system

Abstract

We provide the system. [Solution] A data processing means for generating a virtual environment based on the training content selected by the user, A motion capture means for capturing user movements in real time, A data analysis and display means for analyzing captured motion data and displaying feedback, 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 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] When a user practices individually, there is a problem that it is difficult to realistically reproduce the game situation and gain experience close to actual combat. In particular, it has been difficult with conventional techniques to reproduce the movements of an opponent in a plurality of sports, analyze individual movements, and obtain feedback. An object is to solve this problem and provide an environment in which a user can efficiently improve skills.

Means for Solving the Problems

[0005] This invention provides a system that generates a virtual environment based on training content selected by the user. The system includes motion capture means for capturing the user's movements in real time, and data analysis and display means for analyzing the captured movement data and displaying feedback. This allows the user to train alone in an environment similar to a real match, enabling them to concretely understand and improve their own performance. Furthermore, the virtual environment can include multiple sports scenarios, and the user can freely set the characteristics of their opponent, thus enabling training that can adapt to a variety of situations.

[0006] A "user" is the entity that operates the system, selects training content, and practices in a virtual environment.

[0007] "Training content" refers to the sports or specific training scenarios that users can choose from.

[0008] A "virtual environment" is a digital space generated by the system, representing a match situation that is recreated based on a scenario selected by the user.

[0009] A "motion capture device" is a technological device that records a user's physical movements in real time and provides the data to a system.

[0010] "Motion data" refers to information about a user's movements recorded by motion capture technology.

[0011] "Data analysis and display means" refers to technology that analyzes operational data and visually displays the results to the user.

[0012] "Feedback" refers to information that provides analysis results based on user behavior data, indicating training results and areas for improvement.

[0013] A "sports scenario" is a set of settings used to recreate practice sessions and match situations in a specific sport within a virtual environment.

[0014] "Opponent characteristics" refer to the abilities and behavioral patterns of the opponent that the user can virtually set during practice. [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[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 that virtually provides users with a sports training environment. This system is capable of recreating a match environment according to the selected sport and scenario. The user launches the application via a terminal and selects the type of sport they wish to train in from the main menu. The terminal displays a scenario setting screen based on the selected sport, allowing the user to input detailed information about the opponent's characteristics and the match environment.

[0037] The server generates a virtual environment using the input information and recreates the game situation in real time via the user's motion capture device and VR goggles. The user moves within this virtual environment, and the terminal records and analyzes their movements. The movement data is analyzed by the server, and feedback is provided to improve the user's performance. This feedback includes the success rate of the user's movements, areas for improvement, and suggestions for skill improvement in specific situations.

[0038] For example, if a user chooses to practice volleyball serve reception, the device generates a virtual gymnasium and simulates an opponent with set serve speed and trajectory. The user performs the action of receiving the serve in the VR environment, and motion capture data is collected. The server then analyzes the user's motion data in real time and displays feedback through the device. Based on this feedback, the user can adjust their movements and improve their skills. This system allows users to practice in a realistic, match-like environment even when practicing alone.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user launches the application using their device. The device displays the main menu and provides a screen where the user can select the type of sport they want to practice.

[0042] Step 2:

[0043] The user selects their desired sport and scenario. Based on the selection, the device displays a screen where the user can input details about the opponent's characteristics and scenario settings.

[0044] Step 3:

[0045] The terminal connects to the motion capture device and VR goggles, and is ready. The user puts on the equipment and inputs a ready command into the terminal.

[0046] Step 4:

[0047] The server receives user input data and generates a virtual environment. Based on the selected scenario, a specific match situation is recreated in the VR space.

[0048] Step 5:

[0049] The user begins acting according to a scenario set within the VR environment. The terminal acquires the user's movements in real time from a motion capture device and sends them to the server.

[0050] Step 6:

[0051] The server analyzes the operational data it receives and evaluates user performance. The analysis results generate data such as success rate, operational accuracy, and advice for improvement.

[0052] Step 7:

[0053] The device displays the analysis results and provides feedback to the user. Based on the displayed feedback, the user reflects on their actions and considers the next steps to take.

[0054] Step 8:

[0055] If the user chooses to save their practice results, the device will save the analysis results. This allows the user to refer to past data during their next practice session.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Traditional sports training systems have made it difficult for users to experience realistic virtual environments effectively and flexibly. Furthermore, a lack of specific instruction tailored to individual skill improvement hinders users from efficiently enhancing their abilities. Additionally, conventional technologies have limitations in setting scenarios to meet user needs, sometimes preventing the provision of training optimized for individual users.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user, motion capture means for recording the user's movements in real time, and information analysis and presentation means for analyzing the recorded movement information and displaying the results. This enables the user to experience effective training in the virtual environment in real time and receive specific feedback for improving their individual skills.

[0061] "Information processing means" refers to a device or method for generating an appropriate virtual environment based on the training content selected by the user.

[0062] "Motion capture means" refers to a device or method that records a user's physical movements in real time and acquires those movements as data.

[0063] "Information analysis and presentation means" refers to a device or method that analyzes recorded motion information and provides the results to the user visually or audibly.

[0064] A "generative model" is an algorithm or technology used to generate new scenarios or content based on specific information.

[0065] A "user interface" is a means by which a user interacts with a system and inputs or outputs information, and typically includes a display or input device.

[0066] This invention relates to a system that provides a virtual sports training environment to a user. The system mainly consists of three components: a server, a terminal, and a user.

[0067] The terminal launches the application via a user interface and displays a menu for the user to select the sport they wish to train in. The user then configures details such as the opponent's characteristics and the match environment based on their selection, and sends this information to the server.

[0068] The server generates a virtual match environment using information processing tools based on the information it receives. In this process, it utilizes a generation AI model to construct multiple scenarios and character movements. Specifically, graphics engines such as Unreal Engine and Unity are used to realize a highly accurate and realistic virtual environment. The generated environment is transmitted to the user's terminal via the user's motion capture device and VR goggles, allowing the user to experience the match in real time.

[0069] The user wears a motion capture device and trains in a virtual environment. This device accurately records the user's movements and transmits the data to a server. The server analyzes the movement information using information analysis and presentation tools and generates feedback on the user's performance. This feedback is provided to the user via a terminal and contributes to the user's skill improvement.

[0070] For example, if a user selects a volleyball practice scenario, the device creates a virtual gymnasium and generates a virtual opponent based on the set conditions. The user can then use the VR system to practice serve reception and receive appropriate advice and improvement suggestions from the server.

