system

Abstract

We provide the system. [Solution] Means of obtaining health information, A means for analyzing the user's physical condition based on the aforementioned health information, Based on the aforementioned analysis results, a means for automatically generating an exercise menu suitable for the user, A means for providing the generated exercise menu in an interactive game format, A means for collecting user exercise data through the aforementioned interactive game, A means for providing feedback to the user based on the aforementioned exercise data, A system that includes this.

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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, for a patient with physical symptoms to continue exercising for health maintenance, since existing exercise menus are mainly designed for healthy people, the risk of physical burden and symptom deterioration is high, which has been a major problem. To solve this problem, it is necessary to provide a reasonable exercise menu according to the individual state of the patient and provide an environment in which exercise can be carried out continuously and safely.

Means for Solving the Problems

[0005] This invention employs a means to automatically generate an exercise program tailored to the patient by providing a system that acquires the patient's health information and analyzes the patient's physical condition in detail based on this information. Furthermore, by providing the exercise program in an interactive game format, patients can engage in exercise while having fun. In addition, by aggregating exercise data and providing feedback, it enables the setting of specific goals for the next exercise session, supporting the continuation of safe and effective exercise.

[0006] "Health information" is a general term for data related to a user's physical condition and symptoms, and is obtained from medical institutions and health management systems.

[0007] "Analysis" is the process of evaluating the user's physical condition based on acquired health information and designing an appropriate exercise program.

[0008] An "exercise menu" is a list of exercises designed according to the user's physical condition and exercise goals, and includes individualized content.

[0009] An "interactive game format" is a style of exercise instruction that incorporates game elements to allow users to enjoy exercising.

[0010] "Exercise data" refers to information such as movements and energy expenditure recorded by devices such as terminals when a user performs exercise.

[0011] "Feedback" is information provided to users after exercise, which helps them understand the results of their workout and areas for improvement. [Brief explanation of the drawing]

[0012] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled 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.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled 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, etc.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc. [[ID=I7]]

[0019] <000010I>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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system designed to enable users to continue exercising safely and efficiently. This system aggregates health information, generates personalized exercise menus through analysis, and provides them to users through an interactive game format. Furthermore, it aggregates exercise data and provides specific feedback to users, serving as a guide for achieving future exercise goals. Specific examples are shown below.

[0034] First, the server automatically collects the user's health information in conjunction with the health management system. This information includes diagnostic data from medical institutions and data from daily health monitoring devices. The server uses this data to analyze the user's current physical condition and design appropriate exercise intensity and programs.

[0035] Next, the server generates an exercise program tailored to the user. This program is customized considering the space and equipment available to the user. The generated program is then converted into an interactive game format and sent to the terminal. The advantage of using a game format is that it allows users to continue exercising while having fun.

[0036] The device displays exercise menus received from the server and provides users with visual and audio instructions. This allows users to perform exercises with the correct form. During exercise, the device uses sensors to record the user's movements in real time and calculates calories burned and exercise achievement.

[0037] Once the exercise is complete, the device sends the collected exercise data to a server, which then generates detailed feedback based on that data. This feedback includes exercise results, areas for improvement, and goals for the next exercise session. This allows users to track their progress and approach their next exercise safely and effectively.

[0038] For example, taking a 30-year-old man suffering from chronic lower back pain, the server analyzes his past diagnostic data and daily posture data to generate an exercise program centered on low-impact stretching and yoga. The terminal presents this as an interactive game with a virtual character, instructing him to maintain correct posture during exercise. After the exercise, he can check the results on the terminal and plan his next workout. In this way, users can engage in exercise consistently without overexerting themselves.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server connects with the health management system to retrieve users' health information. This information includes diagnostic data from medical institutions and data from health monitoring devices used in daily life.

[0042] Step 2:

[0043] The server analyzes the acquired health information and evaluates the user's physical condition. Based on this analysis, the exercise intensity and the basis of the workout plan are determined.

[0044] Step 3:

[0045] The server automatically generates an exercise program tailored to the user's analysis results. This program is customized to take into account the user's range of motion and available exercise space.

[0046] Step 4:

[0047] The server converts the generated exercise menu into an interactive game format and sends it to the user's device. This creates an environment where users can enjoy exercising.

[0048] Step 5:

[0049] The device displays the transmitted exercise menu and provides exercise in a game format. It guides the user on how to exercise using visual and audio instructions.

[0050] Step 6:

[0051] The user performs exercises according to the instructions on the device. The device records data during the exercise using sensors and calculates calories burned and the results of the exercise.

[0052] Step 7:

[0053] Once the exercise is complete, the device sends the collected exercise data to the server. The server then generates feedback for the user based on this data.

[0054] Step 8:

[0055] The server sends the generated feedback to the device and presents it to the user. The feedback includes the level of exercise achievement, areas for improvement, and new goals.

[0056] (Example 1)

[0057] 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."

[0058] Recently, many individuals living in urban areas have limited opportunities to engage in effective and sustainable exercise due to time constraints and environmental changes. Furthermore, there are challenges in providing personalized exercise plans and obtaining progress-based feedback. These are important issues in health management, where safety and efficiency are paramount.

[0059] 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.

[0060] In this invention, the server includes means for acquiring health-related data, means for analyzing the user's physiological state based on the data, and means for automatically generating an activity program suitable for the user based on the analysis results. This enables the user to receive a customized activity plan tailored to their individual health condition in an interactive format and to receive feedback based on the results of that activity.

[0061] "Health-related data" refers to information about the user's physiological state and health management, such as heart rate, blood pressure, daily activity level, and past diagnostic history.

[0062] "Physiological state" refers to a state that indicates an individual's physical condition and degree of health, and includes indicators such as heart rate, blood pressure, and physical fitness assessments based on sports motion analysis.

[0063] An "activity program" is a collection of exercises, stretches, and other fitness-enhancing activities designed for users according to their individual health conditions.

[0064] An "interactive entertainment format" is a format that uses games and animations that users can enjoy to encourage exercise and physical activity.

[0065] "Exercise-related data" refers to data that records the movements, activity levels, and calories burned during exercise performed by users.

[0066] "Feedback" refers to information that indicates the results, achievements, and areas for improvement of the exercise performed by the user, and serves as a guide for the next exercise plan.

[0067] The system of the present invention is designed to allow users to perform optimal exercise based on their individual health condition. The embodiments for carrying out the invention are described below in detail.

[0068] The server integrates with health management devices and healthcare systems to acquire health-related data such as the user's heart rate, blood pressure, daily activity level, and past diagnostic history. This includes protocols for securely transferring data using APIs. The server analyzes the acquired data using machine learning algorithms to understand the user's physiological state. This enables the generation of optimal activity programs that take into account each user's unique health condition.

[0069] Next, the server uses a generation AI model to automatically generate an activity program tailored to the user. This program takes into account the user's activity space and available equipment, and is converted into an interactive entertainment format. The generated program data is transmitted to the terminal via the internet.

[0070] The terminal displays the activity program received from the server and provides visual and auditory guidance. Furthermore, the terminal uses sensors to record data about the user's exercise in real time. This allows for detailed monitoring of the user's movements, activity level, and calories burned.

[0071] Once the exercise is complete, the device sends the collected data to the server, which then applies a generative AI model to provide feedback. This feedback indicates the user's exercise performance, achievement level, and next exercise goals, allowing the user to continuously improve their exercise based on this information.

[0072] As a concrete example, consider a woman in her 40s who suffers from chronic shoulder stiffness. The server analyzes her existing health data and daily activity data to generate an activity program that includes stretches and exercises effective in alleviating shoulder stiffness. The terminal provides this program through an animated guide, and the user performs the exercises according to the instructions. After the exercise, she can check the results of her workout on the terminal and plan her next exercise session.

[0073] An example of a prompt would be: "Please propose an activity program, including stretches and exercises, for a woman in her 40s aiming to alleviate shoulder stiffness. Design the program to be presented in an interactive entertainment format, based on existing health data and daily activity data."

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server acquires data related to the user's health. Specifically, it uses APIs to collect information such as heart rate, blood pressure, and activity level from health management devices and healthcare systems. This data serves as input to the server, and the acquired health information becomes the material for the next analysis step.

[0077] Step 2:

[0078] The server analyzes the acquired health data. This process uses machine learning algorithms to evaluate the user's physiological state. The input is the health information collected in step 1, and the output is an evaluation result indicating the individual's health status. This evaluation result provides the necessary information for designing an activity program suitable for the user.

[0079] Step 3:

[0080] The server generates an activity program tailored to the user using a generative AI model based on the analysis results. The input is the analysis results obtained in step 2. As a result of data processing and calculations, a customized activity program is output. This program includes optimized content that takes into account the user's available space and equipment.

[0081] Step 4:

[0082] The server converts the generated activity program into an interactive entertainment format. The input is the activity program obtained in step 3, and the result of the processing is output content that guides the user visually and aurally. The generated content is entertaining while promoting exercise.

[0083] Step 5:

[0084] The device receives interactive content sent from the server and provides it to the user. The input is the content from step 4, which the device displays in the user interface, providing visual and auditory guidance. The user begins exercising as instructed, and the device uses sensors to record exercise data in real time.

[0085] Step 6:

[0086] The device uses sensors to collect and record data related to the user's exercise. The input is the user's exercise behavior, and the output is specific motion data, such as movement precision and calories burned. This data is used to evaluate the effectiveness of the exercise.

[0087] Step 7:

[0088] The server provides feedback using the collected exercise data. The input is the exercise data from step 6, and based on this data, analysis is performed to generate feedback indicating the user's performance, level of achievement, and next steps to take. The output feedback serves as a guide for users to continue improving their exercise.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern society, it is important for users to exercise consistently while maintaining their health, but designing exercise programs tailored to individual conditions and enjoying the effects of those programs is not easy. Therefore, there is a need for a system that provides personalized exercise menus based on the user's physical condition and allows for real-time feedback.

[0092] 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.

[0093] In this invention, the server includes means for acquiring health information, means for analyzing the user's physical condition based on the health information, means for automatically generating an exercise menu suitable for the user based on the analysis results, means for providing the generated exercise menu in an interactive game format and providing feedback in real time, means for aggregating the user's exercise data through the interactive game and processing the data using a cloud service, and means for providing feedback to the user based on the exercise data and automatically setting the next exercise goal. This makes it possible to provide an individualized exercise program and to implement it effectively.

[0094] "Health information" refers to data about a user's physical and health condition, collected from medical institution diagnostic data and health management devices.

[0095] "Analyzing physical condition" refers to a method of evaluating a user's exercise capacity and health status based on health information, and designing an appropriate exercise program.

[0096] "Automatically generating exercise menus" means that a computer automatically creates an appropriate exercise program based on the user's physical condition and available environment.

[0097] "Providing it in an interactive game format" means presenting exercise menus to users in a way that incorporates game elements, so that users can continue exercising while having fun.

[0098] "Providing real-time feedback" means evaluating the user's movements on the spot during exercise and immediately communicating areas for improvement and progress.

[0099] "Using cloud services for data processing" means using remote servers on the internet to quickly store and analyze large amounts of data and provide users with the information they need.

[0100] "Providing feedback and automatically setting the next exercise goal" means that after an exercise session, the system will show the user's achievements and areas for improvement based on their performance, and then present specific goals for their next exercise session.

[0101] The system for implementing this invention includes a program that collects the user's health information and provides a personalized exercise menu based on that information. The server automatically acquires data from medical institutions and home health management devices in cooperation with a health management system and performs data analysis using cloud infrastructure (e.g., Amazon Web Services). Based on the analysis results, it generates an optimal exercise menu for the user and delivers this menu to the terminal in an interactive game format.

[0102] The terminal includes smartphones and wearable devices with motion sensors (e.g., Azure® Kinect DK), which function as hardware for running interactive games. Users exercise through the game, and the terminal records and analyzes the user's movements in real time, providing immediate feedback. This feedback includes information on the accuracy of the movements and the level of achievement.

[0103] As a concrete example, before a user at the gym begins exercising, they launch a dedicated app on their smartphone and scan a QR code (registered trademark) in a designated area. This displays a personalized exercise menu on the device, monitors their movements via motion sensors, and immediately notifies them of areas for improvement and their progress. After the exercise, all data is uploaded to the cloud and used to improve the next exercise program.

[0104] An example of a prompt would be, "A man in his 30s, primarily desk-working, with lower back pain. Please create an optimal exercise plan for three gym sessions per week." By inputting such prompts into an AI model, it is possible to create training menus tailored to individual users.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server retrieves user health information from health management devices and medical institution databases. The input uses the user's authorized health information access rights. This information is aggregated in the cloud and processed into the format necessary for analysis. The output is the user's basic health dataset.

[0108] Step 2:

[0109] The server uses a generated AI model to analyze the acquired health information and evaluate the user's physical condition. The input is the health information obtained in step 1. This analysis determines the appropriate exercise level and objectives. The output is candidate data for exercise programs based on the analysis results.

