement data" refers to data relating to the user's health condition obtained using a fitness device and a body composition monitor.

[1790] A "training plan" is a workout or exercise plan generated based on the user's health status and goals.

[1791] A "meal menu" is a daily meal plan suggested based on the user's health condition and nutritional needs.

[1792] "Emotion data" is data that indicates the user's mental state, such as stress or fatigue.

[1793] A "delivery service" is a service that delivers goods, especially ingredients and food, to a specific location.

[1794] This invention combines a system that manages a user's personal data and measurement data and proposes optimal training plans and meal menus with an emotion engine. This system is designed to support effective health management based on the user's health and emotional state.

[1795] Initial registration and data entry

[1796] First, users install the smartphone app and perform initial registration, entering personal data such as height, weight, and past sports history. The device then sends this data to the server and stores it in a database.

[1797] Integration with fitness devices and body composition monitors

[1798] Users pair the app with fitness devices or body composition scales and periodically upload measurement data to the cloud. The server retrieves the measurement data from the cloud and updates the database, enabling accurate understanding of health status in real time.

[1799] AI and emotion engine for data analysis and plan generation

[1800] The AI ​​engine located on the server analyzes the acquired personal data and measurement data. The AI ​​generates an optimal training plan and meal menu based on the user's physical fitness level and goals. At the same time, the emotion engine acquires and analyzes the user's emotional data and adjusts the plan based on their emotional state (e.g., stress, fatigue). The generated plan is stored on the server and notified to the user via their device.

[1801] Plan notification and execution management

[1802] The device notifies the user of daily training plans and meal menus. The user then follows the training and meal plans and enters their progress and emotional state into the app. The device then sends the entered progress and emotional data to the server. The server receives the progress and emotional data, and the AI ​​engine and emotion engine reanalyze them to evaluate the effectiveness of the training plan and meal menu, and modify the plan as necessary.

[1803] Collaboration with food delivery companies

[1804] Based on the emotion data, the server generates an optimal meal menu and provides ingredients based on the menu in cooperation with a delivery service. Users can quickly obtain ingredients for the suggested meal menu, reducing the effort required for preparation and cooking.

[1805] Specific examples

[1806] User B's case

[1807] User B installs the smartphone app and enters personal data such as height 165 cm, weight 60 kg, and three years of yoga experience. The fitness device and body composition scale are paired, and the measurement data is sent to the server. The AI ​​engine generates a training plan of 30 minutes of yoga three days per week and 20 minutes of walking three days per week, as well as meal menus such as "fruit smoothie," "salad bowl," and "grilled chicken and vegetables." User B records his or her emotional state, entering, for example, "high stress," and the server adjusts the menu to one with a relaxing effect and provides the ingredients via a delivery service.

[1808] Example prompts for generative AI models

[1809] "I'm a 165cm, 60kg woman with three years of yoga experience. I've been feeling a bit stressed lately. Please use this data to suggest the best training plan and meal menu, and provide the ingredients via a delivery service."

[1810] This allows users to always practice optimal training and diet based on their latest health and emotional state, realizing sustainable health management.

[1811] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1812] Step 1:

[1813] The user installs the smartphone app and performs initial registration. The personal data entered (height, weight, past sports history, etc.) is sent from the device to the server and stored in a database. This registration allows the system to grasp the user's basic information.

[1814] Step 2:

[1815] Users pair their fitness devices or body composition scales with the app. The paired devices periodically upload measurement data (weight, body fat percentage, heart rate, etc.) to the cloud, where the server collects and stores the data. This allows users to accurately understand their health status in real time.

[1816] Step 3:

[1817] The AI ​​engine located on the server analyzes the acquired personal data and measurement data. For example, based on data such as height, weight, and heart rate, it generates a training plan and meal menu according to the user's physical fitness level and goals. The input for this process is the personal data and measurement data, and the output is a training plan and meal menu.

[1818] Step 4:

[1819] At the same time, the emotion engine acquires and analyzes the user's emotional data. The input emotional data could be information such as "high stress," and the emotion engine uses this information to analyze the user's emotional state and output an appropriate result. The emotion analysis results obtained here are used to adjust the training plan and meal menu.

[1820] Step 5:

[1821] The server notifies the generated training plan and meal menu to the terminal, and the terminal notifies the user of the daily training and meal menu, allowing the user to understand the tasks to be performed.

[1822] Step 6:

[1823] The user follows the training and meal menu provided and enters their progress and emotional state into the app. The progress and emotional data entered through this operation is then sent from the device to the server.

[1824] Step 7:

[1825] The server re-analyzes the received progress and emotion data. The AI ​​engine and emotion engine use this data to evaluate the effectiveness of the training plan and meal menu and modify the plan as necessary. The input of this re-analysis is the progress and emotion data, and the output is the modified plan.

[1826] Step 8:

[1827] Based on the emotion data, the server generates an optimal meal menu. The server then provides ingredients based on the generated menu in cooperation with a delivery service. The delivery service delivers the ingredients to the user, allowing the user to quickly complete the suggested menu.

[1828] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1829] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1830] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1831] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1832] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1833] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1834] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1835] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1836] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1837] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1838] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1839] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1840] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1841] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1842] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1843] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1844] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1845] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1846] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1847] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1848] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1849] The following is further disclosed regarding the above embodiment.

