system

Abstract

We provide the system. [Solution] A means for collecting biometric and location information of an athlete from a data collection device and for pre-processing the data, A means for running an artificial intelligence model to evaluate the athletic ability of athletes based on preprocessed data, A means of providing the evaluation results to a terminal for visually notifying the athlete, A means of obtaining feedback from exercisers and modifying the exercise plan based on the evaluation results, A means of monitoring the health status of exercisers, suggesting optimal exercise plans, and providing information to maintain motivation, A method for providing real-time exercise guidance in conjunction with home-use machines, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] An object of the present invention is to provide support for highly optimizing a training process for athletes. Conventional training for athletes often depends on uniform programs or subjective evaluations, and there has been a problem that it is difficult to provide a training plan or feedback optimized for individual athletes. In addition, there are also problems that the management of the risk of injury and the support in terms of mental aspects are not sufficient. In order to solve these problems, there is a demand for a system that individually analyzes the biological information and exercise information of athletes and provides appropriate feedback and support based on the results.

Means for Solving the Problems

[0005] This invention provides means for collecting biometric and location information of an athlete from a data collection device and for preprocessing that data. It includes technical means for evaluating the athlete's athletic ability using an artificial intelligence model based on the preprocessed data. By notifying the device of this evaluation result, the athlete can intuitively understand their own condition. Furthermore, by obtaining feedback from the athlete and incorporating it into the training plan, it provides an individually optimized exercise plan. In addition, it ensures the athlete's safety by providing means for predicting potential injury risks and generating instructions to reduce those risks. It also includes means for generating and transmitting information related to mental support to the device, thereby improving the athlete's overall training experience.

[0006] "Data collection devices" is a general term for equipment that enables the acquisition of biometric and location information of athletes.

[0007] "Biometric information" refers to data that shows numerical values ​​or states related to life activities, such as heart rate and calories burned.

[0008] "Location information" refers to data that indicates the geographical location of a person exercising, and can be used to calculate distance traveled and speed.

[0009] "Preprocessing" refers to the preliminary work required to convert raw data into an analyzable format, and includes noise reduction and format conversion.

[0010] An "artificial intelligence model" is a general term for machine learning algorithms used to analyze large amounts of data and extract patterns and insights.

[0011] "Athletic ability" refers to the level of physical strength and skills that an athlete demonstrates during exercise.

[0012] "Feedback" is a general term for information provided to athletes, including training evaluations and advice.

[0013] An "exercise plan" refers to a detailed action plan that outlines the structure and content of a training session undertaken by an exerciser.

[0014] "Injury risk" refers to the predicted value or factors that indicate the likelihood of an exerciser being injured during exercise.

[0015] "Mental support" refers to services that support the psychological state of athletes and include providing information to enhance their motivation and concentration. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The system for carrying out the present invention includes a process for collecting and analyzing various information related to the vital activities of an exerciser in real time. The exerciser wears a wearable device, which instantly acquires biometric information such as heart rate, step count, and location information.

[0038] The server continuously receives the collected data and performs data preprocessing. Preprocessing removes noise and outliers from the acquired data and converts it into a format suitable for machine learning models. Furthermore, by combining this with past training history, it becomes possible to evaluate health status and athletic performance based on the individual athlete's capabilities.

[0039] In this evaluation process, the server uses an artificial intelligence model to analyze the data in detail. The model not only determines the athlete's abilities and health status, but also generates improvements to the training plan and feedback based on this. The generated feedback includes insights to maximize safety and performance, and provides specific instructions, for example, to reduce the risk of injury.

[0040] The terminal presents the results retrieved from the server to the user (exerciser) in an intuitive and easy-to-understand format. This includes infographics, graphs, and specific advice. Visual information allows exercisers to easily check their own condition and make necessary adjustments. Messages aimed at psychological support are also displayed to improve the exerciser's motivation and promote emotional stability.

[0041] As a concrete example, if a user participates in a marathon, this system provides an optimal training plan in advance and monitors heart rate and fatigue levels in real time. Based on this information, it can suggest appropriate pacing and provide instructions to prevent injuries. After the race, it reviews performance and provides new insights for future training.

[0042] Thus, this system provides comprehensive and personalized support to exercisers, enabling the creation of an optimal exercise environment tailored to individual needs.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] When a user puts on a wearable device and starts training, the device acquires data such as heart rate, steps taken, and location information in real time.

[0046] Step 2:

[0047] The device periodically sends acquired data to the server, enabling rapid data collection. This ensures that information on changes and performance during training is reliably recorded.

[0048] Step 3:

[0049] The server preprocesses the received raw data. This process involves noise removal, data cleaning, and data interpolation, generating a clean dataset suitable for analysis.

[0050] Step 4:

[0051] The server operates an artificial intelligence model to evaluate the exercise performance of athletes based on pre-processed data. The AI ​​model analyzes the athletes' physical condition, performance trends, and risk factors, and generates an evaluation report.

[0052] Step 5:

[0053] The server generates feedback and suggestions for improving the training plan based on the AI's analysis results. These suggestions include adjustments to the exercise program and injury prevention measures.

[0054] Step 6:

[0055] The terminal receives results from the server and provides visual feedback to the user. Information is displayed in infographics and graphs, allowing the user to easily understand their exercise status.

[0056] Step 7:

[0057] The system utilizes a feature that allows users to provide feedback via their devices, recording their thoughts on recommended training content and evaluations.

[0058] Step 8:

[0059] The device sends user feedback to the server, and the information received is used to improve future training plans.

[0060] This processing flow allows for personalized training experiences for athletes, providing safe and effective feedback.

[0061] (Example 1)

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

[0063] In modern times, athletes are required to accurately understand their own health status and athletic ability and to create appropriate exercise plans based on that understanding. However, conventional systems struggle to provide detailed, real-time health status monitoring and flexible, personalized exercise plans. Furthermore, challenges remain in the early detection of potential health risks during exercise and the lack of psychological support to enhance exercise motivation.

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

[0065] In this invention, the server includes means for acquiring and processing biometric information and movement information from a device worn by an exerciser, means for using a generative artificial intelligence model to evaluate exercise ability and health status based on the processed information, and means for transmitting the evaluation results and improvement suggestions to a device that notifies the exerciser. This allows the exerciser to understand their own condition in real time and receive an optimal exercise plan based on scientific evidence. They can also receive suggestions to reduce potential risks during exercise and obtain comprehensive support, including psychological support.

[0066] A "device" is a piece of equipment worn by an athlete to acquire biometric information and information about their movements.

[0067] "Biometric information" refers to data that shows the physical condition of an exerciser in real time, such as heart rate, respiratory rate, and body temperature.

[0068] "Motion-related information" refers to data used to identify the motion state of a person, such as their position, velocity, and acceleration.

[0069] "Processing means" refers to the process of removing noise and changing the data format of biological information and operational information acquired from the device, preparing it for subsequent analysis.

[0070] A "generative artificial intelligence model" is a machine learning model used to analyze data and evaluate the athletic ability and health status of athletes.

[0071] A "notification device" is a display terminal that visually displays evaluation results and improvement suggestions sent from the server to the participant.

[0072] "Potential health risks" refer to undiscovered risk factors or conditions that may affect the health status of an exerciser.

[0073] "Psychological support" refers to encouraging and supportive messages provided to boost the motivation of athletes and maintain their mental stability.

[0074] A system implementing this invention includes a process for acquiring biometric and motion-related information in real time from a device worn by an athlete, and for appropriately processing and analyzing this information. The server receives the acquired data and performs data preprocessing, such as noise removal and elimination of outliers. Subsequently, the data is converted into a standardized format and analyzed using a generative AI model.

[0075] The server uses software frameworks such as TENSORFLOW® and PyTorch on a computer to build machine learning models and evaluate the exercise capacity and health status of athletes. These evaluation results are useful for generating optimal training plans for athletes and predicting health risks.

[0076] The terminal visually displays evaluation results and improvement suggestions sent from the server to the exerciser. Utilizing devices such as smartphones and tablets, the terminal provides visual information such as infographics and graphs to make it easier for the exerciser to understand the results. It also includes a function to display encouraging messages as psychological support to boost the exerciser's motivation.

[0077] For example, if a user preparing for a marathon uses this system, they can receive an optimal training plan in advance. During the race, their heart rate and fatigue level are monitored in real time, and they receive suggestions for appropriate exercise pace and rest timing, which helps prevent injuries. After the race, they can analyze their performance and gain new insights for improving their future training.

[0078] An example of a prompt for a generating AI model is: "Analyze the athlete's biometric information, assess their health status, and generate an optimal training plan. Then, suggest feedback that will lead to maximizing performance and reducing the risk of injury."

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user collects biometric and movement-related information through a wearable device. Inputs include heart rate, step count, and location information, which are transmitted to the server in real time. Output is data communication to the server.

[0082] Step 2:

[0083] The server receives data sent from the user and first detects and removes noise data. The input is raw biometric and behavioral data, and outliers are removed and missing values ​​are imputed through data cleansing. The output is pre-processed, more accurate data.

[0084] Step 3:

[0085] The server uses the pre-processed data, performs processing such as data standardization and scaling, and converts it into a format that can be used by the generative AI model. The input is cleansed data, and the output is data in an analyzable format prepared for the generative AI model.

[0086] Step 4:

[0087] The server uses a generative AI model to analyze input data and evaluate the exerciser's physical ability and health status. Specifically, it uses machine learning algorithms to compare current data with past data and generate performance indicators. The output is an evaluation of physical ability and an assessment of health status.

[0088] Step 5:

[0089] The server sends the generated evaluation and suggestion results to the terminal. The input is the analysis results of the AI ​​model, and the output is feedback information for the user. The terminal receives this and prepares it to be provided to the user.

[0090] Step 6:

[0091] The terminal displays evaluation results and improvement suggestions received from the server in infographics and graphs, presenting them in an easy-to-understand format for the user. In addition to visual displays, it also displays psychological support messages to boost motivation. The output is information that the exerciser can visually confirm.

[0092] (Application Example 1)

[0093] 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."

[0094] There is a need to appropriately monitor the biometric information and movements of exercisers in real time and provide optimal exercise plans tailored to each individual's health condition. However, conventional technologies not only collect and analyze data in a fragmented manner, but also lack visual notifications and psychological support, making it difficult to adequately maintain exerciser motivation and prevent injuries. Furthermore, systems that provide real-time exercise guidance through integration with home-use equipment are not yet widely available.

[0095] 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.

[0096] In this invention, the server includes means for collecting biometric and location information of an exerciser from a data collection device and preprocessing the data; means for running an artificial intelligence model to evaluate the exerciser's athletic ability based on the preprocessed data; and means for coordinating with a home appliance to provide real-time exercise guidance. As a result, exercisers can receive an optimal exercise plan based on their health condition even while at home, enabling efficient injury prevention and motivation maintenance.

[0097] A "data collection device" is a device used to acquire biometric and location information of an exerciser.

[0098] "Biometric information of an exerciser" refers to data that indicates the physical activity status of an exerciser, such as heart rate and step count.

[0099] "Location information" refers to information indicating the geographical location of an exerciser, and is data obtained through technologies such as GPS.

[0100] "Preprocessing methods" refer to methods for removing noise and outliers from collected biological information and converting it into a format suitable for analysis.

[0101] "Means of executing artificial intelligence models" refers to methods of performing calculations and analyses using machine learning to evaluate the athletic abilities of athletes.

[0102] A "terminal for visual notification" is a device equipped with a screen and design that clearly displays evaluation results and related information to the exerciser.

[0103] "Means of modifying exercise plans" refer to methods for adjusting the current training program based on feedback and evaluation results from the exerciser.

[0104] "Means of monitoring health status" refers to methods for constantly checking the health status of exercisers and providing appropriate exercise guidance as needed.

[0105] "Means of providing exercise guidance" refers to methods of providing advice and instructions to improve the exercise methods of participants based on collected data.

[0106] "Household machinery" refers to robots and devices used to assist and manage the physical activity of a person exercising within the home.

[0107] The system implementing this invention begins with the exerciser using a device to collect physical information. The server receives biometric and location information acquired from these devices in real time and preprocesses the data. Specifically, it uses a Python program to remove noise and outliers from the collected data and format it into a format suitable for machine learning models. Using this preprocessed data, the server runs an artificial intelligence model to evaluate the exerciser's physical ability and health status. This evaluation utilizes machine learning libraries such as Scikit-learn and employs linear regression models and other appropriate models.

[0108] The device then receives the results from the server and displays the information visually. Users can intuitively check evaluation results, modify their exercise plan, and access health guidelines using their smartphones or tablets. The results are displayed as infographics on the device, and psychological support information is also provided as feedback to the exerciser.

[0109] Furthermore, it can be linked with home-use machines to provide real-time guidance on the exerciser's movements. A generative AI model is used to provide exercise guidance and health management advice tailored to the exerciser's movements. Specific prompts include commands such as, "Please input the exerciser's heart rate data and suggest the optimal pace," which are then used to optimize the exercise plan.