[0110] Step 3:

[0111] The server automatically generates an exercise menu corresponding to the analysis results and converts it into a game format. The input is candidate data for the exercise program, which is the output of step 2. Using the generating AI model, a menu including interactive elements is created. The output is a customized exercise program converted into a game format.

[0112] Step 4:

[0113] The server sends the generated exercise menu to the terminal. The terminal executes this menu as a game, including visual and audio guidance. The input is the game-format menu generated in step 3. The output is the exercise guide displayed to the user.

[0114] Step 5:

[0115] The user initiates exercise using a device, and motion sensors monitor the user's movements in real time. Input is the user's physical movement data. Sensor information is used to analyze the accuracy of the movements and calorie consumption. Output is real-time feedback information.

[0116] Step 6:

[0117] Once the exercise is complete, the device sends the collected exercise data to the server. The server analyzes this data and automatically sets detailed feedback and next exercise goals for the user. The input is the exercise data obtained from the motion sensor. The output is the feedback results and new exercise goal data from the cloud.

[0118] 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.

[0119] This invention is a system aimed at providing a personalized exercise experience while taking into account the user's emotional state. This system includes means for generating exercise menus based on the user's health information and a function for recognizing the user's emotional state using an emotion engine. Through an interactive game format, it provides an environment where users can continue exercising in a fun and effortless way. Specific examples are shown below.

[0120] The server works in conjunction with the health management system to acquire and analyze the user's health information. This analysis generates an appropriate exercise program based on the user's physical condition. The generated exercise program is then sent to the user's terminal.

[0121] The device presents its exercise menu in an interactive game format, acting as a guide for users as they engage in exercise. A key feature here is the emotion engine built into the device. This emotion engine analyzes the user's facial expressions, voice tone, and input data to understand their emotional state in real time. Based on this emotion recognition, it adjusts the difficulty and presentation within the game, ensuring users feel both entertained and challenged.

[0122] For example, if a user shows signs of fatigue during exercise, the emotion engine analyzes this and supports the user by automatically lowering the difficulty of the exercise or inserting relaxation content. On the other hand, if the system recognizes that the user is enjoying themselves, it provides praise messages and incentives within the game to further promote engagement.

[0123] Once an exercise session is complete, the device sends exercise and emotional data to the server. The server uses this data to create feedback and suggest goals for the next exercise session. The feedback is also personalized by the emotional engine, designed to maintain motivation and guide you towards your next challenge.

[0124] Thus, the system of the present invention can provide an optimal exercise experience while considering not only the user's physical health but also their emotional aspects. This improves the ease of engaging in exercise and contributes to sustainable health promotion activities.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server works in conjunction with the health management system to collect user health information. This information includes the user's physical condition, past exercise history, and health checkup data.

[0128] Step 2:

[0129] The server analyzes the user's physical condition based on collected health information and automatically generates an appropriate exercise program based on the analysis results. The generated program is personalized and tailored to the user's fitness level.

[0130] Step 3:

[0131] The server sends the generated exercise menu to the user's device. The menu is converted into an interactive game format, designed to allow users to exercise while having fun.

[0132] Step 4:

[0133] The terminal displays exercise menus received from the server and presents them to the user in an interactive game format. It guides the user through exercise instructions and correct form via visual and audio guidance.

[0134] Step 5:

[0135] The emotion engine built into the device analyzes the user's facial expressions and tone of voice to recognize the user's emotional state in real time.

[0136] Step 6:

[0137] The emotion engine adjusts the difficulty and content of the game based on the user's emotional state. For example, if the user is showing signs of fatigue, it may reduce the intensity of the exercise or insert relaxation content.

[0138] Step 7:

[0139] The user performs exercises according to the instructions on the device. The device records the user's movements and emotional data during exercise and calculates exercise achievement and calories burned in real time.

[0140] Step 8:

[0141] Once the exercise is complete, the device sends the collected exercise data and emotional data to the server.

[0142] Step 9:

[0143] The server generates and provides feedback to the user based on the received data. The feedback is personalized by an emotion engine, and specifically suggests exercise goals and areas for improvement for the next session.

[0144] Step 10:

[0145] Users can check feedback on their devices, maintain their motivation, and prepare for their next exercise session.

[0146] (Example 2)

[0147] 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 will be referred to as the "terminal."

[0148] There is a need to provide personalized exercise plans tailored to each user's physical and emotional state, supporting an enjoyable and sustainable exercise experience. However, existing systems struggle to provide exercise plans that fully consider the user's biometric information and emotional state. Furthermore, it is difficult to appropriately adjust the difficulty level and presentation of exercises in real time.

[0149] 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.

[0150] In this invention, the server includes means for acquiring biometric information, means for analyzing the user's physical condition based on the biometric information, means for automatically generating an exercise plan suitable for the user based on the analysis results, means for recognizing the user's emotional state using an emotion analysis device, and means for adjusting the difficulty level and presentation of the entertainment format according to the recognized emotional state. This makes it possible to provide an exercise experience that takes into account the user's physical health and emotional satisfaction.

[0151] "Biometric information" refers to data that indicates the user's physical health status, including heart rate, weight, and past exercise records.

[0152] "User" refers to an individual who accepts and executes an exercise plan using this system.

[0153] "Physical condition" refers to information indicating the user's health status and fitness level, and includes the results of biometric data analysis.

[0154] An "exercise plan" is a guide that indicates fitness activities suitable for the user, and it is generated by this system.

[0155] A "two-way entertainment format" is a type of game in which the user and the system interact and exchange information while providing entertainment.

[0156] "Activity data" refers to data that shows the status of exercise performed by a user when they engage in exercise.

[0157] An "emotional analysis device" is a device or software that recognizes a user's emotional state by analyzing input such as their facial expressions and voice.

[0158] "Emotional state" refers to the user's psychological or emotional condition, as recognized by the emotion analysis device.

[0159] "Adjusting difficulty level and presentation" means changing the difficulty of the exercise plan and the visual and auditory effects based on the user's emotional state.

[0160] This invention relates to a system for providing users with personalized exercise experiences. It primarily consists of a server, terminals, and users, which work together to realize the system.

[0161] The server communicates with the health management system to collect biometric information. For this purpose, it uses a general-purpose API to acquire biometric information such as heart rate, weight, and past exercise records, and securely stores this information in a database. For analysis, a generative AI model is used, automatically generating personalized exercise plans by utilizing prompts such as, for example, "Please suggest an exercise program suitable for a user with a heart rate of 120."

[0162] This exercise plan is provided to the user in real time in an interactive, entertaining format. After receiving the exercise plan, the device guides the user visually and audibly through a general-purpose game software, which is an interactive platform. The user performs the exercise based on this guide, and the device collects the activity data generated during the process.

[0163] Furthermore, the device incorporates an emotion analysis device that analyzes the user's facial expressions and tone of voice in real time. This allows the system to recognize the user's emotional state and adjust the difficulty of the exercise if the user is tired, or provide a challenging experience if the user is highly motivated.

[0164] For example, if a user shows a focused expression during exercise, the system provides positive feedback and encourages them to continue exercising. Furthermore, once the exercise is complete, the server integrates activity and emotional data sent from the device to create feedback for the next exercise session. This feedback is personalized using a generative AI model to encourage continued exercise and maintain motivation.

[0165] In this way, the system can support both the user's physical health and emotional well-being.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The server retrieves the user's biometric information from the health management system. The input is the user's ID and basic authentication information, which the server uses to request biometric data such as heart rate, weight, and past exercise records via an API. The output is a biometric report compiled from this data. The server stores the obtained data in a database and prepares it for analysis.

[0169] Step 2:

[0170] The server uses a generative AI model to analyze the acquired biometric information. It uses a previously collected biometric information report as input. The server then inputs the prompt "Suggest an exercise program suitable for a user with a heart rate of 120" into the AI ​​model to generate a specific exercise plan. The output is a customized exercise plan based on the user's physical condition.

[0171] Step 3:

[0172] The server sends the generated exercise plan to the user's device. The input is the customized exercise plan, which is converted into the format necessary for the device to correctly receive and display it. The output is the exercise plan data sent to the device as part of an interactive entertainment format.

[0173] Step 4:

[0174] The device visually and audibly guides the user through the received exercise plan. The input is exercise plan data sent from the server. The device processes this data using general-purpose game software to generate animations and audio guides. The output is an exercise guide in a user-friendly format. The user then begins exercising based on this guide and makes adjustments based on the feedback.

[0175] Step 5:

[0176] The device collects real-time activity data and emotional state of the user during exercise. The user's facial expressions and tone of voice are used as input, which is then interpreted by an emotion analysis device. The output consists of data on the user's emotional state and activity data indicating the progress of the exercise.

[0177] Step 6:

[0178] After the exercise session ends, the device sends the collected activity and emotional data to the server. The input consists of all data recorded during the exercise session. The output consists of the data sent to the server for feedback and planning the next exercise session.

[0179] Step 7:

[0180] The server generates feedback based on the received data. It uses activity and emotion data sent from the device as input and evaluates it using a generative AI model. The output is personalized feedback provided to the user, along with suggestions for their next exercise session. The server sends this back to the user to help them with their next workout.

[0181] (Application Example 2)

[0182] 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".

[0183] In modern manufacturing environments, excessive workloads that disregard workers' physical and emotional states pose a problem that hinders work efficiency and health. However, traditional methods make real-time workload adjustment difficult, likely leading to increased stress and decreased work efficiency. To address this challenge, it is necessary to monitor workers' physiological and emotional states and implement individualized exercise adjustments.

[0184] 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.

[0185] In this invention, the server includes means for acquiring health information, means for automatically generating an exercise menu suitable for the user based on the analysis results, and means for evaluating the user's emotional state in real time and adjusting the workload, including an emotion engine that has the function of identifying the emotional state. This makes it possible to optimize the burden on workers in the work environment and promote an efficient and healthy work process.

[0186] "Health information" refers to data that indicates the physiological state of a worker, including vital signs such as heart rate and fatigue level.

[0187] "Analysis results" refer to detailed analytical data obtained after evaluating the physical condition of workers based on acquired health information.

[0188] An "exercise menu" is a set of instructions that includes effective and safe exercises and work procedures for workers, and is automatically generated according to each individual's health condition.

[0189] An "interactive game" is a two-way simulation or training program that encourages workers to actively participate and act while receiving visual and auditory feedback.

[0190] The "emotion engine" is an artificial intelligence system that analyzes the worker's facial expressions, tone of voice, and input data to evaluate their emotional state in real time.

[0191] "Work environment" refers to a broad definition of workplace conditions, including the physical location where workers perform their duties on a daily basis, as well as the equipment and tools used in that location.

[0192] "Workload" is an indicator that shows the amount of work and the difficulty level of the work that a worker is required to perform within a certain period of time.

[0193] "Physiological data" refers to digital information about a worker's physical function and health status, and is closely related to health information.

[0194] "Emotional data" refers to data that quantifies or qualitatively represents the emotional state of a worker, and is evaluated by an emotion engine.

[0195] The system for implementing this invention is an advanced digital platform for managing workers' health information and emotional state in the work environment. This system provides the ability to evaluate workers' physical and emotional states in real time and to interactively optimize their workload.

[0196] The server periodically acquires health information such as heart rate and fatigue level from each worker and uses software to analyze this information. This analysis automatically generates an optimal exercise program for each worker. Physiological sensors are used to acquire health information, and AI-based analysis software is used to analyze the data.

[0197] The terminal provides the worker with a generated exercise menu in an interactive game format. This allows the worker to enjoy exercising while exercise data is collected in the process. The terminal is equipped with an emotion engine that detects the worker's emotional state by analyzing their facial expressions and tone of voice. Image recognition software and voice analysis tools are used for this process.

[0198] When a user begins exercising, the device sends collected exercise and emotional data to a server. The server uses this data to provide feedback to the user and suggest adjustments to the next exercise routine or workload. Throughout this process, data management and analysis are performed using a dedicated cloud computing solution.

[0199] A concrete example would be a scenario where signs of fatigue in workers during long shifts are detected in real time, and break instructions or mitigation tasks are automatically provided according to their condition. An example of input to the generating AI model would be a prompt sentence such as, "If the worker's fatigue level is 'high', what action plan would you suggest to adjust the tasks appropriately?"

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The server receives health information acquired through physiological sensors. This information, including heart rate and fatigue level, is passed to analysis software as input data. Data analysis detects abnormal health patterns and generates output that evaluates the worker's physical condition.

[0203] Step 2:

[0204] The terminal receives analysis results sent from the server and generates an optimal exercise menu in an interactive game format based on them. This exercise menu presents instructions for tasks applicable to the worker and provides output that supports visual navigation using a game engine.

[0205] Step 3:

[0206] When a user begins exercising, an emotion engine built into the device captures the user's facial expressions and voice in real time, and uses this data as input to analyze their emotional state. Based on the results of this analysis, an emotional evaluation is generated, and the game progression and task difficulty are dynamically adjusted.

[0207] Step 4:

[0208] The terminal collects exercise and emotional data and sends this data to the server. In this step, emotion-recognition feedback data is supplied to the server's processing center, which prepares output for suggesting adjustments to the worker's next exercise menu and workload.

