system

Abstract

Provide a system. 【Solution means】 Input means for inputting personal information from a user, Collection means for collecting the user's biological information via a sensor device, Evaluation means for analyzing data in a computing device to evaluate the user's health status, Generation means for creating a user-specific exercise plan using a generated AI model based on the evaluation, Notification means for notifying the generated exercise plan to the user's information terminal, Feedback collection means for receiving feedback information from the user, Retraining means for improving the accuracy of the generated AI model using the feedback information, Guide means for providing exercise guidance as a household robot, A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern middle-aged and elderly businessmen have a problem that although their awareness of health is increasing, it is difficult to incorporate an exercise habit due to their busy lives. In addition, there is a lack of an appropriate method for performing the necessary exercises for maintaining health efficiently and continuously. Without an appropriate exercise plan, it may hinder stress reduction and improvement of work productivity. In response to such problems, it is required to provide health support optimized for individual lifestyles and health conditions.

Means for Solving the Problems

[0005] The present invention comprises an input means for inputting personal information from the user and a collection means for collecting the user's biometric data via a sensor device. Furthermore, it includes an evaluation means for analyzing the data on a server to evaluate the user's health status, and a generation means for creating an individualized exercise plan for the user using a generated AI model based on the evaluation. It also includes a notification means for notifying the user's terminal of the generated exercise plan, a feedback collection means for receiving feedback data from the user, and a retraining means for improving the accuracy of the generated AI model using the feedback data. This makes it possible to propose exercises that are suitable for individual needs, maintain motivation, and promote efficient health.

[0006] "Input means" refers to a device or system that has the function of acquiring and recording personal information from a user.

[0007] "Collection means" refers to a device or system for continuously acquiring a user's biometric data via a sensor device.

[0008] "Evaluation means" refers to a device or system that has the function of analyzing data collected on a server and objectively determining the user's health status.

[0009] "Generation means" refers to a device or system for automatically creating individualized exercise plans for users using a generated AI model based on analysis results.

[0010] A "notification means" is a device or system that has the function of transmitting the generated exercise plan to the user's terminal and proposing specific actions.

[0011] A "feedback collection means" is a device or system that has the function of receiving and recording feedback data from users regarding their exercise performance.

[0012] A "retraining device" is a device or system that has the function of performing a process to improve the performance of a generated AI model using feedback data. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a generative AI instructor system designed to support users in maintaining their health and promoting exercise. This system proposes an exercise plan optimized for each individual user, aiming to maintain their motivation.

[0035] In this system, users first enter personal information using a terminal. This includes their name, age, gender, recent health check results, and lifestyle information. Furthermore, the terminal works in conjunction with the user's smartwatch and other sensor devices to collect biometric data in real time. This data includes heart rate, steps taken, calorie consumption, and sleep patterns.

[0036] The server collects received personal information and biometric data and uses a powerful data analysis engine to assess each user's health status. This assessment process includes calculating health indicators, determining stress levels, and identifying tendencies toward sedentary lifestyles. This generates a detailed health report about the user's current situation.

[0037] Next, the server uses a generation AI model to generate an optimal exercise plan for each user based on the evaluation results. This plan includes a variety of exercises tailored to the user's needs and goals, such as aerobic exercise, strength training, and relaxation exercises. The frequency, duration, and intensity of the exercises are also set individually.

[0038] The generated exercise plan is notified to the user via their device. This allows the user to easily incorporate healthy habits into their daily life. Furthermore, after completing the exercise plan, the user provides feedback and sends it to the server via their device. This feedback includes their physical condition and satisfaction level after the exercise, as well as their perceived effectiveness of the exercise.

[0039] The server uses this feedback data to retrain the AI ​​model, continuously improving the accuracy of plan generation. This ensures that the server can always provide plans that satisfy users and effectively promote their health.

[0040] As a concrete example, consider a middle-aged businessman. This user gets tired easily and finds it difficult to maintain a consistent exercise routine. Based on his information, the system creates an effective relaxation exercise plan that can be done in a short amount of time, supporting him in reducing daily stress. By obtaining feedback, the exercise plan can be further refined to better suit his needs, thereby improving his quality of life.

[0041] Thus, the present invention provides users with individually optimized health support and helps them to continue exercising efficiently.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user enters personal information using their device. This includes age, gender, health check results, and details of their lifestyle. The device then sends this data to the server.

[0045] Step 2:

[0046] The device connects with smartwatches and fitness trackers to continuously collect biometric data. This data includes heart rate, steps taken, calories burned, and sleep patterns.

[0047] Step 3:

[0048] The server receives and stores personal information and biometric data transmitted from the terminal. The received data is stored in a database and used for analysis.

[0049] Step 4:

[0050] The server analyzes the accumulated data. Using a data analysis engine, it assesses the user's health status and determines their stress level and degree of lack of exercise.

[0051] Step 5:

[0052] The server uses a generative AI model to generate an optimal exercise plan tailored to each user. This plan includes the type, frequency, and intensity of exercise that are appropriate for the user's health condition.

[0053] Step 6:

[0054] The device notifies the user of the generated exercise plan. The notification includes specific exercises, timing, and points to note.

[0055] Step 7:

[0056] The user performs exercise according to the notified exercise plan. After the exercise, they input feedback on their physical condition and satisfaction with the exercise into the device.

[0057] Step 8:

[0058] The server collects and stores feedback received from users. This feedback is used to retrain the generative AI model.

[0059] Step 9:

[0060] The server retrains the AI ​​model based on feedback to improve the accuracy of plan generation. This process makes the suggestions to the user more personalized and effective.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In modern society, maintaining personal health and establishing exercise habits are crucial, but developing exercise plans that adapt to diverse lifestyles and health conditions is a challenging task. In particular, there is a need for personalized exercise plans and mechanisms to support their implementation, but conventional methods are insufficient. Furthermore, technologies that incorporate user feedback to improve plan accuracy are still evolving, and there is a need to realize effective health support.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes acquisition means for acquiring personal attribute data from the user, collection means for collecting the user's physiological data using a biometric measurement device, and evaluation means for analyzing the data in an information processing device to evaluate the user's health status. This enables the provision of an advanced exercise plan based on the user's individual health status and the long-term maintenance of health that accompanies it.

[0066] "Personal attribute data" refers to basic information about an individual, including the user's name, age, gender, height, weight, health check results, and lifestyle.

[0067] "Physiological data" refers to numerical data that indicates the user's physical condition, such as heart rate, steps taken, calories burned, and sleep patterns.

[0068] A "biometric measurement device" is a device, such as a smartwatch or other sensor device, that measures a user's physical values ​​in real time.

[0069] An "information processing device" is a hardware device, such as a server or computer, that receives, analyzes, and processes data.

[0070] A "generative artificial intelligence model" is a collection of algorithms that use machine learning and deep learning techniques to learn patterns from data and make predictions.

[0071] An "exercise plan" is a plan of exercise, including the type, frequency, intensity, and duration, that is formulated based on an individual's health condition and goals.

[0072] "Communication devices" refer to electronic devices such as smartphones, tablets, and personal computers that are used to send and receive information with users.

[0073] "Response data" refers to feedback information provided by users after completing an exercise plan, regarding their physical condition, satisfaction level, and the effectiveness of the exercise.

[0074] "Retraining" refers to the process of retraining a generative artificial intelligence model using user feedback data to improve the accuracy of predictions.

[0075] This invention is a system designed to support users in maintaining their health and promoting exercise. Based on personal attribute data and physiological data from the user, the system formulates an individually optimized exercise plan and provides it to the user, while also utilizing feedback to improve the accuracy of the generated AI model.

[0076] Users input their personal attribute data using a device, which includes smartphones and tablets. The device works in conjunction with smartwatches and other biometric devices to collect physiological data such as heart rate, steps, calorie consumption, and sleep patterns in real time. This data is transmitted to a server using wireless technologies such as Bluetooth.

[0077] The server uses Python and R data analysis libraries to analyze the received personal attribute data and physiological data. This data analysis includes calculating health indicators, identifying stress levels, and analyzing trends in sedentary lifestyles. Based on the evaluation results obtained, the server uses a generative AI model to create an exercise plan tailored to each user. Machine learning frameworks such as TENSORFLOW® and PyTorch are used for the generative AI model.

[0078] The developed exercise plan is notified to the user via a device. The device provides a reminder function to help the user incorporate the notified exercise plan into their daily routine. After the user completes the exercise plan, feedback data is sent to the server via the device. The feedback includes information about the user's physical condition after exercise, satisfaction level, and perceived effectiveness of the exercise.

[0079] The server retrains its AI model based on collected feedback, continuously improving the accuracy of exercise plan development. This allows it to provide users with more effective and satisfying health support.

[0080] For example, if a user enters the prompt, "Please suggest a stress-reducing exercise plan for a man in his 50s who is busy with work and doesn't have much time," the server will respond to this request by creating a plan that includes short yoga and stretching exercises and notifying the user via their device.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user enters personal attribute data using a terminal. This data includes name, age, gender, height, weight, health check results, and lifestyle. The terminal sends the entered data to the server. This entered data is used as basic information to diagnose the user's current health status.

[0084] Step 2:

[0085] The device synchronizes with a biometric measurement device to collect physiological data such as heart rate, steps, calories burned, and sleep patterns in real time. The device transmits the collected physiological data to a server using a Bluetooth connection. This physiological data provides dynamic information about the user's activity and health.

[0086] Step 3:

[0087] The server integrates received personal attribute data and physiological data, and analyzes the data using Python or R analysis libraries. Specifically, it calculates health indicators and evaluates stress levels and exercise habits. Based on the input data, it generates a health status assessment report for the user. As output, it provides this assessment result to the next processing step of the generating AI model.

[0088] Step 4:

[0089] The server uses a generative AI model to develop a personalized exercise plan based on the evaluation results. This generative AI model, implemented in TensorFlow or PyTorch, designs the optimal exercise plan tailored to the user's needs. Specifically, it calculates the type of exercise, frequency, duration, and intensity, and customizes the plan individually. The developed exercise plan is then sent from the server to the user's device.

[0090] Step 5:

[0091] The device notifies the user of an exercise plan and displays reminders to incorporate it into their daily activities. By following the notified exercise plan and performing the exercises, the user promotes maintaining their health. The device manages the implementation of this activity and tracks progress.

[0092] Step 6:

[0093] After completing their exercise plan, users input feedback on their physical condition and satisfaction level into the device. This feedback includes information about their physical sensations and mental satisfaction after exercise. The device then sends this feedback data to the server.

[0094] Step 7:

[0095] The server improves the accuracy of the exercise plan by retraining the generated AI model using the collected feedback data. The retraining utilizes collected feedback and new data, improving the AI ​​model's predictive capabilities. This results in more accurate and effective exercise plans for the user in the future.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] In modern society, personalized health management is important, but it is difficult to exercise consistently amidst a busy daily life. This invention aims to solve the problem of supporting the maintenance of a healthy lifestyle by providing a system that automatically generates an exercise plan tailored to the user's health condition and allows for easy exercise guidance at home.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes an input means for inputting personal information from the user, a collection means for collecting the user's biometric information via a sensor device, an evaluation means for analyzing the data in a computing device to evaluate the user's health status, a generation means for creating an individualized exercise plan for the user using a generated AI model based on the evaluation, a notification means for notifying the user's information terminal of the generated exercise plan, a feedback collection means for receiving feedback information from the user, a retraining means for improving the accuracy of the generated AI model using the feedback information, and a guide means for providing exercise guidance as a home robot. As a result, the user can receive exercise guidance at home and continue personalized health management.

[0101] A "user" is an individual who uses this system to receive health management and exercise guidance.

[0102] "Personal information" refers to identifiable data necessary for health management, such as a user's name, age, gender, health check results, and lifestyle.

[0103] A "sensor device" is a measuring instrument used to collect a user's biometric information in real time, and includes heart rate monitors, pedometers, and other similar devices.

[0104] "Biometric information" refers to data that indicates the user's physical activity and health status, such as heart rate, steps taken, calorie consumption, and sleep patterns.

[0105] A "computational device" is an electronic device used to analyze collected data and evaluate the user's health status.

[0106] "Assessing health status" is the process of calculating the user's health indicators and determining their stress levels and tendency towards lack of exercise.

[0107] A "generative AI model" is an artificial intelligence algorithm that automatically generates an optimal exercise plan for each user based on evaluation results.

[0108] An "exercise plan" is an exercise plan that includes aerobic exercise, strength training, and relaxation exercises tailored to the user's needs and goals.

[0109] An "information terminal" is an electronic device used to notify users of exercise plans, and includes smartphones and computers.

[0110] "Feedback information" refers to response data provided by users after they have exercised, including their physical condition, satisfaction level, and the effectiveness of the exercise.

[0111] A "home robot" is an autonomous mechanical system that provides exercise guidance to users within their homes.

[0112] The embodiments for carrying out the present invention are shown below.

[0113] In this system, the terminal first collects personal information from the user and then works with sensor devices to acquire biometric information in real time. This utilizes sensors such as heart rate monitors and pedometers. The collected personal information and biometric information are then transmitted to a server.

[0114] On the server, computing devices analyze this data, and a generative AI model evaluates the user's health status. This evaluation includes a function to determine stress levels based on the user's heart rate variability and step count information. Based on these results, an optimal exercise plan for each user is created using the generative AI model through the evaluation process. This model uses TensorFlow, PyTorch, and other tools to process and analyze the data.

[0115] The generated exercise plan is notified to the information terminal, allowing the user to incorporate it into their daily life. After completing the exercise, the user provides feedback information about their physical condition and the effects of the exercise, and sends it back to the server via the terminal. Based on this feedback information, the server retrains the generating AI model to improve the accuracy of the exercise plan.

[0116] The home robot will use this information to provide real-time exercise guidance to the user. For example, for a home user who has a lot of work scheduled in the morning, it can suggest stretching and deep breathing exercises to help refresh the mind and body. An example of a prompt message for the generating AI model might be, "Please suggest a relaxation exercise plan that can be done this morning for a 45-year-old adult who does desk work."

[0117] Thus, the present invention provides an intelligent evolution system based on user-individualized exercise guidance and feedback, supporting the maintenance of a healthy lifestyle in the home environment.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The terminal receives personal information from the user. This information includes the user's name, age, gender, health check results, and lifestyle. The entered data is sent to the server as foundational information for the user's individual health management.