[0071] Example prompt: "Generate a volleyball practice environment. Focus on serve reception training, set the serve speed to 25 meters per second, and set the trajectory to random."

[0072] This system allows users to easily experience advanced virtual sports training tailored to their individual needs, either at home or in any location.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user launches the training application on their device and selects their desired sport from the main menu. The input is the sport selected by the user. The output is the next interface screen based on the selected option. In this step, the application presents a sport scenario in the user interface according to the user's selection.

[0076] Step 2:

[0077] The terminal displays a scenario settings screen related to the sport selected by the user, allowing the user to input details about the opponent's characteristics and the match environment. The input consists of information about the opponent's characteristics and the match environment settings chosen by the user. The output is the confirmed settings data after reviewing the entered settings. This settings data is then sent to the server as information necessary for the subsequent virtual environment generation.

[0078] Step 3:

[0079] The server generates a virtual environment using information processing tools based on configuration data sent from the terminal. It utilizes a generation AI model to construct realistic match scenarios based on selected conditions. The input is the scenario data set by the user. The output is the constructed virtual environment. The server uses a graphics engine to create a virtual match scene and sends the results to the terminal.

[0080] Step 4:

[0081] The user wears VR goggles and a motion capture device and trains in a virtual environment provided by the server. The input is the user's physical movements within the virtual environment. The output is motion data recorded in real time. The motion capture device precisely captures the user's movements and collects motion data.

[0082] Step 5:

[0083] The server analyzes the acquired motion data using information analysis and presentation tools to generate feedback on the user's performance. The input is the user's recorded motion data. The output is feedback information obtained from the motion analysis. This feedback information is sent to the terminal as effective training advice and presented to the user.

[0084] Step 6:

[0085] The user reviews the feedback displayed on the device and takes action to improve their actions and skills. The input is the feedback provided by the server. The output is the actions and skills that the user corrects or improves based on the feedback. This final step allows the user to continuously improve their skills.

[0086] (Application Example 1)

[0087] 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."

[0088] In modern times, effective training in sports and physical activities requires actual training grounds and instructors. However, frequent use of these resources is difficult for many people due to time constraints and financial burdens. Furthermore, it is difficult to receive feedback when training alone, making effective skill improvement challenging. To solve these problems, there is a need for methods that provide advanced virtual training experiences at home, enabling users to effectively improve their skills.

[0089] 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.

[0090] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user, motion capture means for capturing the user's actions in real time, data analysis and display means for analyzing the captured motion data and suggesting areas for improvement, and means for making suggestions for improving the user's skills using a generated AI model. As a result, the user can practice in a realistic virtual environment from the comfort of their home and receive instant feedback to improve their skills.

[0091] "Information processing means" refers to a device that has the function of generating a virtual environment corresponding to the training content selected by the user.

[0092] A "motion tracking means" is a device that has the function of recording and tracking the user's movements in real time.

[0093] "Data analysis and display means" refers to a device that analyzes captured user behavior data and visually presents the analysis results to the user.

[0094] A "generative AI model" is a computational model that uses machine learning techniques to generate improvement suggestions based on user behavior data.

[0095] "Means for providing suggestions for skill improvement" refers to a device that has the function of providing information and advice that contributes to improving the user's skills using AI models generated by the server.

[0096] This invention is a system that generates a virtual environment based on the sports training content selected by the user and provides feedback by capturing the user's movements in real time. The main components and their functions are described below.

[0097] The server generates a virtual environment based on the training content selected by the user through information processing. The virtual environment embodies scenarios for various sports such as soccer and volleyball, and allows the user to set the characteristics of the simulated opponent.

[0098] The terminal is equipped with motion capture capabilities that record the user's movements in real time. Furthermore, the terminal transmits the captured motion data to a server, where it is analyzed and displayed. The results of this analysis are provided to the user visually as suggestions for improvement and feedback.

[0099] The server uses a generative AI model to provide suggestions for improving the user's skills based on the analysis results. These suggestions are provided in a prompt format, based on the analysis of the user's actions by the generative AI model. For example, specific advice such as, "If you adjust the angle of your kick a little more inward, your shooting accuracy will improve," may be given. This allows the user to improve their skills through practice in a virtual environment.

[0100] An example of a prompt message would be: "User data: {Action data}. Please use this data to tell me how to improve my soccer free kick."

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The user launches the training application on their device and selects the type of sport and scenario they wish to train. The input is the user's selection information, and the output is configuration data for the virtual environment based on this information. The device sends this data to the server.

[0104] Step 2:

[0105] The server uses information processing tools to generate a virtual environment corresponding to the selected sport based on the received configuration data. The input is the configured sport and scenario information, and the output is the scene data of the virtual environment. This data is sent to the terminal, and the virtual environment is displayed to the user.

[0106] Step 3:

[0107] The user starts an action within the virtual environment, and the terminal's action tracking system records the user's actions in real time. The input is the physical data of the user's actions, and the output is the recorded action data. This data is sent to the server.

[0108] Step 4:

[0109] The server analyzes the received motion data using data analysis and display means. The input is the user's motion data, and the output is detailed information about the analyzed motion. The analysis includes motion speed, angle, trajectory, etc.

[0110] Step 5:

[0111] The server uses a generative AI model to generate prompts for improving the user's skills based on the analysis results. The input is the analysis results, and the output is a feedback prompt that includes suggestions. For example, specific advice might be given such as, "If you angle your kick a little more inward, your shooting accuracy will improve."

[0112] Step 6:

[0113] The terminal displays feedback received from the server to the user. The input is a feedback prompt, and the output is a visual feedback presentation to the user. Based on this feedback, the user can adjust their actions and improve their skills.

[0114] 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.

[0115] This invention provides a virtual environment based on training content, enhancing the user's practice experience. Furthermore, this system incorporates an emotion engine that can recognize the user's emotional state in real time and improve the quality of feedback.

[0116] This system begins with the user selecting a sport or scenario using a terminal and starting training in a virtual environment. The terminal considers the characteristics of the opponent entered by the user and generates a realistic virtual environment via a motion capture device and VR goggles. While the user performs actions, the motion capture device collects data on the user's movements and sends it to a server. This motion data is then analyzed, and detailed feedback is provided.