[0209] Step 5:

[0210] The server utilizes a generated AI model based on the collected data to propose the next work plan and exercise goals. This generates feedback and personalized advice for the worker, which is then used as a basis for providing appropriate work instructions.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention is a system designed to enable users to continue exercising safely and efficiently. This system aggregates health information, generates personalized exercise menus through analysis, and provides them to users through an interactive game format. Furthermore, it aggregates exercise data and provides specific feedback to users, serving as a guide for achieving future exercise goals. Specific examples are shown below.

[0228] First, the server automatically collects the user's health information in conjunction with the health management system. This information includes diagnostic data from medical institutions and data from daily health monitoring devices. The server uses this data to analyze the user's current physical condition and design appropriate exercise intensity and programs.

[0229] Next, the server generates an exercise program tailored to the user. This program is customized considering the space and equipment available to the user. The generated program is then converted into an interactive game format and sent to the terminal. The advantage of using a game format is that it allows users to continue exercising while having fun.

[0230] The device displays exercise menus received from the server and provides users with visual and audio instructions. This allows users to perform exercises with the correct form. During exercise, the device uses sensors to record the user's movements in real time and calculates calories burned and exercise achievement.

[0231] Once the exercise is complete, the device sends the collected exercise data to a server, which then generates detailed feedback based on that data. This feedback includes exercise results, areas for improvement, and goals for the next exercise session. This allows users to track their progress and approach their next exercise safely and effectively.

[0232] For example, taking a 30-year-old man suffering from chronic lower back pain, the server analyzes his past diagnostic data and daily posture data to generate an exercise program centered on low-impact stretching and yoga. The terminal presents this as an interactive game with a virtual character, instructing him to maintain correct posture during exercise. After the exercise, he can check the results on the terminal and plan his next workout. In this way, users can engage in exercise consistently without overexerting themselves.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server connects with the health management system to retrieve users' health information. This information includes diagnostic data from medical institutions and data from health monitoring devices used in daily life.

[0236] Step 2:

[0237] The server analyzes the acquired health information and evaluates the user's physical condition. Based on this analysis, the exercise intensity and the basis of the workout plan are determined.

[0238] Step 3:

[0239] The server automatically generates an exercise program tailored to the user's analysis results. This program is customized to take into account the user's range of motion and available exercise space.

[0240] Step 4:

[0241] The server converts the generated exercise menu into an interactive game format and sends it to the user's device. This creates an environment where users can enjoy exercising.

[0242] Step 5:

[0243] The device displays the transmitted exercise menu and provides exercise in a game format. It guides the user on how to exercise using visual and audio instructions.

[0244] Step 6:

[0245] The user performs exercises according to the instructions on the device. The device records data during the exercise using sensors and calculates calories burned and the results of the exercise.

[0246] Step 7:

[0247] Once the exercise is complete, the device sends the collected exercise data to the server. The server then generates feedback for the user based on this data.

[0248] Step 8:

[0249] The server sends the generated feedback to the device and presents it to the user. The feedback includes the level of exercise achievement, areas for improvement, and new goals.

[0250] (Example 1)

[0251] 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."

[0252] Recently, many individuals living in urban areas have limited opportunities to engage in effective and sustainable exercise due to time constraints and environmental changes. Furthermore, there are challenges in providing personalized exercise plans and obtaining progress-based feedback. These are important issues in health management, where safety and efficiency are paramount.

[0253] 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.

[0254] In this invention, the server includes means for acquiring health-related data, means for analyzing the user's physiological state based on the data, and means for automatically generating an activity program suitable for the user based on the analysis results. This enables the user to receive a customized activity plan tailored to their individual health condition in an interactive format and to receive feedback based on the results of that activity.

[0255] "Health-related data" refers to information about the user's physiological state and health management, such as heart rate, blood pressure, daily activity level, and past diagnostic history.

[0256] "Physiological state" refers to a state that indicates an individual's physical condition and degree of health, and includes indicators such as heart rate, blood pressure, and physical fitness assessments based on sports motion analysis.

[0257] An "activity program" is a collection of exercises, stretches, and other fitness-enhancing activities designed for users according to their individual health conditions.

[0258] An "interactive entertainment format" is a format that uses games and animations that users can enjoy to encourage exercise and physical activity.

[0259] "Exercise-related data" refers to data that records the movements, activity levels, and calories burned during exercise performed by users.

[0260] "Feedback" refers to information that indicates the results, achievements, and areas for improvement of the exercise performed by the user, and serves as a guide for the next exercise plan.

[0261] The system of the present invention is designed to allow users to perform optimal exercise based on their individual health condition. The embodiments for carrying out the invention are described below in detail.

[0262] The server integrates with health management devices and healthcare systems to acquire health-related data such as the user's heart rate, blood pressure, daily activity level, and past diagnostic history. This includes protocols for securely transferring data using APIs. The server analyzes the acquired data using machine learning algorithms to understand the user's physiological state. This enables the generation of optimal activity programs that take into account each user's unique health condition.

[0263] Next, the server uses a generation AI model to automatically generate an activity program tailored to the user. This program takes into account the user's activity space and available equipment, and is converted into an interactive entertainment format. The generated program data is transmitted to the terminal via the internet.

[0264] The terminal displays the activity program received from the server and provides visual and auditory guidance. Furthermore, the terminal uses sensors to record data about the user's exercise in real time. This allows for detailed monitoring of the user's movements, activity level, and calories burned.

[0265] Once the exercise is complete, the device sends the collected data to the server, which then applies a generative AI model to provide feedback. This feedback indicates the user's exercise performance, achievement level, and next exercise goals, allowing the user to continuously improve their exercise based on this information.

[0266] As a concrete example, consider a woman in her 40s who suffers from chronic shoulder stiffness. The server analyzes her existing health data and daily activity data to generate an activity program that includes stretches and exercises effective in alleviating shoulder stiffness. The terminal provides this program through an animated guide, and the user performs the exercises according to the instructions. After the exercise, she can check the results of her workout on the terminal and plan her next exercise session.

[0267] An example of a prompt would be: "Please propose an activity program, including stretches and exercises, for a woman in her 40s aiming to alleviate shoulder stiffness. Design the program to be presented in an interactive entertainment format, based on existing health data and daily activity data."

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The server acquires data related to the user's health. Specifically, it uses APIs to collect information such as heart rate, blood pressure, and activity level from health management devices and healthcare systems. This data serves as input to the server, and the acquired health information becomes the material for the next analysis step.

[0271] Step 2:

[0272] The server analyzes the acquired health data. This process uses machine learning algorithms to evaluate the user's physiological state. The input is the health information collected in step 1, and the output is an evaluation result indicating the individual's health status. This evaluation result provides the necessary information for designing an activity program suitable for the user.

[0273] Step 3:

[0274] The server generates an activity program tailored to the user using a generative AI model based on the analysis results. The input is the analysis results obtained in step 2. As a result of data processing and calculations, a customized activity program is output. This program includes optimized content that takes into account the user's available space and equipment.

[0275] Step 4:

[0276] The server converts the generated activity program into an interactive entertainment format. The input is the activity program obtained in step 3, and the result of the processing is output content that guides the user visually and aurally. The generated content is entertaining while promoting exercise.

[0277] Step 5:

[0278] The terminal receives the interactive content sent from the server and provides it to the user. The input is the content in Step 4, and the terminal displays this on the user interface and provides visual and auditory guidance. The user starts moving as instructed, and the terminal uses sensors to record the motion data in real time.

[0279] Step 6:

[0280] The terminal uses sensors to collect and record data related to the user's movement. The input is the user's movement behavior, and as output, specific motion data such as movement accuracy and calories burned can be obtained. Based on this data, the results of the movement are evaluated.

[0281] Step 7:

[0282] The server provides feedback using the collected motion data. The input is the motion data in Step 6. Based on this data, analysis is performed, and feedback indicating the user's results, degree of achievement, and the actions to be taken next is generated. The output feedback serves as a guide for the user to continue improving their movement.

[0283] (Application Example 1)

[0284] 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".

[0285] In modern society, it is important for users to exercise continuously while maintaining their health. However, it is not easy to design an exercise program according to individual conditions or to enjoy the exercise while feeling its effects. Therefore, there is a need for a system that provides an individualized exercise menu based on the user's physical condition and allows the user to receive real-time feedback.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes means for acquiring health information, means for analyzing the physical condition of the user based on the health information, means for automatically generating an exercise menu suitable for the user based on the analysis result, means for providing the generated exercise menu in an interactive game format and performing real-time feedback, means for aggregating the exercise data of the user through the interactive game and performing data processing using cloud services, and means for providing feedback to the user based on the exercise data and automatically setting the next exercise goal. Thereby, it becomes possible to provide an individualized exercise program and effectively implement it.

[0288] "Health information" refers to data related to the physical condition and health status of the user, which is collected from the diagnostic data of medical institutions and health management devices.

[0289] "Analyzing the physical condition" is a method for evaluating the exercise ability and health status of the user based on health information and designing an appropriate exercise menu.

[0290] "Automatically generating an exercise menu" means that the computer automatically creates an appropriate exercise program according to the physical condition of the user and the available environment.

[0291] "Providing in an interactive game format" is a method of presenting the exercise menu to the user in a form incorporating game elements so that the user can continue exercising while enjoying it.

[0292] "Performing real-time feedback" means evaluating the user's actions on the spot during exercise and immediately conveying the improvement points and achievement status.

[0293] "Performing data processing using cloud services" means using a remote server on the Internet to quickly store, analyze a large amount of data, and provide the necessary information to the user.

[0294] "Providing feedback and automatically setting the next exercise goal" means that after an exercise session, the system will show the user's achievements and areas for improvement based on their performance, and then present specific goals for their next exercise session.

[0295] The system for implementing this invention includes a program that collects the user's health information and provides a personalized exercise menu based on that information. The server automatically acquires data from medical institutions and home health management devices in cooperation with a health management system and performs data analysis using cloud infrastructure (e.g., Amazon Web Services). Based on the analysis results, it generates an optimal exercise menu for the user and delivers this menu to the terminal in an interactive game format.

[0296] The system includes smartphones and wearable devices with motion sensors (e.g., Azure Kinect DK), which function as hardware for running interactive games. Users exercise through the games, and the system records and analyzes their movements in real time, providing immediate feedback. This feedback includes information on the accuracy of the movements and the level of achievement.

[0297] As a concrete example, before a user at the gym begins exercising, they launch a dedicated app on their smartphone and scan a QR code in a designated area. This displays a personalized exercise menu on the device, monitors their movements via motion sensors, and immediately notifies them of areas for improvement and their progress. After the exercise, all data is uploaded to the cloud and used to improve the next exercise program.

[0298] An example of a prompt would be, "A man in his 30s, primarily desk-working, with lower back pain. Please create an optimal exercise plan for three gym sessions per week." By inputting such prompts into an AI model, it is possible to create training menus tailored to individual users.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The server retrieves user health information from health management devices and medical institution databases. The input uses the user's authorized health information access rights. This information is aggregated in the cloud and processed into the format necessary for analysis. The output is the user's basic health dataset.

[0302] Step 2:

[0303] The server uses a generated AI model to analyze the acquired health information and evaluate the user's physical condition. The input is the health information obtained in step 1. This analysis determines the appropriate exercise level and objectives. The output is candidate data for exercise programs based on the analysis results.

[0304] Step 3:

[0305] The server automatically generates an exercise menu corresponding to the analysis results and converts it into a game format. The input is candidate data for the exercise program, which is the output of step 2. Using the generating AI model, a menu including interactive elements is created. The output is a customized exercise program converted into a game format.

[0306] Step 4:

[0307] The server transmits the generated exercise menu to the terminal. The terminal executes this menu as a game including visual and voice guidance. The input is the game format menu generated in step 3. The output is the exercise guidance displayed to the user.

[0308] Step 5:

[0309] The user starts exercising using the terminal, and the motion sensor monitors the user's movements in real time. The input is the user's body movement data. The accuracy of the movement and the calories consumed are analyzed using the sensor information. The output is real-time feedback information.

[0310] Step 6:

[0311] When the exercise ends, the terminal transmits the collected exercise data to the server. The server analyzes this and automatically sets detailed feedback for the user and the next exercise goal. The input is the exercise data obtained by the motion sensor. The output is the feedback result by the cloud and the new exercise goal data.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0313] The present invention is a system aimed at providing an individualized exercise experience while considering the user's emotional state. This system has means for generating an exercise menu based on the user's health information and a function for recognizing the user's emotional state using an emotion engine. Through an interactive game format, an environment is provided where the user can continue exercising happily and without difficulty. Specific examples are shown below.

[0314] The server works in conjunction with the health management system to acquire and analyze the user's health information. This analysis generates an appropriate exercise program based on the user's physical condition. The generated exercise program is then sent to the user's terminal.

[0315] The device presents its exercise menu in an interactive game format, acting as a guide for users as they engage in exercise. A key feature here is the emotion engine built into the device. This emotion engine analyzes the user's facial expressions, voice tone, and input data to understand their emotional state in real time. Based on this emotion recognition, it adjusts the difficulty and presentation within the game, ensuring users feel both entertained and challenged.