[0121] Step 2:

[0122] The device collects biometric information in real time via connected sensor devices. Specifically, this includes data such as heart rate, steps taken, calorie consumption, and sleep patterns. The collected biometric information is sent to a server to continuously monitor the user's physical activity.

[0123] Step 3:

[0124] The server analyzes the received personal and biometric information. This analysis uses computing devices to analyze data such as the user's heart rate variability and step count, and evaluates their stress level and health status. Based on the biometric information input, health indicators are calculated, and the results are output.

[0125] Step 4:

[0126] The server uses the evaluation results to call a generating AI model to create an optimal exercise plan for each user. This process uses software such as TensorFlow and PyTorch to process the user's health data as input and output an exercise plan. The generated exercise plan is then customized according to the user's goals and needs.

[0127] Step 5:

[0128] The generated exercise plan is notified to the information terminal. The user then begins their daily exercise based on the notified exercise plan. The terminal can also set reminders and alerts to prompt the user to start and finish their exercise.

[0129] Step 6:

[0130] After completing their workout, users input feedback information about their experience and physical condition into their device. This feedback data is sent to a server to reflect the user's satisfaction level and the effectiveness of their workout, and is used to generate their next workout plan.

[0131] Step 7:

[0132] The server uses feedback information to retrain the generated AI model to improve its accuracy. In this process, the AI ​​model is adaptively improved using feedback as input data, aiming to generate more accurate motor plans.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention combines an emotion engine with a generative AI instructor system to support users' health management and exercise promotion. The emotion engine recognizes the user's emotional state and proposes an individualized health plan based on that state, thereby reducing stress and maintaining motivation.

[0135] This system begins with the user entering basic personal information using a device, including age, gender, occupation, and current health status. The device also connects with the user's smartwatch or fitness tracker to collect biometric data such as heart rate, steps taken, and calorie consumption in real time.

[0136] In addition, the emotion engine captures the user's facial expressions with a camera and analyzes their emotional state based on that data. This analysis allows the system to identify the user's current mental state and their stress and anxiety levels.

[0137] The server receives collected personal information, biometric data, and emotional data, and uses a data analysis engine to assess the overall health status. This assessment process integrates emotional data analyzed by the emotion engine with stress level determination based on the user's heart rate variability and step count data to determine the health status.

[0138] Next, the server uses a generative AI model to generate a personalized exercise plan for each user. This plan not only includes exercises suited to the user's health condition, but also takes into account relaxation exercises and stress-relieving exercises based on their emotional state.

[0139] The generated exercise plan is notified to the user via the device. The notification includes the benefits of exercising, goals to be achieved, and a reward plan to motivate the user. After completing the exercise, the user is prompted to enter feedback on changes in their physical condition and mood into the device.

[0140] The server receives feedback data and uses it to retrain the generative AI model and emotion engine, improving the accuracy of the system's suggestions. This feedback process ensures that users continue to receive plans optimized for them.

[0141] For example, if a user is determined to be experiencing stress due to work-related pressure, the emotion engine will assess the situation and suggest relaxation plans such as yoga or meditation to reduce stress. If this plan is implemented and stress reduction is reported, the emotion engine and generative AI model will be adjusted again to provide better health support to the user.

[0142] Based on the above, we will provide comprehensive health support that takes into account not only the user's physical condition but also their emotional state, realizing a system that efficiently and effectively supports health maintenance.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user enters personal information using their device. This includes age, gender, health check results, and details about their lifestyle. The device then sends this information to a cloud server.

[0146] Step 2:

[0147] The device connects with sensor devices such as smartwatches and fitness trackers to collect biometric data in real time. It periodically records data such as heart rate, steps taken, calorie consumption, and sleep patterns, and sends it to a server.

[0148] Step 3:

[0149] The device uses its built-in camera to capture the user's facial expressions. The emotion engine analyzes the image to determine the user's emotional state. The analysis results in the quantification of emotional states such as stress, anxiety, and joy.

[0150] Step 4:

[0151] The server receives personal information, biometric data, and emotional data sent by the user and stores them in a database. Based on the received data, a data analysis engine operates to evaluate the user's overall health status.

[0152] Step 5:

[0153] The server uses a generated AI model based on the evaluation results to create a personalized exercise plan for each user. This plan takes into account both the user's health and emotional state, and includes optimal exercises and stress-reducing activities.

[0154] Step 6:

[0155] The device notifies the user of the generated exercise plan. The notification includes details of the exercises to be performed, the recommended duration, and the expected effects of the exercises.

[0156] Step 7:

[0157] The user executes an exercise plan based on notifications from their device. After exercising, they input feedback on changes in their physical condition and mood into their device.

[0158] Step 8:

[0159] The server receives user feedback data and records it in a database. Based on this feedback, the generative AI model and emotion engine are retrained to improve accuracy.

[0160] Step 9:

[0161] The server uses a retrained and improved generative AI model to enable more personalized suggestions in subsequent plan generation. This improves the accuracy and satisfaction of health support for users.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] In modern society, lifestyle-related diseases and accumulated stress are becoming apparent health problems. Conventional health management systems have only considered the user's physical health status, making it difficult to take a comprehensive approach that also takes into account emotional state and stress levels. As a result, there has been a problem in that users are unable to sustainably manage their health.

[0165] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0166] In this invention, the server includes emotion analysis means for capturing the user's facial expressions and analyzing their emotional state, health evaluation means for analyzing the data and evaluating the user's health status, and plan generation means for creating an individualized exercise plan for the user using a generated AI model based on the evaluation. This enables comprehensive health management that takes into account not only the user's physical health status but also their emotional state and stress level.

[0167] "Information acquisition means" refers to a device or method for inputting and acquiring personal information from a user.

[0168] "Data collection means" refers to a device or method for collecting a user's biometric data via a sensor device.

[0169] "Emotional analysis means" refers to a device or method for capturing a user's facial expressions, analyzing that data, and evaluating their emotional state.

[0170] A "health assessment tool" is a device or method for analyzing data collected on a server and evaluating the user's health status.

[0171] "Plan generation means" refers to an apparatus or method for creating an individualized exercise plan for a user using a generation AI model based on the results of a health assessment.

[0172] "Plan notification means" refers to a device or method for notifying a user's terminal of a generated exercise plan.

[0173] "Feedback data collection means" refers to a device or method for receiving feedback data from users.

[0174] A "model retraining means" is a device or method for improving the accuracy of a generated AI model using received feedback data.

[0175] This invention implements a system that comprehensively manages the user's health and emotional state and provides an individualized exercise plan. Specifically, it uses the following devices and software.

[0176] The user first enters personal information via a device. This device works in conjunction with smartwatches and fitness trackers to collect biometric data such as the user's heart rate, steps taken, and calorie consumption in real time. The device also uses a camera to capture the user's facial expressions and analyzes their emotional state using software called an emotion engine.

[0177] The server processes personal information, biometric data, and emotional data transmitted from the device using a data analysis engine to assess overall health status. This assessment includes stress level determination from heart rate variability and step count, as well as integrated analysis of emotional data. Based on the assessment results, the server utilizes a generative AI model to generate a personalized exercise plan for the user. This exercise plan includes exercises optimized for the user's health condition and relaxation exercises appropriate for their emotional state.

[0178] The generated exercise plan is notified to the user via their device. The notification includes the benefits of the exercise, the goals to be achieved, and the reward plan. After completing the exercise, the user provides feedback on changes in their physical condition and mood via their device. The server uses this feedback to retrain the generating AI model and emotion engine, improving the accuracy of the suggestions.

[0179] For example, if a user enters information such as, "I'm a 30-year-old woman in a management position, and I experience high levels of daily stress. My heart rate tends to rise easily, and I'd like an exercise plan to help me relax," the system will suggest a relaxation plan that includes yoga and meditation, thereby helping to reduce the user's stress levels.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] Users enter personal information using a terminal. Specifically, they enter information about their age, gender, occupation, and health status. This is registered as the system's initial data. This information is the input data that forms the basis for subsequent data analysis.

[0183] Step 2:

[0184] The device collects biometric data from connected smartwatches and fitness trackers. Specific input data includes heart rate, steps taken, and calorie consumption. This biometric data is then sent to a server as output data for health assessment.

[0185] Step 3:

[0186] The device captures the user's facial expressions using its built-in camera. An emotion analysis engine receives this facial data as input and analyzes the user's emotional state. The analyzed emotional state data becomes the output data used for stress level assessment.

[0187] Step 4:

[0188] The server receives personal information, biometric data, and emotional data transmitted from the terminal and performs a health assessment. A data analysis engine uses this data as input to analyze and evaluate the user's health status. The output is an overall health status and stress level assessment.

[0189] Step 5:

[0190] The server uses a generative AI model to generate a personalized exercise plan for the user based on the results of the health assessment. It uses the assessment results as input and generates an exercise plan that takes into account the user's health and emotional state as output.

[0191] Step 6:

[0192] The server sends the generated exercise plan to the terminal. The terminal notifies the user of the plan. Specifically, it presents the benefits of the exercise, the goals to be achieved, and the reward plan. This becomes the output data for the user.

[0193] Step 7:

[0194] The user performs exercises based on the provided exercise plan and then inputs feedback on changes in their physical condition and emotions into the device. This feedback data is sent to the server as input data for the next processing step.

[0195] Step 8:

[0196] The server retrains its generative AI model and emotion engine based on feedback data received from the user. This retraining process improves the accuracy of subsequent exercise plan suggestions. The improved AI model is output using the feedback data as input.

[0197] (Application Example 2)

[0198] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0199] In modern society, people are becoming more aware of the importance of health management, but it is difficult to achieve effective health management amidst busy daily lives. Furthermore, stress and emotional fluctuations often reduce motivation for exercise, making it difficult to maintain good health. Therefore, there is a need for a system that efficiently provides comprehensive health support, taking into account not only the user's physical condition but also their emotional state.

[0200] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0201] In this invention, the server includes information input means for inputting personal information from the user, data collection means for collecting the user's biometric data via sensor devices, and evaluation means for analyzing the data on the server to evaluate the user's health and emotional state. This makes it possible to provide a personalized exercise plan based on the user's current health and emotional state.

[0202] "Personal information" refers to basic information about a user, including age, gender, occupation, and health status.

[0203] A "sensor device" refers to a device used to collect a user's biometric data, such as heart rate, steps taken, and calorie consumption, in real time.

[0204] "Evaluation methods" refer to the process of analyzing collected data to assess the user's health and emotional state.

[0205] "Generative AI technology" refers to algorithms and their application technologies for creating personalized exercise plans based on a user's health and emotional state.

[0206] The "plan generation means" refers to a function for creating individual exercise plans for users, and uses generation AI technology to determine the content of the exercises.

[0207] "Notification means" refers to methods for informing the user of the created exercise plan, and this information is provided via the user's terminal or home robot.

[0208] "Feedback collection methods" refer to the process of collecting user reactions and data after they have completed their exercise plan.

[0209] "Retraining methods" refer to the process of improving generative AI technology and increasing its accuracy by utilizing feedback data.

[0210] The system for implementing this invention aims to provide an exercise plan that takes into account the user's health management and emotional state. The system mainly consists of a terminal that receives the user's personal information, sensor equipment that collects the user's biometric data, a server that analyzes the data, and a generative AI model.

[0211] The server receives personal information entered by the user through the terminal. This includes age, gender, occupation, and health status. Sensor devices collect biometric data in real time, such as the user's heart rate, steps taken, and calorie consumption. Furthermore, a camera is used to capture the user's facial expressions and analyze their emotional state.

[0212] The server uses evaluation tools to comprehensively analyze biometric and emotional data to assess the user's health status and stress level. Generative AI technology then generates a personalized exercise plan based on the evaluation results. This plan includes refreshing activities and incentive plans tailored to the user's psychological state, thereby providing optimal health support.

[0213] The generated exercise plan is communicated to the user via their device or home robot. This notification includes the exercise content, achievement goals, and benefits of performing the exercise. The user is required to perform the exercise and input the results as feedback into their device. The server receives this feedback and retrains the generating AI model to improve the accuracy of the suggestions. This provides an optimized plan tailored to the user's needs.

[0214] For example, if a user complains of work fatigue, the server can suggest an exercise plan called "Refresh Yoga." After completing this plan, if the user provides feedback to their device such as "My heart rate has calmed down," the generating AI model learns from that data and will then suggest a more appropriate plan for future sessions.

[0215] An example of a prompt message would be: "A man in his 30s is feeling tired from work. He has a high heart rate and high stress levels. Please suggest a suitable way for him to refresh himself."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The terminal receives personal information from the user. This information includes age, gender, occupation, and health status, and is sent to the server. The terminal then processes this input data to format it into an appropriate format.

[0219] Step 2:

[0220] The sensor device collects the user's biometric data in real time. This includes heart rate, steps taken, and calorie consumption. The collected biometric data is transferred from the sensor device to a server. The data is de-noised and signal-processed before being transmitted.

[0221] Step 3:

[0222] The server acquires user facial expression data via the camera and performs emotion analysis. This analysis determines the user's emotional state and stress level, and stores this data as evaluation data. Image processing algorithms are used in this process.

[0223] Step 4:

[0224] The server integrates and analyzes collected personal information, biometric data, and emotional data using evaluation tools to assess the user's health status. This process uses machine learning algorithms to analyze the characteristics of the data and generate indicators of health and mental state.

[0225] Step 5:

[0226] Using a generative AI model, a personalized exercise plan is created for each user based on the evaluation results. The generated plan includes optimal exercise content and incentives for the user, based on prompt messages. Here, features are extracted from the data, and an optimized exercise plan is output using a predictive model.

[0227] Step 6:

[0228] The server sends the generated exercise plan to a terminal or home robot. The terminal uses notification methods to present the user with the plan's contents, achievement goals, and benefits of execution. The information is reformatted and displayed in a way that is easy for the user to understand visually.

[0229] Step 7:

[0230] Users perform exercises and input feedback about their results and changes in physical condition into the device. This feedback includes their impressions of the exercise, their level of achievement, and changes in their physical condition. The device then formats this data into an appropriate data structure and sends it to the server.

[0231] Step 8:

[0232] The server retrains the generating AI model based on the received feedback data. This process compares the feedback results with the prediction accuracy of the plan, learns areas for improvement in the model, and enhances the accuracy of future proposals. The model parameters are automatically adjusted through this feedback loop.

[0233] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0234] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0235] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0239] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0240] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0241] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0242] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0243] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0244] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0245] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0246] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0247] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0248] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0249] This invention is a generative AI instructor system designed to support users in maintaining their health and promoting exercise. This system proposes an exercise plan optimized for each individual user, aiming to maintain their motivation.