[0117] Furthermore, the emotion engine analyzes the user's emotions in real time from their facial expressions and voice. Based on the data obtained from the emotion engine, the device can provide appropriate feedback according to the user's mental state. For example, if the user's motivation decreases during practice, the emotion engine will detect this, and the device will provide encouraging messages or adjust the training content.

[0118] As a concrete example, consider a scenario where a user is practicing tennis serve returns. Within the virtual environment, pre-set serve patterns are reproduced, and the user performs return actions in accordance with those patterns. The user's movements are measured by a motion capture device and analyzed in real time on a server. In addition, an emotion engine determines the user's level of concentration and tension from their facial expressions and provides necessary feedback on the device. This allows the user to obtain a highly personalized practice experience.

[0119] This system makes individual sports training more effective and interactive, supporting the user's skill improvement.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The user launches the application using their device and selects the sport and scenario they want to practice. The device then receives the user's selection and displays a settings screen for creating the virtual environment.

[0123] Step 2:

[0124] The user inputs the opponent's characteristics and various settings necessary for training. The terminal sends this information to the server and requests the creation of a virtual environment.

[0125] Step 3:

[0126] Based on the configuration information received by the server, a virtual environment is generated that reproduces the selected sports scenario. The virtual sports scene is then displayed in real time on the VR goggles connected to the terminal.

[0127] Step 4:

[0128] The user wears a motion capture device and begins performing actual movements within a virtual environment. The terminal acquires the user's movement data from the motion capture device and sends it to the server.

[0129] Step 5:

[0130] The server analyzes user action data in real time to evaluate the effectiveness of practice. Success rates and accuracy of actions are determined, and feedback is formulated accordingly.

[0131] Step 6:

[0132] The emotion engine recognizes and analyzes the user's emotions from their facial expressions and tone of voice. The device receives the emotion data, makes dynamic adjustments based on feedback from the server, and displays the results on the screen.

[0133] Step 7:

[0134] The device provides feedback information to the user and adjusts the training content and situation as needed. Based on the advice provided, the user works to improve their practice.

[0135] Step 8:

[0136] When a user finishes a practice session, the device saves the analysis results. This allows the user to refer to past records in subsequent practice sessions and track their progress.

[0137] (Example 2)

[0138] 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".

[0139] Conventional virtual training systems have faced challenges such as limited feedback on user actions and a lack of appropriate feedback that takes emotional states into account. This made it difficult to achieve sufficient results in improving individual users' skills and maintaining their motivation.

[0140] 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.

[0141] In this invention, the server includes information processing means for generating a simulation environment based on training content selected by the user; motion capture means for capturing the user's actions in real time; information analysis and presentation means for analyzing the captured motion information and presenting the information; emotion analysis means for analyzing the user's facial expressions and voice information and determining their emotional state; and feedback means for providing adaptive advice according to the user's mental state. This enables highly personalized feedback that responds to the user's actions and emotional state.

[0142] "Information processing means" refers to a function within the system that generates a virtual simulation environment based on the training content selected by the user.

[0143] "Motion tracking means" refers to a part of a system that senses the user's physical movements in real time and records them as digital information.

[0144] "Information analysis and presentation means" refers to technology for analyzing digital information obtained by motion capture means and providing the user with the analysis results.

[0145] "Emotional analysis methods" refer to technologies that analyze a user's facial expressions and voice data to estimate their psychological state and emotions.

[0146] "Feedback mechanism" refers to a system function that provides optimal guidance and advice to the user based on analyzed behavioral information and emotional state.

[0147] This invention provides a system that offers a virtual environment based on the training content selected by the user, thereby personalizing the user's practice experience. This system, primarily composed of a server and a terminal, operates as follows:

[0148] The user begins training by selecting the sport or scenario they want to practice via the device. The device then builds a simulation environment based on the user's input. The main hardware used here is VR goggles and motion capture devices. This allows the user to experience training in a realistic virtual environment.

[0149] The user begins moving within the virtual environment. A motion capture device tracks the user's movements in real time and transmits the movement information to the server via the terminal. The server analyzes this data using a generative AI model and generates feedback on the accuracy of the movements and areas for improvement.

[0150] Furthermore, the device incorporates an emotion analysis function that analyzes the user's facial expressions and voice data in real time. Based on this analysis, the device provides feedback tailored to the user's psychological state. For example, if the device determines that the user's concentration is declining, it displays an encouraging message to boost their motivation. This allows the user to engage in training in the most optimal state.

[0151] As a concrete example, when a user practices tennis service returns, various serve scenarios are reproduced on a virtual court. The user's return movements are recorded using motion capture and analyzed on a server. In addition, messages such as "Relax and try your next return" are displayed on the terminal according to the user's emotional state. In this way, the present invention effectively supports skill improvement by providing personalized training for the user.

[0152] An example of a prompt would be: "Simulate tennis serve return practice in a virtual environment. Analyze my motion capture data and provide real-time feedback. Also, take my emotional state into consideration and offer words of encouragement as needed."

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The user uses a terminal to select the sport or scenario they wish to train in. The input is the user's selected sport or scenario, and the output is the selected data. This selected data is used as foundational information for the subsequent virtual environment construction.

[0156] Step 2:

[0157] The terminal activates VR goggles and motion capture devices based on the training content selected by the user, generating a simulation environment. The input is the selected data obtained in step 1, and the output is the constructed virtual environment. This process prepares the user to begin training visually and haptically.

[0158] Step 3:

[0159] The user begins training in a virtual environment, and a motion capture device tracks the user's movements in real time. The input is the user's physical movements, and the output is digitized motion data. This data is transmitted in real time to the server via the terminal.

[0160] Step 4:

[0161] The server analyzes the received motion data using a generating AI model. The input is the motion data sent in step 3, and the output is the user's motion analysis results and feedback information on areas for improvement. This analysis provides a detailed evaluation of the user's actions.

[0162] Step 5:

[0163] As a means of emotion analysis, the device analyzes the user's facial expressions and voice in real time. The input is the user's facial expressions and voice data, and the output is information about the user's emotional state. This emotion analysis allows for an understanding of the user's psychological state.

[0164] Step 6:

[0165] The device provides personalized feedback to the user based on the behavioral and emotional analysis results from the server. The input is the analysis results obtained in steps 4 and 5, and the output is audio or visual feedback messages. For example, if the device determines that the user is not concentrating, a message such as "Relax and try the next action" will be displayed.

[0166] Through these steps, users can efficiently improve their skills while receiving feedback tailored to their individual circumstances.