[0316] For example, if a user shows signs of fatigue during exercise, the emotion engine analyzes this and supports the user by automatically lowering the difficulty of the exercise or inserting relaxation content. On the other hand, if the system recognizes that the user is enjoying themselves, it provides praise messages and incentives within the game to further promote engagement.

[0317] Once an exercise session is complete, the device sends exercise and emotional data to the server. The server uses this data to create feedback and suggest goals for the next exercise session. The feedback is also personalized by the emotional engine, designed to maintain motivation and guide you towards your next challenge.

[0318] Thus, the system of the present invention can provide an optimal exercise experience while considering not only the user's physical health but also their emotional aspects. This improves the ease of engaging in exercise and contributes to sustainable health promotion activities.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The server works in conjunction with the health management system to collect user health information. This information includes the user's physical condition, past exercise history, and health checkup data.

[0322] Step 2:

[0323] The server analyzes the user's physical condition based on collected health information and automatically generates an appropriate exercise program based on the analysis results. The generated program is personalized and tailored to the user's fitness level.

[0324] Step 3:

[0325] The server sends the generated exercise menu to the user's device. The menu is converted into an interactive game format, designed to allow users to exercise while having fun.

[0326] Step 4:

[0327] The terminal displays exercise menus received from the server and presents them to the user in an interactive game format. It guides the user through exercise instructions and correct form via visual and audio guidance.

[0328] Step 5:

[0329] The emotion engine built into the device analyzes the user's facial expressions and tone of voice to recognize the user's emotional state in real time.

[0330] Step 6:

[0331] The emotion engine adjusts the difficulty and content of the game based on the user's emotional state. For example, if the user is showing signs of fatigue, it may reduce the intensity of the exercise or insert relaxation content.

[0332] Step 7:

[0333] The user performs exercises according to the instructions on the device. The device records the user's movements and emotional data during exercise and calculates exercise achievement and calories burned in real time.

[0334] Step 8:

[0335] Once the exercise is complete, the device sends the collected exercise data and emotional data to the server.

[0336] Step 9:

[0337] The server generates and provides feedback to the user based on the received data. The feedback is personalized by an emotion engine, and specifically suggests exercise goals and areas for improvement for the next session.

[0338] Step 10:

[0339] Users can check feedback on their devices, maintain their motivation, and prepare for their next exercise session.

[0340] (Example 2)

[0341] 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".

[0342] There is a need to provide personalized exercise plans tailored to each user's physical and emotional state, supporting an enjoyable and sustainable exercise experience. However, existing systems struggle to provide exercise plans that fully consider the user's biometric information and emotional state. Furthermore, it is difficult to appropriately adjust the difficulty level and presentation of exercises in real time.

[0343] 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.

[0344] In this invention, the server includes means for acquiring biometric information, means for analyzing the user's physical condition based on the biometric information, means for automatically generating an exercise plan suitable for the user based on the analysis results, means for recognizing the user's emotional state using an emotion analysis device, and means for adjusting the difficulty level and presentation of the entertainment format according to the recognized emotional state. This makes it possible to provide an exercise experience that takes into account the user's physical health and emotional satisfaction.

[0345] "Biometric information" refers to data that indicates the user's physical health status, including heart rate, weight, and past exercise records.

[0346] "User" refers to an individual who accepts and executes an exercise plan using this system.

[0347] "Physical condition" refers to information indicating the user's health status and fitness level, and includes the results of biometric data analysis.

[0348] An "exercise plan" is a guide that indicates fitness activities suitable for the user, and it is generated by this system.

[0349] A "two-way entertainment format" is a type of game in which the user and the system interact and exchange information while providing entertainment.

[0350] "Activity data" refers to data that shows the status of exercise performed by a user when they engage in exercise.

[0351] An "emotional analysis device" is a device or software that recognizes a user's emotional state by analyzing input such as their facial expressions and voice.

[0352] "Emotional state" refers to the user's psychological or emotional condition, as recognized by the emotion analysis device.

[0353] "Adjusting difficulty level and presentation" means changing the difficulty of the exercise plan and the visual and auditory effects based on the user's emotional state.

[0354] This invention relates to a system for providing users with personalized exercise experiences. It primarily consists of a server, terminals, and users, which work together to realize the system.

[0355] The server communicates with the health management system to collect biometric information. For this purpose, it uses a general-purpose API to acquire biometric information such as heart rate, weight, and past exercise records, and securely stores this information in a database. For analysis, a generative AI model is used, automatically generating personalized exercise plans by utilizing prompts such as, for example, "Please suggest an exercise program suitable for a user with a heart rate of 120."

[0356] This exercise plan is provided to the user in real time in an interactive, entertaining format. After receiving the exercise plan, the device guides the user visually and audibly through a general-purpose game software, which is an interactive platform. The user performs the exercise based on this guide, and the device collects the activity data generated during the process.

[0357] Furthermore, the device incorporates an emotion analysis device that analyzes the user's facial expressions and tone of voice in real time. This allows the system to recognize the user's emotional state and adjust the difficulty of the exercise if the user is tired, or provide a challenging experience if the user is highly motivated.

[0358] For example, if a user shows a focused expression during exercise, the system provides positive feedback and encourages them to continue exercising. Furthermore, once the exercise is complete, the server integrates activity and emotional data sent from the device to create feedback for the next exercise session. This feedback is personalized using a generative AI model to encourage continued exercise and maintain motivation.

[0359] In this way, the system can support both the user's physical health and emotional well-being.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The server retrieves the user's biometric information from the health management system. The input is the user's ID and basic authentication information, which the server uses to request biometric data such as heart rate, weight, and past exercise records via an API. The output is a biometric report compiled from this data. The server stores the obtained data in a database and prepares it for analysis.

[0363] Step 2:

[0364] The server uses a generative AI model to analyze the acquired biometric information. It uses a previously collected biometric information report as input. The server then inputs the prompt "Suggest an exercise program suitable for a user with a heart rate of 120" into the AI ​​model to generate a specific exercise plan. The output is a customized exercise plan based on the user's physical condition.

[0365] Step 3:

[0366] The server sends the generated exercise plan to the user's device. The input is the customized exercise plan, which is converted into the format necessary for the device to correctly receive and display it. The output is the exercise plan data sent to the device as part of an interactive entertainment format.

[0367] Step 4:

[0368] The device visually and audibly guides the user through the received exercise plan. The input is exercise plan data sent from the server. The device processes this data using general-purpose game software to generate animations and audio guides. The output is an exercise guide in a user-friendly format. The user then begins exercising based on this guide and makes adjustments based on the feedback.

[0369] Step 5:

[0370] The device collects real-time activity data and emotional state of the user during exercise. The user's facial expressions and tone of voice are used as input, which is then interpreted by an emotion analysis device. The output consists of data on the user's emotional state and activity data indicating the progress of the exercise.

[0371] Step 6:

[0372] After the exercise session ends, the device sends the collected activity and emotional data to the server. The input consists of all data recorded during the exercise session. The output consists of the data sent to the server for feedback and planning the next exercise session.

[0373] Step 7:

[0374] The server generates feedback based on the received data. It uses activity and emotion data sent from the device as input and evaluates it using a generative AI model. The output is personalized feedback provided to the user, along with suggestions for their next exercise session. The server sends this back to the user to help them with their next workout.

[0375] (Application Example 2)

[0376] 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."

[0377] In modern manufacturing environments, excessive workloads that disregard workers' physical and emotional states pose a problem that hinders work efficiency and health. However, traditional methods make real-time workload adjustment difficult, likely leading to increased stress and decreased work efficiency. To address this challenge, it is necessary to monitor workers' physiological and emotional states and implement individualized exercise adjustments.

[0378] 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.

[0379] In this invention, the server includes means for acquiring health information, means for automatically generating an exercise menu suitable for the user based on the analysis results, and means for evaluating the user's emotional state in real time and adjusting the workload, including an emotion engine that has the function of identifying the emotional state. This makes it possible to optimize the burden on workers in the work environment and promote an efficient and healthy work process.

[0380] "Health information" refers to data that indicates the physiological state of a worker, including vital signs such as heart rate and fatigue level.

[0381] "Analysis results" refer to detailed analytical data obtained after evaluating the physical condition of workers based on acquired health information.

[0382] An "exercise menu" is a set of instructions that includes effective and safe exercises and work procedures for workers, and is automatically generated according to each individual's health condition.

[0383] An "interactive game" is a two-way simulation or training program that encourages workers to actively participate and act while receiving visual and auditory feedback.

[0384] The "emotion engine" is an artificial intelligence system that analyzes the worker's facial expressions, tone of voice, and input data to evaluate their emotional state in real time.

[0385] "Work environment" refers to a broad definition of workplace conditions, including the physical location where workers perform their duties on a daily basis, as well as the equipment and tools used in that location.

[0386] "Workload" is an indicator that shows the amount of work and the difficulty level of the work that a worker is required to perform within a certain period of time.

[0387] "Physiological data" refers to digital information about a worker's physical function and health status, and is closely related to health information.

[0388] "Emotional data" refers to data that quantifies or qualitatively represents the emotional state of a worker, and is evaluated by an emotion engine.

[0389] The system for implementing this invention is an advanced digital platform for managing workers' health information and emotional state in the work environment. This system provides the ability to evaluate workers' physical and emotional states in real time and to interactively optimize their workload.

[0390] The server periodically acquires health information such as heart rate and fatigue level from each worker and uses software to analyze this information. This analysis automatically generates an optimal exercise program for each worker. Physiological sensors are used to acquire health information, and AI-based analysis software is used to analyze the data.

[0391] The terminal provides the worker with a generated exercise menu in an interactive game format. This allows the worker to enjoy exercising while exercise data is collected in the process. The terminal is equipped with an emotion engine that detects the worker's emotional state by analyzing their facial expressions and tone of voice. Image recognition software and voice analysis tools are used for this process.

[0392] When a user begins exercising, the device sends collected exercise and emotional data to a server. The server uses this data to provide feedback to the user and suggest adjustments to the next exercise routine or workload. Throughout this process, data management and analysis are performed using a dedicated cloud computing solution.

[0393] A concrete example would be a scenario where signs of fatigue in workers during long shifts are detected in real time, and break instructions or mitigation tasks are automatically provided according to their condition. An example of input to the generating AI model would be a prompt sentence such as, "If the worker's fatigue level is 'high', what action plan would you suggest to adjust the tasks appropriately?"

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The server receives health information acquired through physiological sensors. This information, including heart rate and fatigue level, is passed to analysis software as input data. Data analysis detects abnormal health patterns and generates output that evaluates the worker's physical condition.

[0397] Step 2:

[0398] The terminal receives analysis results sent from the server and generates an optimal exercise menu in an interactive game format based on them. This exercise menu presents instructions for tasks applicable to the worker and provides output that supports visual navigation using a game engine.

[0399] Step 3:

[0400] When a user begins exercising, an emotion engine built into the device captures the user's facial expressions and voice in real time, and uses this data as input to analyze their emotional state. Based on the results of this analysis, an emotional evaluation is generated, and the game progression and task difficulty are dynamically adjusted.

[0401] Step 4:

[0402] The terminal collects exercise and emotional data and sends this data to the server. In this step, emotion-recognition feedback data is supplied to the server's processing center, which prepares output for suggesting adjustments to the worker's next exercise menu and workload.

[0403] Step 5:

[0404] The server utilizes a generated AI model based on the collected data to propose the next work plan and exercise goals. This generates feedback and personalized advice for the worker, which is then used as a basis for providing appropriate work instructions.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] This invention is a system designed to enable users to continue exercising safely and efficiently. This system aggregates health information, generates personalized exercise menus through analysis, and provides them to users through an interactive game format. Furthermore, it aggregates exercise data and provides specific feedback to users, serving as a guide for achieving future exercise goals. Specific examples are shown below.

[0422] First, the server automatically collects the user's health information in conjunction with the health management system. This information includes diagnostic data from medical institutions and data from daily health monitoring devices. The server uses this data to analyze the user's current physical condition and design appropriate exercise intensity and programs.

[0423] Next, the server generates an exercise program tailored to the user. This program is customized considering the space and equipment available to the user. The generated program is then converted into an interactive game format and sent to the terminal. The advantage of using a game format is that it allows users to continue exercising while having fun.

[0424] The device displays exercise menus received from the server and provides users with visual and audio instructions. This allows users to perform exercises with the correct form. During exercise, the device uses sensors to record the user's movements in real time and calculates calories burned and exercise achievement.

[0425] Once the exercise is complete, the device sends the collected exercise data to a server, which then generates detailed feedback based on that data. This feedback includes exercise results, areas for improvement, and goals for the next exercise session. This allows users to track their progress and approach their next exercise safely and effectively.

