system

Abstract

We provide the system. [Solution] An input means for collecting user exercise data in real time, A generation means that analyzes the aforementioned exercise data and generates a training plan optimized for the user, An output means for generating a virtual opponent and displaying it to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern busy lifestyles and with limited resources, it is difficult for individuals to conduct effective and optimal sports training. In conventional training methods, guidance according to individual needs is often insufficient, and there is also a problem that motivation is difficult to maintain due to the absence of a training partner. The present invention aims to solve these problems, enable training and feedback suitable for each user, and contribute to skill improvement and injury prevention.

Means for Solving the Problems

[0005] This invention provides an input means using sensors to collect user exercise data in real time, and a generation means that provides a training plan optimized for the user based on the obtained data. Furthermore, a virtual opponent is displayed by an output means, simulating an actual match environment to enhance training effectiveness and improve motivation. In addition, the generation means analyzes the user's past exercise data to analyze continuous improvement and provides feedback, thereby providing a system that supports more effective training.

[0006] A "user" refers to an individual who uses the system to perform sports training.

[0007] "Exercise data" refers to information about a user's physical activity, including data such as movement patterns, speed, and heart rate.

[0008] "Real-time" refers to data being processed almost instantly after it is collected and then fed back to the user.

[0009] "Input means" refers to a collection of interfaces and sensors used to acquire user movement data.

[0010] "Generation means" refers to software or systems that perform analysis based on input exercise data and provide functions to generate training plans and virtual opponents.

[0011] A "training plan" refers to a program or guideline that outlines the steps of exercise and practice, tailored to the user's skills and health condition.

[0012] A "virtual opponent" refers to a computer-generated opponent that allows users to compete during training.

[0013] "Output means" refers to devices or interfaces that provide users with generated training plans and virtual opponent information visually or audibly.

[0014] "Continuous improvement" refers to the process of analyzing user performance over time and adjusting or optimizing training plans to achieve even more effective results. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a system for users to effectively perform sports training, collecting data in real time and providing an optimized training plan and virtual opponent based on that data.

[0037] Specifically, the device collects exercise data from various sensors attached to the user. This exercise data includes physiological data such as location information, speed, acceleration, and heart rate, which can be used to evaluate the user's current exercise capacity.

[0038] The server receives this data in real time and analyzes it using a generating AI. Based on the analysis results, it generates a training plan specifically optimized for the user's athletic ability and training needs. In parallel, it quantitatively evaluates the user's performance based on historical data and provides feedback for continuous improvement.

[0039] The device visually displays the generated training plan to the user. Furthermore, a virtual opponent is visualized, allowing the user to train in a way that simulates actual competition. This training environment is crucial for increasing user motivation and preparing them for actual matches.

[0040] As a concrete example, consider running training. When a user puts on the device and starts running, the device collects movement data and sends it to a server. Based on the data received, the server determines whether the user is maintaining an appropriate pace and whether their form needs improvement, and then creates an appropriate training plan. A virtual pacer is visualized, and the user can train efficiently by following along with it. After the training is complete, the device provides the user with performance feedback for the day, allowing them to adjust the content of their next training session as needed.

[0041] In this way, this system provides users with a highly effective training environment and supports skill improvement tailored to their individual needs.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user puts on the device and starts training. The device uses its built-in sensors to acquire the user's exercise data (e.g., location, speed, acceleration, heart rate, etc.) in real time.

[0045] Step 2:

[0046] The device transmits real-time motion data to the server via a communication module. The data is encrypted and securely transferred.

[0047] Step 3:

[0048] The server analyzes the received motion data. This analysis includes pattern recognition of movements, form evaluation, and performance evaluation using generative AI.

[0049] Step 4:

[0050] The server generates an optimized training plan based on the user's fitness level and needs, using analysis results. The plan includes specific exercises and their intensity.

[0051] Step 5:

[0052] The server sends the generated training plan to the terminal. The terminal then visually presents the training plan to the user.

[0053] Step 6:

[0054] The server generates a virtual opponent based on the user's past data. The opponent's data and behavior patterns are sent to the terminal, creating a virtual match environment.

[0055] Step 7:

[0056] The user exercises according to a training plan and competes against a virtual opponent. The device continues to collect exercise data and sends it back to the server.

[0057] Step 8:

[0058] After the training is complete, the server re-analyzes the collected data and sends the results to the terminal. The results include progress evaluation and suggestions for future improvements.

[0059] Step 9:

[0060] Users review feedback and prepare for their next training session. This process enables continuous skill improvement and health management.

[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] Traditional training systems struggle to provide optimal training plans that fully consider each user's individual physiological state and exercise history. Furthermore, they lack mechanisms to maintain user motivation while providing real-time feedback and health indicators, limiting the potential for improving training effectiveness.

[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 input means including sensors for collecting physiological states in real time, generation means for analyzing the physiological states and generating a training plan tailored to the user, output means for generating a virtual competition and presenting it visually to the user, and means for visualizing progress using data generated in real time. This enables the provision of a personalized training plan tailored to the user, while also providing real-time experiences and feedback to enhance motivation.

[0066] "Physiological state" refers to information that indicates the functional state of the user's body, and includes physiological data such as heart rate, respiratory rate, and body temperature.

[0067] "Input means" refers to a collection of devices and sensors used to acquire the user's physiological state and exercise data in real time.

[0068] "Generation means" refers to the process or device for creating a training plan or virtual competition environment optimized for the user based on acquired physiological state and exercise data.

[0069] "Output means" refers to displays or interfaces used to visually present generated training plans and virtual competition environments to users.

[0070] "Virtual competition" refers to a virtual opponent or simulation environment designed to allow users to experience a simulated real-world competition.

[0071] "Means of visualizing progress" refers to visual tools or mechanisms for displaying a user's training progress in real time.

[0072] This invention is a system that collects data in real time when a user exercises and provides an optimized training plan and virtual competition environment based on that data. This system uses various sensors that the user attaches to a device. These include GPS sensors, accelerometers, and heart rate sensors, which acquire the user's location information, speed, acceleration, heart rate, and other physiological states.

[0073] The device processes information obtained from sensors and transmits the data to the server in real time using a highly secure communication protocol. The server analyzes the received data using a generating AI model and, combined with the user's past exercise data, generates an optimal training plan. This training plan includes setting the pace, rest times, and heart rate targets.

[0074] The server also generates a virtual competition environment in which users can train against virtual opponents. This allows users to improve the quality of their training while simulating real-world competition.

[0075] The device visually presents the user with a training plan and virtual competition environment based on the analysis results. This allows users to check their training progress in real time, making it easier to maintain motivation. After training, the device also provides the user with detailed feedback and suggests areas for improvement in the next training session. This feedback supports effective training and the user's sustained growth.

[0076] As a concrete example, let's consider a scenario where a user is running. When the user puts on the device and starts running, the device collects location information, speed, heart rate, etc., in real time and sends it to the server. The server uses a generative AI model to analyze this data and generate a running plan that includes suggestions for the most effective pace and form improvements for the user. In this process, a virtual pacemaker and opponent are visualized, allowing the user to train effectively by following along with them.

[0077] An example of a prompt for a generative AI model might be: "Based on the user's most recent exercise data, please suggest the optimal pace and distance for the next running training session." This system aims to provide training tailored to the user's specific needs and goals, striving for sustained fitness improvement and maximizing performance.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device collects exercise-related data from various sensors attached to the user. Inputs include location information from the GPS sensor, speed and acceleration from the accelerometer, and heart rate from the heart rate sensor. Based on this information, the data is integrated to understand the user's real-time physiological and physical state.

[0081] Step 2:

[0082] The terminal transmits integrated data to the server in real time. Collected physiological and motor data are used as input, and the data is transmitted using an encrypted, secure communication protocol. The output is raw data, enabling immediate analysis on the server.

[0083] Step 3:

[0084] The server analyzes the received data using a generating AI model. This process uses historical data and the latest real-time data as input. As part of the data processing, the AI ​​model evaluates the user's current performance and proposes an optimal training plan. As output, a customized training plan tailored to the user's athletic ability is generated.

[0085] Step 4:

[0086] The server constructs a virtual competition environment based on the generated training plan. The generated training plan is used as input. It then generates data for virtual opponents and pacemakers. The output is a virtual competition environment that the user follows.

[0087] Step 5:

[0088] The terminal visually presents the user with a training plan and virtual competition environment received from the server. The input uses data from the server, and a user-friendly UI operates. The output displays training content that the user can review in real time and act upon.

[0089] Step 6:

[0090] As the user trains, actual movements progress, and the device collects this data again. New physiological data from the user during training is acquired as input, and the collected data is resent to the server. Detailed feedback on the training is prepared as output.

[0091] Step 7:

[0092] The server analyzes the newly acquired training data and generates detailed feedback for the user. The most recent training data is used as input. The output includes an assessment of achievement, areas for improvement in the next session, and advice regarding health status.

[0093] (Application Example 1)

[0094] 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."

[0095] In modern society, there is a demand for providing personalized sports training plans in real time and efficiently improving their effectiveness. However, conventional technologies have made it difficult to comprehensively consider users' physical data and physiological state when planning, making it impossible to provide individually optimized training. Furthermore, there has been a lack of means to effectively support user motivation and performance improvement by creating a virtual competitive environment.

[0096] 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.

[0097] In this invention, the server includes acquisition means for acquiring the user's biometric data in a time series, creation means for analyzing the biometric data and creating a plan optimized for the user, and presentation means for generating and presenting virtual opponents to the user. As a result, the user can receive a real-time, individually optimized training plan and train efficiently while maintaining high motivation through competition with virtual opponents.

[0098] "User biometric data" refers to time-series and dynamic information obtained from the user's body, including data that shows the body's movements and physiological state during exercise.

[0099] "Acquisition means" refers to a function or device for collecting a user's biometric data over time using various sensors and devices.

[0100] "Analysis" refers to the act of processing and analyzing data based on acquired biometric data to evaluate the user's current state and athletic ability.

[0101] An "optimized plan" is a plan that includes the most suitable training content, designed to support each user's individual athletic ability and goal achievement.

[0102] "Creation method" refers to a function or process for constructing an optimal training plan for the user based on collected biometric data.

[0103] A "virtual opponent" is a virtual opponent generated to support the user's training and promote performance improvement.

[0104] A "presentation means" is a device or part of a device that provides information to a user visually and has the function of transmitting information to the user through a display device.

[0105] This system is designed to help users effectively perform sports training. The device worn by the user is equipped with various sensors that acquire the user's biometric data in real time. The acquired data includes information that represents the user's physical condition and performance status, such as heart rate, speed, acceleration, and location information.

[0106] Data transmitted from the device is received by a server in the cloud and analyzed using a dedicated generative AI model. This analysis utilizes Python libraries such as Pandas and NumPy, as well as TENSORFLOW®, to perform data processing and calculations. Based on the analysis results, the server creates an optimal plan for each user and generates a virtual opponent. This allows users to train with higher motivation through virtual competition against their opponents.

[0107] The information provided to the user is delivered visually to the device's display. The generated plan and information about virtual opponents are presented to the user through a display device such as a smartphone or smart glasses. For example, during marathon training, using smart glasses allows the user to visualize a virtual pacer, enabling more efficient training. After training, a performance evaluation is conducted, and feedback is provided for the next training session.

[0108] An example of a prompt to the generating AI model is, "Based on user A's running data, please suggest the optimal pace and advice for improving form." This enables customized guidance for the user.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user puts on the device and begins exercising. Sensors built into the device acquire the user's biometric data in real time. This data includes heart rate, speed, acceleration, and location information. The output from the sensors is temporarily stored as digital data in the device's memory.