[0250] In this system, users first enter personal information using a terminal. This includes their name, age, gender, recent health check results, and lifestyle information. Furthermore, the terminal works in conjunction with the user's smartwatch and other sensor devices to collect biometric data in real time. This data includes heart rate, steps taken, calorie consumption, and sleep patterns.

[0251] The server collects received personal information and biometric data and uses a powerful data analysis engine to assess each user's health status. This assessment process includes calculating health indicators, determining stress levels, and identifying tendencies toward sedentary lifestyles. This generates a detailed health report about the user's current situation.

[0252] Next, the server uses a generation AI model to generate an optimal exercise plan for each user based on the evaluation results. This plan includes a variety of exercises tailored to the user's needs and goals, such as aerobic exercise, strength training, and relaxation exercises. The frequency, duration, and intensity of the exercises are also set individually.

[0253] The generated exercise plan is notified to the user via their device. This allows the user to easily incorporate healthy habits into their daily life. Furthermore, after completing the exercise plan, the user provides feedback and sends it to the server via their device. This feedback includes their physical condition and satisfaction level after the exercise, as well as their perceived effectiveness of the exercise.

[0254] The server uses this feedback data to retrain the AI ​​model, continuously improving the accuracy of plan generation. This ensures that the server can always provide plans that satisfy users and effectively promote their health.

[0255] As a concrete example, consider a middle-aged businessman. This user gets tired easily and finds it difficult to maintain a consistent exercise routine. Based on his information, the system creates an effective relaxation exercise plan that can be done in a short amount of time, supporting him in reducing daily stress. By obtaining feedback, the exercise plan can be further refined to better suit his needs, thereby improving his quality of life.

[0256] Thus, the present invention provides users with individually optimized health support and helps them to continue exercising efficiently.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] The user enters personal information using their device. This includes age, gender, health check results, and details of their lifestyle. The device then sends this data to the server.

[0260] Step 2:

[0261] The device connects with smartwatches and fitness trackers to continuously collect biometric data. This data includes heart rate, steps taken, calories burned, and sleep patterns.

[0262] Step 3:

[0263] The server receives and stores personal information and biometric data transmitted from the terminal. The received data is stored in a database and used for analysis.

[0264] Step 4:

[0265] The server analyzes the accumulated data. Using a data analysis engine, it assesses the user's health status and determines their stress level and degree of lack of exercise.

[0266] Step 5:

[0267] The server uses a generative AI model to generate an optimal exercise plan tailored to each user. This plan includes the type, frequency, and intensity of exercise that are appropriate for the user's health condition.

[0268] Step 6:

[0269] The device notifies the user of the generated exercise plan. The notification includes specific exercises, timing, and points to note.

[0270] Step 7:

[0271] The user performs exercise according to the notified exercise plan. After the exercise, they input feedback on their physical condition and satisfaction with the exercise into the device.

[0272] Step 8:

[0273] The server collects and stores feedback received from users. This feedback is used to retrain the generative AI model.

[0274] Step 9:

[0275] The server retrains the AI ​​model based on feedback to improve the accuracy of plan generation. This process makes the suggestions to the user more personalized and effective.

[0276] (Example 1)

[0277] Next, we will describe Example 1. 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."

[0278] In modern society, maintaining personal health and establishing exercise habits are crucial, but developing exercise plans that adapt to diverse lifestyles and health conditions is a challenging task. In particular, there is a need for personalized exercise plans and mechanisms to support their implementation, but conventional methods are insufficient. Furthermore, technologies that incorporate user feedback to improve plan accuracy are still evolving, and there is a need to realize effective health support.

[0279] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0280] In this invention, the server includes an acquisition means for acquiring personal attribute data from a user, a collection means for collecting the user's physiological data using a biological measurement device, and an evaluation means for analyzing the data in an information processing device to evaluate the user's health status. As a result, it becomes possible to provide an advanced exercise plan based on the individual health status of the user and to maintain long-term health accordingly.

[0281] "Personal attribute data" is basic information about an individual, including the user's name, age, gender, height, weight, health examination results, lifestyle, etc.

[0282] "Physiological data" is numerical data indicating the user's physical state, such as heart rate, number of steps, calories burned, sleep pattern, etc.

[0283] "Biological measurement device" is a device for measuring the user's physical numerical values in real time, such as a smartwatch or other sensor devices.

[0284] "Information processing device" is a hardware device for receiving, analyzing, and processing data, such as a server or a computer.

[0285] "Generated artificial intelligence model" is a set of algorithms that uses machine learning and deep learning techniques to learn patterns from data and make predictions.

[0286] "Exercise plan" is a plan for the type, frequency, intensity, and time of exercise, formulated based on individual health status and goals.

[0287] "Communication device" is an electronic device for transmitting and receiving information with the user, such as a smartphone, tablet, or personal computer.

[0288] "Reaction data" is feedback information regarding physical condition, satisfaction, and exercise effect provided by the user after the implementation of the exercise plan.

[0289] "Retraining" refers to the process of retraining a generative artificial intelligence model using user feedback data to improve the accuracy of predictions.

[0290] This invention is a system designed to support users in maintaining their health and promoting exercise. Based on personal attribute data and physiological data from the user, the system formulates an individually optimized exercise plan and provides it to the user, while also utilizing feedback to improve the accuracy of the generated AI model.

[0291] Users input their personal attribute data using a device, which includes smartphones and tablets. The device works in conjunction with smartwatches and other biometric devices to collect physiological data such as heart rate, steps, calorie consumption, and sleep patterns in real time. This data is transmitted to a server using wireless technologies such as Bluetooth.

[0292] The server uses Python and R data analysis libraries to analyze the received personal attribute data and physiological data. This data analysis includes calculating health indicators, identifying stress levels, and analyzing trends in sedentary lifestyles. Based on the evaluation results obtained, the server uses a generative AI model to create a personalized exercise plan for each user. Machine learning frameworks such as TensorFlow and PyTorch are used for the generative AI model.

[0293] The developed exercise plan is notified to the user via a device. The device provides a reminder function to help the user incorporate the notified exercise plan into their daily routine. After the user completes the exercise plan, feedback data is sent to the server via the device. The feedback includes information about the user's physical condition after exercise, satisfaction level, and perceived effectiveness of the exercise.

[0294] The server retrains its AI model based on collected feedback, continuously improving the accuracy of exercise plan development. This allows it to provide users with more effective and satisfying health support.

[0295] For example, if a user enters the prompt, "Please suggest a stress-reducing exercise plan for a man in his 50s who is busy with work and doesn't have much time," the server will respond to this request by creating a plan that includes short yoga and stretching exercises and notifying the user via their device.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] The user enters personal attribute data using a terminal. This data includes name, age, gender, height, weight, health check results, and lifestyle. The terminal sends the entered data to the server. This entered data is used as basic information to diagnose the user's current health status.

[0299] Step 2:

[0300] The device synchronizes with a biometric measurement device to collect physiological data such as heart rate, steps, calories burned, and sleep patterns in real time. The device transmits the collected physiological data to a server using a Bluetooth connection. This physiological data provides dynamic information about the user's activity and health.

[0301] Step 3:

[0302] The server integrates received personal attribute data and physiological data, and analyzes the data using Python or R analysis libraries. Specifically, it calculates health indicators and evaluates stress levels and exercise habits. Based on the input data, it generates a health status assessment report for the user. As output, it provides this assessment result to the next processing step of the generating AI model.

[0303] Step 4:

[0304] The server uses a generative AI model to formulate an exercise plan for each user based on the evaluation results. This generative AI model is implemented in TensorFlow or PyTorch and designs an optimal exercise plan that suits the user's needs. As specific operations, it calculates the type, frequency, duration, and intensity of the exercise and customizes the plan individually. The formulated exercise plan is sent from the server to the terminal.

[0305] Step 5:

[0306] The terminal notifies the user of the exercise plan and displays a reminder for incorporating it into daily activities. The user promotes health maintenance by performing the actual exercise according to the notified exercise plan. The terminal manages the implementation of this activity and tracks the progress.

[0307] Step 6:

[0308] The user inputs feedback regarding physical condition and satisfaction to the terminal after implementing the exercise plan. This feedback includes information regarding the physical sensations and mental satisfaction after exercise. The terminal sends this feedback data to the server.

[0309] Step 7:

[0310] The server improves the accuracy of the exercise plan by retraining the generative AI model using the collected feedback data. For retraining, the collected feedback and new data are utilized, and the prediction ability of the AI model is improved. As a result, the subsequent exercise plans for the user become more accurate and effective.

[0311] (Application Example 1)

[0312] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] In modern society, personalized health management is important, but it is difficult to exercise consistently amidst a busy daily life. This invention aims to solve the problem of supporting the maintenance of a healthy lifestyle by providing a system that automatically generates an exercise plan tailored to the user's health condition and allows for easy exercise guidance at home.

[0314] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0315] In this invention, the server includes an input means for inputting personal information from the user, a collection means for collecting the user's biometric information via a sensor device, an evaluation means for analyzing the data in a computing device to evaluate the user's health status, a generation means for creating an individualized exercise plan for the user using a generated AI model based on the evaluation, a notification means for notifying the user's information terminal of the generated exercise plan, a feedback collection means for receiving feedback information from the user, a retraining means for improving the accuracy of the generated AI model using the feedback information, and a guide means for providing exercise guidance as a home robot. As a result, the user can receive exercise guidance at home and continue personalized health management.

[0316] A "user" is an individual who uses this system to receive health management and exercise guidance.

[0317] "Personal information" refers to identifiable data necessary for health management, such as a user's name, age, gender, health check results, and lifestyle.

[0318] A "sensor device" is a measuring instrument used to collect a user's biometric information in real time, and includes heart rate monitors, pedometers, and other similar devices.

[0319] "Biometric information" refers to data that indicates the user's physical activity and health status, such as heart rate, steps taken, calorie consumption, and sleep patterns.

[0320] A "computational device" is an electronic device used to analyze collected data and evaluate the user's health status.

[0321] "Assessing health status" is the process of calculating the user's health indicators and determining their stress levels and tendency towards lack of exercise.

[0322] A "generative AI model" is an artificial intelligence algorithm that automatically generates an optimal exercise plan for each user based on evaluation results.

[0323] An "exercise plan" is an exercise plan that includes aerobic exercise, strength training, and relaxation exercises tailored to the user's needs and goals.

[0324] An "information terminal" is an electronic device used to notify users of exercise plans, and includes smartphones and computers.

[0325] "Feedback information" refers to response data provided by users after they have exercised, including their physical condition, satisfaction level, and the effectiveness of the exercise.

[0326] A "home robot" is an autonomous mechanical system that provides exercise guidance to users within their homes.

[0327] The embodiments for carrying out the present invention are shown below.

[0328] In this system, the terminal first collects personal information from the user and then works with sensor devices to acquire biometric information in real time. This utilizes sensors such as heart rate monitors and pedometers. The collected personal information and biometric information are then transmitted to a server.

[0329] On the server, computing devices analyze this data, and a generative AI model evaluates the user's health status. This evaluation includes a function to determine stress levels based on the user's heart rate variability and step count information. Based on these results, an optimal exercise plan for each user is created using the generative AI model through the evaluation process. This model uses TensorFlow, PyTorch, and other tools to process and analyze the data.

[0330] The generated exercise plan is notified to the information terminal, allowing the user to incorporate it into their daily life. After completing the exercise, the user provides feedback information about their physical condition and the effects of the exercise, and sends it back to the server via the terminal. Based on this feedback information, the server retrains the generating AI model to improve the accuracy of the exercise plan.

[0331] The home robot will use this information to provide real-time exercise guidance to the user. For example, for a home user who has a lot of work scheduled in the morning, it can suggest stretching and deep breathing exercises to help refresh the mind and body. An example of a prompt message for the generating AI model might be, "Please suggest a relaxation exercise plan that can be done this morning for a 45-year-old adult who does desk work."

[0332] Thus, the present invention provides an intelligent evolution system based on user-individualized exercise guidance and feedback, supporting the maintenance of a healthy lifestyle in the home environment.

[0333] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0334] Step 1:

[0335] The terminal receives personal information from the user. This information includes the user's name, age, gender, health check results, and lifestyle. The entered data is sent to the server as foundational information for the user's individual health management.

[0336] Step 2:

[0337] The device collects biometric information in real time via connected sensor devices. Specifically, this includes data such as heart rate, steps taken, calorie consumption, and sleep patterns. The collected biometric information is sent to a server to continuously monitor the user's physical activity.

[0338] Step 3:

[0339] The server analyzes the received personal and biometric information. This analysis uses computing devices to analyze data such as the user's heart rate variability and step count, and evaluates their stress level and health status. Based on the biometric information input, health indicators are calculated, and the results are output.

[0340] Step 4:

[0341] The server uses the evaluation results to call a generating AI model to create an optimal exercise plan for each user. This process uses software such as TensorFlow and PyTorch to process the user's health data as input and output an exercise plan. The generated exercise plan is then customized according to the user's goals and needs.

[0342] Step 5:

[0343] The generated exercise plan is notified to the information terminal. The user then begins their daily exercise based on the notified exercise plan. The terminal can also set reminders and alerts to prompt the user to start and finish their exercise.

[0344] Step 6:

[0345] After completing their workout, users input feedback information about their experience and physical condition into their device. This feedback data is sent to a server to reflect the user's satisfaction level and the effectiveness of their workout, and is used to generate their next workout plan.

[0346] Step 7:

[0347] The server uses feedback information to retrain the generated AI model to improve its accuracy. In this process, the AI ​​model is adaptively improved using feedback as input data, aiming to generate more accurate motor plans.

[0348] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0349] This invention combines an emotion engine with a generative AI instructor system to support users' health management and exercise promotion. The emotion engine recognizes the user's emotional state and proposes an individualized health plan based on that state, thereby reducing stress and maintaining motivation.

[0350] This system begins with the user entering basic personal information using a device, including age, gender, occupation, and current health status. The device also connects with the user's smartwatch or fitness tracker to collect biometric data such as heart rate, steps taken, and calorie consumption in real time.

[0351] In addition, the emotion engine captures the user's facial expressions with a camera and analyzes their emotional state based on that data. This analysis allows the system to identify the user's current mental state and their stress and anxiety levels.