[0110] This system allows users to engage in effective exercise and receive appropriate feedback and guidance, creating an environment where they can maintain optimal health while increasing their motivation to exercise. For example, a user who enjoys jogging can improve their exercise performance without overexertion by receiving guidance on appropriate rest times and acceleration.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server collects biometric and location information in real time from data collection devices worn by the exerciser. This information includes heart rate, step count, and location data. The server accurately receives this data and stores it as foundational data to proceed to the next step.

[0114] Step 2:

[0115] The server preprocesses the collected biometric data. It removes noise and outliers from the input data and performs linear scaling and standardization. This preprocessing allows machine learning models to obtain more accurate results when analyzing the data. The output is a clean dataset with outliers removed.

[0116] Step 3:

[0117] The server uses pre-processed data to run an artificial intelligence model and evaluate the exercise performance and health status of athletes. The input is a pre-processed dataset, and the output consists of performance metrics and health assessment scores for the athletes. This process utilizes Scikit-learn machine learning algorithms to maximize the predictive power of the model.

[0118] Step 4:

[0119] The terminal receives evaluation results sent from the server and visually notifies the user. Inputs include evaluation results and health assessment scores from the server. The terminal displays this information as graphics or infographics, allowing the user to immediately understand the situation visually. Outputs are the information the user sees on the terminal screen.

[0120] Step 5:

[0121] The user uses a device to review feedback received from their exercise and revise their exercise plan as needed. The device receives the user's feedback and updates the suggested exercise plan for the next session based on it. The input is the user's feedback, and the output is the revised exercise plan.

[0122] Step 6:

[0123] The home-use machine provides real-time exercise guidance to the user based on the latest information obtained from the server and terminal. Inputs include exercise guidance information from the server and correction plans from the terminal. Based on this information, the machine monitors the user's movements in real time and instructs them on appropriate exercise posture and pacing. Output is the audio and visual feedback received by the user.

[0124] 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.

[0125] This invention relates to a system that provides personalized feedback and support to an exerciser by integrating an emotion engine that recognizes the user's emotional state. The emotion engine is installed on the user's device and analyzes emotions from data such as voice, facial recognition, and entered text.

[0126] The server collects biometric and location information of the athlete, preprocesses it, and then runs an artificial intelligence model to evaluate their athletic ability. During this process, detailed data on the athlete's performance is obtained and provided as feedback.

[0127] This feedback process includes an emotional engine that analyzes the user's psychological state, and based on that, generates appropriate feedback. Specifically, if an exerciser is experiencing stress during training, the emotional engine can detect this state, and the server can adjust the training intensity or suggest relaxation methods.

[0128] The device provides the user with analysis results obtained from the server and advice based on their psychological state, generated by an emotion engine. The displayed information includes an assessment of exercise performance, motivational messages, and resources for psychological support.

[0129] For example, if a user begins to feel fatigued during a long training session, this system can recognize that emotion through its emotional engine and suggest appropriate rest periods and recovery exercises. As a result, athletes can train in a way that takes into account not only their physical abilities but also their mental health.

[0130] In this form, the present invention realizes training support that takes into account the user's emotional state and provides an exercise environment optimized for the individual. This system aims to improve the exerciser's overall training experience and achieve high training effectiveness.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] When a user puts on a wearable device and starts training, the emotion engine also activates, and a function that recognizes the user's emotions in real time from their voice and facial expressions comes into play.

[0134] Step 2:

[0135] The device acquires user emotional data and, when it detects a change exceeding a pre-set threshold (for example, increased stress or decreased motivation), it sends that data to the server.

[0136] Step 3:

[0137] The server receives biometric and location information from wearable devices and uses an AI model to evaluate athletic performance. This evaluation includes indicators such as heart rate, movement speed, and distance.

[0138] Step 4:

[0139] The server analyzes psychological state data from the emotion engine and combines it with athletic performance evaluation results to comprehensively assess the user's overall health.

[0140] Step 5:

[0141] The server generates specific advice for adjusting training intensity, recommending rest, and managing stress based on the user's psychological state, and sends this information to the terminal.

[0142] Step 6:

[0143] The device visually presents the generated advice to the user. This includes real-time exercise performance graphs, motivational messages, and specific exercises for stress reduction.

[0144] Step 7:

[0145] Users can make necessary adjustments during training based on the information provided. They can also input their feedback through a feedback option, and this feedback will be used to improve future suggestions.

[0146] This processing flow allows users to receive training approaches tailored to their individual emotional states, supporting their physical and mental health.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] The present invention aims to provide a system that allows athletes to effectively understand their emotional state and biometric information during training and receive personalized feedback. This will help athletes reduce stress and potential injury risks, while also addressing the challenges of improved motivation and mental support.

[0150] 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.

[0151] In this invention, the server includes means for analyzing the user's emotional state and performing emotion recognition, means for acquiring biometric and location information of the exerciser from data collection equipment and preprocessing the data, and means for executing a machine learning model to evaluate the exerciser's athletic ability based on the preprocessed data. This enables the exerciser to receive personalized feedback and perform training that improves their mental and physical health.

[0152] "Emotional state" refers to a user's psychological response and is a collection of data determined from sources such as voice, facial expressions, and text input.

[0153] "Biometric information" refers to data that indicates the physical condition of an exerciser, including health-related values ​​such as heart rate and respiratory rate.

[0154] "Location information" refers to data indicating the current location of an exerciser, and is obtained using location tracking technologies such as GPS.

[0155] "Preprocessing" refers to the process of preparing acquired data into an analyzable format, and includes steps such as noise reduction and checking for outliers.

[0156] A "machine learning model" is a statistical method used to identify patterns based on vast amounts of data and to evaluate and predict new data.

[0157] "Feedback" refers to information and advice provided about an athlete's performance to encourage future improvement.

[0158] "Psychological support" refers to providing information and advice to maintain and improve the mental health of athletes.

[0159] This invention is a system for providing user-specific feedback and support. The system's embodiments analyze the user's emotional state, evaluate their motor skills, and provide personalized advice, utilizing multiple hardware and software components.

[0160] The user uses a device with an emotion engine installed. The emotion engine uses speech recognition technology, facial recognition, and text analysis algorithms to evaluate the user's emotional state in real time. For example, if the user says "I'm a little tired," this is incorporated into the emotion analysis through speech recognition.

[0161] The server collects biometric and location information through wearable sensors and smartphones. This data is preprocessed and refined. Data cleansing technology is used to remove noise before processing. Based on the preprocessed information, a generative AI model performs exercise evaluation and analyzes exercise performance.

[0162] The device provides user-optimized feedback based on evaluation results and sentiment analysis data performed on the server. This feedback includes exercise evaluation results, motivational messages, and resources for mental support. For example, the device might advise the user to reduce training intensity or suggest relaxation exercises.

[0163] An example of a prompt for a generative AI model is a question like, "What are some effective ways to cool down based on my current emotional state?" In response to this prompt, the AI ​​can provide user-specific advice and evaluations.

[0164] This format allows users to receive personalized psychological and physical support during training, enabling them to achieve better results.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user activates the emotion engine on their device and begins training. Voice data, facial expression data, and text data are sent to the emotion engine as input. The emotion engine analyzes this data and runs an algorithm to identify the user's emotional state. The output generates the emotional state the user is currently feeling. Specifically, the device analyzes emotions in real time based on the user's voice commands.

[0168] Step 2:

[0169] The server collects biometric and location information from the user's wearable device or smartphone. Inputs include heart rate, respiratory rate, and current location data. The server filters this data to remove noise, cleans the data, and then performs preprocessing. The output is clean, denoised data. Specific operations include detecting and removing abnormal biometric data.

[0170] Step 3:

[0171] The server uses pre-processed biometric information as input to begin evaluating athletic ability against a generated AI model. Based on the input data, a machine learning algorithm evaluates the user's athletic performance and calculates indicators relevant to future training plans. The output provides a detailed evaluation of the user's athletic ability and suggestions for improvement. Specific actions include an analysis of performance changes compared to past data.

[0172] Step 4:

[0173] The device integrates evaluation results from the server with analysis results from the emotion engine to display personalized feedback to the user. Inputs include athletic performance evaluation results and emotional state data. Based on this, the device generates advice including adjustments to the exercise plan, motivational messages, and relaxation methods. Output is the feedback displayed on the user screen. Specific actions include suggesting rest timings that take into account the stress level during the most recent training session.

[0174] (Application Example 2)

[0175] 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".

[0176] Conventional exercise support systems primarily focus on feedback and advice based on the exerciser's physical condition, lacking support that considers the exerciser's mental and emotional state. This can lead to problems such as exercisers not receiving appropriate support when experiencing stress or fatigue, potentially resulting in a decrease in motivation to continue exercising. Furthermore, there is a need for an integrated approach that considers not only physical training but also mental health.

[0177] 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.

[0178] In this invention, the server includes means for collecting biometric and location information of an exerciser from a data collection device and preprocessing the data; means for executing an artificial intelligence model for evaluating the exerciser's athletic ability based on the preprocessed data; and means for analyzing the user's emotional state and generating information for adjusting the exercise menu based on the analysis results. This comprehensively considers the exerciser's physical and emotional state, provides an individualized exercise plan accordingly, and enables the maintenance of motivation for exercise and improvement of mental health.

[0179] A "data collection device" is a device that collects biometric and location information from athletes and provides it to a server.

[0180] "Biometric information" refers to data that represents the physical condition of an exerciser, such as heart rate, blood pressure, and body temperature.

[0181] "Location information" refers to geographical location data that shows the current location and movement patterns of a person exercising.

[0182] "Preprocessing means" refers to technical means for appropriately organizing or converting collected biometric and location information and adjusting it into a format that can be used as input for an artificial intelligence model.

[0183] An "artificial intelligence model" is a computer program that uses machine learning algorithms to evaluate the athletic performance of athletes.

[0184] "Means for evaluating athletic ability" refers to a process that quantifies an athlete's fitness level and performance based on pre-processed biometric and positional information.

[0185] "Methods for analyzing emotional states" refer to technologies that analyze emotions from a person's voice, facial expressions, input text, etc., and understand their emotional state.

[0186] "Means of generating information" refers to the function of designing optimal exercise menus and advice based on the exerciser's physical ability and emotional state.

[0187] "Information for adjusting exercise programs" refers to specific guidelines for determining the optimal exercise content and intensity, taking into account the exerciser's current physical and mental condition.

[0188] The system for carrying out this invention is configured to combine a data collection device, a local terminal, a server, and an artificial intelligence model on the cloud in order to support exercise while taking into account the user's mental and emotional health. The system collects the user's facial expressions, voice, location information, and biometric information, and based on this, provides personalized exercise feedback and mental support.

[0189] The server acquires biometric and location information of the exerciser from data collection devices. This information is collected in real time, for example, using sensors on smartphones or wearable devices. The collected data is preprocessed as needed and sent to an artificial intelligence model in the cloud in an appropriate format. Preprocessing steps may include noise reduction and data normalization.

[0190] The AI ​​model on the cloud processes data to evaluate the user's athletic ability and analyzes emotional data such as the user's facial expressions and voice. Based on the analyzed emotional data, the server generates an exercise menu that takes the user's emotional state into account. This suggests the optimal exercise load and relaxation method for the user.

[0191] The device provides visual and audible feedback to the user based on exercise evaluation results and emotion analysis results obtained from the server. For example, if the user is feeling stressed, it may suggest relaxation exercises and play music to reduce stress.

[0192] As a concrete example, here is an example of a prompt message:

[0193] "Based on the user's emotional data, design the optimal exercise routine and mental support plan for the day. The user appears to be feeling fatigued at the moment."

[0194] This allows users to train in a way that takes into account not only their physical health but also their mental health, which is expected to improve exercise efficiency and consistency.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server acquires biometric information (e.g., heart rate, blood pressure) and location information of the exerciser from data collection devices (smartphones or wearable devices). At this point, the input is raw data from the sensors. The server preprocesses this data, such as removing noise, to format it in a way that is easy for the subsequent artificial intelligence model to process. The output is the preprocessed, clean data.

[0198] Step 2:

[0199] Using pre-processed data, the server runs an artificial intelligence model on the cloud. The inputs here are pre-processed biometric and location data. The server inputs this into the model to evaluate the exerciser's physical ability. The model analyzes the user's fitness level using machine learning algorithms. The output is the result data of the evaluated physical ability.

[0200] Step 3:

[0201] The server analyzes user emotion data (e.g., facial expressions, voice) obtained from the terminal or emotion engine. The input is raw data related to the user's emotions. The server uses an AI algorithm for emotion analysis to identify the emotional state and generates feedback based on that state. The output is the analyzed emotional state data.