[0112] Step 2:

[0113] The device transmits the acquired biometric data to a server in the cloud. A communication protocol (e.g., HTTPS) is used for data transmission. The data includes time-series information, specifically data on changes in each parameter. After transmission, the device waits for the analysis results from the server.

[0114] Step 3:

[0115] The server uses a generative AI model to analyze the received biometric data. It performs data processing and preprocessing using Python libraries such as Pandas and NumPy. After preprocessing, an AI model using TensorFlow performs data analysis to evaluate the user's exercise performance. The analysis output generates a training plan optimized for the user and performance evaluation results.

[0116] Step 4:

[0117] The server visualizes the generated training plan and information about the virtual opponent. The plan includes a training schedule, target pace, and advice for improving form. The virtual opponent is visualized as a benchmark that can be compared to the user's current pace.

[0118] Step 5:

[0119] The server transmits visualized information to the terminal. The terminal provides this information to the user using a display device (smartphone or smart glasses). The user continues training based on the visual information and improves their performance through competition with virtual opponents.

[0120] Step 6:

[0121] After the training is complete, the device sends the user's final performance data back to the server. The server analyzes this data as new feedback and uses it to adjust the plan for the next training session. The feedback specifically shows the user's progress and areas for improvement, providing guidance for the next steps.

[0122] 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.

[0123] This invention is a sports training support system that incorporates an emotion engine to make the user's training experience more personalized. In addition to exercise data and physiological data, this system recognizes the user's emotional state in real time and provides training plans and feedback accordingly.

[0124] Specifically, the device acquires emotional data from the user's facial expressions and voice through an emotion engine. This data is sent to the server along with the user's exercise and physiological data.

[0125] The server analyzes emotional data to determine the user's emotional state and uses generative AI to adjust the training plan accordingly. For example, if the user is feeling stressed, it can incorporate exercises that help them relax.

[0126] Furthermore, the virtual opponent's behavior also changes dynamically based on emotional data. The device adjusts the opponent's difficulty level and reactions according to the user's emotions, providing an environment that allows the user to concentrate more easily.

[0127] As a concrete example, consider a user practicing yoga. If the user is excessively tense, the device's emotion engine detects this state. The server receives and analyzes this information and generates a training plan that incorporates breathing techniques to relieve tension. Additionally, a virtual opponent acts as a guide, demonstrating slow movements to support the user in progressing at their own pace.

[0128] In this way, by taking the user's emotional state into consideration, this system can provide an effective training environment that maintains a balance between mind and body. As a result, users can enjoy a more fulfilling sports experience.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user puts on the device and begins training. The device uses its built-in sensors to acquire the user's exercise data and emotional data (facial expressions and voice) in real time.

[0132] Step 2:

[0133] The device compresses the exercise, emotional, and physiological data acquired in real time and sends it to the server. The data is encrypted using a secure protocol before transmission.

[0134] Step 3:

[0135] The server analyzes the received motion and emotional data. Using generative AI, it recognizes emotional states in addition to evaluating movements, and determines the user's mental state.

[0136] Step 4:

[0137] The server generates an optimized training plan based on the analysis results, adapted to the user's athletic ability, emotional state, and health condition. Users can select relaxation plans or concentration-enhancing plans as needed.

[0138] Step 5:

[0139] The server sends the generated training plan and data on a virtual opponent that dynamically changes according to the user's emotions to the terminal. The terminal then presents the training plan and virtual opponent to the user visually or audibly.

[0140] Step 6:

[0141] The user executes a training plan and competes against a virtual opponent. The device continuously collects data during exercise and transmits it to the server in real time.

[0142] Step 7:

[0143] After the training is complete, the server re-analyzes all the collected data to evaluate the user's progress. It then generates feedback to help improve future training sessions.

[0144] Step 8:

[0145] The device provides feedback to the user from the server. Based on this feedback, the user becomes aware of improvements in their emotional state and athletic ability, and prepares for the next training session.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] Traditional training systems focused solely on the user's athletic performance and failed to provide a training experience that took the user's emotional state into account. As a result, users found it difficult to reduce the psychological and emotional burden during training, and the effectiveness of the training could not be optimized. Furthermore, virtual opponents only provided fixed movements and feedback, and did not offer dynamic responses that corresponded to the user's emotional changes.

[0149] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0150] In this invention, the server includes information acquisition means for collecting user exercise data and emotional data in real time; generation AI means for analyzing the exercise data and emotional data and generating an optimized training plan according to the user's emotional state; and output means for generating a virtual opponent whose behavior dynamically changes based on the emotional state and displaying it to the user. This makes it possible to provide a more personalized training experience based on both the user's emotional state and exercise performance, and to maximize the efficiency of training.

[0151] "Information acquisition means" refers to a device or method for collecting user motor data and emotional data in real time.

[0152] "Generative AI methods" refer to methods that utilize artificial intelligence technology to analyze collected exercise and emotional data and dynamically generate training plans optimized for the user based on that analysis.

[0153] "Output means" refers to a device or method for visually or audibly presenting the generated training plan and the actions of the virtual opponent to the user.

[0154] "Emotional data" refers to data collected from a user's facial expressions, voice, and other sources that indicates the user's psychological and emotional state.

[0155] A "virtual opponent" is a digital opponent generated by a computer or system during a user's training, capable of dynamically responding to the user's emotional state.

[0156] This invention is an advanced training system that provides personalized training plans by utilizing the user's exercise data and emotional data.

[0157] The device interprets the user's facial expressions and voice using an emotion engine and acquires emotional data in real time. It also collects exercise and physiological data from wearable sensors. This utilizes emotion recognition technology using cameras and microphones, as well as accelerometers and heart rate monitors. Specific software includes OpenCV and the Emotion SDK for facial recognition, and libraries for voice analysis.

[0158] The device transmits the collected data to a server via the internet. This data is recorded in a database on the server and analyzed by a generative AI model. The AI ​​model includes deep learning algorithms built using, for example, TensorFlow or PyTorch. This model identifies the user's psychological state and customizes the training plan based on that.

[0159] The server generates an exercise plan tailored to the user by prompting the AI ​​with instructions such as, "Suggest a suitable training method if the user is not relaxed."

[0160] For example, if a user is determined to be experiencing stress, the server creates a plan that includes relaxation exercises and sends it to the user's device. The user interacts with a virtual opponent on the device, and the opponent adjusts its movements according to the user's state, reducing the burden and providing a training environment that makes it easier to concentrate.

[0161] Through these actions, users can enjoy a personalized training experience tailored to their emotional state, enabling them to more effectively maximize the benefits of their exercise.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] When a user begins training, the device activates its emotion engine and uses the camera and microphone to acquire the user's facial and audio data. The input is the user's real-time video feed and audio stream, and the output is emotion data based on this. The emotion engine analyzes, for example, subtle changes in the user's face and tone of voice to identify the user's current emotional state (e.g., stress or tension).

[0165] Step 2:

[0166] The terminal collects emotional data along with motor data (e.g., motion measurements from an accelerometer) and physiological data (e.g., heart rate), packets all the data, and sends it to the server. The input consists of the collected emotional data, motor data, and physiological data, and the output is a single integrated data packet containing all of this data. The terminal encrypts this packet and sends it to the server via a secure communication channel.

[0167] Step 3:

[0168] The server decodes the received data packets and activates the generative AI model. The input is encrypted data packets, and the output is decoded emotional and motor state data. The server applies a deep learning algorithm to analyze the user's emotional state and provides that information to the AI ​​model in the form of a prompt. For example, it might command the AI ​​model to "provide the optimal exercise for when the user is feeling stressed."

[0169] Step 4:

[0170] The generative AI model generates an appropriate training plan based on the input prompt text. The input consists of prompt text and parsed data, and the output is a customized training plan. This plan includes exercises and relaxation techniques that take emotional states into account, resulting in training content that is most suitable for the user.

[0171] Step 5:

[0172] The server sends the generated training plan to the terminal. The input is the generated training plan, and the output is the transmission of its contents to the terminal. The terminal displays the plan to the user and adjusts the movements of the virtual opponent. The opponent acts according to this plan, providing guidance for the user's training.

[0173] Step 6:

[0174] As the user continues training, the device periodically uses an emotion engine to re-evaluate the user's state. Real-time facial and audio data from the training are input, and the updated emotional state is output. The device sends this data back to the server, which re-analyzes it and modifies the training plan if necessary.

[0175] (Application Example 2)

[0176] 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".

[0177] Traditional training systems generate training plans based solely on the user's exercise and physiological data, failing to take into account the user's emotional state and making it difficult to provide a sufficiently personalized service. Therefore, there is a need to reduce the user's psychological burden, maintain motivation, and provide a more effective training experience.

[0178] 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.

[0179] In this invention, the server includes input means for recognizing the user's emotional state in real time, generation means for generating a personalized training plan based on the emotional state, motor data, and physiological data, and output means for adjusting and displaying the actions of a virtual opponent. This makes it possible to provide personalized feedback and a training environment that responds to the user's emotional state.

[0180] "User emotional state" refers to information that shows the user's psychological and emotional state in real time.

[0181] "Input means" refers to a device or method for collecting the user's emotional state, motor data, physiological data, etc.

[0182] "Generation means" refers to an apparatus or method for creating a training plan suitable for a user based on collected data.

[0183] A "virtual opponent" is a computer-generated character or model that operates as if competing or cooperating with the user during user training.

[0184] "Output means" refers to a device or method for presenting a generated training plan or virtual opponent to the user.

[0185] The system that realizes this invention includes multiple hardware and software components to collect the user's emotional state, motor data, and physiological data, and to generate a training plan and feedback based on them.

[0186] First, the device uses built-in sensors (such as cameras and microphones) to identify the user's emotional state in real time from their facial expressions and speech data. This process typically utilizes the Emotion API, which allows for accurate capture of the user's psychological state. In addition, biometric sensors are used to collect motor and physiological data.

[0187] Next, this data is sent to the server. The server uses a generative AI model (for example, OpenAI®'s GPT series) to generate a training plan optimized for the user's current situation. The generated prompts may include questions such as, "If the user is feeling stressed, what relaxation exercises should be recommended?"

[0188] Furthermore, the server dynamically adjusts the actions of the virtual opponent according to the user's emotional state. This is visualized to the user through a VR display or smartphone screen, providing an immersive training experience. This entire process allows the user to enjoy a highly personalized fitness experience tailored to their emotional state at any given time.

[0189] For example, if a user is observed to lose focus during a fast-paced exercise, the system will suggest slowing down and provide real-time feedback to encourage the user to adjust their pace. An example of a prompt using a generative AI model is, "How can you maintain motivation if the user feels fatigued midway through?"

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The device acquires video and audio data of the user's face in real time using its camera and microphone. This input data is sent to the Emotion API to analyze the user's emotional state. The output is the user's emotion tag (e.g., relaxed, stressed, focused).

[0193] Step 2:

[0194] The device uses exercise sensors and biometric sensors to collect the user's exercise data (e.g., heart rate, movement speed) and physiological data. This data is sent directly to the server and used to customize the training plan.

[0195] Step 3:

[0196] The server uses a generative AI model based on received emotional state, exercise data, and physiological data to generate a training plan tailored to the user's current condition. The input is the prompt "If the user is feeling stressed, what relaxation exercises should be recommended?". The output generates specific exercise content.

[0197] Step 4:

[0198] The server adjusts the virtual opponent's behavior based on the generated training plan. It uses prompts that correspond to the user's emotional state to set the opponent's movements and reaction speed. As a result, it provides an environment with an appropriate difficulty level for the user.