[1850] (Claim 1)

[1851] A means for registering personal data of each user;

[1852] A means for automatically acquiring measurement data of each user in conjunction with a fitness device and a body composition monitor;

[1853] A means for analyzing the acquired personal data and measurement data to generate an optimal training plan and meal menu;

[1854] a means for notifying the user of the generated training plan and meal menu;

[1855] a means for inputting user progress data;

[1856] A means for reanalyzing the entered progress data and modifying the training plan and meal menu;

[1857] A system including:

[1858] (Claim 2)

[1859] The system of claim 1, further comprising means for the fitness device and the body composition monitor to transmit measurement data via the cloud, and for the server to acquire the measurement data from the cloud.

[1860] (Claim 3)

[1861] 10. The system of claim 1, further comprising means for adjusting the generated training plan and meal menu based on the user's goals.

[1862] "Example 1"

[1863] (Claim 1)

[1864] means for registering biometric information of each user;

[1865] a means for automatically acquiring measurement data for each user in conjunction with the detection device;

[1866] A means for analyzing the acquired biological information and measurement data and generating an optimal exercise plan and diet plan;

[1867] means for notifying a user of the generated exercise plan and diet plan;

[1868] a means for inputting user progress data;

[1869] means for reanalyzing the input progress data and correcting the exercise plan and diet plan;

[1870] A system including:

[1871] (Claim 2)

[1872] 10. The system of claim 1, further comprising means for the detection device to transmit the measurement data via the cloud and for the server to acquire the measurement data from the cloud.

[1873] (Claim 3)

[1874] 10. The system of claim 1, further comprising means for adjusting the generated exercise and eating plans based on the user's goals.

[1875] "Application Example 1"

[1876] (Claim 1)

[1877] A means for registering personal data of each user;

[1878] A means for automatically acquiring measurement data of each user in conjunction with a fitness device and a body composition monitor;

[1879] A means for analyzing the acquired personal data and measurement data to generate an optimal training plan and meal menu;

[1880] a means for notifying the user of the generated training plan and meal menu;

[1881] a means for inputting user progress data;

[1882] A means for reanalyzing the entered progress data and modifying the training plan and meal menu;

[1883] transmitting any type of progress data of each of the users to a server;

[1884] A means for obtaining progress data from a cloud service;

[1885] A means for analyzing personal and progress data using a generative AI model to generate and modify optimal training plans and meal menus based on prompts;

[1886] means for providing the user with visual feedback of the training plan and meal menu via a head mounted display;

[1887] A system including:

[1888] (Claim 2)

[1889] The system of claim 1, further comprising means for the fitness device and the body composition monitor to transmit measurement data via the cloud, and for the server to acquire the measurement data from the cloud.

[1890] (Claim 3)

[1891] 10. The system of claim 1, further comprising means for adjusting the generated training plan and meal menu based on the user's goals.

[1892] "Example 2: Combining Emotion Engines"

[1893] (Claim 1)

[1894] a means for registering personal data of each user;

[1895] A means for automatically acquiring measurement data of each user in conjunction with the measurement device and the body composition monitor;

[1896] A means for analyzing the acquired personal data and measurement data and generating an optimal exercise program and meal menu;

[1897] means for analyzing the user's emotional data and adjusting an exercise program and a meal menu based on the user's emotional state;

[1898] means for notifying a user of the generated exercise program and meal menu;

[1899] means for inputting user progress data and emotion data;

[1900] means for reanalyzing the input progress data and emotion data and correcting the exercise program and meal menu;

[1901] A system including:

[1902] (Claim 2)

[1903] The system according to claim 1, further comprising means for the various measuring devices and the body composition monitor to transmit measurement data via the cloud, and for the server to acquire the measurement data from the cloud.

[1904] (Claim 3)

[1905] 10. The system of claim 1, further comprising means for adjusting the generated exercise program and meal menu based on the user's goals and emotional state.

[1906] "Application example 2 when combining emotion engines"

[1907] (Claim 1)

[1908] A means for registering personal data of each user;

[1909] A means for automatically acquiring measurement data of each user in conjunction with a fitness device and a body composition monitor;

[1910] A means for analyzing the acquired personal data and measurement data to generate an optimal training plan and meal menu;

[1911] a means for notifying the user of the generated training plan and meal menu;

[1912] means for inputting user progress data and emotion data;

[1913] A means for reanalyzing the input progress data and emotional data and correcting the training plan and meal menu;

[1914] A method for generating optimal meal menus based on emotional data and providing ingredients based on the menus in cooperation with delivery services;

[1915] A system including:

[1916] (Claim 2)

[1917] The system of claim 1, further comprising means for the fitness device and the body composition monitor to transmit measurement data via the cloud, and for the server to acquire the measurement data from the cloud.

[1918] (Claim 3)

[1919] 10. The system of claim 1, further comprising means for adjusting the generated training plan and meal menu based on the user's goals. [Explanation of symbols]

[1920] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for registering personal data of each user; A means for automatically acquiring measurement data of each user in conjunction with a fitness device and a body composition monitor; A means for analyzing the acquired personal data and measurement data to generate an optimal training plan and meal menu; a means for notifying the user of the generated training plan and meal menu; a means for inputting user progress data; A means for reanalyzing the entered progress data and modifying the training plan and meal menu; A system including:

2. The system according to claim 1 , further comprising means for the fitness device and the body composition monitor to transmit measurement data via the cloud, and for the server to acquire the measurement data from the cloud.

3. 10. The system of claim 1, further comprising means for adjusting the generated training plan and meal menu based on the user's goals.

Images