[0202] Step 4:

[0203] The server integrates athletic performance assessment results and emotional state data to create an optimal exercise program and mental support plan for the user. Inputs are athletic performance data and emotional state data. Based on this, the server generates personalized exercise suggestions tailored to the user's condition on that day. Outputs are the created exercise program and support plan.

[0204] Step 5:

[0205] The terminal notifies the user of the exercise menu and mental support plan provided by the server. The input is the generated exercise menu and support plan. The terminal conveys information to the user visually (display) and aurally (voice guidance) and provides exercise instructions. The output is the exercise instructions presented to the user.

[0206] Step 6:

[0207] The user provides feedback according to the suggested exercise menu. This feedback is sent to the server via the terminal. The input is the user's feedback information. The server analyzes this feedback, modifies the exercise plan as needed, and incorporates it into the next training session. The output is the revised exercise plan.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] The system for carrying out the present invention includes a process for collecting and analyzing various information related to the vital activities of an exerciser in real time. The exerciser wears a wearable device, which instantly acquires biometric information such as heart rate, step count, and location information.

[0225] The server continuously receives the collected data and performs data preprocessing. Preprocessing removes noise and outliers from the acquired data and converts it into a format suitable for machine learning models. Furthermore, by combining this with past training history, it becomes possible to evaluate health status and athletic performance based on the individual athlete's capabilities.

[0226] In this evaluation process, the server uses an artificial intelligence model to analyze the data in detail. The model not only determines the athlete's abilities and health status, but also generates improvements to the training plan and feedback based on this. The generated feedback includes insights to maximize safety and performance, and provides specific instructions, for example, to reduce the risk of injury.

[0227] The terminal presents the results retrieved from the server to the user (exerciser) in an intuitive and easy-to-understand format. This includes infographics, graphs, and specific advice. Visual information allows exercisers to easily check their own condition and make necessary adjustments. Messages aimed at psychological support are also displayed to improve the exerciser's motivation and promote emotional stability.

[0228] As a concrete example, if a user participates in a marathon, this system provides an optimal training plan in advance and monitors heart rate and fatigue levels in real time. Based on this information, it can suggest appropriate pacing and provide instructions to prevent injuries. After the race, it reviews performance and provides new insights for future training.

[0229] Thus, this system provides comprehensive and personalized support to exercisers, enabling the creation of an optimal exercise environment tailored to individual needs.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] When a user puts on a wearable device and starts training, the device acquires data such as heart rate, steps taken, and location information in real time.

[0233] Step 2:

[0234] The device periodically sends acquired data to the server, enabling rapid data collection. This ensures that information on changes and performance during training is reliably recorded.

[0235] Step 3:

[0236] The server preprocesses the received raw data. This process involves noise removal, data cleaning, and data interpolation, generating a clean dataset suitable for analysis.

[0237] Step 4:

[0238] The server operates an artificial intelligence model to evaluate the exercise performance of athletes based on pre-processed data. The AI ​​model analyzes the athletes' physical condition, performance trends, and risk factors, and generates an evaluation report.

[0239] Step 5:

[0240] The server generates feedback and suggestions for improving the training plan based on the AI's analysis results. These suggestions include adjustments to the exercise program and injury prevention measures.

[0241] Step 6:

[0242] The terminal receives results from the server and provides visual feedback to the user. Information is displayed in infographics and graphs, allowing the user to easily understand their exercise status.

[0243] Step 7:

[0244] The system utilizes a feature that allows users to provide feedback via their devices, recording their thoughts on recommended training content and evaluations.

[0245] Step 8:

[0246] The device sends user feedback to the server, and the information received is used to improve future training plans.

[0247] This processing flow allows for personalized training experiences for athletes, providing safe and effective feedback.

[0248] (Example 1)

[0249] 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."

[0250] In modern times, athletes are required to accurately understand their own health status and athletic ability and to create appropriate exercise plans based on that understanding. However, conventional systems struggle to provide detailed, real-time health status monitoring and flexible, personalized exercise plans. Furthermore, challenges remain in the early detection of potential health risks during exercise and the lack of psychological support to enhance exercise motivation.

[0251] 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.

[0252] In this invention, the server includes means for acquiring and processing biometric information and movement information from a device worn by an exerciser, means for using a generative artificial intelligence model to evaluate exercise ability and health status based on the processed information, and means for transmitting the evaluation results and improvement suggestions to a device that notifies the exerciser. This allows the exerciser to understand their own condition in real time and receive an optimal exercise plan based on scientific evidence. They can also receive suggestions to reduce potential risks during exercise and obtain comprehensive support, including psychological support.

[0253] A "device" is a piece of equipment worn by an athlete to acquire biometric information and information about their movements.

[0254] "Biometric information" refers to data that shows the physical condition of an exerciser in real time, such as heart rate, respiratory rate, and body temperature.

[0255] "Motion-related information" refers to data used to identify the motion state of a person, such as their position, velocity, and acceleration.

[0256] "Processing means" refers to the process of removing noise and changing the data format of biological information and operational information acquired from the device, preparing it for subsequent analysis.

[0257] A "generative artificial intelligence model" is a machine learning model used to analyze data and evaluate the athletic ability and health status of athletes.

[0258] A "notification device" is a display terminal that visually displays evaluation results and improvement suggestions sent from the server to the participant.

[0259] "Potential health risks" refer to undiscovered risk factors or conditions that may affect the health status of an exerciser.

[0260] "Psychological support" refers to encouraging and supportive messages provided to boost the motivation of athletes and maintain their mental stability.

[0261] A system implementing this invention includes a process for acquiring biometric and motion-related information in real time from a device worn by an athlete, and for appropriately processing and analyzing this information. The server receives the acquired data and performs data preprocessing, such as noise removal and elimination of outliers. Subsequently, the data is converted into a standardized format and analyzed using a generative AI model.

[0262] The server uses software frameworks such as TensorFlow and PyTorch on a computer to build machine learning models and evaluate the exercise capacity and health status of athletes. These evaluation results are useful for generating optimal training plans for athletes and predicting health risks.

[0263] The terminal visually displays evaluation results and improvement suggestions sent from the server to the exerciser. Utilizing devices such as smartphones and tablets, the terminal provides visual information such as infographics and graphs to make it easier for the exerciser to understand the results. It also includes a function to display encouraging messages as psychological support to boost the exerciser's motivation.

[0264] For example, if a user preparing for a marathon uses this system, they can receive an optimal training plan in advance. During the race, their heart rate and fatigue level are monitored in real time, and they receive suggestions for appropriate exercise pace and rest timing, which helps prevent injuries. After the race, they can analyze their performance and gain new insights for improving their future training.

[0265] An example of a prompt for a generating AI model is: "Analyze the athlete's biometric information, assess their health status, and generate an optimal training plan. Then, suggest feedback that will lead to maximizing performance and reducing the risk of injury."

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The user collects biometric and movement-related information through a wearable device. Inputs include heart rate, step count, and location information, which are transmitted to the server in real time. Output is data communication to the server.

[0269] Step 2:

[0270] The server receives data sent from the user and first detects and removes noise data. The input is raw biometric and behavioral data, and outliers are removed and missing values ​​are imputed through data cleansing. The output is pre-processed, more accurate data.

[0271] Step 3:

[0272] The server uses the pre-processed data, performs processing such as data standardization and scaling, and converts it into a format that can be used by the generative AI model. The input is cleansed data, and the output is data in an analyzable format prepared for the generative AI model.

[0273] Step 4:

[0274] The server uses a generative AI model to analyze input data and evaluate the exerciser's physical ability and health status. Specifically, it uses machine learning algorithms to compare current data with past data and generate performance indicators. The output is an evaluation of physical ability and an assessment of health status.

[0275] Step 5:

[0276] The server sends the generated evaluation and suggestion results to the terminal. The input is the analysis results of the AI ​​model, and the output is feedback information for the user. The terminal receives this and prepares it to be provided to the user.

[0277] Step 6:

[0278] The terminal displays evaluation results and improvement suggestions received from the server in infographics and graphs, presenting them in an easy-to-understand format for the user. In addition to visual displays, it also displays psychological support messages to boost motivation. The output is information that the exerciser can visually confirm.

[0279] (Application Example 1)

[0280] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0281] It is required to appropriately monitor the biometric information and movements of athletes in real time and provide an optimal exercise plan according to each individual's health condition. However, in the conventional technology, not only is the collection and analysis of data fragmentary, but there is also a lack of visual notification and mental support, making it difficult to achieve sufficient effects in maintaining the motivation of athletes and preventing injuries. In addition, at present, a system for providing real-time exercise guidance through cooperation with household machines has not been sufficiently popularized.

[0282] 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.

[0283] In this invention, the server includes means for collecting biometric information and position information of an athlete from a device for collecting data, preprocessing the data, means for executing an artificial intelligence model for evaluating the athlete's exercise ability based on the preprocessed data, and means for cooperating with a household machine to provide real-time exercise guidance. As a result, even when the athlete is at home, it becomes possible to receive an optimal exercise plan based on their health condition, and injuries can be prevented and motivation can be maintained efficiently.

[0284] The "device for collecting data" is a device for acquiring biometric information and position information of an athlete.

[0285] The "biometric information of an athlete" is data indicating the physical activity status of an athlete, such as heart rate and number of steps.

[0286] The "position information" is information indicating the geographical position of an athlete, and is data acquired by technologies such as GPS.

[0287] "Preprocessing methods" refer to methods for removing noise and outliers from collected biological information and converting it into a format suitable for analysis.

[0288] "Means of executing artificial intelligence models" refers to methods of performing calculations and analyses using machine learning to evaluate the athletic abilities of athletes.

[0289] A "terminal for visual notification" is a device equipped with a screen and design that clearly displays evaluation results and related information to the exerciser.

[0290] "Means of modifying exercise plans" refer to methods for adjusting the current training program based on feedback and evaluation results from the exerciser.

[0291] "Means of monitoring health status" refers to methods for constantly checking the health status of exercisers and providing appropriate exercise guidance as needed.

[0292] "Means of providing exercise guidance" refers to methods of providing advice and instructions to improve the exercise methods of participants based on collected data.

[0293] "Household machinery" refers to robots and devices used to assist and manage the physical activity of a person exercising within the home.

[0294] The system implementing this invention begins with the exerciser using a device to collect physical information. The server receives biometric and location information acquired from these devices in real time and preprocesses the data. Specifically, it uses a Python program to remove noise and outliers from the collected data and format it into a format suitable for machine learning models. Using this preprocessed data, the server runs an artificial intelligence model to evaluate the exerciser's physical ability and health status. This evaluation utilizes machine learning libraries such as Scikit-learn and employs linear regression models and other appropriate models.

[0295] The device then receives the results from the server and displays the information visually. Users can intuitively check evaluation results, modify their exercise plan, and access health guidelines using their smartphones or tablets. The results are displayed as infographics on the device, and psychological support information is also provided as feedback to the exerciser.

[0296] Furthermore, it can be linked with home-use machines to provide real-time guidance on the exerciser's movements. A generative AI model is used to provide exercise guidance and health management advice tailored to the exerciser's movements. Specific prompts include commands such as, "Please input the exerciser's heart rate data and suggest the optimal pace," which are then used to optimize the exercise plan.

[0297] This system allows users to engage in effective exercise and receive appropriate feedback and guidance, creating an environment where they can maintain optimal health while increasing their motivation to exercise. For example, a user who enjoys jogging can improve their exercise performance without overexertion by receiving guidance on appropriate rest times and acceleration.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server collects biometric and location information in real time from data collection devices worn by the exerciser. This information includes heart rate, step count, and location data. The server accurately receives this data and stores it as foundational data to proceed to the next step.

[0301] Step 2:

[0302] The server performs preprocessing of the data on the collected biological information. It removes noise and outliers from the input data and performs linear scaling and normalization. This preprocessing enables the machine learning model to obtain more accurate results when analyzing the data. The output is a clean dataset with outliers removed.

[0303] Step 3:

[0304] The server uses the preprocessed data to execute an artificial intelligence model and evaluate the athlete's athletic ability and health status. The preprocessed dataset is used as the input, and the output is the athlete's performance index and health evaluation score. In this process, the machine learning algorithms of Scikit-learn are used to maximize the predictive ability of the model.

[0305] Step 4:

[0306] The terminal receives the evaluation results sent from the server and notifies the user visually. The inputs are the evaluation results and health evaluation scores from the server. The terminal displays this as graphics or infographics so that the user can immediately understand the situation from the visual information. The output is the information that the user checks on the terminal screen.

[0307] Step 5:

[0308] The user uses the terminal to check the feedback obtained from the exercise and modify the exercise plan as needed. The terminal obtains the user's feedback and updates the proposal for the next exercise plan based on it. The input is the user's feedback, and the output is the revised exercise plan proposal.

[0309] Step 6:

[0310] The home-use machine provides real-time exercise guidance to the user based on the latest information obtained from the server and terminal. Inputs include exercise guidance information from the server and correction plans from the terminal. Based on this information, the machine monitors the user's movements in real time and instructs them on appropriate exercise posture and pacing. Output is the audio and visual feedback received by the user.