[0199] Step 5:

[0200] The device displays a customized training plan and a virtual opponent to the user. Through the display screen and audio output, it provides real-time feedback, showing the user the training content and the opponent's movements.

[0201] Step 6:

[0202] If the user's emotional state or performance changes during training, the device collects new data again and sends it to the server. The server then updates the training plan in real time as needed, continuously providing the optimal training experience.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention is a system for users to effectively perform sports training, collecting data in real time and providing an optimized training plan and virtual opponent based on that data.

[0220] Specifically, the device collects exercise data from various sensors attached to the user. This exercise data includes physiological data such as location information, speed, acceleration, and heart rate, which can be used to evaluate the user's current exercise capacity.

[0221] The server receives this data in real time and analyzes it using a generating AI. Based on the analysis results, it generates a training plan specifically optimized for the user's athletic ability and training needs. In parallel, it quantitatively evaluates the user's performance based on historical data and provides feedback for continuous improvement.

[0222] The device visually displays the generated training plan to the user. Furthermore, a virtual opponent is visualized, allowing the user to train in a way that simulates actual competition. This training environment is crucial for increasing user motivation and preparing them for actual matches.

[0223] As a concrete example, consider running training. When a user puts on the device and starts running, the device collects movement data and sends it to a server. Based on the data received, the server determines whether the user is maintaining an appropriate pace and whether their form needs improvement, and then creates an appropriate training plan. A virtual pacer is visualized, and the user can train efficiently by following along with it. After the training is complete, the device provides the user with performance feedback for the day, allowing them to adjust the content of their next training session as needed.

[0224] In this way, this system provides users with a highly effective training environment and supports skill improvement tailored to their individual needs.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The user puts on the device and starts training. The device uses its built-in sensors to acquire the user's exercise data (e.g., location, speed, acceleration, heart rate, etc.) in real time.

[0228] Step 2:

[0229] The device transmits real-time motion data to the server via a communication module. The data is encrypted and securely transferred.

[0230] Step 3:

[0231] The server analyzes the received motion data. This analysis includes pattern recognition of movements, form evaluation, and performance evaluation using generative AI.

[0232] Step 4:

[0233] The server generates an optimized training plan based on the user's fitness level and needs, using analysis results. The plan includes specific exercises and their intensity.

[0234] Step 5:

[0235] The server sends the generated training plan to the terminal. The terminal then visually presents the training plan to the user.

[0236] Step 6:

[0237] The server generates a virtual opponent based on the user's past data. The opponent's data and behavior patterns are sent to the terminal, creating a virtual match environment.

[0238] Step 7:

[0239] The user exercises according to a training plan and competes against a virtual opponent. The device continues to collect exercise data and sends it back to the server.

[0240] Step 8:

[0241] After the training is complete, the server re-analyzes the collected data and sends the results to the terminal. The results include progress evaluation and suggestions for future improvements.

[0242] Step 9:

[0243] Users review feedback and prepare for their next training session. This process enables continuous skill improvement and health management.

[0244] (Example 1)

[0245] 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."

[0246] Traditional training systems struggle to provide optimal training plans that fully consider each user's individual physiological state and exercise history. Furthermore, they lack mechanisms to maintain user motivation while providing real-time feedback and health indicators, limiting the potential for improving training effectiveness.

[0247] 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.

[0248] In this invention, the server includes input means including sensors for collecting physiological states in real time, generation means for analyzing the physiological states and generating a training plan tailored to the user, output means for generating a virtual competition and presenting it visually to the user, and means for visualizing progress using data generated in real time. This enables the provision of a personalized training plan tailored to the user, while also providing real-time experiences and feedback to enhance motivation.

[0249] "Physiological state" refers to information that indicates the functional state of the user's body, and includes physiological data such as heart rate, respiratory rate, and body temperature.

[0250] "Input means" refers to a collection of devices and sensors used to acquire the user's physiological state and exercise data in real time.

[0251] "Generation means" refers to the process or device for creating a training plan or virtual competition environment optimized for the user based on acquired physiological state and exercise data.

[0252] "Output means" refers to displays or interfaces used to visually present generated training plans and virtual competition environments to users.

[0253] "Virtual competition" refers to a virtual opponent or simulation environment designed to allow users to experience a simulated real-world competition.

[0254] "Means of visualizing progress" refers to visual tools or mechanisms for displaying a user's training progress in real time.

[0255] This invention is a system that collects data in real time when a user exercises and provides an optimized training plan and virtual competition environment based on that data. This system uses various sensors that the user attaches to a device. These include GPS sensors, accelerometers, and heart rate sensors, which acquire the user's location information, speed, acceleration, heart rate, and other physiological states.

[0256] The device processes information obtained from sensors and transmits the data to the server in real time using a highly secure communication protocol. The server analyzes the received data using a generating AI model and, combined with the user's past exercise data, generates an optimal training plan. This training plan includes setting the pace, rest times, and heart rate targets.

[0257] The server also generates a virtual competition environment in which users can train against virtual opponents. This allows users to improve the quality of their training while simulating real-world competition.

[0258] The device visually presents the user with a training plan and virtual competition environment based on the analysis results. This allows users to check their training progress in real time, making it easier to maintain motivation. After training, the device also provides the user with detailed feedback and suggests areas for improvement in the next training session. This feedback supports effective training and the user's sustained growth.

[0259] As a concrete example, let's consider a scenario where a user is running. When the user puts on the device and starts running, the device collects location information, speed, heart rate, etc., in real time and sends it to the server. The server uses a generative AI model to analyze this data and generate a running plan that includes suggestions for the most effective pace and form improvements for the user. In this process, a virtual pacemaker and opponent are visualized, allowing the user to train effectively by following along with them.

[0260] An example of a prompt for a generative AI model might be: "Based on the user's most recent exercise data, please suggest the optimal pace and distance for the next running training session." This system aims to provide training tailored to the user's specific needs and goals, striving for sustained fitness improvement and maximizing performance.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The device collects exercise-related data from various sensors attached to the user. Inputs include location information from the GPS sensor, speed and acceleration from the accelerometer, and heart rate from the heart rate sensor. Based on this information, the data is integrated to understand the user's real-time physiological and physical state.

[0264] Step 2:

[0265] The terminal transmits integrated data to the server in real time. Collected physiological and motor data are used as input, and the data is transmitted using an encrypted, secure communication protocol. The output is raw data, enabling immediate analysis on the server.

[0266] Step 3:

[0267] The server analyzes the received data using a generating AI model. This process uses historical data and the latest real-time data as input. As part of the data processing, the AI ​​model evaluates the user's current performance and proposes an optimal training plan. As output, a customized training plan tailored to the user's athletic ability is generated.

[0268] Step 4:

[0269] The server constructs a virtual competition environment based on the generated training plan. The generated training plan is used as input. It then generates data for virtual opponents and pacemakers. The output is a virtual competition environment that the user follows.

[0270] Step 5:

[0271] The terminal visually presents the user with a training plan and virtual competition environment received from the server. The input uses data from the server, and a user-friendly UI operates. The output displays training content that the user can review in real time and act upon.

[0272] Step 6:

[0273] As the user trains, actual movements progress, and the device collects this data again. New physiological data from the user during training is acquired as input, and the collected data is resent to the server. Detailed feedback on the training is prepared as output.

[0274] Step 7:

[0275] The server analyzes the newly acquired training data and generates detailed feedback for the user. The most recent training data is used as input. The output includes an assessment of achievement, areas for improvement in the next session, and advice regarding health status.

[0276] (Application Example 1)

[0277] 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 glasses 214 will be referred to as the "terminal."

[0278] In modern society, there is a demand for providing personalized sports training plans in real time and efficiently improving their effectiveness. However, conventional technologies have made it difficult to comprehensively consider users' physical data and physiological state when planning, making it impossible to provide individually optimized training. Furthermore, there has been a lack of means to effectively support user motivation and performance improvement by creating a virtual competitive environment.

[0279] 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.

[0280] In this invention, the server includes an acquisition means for acquiring the user's biological data in chronological order, a creation means for analyzing the biological data and creating a plan optimized for the user, and a presentation means for generating a virtual competition opponent and presenting it to the user. As a result, the user can receive a real-time and individually optimized training plan, and through competition with the virtual competition opponent, it becomes possible to perform efficient training while maintaining high motivation.

[0281] The "user's biological data" is time-series and dynamic information acquired from the user's body, and is data indicating the movement and physiological state of the body during exercise.

[0282] The "acquisition means" is a function or device for collecting the user's biological data in chronological order using various sensors and devices.

[0283] "Analysis" is an act of performing data processing and analysis for evaluating the user's current state and exercise ability based on the acquired biological data.

[0284] The "optimized plan" is a plan including optimal training content designed to support the user's individual exercise ability and goal achievement.

[0285] The "creation means" is a function or process for constructing an optimal training plan for the user based on the collected biological data.

[0286] The "virtual competition opponent" is a virtual opponent generated to support the user's training and promote performance improvement.

[0287] The "presentation means" is a device or part of a device that visually provides information to the user and has a function of transmitting information to the user through a display device.

[0288] This system is designed to help users effectively perform sports training. The device worn by the user is equipped with various sensors that acquire the user's biometric data in real time. The acquired data includes information that represents the user's physical condition and performance status, such as heart rate, speed, acceleration, and location information.

[0289] Data sent from the device is received by a server in the cloud and analyzed using a dedicated generative AI model. This analysis utilizes Python libraries such as Pandas, NumPy, and TensorFlow to perform data processing and calculations. Based on the analysis results, the server creates an optimal plan for each user and generates a virtual opponent. This allows users to train with higher motivation through virtual competition against their opponents.

[0290] The information provided to the user is delivered visually to the device's display. The generated plan and information about virtual opponents are presented to the user through a display device such as a smartphone or smart glasses. For example, during marathon training, using smart glasses allows the user to visualize a virtual pacer, enabling more efficient training. After training, a performance evaluation is conducted, and feedback is provided for the next training session.

[0291] An example of a prompt to the generating AI model is, "Based on user A's running data, please suggest the optimal pace and advice for improving form." This enables customized guidance for the user.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The user puts on the device and begins exercising. Sensors built into the device acquire the user's biometric data in real time. This data includes heart rate, speed, acceleration, and location information. The output from the sensors is temporarily stored as digital data in the device's memory.

[0295] Step 2:

[0296] The device transmits the acquired biometric data to a server in the cloud. A communication protocol (e.g., HTTPS) is used for data transmission. The data includes time-series information, specifically data on changes in each parameter. After transmission, the device waits for the analysis results from the server.

[0297] Step 3:

[0298] The server uses a generative AI model to analyze the received biometric data. It performs data processing and preprocessing using Python libraries such as Pandas and NumPy. After preprocessing, an AI model using TensorFlow performs data analysis to evaluate the user's exercise performance. The analysis output generates a training plan optimized for the user and performance evaluation results.

[0299] Step 4:

[0300] The server visualizes the generated training plan and information about the virtual opponent. The plan includes a training schedule, target pace, and advice for improving form. The virtual opponent is visualized as a benchmark that can be compared to the user's current pace.

[0301] Step 5:

[0302] The server transmits visualized information to the terminal. The terminal provides this information to the user using a display device (smartphone or smart glasses). The user continues training based on the visual information and improves their performance through competition with virtual opponents.

[0303] Step 6:

[0304] After the training is completed, the terminal resends the user's final performance data to the server. The server analyzes this data as new feedback and uses it to adjust the plan for the next training. The feedback specifically shows the user's growth status and areas for improvement, and provides guidance for the next step.