[0167] (Application Example 2)

[0168] 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 device 14 will be referred to as the "terminal."

[0169] Training robot operators in factories presents a challenge: the lack of real-time feedback based on emotional states and motion data in typical training environments hinders the efficient improvement of operator skills.

[0170] 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.

[0171] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user; motion capture means for recording the user's movements in real time; information analysis and presentation means for analyzing the recorded movement information and providing feedback; emotion analysis means for analyzing the user's emotional state and reflecting it in the feedback; and visual presentation means for visually presenting the feedback on a display device. This enables the operator to receive detailed feedback based on their individual movements and emotional state.

[0172] "Information processing means" refers to a device or system that has the function of generating a virtual environment based on the training content selected by the user.

[0173] "Motion capture means" refers to a device or technology for recording a user's movements in real time and acquiring that data.

[0174] "Information analysis and presentation means" refers to a device or function for analyzing recorded operational information and presenting the results to the user as feedback.

[0175] "Emotional analysis means" refers to a technology or system for analyzing a user's emotional state and using that data for training feedback.

[0176] A "visual presentation means" is a display device or interface for visually presenting data-based feedback to a user.

[0177] This invention is a system for training robots in environments such as factories. The system uses information processing means to generate a virtual environment based on training content selected by the user. Motion capture devices worn by the user record movements in real time, and this data is transmitted to a server. The server analyzes the motion data using Xsens' MVN Animate. In addition, Affectiva's emotion analysis engine is used to evaluate the user's emotional state in real time from their facial expressions and voice, and the results are reflected in the training feedback.

[0178] Feedback information is visually presented on the user's smart glasses. For example, using Microsoft HoloLens®, suggestions for improving operation and guidance are overlaid on the user's field of view. If the user's movements are analyzed as unstable while they are virtually operating the robot, a message such as "Your movements are clumsy, try to operate calmly" will be displayed on HoloLens, and hints on how to operate will be provided. This allows users to receive immediate feedback tailored to their individual situation, enabling them to efficiently improve their skills.

[0179] Specific examples of prompts include phrases like, "Please consider strategies to adjust the training environment to help the user concentrate." This allows the generative AI model to provide the optimal training methods and strategies for the user.

[0180] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0181] Step 1:

[0182] The terminal receives input from the user and determines the training content and the characteristics of the opponent. This input consists of the training menu selected by the user and attribute data of the opponent. Based on this information, the terminal sends commands to the server to generate the virtual environment.

[0183] Step 2:

[0184] The server generates a virtual environment based on the received training data. Using Xsens' MVN Animate and related software, it prepares to record the user's actions in real time. The output is the generated virtual environment data, which is sent to the user's terminal.

[0185] Step 3:

[0186] The user puts on smart glasses and begins training in a virtual environment. This allows the user's motion data to be collected in real time by a motion capture device and sent to a server. The input is the user's motion data, and the output is the motion information sent to the server.

[0187] Step 4:

[0188] The system analyzes the operational data received by the server. Here, a generative AI model is used to evaluate the accuracy and stability of the operations. The data calculation results in analyzed operational information, which influences subsequent feedback. The output is the operational analysis result.

[0189] Step 5:

[0190] The emotion analysis system collects the user's facial expressions and voice data and analyzes their emotional state in real time. The emotion engine handles this, taking the user's facial expressions and voice data as input. The analyzed results are used in the next step. The output is the emotion analysis result.

[0191] Step 6:

[0192] The server integrates the results of motion analysis and emotion analysis to generate user-specific feedback. This feedback is presented to the user's smart glasses using visual means. This feedback includes suggestions for motion improvement and emotion-based advice. The input is the results of motion and emotion analysis, and the output is the feedback information presented to the user.

[0193] Step 7:

[0194] The feedback provided by the user is reviewed and used to improve subsequent actions and training. This allows the user to enhance their training effectiveness and further improve their skills.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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".

[0211] This invention is a system that virtually provides users with a sports training environment. This system is capable of recreating a match environment according to the selected sport and scenario. The user launches the application via a terminal and selects the type of sport they wish to train in from the main menu. The terminal displays a scenario setting screen based on the selected sport, allowing the user to input detailed information about the opponent's characteristics and the match environment.

[0212] The server generates a virtual environment using the input information and recreates the game situation in real time via the user's motion capture device and VR goggles. The user moves within this virtual environment, and the terminal records and analyzes their movements. The movement data is analyzed by the server, and feedback is provided to improve the user's performance. This feedback includes the success rate of the user's movements, areas for improvement, and suggestions for skill improvement in specific situations.

[0213] For example, if a user chooses to practice volleyball serve reception, the device generates a virtual gymnasium and simulates an opponent with set serve speed and trajectory. The user performs the action of receiving the serve in the VR environment, and motion capture data is collected. The server then analyzes the user's motion data in real time and displays feedback through the device. Based on this feedback, the user can adjust their movements and improve their skills. This system allows users to practice in a realistic, match-like environment even when practicing alone.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The user launches the application using their device. The device displays the main menu and provides a screen where the user can select the type of sport they want to practice.

[0217] Step 2:

[0218] The user selects their desired sport and scenario. Based on the selection, the device displays a screen where the user can input details about the opponent's characteristics and scenario settings.

[0219] Step 3:

[0220] The terminal connects to the motion capture device and VR goggles, and is ready. The user puts on the equipment and inputs a ready command into the terminal.

[0221] Step 4:

[0222] The server receives user input data and generates a virtual environment. Based on the selected scenario, a specific match situation is recreated in the VR space.

[0223] Step 5:

[0224] The user begins acting according to a scenario set within the VR environment. The terminal acquires the user's movements in real time from a motion capture device and sends them to the server.

[0225] Step 6:

[0226] The server analyzes the operational data it receives and evaluates user performance. The analysis results generate data such as success rate, operational accuracy, and advice for improvement.

[0227] Step 7:

[0228] The device displays the analysis results and provides feedback to the user. Based on the displayed feedback, the user reflects on their actions and considers the next steps to take.

[0229] Step 8:

[0230] If the user chooses to save their practice results, the device will save the analysis results. This allows the user to refer to past data during their next practice session.

[0231] (Example 1)

[0232] 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."

[0233] Traditional sports training systems have made it difficult for users to experience realistic virtual environments effectively and flexibly. Furthermore, a lack of specific instruction tailored to individual skill improvement hinders users from efficiently enhancing their abilities. Additionally, conventional technologies have limitations in setting scenarios to meet user needs, sometimes preventing the provision of training optimized for individual users.