[0352] The server receives collected personal information, biometric data, and emotional data, and uses a data analysis engine to assess the overall health status. This assessment process integrates emotional data analyzed by the emotion engine with stress level determination based on the user's heart rate variability and step count data to determine the health status.

[0353] Next, the server uses a generative AI model to generate a personalized exercise plan for each user. This plan not only includes exercises suited to the user's health condition, but also takes into account relaxation exercises and stress-relieving exercises based on their emotional state.

[0354] The generated exercise plan is notified to the user via the device. The notification includes the benefits of exercising, goals to be achieved, and a reward plan to motivate the user. After completing the exercise, the user is prompted to enter feedback on changes in their physical condition and mood into the device.

[0355] The server receives feedback data and uses it to retrain the generative AI model and emotion engine, improving the accuracy of the system's suggestions. This feedback process ensures that users continue to receive plans optimized for them.

[0356] For example, if a user is determined to be experiencing stress due to work-related pressure, the emotion engine will assess the situation and suggest relaxation plans such as yoga or meditation to reduce stress. If this plan is implemented and stress reduction is reported, the emotion engine and generative AI model will be adjusted again to provide better health support to the user.

[0357] Based on the above, we will provide comprehensive health support that takes into account not only the user's physical condition but also their emotional state, realizing a system that efficiently and effectively supports health maintenance.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] The user enters personal information using their device. This includes age, gender, health check results, and details about their lifestyle. The device then sends this information to a cloud server.

[0361] Step 2:

[0362] The device connects with sensor devices such as smartwatches and fitness trackers to collect biometric data in real time. It periodically records data such as heart rate, steps taken, calorie consumption, and sleep patterns, and sends it to a server.

[0363] Step 3:

[0364] The device uses its built-in camera to capture the user's facial expressions. The emotion engine analyzes the image to determine the user's emotional state. The analysis results in the quantification of emotional states such as stress, anxiety, and joy.

[0365] Step 4:

[0366] The server receives personal information, biometric data, and emotional data sent by the user and stores them in a database. Based on the received data, a data analysis engine operates to evaluate the user's overall health status.

[0367] Step 5:

[0368] The server uses a generated AI model based on the evaluation results to create a personalized exercise plan for each user. This plan takes into account both the user's health and emotional state, and includes optimal exercises and stress-reducing activities.

[0369] Step 6:

[0370] The device notifies the user of the generated exercise plan. The notification includes details of the exercises to be performed, the recommended duration, and the expected effects of the exercises.

[0371] Step 7:

[0372] The user executes an exercise plan based on notifications from their device. After exercising, they input feedback on changes in their physical condition and mood into their device.

[0373] Step 8:

[0374] The server receives user feedback data and records it in a database. Based on this feedback, the generative AI model and emotion engine are retrained to improve accuracy.

[0375] Step 9:

[0376] The server uses a retrained and improved generative AI model to enable more personalized suggestions in subsequent plan generation. This improves the accuracy and satisfaction of health support for users.

[0377] (Example 2)

[0378] Next, we will describe Example 2. 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".

[0379] In modern society, lifestyle-related diseases and accumulated stress are becoming apparent health problems. Conventional health management systems have only considered the user's physical health status, making it difficult to take a comprehensive approach that also takes into account emotional state and stress levels. As a result, there has been a problem in that users are unable to sustainably manage their health.

[0380] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0381] In this invention, the server includes emotion analysis means for capturing the user's facial expressions and analyzing their emotional state, health evaluation means for analyzing the data and evaluating the user's health status, and plan generation means for creating an individualized exercise plan for the user using a generated AI model based on the evaluation. This enables comprehensive health management that takes into account not only the user's physical health status but also their emotional state and stress level.

[0382] "Information acquisition means" refers to a device or method for inputting and acquiring personal information from a user.

[0383] "Data collection means" refers to a device or method for collecting a user's biometric data via a sensor device.

[0384] "Emotional analysis means" refers to a device or method for capturing a user's facial expressions, analyzing that data, and evaluating their emotional state.

[0385] A "health assessment tool" is a device or method for analyzing data collected on a server and evaluating the user's health status.

[0386] "Plan generation means" refers to an apparatus or method for creating an individualized exercise plan for a user using a generation AI model based on the results of a health assessment.

[0387] "Plan notification means" refers to a device or method for notifying a user's terminal of a generated exercise plan.

[0388] "Feedback data collection means" refers to a device or method for receiving feedback data from users.

[0389] A "model retraining means" is a device or method for improving the accuracy of a generated AI model using received feedback data.

[0390] This invention implements a system that comprehensively manages the user's health and emotional state and provides an individualized exercise plan. Specifically, it uses the following devices and software.

[0391] The user first enters personal information via a device. This device works in conjunction with smartwatches and fitness trackers to collect biometric data such as the user's heart rate, steps taken, and calorie consumption in real time. The device also uses a camera to capture the user's facial expressions and analyzes their emotional state using software called an emotion engine.

[0392] The server processes personal information, biometric data, and emotional data transmitted from the device using a data analysis engine to assess overall health status. This assessment includes stress level determination from heart rate variability and step count, as well as integrated analysis of emotional data. Based on the assessment results, the server utilizes a generative AI model to generate a personalized exercise plan for the user. This exercise plan includes exercises optimized for the user's health condition and relaxation exercises appropriate for their emotional state.

[0393] The generated exercise plan is notified to the user via their device. The notification includes the benefits of the exercise, the goals to be achieved, and the reward plan. After completing the exercise, the user provides feedback on changes in their physical condition and mood via their device. The server uses this feedback to retrain the generating AI model and emotion engine, improving the accuracy of the suggestions.

[0394] For example, if a user enters information such as, "I'm a 30-year-old woman in a management position, and I experience high levels of daily stress. My heart rate tends to rise easily, and I'd like an exercise plan to help me relax," the system will suggest a relaxation plan that includes yoga and meditation, thereby helping to reduce the user's stress levels.

[0395] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0396] Step 1:

[0397] Users enter personal information using a terminal. Specifically, they enter information about their age, gender, occupation, and health status. This is registered as the system's initial data. This information is the input data that forms the basis for subsequent data analysis.

[0398] Step 2:

[0399] The device collects biometric data from connected smartwatches and fitness trackers. Specific input data includes heart rate, steps taken, and calorie consumption. This biometric data is then sent to a server as output data for health assessment.

[0400] Step 3:

[0401] The device captures the user's facial expressions using its built-in camera. An emotion analysis engine receives this facial data as input and analyzes the user's emotional state. The analyzed emotional state data becomes the output data used for stress level assessment.

[0402] Step 4:

[0403] The server receives personal information, biometric data, and emotional data transmitted from the terminal and performs a health assessment. A data analysis engine uses this data as input to analyze and evaluate the user's health status. The output is an overall health status and stress level assessment.

[0404] Step 5:

[0405] The server uses a generative AI model to generate a personalized exercise plan for the user based on the results of the health assessment. It uses the assessment results as input and generates an exercise plan that takes into account the user's health and emotional state as output.

[0406] Step 6:

[0407] The server sends the generated exercise plan to the terminal. The terminal notifies the user of the plan. Specifically, it presents the benefits of the exercise, the goals to be achieved, and the reward plan. This becomes the output data for the user.

[0408] Step 7:

[0409] The user performs exercises based on the provided exercise plan and then inputs feedback on changes in their physical condition and emotions into the device. This feedback data is sent to the server as input data for the next processing step.

[0410] Step 8:

[0411] The server retrains its generative AI model and emotion engine based on feedback data received from the user. This retraining process improves the accuracy of subsequent exercise plan suggestions. The improved AI model is output using the feedback data as input.

[0412] (Application Example 2)

[0413] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0414] In modern society, people are becoming more aware of the importance of health management, but it is difficult to achieve effective health management amidst busy daily lives. Furthermore, stress and emotional fluctuations often reduce motivation for exercise, making it difficult to maintain good health. Therefore, there is a need for a system that efficiently provides comprehensive health support, taking into account not only the user's physical condition but also their emotional state.

[0415] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0416] In this invention, the server includes information input means for inputting personal information from the user, data collection means for collecting the user's biometric data via sensor devices, and evaluation means for analyzing the data on the server to evaluate the user's health and emotional state. This makes it possible to provide a personalized exercise plan based on the user's current health and emotional state.

[0417] "Personal information" refers to basic information about a user, including age, gender, occupation, and health status.

[0418] A "sensor device" refers to a device used to collect a user's biometric data, such as heart rate, steps taken, and calorie consumption, in real time.

[0419] "Evaluation methods" refer to the process of analyzing collected data to assess the user's health and emotional state.

[0420] "Generative AI technology" refers to algorithms and their application technologies for creating personalized exercise plans based on a user's health and emotional state.

[0421] The "plan generation means" refers to a function for creating individual exercise plans for users, and uses generation AI technology to determine the content of the exercises.

[0422] "Notification means" refers to methods for informing the user of the created exercise plan, and this information is provided via the user's terminal or home robot.

[0423] "Feedback collection methods" refer to the process of collecting user reactions and data after they have completed their exercise plan.

[0424] "Retraining methods" refer to the process of improving generative AI technology and increasing its accuracy by utilizing feedback data.

[0425] The system for implementing this invention aims to provide an exercise plan that takes into account the user's health management and emotional state. The system mainly consists of a terminal that receives the user's personal information, sensor equipment that collects the user's biometric data, a server that analyzes the data, and a generative AI model.

[0426] The server receives personal information entered by the user through the terminal. This includes age, gender, occupation, and health status. Sensor devices collect biometric data in real time, such as the user's heart rate, steps taken, and calorie consumption. Furthermore, a camera is used to capture the user's facial expressions and analyze their emotional state.

[0427] The server uses evaluation tools to comprehensively analyze biometric and emotional data to assess the user's health status and stress level. Generative AI technology then generates a personalized exercise plan based on the evaluation results. This plan includes refreshing activities and incentive plans tailored to the user's psychological state, thereby providing optimal health support.

[0428] The generated exercise plan is communicated to the user via their device or home robot. This notification includes the exercise content, achievement goals, and benefits of performing the exercise. The user is required to perform the exercise and input the results as feedback into their device. The server receives this feedback and retrains the generating AI model to improve the accuracy of the suggestions. This provides an optimized plan tailored to the user's needs.

[0429] For example, if a user complains of work fatigue, the server can suggest an exercise plan called "Refresh Yoga." After completing this plan, if the user provides feedback to their device such as "My heart rate has calmed down," the generating AI model learns from that data and will then suggest a more appropriate plan for future sessions.

[0430] An example of a prompt message would be: "A man in his 30s is feeling tired from work. He has a high heart rate and high stress levels. Please suggest a suitable way for him to refresh himself."

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] The terminal receives personal information from the user. This information includes age, gender, occupation, and health status, and is sent to the server. The terminal then processes this input data to format it into an appropriate format.

[0434] Step 2:

[0435] The sensor device collects the user's biometric data in real time. This includes heart rate, steps taken, and calorie consumption. The collected biometric data is transferred from the sensor device to a server. The data is de-noised and signal-processed before being transmitted.

[0436] Step 3:

[0437] The server acquires user facial expression data via the camera and performs emotion analysis. This analysis determines the user's emotional state and stress level, and stores this data as evaluation data. Image processing algorithms are used in this process.

[0438] Step 4:

[0439] The server integrates and analyzes collected personal information, biometric data, and emotional data using evaluation tools to assess the user's health status. This process uses machine learning algorithms to analyze the characteristics of the data and generate indicators of health and mental state.

[0440] Step 5:

[0441] Using a generative AI model, a personalized exercise plan is created for each user based on the evaluation results. The generated plan includes optimal exercise content and incentives for the user, based on prompt messages. Here, features are extracted from the data, and an optimized exercise plan is output using a predictive model.

[0442] Step 6:

[0443] The server sends the generated exercise plan to a terminal or home robot. The terminal uses notification methods to present the user with the plan's contents, achievement goals, and benefits of execution. The information is reformatted and displayed in a way that is easy for the user to understand visually.

[0444] Step 7:

[0445] Users perform exercises and input feedback about their results and changes in physical condition into the device. This feedback includes their impressions of the exercise, their level of achievement, and changes in their physical condition. The device then formats this data into an appropriate data structure and sends it to the server.

[0446] Step 8:

[0447] The server retrains the generating AI model based on the received feedback data. This process compares the feedback results with the prediction accuracy of the plan, learns areas for improvement in the model, and enhances the accuracy of future proposals. The model parameters are automatically adjusted through this feedback loop.

[0448] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0449] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0450] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0451] [Third Embodiment]

[0452] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0453] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0454] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0455] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0456] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0457] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0458] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0459] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0460] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0461] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0462] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0463] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0464] This invention is a generative AI instructor system designed to support users in maintaining their health and promoting exercise. This system proposes an exercise plan optimized for each individual user, aiming to maintain their motivation.

[0465] In this system, users first enter personal information using a terminal. This includes their name, age, gender, recent health check results, and lifestyle information. Furthermore, the terminal works in conjunction with the user's smartwatch and other sensor devices to collect biometric data in real time. This data includes heart rate, steps taken, calorie consumption, and sleep patterns.

[0466] The server collects received personal information and biometric data and uses a powerful data analysis engine to assess each user's health status. This assessment process includes calculating health indicators, determining stress levels, and identifying tendencies toward sedentary lifestyles. This generates a detailed health report about the user's current situation.

[0467] Next, the server uses a generation AI model to generate an optimal exercise plan for each user based on the evaluation results. This plan includes a variety of exercises tailored to the user's needs and goals, such as aerobic exercise, strength training, and relaxation exercises. The frequency, duration, and intensity of the exercises are also set individually.

[0468] The generated exercise plan is notified to the user via their device. This allows the user to easily incorporate healthy habits into their daily life. Furthermore, after completing the exercise plan, the user provides feedback and sends it to the server via their device. This feedback includes their physical condition and satisfaction level after the exercise, as well as their perceived effectiveness of the exercise.

[0469] The server uses this feedback data to retrain the AI ​​model, continuously improving the accuracy of plan generation. This ensures that the server can always provide plans that satisfy users and effectively promote their health.

[0470] As a concrete example, consider a middle-aged businessman. This user gets tired easily and finds it difficult to maintain a consistent exercise routine. Based on his information, the system creates an effective relaxation exercise plan that can be done in a short amount of time, supporting him in reducing daily stress. By obtaining feedback, the exercise plan can be further refined to better suit his needs, thereby improving his quality of life.