[0305] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0306] The present invention is a sports training support system incorporating an emotion engine to make the user's training experience more personalized. This system recognizes the user's emotional state in real time in addition to motion data and physiological data, and provides a training plan and feedback accordingly.

[0307] Specifically, the terminal obtains emotion data from the user's expression and voice through the emotion engine. This data is transmitted to the server together with the user's motion data and physiological data.

[0308] The server determines the user's emotional state by analyzing the emotion data and adjusts the training plan according to that state using a generative AI. For example, when the user is feeling stressed, it is possible to incorporate exercises that can relax.

[0309] Also, the actions of the virtual opponent also change dynamically based on the emotion data. The terminal adjusts the difficulty level and reaction of the opponent according to the user's emotion, and provides an environment in which the user can concentrate more easily.

[0310] As a concrete example, consider a user practicing yoga. If the user is excessively tense, the device's emotion engine detects this state. The server receives and analyzes this information and generates a training plan that incorporates breathing techniques to relieve tension. Additionally, a virtual opponent acts as a guide, demonstrating slow movements to support the user in progressing at their own pace.

[0311] In this way, by taking the user's emotional state into consideration, this system can provide an effective training environment that maintains a balance between mind and body. As a result, users can enjoy a more fulfilling sports experience.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The user puts on the device and begins training. The device uses its built-in sensors to acquire the user's exercise data and emotional data (facial expressions and voice) in real time.

[0315] Step 2:

[0316] The device compresses the exercise, emotional, and physiological data acquired in real time and sends it to the server. The data is encrypted using a secure protocol before transmission.

[0317] Step 3:

[0318] The server analyzes the received motion and emotional data. Using generative AI, it recognizes emotional states in addition to evaluating movements, and determines the user's mental state.

[0319] Step 4:

[0320] The server generates an optimized training plan based on the analysis results, adapted to the user's athletic ability, emotional state, and health condition. Users can select relaxation plans or concentration-enhancing plans as needed.

[0321] Step 5:

[0322] The server sends the generated training plan and data on a virtual opponent that dynamically changes according to the user's emotions to the terminal. The terminal then presents the training plan and virtual opponent to the user visually or audibly.

[0323] Step 6:

[0324] The user executes a training plan and competes against a virtual opponent. The device continuously collects data during exercise and transmits it to the server in real time.

[0325] Step 7:

[0326] After the training is complete, the server re-analyzes all the collected data to evaluate the user's progress. It then generates feedback to help improve future training sessions.

[0327] Step 8:

[0328] The device provides feedback to the user from the server. Based on this feedback, the user becomes aware of improvements in their emotional state and athletic ability, and prepares for the next training session.

[0329] (Example 2)

[0330] 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".

[0331] Traditional training systems focused solely on the user's athletic performance and failed to provide a training experience that took the user's emotional state into account. As a result, users found it difficult to reduce the psychological and emotional burden during training, and the effectiveness of the training could not be optimized. Furthermore, virtual opponents only provided fixed movements and feedback, and did not offer dynamic responses that corresponded to the user's emotional changes.

[0332] 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.

[0333] In this invention, the server includes information acquisition means for collecting user exercise data and emotional data in real time; generation AI means for analyzing the exercise data and emotional data and generating an optimized training plan according to the user's emotional state; and output means for generating a virtual opponent whose behavior dynamically changes based on the emotional state and displaying it to the user. This makes it possible to provide a more personalized training experience based on both the user's emotional state and exercise performance, and to maximize the efficiency of training.

[0334] "Information acquisition means" refers to a device or method for collecting user motor data and emotional data in real time.

[0335] "Generative AI methods" refer to methods that utilize artificial intelligence technology to analyze collected exercise and emotional data and dynamically generate training plans optimized for the user based on that analysis.

[0336] "Output means" refers to a device or method for visually or audibly presenting the generated training plan and the actions of the virtual opponent to the user.

[0337] "Emotional data" refers to data collected from a user's facial expressions, voice, and other sources that indicates the user's psychological and emotional state.

[0338] A "virtual opponent" is a digital opponent generated by a computer or system during a user's training, capable of dynamically responding to the user's emotional state.

[0339] This invention is an advanced training system that provides personalized training plans by utilizing the user's exercise data and emotional data.

[0340] The device interprets the user's facial expressions and voice using an emotion engine and acquires emotional data in real time. It also collects exercise and physiological data from wearable sensors. This utilizes emotion recognition technology using cameras and microphones, as well as accelerometers and heart rate monitors. Specific software includes OpenCV and the Emotion SDK for facial recognition, and libraries for voice analysis.

[0341] The device transmits the collected data to a server via the internet. This data is recorded in a database on the server and analyzed by a generative AI model. The AI ​​model includes deep learning algorithms built using, for example, TensorFlow or PyTorch. This model identifies the user's psychological state and customizes the training plan based on that.

[0342] The server generates an exercise plan tailored to the user by prompting the AI ​​with instructions such as, "Suggest a suitable training method if the user is not relaxed."

[0343] For example, if a user is determined to be experiencing stress, the server creates a plan that includes relaxation exercises and sends it to the user's device. The user interacts with a virtual opponent on the device, and the opponent adjusts its movements according to the user's state, reducing the burden and providing a training environment that makes it easier to concentrate.

[0344] Through these actions, users can enjoy a personalized training experience tailored to their emotional state, enabling them to more effectively maximize the benefits of their exercise.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] When a user begins training, the device activates its emotion engine and uses the camera and microphone to acquire the user's facial and audio data. The input is the user's real-time video feed and audio stream, and the output is emotion data based on this. The emotion engine analyzes, for example, subtle changes in the user's face and tone of voice to identify the user's current emotional state (e.g., stress or tension).

[0348] Step 2:

[0349] The terminal collects emotional data along with motor data (e.g., motion measurements from an accelerometer) and physiological data (e.g., heart rate), packets all the data, and sends it to the server. The input consists of the collected emotional data, motor data, and physiological data, and the output is a single integrated data packet containing all of this data. The terminal encrypts this packet and sends it to the server via a secure communication channel.

[0350] Step 3:

[0351] The server decodes the received data packets and activates the generative AI model. The input is encrypted data packets, and the output is decoded emotional and motor state data. The server applies a deep learning algorithm to analyze the user's emotional state and provides that information to the AI ​​model in the form of a prompt. For example, it might command the AI ​​model to "provide the optimal exercise for when the user is feeling stressed."

[0352] Step 4:

[0353] The generative AI model generates an appropriate training plan based on the input prompt text. The input consists of prompt text and parsed data, and the output is a customized training plan. This plan includes exercises and relaxation techniques that take emotional states into account, resulting in training content that is most suitable for the user.

[0354] Step 5:

[0355] The server sends the generated training plan to the terminal. The input is the generated training plan, and the output is the transmission of its contents to the terminal. The terminal displays the plan to the user and adjusts the movements of the virtual opponent. The opponent acts according to this plan, providing guidance for the user's training.

[0356] Step 6:

[0357] As the user continues training, the device periodically uses an emotion engine to re-evaluate the user's state. Real-time facial and audio data from the training are input, and the updated emotional state is output. The device sends this data back to the server, which re-analyzes it and modifies the training plan if necessary.

[0358] (Application Example 2)

[0359] 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."

[0360] Traditional training systems generate training plans based solely on the user's exercise and physiological data, failing to take into account the user's emotional state and making it difficult to provide a sufficiently personalized service. Therefore, there is a need to reduce the user's psychological burden, maintain motivation, and provide a more effective training experience.

[0361] 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.

[0362] In this invention, the server includes input means for recognizing the user's emotional state in real time, generation means for generating a personalized training plan based on the emotional state, motor data, and physiological data, and output means for adjusting and displaying the actions of a virtual opponent. This makes it possible to provide personalized feedback and a training environment that responds to the user's emotional state.

[0363] "User emotional state" refers to information that shows the user's psychological and emotional state in real time.

[0364] "Input means" refers to a device or method for collecting the user's emotional state, motor data, physiological data, etc.

[0365] "Generation means" refers to an apparatus or method for creating a training plan suitable for a user based on collected data.

[0366] A "virtual opponent" is a computer-generated character or model that operates as if competing or cooperating with the user during user training.

[0367] "Output means" refers to a device or method for presenting a generated training plan or virtual opponent to the user.

[0368] The system that realizes this invention includes multiple hardware and software components to collect the user's emotional state, motor data, and physiological data, and to generate a training plan and feedback based on them.

[0369] First, the device uses built-in sensors (such as cameras and microphones) to identify the user's emotional state in real time from their facial expressions and speech data. This process typically utilizes the Emotion API, which allows for accurate capture of the user's psychological state. In addition, biometric sensors are used to collect motor and physiological data.

[0370] Next, this data is sent to a server. The server uses a generative AI model (for example, OpenAI's GPT series) to generate a training plan optimized for the user's current situation. The generated prompts may include questions such as, "If the user is feeling stressed, what relaxation exercises should be recommended?"

[0371] Furthermore, the server dynamically adjusts the actions of the virtual opponent according to the user's emotional state. This is visualized to the user through a VR display or smartphone screen, providing an immersive training experience. This entire process allows the user to enjoy a highly personalized fitness experience tailored to their emotional state at any given time.

[0372] For example, if a user is observed to lose focus during a fast-paced exercise, the system will suggest slowing down and provide real-time feedback to encourage the user to adjust their pace. An example of a prompt using a generative AI model is, "How can you maintain motivation if the user feels fatigued midway through?"

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The device acquires video and audio data of the user's face in real time using its camera and microphone. This input data is sent to the Emotion API to analyze the user's emotional state. The output is the user's emotion tag (e.g., relaxed, stressed, focused).

[0376] Step 2:

[0377] The device uses exercise sensors and biometric sensors to collect the user's exercise data (e.g., heart rate, movement speed) and physiological data. This data is sent directly to the server and used to customize the training plan.

[0378] Step 3:

[0379] The server uses a generative AI model based on received emotional state, exercise data, and physiological data to generate a training plan tailored to the user's current condition. The input is the prompt "If the user is feeling stressed, what relaxation exercises should be recommended?". The output generates specific exercise content.

[0380] Step 4:

[0381] The server adjusts the virtual opponent's behavior based on the generated training plan. It uses prompts that correspond to the user's emotional state to set the opponent's movements and reaction speed. As a result, it provides an environment with an appropriate difficulty level for the user.

[0382] Step 5:

[0383] The device displays a customized training plan and a virtual opponent to the user. Through the display screen and audio output, it provides real-time feedback, showing the user the training content and the opponent's movements.

[0384] Step 6:

[0385] If the user's emotional state or performance changes during training, the device collects new data again and sends it to the server. The server then updates the training plan in real time as needed, continuously providing the optimal training experience.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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".

[0402] This invention is a system for users to effectively perform sports training, collecting data in real time and providing an optimized training plan and virtual opponent based on that data.

[0403] Specifically, the device collects exercise data from various sensors attached to the user. This exercise data includes physiological data such as location information, speed, acceleration, and heart rate, which can be used to evaluate the user's current exercise capacity.

[0404] The server receives this data in real time and analyzes it using a generating AI. Based on the analysis results, it generates a training plan specifically optimized for the user's athletic ability and training needs. In parallel, it quantitatively evaluates the user's performance based on historical data and provides feedback for continuous improvement.

[0405] The device visually displays the generated training plan to the user. Furthermore, a virtual opponent is visualized, allowing the user to train in a way that simulates actual competition. This training environment is crucial for increasing user motivation and preparing them for actual matches.