[0234] 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.

[0235] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user, motion capture means for recording the user's movements in real time, and information analysis and presentation means for analyzing the recorded movement information and displaying the results. This enables the user to experience effective training in the virtual environment in real time and receive specific feedback for improving their individual skills.

[0236] "Information processing means" refers to a device or method for generating an appropriate virtual environment based on the training content selected by the user.

[0237] "Motion capture means" refers to a device or method that records a user's physical movements in real time and acquires those movements as data.

[0238] "Information analysis and presentation means" refers to a device or method that analyzes recorded motion information and provides the results to the user visually or audibly.

[0239] A "generative model" is an algorithm or technology used to generate new scenarios or content based on specific information.

[0240] A "user interface" is a means by which a user interacts with a system and inputs or outputs information, and typically includes a display or input device.

[0241] This invention relates to a system that provides a virtual sports training environment to a user. The system mainly consists of three components: a server, a terminal, and a user.

[0242] The terminal launches the application via a user interface and displays a menu for the user to select the sport they wish to train in. The user then configures details such as the opponent's characteristics and the match environment based on their selection, and sends this information to the server.

[0243] The server generates a virtual match environment using information processing tools based on the information it receives. In this process, it utilizes a generation AI model to construct multiple scenarios and character movements. Specifically, graphics engines such as Unreal Engine and Unity are used to realize a highly accurate and realistic virtual environment. The generated environment is transmitted to the user's terminal via the user's motion capture device and VR goggles, allowing the user to experience the match in real time.

[0244] The user wears a motion capture device and trains in a virtual environment. This device accurately records the user's movements and transmits the data to a server. The server analyzes the movement information using information analysis and presentation tools and generates feedback on the user's performance. This feedback is provided to the user via a terminal and contributes to the user's skill improvement.

[0245] For example, if a user selects a volleyball practice scenario, the device creates a virtual gymnasium and generates a virtual opponent based on the set conditions. The user can then use the VR system to practice serve reception and receive appropriate advice and improvement suggestions from the server.

[0246] Example prompt: "Generate a volleyball practice environment. Focus on serve reception training, set the serve speed to 25 meters per second, and set the trajectory to random."

[0247] This system allows users to easily experience advanced virtual sports training tailored to their individual needs, either at home or in any location.

[0248] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0249] Step 1:

[0250] The user launches the training application on their device and selects their desired sport from the main menu. The input is the sport selected by the user. The output is the next interface screen based on the selected option. In this step, the application presents a sport scenario in the user interface according to the user's selection.

[0251] Step 2:

[0252] The terminal displays a scenario settings screen related to the sport selected by the user, allowing the user to input details about the opponent's characteristics and the match environment. The input consists of information about the opponent's characteristics and the match environment settings chosen by the user. The output is the confirmed settings data after reviewing the entered settings. This settings data is then sent to the server as information necessary for the subsequent virtual environment generation.

[0253] Step 3:

[0254] The server generates a virtual environment using information processing tools based on configuration data sent from the terminal. It utilizes a generation AI model to construct realistic match scenarios based on selected conditions. The input is the scenario data set by the user. The output is the constructed virtual environment. The server uses a graphics engine to create a virtual match scene and sends the results to the terminal.

[0255] Step 4:

[0256] The user wears VR goggles and a motion capture device and trains in a virtual environment provided by the server. The input is the user's physical movements within the virtual environment. The output is motion data recorded in real time. The motion capture device precisely captures the user's movements and collects motion data.

[0257] Step 5:

[0258] The server analyzes the acquired motion data using information analysis and presentation tools to generate feedback on the user's performance. The input is the user's recorded motion data. The output is feedback information obtained from the motion analysis. This feedback information is sent to the terminal as effective training advice and presented to the user.

[0259] Step 6:

[0260] The user reviews the feedback displayed on the device and takes action to improve their actions and skills. The input is the feedback provided by the server. The output is the actions and skills that the user corrects or improves based on the feedback. This final step allows the user to continuously improve their skills.

[0261] (Application Example 1)

[0262] 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."

[0263] In modern times, effective training in sports and physical activities requires actual training grounds and instructors. However, frequent use of these resources is difficult for many people due to time constraints and financial burdens. Furthermore, it is difficult to receive feedback when training alone, making effective skill improvement challenging. To solve these problems, there is a need for methods that provide advanced virtual training experiences at home, enabling users to effectively improve their skills.

[0264] 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.

[0265] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user, motion capture means for capturing the user's actions in real time, data analysis and display means for analyzing the captured motion data and suggesting areas for improvement, and means for making suggestions for improving the user's skills using a generated AI model. As a result, the user can practice in a realistic virtual environment from the comfort of their home and receive instant feedback to improve their skills.

[0266] "Information processing means" refers to a device that has the function of generating a virtual environment corresponding to the training content selected by the user.

[0267] A "motion tracking means" is a device that has the function of recording and tracking the user's movements in real time.

[0268] "Data analysis and display means" refers to a device that analyzes captured user behavior data and visually presents the analysis results to the user.

[0269] A "generative AI model" is a computational model that uses machine learning techniques to generate improvement suggestions based on user behavior data.

[0270] "Means for providing suggestions for skill improvement" refers to a device that has the function of providing information and advice that contributes to improving the user's skills using AI models generated by the server.

[0271] This invention is a system that generates a virtual environment based on the sports training content selected by the user and provides feedback by capturing the user's movements in real time. The main components and their functions are described below.

[0272] The server generates a virtual environment based on the training content selected by the user through information processing. The virtual environment embodies scenarios for various sports such as soccer and volleyball, and allows the user to set the characteristics of the simulated opponent.

[0273] The terminal is equipped with motion capture capabilities that record the user's movements in real time. Furthermore, the terminal transmits the captured motion data to a server, where it is analyzed and displayed. The results of this analysis are provided to the user visually as suggestions for improvement and feedback.

[0274] The server uses a generative AI model to provide suggestions for improving the user's skills based on the analysis results. These suggestions are provided in a prompt format, based on the analysis of the user's actions by the generative AI model. For example, specific advice such as, "If you adjust the angle of your kick a little more inward, your shooting accuracy will improve," may be given. This allows the user to improve their skills through practice in a virtual environment.

[0275] An example of a prompt message would be: "User data: {Action data}. Please use this data to tell me how to improve my soccer free kick."