[0471] Thus, the present invention provides users with individually optimized health support and helps them to continue exercising efficiently.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The user enters personal information using their device. This includes age, gender, health check results, and details of their lifestyle. The device then sends this data to the server.

[0475] Step 2:

[0476] The device connects with smartwatches and fitness trackers to continuously collect biometric data. This data includes heart rate, steps taken, calories burned, and sleep patterns.

[0477] Step 3:

[0478] The server receives and stores personal information and biometric data transmitted from the terminal. The received data is stored in a database and used for analysis.

[0479] Step 4:

[0480] The server analyzes the accumulated data. Using a data analysis engine, it assesses the user's health status and determines their stress level and degree of lack of exercise.

[0481] Step 5:

[0482] The server uses a generative AI model to generate an optimal exercise plan tailored to each user. This plan includes the type, frequency, and intensity of exercise that are appropriate for the user's health condition.

[0483] Step 6:

[0484] The device notifies the user of the generated exercise plan. The notification includes specific exercises, timing, and points to note.

[0485] Step 7:

[0486] The user performs exercise according to the notified exercise plan. After the exercise, they input feedback on their physical condition and satisfaction with the exercise into the device.

[0487] Step 8:

[0488] The server collects and stores feedback received from users. This feedback is used to retrain the generative AI model.

[0489] Step 9:

[0490] The server retrains the AI ​​model based on feedback to improve the accuracy of plan generation. This process makes the suggestions to the user more personalized and effective.

[0491] (Example 1)

[0492] Next, we will describe Example 1. 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."

[0493] In modern society, maintaining personal health and establishing exercise habits are crucial, but developing exercise plans that adapt to diverse lifestyles and health conditions is a challenging task. In particular, there is a need for personalized exercise plans and mechanisms to support their implementation, but conventional methods are insufficient. Furthermore, technologies that incorporate user feedback to improve plan accuracy are still evolving, and there is a need to realize effective health support.

[0494] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0495] In this invention, the server includes acquisition means for acquiring personal attribute data from the user, collection means for collecting the user's physiological data using a biometric measurement device, and evaluation means for analyzing the data in an information processing device to evaluate the user's health status. This enables the provision of an advanced exercise plan based on the user's individual health status and the long-term maintenance of health that accompanies it.

[0496] "Personal attribute data" refers to basic information about an individual, including the user's name, age, gender, height, weight, health check results, and lifestyle.

[0497] "Physiological data" refers to numerical data that indicates the user's physical condition, such as heart rate, steps taken, calories burned, and sleep patterns.

[0498] A "biometric measurement device" is a device, such as a smartwatch or other sensor device, that measures a user's physical values ​​in real time.

[0499] An "information processing device" is a hardware device, such as a server or computer, that receives, analyzes, and processes data.

[0500] A "generative artificial intelligence model" is a collection of algorithms that use machine learning and deep learning techniques to learn patterns from data and make predictions.

[0501] An "exercise plan" is a plan of exercise, including the type, frequency, intensity, and duration, that is formulated based on an individual's health condition and goals.

[0502] "Communication devices" refer to electronic devices such as smartphones, tablets, and personal computers that are used to send and receive information with users.

[0503] "Response data" refers to feedback information provided by users after completing an exercise plan, regarding their physical condition, satisfaction level, and the effectiveness of the exercise.

[0504] "Retraining" refers to the process of retraining a generative artificial intelligence model using user feedback data to improve the accuracy of predictions.

[0505] This invention is a system designed to support users in maintaining their health and promoting exercise. Based on personal attribute data and physiological data from the user, the system formulates an individually optimized exercise plan and provides it to the user, while also utilizing feedback to improve the accuracy of the generated AI model.

[0506] Users input their personal attribute data using a device, which includes smartphones and tablets. The device works in conjunction with smartwatches and other biometric devices to collect physiological data such as heart rate, steps, calorie consumption, and sleep patterns in real time. This data is transmitted to a server using wireless technologies such as Bluetooth.

[0507] The server uses Python and R data analysis libraries to analyze the received personal attribute data and physiological data. This data analysis includes calculating health indicators, identifying stress levels, and analyzing trends in sedentary lifestyles. Based on the evaluation results obtained, the server uses a generative AI model to create a personalized exercise plan for each user. Machine learning frameworks such as TensorFlow and PyTorch are used for the generative AI model.

[0508] The developed exercise plan is notified to the user via a device. The device provides a reminder function to help the user incorporate the notified exercise plan into their daily routine. After the user completes the exercise plan, feedback data is sent to the server via the device. The feedback includes information about the user's physical condition after exercise, satisfaction level, and perceived effectiveness of the exercise.

[0509] The server retrains its AI model based on collected feedback, continuously improving the accuracy of exercise plan development. This allows it to provide users with more effective and satisfying health support.

[0510] For example, if a user enters the prompt, "Please suggest a stress-reducing exercise plan for a man in his 50s who is busy with work and doesn't have much time," the server will respond to this request by creating a plan that includes short yoga and stretching exercises and notifying the user via their device.

[0511] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0512] Step 1:

[0513] The user enters personal attribute data using a terminal. This data includes name, age, gender, height, weight, health check results, and lifestyle. The terminal sends the entered data to the server. This entered data is used as basic information to diagnose the user's current health status.

[0514] Step 2:

[0515] The device synchronizes with a biometric measurement device to collect physiological data such as heart rate, steps, calories burned, and sleep patterns in real time. The device transmits the collected physiological data to a server using a Bluetooth connection. This physiological data provides dynamic information about the user's activity and health.

[0516] Step 3:

[0517] The server integrates received personal attribute data and physiological data, and analyzes the data using Python or R analysis libraries. Specifically, it calculates health indicators and evaluates stress levels and exercise habits. Based on the input data, it generates a health status assessment report for the user. As output, it provides this assessment result to the next processing step of the generating AI model.

[0518] Step 4:

[0519] The server uses a generative AI model to develop a personalized exercise plan based on the evaluation results. This generative AI model, implemented in TensorFlow or PyTorch, designs the optimal exercise plan tailored to the user's needs. Specifically, it calculates the type of exercise, frequency, duration, and intensity, and customizes the plan individually. The developed exercise plan is then sent from the server to the user's device.

[0520] Step 5:

[0521] The device notifies the user of an exercise plan and displays reminders to incorporate it into their daily activities. By following the notified exercise plan and performing the exercises, the user promotes maintaining their health. The device manages the implementation of this activity and tracks progress.

[0522] Step 6:

[0523] After completing their exercise plan, users input feedback on their physical condition and satisfaction level into the device. This feedback includes information about their physical sensations and mental satisfaction after exercise. The device then sends this feedback data to the server.

[0524] Step 7:

[0525] The server improves the accuracy of the exercise plan by retraining the generated AI model using the collected feedback data. The retraining utilizes collected feedback and new data, improving the AI ​​model's predictive capabilities. This results in more accurate and effective exercise plans for the user in the future.

[0526] (Application Example 1)

[0527] Next, we will explain Application Example 1. In the following explanation, 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."

[0528] In modern society, personalized health management is important, but it is difficult to exercise consistently amidst a busy daily life. This invention aims to solve the problem of supporting the maintenance of a healthy lifestyle by providing a system that automatically generates an exercise plan tailored to the user's health condition and allows for easy exercise guidance at home.

[0529] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0530] In this invention, the server includes an input means for inputting personal information from the user, a collection means for collecting the user's biometric information via a sensor device, an evaluation means for analyzing the data in a computing device to evaluate the user's health status, a generation means for creating an individualized exercise plan for the user using a generated AI model based on the evaluation, a notification means for notifying the user's information terminal of the generated exercise plan, a feedback collection means for receiving feedback information from the user, a retraining means for improving the accuracy of the generated AI model using the feedback information, and a guide means for providing exercise guidance as a home robot. As a result, the user can receive exercise guidance at home and continue personalized health management.

[0531] A "user" is an individual who uses this system to receive health management and exercise guidance.

[0532] "Personal information" refers to identifiable data necessary for health management, such as a user's name, age, gender, health check results, and lifestyle.

[0533] A "sensor device" is a measuring instrument used to collect a user's biometric information in real time, and includes heart rate monitors, pedometers, and other similar devices.

[0534] "Biometric information" refers to data that indicates the user's physical activity and health status, such as heart rate, steps taken, calorie consumption, and sleep patterns.

[0535] A "computational device" is an electronic device used to analyze collected data and evaluate the user's health status.

[0536] "Assessing health status" is the process of calculating the user's health indicators and determining their stress levels and tendency towards lack of exercise.

[0537] A "generative AI model" is an artificial intelligence algorithm that automatically generates an optimal exercise plan for each user based on evaluation results.

[0538] An "exercise plan" is an exercise plan that includes aerobic exercise, strength training, and relaxation exercises tailored to the user's needs and goals.

[0539] An "information terminal" is an electronic device used to notify users of exercise plans, and includes smartphones and computers.

[0540] "Feedback information" refers to response data provided by users after they have exercised, including their physical condition, satisfaction level, and the effectiveness of the exercise.

[0541] A "home robot" is an autonomous mechanical system that provides exercise guidance to users within their homes.

[0542] The embodiments for carrying out the present invention are shown below.

[0543] In this system, the terminal first collects personal information from the user and then works with sensor devices to acquire biometric information in real time. This utilizes sensors such as heart rate monitors and pedometers. The collected personal information and biometric information are then transmitted to a server.

[0544] On the server, computing devices analyze this data, and a generative AI model evaluates the user's health status. This evaluation includes a function to determine stress levels based on the user's heart rate variability and step count information. Based on these results, an optimal exercise plan for each user is created using the generative AI model through the evaluation process. This model uses TensorFlow, PyTorch, and other tools to process and analyze the data.

[0545] The generated exercise plan is notified to the information terminal, allowing the user to incorporate it into their daily life. After completing the exercise, the user provides feedback information about their physical condition and the effects of the exercise, and sends it back to the server via the terminal. Based on this feedback information, the server retrains the generating AI model to improve the accuracy of the exercise plan.

[0546] The home robot will use this information to provide real-time exercise guidance to the user. For example, for a home user who has a lot of work scheduled in the morning, it can suggest stretching and deep breathing exercises to help refresh the mind and body. An example of a prompt message for the generating AI model might be, "Please suggest a relaxation exercise plan that can be done this morning for a 45-year-old adult who does desk work."

[0547] Thus, the present invention provides an intelligent evolution system based on user-individualized exercise guidance and feedback, supporting the maintenance of a healthy lifestyle in the home environment.

[0548] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0549] Step 1:

[0550] The terminal receives personal information from the user. This information includes the user's name, age, gender, health check results, and lifestyle. The entered data is sent to the server as foundational information for the user's individual health management.

[0551] Step 2:

[0552] The device collects biometric information in real time via connected sensor devices. Specifically, this includes data such as heart rate, steps taken, calorie consumption, and sleep patterns. The collected biometric information is sent to a server to continuously monitor the user's physical activity.

[0553] Step 3:

[0554] The server analyzes the received personal and biometric information. This analysis uses computing devices to analyze data such as the user's heart rate variability and step count, and evaluates their stress level and health status. Based on the biometric information input, health indicators are calculated, and the results are output.

[0555] Step 4:

[0556] The server uses the evaluation results to call a generating AI model to create an optimal exercise plan for each user. This process uses software such as TensorFlow and PyTorch to process the user's health data as input and output an exercise plan. The generated exercise plan is then customized according to the user's goals and needs.

[0557] Step 5:

[0558] The generated exercise plan is notified to the information terminal. The user then begins their daily exercise based on the notified exercise plan. The terminal can also set reminders and alerts to prompt the user to start and finish their exercise.

[0559] Step 6:

[0560] After completing their workout, users input feedback information about their experience and physical condition into their device. This feedback data is sent to a server to reflect the user's satisfaction level and the effectiveness of their workout, and is used to generate their next workout plan.

[0561] Step 7:

[0562] The server uses feedback information to retrain the generated AI model to improve its accuracy. In this process, the AI ​​model is adaptively improved using feedback as input data, aiming to generate more accurate motor plans.

[0563] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0564] This invention combines an emotion engine with a generative AI instructor system to support users' health management and exercise promotion. The emotion engine recognizes the user's emotional state and proposes an individualized health plan based on that state, thereby reducing stress and maintaining motivation.

[0565] This system begins with the user entering basic personal information using a device, including age, gender, occupation, and current health status. The device also connects with the user's smartwatch or fitness tracker to collect biometric data such as heart rate, steps taken, and calorie consumption in real time.

[0566] In addition, the emotion engine captures the user's facial expressions with a camera and analyzes their emotional state based on that data. This analysis allows the system to identify the user's current mental state and their stress and anxiety levels.

[0567] The server receives collected personal information, biometric data, and emotional data, and uses a data analysis engine to assess the overall health status. This assessment process integrates emotional data analyzed by the emotion engine with stress level determination based on the user's heart rate variability and step count data to determine the health status.

[0568] Next, the server uses a generative AI model to generate a personalized exercise plan for each user. This plan not only includes exercises suited to the user's health condition, but also takes into account relaxation exercises and stress-relieving exercises based on their emotional state.

[0569] The generated exercise plan is notified to the user via the device. The notification includes the benefits of exercising, goals to be achieved, and a reward plan to motivate the user. After completing the exercise, the user is prompted to enter feedback on changes in their physical condition and mood into the device.

[0570] The server receives feedback data and uses it to retrain the generative AI model and emotion engine, improving the accuracy of the system's suggestions. This feedback process ensures that users continue to receive plans optimized for them.

[0571] For example, if a user is determined to be experiencing stress due to work-related pressure, the emotion engine will assess the situation and suggest relaxation plans such as yoga or meditation to reduce stress. If this plan is implemented and stress reduction is reported, the emotion engine and generative AI model will be adjusted again to provide better health support to the user.

[0572] Based on the above, we will provide comprehensive health support that takes into account not only the user's physical condition but also their emotional state, realizing a system that efficiently and effectively supports health maintenance.

[0573] The following describes the processing flow.

[0574] Step 1:

[0575] The user enters personal information using their device. This includes age, gender, health check results, and details about their lifestyle. The device then sends this information to a cloud server.

[0576] Step 2:

[0577] The device connects with sensor devices such as smartwatches and fitness trackers to collect biometric data in real time. It periodically records data such as heart rate, steps taken, calorie consumption, and sleep patterns, and sends it to a server.

[0578] Step 3:

[0579] The device uses its built-in camera to capture the user's facial expressions. The emotion engine analyzes the image to determine the user's emotional state. The analysis results in the quantification of emotional states such as stress, anxiety, and joy.