[0406] As a concrete example, consider running training. When a user puts on the device and starts running, the device collects movement data and sends it to a server. Based on the data received, the server determines whether the user is maintaining an appropriate pace and whether their form needs improvement, and then creates an appropriate training plan. A virtual pacer is visualized, and the user can train efficiently by following along with it. After the training is complete, the device provides the user with performance feedback for the day, allowing them to adjust the content of their next training session as needed.

[0407] In this way, this system provides users with a highly effective training environment and supports skill improvement tailored to their individual needs.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The user puts on the device and starts training. The device uses its built-in sensors to acquire the user's exercise data (e.g., location, speed, acceleration, heart rate, etc.) in real time.

[0411] Step 2:

[0412] The device transmits real-time motion data to the server via a communication module. The data is encrypted and securely transferred.

[0413] Step 3:

[0414] The server analyzes the received motion data. This analysis includes pattern recognition of movements, form evaluation, and performance evaluation using generative AI.

[0415] Step 4:

[0416] The server generates an optimized training plan based on the user's fitness level and needs, using analysis results. The plan includes specific exercises and their intensity.

[0417] Step 5:

[0418] The server sends the generated training plan to the terminal. The terminal then visually presents the training plan to the user.

[0419] Step 6:

[0420] The server generates a virtual opponent based on the user's past data. The opponent's data and behavior patterns are sent to the terminal, creating a virtual match environment.

[0421] Step 7:

[0422] The user exercises according to a training plan and competes against a virtual opponent. The device continues to collect exercise data and sends it back to the server.

[0423] Step 8:

[0424] After the training is complete, the server re-analyzes the collected data and sends the results to the terminal. The results include progress evaluation and suggestions for future improvements.

[0425] Step 9:

[0426] Users review feedback and prepare for their next training session. This process enables continuous skill improvement and health management.

[0427] (Example 1)

[0428] 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."

[0429] Traditional training systems struggle to provide optimal training plans that fully consider each user's individual physiological state and exercise history. Furthermore, they lack mechanisms to maintain user motivation while providing real-time feedback and health indicators, limiting the potential for improving training effectiveness.

[0430] 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.

[0431] In this invention, the server includes input means including sensors for collecting physiological states in real time, generation means for analyzing the physiological states and generating a training plan tailored to the user, output means for generating a virtual competition and presenting it visually to the user, and means for visualizing progress using data generated in real time. This enables the provision of a personalized training plan tailored to the user, while also providing real-time experiences and feedback to enhance motivation.

[0432] "Physiological state" refers to information that indicates the functional state of the user's body, and includes physiological data such as heart rate, respiratory rate, and body temperature.

[0433] "Input means" refers to a collection of devices and sensors used to acquire the user's physiological state and exercise data in real time.

[0434] "Generation means" refers to the process or device for creating a training plan or virtual competition environment optimized for the user based on acquired physiological state and exercise data.

[0435] "Output means" refers to displays or interfaces used to visually present generated training plans and virtual competition environments to users.

[0436] "Virtual competition" refers to a virtual opponent or simulation environment designed to allow users to experience a simulated real-world competition.

[0437] "Means of visualizing progress" refers to visual tools or mechanisms for displaying a user's training progress in real time.

[0438] This invention is a system that collects data in real time when a user exercises and provides an optimized training plan and virtual competition environment based on that data. This system uses various sensors that the user attaches to a device. These include GPS sensors, accelerometers, and heart rate sensors, which acquire the user's location information, speed, acceleration, heart rate, and other physiological states.

[0439] The device processes information obtained from sensors and transmits the data to the server in real time using a highly secure communication protocol. The server analyzes the received data using a generating AI model and, combined with the user's past exercise data, generates an optimal training plan. This training plan includes setting the pace, rest times, and heart rate targets.

[0440] The server also generates a virtual competition environment in which users can train against virtual opponents. This allows users to improve the quality of their training while simulating real-world competition.

[0441] The device visually presents the user with a training plan and virtual competition environment based on the analysis results. This allows users to check their training progress in real time, making it easier to maintain motivation. After training, the device also provides the user with detailed feedback and suggests areas for improvement in the next training session. This feedback supports effective training and the user's sustained growth.

[0442] As a concrete example, let's consider a scenario where a user is running. When the user puts on the device and starts running, the device collects location information, speed, heart rate, etc., in real time and sends it to the server. The server uses a generative AI model to analyze this data and generate a running plan that includes suggestions for the most effective pace and form improvements for the user. In this process, a virtual pacemaker and opponent are visualized, allowing the user to train effectively by following along with them.

[0443] An example of a prompt for a generative AI model might be: "Based on the user's most recent exercise data, please suggest the optimal pace and distance for the next running training session." This system aims to provide training tailored to the user's specific needs and goals, striving for sustained fitness improvement and maximizing performance.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The device collects exercise-related data from various sensors attached to the user. Inputs include location information from the GPS sensor, speed and acceleration from the accelerometer, and heart rate from the heart rate sensor. Based on this information, the data is integrated to understand the user's real-time physiological and physical state.

[0447] Step 2:

[0448] The terminal transmits integrated data to the server in real time. Collected physiological and motor data are used as input, and the data is transmitted using an encrypted, secure communication protocol. The output is raw data, enabling immediate analysis on the server.

[0449] Step 3:

[0450] The server analyzes the received data using a generating AI model. This process uses historical data and the latest real-time data as input. As part of the data processing, the AI ​​model evaluates the user's current performance and proposes an optimal training plan. As output, a customized training plan tailored to the user's athletic ability is generated.

[0451] Step 4:

[0452] The server constructs a virtual competition environment based on the generated training plan. The generated training plan is used as input. It then generates data for virtual opponents and pacemakers. The output is a virtual competition environment that the user follows.

[0453] Step 5:

[0454] The terminal visually presents the user with a training plan and virtual competition environment received from the server. The input uses data from the server, and a user-friendly UI operates. The output displays training content that the user can review in real time and act upon.

[0455] Step 6:

[0456] As the user trains, actual movements progress, and the device collects this data again. New physiological data from the user during training is acquired as input, and the collected data is resent to the server. Detailed feedback on the training is prepared as output.

[0457] Step 7:

[0458] The server analyzes the newly acquired training data and generates detailed feedback for the user. The most recent training data is used as input. The output includes an assessment of achievement, areas for improvement in the next session, and advice regarding health status.

[0459] (Application Example 1)

[0460] 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."

[0461] In modern society, there is a demand for providing personalized sports training plans in real time and efficiently improving their effectiveness. However, conventional technologies have made it difficult to comprehensively consider users' physical data and physiological state when planning, making it impossible to provide individually optimized training. Furthermore, there has been a lack of means to effectively support user motivation and performance improvement by creating a virtual competitive environment.

[0462] 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.

[0463] In this invention, the server includes acquisition means for acquiring the user's biometric data in a time series, creation means for analyzing the biometric data and creating a plan optimized for the user, and presentation means for generating and presenting virtual opponents to the user. As a result, the user can receive a real-time, individually optimized training plan and train efficiently while maintaining high motivation through competition with virtual opponents.

[0464] "User biometric data" refers to time-series and dynamic information obtained from the user's body, including data that shows the body's movements and physiological state during exercise.

[0465] "Acquisition means" refers to a function or device for collecting a user's biometric data over time using various sensors and devices.

[0466] "Analysis" refers to the act of processing and analyzing data based on acquired biometric data to evaluate the user's current state and athletic ability.

[0467] An "optimized plan" is a plan that includes the most suitable training content, designed to support each user's individual athletic ability and goal achievement.

[0468] "Creation method" refers to a function or process for constructing an optimal training plan for the user based on collected biometric data.

[0469] A "virtual opponent" is a virtual opponent generated to support the user's training and promote performance improvement.

[0470] A "presentation means" is a device or part of a device that provides information to a user visually and has the function of transmitting information to the user through a display device.

[0471] This system is designed to help users effectively perform sports training. The device worn by the user is equipped with various sensors that acquire the user's biometric data in real time. The acquired data includes information that represents the user's physical condition and performance status, such as heart rate, speed, acceleration, and location information.

[0472] Data sent from the device is received by a server in the cloud and analyzed using a dedicated generative AI model. This analysis utilizes Python libraries such as Pandas, NumPy, and TensorFlow to perform data processing and calculations. Based on the analysis results, the server creates an optimal plan for each user and generates a virtual opponent. This allows users to train with higher motivation through virtual competition against their opponents.

[0473] The information provided to the user is delivered visually to the device's display. The generated plan and information about virtual opponents are presented to the user through a display device such as a smartphone or smart glasses. For example, during marathon training, using smart glasses allows the user to visualize a virtual pacer, enabling more efficient training. After training, a performance evaluation is conducted, and feedback is provided for the next training session.

[0474] An example of a prompt to the generating AI model is, "Based on user A's running data, please suggest the optimal pace and advice for improving form." This enables customized guidance for the user.

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] The user puts on the device and begins exercising. Sensors built into the device acquire the user's biometric data in real time. This data includes heart rate, speed, acceleration, and location information. The output from the sensors is temporarily stored as digital data in the device's memory.

[0478] Step 2:

[0479] The device transmits the acquired biometric data to a server in the cloud. A communication protocol (e.g., HTTPS) is used for data transmission. The data includes time-series information, specifically data on changes in each parameter. After transmission, the device waits for the analysis results from the server.

[0480] Step 3:

[0481] The server uses a generative AI model to analyze the received biometric data. It performs data processing and preprocessing using Python libraries such as Pandas and NumPy. After preprocessing, an AI model using TensorFlow performs data analysis to evaluate the user's exercise performance. The analysis output generates a training plan optimized for the user and performance evaluation results.

[0482] Step 4:

[0483] The server visualizes the generated training plan and information about the virtual opponent. The plan includes a training schedule, target pace, and advice for improving form. The virtual opponent is visualized as a benchmark that can be compared to the user's current pace.

[0484] Step 5:

[0485] The server transmits visualized information to the terminal. The terminal provides this information to the user using a display device (smartphone or smart glasses). The user continues training based on the visual information and improves their performance through competition with virtual opponents.

[0486] Step 6:

[0487] After the training is complete, the device sends the user's final performance data back to the server. The server analyzes this data as new feedback and uses it to adjust the plan for the next training session. The feedback specifically shows the user's progress and areas for improvement, providing guidance for the next steps.

[0488] 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.

[0489] This invention is a sports training support system that incorporates an emotion engine to make the user's training experience more personalized. In addition to exercise data and physiological data, this system recognizes the user's emotional state in real time and provides training plans and feedback accordingly.

[0490] Specifically, the device acquires emotional data from the user's facial expressions and voice through an emotion engine. This data is sent to the server along with the user's exercise and physiological data.

[0491] The server analyzes emotional data to determine the user's emotional state and uses generative AI to adjust the training plan accordingly. For example, if the user is feeling stressed, it can incorporate exercises that help them relax.

[0492] Furthermore, the virtual opponent's behavior also changes dynamically based on emotional data. The device adjusts the opponent's difficulty level and reactions according to the user's emotions, providing an environment that allows the user to concentrate more easily.

[0493] As a concrete example, consider a user practicing yoga. If the user is excessively tense, the device's emotion engine detects this state. The server receives and analyzes this information and generates a training plan that incorporates breathing techniques to relieve tension. Additionally, a virtual opponent acts as a guide, demonstrating slow movements to support the user in progressing at their own pace.

[0494] In this way, by taking the user's emotional state into consideration, this system can provide an effective training environment that maintains a balance between mind and body. As a result, users can enjoy a more fulfilling sports experience.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The user puts on the device and begins training. The device uses its built-in sensors to acquire the user's exercise data and emotional data (facial expressions and voice) in real time.