[0276] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0277] Step 1:

[0278] The user starts the training application on the terminal and selects the type of sport and scenario to be trained. The input is the user's selection information, and the output is the setting data of the virtual environment based on this information. The terminal sends this data to the server.

[0279] Step 2:

[0280] Based on the received setting data, the server uses the information processing means to generate a virtual environment corresponding to the selected sport. The input is the set sport and scenario information, and the output is the scene data of the virtual environment. This data is sent to the terminal and the virtual environment is displayed to the user.

[0281] Step 3:

[0282] The user starts to move in the virtual environment, and the motion capture means of the terminal records the user's motion in real time. The input is the physical data of the user's motion, and the output is the recorded motion data. This data is sent to the server.

[0283] Step 4:

[0284] The server analyzes the received motion data by means of data analysis and display. The input is the user's motion data, and the output is the detailed information of the analyzed motion. The analysis includes the speed, angle, trajectory, etc. of the motion.

[0285] Step 5:

[0286] The server uses a generative AI model to generate prompt texts for improving the user's skills based on the analysis results. The input is the analysis result, and the output is the prompt text of the feedback including suggestions. As a specific advice, it is shown that "if you make the angle of your kick a little more inside, the shooting accuracy will improve".

[0287] Step 6:

[0288] The terminal displays the feedback received from the server to the user. The input is the prompt text of the feedback, and the output is the visual feedback presentation to the user. Based on this feedback, the user can adjust their actions and improve their skills.

[0289] 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.

[0290] The present invention is a system for providing a virtual environment based on training content and enhancing the user's practice experience. Furthermore, an emotion engine is incorporated into this system, which can recognize the user's emotional state in real time and improve the quality of feedback.

[0291] This system starts with the user selecting a sport or scenario using a terminal and starting training in a virtual environment. The terminal generates a realistic virtual environment through a motion capture device and VR goggles considering the characteristics of the opponent input by the user. While the user is performing an action, the motion capture device collects data on the user's movement and transmits it to the server. Thereby, the motion data is analyzed and detailed feedback is provided.

[0292] Furthermore, the emotion engine analyzes the user's emotions in real time from their facial expressions and voice. Based on the data obtained from the emotion engine, the device can provide appropriate feedback according to the user's mental state. For example, if the user's motivation decreases during practice, the emotion engine will detect this, and the device will provide encouraging messages or adjust the training content.

[0293] As a concrete example, consider a scenario where a user is practicing tennis serve returns. Within the virtual environment, pre-set serve patterns are reproduced, and the user performs return actions in accordance with those patterns. The user's movements are measured by a motion capture device and analyzed in real time on a server. In addition, an emotion engine determines the user's level of concentration and tension from their facial expressions and provides necessary feedback on the device. This allows the user to obtain a highly personalized practice experience.

[0294] This system makes individual sports training more effective and interactive, supporting the user's skill improvement.

[0295] The following describes the processing flow.

[0296] Step 1:

[0297] The user launches the application using their device and selects the sport and scenario they want to practice. The device then receives the user's selection and displays a settings screen for creating the virtual environment.

[0298] Step 2:

[0299] The user inputs the opponent's characteristics and various settings necessary for training. The terminal sends this information to the server and requests the creation of a virtual environment.

[0300] Step 3:

[0301] Based on the configuration information received by the server, a virtual environment is generated to reproduce the selected sports scenario. In the VR goggles connected to the terminal, the virtual sports scene is displayed in real time.

[0302] Step 4:

[0303] The user wears a motion capture device and starts actual operations within the virtual environment. The terminal acquires the user's motion data from the motion capture device and transmits it to the server.

[0304] Step 5:

[0305] The server analyzes the user's motion data in real time and evaluates the effectiveness of the practice. The success rate and the accuracy of the operations are determined, and the content of the feedback is formulated.

[0306] Step 6:

[0307] The emotion engine recognizes and analyzes emotions from the user's expressions and voice tones. The terminal receives the emotion data, makes dynamic adjustments according to the user based on the feedback from the server, and displays it on the screen.

[0308] Step 7:

[0309] The terminal presents the feedback information to the user and adjusts the training content and situation as necessary. The user makes efforts to improve the practice based on the presented advice.

[0310] Step 8:

[0311] When the user finishes the practice session, the terminal saves the analysis results. Thereby, the user can refer to the past records in subsequent practices and confirm their progress.

[0312] (Example 2)

[0313] 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".

[0314] Conventional virtual training systems have faced challenges such as limited feedback on user actions and a lack of appropriate feedback that takes emotional states into account. This made it difficult to achieve sufficient results in improving individual users' skills and maintaining their motivation.

[0315] 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.

[0316] In this invention, the server includes information processing means for generating a simulation environment based on training content selected by the user; motion capture means for capturing the user's actions in real time; information analysis and presentation means for analyzing the captured motion information and presenting the information; emotion analysis means for analyzing the user's facial expressions and voice information and determining their emotional state; and feedback means for providing adaptive advice according to the user's mental state. This enables highly personalized feedback that responds to the user's actions and emotional state.

[0317] "Information processing means" refers to a function within the system that generates a virtual simulation environment based on the training content selected by the user.

[0318] "Motion tracking means" refers to a part of a system that senses the user's physical movements in real time and records them as digital information.

[0319] "Information analysis and presentation means" refers to technology for analyzing digital information obtained by motion capture means and providing the user with the analysis results.

[0320] "Emotional analysis methods" refer to technologies that analyze a user's facial expressions and voice data to estimate their psychological state and emotions.

[0321] "Feedback mechanism" refers to a system function that provides optimal guidance and advice to the user based on analyzed behavioral information and emotional state.

[0322] This invention provides a system that offers a virtual environment based on the training content selected by the user, thereby personalizing the user's practice experience. This system, primarily composed of a server and a terminal, operates as follows:

[0323] The user begins training by selecting the sport or scenario they want to practice via the device. The device then builds a simulation environment based on the user's input. The main hardware used here is VR goggles and motion capture devices. This allows the user to experience training in a realistic virtual environment.

[0324] The user begins moving within the virtual environment. A motion capture device tracks the user's movements in real time and transmits the movement information to the server via the terminal. The server analyzes this data using a generative AI model and generates feedback on the accuracy of the movements and areas for improvement.