[0426] For example, taking a 30-year-old man suffering from chronic lower back pain, the server analyzes his past diagnostic data and daily posture data to generate an exercise program centered on low-impact stretching and yoga. The terminal presents this as an interactive game with a virtual character, instructing him to maintain correct posture during exercise. After the exercise, he can check the results on the terminal and plan his next workout. In this way, users can engage in exercise consistently without overexerting themselves.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The server connects with the health management system to retrieve users' health information. This information includes diagnostic data from medical institutions and data from health monitoring devices used in daily life.

[0430] Step 2:

[0431] The server analyzes the acquired health information and evaluates the user's physical condition. Based on this analysis, the exercise intensity and the basis of the workout plan are determined.

[0432] Step 3:

[0433] The server automatically generates an exercise program tailored to the user's analysis results. This program is customized to take into account the user's range of motion and available exercise space.

[0434] Step 4:

[0435] The server converts the generated exercise menu into an interactive game format and sends it to the user's device. This creates an environment where users can enjoy exercising.

[0436] Step 5:

[0437] The device displays the transmitted exercise menu and provides exercise in a game format. It guides the user on how to exercise using visual and audio instructions.

[0438] Step 6:

[0439] The user performs exercises according to the instructions on the device. The device records data during the exercise using sensors and calculates calories burned and the results of the exercise.

[0440] Step 7:

[0441] Once the exercise is complete, the device sends the collected exercise data to the server. The server then generates feedback for the user based on this data.

[0442] Step 8:

[0443] The server sends the generated feedback to the device and presents it to the user. The feedback includes the level of exercise achievement, areas for improvement, and new goals.

[0444] (Example 1)

[0445] 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."

[0446] Recently, many individuals living in urban areas have limited opportunities to engage in effective and sustainable exercise due to time constraints and environmental changes. Furthermore, there are challenges in providing personalized exercise plans and obtaining progress-based feedback. These are important issues in health management, where safety and efficiency are paramount.

[0447] 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.

[0448] In this invention, the server includes means for acquiring health-related data, means for analyzing the user's physiological state based on the data, and means for automatically generating an activity program suitable for the user based on the analysis results. This enables the user to receive a customized activity plan tailored to their individual health condition in an interactive format and to receive feedback based on the results of that activity.

[0449] "Health-related data" refers to information about the user's physiological state and health management, such as heart rate, blood pressure, daily activity level, and past diagnostic history.

[0450] "Physiological state" refers to a state that indicates an individual's physical condition and degree of health, and includes indicators such as heart rate, blood pressure, and physical fitness assessments based on sports motion analysis.

[0451] An "activity program" is a collection of exercises, stretches, and other fitness-enhancing activities designed for users according to their individual health conditions.

[0452] An "interactive entertainment format" is a format that uses games and animations that users can enjoy to encourage exercise and physical activity.

[0453] "Exercise-related data" refers to data that records the movements, activity levels, and calories burned during exercise performed by users.

[0454] "Feedback" refers to information that indicates the results, achievements, and areas for improvement of the exercise performed by the user, and serves as a guide for the next exercise plan.

[0455] The system of the present invention is designed to allow users to perform optimal exercise based on their individual health condition. The embodiments for carrying out the invention are described below in detail.

[0456] The server integrates with health management devices and healthcare systems to acquire health-related data such as the user's heart rate, blood pressure, daily activity level, and past diagnostic history. This includes protocols for securely transferring data using APIs. The server analyzes the acquired data using machine learning algorithms to understand the user's physiological state. This enables the generation of optimal activity programs that take into account each user's unique health condition.

[0457] Next, the server uses a generation AI model to automatically generate an activity program tailored to the user. This program takes into account the user's activity space and available equipment, and is converted into an interactive entertainment format. The generated program data is transmitted to the terminal via the internet.

[0458] The terminal displays the activity program received from the server and provides visual and auditory guidance. Furthermore, the terminal uses sensors to record data about the user's exercise in real time. This allows for detailed monitoring of the user's movements, activity level, and calories burned.

[0459] Once the exercise is complete, the device sends the collected data to the server, which then applies a generative AI model to provide feedback. This feedback indicates the user's exercise performance, achievement level, and next exercise goals, allowing the user to continuously improve their exercise based on this information.

[0460] As a concrete example, consider a woman in her 40s who suffers from chronic shoulder stiffness. The server analyzes her existing health data and daily activity data to generate an activity program that includes stretches and exercises effective in alleviating shoulder stiffness. The terminal provides this program through an animated guide, and the user performs the exercises according to the instructions. After the exercise, she can check the results of her workout on the terminal and plan her next exercise session.

[0461] An example of a prompt would be: "Please propose an activity program, including stretches and exercises, for a woman in her 40s aiming to alleviate shoulder stiffness. Design the program to be presented in an interactive entertainment format, based on existing health data and daily activity data."

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The server acquires data related to the user's health. Specifically, it uses APIs to collect information such as heart rate, blood pressure, and activity level from health management devices and healthcare systems. This data serves as input to the server, and the acquired health information becomes the material for the next analysis step.

[0465] Step 2:

[0466] The server analyzes the acquired health data. This process uses machine learning algorithms to evaluate the user's physiological state. The input is the health information collected in step 1, and the output is an evaluation result indicating the individual's health status. This evaluation result provides the necessary information for designing an activity program suitable for the user.

[0467] Step 3:

[0468] The server generates an activity program tailored to the user using a generative AI model based on the analysis results. The input is the analysis results obtained in step 2. As a result of data processing and calculations, a customized activity program is output. This program includes optimized content that takes into account the user's available space and equipment.

[0469] Step 4:

[0470] The server converts the generated activity program into an interactive entertainment format. The input is the activity program obtained in step 3, and the result of the processing is output content that guides the user visually and aurally. The generated content is entertaining while promoting exercise.

[0471] Step 5:

[0472] The device receives interactive content sent from the server and provides it to the user. The input is the content from step 4, which the device displays in the user interface, providing visual and auditory guidance. The user begins exercising as instructed, and the device uses sensors to record exercise data in real time.

[0473] Step 6:

[0474] The device uses sensors to collect and record data related to the user's exercise. The input is the user's exercise behavior, and the output is specific motion data, such as movement precision and calories burned. This data is used to evaluate the effectiveness of the exercise.

[0475] Step 7:

[0476] The server provides feedback using the collected exercise data. The input is the exercise data from step 6, and based on this data, analysis is performed to generate feedback indicating the user's performance, level of achievement, and next steps to take. The output feedback serves as a guide for users to continue improving their exercise.

[0477] (Application Example 1)

[0478] 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."

[0479] In modern society, it is important for users to exercise consistently while maintaining their health, but designing exercise programs tailored to individual conditions and enjoying the effects of those programs is not easy. Therefore, there is a need for a system that provides personalized exercise menus based on the user's physical condition and allows for real-time feedback.

[0480] 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.

[0481] In this invention, the server includes means for acquiring health information, means for analyzing the user's physical condition based on the health information, means for automatically generating an exercise menu suitable for the user based on the analysis results, means for providing the generated exercise menu in an interactive game format and providing feedback in real time, means for aggregating the user's exercise data through the interactive game and processing the data using a cloud service, and means for providing feedback to the user based on the exercise data and automatically setting the next exercise goal. This makes it possible to provide an individualized exercise program and to implement it effectively.

[0482] "Health information" refers to data about a user's physical and health condition, collected from medical institution diagnostic data and health management devices.

[0483] "Analyzing physical condition" refers to a method of evaluating a user's exercise capacity and health status based on health information, and designing an appropriate exercise program.

[0484] "Automatically generating exercise menus" means that a computer automatically creates an appropriate exercise program based on the user's physical condition and available environment.

[0485] "Providing it in an interactive game format" means presenting exercise menus to users in a way that incorporates game elements, so that users can continue exercising while having fun.

[0486] "Providing real-time feedback" means evaluating the user's movements on the spot during exercise and immediately communicating areas for improvement and progress.

[0487] "Using cloud services for data processing" means using remote servers on the internet to quickly store and analyze large amounts of data and provide users with the information they need.

[0488] "Providing feedback and automatically setting the next exercise goal" means that after an exercise session, the system will show the user's achievements and areas for improvement based on their performance, and then present specific goals for their next exercise session.

[0489] The system for implementing this invention includes a program that collects the user's health information and provides a personalized exercise menu based on that information. The server automatically acquires data from medical institutions and home health management devices in cooperation with a health management system and performs data analysis using cloud infrastructure (e.g., Amazon Web Services). Based on the analysis results, it generates an optimal exercise menu for the user and delivers this menu to the terminal in an interactive game format.

[0490] The system includes smartphones and wearable devices with motion sensors (e.g., Azure Kinect DK), which function as hardware for running interactive games. Users exercise through the games, and the system records and analyzes their movements in real time, providing immediate feedback. This feedback includes information on the accuracy of the movements and the level of achievement.

[0491] As a concrete example, before a user at the gym begins exercising, they launch a dedicated app on their smartphone and scan a QR code in a designated area. This displays a personalized exercise menu on the device, monitors their movements via motion sensors, and immediately notifies them of areas for improvement and their progress. After the exercise, all data is uploaded to the cloud and used to improve the next exercise program.

[0492] An example of a prompt would be, "A man in his 30s, primarily desk-working, with lower back pain. Please create an optimal exercise plan for three gym sessions per week." By inputting such prompts into an AI model, it is possible to create training menus tailored to individual users.

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] The server retrieves user health information from health management devices and medical institution databases. The input uses the user's authorized health information access rights. This information is aggregated in the cloud and processed into the format necessary for analysis. The output is the user's basic health dataset.

[0496] Step 2:

[0497] The server uses a generated AI model to analyze the acquired health information and evaluate the user's physical condition. The input is the health information obtained in step 1. This analysis determines the appropriate exercise level and objectives. The output is candidate data for exercise programs based on the analysis results.

[0498] Step 3:

[0499] The server automatically generates an exercise menu corresponding to the analysis results and converts it into a game format. The input is candidate data for the exercise program, which is the output of step 2. Using the generating AI model, a menu including interactive elements is created. The output is a customized exercise program converted into a game format.

[0500] Step 4:

[0501] The server sends the generated exercise menu to the terminal. The terminal executes this menu as a game, including visual and audio guidance. The input is the game-format menu generated in step 3. The output is the exercise guide displayed to the user.

[0502] Step 5:

[0503] The user initiates exercise using a device, and motion sensors monitor the user's movements in real time. Input is the user's physical movement data. Sensor information is used to analyze the accuracy of the movements and calorie consumption. Output is real-time feedback information.

[0504] Step 6:

[0505] Once the exercise is complete, the device sends the collected exercise data to the server. The server analyzes this data and automatically sets detailed feedback and next exercise goals for the user. The input is the exercise data obtained from the motion sensor. The output is the feedback results and new exercise goal data from the cloud.

[0506] 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.

[0507] This invention is a system aimed at providing a personalized exercise experience while taking into account the user's emotional state. This system includes means for generating exercise menus based on the user's health information and a function for recognizing the user's emotional state using an emotion engine. Through an interactive game format, it provides an environment where users can continue exercising in a fun and effortless way. Specific examples are shown below.

[0508] The server works in conjunction with the health management system to acquire and analyze the user's health information. This analysis generates an appropriate exercise program based on the user's physical condition. The generated exercise program is then sent to the user's terminal.

[0509] The device presents its exercise menu in an interactive game format, acting as a guide for users as they engage in exercise. A key feature here is the emotion engine built into the device. This emotion engine analyzes the user's facial expressions, voice tone, and input data to understand their emotional state in real time. Based on this emotion recognition, it adjusts the difficulty and presentation within the game, ensuring users feel both entertained and challenged.

[0510] For example, if a user shows signs of fatigue during exercise, the emotion engine analyzes this and supports the user by automatically lowering the difficulty of the exercise or inserting relaxation content. On the other hand, if the system recognizes that the user is enjoying themselves, it provides praise messages and incentives within the game to further promote engagement.

[0511] Once an exercise session is complete, the device sends exercise and emotional data to the server. The server uses this data to create feedback and suggest goals for the next exercise session. The feedback is also personalized by the emotional engine, designed to maintain motivation and guide you towards your next challenge.

[0512] Thus, the system of the present invention can provide an optimal exercise experience while considering not only the user's physical health but also their emotional aspects. This improves the ease of engaging in exercise and contributes to sustainable health promotion activities.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The server works in conjunction with the health management system to collect user health information. This information includes the user's physical condition, past exercise history, and health checkup data.

[0516] Step 2:

[0517] The server analyzes the user's physical condition based on collected health information and automatically generates an appropriate exercise program based on the analysis results. The generated program is personalized and tailored to the user's fitness level.

[0518] Step 3:

[0519] The server sends the generated exercise menu to the user's device. The menu is converted into an interactive game format, designed to allow users to exercise while having fun.

[0520] Step 4:

[0521] The terminal displays exercise menus received from the server and presents them to the user in an interactive game format. It guides the user through exercise instructions and correct form via visual and audio guidance.

[0522] Step 5:

[0523] The emotion engine built into the device analyzes the user's facial expressions and tone of voice to recognize the user's emotional state in real time.