[0311] 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.

[0312] This invention relates to a system that provides personalized feedback and support to an exerciser by integrating an emotion engine that recognizes the user's emotional state. The emotion engine is installed on the user's device and analyzes emotions from data such as voice, facial recognition, and entered text.

[0313] The server collects biometric and location information of the athlete, preprocesses it, and then runs an artificial intelligence model to evaluate their athletic ability. During this process, detailed data on the athlete's performance is obtained and provided as feedback.

[0314] This feedback process includes an emotional engine that analyzes the user's psychological state, and based on that, generates appropriate feedback. Specifically, if an exerciser is experiencing stress during training, the emotional engine can detect this state, and the server can adjust the training intensity or suggest relaxation methods.

[0315] The device provides the user with analysis results obtained from the server and advice based on their psychological state, generated by an emotion engine. The displayed information includes an assessment of exercise performance, motivational messages, and resources for psychological support.

[0316] For example, if a user begins to feel fatigued during a long training session, this system can recognize that emotion through its emotional engine and suggest appropriate rest periods and recovery exercises. As a result, athletes can train in a way that takes into account not only their physical abilities but also their mental health.

[0317] In this form, the present invention realizes training support that takes into account the user's emotional state and provides an exercise environment optimized for the individual. This system aims to improve the exerciser's overall training experience and achieve high training effectiveness.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] When a user puts on a wearable device and starts training, the emotion engine also activates, and a function that recognizes the user's emotions in real time from their voice and facial expressions comes into play.

[0321] Step 2:

[0322] The device acquires user emotional data and, when it detects a change exceeding a pre-set threshold (for example, increased stress or decreased motivation), it sends that data to the server.

[0323] Step 3:

[0324] The server receives biometric and location information from wearable devices and uses an AI model to evaluate athletic performance. This evaluation includes indicators such as heart rate, movement speed, and distance.

[0325] Step 4:

[0326] The server analyzes psychological state data from the emotion engine and combines it with athletic performance evaluation results to comprehensively assess the user's overall health.

[0327] Step 5:

[0328] The server generates specific advice for adjusting training intensity, recommending rest, and managing stress based on the user's psychological state, and sends this information to the terminal.

[0329] Step 6:

[0330] The device visually presents the generated advice to the user. This includes real-time exercise performance graphs, motivational messages, and specific exercises for stress reduction.

[0331] Step 7:

[0332] Users can make necessary adjustments during training based on the information provided. They can also input their feedback through a feedback option, and this feedback will be used to improve future suggestions.

[0333] This processing flow allows users to receive training approaches tailored to their individual emotional states, supporting their physical and mental health.

[0334] (Example 2)

[0335] 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".

[0336] The present invention aims to provide a system that allows athletes to effectively understand their emotional state and biometric information during training and receive personalized feedback. This will help athletes reduce stress and potential injury risks, while also addressing the challenges of improved motivation and mental support.

[0337] 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.

[0338] In this invention, the server includes means for analyzing the user's emotional state and performing emotion recognition, means for acquiring biometric and location information of the exerciser from data collection equipment and preprocessing the data, and means for executing a machine learning model to evaluate the exerciser's athletic ability based on the preprocessed data. This enables the exerciser to receive personalized feedback and perform training that improves their mental and physical health.

[0339] "Emotional state" refers to a user's psychological response and is a collection of data determined from sources such as voice, facial expressions, and text input.

[0340] "Biometric information" refers to data that indicates the physical condition of an exerciser, including health-related values ​​such as heart rate and respiratory rate.

[0341] "Location information" refers to data indicating the current location of an exerciser, and is obtained using location tracking technologies such as GPS.

[0342] "Preprocessing" refers to the process of preparing acquired data into an analyzable format, and includes steps such as noise reduction and checking for outliers.

[0343] A "machine learning model" is a statistical method used to identify patterns based on vast amounts of data and to evaluate and predict new data.

[0344] "Feedback" refers to information and advice provided about an athlete's performance to encourage future improvement.

[0345] "Psychological support" refers to providing information and advice to maintain and improve the mental health of athletes.

[0346] This invention is a system for providing user-specific feedback and support. The system's embodiments analyze the user's emotional state, evaluate their motor skills, and provide personalized advice, utilizing multiple hardware and software components.

[0347] The user uses a device with an emotion engine installed. The emotion engine uses speech recognition technology, facial recognition, and text analysis algorithms to evaluate the user's emotional state in real time. For example, if the user says "I'm a little tired," this is incorporated into the emotion analysis through speech recognition.

[0348] The server collects biometric and location information through wearable sensors and smartphones. This data is preprocessed and refined. Data cleansing technology is used to remove noise before processing. Based on the preprocessed information, a generative AI model performs exercise evaluation and analyzes exercise performance.

[0349] The device provides user-optimized feedback based on evaluation results and sentiment analysis data performed on the server. This feedback includes exercise evaluation results, motivational messages, and resources for mental support. For example, the device might advise the user to reduce training intensity or suggest relaxation exercises.

[0350] An example of a prompt for a generative AI model is a question like, "What are some effective ways to cool down based on my current emotional state?" In response to this prompt, the AI ​​can provide user-specific advice and evaluations.

[0351] This format allows users to receive personalized psychological and physical support during training, enabling them to achieve better results.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The user activates the emotion engine on their device and begins training. Voice data, facial expression data, and text data are sent to the emotion engine as input. The emotion engine analyzes this data and runs an algorithm to identify the user's emotional state. The output generates the emotional state the user is currently feeling. Specifically, the device analyzes emotions in real time based on the user's voice commands.

[0355] Step 2:

[0356] The server collects biometric and location information from the user's wearable device or smartphone. Inputs include heart rate, respiratory rate, and current location data. The server filters this data to remove noise, cleans the data, and then performs preprocessing. The output is clean, denoised data. Specific operations include detecting and removing abnormal biometric data.

[0357] Step 3:

[0358] The server uses pre-processed biometric information as input to begin evaluating athletic ability against a generated AI model. Based on the input data, a machine learning algorithm evaluates the user's athletic performance and calculates indicators relevant to future training plans. The output provides a detailed evaluation of the user's athletic ability and suggestions for improvement. Specific actions include an analysis of performance changes compared to past data.

[0359] Step 4:

[0360] The device integrates evaluation results from the server with analysis results from the emotion engine to display personalized feedback to the user. Inputs include athletic performance evaluation results and emotional state data. Based on this, the device generates advice including adjustments to the exercise plan, motivational messages, and relaxation methods. Output is the feedback displayed on the user screen. Specific actions include suggesting rest timings that take into account the stress level during the most recent training session.

[0361] (Application Example 2)

[0362] 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."

[0363] Conventional exercise support systems primarily focus on feedback and advice based on the exerciser's physical condition, lacking support that considers the exerciser's mental and emotional state. This can lead to problems such as exercisers not receiving appropriate support when experiencing stress or fatigue, potentially resulting in a decrease in motivation to continue exercising. Furthermore, there is a need for an integrated approach that considers not only physical training but also mental health.

[0364] 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.

[0365] In this invention, the server includes means for collecting biometric and location information of an exerciser from a data collection device and preprocessing the data; means for executing an artificial intelligence model for evaluating the exerciser's athletic ability based on the preprocessed data; and means for analyzing the user's emotional state and generating information for adjusting the exercise menu based on the analysis results. This comprehensively considers the exerciser's physical and emotional state, provides an individualized exercise plan accordingly, and enables the maintenance of motivation for exercise and improvement of mental health.

[0366] A "data collection device" is a device that collects biometric and location information from athletes and provides it to a server.

[0367] "Biometric information" refers to data that represents the physical condition of an exerciser, such as heart rate, blood pressure, and body temperature.

[0368] "Location information" refers to geographical location data that shows the current location and movement patterns of a person exercising.

[0369] "Preprocessing means" refers to technical means for appropriately organizing or converting collected biometric and location information and adjusting it into a format that can be used as input for an artificial intelligence model.

[0370] An "artificial intelligence model" is a computer program that uses machine learning algorithms to evaluate the athletic performance of athletes.

[0371] "Means for evaluating athletic ability" refers to a process that quantifies an athlete's fitness level and performance based on pre-processed biometric and positional information.

[0372] "Methods for analyzing emotional states" refer to technologies that analyze emotions from a person's voice, facial expressions, input text, etc., and understand their emotional state.

[0373] "Means of generating information" refers to the function of designing optimal exercise menus and advice based on the exerciser's physical ability and emotional state.

[0374] "Information for adjusting exercise programs" refers to specific guidelines for determining the optimal exercise content and intensity, taking into account the exerciser's current physical and mental condition.

[0375] The system for carrying out this invention is configured to combine a data collection device, a local terminal, a server, and an artificial intelligence model on the cloud in order to support exercise while taking into account the user's mental and emotional health. The system collects the user's facial expressions, voice, location information, and biometric information, and based on this, provides personalized exercise feedback and mental support.

[0376] The server acquires biometric and location information of the exerciser from data collection devices. This information is collected in real time, for example, using sensors on smartphones or wearable devices. The collected data is preprocessed as needed and sent to an artificial intelligence model in the cloud in an appropriate format. Preprocessing steps may include noise reduction and data normalization.

[0377] The AI ​​model on the cloud processes data to evaluate the user's athletic ability and analyzes emotional data such as the user's facial expressions and voice. Based on the analyzed emotional data, the server generates an exercise menu that takes the user's emotional state into account. This suggests the optimal exercise load and relaxation method for the user.

[0378] The device provides visual and audible feedback to the user based on exercise evaluation results and emotion analysis results obtained from the server. For example, if the user is feeling stressed, it may suggest relaxation exercises and play music to reduce stress.

[0379] As a concrete example, here is an example of a prompt message:

[0380] "Based on the user's emotional data, design the optimal exercise routine and mental support plan for the day. The user appears to be feeling fatigued at the moment."

[0381] This allows users to train in a way that takes into account not only their physical health but also their mental health, which is expected to improve exercise efficiency and consistency.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The server acquires biometric information (e.g., heart rate, blood pressure) and location information of the exerciser from data collection devices (smartphones or wearable devices). At this point, the input is raw data from the sensors. The server preprocesses this data, such as removing noise, to format it in a way that is easy for the subsequent artificial intelligence model to process. The output is the preprocessed, clean data.

[0385] Step 2:

[0386] Using pre-processed data, the server runs an artificial intelligence model on the cloud. The inputs here are pre-processed biometric and location data. The server inputs this into the model to evaluate the exerciser's physical ability. The model analyzes the user's fitness level using machine learning algorithms. The output is the result data of the evaluated physical ability.

[0387] Step 3:

[0388] The server analyzes user emotion data (e.g., facial expressions, voice) obtained from the terminal or emotion engine. The input is raw data related to the user's emotions. The server uses an AI algorithm for emotion analysis to identify the emotional state and generates feedback based on that state. The output is the analyzed emotional state data.

[0389] Step 4:

[0390] The server integrates athletic performance assessment results and emotional state data to create an optimal exercise program and mental support plan for the user. Inputs are athletic performance data and emotional state data. Based on this, the server generates personalized exercise suggestions tailored to the user's condition on that day. Outputs are the created exercise program and support plan.

[0391] Step 5:

[0392] The terminal notifies the user of the exercise menu and mental support plan provided by the server. The input is the generated exercise menu and support plan. The terminal conveys information to the user visually (display) and aurally (voice guidance) and provides exercise instructions. The output is the exercise instructions presented to the user.

[0393] Step 6:

[0394] The user provides feedback according to the suggested exercise menu. This feedback is sent to the server via the terminal. The input is the user's feedback information. The server analyzes this feedback, modifies the exercise plan as needed, and incorporates it into the next training session. The output is the revised exercise plan.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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".

[0411] The system for carrying out the present invention includes a process for collecting and analyzing various information related to the vital activities of an exerciser in real time. The exerciser wears a wearable device, which instantly acquires biometric information such as heart rate, step count, and location information.

[0412] The server continuously receives the collected data and performs data preprocessing. Preprocessing removes noise and outliers from the acquired data and converts it into a format suitable for machine learning models. Furthermore, by combining this with past training history, it becomes possible to evaluate health status and athletic performance based on the individual athlete's capabilities.

[0413] In this evaluation process, the server uses an artificial intelligence model to analyze the data in detail. The model not only determines the athlete's abilities and health status, but also generates improvements to the training plan and feedback based on this. The generated feedback includes insights to maximize safety and performance, and provides specific instructions, for example, to reduce the risk of injury.

[0414] The terminal presents the results retrieved from the server to the user (exerciser) in an intuitive and easy-to-understand format. This includes infographics, graphs, and specific advice. Visual information allows exercisers to easily check their own condition and make necessary adjustments. Messages aimed at psychological support are also displayed to improve the exerciser's motivation and promote emotional stability.