[0325] Furthermore, the device incorporates an emotion analysis function that analyzes the user's facial expressions and voice data in real time. Based on this analysis, the device provides feedback tailored to the user's psychological state. For example, if the device determines that the user's concentration is declining, it displays an encouraging message to boost their motivation. This allows the user to engage in training in the most optimal state.

[0326] As a concrete example, when a user practices tennis service returns, various serve scenarios are reproduced on a virtual court. The user's return movements are recorded using motion capture and analyzed on a server. In addition, messages such as "Relax and try your next return" are displayed on the terminal according to the user's emotional state. In this way, the present invention effectively supports skill improvement by providing personalized training for the user.

[0327] An example of a prompt would be: "Simulate tennis serve return practice in a virtual environment. Analyze my motion capture data and provide real-time feedback. Also, take my emotional state into consideration and offer words of encouragement as needed."

[0328] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0329] Step 1:

[0330] The user uses a terminal to select the sport or scenario they wish to train in. The input is the user's selected sport or scenario, and the output is the selected data. This selected data is used as foundational information for the subsequent virtual environment construction.

[0331] Step 2:

[0332] The terminal activates VR goggles and motion capture devices based on the training content selected by the user, generating a simulation environment. The input is the selected data obtained in step 1, and the output is the constructed virtual environment. This process prepares the user to begin training visually and haptically.

[0333] Step 3:

[0334] The user begins training in a virtual environment, and a motion capture device tracks the user's movements in real time. The input is the user's physical movements, and the output is digitized motion data. This data is transmitted in real time to the server via the terminal.

[0335] Step 4:

[0336] The server analyzes the received motion data using a generating AI model. The input is the motion data sent in step 3, and the output is the user's motion analysis results and feedback information on areas for improvement. This analysis provides a detailed evaluation of the user's actions.

[0337] Step 5:

[0338] As a means of emotion analysis, the device analyzes the user's facial expressions and voice in real time. The input is the user's facial expressions and voice data, and the output is information about the user's emotional state. This emotion analysis allows for an understanding of the user's psychological state.

[0339] Step 6:

[0340] The device provides personalized feedback to the user based on the behavioral and emotional analysis results from the server. The input is the analysis results obtained in steps 4 and 5, and the output is audio or visual feedback messages. For example, if the device determines that the user is not concentrating, a message such as "Relax and try the next action" will be displayed.

[0341] Through these steps, users can efficiently improve their skills while receiving feedback tailored to their individual circumstances.

[0342] (Application Example 2)

[0343] 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 as the "terminal".

[0344] Training robot operators in factories presents a challenge: the lack of real-time feedback based on emotional states and motion data in typical training environments hinders the efficient improvement of operator skills.

[0345] 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.

[0346] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user; motion capture means for recording the user's movements in real time; information analysis and presentation means for analyzing the recorded movement information and providing feedback; emotion analysis means for analyzing the user's emotional state and reflecting it in the feedback; and visual presentation means for visually presenting the feedback on a display device. This enables the operator to receive detailed feedback based on their individual movements and emotional state.

[0347] "Information processing means" refers to a device or system that has the function of generating a virtual environment based on the training content selected by the user.

[0348] "Motion capture means" refers to a device or technology for recording a user's movements in real time and acquiring that data.

[0349] "Information analysis and presentation means" refers to a device or function for analyzing recorded operational information and presenting the results to the user as feedback.

[0350] "Emotional analysis means" refers to a technology or system for analyzing a user's emotional state and using that data for training feedback.

[0351] A "visual presentation means" is a display device or interface for visually presenting data-based feedback to a user.

[0352] This invention is a system for training robots in environments such as factories. The system uses information processing means to generate a virtual environment based on training content selected by the user. Motion capture devices worn by the user record movements in real time, and this data is transmitted to a server. The server analyzes the motion data using Xsens' MVN Animate. In addition, Affectiva's emotion analysis engine is used to evaluate the user's emotional state in real time from their facial expressions and voice, and the results are reflected in the training feedback.

[0353] The user's smart glasses visually display feedback information. For example, using Microsoft HoloLens, suggestions and guidance for improving operation are overlaid on the user's field of view. If the user's movements are analyzed as unstable while they are virtually controlling a robot, a message such as "Your movements are clumsy, try to operate calmly" will be displayed on the HoloLens, along with hints on how to operate it. This allows the user to receive immediate feedback tailored to their individual situation, enabling them to efficiently improve their skills.

[0354] Specific examples of prompts include phrases like, "Please consider strategies to adjust the training environment to help the user concentrate." This allows the generative AI model to provide the optimal training methods and strategies for the user.

[0355] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0356] Step 1:

[0357] The terminal receives input from the user and determines the training content and the characteristics of the opponent. This input consists of the training menu selected by the user and attribute data of the opponent. Based on this information, the terminal sends commands to the server to generate the virtual environment.

[0358] Step 2:

[0359] The server generates a virtual environment based on the received training data. Using Xsens' MVN Animate and related software, it prepares to record the user's actions in real time. The output is the generated virtual environment data, which is sent to the user's terminal.

[0360] Step 3:

[0361] The user puts on smart glasses and begins training in a virtual environment. This allows the user's motion data to be collected in real time by a motion capture device and sent to a server. The input is the user's motion data, and the output is the motion information sent to the server.

[0362] Step 4:

[0363] The system analyzes the operational data received by the server. Here, a generative AI model is used to evaluate the accuracy and stability of the operations. The data calculation results in analyzed operational information, which influences subsequent feedback. The output is the operational analysis result.

[0364] Step 5:

[0365] The emotion analysis system collects the user's facial expressions and voice data and analyzes their emotional state in real time. The emotion engine handles this, taking the user's facial expressions and voice data as input. The analyzed results are used in the next step. The output is the emotion analysis result.

[0366] Step 6:

[0367] The server integrates the results of motion analysis and emotion analysis to generate user-specific feedback. This feedback is presented to the user's smart glasses using visual means. This feedback includes suggestions for motion improvement and emotion-based advice. The input is the results of motion and emotion analysis, and the output is the feedback information presented to the user.

[0368] Step 7:

[0369] The feedback provided by the user is reviewed and used to improve subsequent actions and training. This allows the user to enhance their training effectiveness and further improve their skills.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] [Third Embodiment]

[0374] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0375] 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.

[0376] 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).

[0377] 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.

[0378] 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.

[0379] 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).

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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".

[0386] This invention is a system that virtually provides users with a sports training environment. This system is capable of recreating a match environment according to the selected sport and scenario. The user launches the application via a terminal and selects the type of sport they wish to train in from the main menu. The terminal displays a scenario setting screen based on the selected sport, allowing the user to input detailed information about the opponent's characteristics and the match environment.