[0580] Step 4:

[0581] The server receives personal information, biometric data, and emotional data sent by the user and stores them in a database. Based on the received data, a data analysis engine operates to evaluate the user's overall health status.

[0582] Step 5:

[0583] The server uses a generated AI model based on the evaluation results to create a personalized exercise plan for each user. This plan takes into account both the user's health and emotional state, and includes optimal exercises and stress-reducing activities.

[0584] Step 6:

[0585] The device notifies the user of the generated exercise plan. The notification includes details of the exercises to be performed, the recommended duration, and the expected effects of the exercises.

[0586] Step 7:

[0587] The user executes an exercise plan based on notifications from their device. After exercising, they input feedback on changes in their physical condition and mood into their device.

[0588] Step 8:

[0589] The server receives user feedback data and records it in a database. Based on this feedback, the generative AI model and emotion engine are retrained to improve accuracy.

[0590] Step 9:

[0591] The server uses a retrained and improved generative AI model to enable more personalized suggestions in subsequent plan generation. This improves the accuracy and satisfaction of health support for users.

[0592] (Example 2)

[0593] Next, we will describe Example 2. 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."

[0594] In modern society, lifestyle-related diseases and accumulated stress are becoming apparent health problems. Conventional health management systems have only considered the user's physical health status, making it difficult to take a comprehensive approach that also takes into account emotional state and stress levels. As a result, there has been a problem in that users are unable to sustainably manage their health.

[0595] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0596] In this invention, the server includes emotion analysis means for capturing the user's facial expressions and analyzing their emotional state, health evaluation means for analyzing the data and evaluating the user's health status, and plan generation means for creating an individualized exercise plan for the user using a generated AI model based on the evaluation. This enables comprehensive health management that takes into account not only the user's physical health status but also their emotional state and stress level.

[0597] "Information acquisition means" refers to a device or method for inputting and acquiring personal information from a user.

[0598] "Data collection means" refers to a device or method for collecting a user's biometric data via a sensor device.

[0599] "Emotional analysis means" refers to a device or method for capturing a user's facial expressions, analyzing that data, and evaluating their emotional state.

[0600] A "health assessment tool" is a device or method for analyzing data collected on a server and evaluating the user's health status.

[0601] "Plan generation means" refers to an apparatus or method for creating an individualized exercise plan for a user using a generation AI model based on the results of a health assessment.

[0602] "Plan notification means" refers to a device or method for notifying a user's terminal of a generated exercise plan.

[0603] "Feedback data collection means" refers to a device or method for receiving feedback data from users.

[0604] A "model retraining means" is a device or method for improving the accuracy of a generated AI model using received feedback data.

[0605] This invention implements a system that comprehensively manages the user's health and emotional state and provides an individualized exercise plan. Specifically, it uses the following devices and software.

[0606] The user first enters personal information via a device. This device works in conjunction with smartwatches and fitness trackers to collect biometric data such as the user's heart rate, steps taken, and calorie consumption in real time. The device also uses a camera to capture the user's facial expressions and analyzes their emotional state using software called an emotion engine.

[0607] The server processes personal information, biometric data, and emotional data transmitted from the device using a data analysis engine to assess overall health status. This assessment includes stress level determination from heart rate variability and step count, as well as integrated analysis of emotional data. Based on the assessment results, the server utilizes a generative AI model to generate a personalized exercise plan for the user. This exercise plan includes exercises optimized for the user's health condition and relaxation exercises appropriate for their emotional state.

[0608] The generated exercise plan is notified to the user via their device. The notification includes the benefits of the exercise, the goals to be achieved, and the reward plan. After completing the exercise, the user provides feedback on changes in their physical condition and mood via their device. The server uses this feedback to retrain the generating AI model and emotion engine, improving the accuracy of the suggestions.

[0609] For example, if a user enters information such as, "I'm a 30-year-old woman in a management position, and I experience high levels of daily stress. My heart rate tends to rise easily, and I'd like an exercise plan to help me relax," the system will suggest a relaxation plan that includes yoga and meditation, thereby helping to reduce the user's stress levels.

[0610] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0611] Step 1:

[0612] Users enter personal information using a terminal. Specifically, they enter information about their age, gender, occupation, and health status. This is registered as the system's initial data. This information is the input data that forms the basis for subsequent data analysis.

[0613] Step 2:

[0614] The device collects biometric data from connected smartwatches and fitness trackers. Specific input data includes heart rate, steps taken, and calorie consumption. This biometric data is then sent to a server as output data for health assessment.

[0615] Step 3:

[0616] The device captures the user's facial expressions using its built-in camera. An emotion analysis engine receives this facial data as input and analyzes the user's emotional state. The analyzed emotional state data becomes the output data used for stress level assessment.

[0617] Step 4:

[0618] The server receives personal information, biometric data, and emotional data transmitted from the terminal and performs a health assessment. A data analysis engine uses this data as input to analyze and evaluate the user's health status. The output is an overall health status and stress level assessment.

[0619] Step 5:

[0620] The server uses a generative AI model to generate a personalized exercise plan for the user based on the results of the health assessment. It uses the assessment results as input and generates an exercise plan that takes into account the user's health and emotional state as output.

[0621] Step 6:

[0622] The server sends the generated exercise plan to the terminal. The terminal notifies the user of the plan. Specifically, it presents the benefits of the exercise, the goals to be achieved, and the reward plan. This becomes the output data for the user.

[0623] Step 7:

[0624] The user performs exercises based on the provided exercise plan and then inputs feedback on changes in their physical condition and emotions into the device. This feedback data is sent to the server as input data for the next processing step.

[0625] Step 8:

[0626] The server retrains its generative AI model and emotion engine based on feedback data received from the user. This retraining process improves the accuracy of subsequent exercise plan suggestions. The improved AI model is output using the feedback data as input.

[0627] (Application Example 2)

[0628] Next, we will explain application example 2. In the following explanation, 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."

[0629] In modern society, people are becoming more aware of the importance of health management, but it is difficult to achieve effective health management amidst busy daily lives. Furthermore, stress and emotional fluctuations often reduce motivation for exercise, making it difficult to maintain good health. Therefore, there is a need for a system that efficiently provides comprehensive health support, taking into account not only the user's physical condition but also their emotional state.

[0630] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0631] In this invention, the server includes information input means for inputting personal information from the user, data collection means for collecting the user's biometric data via sensor devices, and evaluation means for analyzing the data on the server to evaluate the user's health and emotional state. This makes it possible to provide a personalized exercise plan based on the user's current health and emotional state.

[0632] "Personal information" refers to basic information about a user, including age, gender, occupation, and health status.

[0633] A "sensor device" refers to a device used to collect a user's biometric data, such as heart rate, steps taken, and calorie consumption, in real time.

[0634] "Evaluation methods" refer to the process of analyzing collected data to assess the user's health and emotional state.

[0635] "Generative AI technology" refers to algorithms and their application technologies for creating personalized exercise plans based on a user's health and emotional state.

[0636] The "plan generation means" refers to a function for creating individual exercise plans for users, and uses generation AI technology to determine the content of the exercises.

[0637] "Notification means" refers to methods for informing the user of the created exercise plan, and this information is provided via the user's terminal or home robot.

[0638] "Feedback collection methods" refer to the process of collecting user reactions and data after they have completed their exercise plan.

[0639] "Retraining methods" refer to the process of improving generative AI technology and increasing its accuracy by utilizing feedback data.

[0640] The system for implementing this invention aims to provide an exercise plan that takes into account the user's health management and emotional state. The system mainly consists of a terminal that receives the user's personal information, sensor equipment that collects the user's biometric data, a server that analyzes the data, and a generative AI model.

[0641] The server receives personal information entered by the user through the terminal. This includes age, gender, occupation, and health status. Sensor devices collect biometric data in real time, such as the user's heart rate, steps taken, and calorie consumption. Furthermore, a camera is used to capture the user's facial expressions and analyze their emotional state.

[0642] The server uses evaluation tools to comprehensively analyze biometric and emotional data to assess the user's health status and stress level. Generative AI technology then generates a personalized exercise plan based on the evaluation results. This plan includes refreshing activities and incentive plans tailored to the user's psychological state, thereby providing optimal health support.

[0643] The generated exercise plan is communicated to the user via their device or home robot. This notification includes the exercise content, achievement goals, and benefits of performing the exercise. The user is required to perform the exercise and input the results as feedback into their device. The server receives this feedback and retrains the generating AI model to improve the accuracy of the suggestions. This provides an optimized plan tailored to the user's needs.

[0644] For example, if a user complains of work fatigue, the server can suggest an exercise plan called "Refresh Yoga." After completing this plan, if the user provides feedback to their device such as "My heart rate has calmed down," the generating AI model learns from that data and will then suggest a more appropriate plan for future sessions.

[0645] An example of a prompt message would be: "A man in his 30s is feeling tired from work. He has a high heart rate and high stress levels. Please suggest a suitable way for him to refresh himself."

[0646] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0647] Step 1:

[0648] The terminal receives personal information from the user. This information includes age, gender, occupation, and health status, and is sent to the server. The terminal then processes this input data to format it into an appropriate format.

[0649] Step 2:

[0650] The sensor device collects the user's biometric data in real time. This includes heart rate, steps taken, and calorie consumption. The collected biometric data is transferred from the sensor device to a server. The data is de-noised and signal-processed before being transmitted.

[0651] Step 3:

[0652] The server acquires user facial expression data via the camera and performs emotion analysis. This analysis determines the user's emotional state and stress level, and stores this data as evaluation data. Image processing algorithms are used in this process.

[0653] Step 4:

[0654] The server integrates and analyzes collected personal information, biometric data, and emotional data using evaluation tools to assess the user's health status. This process uses machine learning algorithms to analyze the characteristics of the data and generate indicators of health and mental state.

[0655] Step 5:

[0656] Using a generative AI model, a personalized exercise plan is created for each user based on the evaluation results. The generated plan includes optimal exercise content and incentives for the user, based on prompt messages. Here, features are extracted from the data, and an optimized exercise plan is output using a predictive model.

[0657] Step 6:

[0658] The server sends the generated exercise plan to a terminal or home robot. The terminal uses notification methods to present the user with the plan's contents, achievement goals, and benefits of execution. The information is reformatted and displayed in a way that is easy for the user to understand visually.

[0659] Step 7:

[0660] Users perform exercises and input feedback about their results and changes in physical condition into the device. This feedback includes their impressions of the exercise, their level of achievement, and changes in their physical condition. The device then formats this data into an appropriate data structure and sends it to the server.

[0661] Step 8:

[0662] The server retrains the generating AI model based on the received feedback data. This process compares the feedback results with the prediction accuracy of the plan, learns areas for improvement in the model, and enhances the accuracy of future proposals. The model parameters are automatically adjusted through this feedback loop.

[0663] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0664] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0665] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0666] [Fourth Embodiment]

[0667] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0668] As shown in Figure 7, the 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.

[0669] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0670] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0671] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0672] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0673] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0674] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0675] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0676] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0677] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0678] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0679] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] This invention is a generative AI instructor system designed to support users in maintaining their health and promoting exercise. This system proposes an exercise plan optimized for each individual user, aiming to maintain their motivation.

[0681] In this system, users first enter personal information using a terminal. This includes their name, age, gender, recent health check results, and lifestyle information. Furthermore, the terminal works in conjunction with the user's smartwatch and other sensor devices to collect biometric data in real time. This data includes heart rate, steps taken, calorie consumption, and sleep patterns.

[0682] The server collects received personal information and biometric data and uses a powerful data analysis engine to assess each user's health status. This assessment process includes calculating health indicators, determining stress levels, and identifying tendencies toward sedentary lifestyles. This generates a detailed health report about the user's current situation.

[0683] Next, the server uses a generation AI model to generate an optimal exercise plan for each user based on the evaluation results. This plan includes a variety of exercises tailored to the user's needs and goals, such as aerobic exercise, strength training, and relaxation exercises. The frequency, duration, and intensity of the exercises are also set individually.

[0684] The generated exercise plan is notified to the user via their device. This allows the user to easily incorporate healthy habits into their daily life. Furthermore, after completing the exercise plan, the user provides feedback and sends it to the server via their device. This feedback includes their physical condition and satisfaction level after the exercise, as well as their perceived effectiveness of the exercise.

[0685] The server uses this feedback data to retrain the AI ​​model, continuously improving the accuracy of plan generation. This ensures that the server can always provide plans that satisfy users and effectively promote their health.

[0686] As a concrete example, consider a middle-aged businessman. This user gets tired easily and finds it difficult to maintain a consistent exercise routine. Based on his information, the system creates an effective relaxation exercise plan that can be done in a short amount of time, supporting him in reducing daily stress. By obtaining feedback, the exercise plan can be further refined to better suit his needs, thereby improving his quality of life.

[0687] Thus, the present invention provides users with individually optimized health support and helps them to continue exercising efficiently.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] The user enters personal information using their device. This includes age, gender, health check results, and details of their lifestyle. The device then sends this data to the server.

[0691] Step 2:

[0692] The device connects with smartwatches and fitness trackers to continuously collect biometric data. This data includes heart rate, steps taken, calories burned, and sleep patterns.

[0693] Step 3:

[0694] The server receives and stores personal information and biometric data transmitted from the terminal. The received data is stored in a database and used for analysis.

[0695] Step 4:

[0696] The server analyzes the accumulated data. Using a data analysis engine, it assesses the user's health status and determines their stress level and degree of lack of exercise.

[0697] Step 5:

[0698] The server uses a generative AI model to generate an optimal exercise plan tailored to each user. This plan includes the type, frequency, and intensity of exercise that are appropriate for the user's health condition.

[0699] Step 6:

[0700] The device notifies the user of the generated exercise plan. The notification includes specific exercises, timing, and points to note.

[0701] Step 7:

[0702] The user performs exercise according to the notified exercise plan. After the exercise, they input feedback on their physical condition and satisfaction with the exercise into the device.

[0703] Step 8:

[0704] The server collects and stores feedback received from users. This feedback is used to retrain the generative AI model.

[0705] Step 9:

[0706] The server retrains the AI ​​model based on feedback to improve the accuracy of plan generation. This process makes the suggestions to the user more personalized and effective.