[0415] As a concrete example, if a user participates in a marathon, this system provides an optimal training plan in advance and monitors heart rate and fatigue levels in real time. Based on this information, it can suggest appropriate pacing and provide instructions to prevent injuries. After the race, it reviews performance and provides new insights for future training.

[0416] Thus, this system provides comprehensive and personalized support to exercisers, enabling the creation of an optimal exercise environment tailored to individual needs.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] When a user puts on a wearable device and starts training, the device acquires data such as heart rate, steps taken, and location information in real time.

[0420] Step 2:

[0421] The device periodically sends acquired data to the server, enabling rapid data collection. This ensures that information on changes and performance during training is reliably recorded.

[0422] Step 3:

[0423] The server preprocesses the received raw data. This process involves noise removal, data cleaning, and data interpolation, generating a clean dataset suitable for analysis.

[0424] Step 4:

[0425] The server operates an artificial intelligence model to evaluate the exercise performance of athletes based on pre-processed data. The AI ​​model analyzes the athletes' physical condition, performance trends, and risk factors, and generates an evaluation report.

[0426] Step 5:

[0427] The server generates feedback and suggestions for improving the training plan based on the AI's analysis results. These suggestions include adjustments to the exercise program and injury prevention measures.

[0428] Step 6:

[0429] The terminal receives results from the server and provides visual feedback to the user. Information is displayed in infographics and graphs, allowing the user to easily understand their exercise status.

[0430] Step 7:

[0431] The system utilizes a feature that allows users to provide feedback via their devices, recording their thoughts on recommended training content and evaluations.

[0432] Step 8:

[0433] The device sends user feedback to the server, and the information received is used to improve future training plans.

[0434] This processing flow allows for personalized training experiences for athletes, providing safe and effective feedback.

[0435] (Example 1)

[0436] 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."

[0437] In modern times, athletes are required to accurately understand their own health status and athletic ability and to create appropriate exercise plans based on that understanding. However, conventional systems struggle to provide detailed, real-time health status monitoring and flexible, personalized exercise plans. Furthermore, challenges remain in the early detection of potential health risks during exercise and the lack of psychological support to enhance exercise motivation.

[0438] 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.

[0439] In this invention, the server includes means for acquiring and processing biometric information and movement information from a device worn by an exerciser, means for using a generative artificial intelligence model to evaluate exercise ability and health status based on the processed information, and means for transmitting the evaluation results and improvement suggestions to a device that notifies the exerciser. This allows the exerciser to understand their own condition in real time and receive an optimal exercise plan based on scientific evidence. They can also receive suggestions to reduce potential risks during exercise and obtain comprehensive support, including psychological support.

[0440] A "device" is a piece of equipment worn by an athlete to acquire biometric information and information about their movements.

[0441] "Biometric information" refers to data that shows the physical condition of an exerciser in real time, such as heart rate, respiratory rate, and body temperature.

[0442] "Motion-related information" refers to data used to identify the motion state of a person, such as their position, velocity, and acceleration.

[0443] "Processing means" refers to the process of removing noise and changing the data format of biological information and operational information acquired from the device, preparing it for subsequent analysis.

[0444] A "generative artificial intelligence model" is a machine learning model used to analyze data and evaluate the athletic ability and health status of athletes.

[0445] A "notification device" is a display terminal that visually displays evaluation results and improvement suggestions sent from the server to the participant.

[0446] "Potential health risks" refer to undiscovered risk factors or conditions that may affect the health status of an exerciser.

[0447] "Psychological support" refers to encouraging and supportive messages provided to boost the motivation of athletes and maintain their mental stability.

[0448] A system implementing this invention includes a process for acquiring biometric and motion-related information in real time from a device worn by an athlete, and for appropriately processing and analyzing this information. The server receives the acquired data and performs data preprocessing, such as noise removal and elimination of outliers. Subsequently, the data is converted into a standardized format and analyzed using a generative AI model.

[0449] The server uses software frameworks such as TensorFlow and PyTorch on a computer to build machine learning models and evaluate the exercise capacity and health status of athletes. These evaluation results are useful for generating optimal training plans for athletes and predicting health risks.

[0450] The terminal visually displays evaluation results and improvement suggestions sent from the server to the exerciser. Utilizing devices such as smartphones and tablets, the terminal provides visual information such as infographics and graphs to make it easier for the exerciser to understand the results. It also includes a function to display encouraging messages as psychological support to boost the exerciser's motivation.

[0451] For example, if a user preparing for a marathon uses this system, they can receive an optimal training plan in advance. During the race, their heart rate and fatigue level are monitored in real time, and they receive suggestions for appropriate exercise pace and rest timing, which helps prevent injuries. After the race, they can analyze their performance and gain new insights for improving their future training.

[0452] An example of a prompt for a generating AI model is: "Analyze the athlete's biometric information, assess their health status, and generate an optimal training plan. Then, suggest feedback that will lead to maximizing performance and reducing the risk of injury."

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The user collects biometric and movement-related information through a wearable device. Inputs include heart rate, step count, and location information, which are transmitted to the server in real time. Output is data communication to the server.

[0456] Step 2:

[0457] The server receives data sent from the user and first detects and removes noise data. The input is raw biometric and behavioral data, and outliers are removed and missing values ​​are imputed through data cleansing. The output is pre-processed, more accurate data.

[0458] Step 3:

[0459] The server uses the pre-processed data, performs processing such as data standardization and scaling, and converts it into a format that can be used by the generative AI model. The input is cleansed data, and the output is data in an analyzable format prepared for the generative AI model.

[0460] Step 4:

[0461] The server uses a generative AI model to analyze input data and evaluate the exerciser's physical ability and health status. Specifically, it uses machine learning algorithms to compare current data with past data and generate performance indicators. The output is an evaluation of physical ability and an assessment of health status.

[0462] Step 5:

[0463] The server sends the generated evaluation and suggestion results to the terminal. The input is the analysis results of the AI ​​model, and the output is feedback information for the user. The terminal receives this and prepares it to be provided to the user.

[0464] Step 6:

[0465] The terminal displays evaluation results and improvement suggestions received from the server in infographics and graphs, presenting them in an easy-to-understand format for the user. In addition to visual displays, it also displays psychological support messages to boost motivation. The output is information that the exerciser can visually confirm.

[0466] (Application Example 1)

[0467] 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."

[0468] There is a need to appropriately monitor the biometric information and movements of exercisers in real time and provide optimal exercise plans tailored to each individual's health condition. However, conventional technologies not only collect and analyze data in a fragmented manner, but also lack visual notifications and psychological support, making it difficult to adequately maintain exerciser motivation and prevent injuries. Furthermore, systems that provide real-time exercise guidance through integration with home-use equipment are not yet widely available.

[0469] 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.

[0470] In this invention, the server includes means for collecting biometric and location information of an exerciser from a data collection device and preprocessing the data; means for running an artificial intelligence model to evaluate the exerciser's athletic ability based on the preprocessed data; and means for coordinating with a home appliance to provide real-time exercise guidance. As a result, exercisers can receive an optimal exercise plan based on their health condition even while at home, enabling efficient injury prevention and motivation maintenance.

[0471] A "data collection device" is a device used to acquire biometric and location information of an exerciser.

[0472] "Biometric information of an exerciser" refers to data that indicates the physical activity status of an exerciser, such as heart rate and step count.

[0473] "Location information" refers to information indicating the geographical location of an exerciser, and is data obtained through technologies such as GPS.

[0474] "Preprocessing methods" refer to methods for removing noise and outliers from collected biological information and converting it into a format suitable for analysis.

[0475] "Means of executing artificial intelligence models" refers to methods of performing calculations and analyses using machine learning to evaluate the athletic abilities of athletes.

[0476] A "terminal for visual notification" is a device equipped with a screen and design that clearly displays evaluation results and related information to the exerciser.

[0477] "Means of modifying exercise plans" refer to methods for adjusting the current training program based on feedback and evaluation results from the exerciser.

[0478] "Means of monitoring health status" refers to methods for constantly checking the health status of exercisers and providing appropriate exercise guidance as needed.

[0479] "Means of providing exercise guidance" refers to methods of providing advice and instructions to improve the exercise methods of participants based on collected data.

[0480] "Household machinery" refers to robots and devices used to assist and manage the physical activity of a person exercising within the home.

[0481] The system implementing this invention begins with the exerciser using a device to collect physical information. The server receives biometric and location information acquired from these devices in real time and preprocesses the data. Specifically, it uses a Python program to remove noise and outliers from the collected data and format it into a format suitable for machine learning models. Using this preprocessed data, the server runs an artificial intelligence model to evaluate the exerciser's physical ability and health status. This evaluation utilizes machine learning libraries such as Scikit-learn and employs linear regression models and other appropriate models.

[0482] The device then receives the results from the server and displays the information visually. Users can intuitively check evaluation results, modify their exercise plan, and access health guidelines using their smartphones or tablets. The results are displayed as infographics on the device, and psychological support information is also provided as feedback to the exerciser.

[0483] Furthermore, it can be linked with home-use machines to provide real-time guidance on the exerciser's movements. A generative AI model is used to provide exercise guidance and health management advice tailored to the exerciser's movements. Specific prompts include commands such as, "Please input the exerciser's heart rate data and suggest the optimal pace," which are then used to optimize the exercise plan.

[0484] This system allows users to engage in effective exercise and receive appropriate feedback and guidance, creating an environment where they can maintain optimal health while increasing their motivation to exercise. For example, a user who enjoys jogging can improve their exercise performance without overexertion by receiving guidance on appropriate rest times and acceleration.

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The server collects biometric and location information in real time from data collection devices worn by the exerciser. This information includes heart rate, step count, and location data. The server accurately receives this data and stores it as foundational data to proceed to the next step.

[0488] Step 2:

[0489] The server preprocesses the collected biometric data. It removes noise and outliers from the input data and performs linear scaling and standardization. This preprocessing allows machine learning models to obtain more accurate results when analyzing the data. The output is a clean dataset with outliers removed.

[0490] Step 3:

[0491] The server uses pre-processed data to run an artificial intelligence model and evaluate the exercise performance and health status of athletes. The input is a pre-processed dataset, and the output consists of performance metrics and health assessment scores for the athletes. This process utilizes Scikit-learn machine learning algorithms to maximize the predictive power of the model.

[0492] Step 4:

[0493] The terminal receives evaluation results sent from the server and visually notifies the user. Inputs include evaluation results and health assessment scores from the server. The terminal displays this information as graphics or infographics, allowing the user to immediately understand the situation visually. Outputs are the information the user sees on the terminal screen.

[0494] Step 5:

[0495] The user uses a device to review feedback received from their exercise and revise their exercise plan as needed. The device receives the user's feedback and updates the suggested exercise plan for the next session based on it. The input is the user's feedback, and the output is the revised exercise plan.

[0496] Step 6:

[0497] The home-use machine provides real-time exercise guidance to the user based on the latest information obtained from the server and terminal. Inputs include exercise guidance information from the server and correction plans from the terminal. Based on this information, the machine monitors the user's movements in real time and instructs them on appropriate exercise posture and pacing. Output is the audio and visual feedback received by the user.

[0498] 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.

[0499] This invention relates to a system that provides personalized feedback and support to an exerciser by integrating an emotion engine that recognizes the user's emotional state. The emotion engine is installed on the user's device and analyzes emotions from data such as voice, facial recognition, and entered text.

[0500] The server collects biometric and location information of the athlete, preprocesses it, and then runs an artificial intelligence model to evaluate their athletic ability. During this process, detailed data on the athlete's performance is obtained and provided as feedback.

[0501] This feedback process includes an emotional engine that analyzes the user's psychological state, and based on that, generates appropriate feedback. Specifically, if an exerciser is experiencing stress during training, the emotional engine can detect this state, and the server can adjust the training intensity or suggest relaxation methods.

[0502] The device provides the user with analysis results obtained from the server and advice based on their psychological state, generated by an emotion engine. The displayed information includes exercise evaluations, motivational messages, and resources for psychological support.

[0503] For example, if a user begins to feel fatigued during a long training session, this system can recognize that emotion through its emotional engine and suggest appropriate rest periods and recovery exercises. As a result, athletes can train in a way that takes into account not only their physical abilities but also their mental health.

[0504] In this form, the present invention realizes training support that takes into account the user's emotional state and provides an exercise environment optimized for the individual. This system aims to improve the exerciser's overall training experience and achieve high training effectiveness.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] When a user puts on a wearable device and starts training, the emotion engine also activates, and a function that recognizes the user's emotions in real time from their voice and facial expressions comes into play.

[0508] Step 2:

[0509] The device acquires user emotional data and, when it detects a change exceeding a pre-set threshold (for example, increased stress or decreased motivation), it sends that data to the server.

[0510] Step 3:

[0511] The server receives biometric and location information from wearable devices and uses an AI model to evaluate athletic performance. This evaluation includes indicators such as heart rate, movement speed, and distance.