[0498] Step 2:

[0499] The device compresses the exercise, emotional, and physiological data acquired in real time and sends it to the server. The data is encrypted using a secure protocol before transmission.

[0500] Step 3:

[0501] The server analyzes the received motion and emotional data. Using generative AI, it recognizes emotional states in addition to evaluating movements, and determines the user's mental state.

[0502] Step 4:

[0503] The server generates an optimized training plan based on the analysis results, adapted to the user's athletic ability, emotional state, and health condition. Users can select relaxation plans or concentration-enhancing plans as needed.

[0504] Step 5:

[0505] The server sends the generated training plan and data on a virtual opponent that dynamically changes according to the user's emotions to the terminal. The terminal then presents the training plan and virtual opponent to the user visually or audibly.

[0506] Step 6:

[0507] The user executes a training plan and competes against a virtual opponent. The device continuously collects data during exercise and transmits it to the server in real time.

[0508] Step 7:

[0509] After the training is complete, the server re-analyzes all the collected data to evaluate the user's progress. It then generates feedback to help improve future training sessions.

[0510] Step 8:

[0511] The device provides feedback to the user from the server. Based on this feedback, the user becomes aware of improvements in their emotional state and athletic ability, and prepares for the next training session.

[0512] (Example 2)

[0513] 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."

[0514] Traditional training systems focused solely on the user's athletic performance and failed to provide a training experience that took the user's emotional state into account. As a result, users found it difficult to reduce the psychological and emotional burden during training, and the effectiveness of the training could not be optimized. Furthermore, virtual opponents only provided fixed movements and feedback, and did not offer dynamic responses that corresponded to the user's emotional changes.

[0515] 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.

[0516] In this invention, the server includes information acquisition means for collecting user exercise data and emotional data in real time; generation AI means for analyzing the exercise data and emotional data and generating an optimized training plan according to the user's emotional state; and output means for generating a virtual opponent whose behavior dynamically changes based on the emotional state and displaying it to the user. This makes it possible to provide a more personalized training experience based on both the user's emotional state and exercise performance, and to maximize the efficiency of training.

[0517] "Information acquisition means" refers to a device or method for collecting user motor data and emotional data in real time.

[0518] "Generative AI methods" refer to methods that utilize artificial intelligence technology to analyze collected exercise and emotional data and dynamically generate training plans optimized for the user based on that analysis.

[0519] "Output means" refers to a device or method for visually or audibly presenting the generated training plan and the actions of the virtual opponent to the user.

[0520] "Emotional data" refers to data collected from a user's facial expressions, voice, and other sources that indicates the user's psychological and emotional state.

[0521] A "virtual opponent" is a digital opponent generated by a computer or system during a user's training, capable of dynamically responding to the user's emotional state.

[0522] This invention is an advanced training system that provides personalized training plans by utilizing the user's exercise data and emotional data.

[0523] The device interprets the user's facial expressions and voice using an emotion engine and acquires emotional data in real time. It also collects exercise and physiological data from wearable sensors. This utilizes emotion recognition technology using cameras and microphones, as well as accelerometers and heart rate monitors. Specific software includes OpenCV and the Emotion SDK for facial recognition, and libraries for voice analysis.

[0524] The device transmits the collected data to a server via the internet. This data is recorded in a database on the server and analyzed by a generative AI model. The AI ​​model includes deep learning algorithms built using, for example, TensorFlow or PyTorch. This model identifies the user's psychological state and customizes the training plan based on that.

[0525] The server generates an exercise plan tailored to the user by prompting the AI ​​with instructions such as, "Suggest a suitable training method if the user is not relaxed."

[0526] For example, if a user is determined to be experiencing stress, the server creates a plan that includes relaxation exercises and sends it to the user's device. The user interacts with a virtual opponent on the device, and the opponent adjusts its movements according to the user's state, reducing the burden and providing a training environment that makes it easier to concentrate.

[0527] Through these actions, users can enjoy a personalized training experience tailored to their emotional state, enabling them to more effectively maximize the benefits of their exercise.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] When a user begins training, the device activates its emotion engine and uses the camera and microphone to acquire the user's facial and audio data. The input is the user's real-time video feed and audio stream, and the output is emotion data based on this. The emotion engine analyzes, for example, subtle changes in the user's face and tone of voice to identify the user's current emotional state (e.g., stress or tension).

[0531] Step 2:

[0532] The terminal collects emotional data along with motor data (e.g., motion measurements from an accelerometer) and physiological data (e.g., heart rate), packets all the data, and sends it to the server. The input consists of the collected emotional data, motor data, and physiological data, and the output is a single integrated data packet containing all of this data. The terminal encrypts this packet and sends it to the server via a secure communication channel.

[0533] Step 3:

[0534] The server decodes the received data packets and activates the generative AI model. The input is encrypted data packets, and the output is decoded emotional and motor state data. The server applies a deep learning algorithm to analyze the user's emotional state and provides that information to the AI ​​model in the form of a prompt. For example, it might command the AI ​​model to "provide the optimal exercise for when the user is feeling stressed."

[0535] Step 4:

[0536] The generative AI model generates an appropriate training plan based on the input prompt text. The input consists of prompt text and parsed data, and the output is a customized training plan. This plan includes exercises and relaxation techniques that take emotional states into account, resulting in training content that is most suitable for the user.

[0537] Step 5:

[0538] The server sends the generated training plan to the terminal. The input is the generated training plan, and the output is the transmission of its contents to the terminal. The terminal displays the plan to the user and adjusts the movements of the virtual opponent. The opponent acts according to this plan, providing guidance for the user's training.

[0539] Step 6:

[0540] As the user continues training, the device periodically uses an emotion engine to re-evaluate the user's state. Real-time facial and audio data from the training are input, and the updated emotional state is output. The device sends this data back to the server, which re-analyzes it and modifies the training plan if necessary.

[0541] (Application Example 2)

[0542] 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."

[0543] Traditional training systems generate training plans based solely on the user's exercise and physiological data, failing to take into account the user's emotional state and making it difficult to provide a sufficiently personalized service. Therefore, there is a need to reduce the user's psychological burden, maintain motivation, and provide a more effective training experience.

[0544] 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.

[0545] In this invention, the server includes input means for recognizing the user's emotional state in real time, generation means for generating a personalized training plan based on the emotional state, motor data, and physiological data, and output means for adjusting and displaying the actions of a virtual opponent. This makes it possible to provide personalized feedback and a training environment that responds to the user's emotional state.

[0546] "User emotional state" refers to information that shows the user's psychological and emotional state in real time.

[0547] "Input means" refers to a device or method for collecting the user's emotional state, motor data, physiological data, etc.

[0548] "Generation means" refers to an apparatus or method for creating a training plan suitable for a user based on collected data.

[0549] A "virtual opponent" is a computer-generated character or model that operates as if competing or cooperating with the user during user training.

[0550] "Output means" refers to a device or method for presenting a generated training plan or virtual opponent to the user.

[0551] The system that realizes this invention includes multiple hardware and software components to collect the user's emotional state, motor data, and physiological data, and to generate a training plan and feedback based on them.

[0552] First, the device uses built-in sensors (such as cameras and microphones) to identify the user's emotional state in real time from their facial expressions and speech data. This process typically utilizes the Emotion API, which allows for accurate capture of the user's psychological state. In addition, biometric sensors are used to collect motor and physiological data.

[0553] Next, this data is sent to a server. The server uses a generative AI model (for example, OpenAI's GPT series) to generate a training plan optimized for the user's current situation. The generated prompts may include questions such as, "If the user is feeling stressed, what relaxation exercises should be recommended?"

[0554] Furthermore, the server dynamically adjusts the actions of the virtual opponent according to the user's emotional state. This is visualized to the user through a VR display or smartphone screen, providing an immersive training experience. This entire process allows the user to enjoy a highly personalized fitness experience tailored to their emotional state at any given time.

[0555] For example, if a user is observed to lose focus during a fast-paced exercise, the system will suggest slowing down and provide real-time feedback to encourage the user to adjust their pace. An example of a prompt using a generative AI model is, "How can you maintain motivation if the user feels fatigued midway through?"

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The device acquires video and audio data of the user's face in real time using its camera and microphone. This input data is sent to the Emotion API to analyze the user's emotional state. The output is the user's emotion tag (e.g., relaxed, stressed, focused).

[0559] Step 2:

[0560] The device uses exercise sensors and biometric sensors to collect the user's exercise data (e.g., heart rate, movement speed) and physiological data. This data is sent directly to the server and used to customize the training plan.

[0561] Step 3:

[0562] The server uses a generative AI model based on received emotional state, exercise data, and physiological data to generate a training plan tailored to the user's current condition. The input is the prompt "If the user is feeling stressed, what relaxation exercises should be recommended?". The output generates specific exercise content.

[0563] Step 4:

[0564] The server adjusts the virtual opponent's behavior based on the generated training plan. It uses prompts that correspond to the user's emotional state to set the opponent's movements and reaction speed. As a result, it provides an environment with an appropriate difficulty level for the user.

[0565] Step 5:

[0566] The device displays a customized training plan and a virtual opponent to the user. Through the display screen and audio output, it provides real-time feedback, showing the user the training content and the opponent's movements.

[0567] Step 6:

[0568] If the user's emotional state or performance changes during training, the device collects new data again and sends it to the server. The server then updates the training plan in real time as needed, continuously providing the optimal training experience.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] [Fourth Embodiment]

[0573] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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".

[0586] This invention is a system for users to effectively perform sports training, collecting data in real time and providing an optimized training plan and virtual opponent based on that data.

[0587] Specifically, the device collects exercise data from various sensors attached to the user. This exercise data includes physiological data such as location information, speed, acceleration, and heart rate, which can be used to evaluate the user's current exercise capacity.

[0588] The server receives this data in real time and analyzes it using a generating AI. Based on the analysis results, it generates a training plan specifically optimized for the user's athletic ability and training needs. In parallel, it quantitatively evaluates the user's performance based on historical data and provides feedback for continuous improvement.

[0589] The device visually displays the generated training plan to the user. Furthermore, a virtual opponent is visualized, allowing the user to train in a way that simulates actual competition. This training environment is crucial for increasing user motivation and preparing them for actual matches.

[0590] As a concrete example, consider running training. When a user puts on the device and starts running, the device collects movement data and sends it to a server. Based on the data received, the server determines whether the user is maintaining an appropriate pace and whether their form needs improvement, and then creates an appropriate training plan. A virtual pacer is visualized, and the user can train efficiently by following along with it. After the training is complete, the device provides the user with performance feedback for the day, allowing them to adjust the content of their next training session as needed.

[0591] In this way, this system provides users with a highly effective training environment and supports skill improvement tailored to their individual needs.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The user puts on the device and starts training. The device uses its built-in sensors to acquire the user's exercise data (e.g., location, speed, acceleration, heart rate, etc.) in real time.

[0595] Step 2:

[0596] The device transmits real-time motion data to the server via a communication module. The data is encrypted and securely transferred.

[0597] Step 3:

[0598] The server analyzes the received motion data. This analysis includes pattern recognition of movements, form evaluation, and performance evaluation using generative AI.

[0599] Step 4:

[0600] The server generates an optimized training plan based on the user's fitness level and needs, using analysis results. The plan includes specific exercises and their intensity.

[0601] Step 5:

[0602] The server sends the generated training plan to the terminal. The terminal then visually presents the training plan to the user.

[0603] Step 6:

[0604] The server generates a virtual opponent based on the user's past data. The opponent's data and behavior patterns are sent to the terminal, creating a virtual match environment.

[0605] Step 7:

[0606] The user exercises according to a training plan and competes against a virtual opponent. The device continues to collect exercise data and sends it back to the server.