[0707] (Example 1)

[0708] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] In modern society, maintaining personal health and establishing exercise habits are crucial, but developing exercise plans that adapt to diverse lifestyles and health conditions is a challenging task. In particular, there is a need for personalized exercise plans and mechanisms to support their implementation, but conventional methods are insufficient. Furthermore, technologies that incorporate user feedback to improve plan accuracy are still evolving, and there is a need to realize effective health support.

[0710] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0711] In this invention, the server includes acquisition means for acquiring personal attribute data from the user, collection means for collecting the user's physiological data using a biometric measurement device, and evaluation means for analyzing the data in an information processing device to evaluate the user's health status. This enables the provision of an advanced exercise plan based on the user's individual health status and the long-term maintenance of health that accompanies it.

[0712] "Personal attribute data" refers to basic information about an individual, including the user's name, age, gender, height, weight, health check results, and lifestyle.

[0713] "Physiological data" refers to numerical data that indicates the user's physical condition, such as heart rate, steps taken, calories burned, and sleep patterns.

[0714] A "biometric measurement device" is a device, such as a smartwatch or other sensor device, that measures a user's physical values ​​in real time.

[0715] An "information processing device" is a hardware device, such as a server or computer, that receives, analyzes, and processes data.

[0716] A "generative artificial intelligence model" is a collection of algorithms that use machine learning and deep learning techniques to learn patterns from data and make predictions.

[0717] An "exercise plan" is a plan of exercise, including the type, frequency, intensity, and duration, that is formulated based on an individual's health condition and goals.

[0718] "Communication devices" refer to electronic devices such as smartphones, tablets, and personal computers that are used to send and receive information with users.

[0719] "Response data" refers to feedback information provided by users after completing an exercise plan, regarding their physical condition, satisfaction level, and the effectiveness of the exercise.

[0720] "Retraining" refers to the process of retraining a generative artificial intelligence model using user feedback data to improve the accuracy of predictions.

[0721] This invention is a system designed to support users in maintaining their health and promoting exercise. Based on personal attribute data and physiological data from the user, the system formulates an individually optimized exercise plan and provides it to the user, while also utilizing feedback to improve the accuracy of the generated AI model.

[0722] Users input their personal attribute data using a device, which includes smartphones and tablets. The device works in conjunction with smartwatches and other biometric devices to collect physiological data such as heart rate, steps, calorie consumption, and sleep patterns in real time. This data is transmitted to a server using wireless technologies such as Bluetooth.

[0723] The server uses Python and R data analysis libraries to analyze the received personal attribute data and physiological data. This data analysis includes calculating health indicators, identifying stress levels, and analyzing trends in sedentary lifestyles. Based on the evaluation results obtained, the server uses a generative AI model to create a personalized exercise plan for each user. Machine learning frameworks such as TensorFlow and PyTorch are used for the generative AI model.

[0724] The developed exercise plan is notified to the user via a device. The device provides a reminder function to help the user incorporate the notified exercise plan into their daily routine. After the user completes the exercise plan, feedback data is sent to the server via the device. The feedback includes information about the user's physical condition after exercise, satisfaction level, and perceived effectiveness of the exercise.

[0725] The server retrains its AI model based on collected feedback, continuously improving the accuracy of exercise plan development. This allows it to provide users with more effective and satisfying health support.

[0726] For example, if a user enters the prompt, "Please suggest a stress-reducing exercise plan for a man in his 50s who is busy with work and doesn't have much time," the server will respond to this request by creating a plan that includes short yoga and stretching exercises and notifying the user via their device.

[0727] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0728] Step 1:

[0729] The user enters personal attribute data using a terminal. This data includes name, age, gender, height, weight, health check results, and lifestyle. The terminal sends the entered data to the server. This entered data is used as basic information to diagnose the user's current health status.

[0730] Step 2:

[0731] The device synchronizes with a biometric measurement device to collect physiological data such as heart rate, steps, calories burned, and sleep patterns in real time. The device transmits the collected physiological data to a server using a Bluetooth connection. This physiological data provides dynamic information about the user's activity and health.

[0732] Step 3:

[0733] The server integrates received personal attribute data and physiological data, and analyzes the data using Python or R analysis libraries. Specifically, it calculates health indicators and evaluates stress levels and exercise habits. Based on the input data, it generates a health status assessment report for the user. As output, it provides this assessment result to the next processing step of the generating AI model.

[0734] Step 4:

[0735] The server uses a generative AI model to develop a personalized exercise plan based on the evaluation results. This generative AI model, implemented in TensorFlow or PyTorch, designs the optimal exercise plan tailored to the user's needs. Specifically, it calculates the type of exercise, frequency, duration, and intensity, and customizes the plan individually. The developed exercise plan is then sent from the server to the user's device.

[0736] Step 5:

[0737] The device notifies the user of an exercise plan and displays reminders to incorporate it into their daily activities. By following the notified exercise plan and performing the exercises, the user promotes maintaining their health. The device manages the implementation of this activity and tracks progress.

[0738] Step 6:

[0739] After completing their exercise plan, users input feedback on their physical condition and satisfaction level into the device. This feedback includes information about their physical sensations and mental satisfaction after exercise. The device then sends this feedback data to the server.

[0740] Step 7:

[0741] The server improves the accuracy of the exercise plan by retraining the generated AI model using the collected feedback data. The retraining utilizes collected feedback and new data, improving the AI ​​model's predictive capabilities. This results in more accurate and effective exercise plans for the user in the future.

[0742] (Application Example 1)

[0743] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] In modern society, personalized health management is important, but it is difficult to exercise consistently amidst a busy daily life. This invention aims to solve the problem of supporting the maintenance of a healthy lifestyle by providing a system that automatically generates an exercise plan tailored to the user's health condition and allows for easy exercise guidance at home.

[0745] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0746] In this invention, the server includes an input means for inputting personal information from the user, a collection means for collecting the user's biometric information via a sensor device, an evaluation means for analyzing the data in a computing device to evaluate the user's health status, a generation means for creating an individualized exercise plan for the user using a generated AI model based on the evaluation, a notification means for notifying the user's information terminal of the generated exercise plan, a feedback collection means for receiving feedback information from the user, a retraining means for improving the accuracy of the generated AI model using the feedback information, and a guide means for providing exercise guidance as a home robot. As a result, the user can receive exercise guidance at home and continue personalized health management.

[0747] A "user" is an individual who uses this system to receive health management and exercise guidance.

[0748] "Personal information" refers to identifiable data necessary for health management, such as a user's name, age, gender, health check results, and lifestyle.

[0749] A "sensor device" is a measuring instrument used to collect a user's biometric information in real time, and includes heart rate monitors, pedometers, and other similar devices.

[0750] "Biometric information" refers to data that indicates the user's physical activity and health status, such as heart rate, steps taken, calorie consumption, and sleep patterns.

[0751] A "computational device" is an electronic device used to analyze collected data and evaluate the user's health status.

[0752] "Assessing health status" is the process of calculating the user's health indicators and determining their stress levels and tendency towards lack of exercise.

[0753] A "generative AI model" is an artificial intelligence algorithm that automatically generates an optimal exercise plan for each user based on evaluation results.

[0754] An "exercise plan" is an exercise plan that includes aerobic exercise, strength training, and relaxation exercises tailored to the user's needs and goals.

[0755] An "information terminal" is an electronic device used to notify users of exercise plans, and includes smartphones and computers.

[0756] "Feedback information" refers to response data provided by users after they have exercised, including their physical condition, satisfaction level, and the effectiveness of the exercise.

[0757] A "home robot" is an autonomous mechanical system that provides exercise guidance to users within their homes.

[0758] The embodiments for carrying out the present invention are shown below.

[0759] In this system, the terminal first collects personal information from the user and then works with sensor devices to acquire biometric information in real time. This utilizes sensors such as heart rate monitors and pedometers. The collected personal information and biometric information are then transmitted to a server.

[0760] On the server, computing devices analyze this data, and a generative AI model evaluates the user's health status. This evaluation includes a function to determine stress levels based on the user's heart rate variability and step count information. Based on these results, an optimal exercise plan for each user is created using the generative AI model through the evaluation process. This model uses TensorFlow, PyTorch, and other tools to process and analyze the data.

[0761] The generated exercise plan is notified to the information terminal, allowing the user to incorporate it into their daily life. After completing the exercise, the user provides feedback information about their physical condition and the effects of the exercise, and sends it back to the server via the terminal. Based on this feedback information, the server retrains the generating AI model to improve the accuracy of the exercise plan.

[0762] The home robot will use this information to provide real-time exercise guidance to the user. For example, for a home user who has a lot of work scheduled in the morning, it can suggest stretching and deep breathing exercises to help refresh the mind and body. An example of a prompt message for the generating AI model might be, "Please suggest a relaxation exercise plan that can be done this morning for a 45-year-old adult who does desk work."

[0763] Thus, the present invention provides an intelligent evolution system based on user-individualized exercise guidance and feedback, supporting the maintenance of a healthy lifestyle in the home environment.

[0764] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0765] Step 1:

[0766] The terminal receives personal information from the user. This information includes the user's name, age, gender, health check results, and lifestyle. The entered data is sent to the server as foundational information for the user's individual health management.

[0767] Step 2:

[0768] The device collects biometric information in real time via connected sensor devices. Specifically, this includes data such as heart rate, steps taken, calorie consumption, and sleep patterns. The collected biometric information is sent to a server to continuously monitor the user's physical activity.

[0769] Step 3:

[0770] The server analyzes the received personal and biometric information. This analysis uses computing devices to analyze data such as the user's heart rate variability and step count, and evaluates their stress level and health status. Based on the biometric information input, health indicators are calculated, and the results are output.

[0771] Step 4:

[0772] The server uses the evaluation results to call a generating AI model to create an optimal exercise plan for each user. This process uses software such as TensorFlow and PyTorch to process the user's health data as input and output an exercise plan. The generated exercise plan is then customized according to the user's goals and needs.

[0773] Step 5:

[0774] The generated exercise plan is notified to the information terminal. The user then begins their daily exercise based on the notified exercise plan. The terminal can also set reminders and alerts to prompt the user to start and finish their exercise.

[0775] Step 6:

[0776] After completing their workout, users input feedback information about their experience and physical condition into their device. This feedback data is sent to a server to reflect the user's satisfaction level and the effectiveness of their workout, and is used to generate their next workout plan.

[0777] Step 7:

[0778] The server uses feedback information to retrain the generated AI model to improve its accuracy. In this process, the AI ​​model is adaptively improved using feedback as input data, aiming to generate more accurate motor plans.

[0779] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0780] This invention combines an emotion engine with a generative AI instructor system to support users' health management and exercise promotion. The emotion engine recognizes the user's emotional state and proposes an individualized health plan based on that state, thereby reducing stress and maintaining motivation.

[0781] This system begins with the user entering basic personal information using a device, including age, gender, occupation, and current health status. The device also connects with the user's smartwatch or fitness tracker to collect biometric data such as heart rate, steps taken, and calorie consumption in real time.

[0782] In addition, the emotion engine captures the user's facial expressions with a camera and analyzes their emotional state based on that data. This analysis allows the system to identify the user's current mental state and their stress and anxiety levels.

[0783] The server receives collected personal information, biometric data, and emotional data, and uses a data analysis engine to assess the overall health status. This assessment process integrates emotional data analyzed by the emotion engine with stress level determination based on the user's heart rate variability and step count data to determine the health status.

[0784] Next, the server uses a generative AI model to generate a personalized exercise plan for each user. This plan not only includes exercises suited to the user's health condition, but also takes into account relaxation exercises and stress-relieving exercises based on their emotional state.

[0785] The generated exercise plan is notified to the user via the device. The notification includes the benefits of exercising, goals to be achieved, and a reward plan to motivate the user. After completing the exercise, the user is prompted to enter feedback on changes in their physical condition and mood into the device.

[0786] The server receives feedback data and uses it to retrain the generative AI model and emotion engine, improving the accuracy of the system's suggestions. This feedback process ensures that users continue to receive plans optimized for them.

[0787] For example, if a user is determined to be experiencing stress due to work-related pressure, the emotion engine will assess the situation and suggest relaxation plans such as yoga or meditation to reduce stress. If this plan is implemented and stress reduction is reported, the emotion engine and generative AI model will be adjusted again to provide better health support to the user.

[0788] Based on the above, we will provide comprehensive health support that takes into account not only the user's physical condition but also their emotional state, realizing a system that efficiently and effectively supports health maintenance.

[0789] The following describes the processing flow.

[0790] Step 1:

[0791] The user enters personal information using their device. This includes age, gender, health check results, and details about their lifestyle. The device then sends this information to a cloud server.

[0792] Step 2:

[0793] The device connects with sensor devices such as smartwatches and fitness trackers to collect biometric data in real time. It periodically records data such as heart rate, steps taken, calorie consumption, and sleep patterns, and sends it to a server.

[0794] Step 3:

[0795] The device uses its built-in camera to capture the user's facial expressions. The emotion engine analyzes the image to determine the user's emotional state. The analysis results in the quantification of emotional states such as stress, anxiety, and joy.

[0796] Step 4:

[0797] The server receives personal information, biometric data, and emotional data sent by the user and stores them in a database. Based on the received data, a data analysis engine operates to evaluate the user's overall health status.

[0798] Step 5:

[0799] The server uses a generated AI model based on the evaluation results to create a personalized exercise plan for each user. This plan takes into account both the user's health and emotional state, and includes optimal exercises and stress-reducing activities.

[0800] Step 6:

[0801] The device notifies the user of the generated exercise plan. The notification includes details of the exercises to be performed, the recommended duration, and the expected effects of the exercises.

[0802] Step 7:

[0803] The user executes an exercise plan based on notifications from their device. After exercising, they input feedback on changes in their physical condition and mood into their device.

[0804] Step 8:

[0805] The server receives user feedback data and records it in a database. Based on this feedback, the generative AI model and emotion engine are retrained to improve accuracy.

[0806] Step 9:

[0807] The server uses a retrained and improved generative AI model to enable more personalized suggestions in subsequent plan generation. This improves the accuracy and satisfaction of health support for users.

[0808] (Example 2)

[0809] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0810] In modern society, lifestyle-related diseases and accumulated stress are becoming apparent health problems. Conventional health management systems have only considered the user's physical health status, making it difficult to take a comprehensive approach that also takes into account emotional state and stress levels. As a result, there has been a problem in that users are unable to sustainably manage their health.

[0811] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0812] In this invention, the server includes emotion analysis means for capturing the user's facial expressions and analyzing their emotional state, health evaluation means for analyzing the data and evaluating the user's health status, and plan generation means for creating an individualized exercise plan for the user using a generated AI model based on the evaluation. This enables comprehensive health management that takes into account not only the user's physical health status but also their emotional state and stress level.