[0512] Step 4:

[0513] The server analyzes psychological state data from the emotion engine and combines it with athletic performance evaluation results to comprehensively assess the user's overall health.

[0514] Step 5:

[0515] The server generates specific advice for adjusting training intensity, recommending rest, and managing stress based on the user's psychological state, and sends this information to the terminal.

[0516] Step 6:

[0517] The device visually presents the generated advice to the user. This includes real-time exercise performance graphs, motivational messages, and specific exercises for stress reduction.

[0518] Step 7:

[0519] Users can make necessary adjustments during training based on the information provided. They can also input their feedback through a feedback option, and this feedback will be used to improve future suggestions.

[0520] This processing flow allows users to receive training approaches tailored to their individual emotional states, supporting their physical and mental health.

[0521] (Example 2)

[0522] 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."

[0523] The present invention aims to provide a system that allows athletes to effectively understand their emotional state and biometric information during training and receive personalized feedback. This will help athletes reduce stress and potential injury risks, while also addressing the challenges of improved motivation and mental support.

[0524] 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.

[0525] In this invention, the server includes means for analyzing the user's emotional state and performing emotion recognition, means for acquiring biometric and location information of the exerciser from data collection equipment and preprocessing the data, and means for executing a machine learning model to evaluate the exerciser's athletic ability based on the preprocessed data. This enables the exerciser to receive personalized feedback and perform training that improves their mental and physical health.

[0526] "Emotional state" refers to a user's psychological response and is a collection of data determined from sources such as voice, facial expressions, and text input.

[0527] "Biometric information" refers to data that indicates the physical condition of an exerciser, including health-related values ​​such as heart rate and respiratory rate.

[0528] "Location information" refers to data indicating the current location of an exerciser, and is obtained using location tracking technologies such as GPS.

[0529] "Preprocessing" refers to the process of preparing acquired data into an analyzable format, and includes steps such as noise reduction and checking for outliers.

[0530] A "machine learning model" is a statistical method used to identify patterns based on vast amounts of data and to evaluate and predict new data.

[0531] "Feedback" refers to information and advice provided about an athlete's performance to encourage future improvement.

[0532] "Psychological support" refers to providing information and advice to maintain and improve the mental health of athletes.

[0533] This invention is a system for providing user-specific feedback and support. The system's embodiments analyze the user's emotional state, evaluate their motor skills, and provide personalized advice, utilizing multiple hardware and software components.

[0534] The user uses a device with an emotion engine installed. The emotion engine uses speech recognition technology, facial recognition, and text analysis algorithms to evaluate the user's emotional state in real time. For example, if the user says "I'm a little tired," this is incorporated into the emotion analysis through speech recognition.

[0535] The server collects biometric and location information through wearable sensors and smartphones. This data is preprocessed and refined. Data cleansing technology is used to remove noise before processing. Based on the preprocessed information, a generative AI model performs exercise evaluation and analyzes exercise performance.

[0536] The device provides user-optimized feedback based on evaluation results and sentiment analysis data performed on the server. This feedback includes exercise evaluation results, motivational messages, and resources for mental support. For example, the device might advise the user to reduce training intensity or suggest relaxation exercises.

[0537] An example of a prompt for a generative AI model is a question like, "What are some effective ways to cool down based on my current emotional state?" In response to this prompt, the AI ​​can provide user-specific advice and evaluations.

[0538] This format allows users to receive personalized psychological and physical support during training, enabling them to achieve better results.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The user activates the emotion engine on their device and begins training. Voice data, facial expression data, and text data are sent to the emotion engine as input. The emotion engine analyzes this data and runs an algorithm to identify the user's emotional state. The output generates the emotional state the user is currently feeling. Specifically, the device analyzes emotions in real time based on the user's voice commands.

[0542] Step 2:

[0543] The server collects biometric and location information from the user's wearable device or smartphone. Inputs include heart rate, respiratory rate, and current location data. The server filters this data to remove noise, cleans the data, and then performs preprocessing. The output is clean, denoised data. Specific operations include detecting and removing abnormal biometric data.

[0544] Step 3:

[0545] The server uses pre-processed biometric information as input to begin evaluating athletic ability against a generated AI model. Based on the input data, a machine learning algorithm evaluates the user's athletic performance and calculates indicators relevant to future training plans. The output provides a detailed evaluation of the user's athletic ability and suggestions for improvement. Specific actions include an analysis of performance changes compared to past data.

[0546] Step 4:

[0547] The device integrates evaluation results from the server with analysis results from the emotion engine to display personalized feedback to the user. Inputs include athletic performance evaluation results and emotional state data. Based on this, the device generates advice including adjustments to the exercise plan, motivational messages, and relaxation methods. Output is the feedback displayed on the user screen. Specific actions include suggesting rest timings that take into account the stress level during the most recent training session.

[0548] (Application Example 2)

[0549] 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."

[0550] Conventional exercise support systems primarily focus on feedback and advice based on the exerciser's physical condition, lacking support that considers the exerciser's mental and emotional state. This can lead to problems such as exercisers not receiving appropriate support when experiencing stress or fatigue, potentially resulting in a decrease in motivation to continue exercising. Furthermore, there is a need for an integrated approach that considers not only physical training but also mental health.

[0551] 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.

[0552] In this invention, the server includes means for collecting biometric and location information of an exerciser from a data collection device and preprocessing the data; means for executing an artificial intelligence model for evaluating the exerciser's athletic ability based on the preprocessed data; and means for analyzing the user's emotional state and generating information for adjusting the exercise menu based on the analysis results. This comprehensively considers the exerciser's physical and emotional state, provides an individualized exercise plan accordingly, and enables the maintenance of motivation for exercise and improvement of mental health.

[0553] A "data collection device" is a device that collects biometric and location information from athletes and provides it to a server.

[0554] "Biometric information" refers to data that represents the physical condition of an exerciser, such as heart rate, blood pressure, and body temperature.

[0555] "Location information" refers to geographical location data that shows the current location and movement patterns of a person exercising.

[0556] "Preprocessing means" refers to technical means for appropriately organizing or converting collected biometric and location information and adjusting it into a format that can be used as input for an artificial intelligence model.

[0557] An "artificial intelligence model" is a computer program that uses machine learning algorithms to evaluate the athletic performance of athletes.

[0558] "Means for evaluating athletic ability" refers to a process that quantifies an athlete's fitness level and performance based on pre-processed biometric and positional information.

[0559] "Methods for analyzing emotional states" refer to technologies that analyze emotions from a person's voice, facial expressions, input text, etc., and understand their emotional state.

[0560] "Means of generating information" refers to the function of designing optimal exercise menus and advice based on the exerciser's physical ability and emotional state.

[0561] "Information for adjusting exercise programs" refers to specific guidelines for determining the optimal exercise content and intensity, taking into account the exerciser's current physical and mental condition.

[0562] The system for carrying out this invention is configured to combine a data collection device, a local terminal, a server, and an artificial intelligence model on the cloud in order to support exercise while taking into account the user's mental and emotional health. The system collects the user's facial expressions, voice, location information, and biometric information, and based on this, provides personalized exercise feedback and mental support.

[0563] The server acquires biometric and location information of the exerciser from data collection devices. This information is collected in real time, for example, using sensors on smartphones or wearable devices. The collected data is preprocessed as needed and sent to an artificial intelligence model in the cloud in an appropriate format. Preprocessing steps may include noise reduction and data normalization.

[0564] The AI ​​model on the cloud processes data to evaluate the user's athletic ability and analyzes emotional data such as the user's facial expressions and voice. Based on the analyzed emotional data, the server generates an exercise menu that takes the user's emotional state into account. This suggests the optimal exercise load and relaxation method for the user.

[0565] The device provides visual and audible feedback to the user based on exercise evaluation results and emotion analysis results obtained from the server. For example, if the user is feeling stressed, it may suggest relaxation exercises and play music to reduce stress.

[0566] As a concrete example, here is an example of a prompt message:

[0567] "Based on the user's emotional data, design the optimal exercise routine and mental support plan for the day. The user appears to be feeling fatigued at the moment."

[0568] This allows users to train in a way that takes into account not only their physical health but also their mental health, which is expected to improve exercise efficiency and consistency.

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The server acquires biometric information (e.g., heart rate, blood pressure) and location information of the exerciser from data collection devices (smartphones or wearable devices). At this point, the input is raw data from the sensors. The server preprocesses this data, such as removing noise, to format it in a way that is easy for the subsequent artificial intelligence model to process. The output is the preprocessed, clean data.

[0572] Step 2:

[0573] Using pre-processed data, the server runs an artificial intelligence model on the cloud. The inputs here are pre-processed biometric and location data. The server inputs this into the model to evaluate the exerciser's physical ability. The model analyzes the user's fitness level using machine learning algorithms. The output is the result data of the evaluated physical ability.

[0574] Step 3:

[0575] The server analyzes user emotion data (e.g., facial expressions, voice) obtained from the terminal or emotion engine. The input is raw data related to the user's emotions. The server uses an AI algorithm for emotion analysis to identify the emotional state and generates feedback based on that state. The output is the analyzed emotional state data.

[0576] Step 4:

[0577] The server integrates athletic performance assessment results and emotional state data to create an optimal exercise program and mental support plan for the user. Inputs are athletic performance data and emotional state data. Based on this, the server generates personalized exercise suggestions tailored to the user's condition on that day. Outputs are the created exercise program and support plan.

[0578] Step 5:

[0579] The terminal notifies the user of the exercise menu and mental support plan provided by the server. The input is the generated exercise menu and support plan. The terminal conveys information to the user visually (display) and aurally (voice guidance) and provides exercise instructions. The output is the exercise instructions presented to the user.

[0580] Step 6:

[0581] The user provides feedback according to the suggested exercise menu. This feedback is sent to the server via the terminal. The input is the user's feedback information. The server analyzes this feedback, modifies the exercise plan as needed, and incorporates it into the next training session. The output is the revised exercise plan.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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".

[0599] The system for carrying out the present invention includes a process for collecting and analyzing various information related to the vital activities of an exerciser in real time. The exerciser wears a wearable device, which instantly acquires biometric information such as heart rate, step count, and location information.

[0600] The server continuously receives the collected data and performs data preprocessing. Preprocessing removes noise and outliers from the acquired data and converts it into a format suitable for machine learning models. Furthermore, by combining this with past training history, it becomes possible to evaluate health status and athletic performance based on the individual athlete's capabilities.

[0601] In this evaluation process, the server uses an artificial intelligence model to analyze the data in detail. The model not only determines the athlete's abilities and health status, but also generates improvements to the training plan and feedback based on this. The generated feedback includes insights to maximize safety and performance, and provides specific instructions, for example, to reduce the risk of injury.

[0602] The terminal presents the results retrieved from the server to the user (exerciser) in an intuitive and easy-to-understand format. This includes infographics, graphs, and specific advice. Visual information allows exercisers to easily check their own condition and make necessary adjustments. Messages aimed at psychological support are also displayed to improve the exerciser's motivation and promote emotional stability.

[0603] As a concrete example, if a user participates in a marathon, this system provides an optimal training plan in advance and monitors heart rate and fatigue levels in real time. Based on this information, it can suggest appropriate pacing and provide instructions to prevent injuries. After the race, it reviews performance and provides new insights for future training.

[0604] Thus, this system provides comprehensive and personalized support to exercisers, enabling the creation of an optimal exercise environment tailored to individual needs.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] When a user puts on a wearable device and starts training, the device acquires data such as heart rate, steps taken, and location information in real time.

[0608] Step 2:

[0609] The device periodically sends acquired data to the server, enabling rapid data collection. This ensures that information on changes and performance during training is reliably recorded.

[0610] Step 3:

[0611] The server preprocesses the received raw data. This process involves noise removal, data cleaning, and data interpolation, generating a clean dataset suitable for analysis.

[0612] Step 4:

[0613] The server operates an artificial intelligence model to evaluate the exercise performance of athletes based on pre-processed data. The AI ​​model analyzes the athletes' physical condition, performance trends, and risk factors, and generates an evaluation report.

[0614] Step 5:

[0615] The server generates feedback and suggestions for improving the training plan based on the AI's analysis results. These suggestions include adjustments to the exercise program and injury prevention measures.

[0616] Step 6:

[0617] The terminal receives results from the server and provides visual feedback to the user. Information is displayed in infographics and graphs, allowing the user to easily understand their exercise status.

[0618] Step 7:

[0619] The system utilizes a feature that allows users to provide feedback via their devices, recording their thoughts on recommended training content and evaluations.

[0620] Step 8:

[0621] The device sends user feedback to the server, and the information received is used to improve future training plans.

[0622] This processing flow allows for personalized training experiences for athletes, providing safe and effective feedback.

[0623] (Example 1)

[0624] 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".