[0607] Step 8:

[0608] After the training is complete, the server re-analyzes the collected data and sends the results to the terminal. The results include progress evaluation and suggestions for future improvements.

[0609] Step 9:

[0610] Users review feedback and prepare for their next training session. This process enables continuous skill improvement and health management.

[0611] (Example 1)

[0612] 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".

[0613] Traditional training systems struggle to provide optimal training plans that fully consider each user's individual physiological state and exercise history. Furthermore, they lack mechanisms to maintain user motivation while providing real-time feedback and health indicators, limiting the potential for improving training effectiveness.

[0614] 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.

[0615] In this invention, the server includes input means including sensors for collecting physiological states in real time, generation means for analyzing the physiological states and generating a training plan tailored to the user, output means for generating a virtual competition and presenting it visually to the user, and means for visualizing progress using data generated in real time. This enables the provision of a personalized training plan tailored to the user, while also providing real-time experiences and feedback to enhance motivation.

[0616] "Physiological state" refers to information that indicates the functional state of the user's body, and includes physiological data such as heart rate, respiratory rate, and body temperature.

[0617] "Input means" refers to a collection of devices and sensors used to acquire the user's physiological state and exercise data in real time.

[0618] "Generation means" refers to the process or device for creating a training plan or virtual competition environment optimized for the user based on acquired physiological state and exercise data.

[0619] "Output means" refers to displays or interfaces used to visually present generated training plans and virtual competition environments to users.

[0620] "Virtual competition" refers to a virtual opponent or simulation environment designed to allow users to experience a simulated real-world competition.

[0621] "Means of visualizing progress" refers to visual tools or mechanisms for displaying a user's training progress in real time.

[0622] This invention is a system that collects data in real time when a user exercises and provides an optimized training plan and virtual competition environment based on that data. This system uses various sensors that the user attaches to a device. These include GPS sensors, accelerometers, and heart rate sensors, which acquire the user's location information, speed, acceleration, heart rate, and other physiological states.

[0623] The device processes information obtained from sensors and transmits the data to the server in real time using a highly secure communication protocol. The server analyzes the received data using a generating AI model and, combined with the user's past exercise data, generates an optimal training plan. This training plan includes setting the pace, rest times, and heart rate targets.

[0624] The server also generates a virtual competition environment in which users can train against virtual opponents. This allows users to improve the quality of their training while simulating real-world competition.

[0625] The device visually presents the user with a training plan and virtual competition environment based on the analysis results. This allows users to check their training progress in real time, making it easier to maintain motivation. After training, the device also provides the user with detailed feedback and suggests areas for improvement in the next training session. This feedback supports effective training and the user's sustained growth.

[0626] As a concrete example, let's consider a scenario where a user is running. When the user puts on the device and starts running, the device collects location information, speed, heart rate, etc., in real time and sends it to the server. The server uses a generative AI model to analyze this data and generate a running plan that includes suggestions for the most effective pace and form improvements for the user. In this process, a virtual pacemaker and opponent are visualized, allowing the user to train effectively by following along with them.

[0627] An example of a prompt for a generative AI model might be: "Based on the user's most recent exercise data, please suggest the optimal pace and distance for the next running training session." This system aims to provide training tailored to the user's specific needs and goals, striving for sustained fitness improvement and maximizing performance.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The device collects exercise-related data from various sensors attached to the user. Inputs include location information from the GPS sensor, speed and acceleration from the accelerometer, and heart rate from the heart rate sensor. Based on this information, the data is integrated to understand the user's real-time physiological and physical state.

[0631] Step 2:

[0632] The terminal transmits integrated data to the server in real time. Collected physiological and motor data are used as input, and the data is transmitted using an encrypted, secure communication protocol. The output is raw data, enabling immediate analysis on the server.

[0633] Step 3:

[0634] The server analyzes the received data using a generating AI model. This process uses historical data and the latest real-time data as input. As part of the data processing, the AI ​​model evaluates the user's current performance and proposes an optimal training plan. As output, a customized training plan tailored to the user's athletic ability is generated.

[0635] Step 4:

[0636] The server constructs a virtual competition environment based on the generated training plan. The generated training plan is used as input. It then generates data for virtual opponents and pacemakers. The output is a virtual competition environment that the user follows.

[0637] Step 5:

[0638] The terminal visually presents the user with a training plan and virtual competition environment received from the server. The input uses data from the server, and a user-friendly UI operates. The output displays training content that the user can review in real time and act upon.

[0639] Step 6:

[0640] As the user trains, actual movements progress, and the device collects this data again. New physiological data from the user during training is acquired as input, and the collected data is resent to the server. Detailed feedback on the training is prepared as output.

[0641] Step 7:

[0642] The server analyzes the newly acquired training data and generates detailed feedback for the user. The most recent training data is used as input. The output includes an assessment of achievement, areas for improvement in the next session, and advice regarding health status.

[0643] (Application Example 1)

[0644] 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".

[0645] In modern society, there is a demand for providing personalized sports training plans in real time and efficiently improving their effectiveness. However, conventional technologies have made it difficult to comprehensively consider users' physical data and physiological state when planning, making it impossible to provide individually optimized training. Furthermore, there has been a lack of means to effectively support user motivation and performance improvement by creating a virtual competitive environment.

[0646] 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.

[0647] In this invention, the server includes acquisition means for acquiring the user's biometric data in a time series, creation means for analyzing the biometric data and creating a plan optimized for the user, and presentation means for generating and presenting virtual opponents to the user. As a result, the user can receive a real-time, individually optimized training plan and train efficiently while maintaining high motivation through competition with virtual opponents.

[0648] "User biometric data" refers to time-series and dynamic information obtained from the user's body, including data that shows the body's movements and physiological state during exercise.

[0649] "Acquisition means" refers to a function or device for collecting a user's biometric data over time using various sensors and devices.

[0650] "Analysis" refers to the act of processing and analyzing data based on acquired biometric data to evaluate the user's current state and athletic ability.

[0651] An "optimized plan" is a plan that includes the most suitable training content, designed to support each user's individual athletic ability and goal achievement.

[0652] "Creation method" refers to a function or process for constructing an optimal training plan for the user based on collected biometric data.

[0653] A "virtual opponent" is a virtual opponent generated to support the user's training and promote performance improvement.

[0654] A "presentation means" is a device or part of a device that provides information to a user visually and has the function of transmitting information to the user through a display device.

[0655] This system is designed to help users effectively perform sports training. The device worn by the user is equipped with various sensors that acquire the user's biometric data in real time. The acquired data includes information that represents the user's physical condition and performance status, such as heart rate, speed, acceleration, and location information.

[0656] Data sent from the device is received by a server in the cloud and analyzed using a dedicated generative AI model. This analysis utilizes Python libraries such as Pandas, NumPy, and TensorFlow to perform data processing and calculations. Based on the analysis results, the server creates an optimal plan for each user and generates a virtual opponent. This allows users to train with higher motivation through virtual competition against their opponents.

[0657] The information provided to the user is delivered visually to the device's display. The generated plan and information about virtual opponents are presented to the user through a display device such as a smartphone or smart glasses. For example, during marathon training, using smart glasses allows the user to visualize a virtual pacer, enabling more efficient training. After training, a performance evaluation is conducted, and feedback is provided for the next training session.

[0658] An example of a prompt to the generating AI model is, "Based on user A's running data, please suggest the optimal pace and advice for improving form." This enables customized guidance for the user.

[0659] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0660] Step 1:

[0661] The user puts on the device and begins exercising. Sensors built into the device acquire the user's biometric data in real time. This data includes heart rate, speed, acceleration, and location information. The output from the sensors is temporarily stored as digital data in the device's memory.

[0662] Step 2:

[0663] The device transmits the acquired biometric data to a server in the cloud. A communication protocol (e.g., HTTPS) is used for data transmission. The data includes time-series information, specifically data on changes in each parameter. After transmission, the device waits for the analysis results from the server.

[0664] Step 3:

[0665] The server uses a generative AI model to analyze the received biometric data. It performs data processing and preprocessing using Python libraries such as Pandas and NumPy. After preprocessing, an AI model using TensorFlow performs data analysis to evaluate the user's exercise performance. The analysis output generates a training plan optimized for the user and performance evaluation results.

[0666] Step 4:

[0667] The server visualizes the generated training plan and information about the virtual opponent. The plan includes a training schedule, target pace, and advice for improving form. The virtual opponent is visualized as a benchmark that can be compared to the user's current pace.

[0668] Step 5:

[0669] The server transmits visualized information to the terminal. The terminal provides this information to the user using a display device (smartphone or smart glasses). The user continues training based on the visual information and improves their performance through competition with virtual opponents.

[0670] Step 6:

[0671] After the training is complete, the device sends the user's final performance data back to the server. The server analyzes this data as new feedback and uses it to adjust the plan for the next training session. The feedback specifically shows the user's progress and areas for improvement, providing guidance for the next steps.

[0672] 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.

[0673] This invention is a sports training support system that incorporates an emotion engine to make the user's training experience more personalized. In addition to exercise data and physiological data, this system recognizes the user's emotional state in real time and provides training plans and feedback accordingly.

[0674] Specifically, the device acquires emotional data from the user's facial expressions and voice through an emotion engine. This data is sent to the server along with the user's exercise and physiological data.

[0675] The server analyzes emotional data to determine the user's emotional state and uses generative AI to adjust the training plan accordingly. For example, if the user is feeling stressed, it can incorporate exercises that help them relax.

[0676] Furthermore, the virtual opponent's behavior also changes dynamically based on emotional data. The device adjusts the opponent's difficulty level and reactions according to the user's emotions, providing an environment that allows the user to concentrate more easily.

[0677] As a concrete example, consider a user practicing yoga. If the user is excessively tense, the device's emotion engine detects this state. The server receives and analyzes this information and generates a training plan that incorporates breathing techniques to relieve tension. Additionally, a virtual opponent acts as a guide, demonstrating slow movements to support the user in progressing at their own pace.

[0678] In this way, by taking the user's emotional state into consideration, this system can provide an effective training environment that maintains a balance between mind and body. As a result, users can enjoy a more fulfilling sports experience.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The user puts on the device and begins training. The device uses its built-in sensors to acquire the user's exercise data and emotional data (facial expressions and voice) in real time.

[0682] Step 2:

[0683] The device compresses the exercise, emotional, and physiological data acquired in real time and sends it to the server. The data is encrypted using a secure protocol before transmission.

[0684] Step 3:

[0685] The server analyzes the received motion and emotional data. Using generative AI, it recognizes emotional states in addition to evaluating movements, and determines the user's mental state.

[0686] Step 4:

[0687] The server generates an optimized training plan based on the analysis results, adapted to the user's athletic ability, emotional state, and health condition. Users can select relaxation plans or concentration-enhancing plans as needed.

[0688] Step 5:

[0689] The server sends the generated training plan and data on a virtual opponent that dynamically changes according to the user's emotions to the terminal. The terminal then presents the training plan and virtual opponent to the user visually or audibly.

[0690] Step 6:

[0691] The user executes a training plan and competes against a virtual opponent. The device continuously collects data during exercise and transmits it to the server in real time.

[0692] Step 7:

[0693] After the training is complete, the server re-analyzes all the collected data to evaluate the user's progress. It then generates feedback to help improve future training sessions.

[0694] Step 8:

[0695] The device provides feedback to the user from the server. Based on this feedback, the user becomes aware of improvements in their emotional state and athletic ability, and prepares for the next training session.

[0696] (Example 2)

[0697] 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".