[0813] "Information acquisition means" refers to a device or method for inputting and acquiring personal information from a user.

[0814] "Data collection means" refers to a device or method for collecting a user's biometric data via a sensor device.

[0815] "Emotional analysis means" refers to a device or method for capturing a user's facial expressions, analyzing that data, and evaluating their emotional state.

[0816] A "health assessment tool" is a device or method for analyzing data collected on a server and evaluating the user's health status.

[0817] "Plan generation means" refers to an apparatus or method for creating an individualized exercise plan for a user using a generation AI model based on the results of a health assessment.

[0818] "Plan notification means" refers to a device or method for notifying a user's terminal of a generated exercise plan.

[0819] "Feedback data collection means" refers to a device or method for receiving feedback data from users.

[0820] A "model retraining means" is a device or method for improving the accuracy of a generated AI model using received feedback data.

[0821] This invention implements a system that comprehensively manages the user's health and emotional state and provides an individualized exercise plan. Specifically, it uses the following devices and software.

[0822] The user first enters personal information via a device. This device works in conjunction with smartwatches and fitness trackers to collect biometric data such as the user's heart rate, steps taken, and calorie consumption in real time. The device also uses a camera to capture the user's facial expressions and analyzes their emotional state using software called an emotion engine.

[0823] The server processes personal information, biometric data, and emotional data transmitted from the device using a data analysis engine to assess overall health status. This assessment includes stress level determination from heart rate variability and step count, as well as integrated analysis of emotional data. Based on the assessment results, the server utilizes a generative AI model to generate a personalized exercise plan for the user. This exercise plan includes exercises optimized for the user's health condition and relaxation exercises appropriate for their emotional state.

[0824] The generated exercise plan is notified to the user via their device. The notification includes the benefits of the exercise, the goals to be achieved, and the reward plan. After completing the exercise, the user provides feedback on changes in their physical condition and mood via their device. The server uses this feedback to retrain the generating AI model and emotion engine, improving the accuracy of the suggestions.

[0825] For example, if a user enters information such as, "I'm a 30-year-old woman in a management position, and I experience high levels of daily stress. My heart rate tends to rise easily, and I'd like an exercise plan to help me relax," the system will suggest a relaxation plan that includes yoga and meditation, thereby helping to reduce the user's stress levels.

[0826] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0827] Step 1:

[0828] Users enter personal information using a terminal. Specifically, they enter information about their age, gender, occupation, and health status. This is registered as the system's initial data. This information is the input data that forms the basis for subsequent data analysis.

[0829] Step 2:

[0830] The device collects biometric data from connected smartwatches and fitness trackers. Specific input data includes heart rate, steps taken, and calorie consumption. This biometric data is then sent to a server as output data for health assessment.

[0831] Step 3:

[0832] The device captures the user's facial expressions using its built-in camera. An emotion analysis engine receives this facial data as input and analyzes the user's emotional state. The analyzed emotional state data becomes the output data used for stress level assessment.

[0833] Step 4:

[0834] The server receives personal information, biometric data, and emotional data transmitted from the terminal and performs a health assessment. A data analysis engine uses this data as input to analyze and evaluate the user's health status. The output is an overall health status and stress level assessment.

[0835] Step 5:

[0836] The server uses a generative AI model to generate a personalized exercise plan for the user based on the results of the health assessment. It uses the assessment results as input and generates an exercise plan that takes into account the user's health and emotional state as output.

[0837] Step 6:

[0838] The server sends the generated exercise plan to the terminal. The terminal notifies the user of the plan. Specifically, it presents the benefits of the exercise, the goals to be achieved, and the reward plan. This becomes the output data for the user.

[0839] Step 7:

[0840] The user performs exercises based on the provided exercise plan and then inputs feedback on changes in their physical condition and emotions into the device. This feedback data is sent to the server as input data for the next processing step.

[0841] Step 8:

[0842] The server retrains its generative AI model and emotion engine based on feedback data received from the user. This retraining process improves the accuracy of subsequent exercise plan suggestions. The improved AI model is output using the feedback data as input.

[0843] (Application Example 2)

[0844] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0845] In modern society, people are becoming more aware of the importance of health management, but it is difficult to achieve effective health management amidst busy daily lives. Furthermore, stress and emotional fluctuations often reduce motivation for exercise, making it difficult to maintain good health. Therefore, there is a need for a system that efficiently provides comprehensive health support, taking into account not only the user's physical condition but also their emotional state.

[0846] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0847] In this invention, the server includes information input means for inputting personal information from the user, data collection means for collecting the user's biometric data via sensor devices, and evaluation means for analyzing the data on the server to evaluate the user's health and emotional state. This makes it possible to provide a personalized exercise plan based on the user's current health and emotional state.

[0848] "Personal information" refers to basic information about a user, including age, gender, occupation, and health status.

[0849] A "sensor device" refers to a device used to collect a user's biometric data, such as heart rate, steps taken, and calorie consumption, in real time.

[0850] "Evaluation methods" refer to the process of analyzing collected data to assess the user's health and emotional state.

[0851] "Generative AI technology" refers to algorithms and their application technologies for creating personalized exercise plans based on a user's health and emotional state.

[0852] The "plan generation means" refers to a function for creating individual exercise plans for users, and uses generation AI technology to determine the content of the exercises.

[0853] "Notification means" refers to methods for informing the user of the created exercise plan, and this information is provided via the user's terminal or home robot.

[0854] "Feedback collection methods" refer to the process of collecting user reactions and data after they have completed their exercise plan.

[0855] "Retraining methods" refer to the process of improving generative AI technology and increasing its accuracy by utilizing feedback data.

[0856] The system for implementing this invention aims to provide an exercise plan that takes into account the user's health management and emotional state. The system mainly consists of a terminal that receives the user's personal information, sensor equipment that collects the user's biometric data, a server that analyzes the data, and a generative AI model.

[0857] The server receives personal information entered by the user through the terminal. This includes age, gender, occupation, and health status. Sensor devices collect biometric data in real time, such as the user's heart rate, steps taken, and calorie consumption. Furthermore, a camera is used to capture the user's facial expressions and analyze their emotional state.

[0858] The server uses evaluation tools to comprehensively analyze biometric and emotional data to assess the user's health status and stress level. Generative AI technology then generates a personalized exercise plan based on the evaluation results. This plan includes refreshing activities and incentive plans tailored to the user's psychological state, thereby providing optimal health support.

[0859] The generated exercise plan is communicated to the user via their device or home robot. This notification includes the exercise content, achievement goals, and benefits of performing the exercise. The user is required to perform the exercise and input the results as feedback into their device. The server receives this feedback and retrains the generating AI model to improve the accuracy of the suggestions. This provides an optimized plan tailored to the user's needs.

[0860] For example, if a user complains of work fatigue, the server can suggest an exercise plan called "Refresh Yoga." After completing this plan, if the user provides feedback to their device such as "My heart rate has calmed down," the generating AI model learns from that data and will then suggest a more appropriate plan for future sessions.

[0861] An example of a prompt message would be: "A man in his 30s is feeling tired from work. He has a high heart rate and high stress levels. Please suggest a suitable way for him to refresh himself."

[0862] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0863] Step 1:

[0864] The terminal receives personal information from the user. This information includes age, gender, occupation, and health status, and is sent to the server. The terminal then processes this input data to format it into an appropriate format.

[0865] Step 2:

[0866] The sensor device collects the user's biometric data in real time. This includes heart rate, steps taken, and calorie consumption. The collected biometric data is transferred from the sensor device to a server. The data is de-noised and signal-processed before being transmitted.

[0867] Step 3:

[0868] The server acquires user facial expression data via the camera and performs emotion analysis. This analysis determines the user's emotional state and stress level, and stores this data as evaluation data. Image processing algorithms are used in this process.

[0869] Step 4:

[0870] The server integrates and analyzes collected personal information, biometric data, and emotional data using evaluation tools to assess the user's health status. This process uses machine learning algorithms to analyze the characteristics of the data and generate indicators of health and mental state.

[0871] Step 5:

[0872] Using a generative AI model, a personalized exercise plan is created for each user based on the evaluation results. The generated plan includes optimal exercise content and incentives for the user, based on prompt messages. Here, features are extracted from the data, and an optimized exercise plan is output using a predictive model.

[0873] Step 6:

[0874] The server sends the generated exercise plan to a terminal or home robot. The terminal uses notification methods to present the user with the plan's contents, achievement goals, and benefits of execution. The information is reformatted and displayed in a way that is easy for the user to understand visually.

[0875] Step 7:

[0876] Users perform exercises and input feedback about their results and changes in physical condition into the device. This feedback includes their impressions of the exercise, their level of achievement, and changes in their physical condition. The device then formats this data into an appropriate data structure and sends it to the server.

[0877] Step 8:

[0878] The server retrains the generating AI model based on the received feedback data. This process compares the feedback results with the prediction accuracy of the plan, learns areas for improvement in the model, and enhances the accuracy of future proposals. The model parameters are automatically adjusted through this feedback loop.

[0879] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0880] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0881] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0882] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0883] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0884] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0885] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0886] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0887] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0888] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0889] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0890] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0891] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0892] 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.

[0893] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0894] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0895] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0896] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0897] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0898] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0899] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0900] The following is further disclosed regarding the embodiments described above.

[0901] (Claim 1)

[0902] Input method for entering personal information from users,

[0903] A collection means for collecting user biometric data via a sensor device,

[0904] An evaluation means that analyzes data on a server to evaluate the user's health status,

[0905] A generation means that creates a user-specific exercise plan using a generated AI model based on evaluation,

[0906] A notification means that notifies the user's device of the generated exercise plan,

[0907] A means of collecting feedback data from users,

[0908] A retraining method to improve the accuracy of a generated AI model using feedback data,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, characterized in that the evaluation means identifies the stress level through the user's heart rate variability and step count data.

[0912] (Claim 3)

[0913] The system according to claim 1, characterized in that the generation means sets up a reward plan as an incentive for the user to perform exercise.

[0914] "Example 1"

[0915] (Claim 1)

[0916] A means of obtaining personal attribute data from users,

[0917] A collection means for collecting user physiological data using a biometric measurement device,

[0918] An evaluation means for analyzing data in an information processing device to evaluate the user's health status,

[0919] A method for formulating individual exercise plans for users by utilizing a generative artificial intelligence model based on evaluation,

[0920] A notification means for informing the user's communication device of the formulated exercise plan,

[0921] A means of collecting user response data,

[0922] A retraining method to improve the accuracy of a generative artificial intelligence model using reaction data,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, characterized in that the evaluation means identifies psychological stress through the user's heart rate variability and step count data.

[0926] (Claim 3)

[0927] The system according to claim 1, characterized in that the planning means sets up a reward plan as a means of motivating users to perform exercise.

[0928] "Application Example 1"

[0929] (Claim 1)

[0930] Input method for entering personal information from users,

[0931] A means for collecting user biometric information via a sensor device,

[0932] An evaluation means that analyzes data in a computing device to evaluate the user's health status,

[0933] A generation means that creates a user-specific exercise plan using a generation AI model based on evaluation,

[0934] A notification means for notifying the user's information terminal of the generated exercise plan,

[0935] A means of collecting feedback information from users,

[0936] A retraining method to improve the accuracy of a generated AI model using feedback information,

[0937] A guide device for providing exercise instruction as a home robot,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, characterized in that the evaluation means identifies the stress level through the user's heart rate variability and step count information.

[0941] (Claim 3)

[0942] The system according to claim 1, characterized in that the generation means sets up a reward program as an incentive for the user to perform exercise.

[0943] "Example 2 of combining an emotion engine"

[0944] (Claim 1)

[0945] A means of obtaining information by having users input their personal information,

[0946] A data collection means for collecting user biometric data via a sensor device,

[0947] An emotion analysis method that captures the user's facial expressions and analyzes their emotional state,

[0948] A health assessment means that analyzes data on a server to evaluate the user's health status,

[0949] A plan generation means that creates a user-specific exercise plan using an AI model based on evaluation,

[0950] A plan notification means that notifies the user's device of the generated exercise plan,

[0951] A means of collecting feedback data to receive feedback data from users,

[0952] A model retraining method that improves the accuracy of the generated AI model using feedback data,

[0953] A system that includes this.

[0954] (Claim 2)

[0955] The system according to claim 1, characterized in that the health assessment means identifies the stress level through the user's heart rate variability and step count data, and performs an integrated analysis of the emotional state using the emotion analysis means.

[0956] (Claim 3)

[0957] The system according to claim 1, characterized in that the plan generation means sets a reward plan as an incentive for the user to perform exercise and proposes relaxation exercises according to the user's emotional state.

[0958] "Application example 2 when combining with an emotional engine"

[0959] (Claim 1)

[0960] A means of inputting personal information from users,

[0961] A data collection means for collecting user biometric data via sensor devices,

[0962] An evaluation means that analyzes data on a server to evaluate the user's health and emotional state,

[0963] A plan generation means that creates a user-specific exercise plan using generation AI technology based on evaluation,

[0964] A notification means for notifying the user's terminal or home robot of the generated exercise plan,

[0965] A means of collecting feedback data from users,

[0966] A retraining method to improve the accuracy of generative AI technology using feedback data,

[0967] An interactive system including...

[0968] (Claim 2)

[0969] The interactive system according to claim 1, characterized in that the evaluation means identifies the stress level through the user's heart rate variability and step count data, and further analyzes the user's emotional state using a camera.

[0970] (Claim 3)

[0971] The interactive system according to claim 1, characterized in that the plan generation means sets a reward plan as an incentive for the user to perform exercise, and also proposes refreshing activities based on the user's emotional state. [Explanation of Symbols]

[0972] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Input method for entering personal information from users, A means for collecting user biometric information via a sensor device, An evaluation means that analyzes data in a computing device to evaluate the user's health status, A generation means that creates a user-specific exercise plan using a generation AI model based on evaluation, A notification means for notifying the user's information terminal of the generated exercise plan, A means of collecting feedback information from users, A retraining method to improve the accuracy of a generated AI model using feedback information, A guide device for providing exercise instruction as a home robot, A system that includes this.

2. The system according to claim 1, characterized in that the evaluation means identifies the stress level through the user's heart rate variability and step count information.

3. The system according to claim 1, characterized in that the generation means sets up a reward program as an incentive for the user to perform exercise.

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