[0625] In modern times, athletes are required to accurately understand their own health status and athletic ability and to create appropriate exercise plans based on that understanding. However, conventional systems struggle to provide detailed, real-time health status monitoring and flexible, personalized exercise plans. Furthermore, challenges remain in the early detection of potential health risks during exercise and the lack of psychological support to enhance exercise motivation.

[0626] 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.

[0627] In this invention, the server includes means for acquiring and processing biometric information and movement information from a device worn by an exerciser, means for using a generative artificial intelligence model to evaluate exercise ability and health status based on the processed information, and means for transmitting the evaluation results and improvement suggestions to a device that notifies the exerciser. This allows the exerciser to understand their own condition in real time and receive an optimal exercise plan based on scientific evidence. They can also receive suggestions to reduce potential risks during exercise and obtain comprehensive support, including psychological support.

[0628] A "device" is a piece of equipment worn by an athlete to acquire biometric information and information about their movements.

[0629] "Biometric information" refers to data that shows the physical condition of an exerciser in real time, such as heart rate, respiratory rate, and body temperature.

[0630] "Motion-related information" refers to data used to identify the motion state of a person, such as their position, velocity, and acceleration.

[0631] "Processing means" refers to the process of removing noise and changing the data format of biological information and operational information acquired from the device, preparing it for subsequent analysis.

[0632] A "generative artificial intelligence model" is a machine learning model used to analyze data and evaluate the athletic ability and health status of athletes.

[0633] A "notification device" is a display terminal that visually displays evaluation results and improvement suggestions sent from the server to the participant.

[0634] "Potential health risks" refer to undiscovered risk factors or conditions that may affect the health status of an exerciser.

[0635] "Psychological support" refers to encouraging and supportive messages provided to boost the motivation of athletes and maintain their mental stability.

[0636] A system implementing this invention includes a process for acquiring biometric and motion-related information in real time from a device worn by an athlete, and for appropriately processing and analyzing this information. The server receives the acquired data and performs data preprocessing, such as noise removal and elimination of outliers. Subsequently, the data is converted into a standardized format and analyzed using a generative AI model.

[0637] The server uses software frameworks such as TensorFlow and PyTorch on a computer to build machine learning models and evaluate the exercise capacity and health status of athletes. These evaluation results are useful for generating optimal training plans for athletes and predicting health risks.

[0638] The terminal visually displays evaluation results and improvement suggestions sent from the server to the exerciser. Utilizing devices such as smartphones and tablets, the terminal provides visual information such as infographics and graphs to make it easier for the exerciser to understand the results. It also includes a function to display encouraging messages as psychological support to boost the exerciser's motivation.

[0639] For example, if a user preparing for a marathon uses this system, they can receive an optimal training plan in advance. During the race, their heart rate and fatigue level are monitored in real time, and they receive suggestions for appropriate exercise pace and rest timing, which helps prevent injuries. After the race, they can analyze their performance and gain new insights for improving their future training.

[0640] An example of a prompt for a generating AI model is: "Analyze the athlete's biometric information, assess their health status, and generate an optimal training plan. Then, suggest feedback that will lead to maximizing performance and reducing the risk of injury."

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] The user collects biometric and movement-related information through a wearable device. Inputs include heart rate, step count, and location information, which are transmitted to the server in real time. Output is data communication to the server.

[0644] Step 2:

[0645] The server receives data sent from the user and first detects and removes noise data. The input is raw biometric and behavioral data, and outliers are removed and missing values ​​are imputed through data cleansing. The output is pre-processed, more accurate data.

[0646] Step 3:

[0647] The server uses the pre-processed data, performs processing such as data standardization and scaling, and converts it into a format that can be used by the generative AI model. The input is cleansed data, and the output is data in an analyzable format prepared for the generative AI model.

[0648] Step 4:

[0649] The server uses a generative AI model to analyze input data and evaluate the exerciser's physical ability and health status. Specifically, it uses machine learning algorithms to compare current data with past data and generate performance indicators. The output is an evaluation of physical ability and an assessment of health status.

[0650] Step 5:

[0651] The server sends the generated evaluation and suggestion results to the terminal. The input is the analysis results of the AI ​​model, and the output is feedback information for the user. The terminal receives this and prepares it to be provided to the user.

[0652] Step 6:

[0653] The terminal displays evaluation results and improvement suggestions received from the server in infographics and graphs, presenting them in an easy-to-understand format for the user. In addition to visual displays, it also displays psychological support messages to boost motivation. The output is information that the exerciser can visually confirm.

[0654] (Application Example 1)

[0655] 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".

[0656] There is a need to appropriately monitor the biometric information and movements of exercisers in real time and provide optimal exercise plans tailored to each individual's health condition. However, conventional technologies not only collect and analyze data in a fragmented manner, but also lack visual notifications and psychological support, making it difficult to adequately maintain exerciser motivation and prevent injuries. Furthermore, systems that provide real-time exercise guidance through integration with home-use equipment are not yet widely available.

[0657] 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.

[0658] In this invention, the server includes means for collecting biometric and location information of an exerciser from a data collection device and preprocessing the data; means for running an artificial intelligence model to evaluate the exerciser's athletic ability based on the preprocessed data; and means for coordinating with a home appliance to provide real-time exercise guidance. As a result, exercisers can receive an optimal exercise plan based on their health condition even while at home, enabling efficient injury prevention and motivation maintenance.

[0659] A "data collection device" is a device used to acquire biometric and location information of an exerciser.

[0660] "Biometric information of an exerciser" refers to data that indicates the physical activity status of an exerciser, such as heart rate and step count.

[0661] "Location information" refers to information indicating the geographical location of an exerciser, and is data obtained through technologies such as GPS.

[0662] "Preprocessing methods" refer to methods for removing noise and outliers from collected biological information and converting it into a format suitable for analysis.

[0663] "Means of executing artificial intelligence models" refers to methods of performing calculations and analyses using machine learning to evaluate the athletic abilities of athletes.

[0664] A "terminal for visual notification" is a device equipped with a screen and design that clearly displays evaluation results and related information to the exerciser.

[0665] "Means of modifying exercise plans" refer to methods for adjusting the current training program based on feedback and evaluation results from the exerciser.

[0666] "Means of monitoring health status" refers to methods for constantly checking the health status of exercisers and providing appropriate exercise guidance as needed.

[0667] "Means of providing exercise guidance" refers to methods of providing advice and instructions to improve the exercise methods of participants based on collected data.

[0668] "Household machinery" refers to robots and devices used to assist and manage the physical activity of a person exercising within the home.

[0669] The system implementing this invention begins with the exerciser using a device to collect physical information. The server receives biometric and location information acquired from these devices in real time and preprocesses the data. Specifically, it uses a Python program to remove noise and outliers from the collected data and format it into a format suitable for machine learning models. Using this preprocessed data, the server runs an artificial intelligence model to evaluate the exerciser's physical ability and health status. This evaluation utilizes machine learning libraries such as Scikit-learn and employs linear regression models and other appropriate models.

[0670] The device then receives the results from the server and displays the information visually. Users can intuitively check evaluation results, modify their exercise plan, and access health guidelines using their smartphones or tablets. The results are displayed as infographics on the device, and psychological support information is also provided as feedback to the exerciser.

[0671] Furthermore, it can be linked with home-use machines to provide real-time guidance on the exerciser's movements. A generative AI model is used to provide exercise guidance and health management advice tailored to the exerciser's movements. Specific prompts include commands such as, "Please input the exerciser's heart rate data and suggest the optimal pace," which are then used to optimize the exercise plan.

[0672] This system allows users to engage in effective exercise and receive appropriate feedback and guidance, creating an environment where they can maintain optimal health while increasing their motivation to exercise. For example, a user who enjoys jogging can improve their exercise performance without overexertion by receiving guidance on appropriate rest times and acceleration.

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The server collects biometric and location information in real time from data collection devices worn by the exerciser. This information includes heart rate, step count, and location data. The server accurately receives this data and stores it as foundational data to proceed to the next step.

[0676] Step 2:

[0677] The server preprocesses the collected biometric data. It removes noise and outliers from the input data and performs linear scaling and standardization. This preprocessing allows machine learning models to obtain more accurate results when analyzing the data. The output is a clean dataset with outliers removed.

[0678] Step 3:

[0679] The server uses pre-processed data to run an artificial intelligence model and evaluate the exercise performance and health status of athletes. The input is a pre-processed dataset, and the output consists of performance metrics and health assessment scores for the athletes. This process utilizes Scikit-learn machine learning algorithms to maximize the predictive power of the model.

[0680] Step 4:

[0681] The terminal receives evaluation results sent from the server and visually notifies the user. Inputs include evaluation results and health assessment scores from the server. The terminal displays this information as graphics or infographics, allowing the user to immediately understand the situation visually. Outputs are the information the user sees on the terminal screen.

[0682] Step 5:

[0683] The user uses a device to review feedback received from their exercise and revise their exercise plan as needed. The device receives the user's feedback and updates the suggested exercise plan for the next session based on it. The input is the user's feedback, and the output is the revised exercise plan.

[0684] Step 6:

[0685] The home-use machine provides real-time exercise guidance to the user based on the latest information obtained from the server and terminal. Inputs include exercise guidance information from the server and correction plans from the terminal. Based on this information, the machine monitors the user's movements in real time and instructs them on appropriate exercise posture and pacing. Output is the audio and visual feedback received by the user.

[0686] 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.

[0687] This invention relates to a system that provides personalized feedback and support to an exerciser by integrating an emotion engine that recognizes the user's emotional state. The emotion engine is installed on the user's device and analyzes emotions from data such as voice, facial recognition, and entered text.

[0688] The server collects biometric and location information of the athlete, preprocesses it, and then runs an artificial intelligence model to evaluate their athletic ability. During this process, detailed data on the athlete's performance is obtained and provided as feedback.

[0689] This feedback process includes an emotional engine that analyzes the user's psychological state, and based on that, generates appropriate feedback. Specifically, if an exerciser is experiencing stress during training, the emotional engine can detect this state, and the server can adjust the training intensity or suggest relaxation methods.

[0690] The device provides the user with analysis results obtained from the server and advice based on their psychological state, generated by an emotion engine. The displayed information includes exercise evaluations, motivational messages, and resources for psychological support.

[0691] For example, if a user begins to feel fatigued during a long training session, this system can recognize that emotion through its emotional engine and suggest appropriate rest periods and recovery exercises. As a result, athletes can train in a way that takes into account not only their physical abilities but also their mental health.

[0692] In this form, the present invention realizes training support that takes into account the user's emotional state and provides an exercise environment optimized for the individual. This system aims to improve the exerciser's overall training experience and achieve high training effectiveness.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] When a user puts on a wearable device and starts training, the emotion engine also activates, and a function that recognizes the user's emotions in real time from their voice and facial expressions comes into play.

[0696] Step 2:

[0697] The device acquires user emotional data and, when it detects a change exceeding a pre-set threshold (for example, increased stress or decreased motivation), it sends that data to the server.

[0698] Step 3:

[0699] The server receives biometric and location information from wearable devices and uses an AI model to evaluate athletic performance. This evaluation includes indicators such as heart rate, movement speed, and distance.

[0700] Step 4:

[0701] The server analyzes psychological state data from the emotion engine and combines it with athletic performance evaluation results to comprehensively assess the user's overall health.

[0702] Step 5:

[0703] The server generates specific advice for adjusting training intensity, recommending rest, and managing stress based on the user's psychological state, and sends this information to the terminal.

[0704] Step 6:

[0705] The device visually presents the generated advice to the user. This includes real-time exercise performance graphs, motivational messages, and specific exercises for stress reduction.

[0706] Step 7:

[0707] Users can make necessary adjustments during training based on the information provided. They can also input their feedback through a feedback option, and this feedback will be used to improve future suggestions.

[0708] This processing flow allows users to receive training approaches tailored to their individual emotional states, supporting their physical and mental health.

[0709] (Example 2)

[0710] 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".

[0711] The present invention aims to provide a system that allows athletes to effectively understand their emotional state and biometric information during training and receive personalized feedback. This will help athletes reduce stress and potential injury risks, while also addressing the challenges of improved motivation and mental support.

[0712] 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.

[0713] In this invention, the server includes means for analyzing the user's emotional state and performing emotion recognition, means for acquiring biometric and location information of the exerciser from data collection equipment and preprocessing the data, and means for executing a machine learning model to evaluate the exerciser's athletic ability based on the preprocessed data. This enables the exerciser to receive personalized feedback and perform training that improves their mental and physical health.

[0714] "Emotional state" refers to a user's psychological response and is a collection of data determined from sources such as voice, facial expressions, and text input.

[0715] "Biometric information" refers to data that indicates the physical condition of an exerciser, including health-related values ​​such as heart rate and respiratory rate.

[0716] "Location information" refers to data indicating the current location of an exerciser, and is obtained using location tracking technologies such as GPS.

[0717] "Preprocessing" refers to the process of preparing acquired data into an analyzable format, and includes steps such as noise reduction and checking for outliers.