[0524] Step 6:

[0525] The emotion engine adjusts the difficulty and content of the game based on the user's emotional state. For example, if the user is showing signs of fatigue, it may reduce the intensity of the exercise or insert relaxation content.

[0526] Step 7:

[0527] The user performs exercises according to the instructions on the device. The device records the user's movements and emotional data during exercise and calculates exercise achievement and calories burned in real time.

[0528] Step 8:

[0529] Once the exercise is complete, the device sends the collected exercise data and emotional data to the server.

[0530] Step 9:

[0531] The server generates and provides feedback to the user based on the received data. The feedback is personalized by an emotion engine, and specifically suggests exercise goals and areas for improvement for the next session.

[0532] Step 10:

[0533] Users can check feedback on their devices, maintain their motivation, and prepare for their next exercise session.

[0534] (Example 2)

[0535] 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."

[0536] There is a need to provide personalized exercise plans tailored to each user's physical and emotional state, supporting an enjoyable and sustainable exercise experience. However, existing systems struggle to provide exercise plans that fully consider the user's biometric information and emotional state. Furthermore, it is difficult to appropriately adjust the difficulty level and presentation of exercises in real time.

[0537] 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.

[0538] In this invention, the server includes means for acquiring biometric information, means for analyzing the user's physical condition based on the biometric information, means for automatically generating an exercise plan suitable for the user based on the analysis results, means for recognizing the user's emotional state using an emotion analysis device, and means for adjusting the difficulty level and presentation of the entertainment format according to the recognized emotional state. This makes it possible to provide an exercise experience that takes into account the user's physical health and emotional satisfaction.

[0539] "Biometric information" refers to data that indicates the user's physical health status, including heart rate, weight, and past exercise records.

[0540] "User" refers to an individual who accepts and executes an exercise plan using this system.

[0541] "Physical condition" refers to information indicating the user's health status and fitness level, and includes the results of biometric data analysis.

[0542] An "exercise plan" is a guide that indicates fitness activities suitable for the user, and it is generated by this system.

[0543] A "two-way entertainment format" is a type of game in which the user and the system interact and exchange information while providing entertainment.

[0544] "Activity data" refers to data that shows the status of exercise performed by a user when they engage in exercise.

[0545] An "emotional analysis device" is a device or software that recognizes a user's emotional state by analyzing input such as their facial expressions and voice.

[0546] "Emotional state" refers to the user's psychological or emotional condition, as recognized by the emotion analysis device.

[0547] "Adjusting difficulty level and presentation" means changing the difficulty of the exercise plan and the visual and auditory effects based on the user's emotional state.

[0548] This invention relates to a system for providing users with personalized exercise experiences. It primarily consists of a server, terminals, and users, which work together to realize the system.

[0549] The server communicates with the health management system to collect biometric information. For this purpose, it uses a general-purpose API to acquire biometric information such as heart rate, weight, and past exercise records, and securely stores this information in a database. For analysis, a generative AI model is used, automatically generating personalized exercise plans by utilizing prompts such as, for example, "Please suggest an exercise program suitable for a user with a heart rate of 120."

[0550] This exercise plan is provided to the user in real time in an interactive, entertaining format. After receiving the exercise plan, the device guides the user visually and audibly through a general-purpose game software, which is an interactive platform. The user performs the exercise based on this guide, and the device collects the activity data generated during the process.

[0551] Furthermore, the device incorporates an emotion analysis device that analyzes the user's facial expressions and tone of voice in real time. This allows the system to recognize the user's emotional state and adjust the difficulty of the exercise if the user is tired, or provide a challenging experience if the user is highly motivated.

[0552] For example, if a user shows a focused expression during exercise, the system provides positive feedback and encourages them to continue exercising. Furthermore, once the exercise is complete, the server integrates activity and emotional data sent from the device to create feedback for the next exercise session. This feedback is personalized using a generative AI model to encourage continued exercise and maintain motivation.

[0553] In this way, the system can support both the user's physical health and emotional well-being.

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] The server retrieves the user's biometric information from the health management system. The input is the user's ID and basic authentication information, which the server uses to request biometric data such as heart rate, weight, and past exercise records via an API. The output is a biometric report compiled from this data. The server stores the obtained data in a database and prepares it for analysis.

[0557] Step 2:

[0558] The server uses a generative AI model to analyze the acquired biometric information. It uses a previously collected biometric information report as input. The server then inputs the prompt "Suggest an exercise program suitable for a user with a heart rate of 120" into the AI ​​model to generate a specific exercise plan. The output is a customized exercise plan based on the user's physical condition.

[0559] Step 3:

[0560] The server sends the generated exercise plan to the user's device. The input is the customized exercise plan, which is converted into the format necessary for the device to correctly receive and display it. The output is the exercise plan data sent to the device as part of an interactive entertainment format.

[0561] Step 4:

[0562] The device visually and audibly guides the user through the received exercise plan. The input is exercise plan data sent from the server. The device processes this data using general-purpose game software to generate animations and audio guides. The output is an exercise guide in a user-friendly format. The user then begins exercising based on this guide and makes adjustments based on the feedback.

[0563] Step 5:

[0564] The device collects real-time activity data and emotional state of the user during exercise. The user's facial expressions and tone of voice are used as input, which is then interpreted by an emotion analysis device. The output consists of data on the user's emotional state and activity data indicating the progress of the exercise.

[0565] Step 6:

[0566] After the exercise session ends, the device sends the collected activity and emotional data to the server. The input consists of all data recorded during the exercise session. The output consists of the data sent to the server for feedback and planning the next exercise session.

[0567] Step 7:

[0568] The server generates feedback based on the received data. It uses activity and emotion data sent from the device as input and evaluates it using a generative AI model. The output is personalized feedback provided to the user, along with suggestions for their next exercise session. The server sends this back to the user to help them with their next workout.

[0569] (Application Example 2)

[0570] 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."

[0571] In modern manufacturing environments, excessive workloads that disregard workers' physical and emotional states pose a problem that hinders work efficiency and health. However, traditional methods make real-time workload adjustment difficult, likely leading to increased stress and decreased work efficiency. To address this challenge, it is necessary to monitor workers' physiological and emotional states and implement individualized exercise adjustments.

[0572] 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.

[0573] In this invention, the server includes means for acquiring health information, means for automatically generating an exercise menu suitable for the user based on the analysis results, and means for evaluating the user's emotional state in real time and adjusting the workload, including an emotion engine that has the function of identifying the emotional state. This makes it possible to optimize the burden on workers in the work environment and promote an efficient and healthy work process.

[0574] "Health information" refers to data that indicates the physiological state of a worker, including vital signs such as heart rate and fatigue level.

[0575] "Analysis results" refer to detailed analytical data obtained after evaluating the physical condition of workers based on acquired health information.

[0576] An "exercise menu" is a set of instructions that includes effective and safe exercises and work procedures for workers, and is automatically generated according to each individual's health condition.

[0577] An "interactive game" is a two-way simulation or training program that encourages workers to actively participate and act while receiving visual and auditory feedback.

[0578] The "emotion engine" is an artificial intelligence system that analyzes the worker's facial expressions, tone of voice, and input data to evaluate their emotional state in real time.

[0579] "Work environment" refers to a broad definition of workplace conditions, including the physical location where workers perform their duties on a daily basis, as well as the equipment and tools used in that location.

[0580] "Workload" is an indicator that shows the amount of work and the difficulty level of the work that a worker is required to perform within a certain period of time.

[0581] "Physiological data" refers to digital information about a worker's physical function and health status, and is closely related to health information.

[0582] "Emotional data" refers to data that quantifies or qualitatively represents the emotional state of a worker, and is evaluated by an emotion engine.

[0583] The system for implementing this invention is an advanced digital platform for managing workers' health information and emotional state in the work environment. This system provides the ability to evaluate workers' physical and emotional states in real time and to interactively optimize their workload.

[0584] The server periodically acquires health information such as heart rate and fatigue level from each worker and uses software to analyze this information. This analysis automatically generates an optimal exercise program for each worker. Physiological sensors are used to acquire health information, and AI-based analysis software is used to analyze the data.

[0585] The terminal provides the worker with a generated exercise menu in an interactive game format. This allows the worker to enjoy exercising while exercise data is collected in the process. The terminal is equipped with an emotion engine that detects the worker's emotional state by analyzing their facial expressions and tone of voice. Image recognition software and voice analysis tools are used for this process.

[0586] When a user begins exercising, the device sends collected exercise and emotional data to a server. The server uses this data to provide feedback to the user and suggest adjustments to the next exercise routine or workload. Throughout this process, data management and analysis are performed using a dedicated cloud computing solution.

[0587] A concrete example would be a scenario where signs of fatigue in workers during long shifts are detected in real time, and break instructions or mitigation tasks are automatically provided according to their condition. An example of input to the generating AI model would be a prompt sentence such as, "If the worker's fatigue level is 'high', what action plan would you suggest to adjust the tasks appropriately?"

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The server receives health information acquired through physiological sensors. This information, including heart rate and fatigue level, is passed to analysis software as input data. Data analysis detects abnormal health patterns and generates output that evaluates the worker's physical condition.

[0591] Step 2:

[0592] The terminal receives analysis results sent from the server and generates an optimal exercise menu in an interactive game format based on them. This exercise menu presents instructions for tasks applicable to the worker and provides output that supports visual navigation using a game engine.

[0593] Step 3:

[0594] When a user begins exercising, an emotion engine built into the device captures the user's facial expressions and voice in real time, and uses this data as input to analyze their emotional state. Based on the results of this analysis, an emotional evaluation is generated, and the game progression and task difficulty are dynamically adjusted.

[0595] Step 4:

[0596] The terminal collects exercise and emotional data and sends this data to the server. In this step, emotion-recognition feedback data is supplied to the server's processing center, which prepares output for suggesting adjustments to the worker's next exercise menu and workload.

[0597] Step 5:

[0598] The server utilizes a generated AI model based on the collected data to propose the next work plan and exercise goals. This generates feedback and personalized advice for the worker, which is then used as a basis for providing appropriate work instructions.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] [Fourth Embodiment]

[0603] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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".

[0616] This invention is a system designed to enable users to continue exercising safely and efficiently. This system aggregates health information, generates personalized exercise menus through analysis, and provides them to users through an interactive game format. Furthermore, it aggregates exercise data and provides specific feedback to users, serving as a guide for achieving future exercise goals. Specific examples are shown below.

[0617] First, the server automatically collects the user's health information in conjunction with the health management system. This information includes diagnostic data from medical institutions and data from daily health monitoring devices. The server uses this data to analyze the user's current physical condition and design appropriate exercise intensity and programs.

[0618] Next, the server generates an exercise program tailored to the user. This program is customized considering the space and equipment available to the user. The generated program is then converted into an interactive game format and sent to the terminal. The advantage of using a game format is that it allows users to continue exercising while having fun.

[0619] The device displays exercise menus received from the server and provides users with visual and audio instructions. This allows users to perform exercises with the correct form. During exercise, the device uses sensors to record the user's movements in real time and calculates calories burned and exercise achievement.

[0620] Once the exercise is complete, the device sends the collected exercise data to a server, which then generates detailed feedback based on that data. This feedback includes exercise results, areas for improvement, and goals for the next exercise session. This allows users to track their progress and approach their next exercise safely and effectively.

[0621] For example, taking a 30-year-old man suffering from chronic lower back pain, the server analyzes his past diagnostic data and daily posture data to generate an exercise program centered on low-impact stretching and yoga. The terminal presents this as an interactive game with a virtual character, instructing him to maintain correct posture during exercise. After the exercise, he can check the results on the terminal and plan his next workout. In this way, users can engage in exercise consistently without overexerting themselves.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The server connects with the health management system to retrieve users' health information. This information includes diagnostic data from medical institutions and data from health monitoring devices used in daily life.

[0625] Step 2:

[0626] The server analyzes the acquired health information and evaluates the user's physical condition. Based on this analysis, the exercise intensity and the basis of the workout plan are determined.

[0627] Step 3:

[0628] The server automatically generates an exercise program tailored to the user's analysis results. This program is customized to take into account the user's range of motion and available exercise space.

[0629] Step 4:

[0630] The server converts the generated exercise menu into an interactive game format and sends it to the user's device. This creates an environment where users can enjoy exercising.

[0631] Step 5:

[0632] The device displays the transmitted exercise menu and provides exercise in a game format. It guides the user on how to exercise using visual and audio instructions.

[0633] Step 6:

[0634] The user performs exercises according to the instructions on the device. The device records data during the exercise using sensors and calculates calories burned and the results of the exercise.

[0635] Step 7:

[0636] Once the exercise is complete, the device sends the collected exercise data to the server. The server then generates feedback for the user based on this data.

[0637] Step 8:

[0638] The server sends the generated feedback to the device and presents it to the user. The feedback includes the level of exercise achievement, areas for improvement, and new goals.

[0639] (Example 1)

[0640] 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".

[0641] Recently, many individuals living in urban areas have limited opportunities to engage in effective and sustainable exercise due to time constraints and environmental changes. Furthermore, there are challenges in providing personalized exercise plans and obtaining progress-based feedback. These are important issues in health management, where safety and efficiency are paramount.