[0698] Traditional training systems focused solely on the user's athletic performance and failed to provide a training experience that took the user's emotional state into account. As a result, users found it difficult to reduce the psychological and emotional burden during training, and the effectiveness of the training could not be optimized. Furthermore, virtual opponents only provided fixed movements and feedback, and did not offer dynamic responses that corresponded to the user's emotional changes.

[0699] 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.

[0700] In this invention, the server includes information acquisition means for collecting user exercise data and emotional data in real time; generation AI means for analyzing the exercise data and emotional data and generating an optimized training plan according to the user's emotional state; and output means for generating a virtual opponent whose behavior dynamically changes based on the emotional state and displaying it to the user. This makes it possible to provide a more personalized training experience based on both the user's emotional state and exercise performance, and to maximize the efficiency of training.

[0701] "Information acquisition means" refers to a device or method for collecting user motor data and emotional data in real time.

[0702] "Generative AI methods" refer to methods that utilize artificial intelligence technology to analyze collected exercise and emotional data and dynamically generate training plans optimized for the user based on that analysis.

[0703] "Output means" refers to a device or method for visually or audibly presenting the generated training plan and the actions of the virtual opponent to the user.

[0704] "Emotional data" refers to data collected from a user's facial expressions, voice, and other sources that indicates the user's psychological and emotional state.

[0705] A "virtual opponent" is a digital opponent generated by a computer or system during a user's training, capable of dynamically responding to the user's emotional state.

[0706] This invention is an advanced training system that provides personalized training plans by utilizing the user's exercise data and emotional data.

[0707] The device interprets the user's facial expressions and voice using an emotion engine and acquires emotional data in real time. It also collects exercise and physiological data from wearable sensors. This utilizes emotion recognition technology using cameras and microphones, as well as accelerometers and heart rate monitors. Specific software includes OpenCV and the Emotion SDK for facial recognition, and libraries for voice analysis.

[0708] The device transmits the collected data to a server via the internet. This data is recorded in a database on the server and analyzed by a generative AI model. The AI ​​model includes deep learning algorithms built using, for example, TensorFlow or PyTorch. This model identifies the user's psychological state and customizes the training plan based on that.

[0709] The server generates an exercise plan tailored to the user by prompting the AI ​​with instructions such as, "Suggest a suitable training method if the user is not relaxed."

[0710] For example, if a user is determined to be experiencing stress, the server creates a plan that includes relaxation exercises and sends it to the user's device. The user interacts with a virtual opponent on the device, and the opponent adjusts its movements according to the user's state, reducing the burden and providing a training environment that makes it easier to concentrate.

[0711] Through these actions, users can enjoy a personalized training experience tailored to their emotional state, enabling them to more effectively maximize the benefits of their exercise.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] When a user begins training, the device activates its emotion engine and uses the camera and microphone to acquire the user's facial and audio data. The input is the user's real-time video feed and audio stream, and the output is emotion data based on this. The emotion engine analyzes, for example, subtle changes in the user's face and tone of voice to identify the user's current emotional state (e.g., stress or tension).

[0715] Step 2:

[0716] The terminal collects emotional data along with motor data (e.g., motion measurements from an accelerometer) and physiological data (e.g., heart rate), packets all the data, and sends it to the server. The input consists of the collected emotional data, motor data, and physiological data, and the output is a single integrated data packet containing all of this data. The terminal encrypts this packet and sends it to the server via a secure communication channel.

[0717] Step 3:

[0718] The server decodes the received data packets and activates the generative AI model. The input is encrypted data packets, and the output is decoded emotional and motor state data. The server applies a deep learning algorithm to analyze the user's emotional state and provides that information to the AI ​​model in the form of a prompt. For example, it might command the AI ​​model to "provide the optimal exercise for when the user is feeling stressed."

[0719] Step 4:

[0720] The generative AI model generates an appropriate training plan based on the input prompt text. The input consists of prompt text and parsed data, and the output is a customized training plan. This plan includes exercises and relaxation techniques that take emotional states into account, resulting in training content that is most suitable for the user.

[0721] Step 5:

[0722] The server sends the generated training plan to the terminal. The input is the generated training plan, and the output is the transmission of its contents to the terminal. The terminal displays the plan to the user and adjusts the movements of the virtual opponent. The opponent acts according to this plan, providing guidance for the user's training.

[0723] Step 6:

[0724] As the user continues training, the device periodically uses an emotion engine to re-evaluate the user's state. Real-time facial and audio data from the training are input, and the updated emotional state is output. The device sends this data back to the server, which re-analyzes it and modifies the training plan if necessary.

[0725] (Application Example 2)

[0726] 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".

[0727] Traditional training systems generate training plans based solely on the user's exercise and physiological data, failing to take into account the user's emotional state and making it difficult to provide a sufficiently personalized service. Therefore, there is a need to reduce the user's psychological burden, maintain motivation, and provide a more effective training experience.

[0728] 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.

[0729] In this invention, the server includes input means for recognizing the user's emotional state in real time, generation means for generating a personalized training plan based on the emotional state, motor data, and physiological data, and output means for adjusting and displaying the actions of a virtual opponent. This makes it possible to provide personalized feedback and a training environment that responds to the user's emotional state.

[0730] "User emotional state" refers to information that shows the user's psychological and emotional state in real time.

[0731] "Input means" refers to a device or method for collecting the user's emotional state, motor data, physiological data, etc.

[0732] "Generation means" refers to an apparatus or method for creating a training plan suitable for a user based on collected data.

[0733] A "virtual opponent" is a computer-generated character or model that operates as if competing or cooperating with the user during user training.

[0734] "Output means" refers to a device or method for presenting a generated training plan or virtual opponent to the user.

[0735] The system that realizes this invention includes multiple hardware and software components to collect the user's emotional state, motor data, and physiological data, and to generate a training plan and feedback based on them.

[0736] First, the device uses built-in sensors (such as cameras and microphones) to identify the user's emotional state in real time from their facial expressions and speech data. This process typically utilizes the Emotion API, which allows for accurate capture of the user's psychological state. In addition, biometric sensors are used to collect motor and physiological data.

[0737] Next, this data is sent to a server. The server uses a generative AI model (for example, OpenAI's GPT series) to generate a training plan optimized for the user's current situation. The generated prompts may include questions such as, "If the user is feeling stressed, what relaxation exercises should be recommended?"

[0738] Furthermore, the server dynamically adjusts the actions of the virtual opponent according to the user's emotional state. This is visualized to the user through a VR display or smartphone screen, providing an immersive training experience. This entire process allows the user to enjoy a highly personalized fitness experience tailored to their emotional state at any given time.

[0739] For example, if a user is observed to lose focus during a fast-paced exercise, the system will suggest slowing down and provide real-time feedback to encourage the user to adjust their pace. An example of a prompt using a generative AI model is, "How can you maintain motivation if the user feels fatigued midway through?"

[0740] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0741] Step 1:

[0742] The device acquires video and audio data of the user's face in real time using its camera and microphone. This input data is sent to the Emotion API to analyze the user's emotional state. The output is the user's emotion tag (e.g., relaxed, stressed, focused).

[0743] Step 2:

[0744] The device uses exercise sensors and biometric sensors to collect the user's exercise data (e.g., heart rate, movement speed) and physiological data. This data is sent directly to the server and used to customize the training plan.

[0745] Step 3:

[0746] The server uses a generative AI model based on received emotional state, exercise data, and physiological data to generate a training plan tailored to the user's current condition. The input is the prompt "If the user is feeling stressed, what relaxation exercises should be recommended?". The output generates specific exercise content.

[0747] Step 4:

[0748] The server adjusts the virtual opponent's behavior based on the generated training plan. It uses prompts that correspond to the user's emotional state to set the opponent's movements and reaction speed. As a result, it provides an environment with an appropriate difficulty level for the user.

[0749] Step 5:

[0750] The device displays a customized training plan and a virtual opponent to the user. Through the display screen and audio output, it provides real-time feedback, showing the user the training content and the opponent's movements.

[0751] Step 6:

[0752] If the user's emotional state or performance changes during training, the device collects new data again and sends it to the server. The server then updates the training plan in real time as needed, continuously providing the optimal training experience.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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."

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0774] The following is further disclosed regarding the embodiments described above.

[0775] (Claim 1)

[0776] An input means for collecting user exercise data in real time,

[0777] A generation means that analyzes the aforementioned exercise data and generates a training plan optimized for the user,

[0778] An output means for generating a virtual opponent and displaying it to the user,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, characterized in that the generation means analyzes the user's past exercise data to provide continuous improvement and feedback.

[0782] (Claim 3)

[0783] The system according to claim 1, characterized in that the input means includes a sensor for collecting physiological data, and the user's health status is evaluated based on the data from the sensor.

[0784] "Example 1"

[0785] (Claim 1)

[0786] An input means including a sensor that collects the user's physiological state in real time,

[0787] A generation means that analyzes the physiological state and generates a training plan tailored to the user,

[0788] An output means for generating a virtual competition and presenting it visually to the user,

[0789] A means of visualizing progress using data generated in real time,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, characterized in that it has a generating means for evaluating continuous growth based on exercise history and providing feedback.

[0793] (Claim 3)

[0794] The system according to claim 1, characterized in that it includes sensors to acquire physiological information and evaluates health indicators based on the collected information.

[0795] "Application Example 1"

[0796] (Claim 1)

[0797] A means of acquiring user biometric data in a time series,

[0798] A means for analyzing the aforementioned biometric data and creating a plan optimized for the user,

[0799] A means of generating and presenting virtual opponents to the user,

[0800] A supply means for supplying visual information using a user-worn display device,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, characterized in that the creation means analyzes continuous improvement using the user's historical motion data and provides feedback.

[0804] (Claim 3)

[0805] The system according to claim 1, characterized in that the acquisition means includes a device for acquiring physical data, and the user's health status is evaluated based on the data from the device.

[0806] "Example 2 of combining an emotion engine"

[0807] (Claim 1)

[0808] A means for acquiring information to collect user exercise data and emotional data in real time,

[0809] A generation AI means that analyzes the aforementioned exercise data and emotional data and generates an optimized training plan according to the user's emotional state,

[0810] An output means that generates a virtual opponent whose behavior changes dynamically based on the aforementioned emotional state and displays it to the user,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, characterized in that the generating AI means analyzes the user's past exercise data and emotional data to provide continuous improvement and feedback.

[0814] (Claim 3)

[0815] The system according to claim 1, characterized in that the information acquisition means includes a sensor that collects physiological information, and the system evaluates the user's physiological state based on the information from the sensor.

[0816] "Application example 2 when combining with an emotional engine"

[0817] (Claim 1)

[0818] An input method that recognizes the user's emotional state in real time,

[0819] A generation means that generates a personalized training plan for the user based on the aforementioned emotional state, exercise data, and physiological data,

[0820] An output means that adjusts and displays the actions of the virtual opponent according to the user's emotional state,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, characterized in that the generation means dynamically adjusts the training plan based on the user's emotional state and provides feedback.

[0824] (Claim 3)

[0825] The system according to claim 1, characterized in that the input means includes a sensor that acquires user speech data and analyzes the user's emotional state based on the data. [Explanation of symbols]

[0826] 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. An input means for collecting user exercise data in real time, A generation means that analyzes the aforementioned exercise data and generates a training plan optimized for the user, An output means for generating a virtual opponent and displaying it to the user, A system that includes this.

2. The system according to claim 1, characterized in that the generation means analyzes continuous improvement based on the user's past exercise data and provides feedback.

3. The system according to claim 1, characterized in that the input means includes a sensor for collecting physiological data, and the user's health status is evaluated based on the data from the sensor.

Images