[0718] A "machine learning model" is a statistical method used to identify patterns based on vast amounts of data and to evaluate and predict new data.

[0719] "Feedback" refers to information and advice provided about an athlete's performance to encourage future improvement.

[0720] "Psychological support" refers to providing information and advice to maintain and improve the mental health of athletes.

[0721] This invention is a system for providing user-specific feedback and support. The system's embodiments analyze the user's emotional state, evaluate their motor skills, and provide personalized advice, utilizing multiple hardware and software components.

[0722] The user uses a device with an emotion engine installed. The emotion engine uses speech recognition technology, facial recognition, and text analysis algorithms to evaluate the user's emotional state in real time. For example, if the user says "I'm a little tired," this is incorporated into the emotion analysis through speech recognition.

[0723] The server collects biometric and location information through wearable sensors and smartphones. This data is preprocessed and refined. Data cleansing technology is used to remove noise before processing. Based on the preprocessed information, a generative AI model performs exercise evaluation and analyzes exercise performance.

[0724] The device provides user-optimized feedback based on evaluation results and sentiment analysis data performed on the server. This feedback includes exercise evaluation results, motivational messages, and resources for mental support. For example, the device might advise the user to reduce training intensity or suggest relaxation exercises.

[0725] An example of a prompt for a generative AI model is a question like, "What are some effective ways to cool down based on my current emotional state?" In response to this prompt, the AI ​​can provide user-specific advice and evaluations.

[0726] This format allows users to receive personalized psychological and physical support during training, enabling them to achieve better results.

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The user activates the emotion engine on their device and begins training. Voice data, facial expression data, and text data are sent to the emotion engine as input. The emotion engine analyzes this data and runs an algorithm to identify the user's emotional state. The output generates the emotional state the user is currently feeling. Specifically, the device analyzes emotions in real time based on the user's voice commands.

[0730] Step 2:

[0731] The server collects biometric and location information from the user's wearable device or smartphone. Inputs include heart rate, respiratory rate, and current location data. The server filters this data to remove noise, cleans the data, and then performs preprocessing. The output is clean, denoised data. Specific operations include detecting and removing abnormal biometric data.

[0732] Step 3:

[0733] The server uses pre-processed biometric information as input to begin evaluating athletic ability against a generated AI model. Based on the input data, a machine learning algorithm evaluates the user's athletic performance and calculates indicators relevant to future training plans. The output provides a detailed evaluation of the user's athletic ability and suggestions for improvement. Specific actions include an analysis of performance changes compared to past data.

[0734] Step 4:

[0735] The device integrates evaluation results from the server with analysis results from the emotion engine to display personalized feedback to the user. Inputs include athletic performance evaluation results and emotional state data. Based on this, the device generates advice including adjustments to the exercise plan, motivational messages, and relaxation methods. Output is the feedback displayed on the user screen. Specific actions include suggesting rest timings that take into account the stress level during the most recent training session.

[0736] (Application Example 2)

[0737] 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".

[0738] Conventional exercise support systems primarily focus on feedback and advice based on the exerciser's physical condition, lacking support that considers the exerciser's mental and emotional state. This can lead to problems such as exercisers not receiving appropriate support when experiencing stress or fatigue, potentially resulting in a decrease in motivation to continue exercising. Furthermore, there is a need for an integrated approach that considers not only physical training but also mental health.

[0739] 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.

[0740] In this invention, the server includes means for collecting biometric and location information of an exerciser from a data collection device and preprocessing the data; means for executing an artificial intelligence model for evaluating the exerciser's athletic ability based on the preprocessed data; and means for analyzing the user's emotional state and generating information for adjusting the exercise menu based on the analysis results. This comprehensively considers the exerciser's physical and emotional state, provides an individualized exercise plan accordingly, and enables the maintenance of motivation for exercise and improvement of mental health.

[0741] A "data collection device" is a device that collects biometric and location information from athletes and provides it to a server.

[0742] "Biometric information" refers to data that represents the physical condition of an exerciser, such as heart rate, blood pressure, and body temperature.

[0743] "Location information" refers to geographical location data that shows the current location and movement patterns of a person exercising.

[0744] "Preprocessing means" refers to technical means for appropriately organizing or converting collected biometric and location information and adjusting it into a format that can be used as input for an artificial intelligence model.

[0745] An "artificial intelligence model" is a computer program that uses machine learning algorithms to evaluate the athletic performance of athletes.

[0746] "Means for evaluating athletic ability" refers to a process that quantifies an athlete's fitness level and performance based on pre-processed biometric and positional information.

[0747] "Methods for analyzing emotional states" refer to technologies that analyze emotions from a person's voice, facial expressions, input text, etc., and understand their emotional state.

[0748] "Means of generating information" refers to the function of designing optimal exercise menus and advice based on the exerciser's physical ability and emotional state.

[0749] "Information for adjusting exercise programs" refers to specific guidelines for determining the optimal exercise content and intensity, taking into account the exerciser's current physical and mental condition.

[0750] The system for carrying out this invention is configured to combine a data collection device, a local terminal, a server, and an artificial intelligence model on the cloud in order to support exercise while taking into account the user's mental and emotional health. The system collects the user's facial expressions, voice, location information, and biometric information, and based on this, provides personalized exercise feedback and mental support.

[0751] The server acquires biometric and location information of the exerciser from data collection devices. This information is collected in real time, for example, using sensors on smartphones or wearable devices. The collected data is preprocessed as needed and sent to an artificial intelligence model in the cloud in an appropriate format. Preprocessing steps may include noise reduction and data normalization.

[0752] The AI ​​model on the cloud processes data to evaluate the user's athletic ability and analyzes emotional data such as the user's facial expressions and voice. Based on the analyzed emotional data, the server generates an exercise menu that takes the user's emotional state into account. This suggests the optimal exercise load and relaxation method for the user.

[0753] The device provides visual and audible feedback to the user based on exercise evaluation results and emotion analysis results obtained from the server. For example, if the user is feeling stressed, it may suggest relaxation exercises and play music to reduce stress.

[0754] As a concrete example, here is an example of a prompt message:

[0755] "Based on the user's emotional data, design the optimal exercise routine and mental support plan for the day. The user appears to be feeling fatigued at the moment."

[0756] This allows users to train in a way that takes into account not only their physical health but also their mental health, which is expected to improve exercise efficiency and consistency.

[0757] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0758] Step 1:

[0759] The server acquires biometric information (e.g., heart rate, blood pressure) and location information of the exerciser from data collection devices (smartphones or wearable devices). At this point, the input is raw data from the sensors. The server preprocesses this data, such as removing noise, to format it in a way that is easy for the subsequent artificial intelligence model to process. The output is the preprocessed, clean data.

[0760] Step 2:

[0761] Using pre-processed data, the server runs an artificial intelligence model on the cloud. The inputs here are pre-processed biometric and location data. The server inputs this into the model to evaluate the exerciser's physical ability. The model analyzes the user's fitness level using machine learning algorithms. The output is the result data of the evaluated physical ability.

[0762] Step 3:

[0763] The server analyzes user emotion data (e.g., facial expressions, voice) obtained from the terminal or emotion engine. The input is raw data related to the user's emotions. The server uses an AI algorithm for emotion analysis to identify the emotional state and generates feedback based on that state. The output is the analyzed emotional state data.

[0764] Step 4:

[0765] The server integrates athletic performance assessment results and emotional state data to create an optimal exercise program and mental support plan for the user. Inputs are athletic performance data and emotional state data. Based on this, the server generates personalized exercise suggestions tailored to the user's condition on that day. Outputs are the created exercise program and support plan.

[0766] Step 5:

[0767] The terminal notifies the user of the exercise menu and mental support plan provided by the server. The input is the generated exercise menu and support plan. The terminal conveys information to the user visually (display) and aurally (voice guidance) and provides exercise instructions. The output is the exercise instructions presented to the user.

[0768] Step 6:

[0769] The user provides feedback according to the suggested exercise menu. This feedback is sent to the server via the terminal. The input is the user's feedback information. The server analyzes this feedback, modifies the exercise plan as needed, and incorporates it into the next training session. The output is the revised exercise plan.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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."

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0791] The following is further disclosed regarding the embodiments described above.

[0792] (Claim 1)

[0793] A means for collecting biometric and location information of an athlete from a data collection device and for pre-processing the data,

[0794] A means for running an artificial intelligence model to evaluate the athletic ability of athletes based on preprocessed data,

[0795] A means for providing evaluation results to a device for visually notifying the athlete,

[0796] A means of obtaining feedback from exercisers and modifying the exercise plan based on the evaluation results,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, further comprising means for predicting the potential injury risk of an athlete and generating instructions for reducing said risk.

[0800] (Claim 3)

[0801] The system according to claim 1, further comprising means for generating and transmitting information to a device for providing mental support to an athlete.

[0802] "Example 1"

[0803] (Claim 1)

[0804] A means for acquiring biological information and information related to movement from a device worn by an athlete, and for processing said information,

[0805] A means of using a generative artificial intelligence model to evaluate the athletic ability and health status of athletes based on processed information,

[0806] A means for transmitting evaluation results and improvement suggestions to a device for notifying the participant,

[0807] A means for obtaining responses from the exerciser and dynamically modifying the exercise plan based on the evaluation results,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, further comprising means for analyzing the potential health risks of an exerciser and generating suggestions for mitigating those risks.

[0811] (Claim 3)

[0812] The system according to claim 1, further comprising means for generating information to provide psychological support to an exerciser and transmitting said information to a display device.

[0813] "Application Example 1"

[0814] (Claim 1)

[0815] A means for collecting biometric and location information of an athlete from a data collection device and for pre-processing the data,

[0816] A means for running an artificial intelligence model to evaluate the athletic ability of athletes based on preprocessed data,

[0817] A means of providing the evaluation results to a terminal for visually notifying the athlete,

[0818] A means of obtaining feedback from exercisers and modifying the exercise plan based on the evaluation results,

[0819] A means of monitoring the health status of exercisers, suggesting optimal exercise plans, and providing information to maintain motivation,

[0820] A method for providing real-time exercise guidance in conjunction with home-use machines,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising means for predicting the potential injury risk of an exerciser, generating instructions to reduce said risk, and providing said instructions to the exerciser while the home machine is exercising.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising means for providing mental support to an exerciser, generating information to improve motivation for exercise, and transmitting it to a terminal.

[0826] "Example 2 of combining an emotion engine"

[0827] (Claim 1)

[0828] A means of analyzing the user's emotional state and performing emotion recognition,

[0829] A means for acquiring biometric and location information of an athlete from a data collection device and for preprocessing said data,

[0830] A means for running a machine learning model to evaluate the athletic ability of athletes based on preprocessed data,

[0831] A means to adjust the exercise plan and suggest appropriate relaxation methods to users when they experience stress during training,

[0832] Based on the analysis results, a means for generating motivational messages and psychological support information tailored to the psychological state of the exerciser, and displaying them on the device,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, further comprising means for predicting the potential injury risk and stress level of an athlete and generating instructions for reducing said risk and stress.

[0836] (Claim 3)

[0837] The system according to claim 1, further comprising means for generating and transmitting information to a device for providing psychological support to an exerciser.

[0838] "Application example 2 when combining with an emotional engine"

[0839] (Claim 1)

[0840] A means for collecting biometric and location information of an athlete from a data collection device and for pre-processing the data,

[0841] A means for running an artificial intelligence model to evaluate the athletic ability of athletes based on preprocessed data,

[0842] A means for analyzing the user's emotional state and generating information to adjust the exercise menu based on the analysis results,

[0843] A means for providing evaluation results and emotion analysis results to a device for visually notifying the person performing the action,

[0844] A means of obtaining feedback from the exerciser and modifying the exercise plan based on the evaluation and analysis results,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, further comprising means for predicting the potential injury risk of an athlete and generating instructions for reducing said risk.

[0848] (Claim 3)

[0849] The system according to claim 1, further comprising means for generating and transmitting information to a device for providing mental support to an athlete. [Explanation of Symbols]

[0850] 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. A means for collecting biometric and location information of an athlete from a data collection device and for pre-processing the data, A means for running an artificial intelligence model to evaluate the athletic ability of athletes based on preprocessed data, A means of providing the evaluation results to a terminal for visually notifying the athlete, A means of obtaining feedback from exercisers and modifying the exercise plan based on the evaluation results, A means of monitoring the health status of exercisers, suggesting optimal exercise plans, and providing information to maintain motivation, A method for providing real-time exercise guidance in conjunction with home-use machines, A system that includes this.

2. The system according to claim 1, further comprising means for predicting the potential injury risk of an exerciser, generating instructions to reduce said risk, and providing these instructions to the exerciser while the home machine is exercising.

3. The system according to claim 1, further comprising means for providing mental support to an exerciser, generating information to improve motivation for exercise, and transmitting it to a terminal.

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