[0642] 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.

[0643] In this invention, the server includes means for acquiring health-related data, means for analyzing the user's physiological state based on the data, and means for automatically generating an activity program suitable for the user based on the analysis results. This enables the user to receive a customized activity plan tailored to their individual health condition in an interactive format and to receive feedback based on the results of that activity.

[0644] "Health-related data" refers to information about the user's physiological state and health management, such as heart rate, blood pressure, daily activity level, and past diagnostic history.

[0645] "Physiological state" refers to a state that indicates an individual's physical condition and degree of health, and includes indicators such as heart rate, blood pressure, and physical fitness assessments based on sports motion analysis.

[0646] An "activity program" is a collection of exercises, stretches, and other fitness-enhancing activities designed for users according to their individual health conditions.

[0647] An "interactive entertainment format" is a format that uses games and animations that users can enjoy to encourage exercise and physical activity.

[0648] "Exercise-related data" refers to data that records the movements, activity levels, and calories burned during exercise performed by users.

[0649] "Feedback" refers to information that indicates the results, achievements, and areas for improvement of the exercise performed by the user, and serves as a guide for the next exercise plan.

[0650] The system of the present invention is designed to allow users to perform optimal exercise based on their individual health condition. The embodiments for carrying out the invention are described below in detail.

[0651] The server integrates with health management devices and healthcare systems to acquire health-related data such as the user's heart rate, blood pressure, daily activity level, and past diagnostic history. This includes protocols for securely transferring data using APIs. The server analyzes the acquired data using machine learning algorithms to understand the user's physiological state. This enables the generation of optimal activity programs that take into account each user's unique health condition.

[0652] Next, the server uses a generation AI model to automatically generate an activity program tailored to the user. This program takes into account the user's activity space and available equipment, and is converted into an interactive entertainment format. The generated program data is transmitted to the terminal via the internet.

[0653] The terminal displays the activity program received from the server and provides visual and auditory guidance. Furthermore, the terminal uses sensors to record data about the user's exercise in real time. This allows for detailed monitoring of the user's movements, activity level, and calories burned.

[0654] Once the exercise is complete, the device sends the collected data to the server, which then applies a generative AI model to provide feedback. This feedback indicates the user's exercise performance, achievement level, and next exercise goals, allowing the user to continuously improve their exercise based on this information.

[0655] As a concrete example, consider a woman in her 40s who suffers from chronic shoulder stiffness. The server analyzes her existing health data and daily activity data to generate an activity program that includes stretches and exercises effective in alleviating shoulder stiffness. The terminal provides this program through an animated guide, and the user performs the exercises according to the instructions. After the exercise, she can check the results of her workout on the terminal and plan her next exercise session.

[0656] An example of a prompt would be: "Please propose an activity program, including stretches and exercises, for a woman in her 40s aiming to alleviate shoulder stiffness. Design the program to be presented in an interactive entertainment format, based on existing health data and daily activity data."

[0657] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0658] Step 1:

[0659] The server acquires data related to the user's health. Specifically, it uses APIs to collect information such as heart rate, blood pressure, and activity level from health management devices and healthcare systems. This data serves as input to the server, and the acquired health information becomes the material for the next analysis step.

[0660] Step 2:

[0661] The server analyzes the acquired health data. This process uses machine learning algorithms to evaluate the user's physiological state. The input is the health information collected in step 1, and the output is an evaluation result indicating the individual's health status. This evaluation result provides the necessary information for designing an activity program suitable for the user.

[0662] Step 3:

[0663] The server generates an activity program tailored to the user using a generative AI model based on the analysis results. The input is the analysis results obtained in step 2. As a result of data processing and calculations, a customized activity program is output. This program includes optimized content that takes into account the user's available space and equipment.

[0664] Step 4:

[0665] The server converts the generated activity program into an interactive entertainment format. The input is the activity program obtained in step 3, and the result of the processing is output content that guides the user visually and aurally. The generated content is entertaining while promoting exercise.

[0666] Step 5:

[0667] The device receives interactive content sent from the server and provides it to the user. The input is the content from step 4, which the device displays in the user interface, providing visual and auditory guidance. The user begins exercising as instructed, and the device uses sensors to record exercise data in real time.

[0668] Step 6:

[0669] The device uses sensors to collect and record data related to the user's exercise. The input is the user's exercise behavior, and the output is specific motion data, such as movement precision and calories burned. This data is used to evaluate the effectiveness of the exercise.

[0670] Step 7:

[0671] The server provides feedback using the collected exercise data. The input is the exercise data from step 6, and based on this data, analysis is performed to generate feedback indicating the user's performance, level of achievement, and next steps to take. The output feedback serves as a guide for users to continue improving their exercise.

[0672] (Application Example 1)

[0673] 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".

[0674] In modern society, it is important for users to exercise consistently while maintaining their health, but designing exercise programs tailored to individual conditions and enjoying the effects of those programs is not easy. Therefore, there is a need for a system that provides personalized exercise menus based on the user's physical condition and allows for real-time feedback.

[0675] 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.

[0676] In this invention, the server includes means for acquiring health information, means for analyzing the user's physical condition based on the health information, means for automatically generating an exercise menu suitable for the user based on the analysis results, means for providing the generated exercise menu in an interactive game format and providing feedback in real time, means for aggregating the user's exercise data through the interactive game and processing the data using a cloud service, and means for providing feedback to the user based on the exercise data and automatically setting the next exercise goal. This makes it possible to provide an individualized exercise program and to implement it effectively.

[0677] "Health information" refers to data about a user's physical and health condition, collected from medical institution diagnostic data and health management devices.

[0678] "Analyzing physical condition" refers to a method of evaluating a user's exercise capacity and health status based on health information, and designing an appropriate exercise program.

[0679] "Automatically generating exercise menus" means that a computer automatically creates an appropriate exercise program based on the user's physical condition and available environment.

[0680] "Providing it in an interactive game format" means presenting exercise menus to users in a way that incorporates game elements, so that users can continue exercising while having fun.

[0681] "Providing real-time feedback" means evaluating the user's movements on the spot during exercise and immediately communicating areas for improvement and progress.

[0682] "Using cloud services for data processing" means using remote servers on the internet to quickly store and analyze large amounts of data and provide users with the information they need.

[0683] "Providing feedback and automatically setting the next exercise goal" means that after an exercise session, the system will show the user's achievements and areas for improvement based on their performance, and then present specific goals for their next exercise session.

[0684] The system for implementing this invention includes a program that collects the user's health information and provides a personalized exercise menu based on that information. The server automatically acquires data from medical institutions and home health management devices in cooperation with a health management system and performs data analysis using cloud infrastructure (e.g., Amazon Web Services). Based on the analysis results, it generates an optimal exercise menu for the user and delivers this menu to the terminal in an interactive game format.

[0685] The system includes smartphones and wearable devices with motion sensors (e.g., Azure Kinect DK), which function as hardware for running interactive games. Users exercise through the games, and the system records and analyzes their movements in real time, providing immediate feedback. This feedback includes information on the accuracy of the movements and the level of achievement.

[0686] As a concrete example, before a user at the gym begins exercising, they launch a dedicated app on their smartphone and scan a QR code in a designated area. This displays a personalized exercise menu on the device, monitors their movements via motion sensors, and immediately notifies them of areas for improvement and their progress. After the exercise, all data is uploaded to the cloud and used to improve the next exercise program.

[0687] An example of a prompt would be, "A man in his 30s, primarily desk-working, with lower back pain. Please create an optimal exercise plan for three gym sessions per week." By inputting such prompts into an AI model, it is possible to create training menus tailored to individual users.

[0688] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0689] Step 1:

[0690] The server retrieves user health information from health management devices and medical institution databases. The input uses the user's authorized health information access rights. This information is aggregated in the cloud and processed into the format necessary for analysis. The output is the user's basic health dataset.

[0691] Step 2:

[0692] The server uses a generated AI model to analyze the acquired health information and evaluate the user's physical condition. The input is the health information obtained in step 1. This analysis determines the appropriate exercise level and objectives. The output is candidate data for exercise programs based on the analysis results.

[0693] Step 3:

[0694] The server automatically generates an exercise menu corresponding to the analysis results and converts it into a game format. The input is candidate data for the exercise program, which is the output of step 2. Using the generating AI model, a menu including interactive elements is created. The output is a customized exercise program converted into a game format.

[0695] Step 4:

[0696] The server sends the generated exercise menu to the terminal. The terminal executes this menu as a game, including visual and audio guidance. The input is the game-format menu generated in step 3. The output is the exercise guide displayed to the user.

[0697] Step 5:

[0698] The user initiates exercise using a device, and motion sensors monitor the user's movements in real time. Input is the user's physical movement data. Sensor information is used to analyze the accuracy of the movements and calorie consumption. Output is real-time feedback information.

[0699] Step 6:

[0700] Once the exercise is complete, the device sends the collected exercise data to the server. The server analyzes this data and automatically sets detailed feedback and next exercise goals for the user. The input is the exercise data obtained from the motion sensor. The output is the feedback results and new exercise goal data from the cloud.

[0701] 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.

[0702] This invention is a system aimed at providing a personalized exercise experience while taking into account the user's emotional state. This system includes means for generating exercise menus based on the user's health information and a function for recognizing the user's emotional state using an emotion engine. Through an interactive game format, it provides an environment where users can continue exercising in a fun and effortless way. Specific examples are shown below.

[0703] The server works in conjunction with the health management system to acquire and analyze the user's health information. This analysis generates an appropriate exercise program based on the user's physical condition. The generated exercise program is then sent to the user's terminal.

[0704] The device presents its exercise menu in an interactive game format, acting as a guide for users as they engage in exercise. A key feature here is the emotion engine built into the device. This emotion engine analyzes the user's facial expressions, voice tone, and input data to understand their emotional state in real time. Based on this emotion recognition, it adjusts the difficulty and presentation within the game, ensuring users feel both entertained and challenged.

[0705] For example, if a user shows signs of fatigue during exercise, the emotion engine analyzes this and supports the user by automatically lowering the difficulty of the exercise or inserting relaxation content. On the other hand, if the system recognizes that the user is enjoying themselves, it provides praise messages and incentives within the game to further promote engagement.

[0706] Once an exercise session is complete, the device sends exercise and emotional data to the server. The server uses this data to create feedback and suggest goals for the next exercise session. The feedback is also personalized by the emotional engine, designed to maintain motivation and guide you towards your next challenge.

[0707] Thus, the system of the present invention can provide an optimal exercise experience while considering not only the user's physical health but also their emotional aspects. This improves the ease of engaging in exercise and contributes to sustainable health promotion activities.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The server works in conjunction with the health management system to collect user health information. This information includes the user's physical condition, past exercise history, and health checkup data.

[0711] Step 2:

[0712] The server analyzes the user's physical condition based on collected health information and automatically generates an appropriate exercise program based on the analysis results. The generated program is personalized and tailored to the user's fitness level.

[0713] Step 3:

[0714] The server sends the generated exercise menu to the user's device. The menu is converted into an interactive game format, designed to allow users to exercise while having fun.

[0715] Step 4:

[0716] The terminal displays exercise menus received from the server and presents them to the user in an interactive game format. It guides the user through exercise instructions and correct form via visual and audio guidance.

[0717] Step 5:

[0718] The emotion engine built into the device analyzes the user's facial expressions and tone of voice to recognize the user's emotional state in real time.

[0719] Step 6:

[0720] The emotion engine adjusts the difficulty and content of the game based on the user's emotional state. For example, if the user is showing signs of fatigue, it may reduce the intensity of the exercise or insert relaxation content.

[0721] Step 7:

[0722] The user performs exercises according to the instructions on the device. The device records the user's movements and emotional data during exercise and calculates exercise achievement and calories burned in real time.

[0723] Step 8:

[0724] Once the exercise is complete, the device sends the collected exercise data and emotional data to the server.

[0725] Step 9:

[0726] The server generates and provides feedback to the user based on the received data. The feedback is personalized by an emotion engine, and specifically suggests exercise goals and areas for improvement for the next session.

[0727] Step 10:

[0728] Users can check feedback on their devices, maintain their motivation, and prepare for their next exercise session.

[0729] (Example 2)

[0730] 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".

[0731] There is a need to provide personalized exercise plans tailored to each user's physical and emotional state, supporting an enjoyable and sustainable exercise experience. However, existing systems struggle to provide exercise plans that fully consider the user's biometric information and emotional state. Furthermore, it is difficult to appropriately adjust the difficulty level and presentation of exercises in real time.

[0732] 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.

[0733] In this invention, the server includes means for acquiring biometric information, means for analyzing the user's physical condition based on the biometric information, means for automatically generating an exercise plan suitable for the user based on the analysis results, means for recognizing the user's emotional state using an emotion analysis device, and means for adjusting the difficulty level and presentation of the entertainment format according to the recognized emotional state. This makes it possible to provide an exercise experience that takes into account the user's physical health and emotional satisfaction.

[0734] "Biometric information" refers to data that indicates the user's physical health status, including heart rate, weight, and past exercise records.

[0735] "User" refers to an individual who accepts and executes an exercise plan using this system.

[0736] "Physical condition" refers to information indicating the user's health status and fitness level, and includes the results of biometric data analysis.

[0737] An "exercise plan" is a guide that indicates fitness activities suitable for the user, and it is generated by this system.

[0738] A "two-way entertainment format" is a type of game in which the user and the system interact and exchange information while providing entertainment.

[0739] "Activity data" refers to data that shows the status of exercise performed by a user when they engage in exercise.

[0740] An "emotional analysis device" is a device or software that recognizes a user's emotional state by analyzing input such as their facial expressions and voice.

[0741] "Emotional state" refers to the user's psychological or emotional condition, as recognized by the emotion analysis device.

[0742] "Adjusting difficulty level and presentation" means changing the difficulty of the exercise plan and the visual and auditory effects based on the user's emotional state.

[0743] This invention relates to a system for providing users with personalized exercise experiences. It primarily consists of a server, terminals, and users, which work together to realize the system.

[0744] The server communicates with the health management system to collect biometric information. For this purpose, it uses a general-purpose API to acquire biometric information such as heart rate, weight, and past exercise records, and securely stores this information in a database. For analysis, a generative AI model is used, automatically generating personalized exercise plans by utilizing prompts such as, for example, "Please suggest an exercise program suitable for a user with a heart rate of 120."

[0745] This exercise plan is provided to the user in real time in an interactive, entertaining format. After receiving the exercise plan, the device guides the user visually and audibly through a general-purpose game software, which is an interactive platform. The user performs the exercise based on this guide, and the device collects the activity data generated during the process.

[0746] Furthermore, the device incorporates an emotion analysis device that analyzes the user's facial expressions and tone of voice in real time. This allows the system to recognize the user's emotional state and adjust the difficulty of the exercise if the user is tired, or provide a challenging experience if the user is highly motivated.

[0747] For example, if a user shows a focused expression during exercise, the system provides positive feedback and encourages them to continue exercising. Furthermore, once the exercise is complete, the server integrates activity and emotional data sent from the device to create feedback for the next exercise session. This feedback is personalized using a generative AI model to encourage continued exercise and maintain motivation.

[0748] In this way, the system can support both the user's physical health and emotional well-being.

[0749] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0750] Step 1:

[0751] The server retrieves the user's biometric information from the health management system. The input is the user's ID and basic authentication information, which the server uses to request biometric data such as heart rate, weight, and past exercise records via an API. The output is a biometric report compiled from this data. The server stores the obtained data in a database and prepares it for analysis.

[0752] Step 2:

[0753] The server uses a generative AI model to analyze the acquired biometric information. It uses a previously collected biometric information report as input. The server then inputs the prompt "Suggest an exercise program suitable for a user with a heart rate of 120" into the AI ​​model to generate a specific exercise plan. The output is a customized exercise plan based on the user's physical condition.

[0754] Step 3:

[0755] The server sends the generated exercise plan to the user's device. The input is the customized exercise plan, which is converted into the format necessary for the device to correctly receive and display it. The output is the exercise plan data sent to the device as part of an interactive entertainment format.

[0756] Step 4:

[0757] The device visually and audibly guides the user through the received exercise plan. The input is exercise plan data sent from the server. The device processes this data using general-purpose game software to generate animations and audio guides. The output is an exercise guide in a user-friendly format. The user then begins exercising based on this guide and makes adjustments based on the feedback.

[0758] Step 5:

[0759] The device collects real-time activity data and emotional state of the user during exercise. The user's facial expressions and tone of voice are used as input, which is then interpreted by an emotion analysis device. The output consists of data on the user's emotional state and activity data indicating the progress of the exercise.

[0760] Step 6:

[0761] After the exercise session ends, the device sends the collected activity and emotional data to the server. The input consists of all data recorded during the exercise session. The output consists of the data sent to the server for feedback and planning the next exercise session.

[0762] Step 7:

[0763] The server generates feedback based on the received data. It uses activity and emotion data sent from the device as input and evaluates it using a generative AI model. The output is personalized feedback provided to the user, along with suggestions for their next exercise session. The server sends this back to the user to help them with their next workout.

[0764] (Application Example 2)

[0765] 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".

[0766] In modern manufacturing environments, excessive workloads that disregard workers' physical and emotional states pose a problem that hinders work efficiency and health. However, traditional methods make real-time workload adjustment difficult, likely leading to increased stress and decreased work efficiency. To address this challenge, it is necessary to monitor workers' physiological and emotional states and implement individualized exercise adjustments.

[0767] 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.

[0768] In this invention, the server includes means for acquiring health information, means for automatically generating an exercise menu suitable for the user based on the analysis results, and means for evaluating the user's emotional state in real time and adjusting the workload, including an emotion engine that has the function of identifying the emotional state. This makes it possible to optimize the burden on workers in the work environment and promote an efficient and healthy work process.

[0769] "Health information" refers to data that indicates the physiological state of a worker, including vital signs such as heart rate and fatigue level.

[0770] "Analysis results" refer to detailed analytical data obtained after evaluating the physical condition of workers based on acquired health information.

[0771] An "exercise menu" is a set of instructions that includes effective and safe exercises and work procedures for workers, and is automatically generated according to each individual's health condition.

[0772] An "interactive game" is a two-way simulation or training program that encourages workers to actively participate and act while receiving visual and auditory feedback.

[0773] The "emotion engine" is an artificial intelligence system that analyzes the worker's facial expressions, tone of voice, and input data to evaluate their emotional state in real time.

[0774] "Work environment" refers to a broad definition of workplace conditions, including the physical location where workers perform their duties on a daily basis, as well as the equipment and tools used in that location.

[0775] "Workload" is an indicator that shows the amount of work and the difficulty level of the work that a worker is required to perform within a certain period of time.

[0776] "Physiological data" refers to digital information about a worker's physical function and health status, and is closely related to health information.

[0777] "Emotional data" refers to data that quantifies or qualitatively represents the emotional state of a worker, and is evaluated by an emotion engine.

[0778] The system for implementing this invention is an advanced digital platform for managing workers' health information and emotional state in the work environment. This system provides the ability to evaluate workers' physical and emotional states in real time and to interactively optimize their workload.

[0779] The server periodically acquires health information such as heart rate and fatigue level from each worker and uses software to analyze this information. This analysis automatically generates an optimal exercise program for each worker. Physiological sensors are used to acquire health information, and AI-based analysis software is used to analyze the data.

[0780] The terminal provides the worker with a generated exercise menu in an interactive game format. This allows the worker to enjoy exercising while exercise data is collected in the process. The terminal is equipped with an emotion engine that detects the worker's emotional state by analyzing their facial expressions and tone of voice. Image recognition software and voice analysis tools are used for this process.

[0781] When a user begins exercising, the device sends collected exercise and emotional data to a server. The server uses this data to provide feedback to the user and suggest adjustments to the next exercise routine or workload. Throughout this process, data management and analysis are performed using a dedicated cloud computing solution.

[0782] A concrete example would be a scenario where signs of fatigue in workers during long shifts are detected in real time, and break instructions or mitigation tasks are automatically provided according to their condition. An example of input to the generating AI model would be a prompt sentence such as, "If the worker's fatigue level is 'high', what action plan would you suggest to adjust the tasks appropriately?"

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The server receives health information acquired through physiological sensors. This information, including heart rate and fatigue level, is passed to analysis software as input data. Data analysis detects abnormal health patterns and generates output that evaluates the worker's physical condition.

[0786] Step 2:

[0787] The terminal receives analysis results sent from the server and generates an optimal exercise menu in an interactive game format based on them. This exercise menu presents instructions for tasks applicable to the worker and provides output that supports visual navigation using a game engine.

[0788] Step 3:

[0789] When a user begins exercising, an emotion engine built into the device captures the user's facial expressions and voice in real time, and uses this data as input to analyze their emotional state. Based on the results of this analysis, an emotional evaluation is generated, and the game progression and task difficulty are dynamically adjusted.

[0790] Step 4:

[0791] The terminal collects exercise and emotional data and sends this data to the server. In this step, emotion-recognition feedback data is supplied to the server's processing center, which prepares output for suggesting adjustments to the worker's next exercise menu and workload.

[0792] Step 5:

[0793] The server utilizes a generated AI model based on the collected data to propose the next work plan and exercise goals. This generates feedback and personalized advice for the worker, which is then used as a basis for providing appropriate work instructions.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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."

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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 to be incorporated by reference.

[0815] The following is further disclosed regarding the embodiments described above.

[0816] (Claim 1)

[0817] Means of obtaining health information,

[0818] A means for analyzing the user's physical condition based on the aforementioned health information,

[0819] Based on the aforementioned analysis results, a means for automatically generating an exercise menu suitable for the user,

[0820] A means for providing the generated exercise menu in an interactive game format,

[0821] A means for collecting user exercise data through the aforementioned interactive game,

[0822] A means for providing feedback to the user based on the aforementioned exercise data,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the exercise menu is customized taking into account the user's exercise space and available equipment.

[0826] (Claim 3)

[0827] The system according to claim 1, wherein the interactive game guides movement through visual and auditory guidance.

[0828] "Example 1"

[0829] (Claim 1)

[0830] Means of acquiring health-related data,

[0831] A means for analyzing the user's physiological state based on the aforementioned data,

[0832] A means for automatically generating an activity program suitable for the user based on the aforementioned analysis results,

[0833] A means for providing the generated activity program in an interactive entertainment format,

[0834] A means for collecting data on users' exercise through the aforementioned interactive entertainment,

[0835] A means of providing feedback to the user based on the data related to the exercise,

[0836] A device that includes this.

[0837] (Claim 2)

[0838] The apparatus according to claim 1, wherein the activity program is customized in consideration of the user's available locations and available equipment.

[0839] (Claim 3)

[0840] The apparatus according to claim 1, wherein the interactive entertainment guides movement through visual and auditory guidance.

[0841] "Application Example 1"

[0842] (Claim 1)

[0843] Means of obtaining health information,

[0844] A means for analyzing the user's physical condition based on the aforementioned health information,

[0845] Based on the aforementioned analysis results, a means for automatically generating an exercise menu suitable for the user,

[0846] A means of providing the generated exercise menu in an interactive game format and providing feedback in real time,

[0847] A means for collecting user exercise data through the aforementioned interactive game and processing the data using a cloud service,

[0848] A means for providing feedback to the user based on the aforementioned exercise data and automatically setting the next exercise goal,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, wherein the exercise menu is customized taking into account the user's exercise environment and available equipment.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the interactive game navigates the movement through visual and audio guidance and the accuracy of the movement is evaluated by motion sensors.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] Means for acquiring biometric information,

[0857] A means for analyzing the user's physical condition based on the aforementioned biometric information,

[0858] A means for automatically generating an exercise plan suitable for the user based on the aforementioned analysis results,

[0859] A means for providing the generated exercise plan in an interactive entertainment format,

[0860] A means for aggregating user activity data through the aforementioned entertainment format,

[0861] A means for providing feedback to the user based on the aforementioned activity data and emotional data,

[0862] A means of recognizing the user's emotional state using an emotion analysis device,

[0863] A means for adjusting the difficulty level and presentation of the entertainment format according to the recognized emotional state,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, wherein the exercise plan is adjusted to take into account the user's exercise environment and available equipment.

[0867] (Claim 3)

[0868] The system according to claim 1, wherein the interactive entertainment guides movement through visual and auditory instructions.

[0869] "Application example 2 when combining with an emotional engine"

[0870] (Claim 1)

[0871] Means of obtaining health information,

[0872] A means for analyzing the user's physical condition based on the aforementioned health information,

[0873] Based on the aforementioned analysis results, a means for automatically generating an exercise menu suitable for the user,

[0874] A means for providing the generated exercise menu in an interactive game format,

[0875] A means for collecting user exercise data through the aforementioned interactive game,

[0876] A means for providing feedback to the user based on the aforementioned exercise data,

[0877] It includes an emotion engine that has the function of identifying emotional states, and means for evaluating the user's emotional state in real time and adjusting the workload,

[0878] A means of adjusting the work process based on the worker's physiological and emotional data using equipment in the work environment,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, wherein the exercise menu is customized taking into account the user's location and available equipment.

[0882] (Claim 3)

[0883] The system according to claim 1, wherein the interactive game guides movement through visual and auditory guidance. [Explanation of symbols]

[0884] 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. Means of obtaining health information, A means for analyzing the user's physical condition based on the aforementioned health information, Based on the aforementioned analysis results, a means for automatically generating an exercise menu suitable for the user, A means for providing the generated exercise menu in an interactive game format, A means for collecting user exercise data through the aforementioned interactive game, A means for providing feedback to the user based on the aforementioned exercise data, A system that includes this.

2. The system according to claim 1, wherein the exercise menu is customized taking into account the user's exercise space and available equipment.

3. The system according to claim 1, wherein the interactive game guides movement through visual and auditory guidance.

Images