[0387] The server generates a virtual environment using the input information and recreates the game situation in real time via the user's motion capture device and VR goggles. The user moves within this virtual environment, and the terminal records and analyzes their movements. The movement data is analyzed by the server, and feedback is provided to improve the user's performance. This feedback includes the success rate of the user's movements, areas for improvement, and suggestions for skill improvement in specific situations.

[0388] For example, if a user chooses to practice volleyball serve reception, the device generates a virtual gymnasium and simulates an opponent with set serve speed and trajectory. The user performs the action of receiving the serve in the VR environment, and motion capture data is collected. The server then analyzes the user's motion data in real time and displays feedback through the device. Based on this feedback, the user can adjust their movements and improve their skills. This system allows users to practice in a realistic, match-like environment even when practicing alone.

[0389] The following describes the processing flow.

[0390] Step 1:

[0391] The user launches the application using their device. The device displays the main menu and provides a screen where the user can select the type of sport they want to practice.

[0392] Step 2:

[0393] The user selects their desired sport and scenario. Based on the selection, the device displays a screen where the user can input details about the opponent's characteristics and scenario settings.

[0394] Step 3:

[0395] The terminal connects to the motion capture device and VR goggles, and is ready. The user puts on the equipment and inputs a ready command into the terminal.

[0396] Step 4:

[0397] The server receives user input data and generates a virtual environment. Based on the selected scenario, a specific match situation is recreated in the VR space.

[0398] Step 5:

[0399] The user begins acting according to a scenario set within the VR environment. The terminal acquires the user's movements in real time from a motion capture device and sends them to the server.

[0400] Step 6:

[0401] The server analyzes the operational data it receives and evaluates user performance. The analysis results generate data such as success rate, operational accuracy, and advice for improvement.

[0402] Step 7:

[0403] The device displays the analysis results and provides feedback to the user. Based on the displayed feedback, the user reflects on their actions and considers the next steps to take.

[0404] Step 8:

[0405] If the user chooses to save their practice results, the device will save the analysis results. This allows the user to refer to past data during their next practice session.

[0406] (Example 1)

[0407] 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."

[0408] Traditional sports training systems have made it difficult for users to experience realistic virtual environments effectively and flexibly. Furthermore, a lack of specific instruction tailored to individual skill improvement hinders users from efficiently enhancing their abilities. Additionally, conventional technologies have limitations in setting scenarios to meet user needs, sometimes preventing the provision of training optimized for individual users.

[0409] 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.

[0410] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user, motion capture means for recording the user's movements in real time, and information analysis and presentation means for analyzing the recorded movement information and displaying the results. This enables the user to experience effective training in the virtual environment in real time and receive specific feedback for improving their individual skills.

[0411] "Information processing means" refers to a device or method for generating an appropriate virtual environment based on the training content selected by the user.

[0412] "Motion capture means" refers to a device or method that records a user's physical movements in real time and acquires those movements as data.

[0413] "Information analysis and presentation means" refers to a device or method that analyzes recorded motion information and provides the results to the user visually or audibly.

[0414] A "generative model" is an algorithm or technology used to generate new scenarios or content based on specific information.

[0415] A "user interface" is a means by which a user interacts with a system and inputs or outputs information, and typically includes a display or input device.

[0416] This invention relates to a system that provides a virtual sports training environment to a user. The system mainly consists of three components: a server, a terminal, and a user.

[0417] The terminal launches the application via a user interface and displays a menu for the user to select the sport they wish to train in. The user then configures details such as the opponent's characteristics and the match environment based on their selection, and sends this information to the server.

[0418] The server generates a virtual match environment using information processing tools based on the information it receives. In this process, it utilizes a generation AI model to construct multiple scenarios and character movements. Specifically, graphics engines such as Unreal Engine and Unity are used to realize a highly accurate and realistic virtual environment. The generated environment is transmitted to the user's terminal via the user's motion capture device and VR goggles, allowing the user to experience the match in real time.

[0419] The user wears a motion capture device and trains in a virtual environment. This device accurately records the user's movements and transmits the data to a server. The server analyzes the movement information using information analysis and presentation tools and generates feedback on the user's performance. This feedback is provided to the user via a terminal and contributes to the user's skill improvement.

[0420] For example, if a user selects a volleyball practice scenario, the device creates a virtual gymnasium and generates a virtual opponent based on the set conditions. The user can then use the VR system to practice serve reception and receive appropriate advice and improvement suggestions from the server.

[0421] Example prompt: "Generate a volleyball practice environment. Focus on serve reception training, set the serve speed to 25 meters per second, and set the trajectory to random."

[0422] This system allows users to easily experience advanced virtual sports training tailored to their individual needs, either at home or in any location.

[0423] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0424] Step 1:

[0425] The user launches the training application on their device and selects their desired sport from the main menu. The input is the sport selected by the user. The output is the next interface screen based on the selected option. In this step, the application presents a sport scenario in the user interface according to the user's selection.

[0426] Step 2:

[0427] The terminal displays a scenario settings screen related to the sport selected by the user, allowing the user to input details about the opponent's characteristics and the match environment. The input consists of information about the opponent's characteristics and the match environment settings chosen by the user. The output is the confirmed settings data after reviewing the entered settings. This settings data is then sent to the server as information necessary for the subsequent virtual environment generation.

[0428] Step 3:

[0429] The server generates a virtual environment using information processing tools based on configuration data sent from the terminal. It utilizes a generation AI model to construct realistic match scenarios based on selected conditions. The input is the scenario data set by the user. The output is the constructed virtual environment. The server uses a graphics engine to create a virtual match scene and sends the results to the terminal.

[0430] Step 4:

[0431] The user wears VR goggles and a motion capture device and trains in a virtual environment provided by the server. The input is the user's physical movements within the virtual environment. The output is motion data recorded in real time. The motion capture device precisely captures the user's movements and collects motion data.

[0432] Step 5:

[0433] The server analyzes the acquired motion data using information analysis and presentation tools to generate feedback on the user's performance. The input is the user's recorded motion data. The output is feedback information obtained from the motion analysis. This feedback information is sent to the terminal as effective training advice and presented to the user.

[0434] Step 6:

[0435] The user reviews the feedback displayed on the device and takes action to improve their actions and skills. The input is the feedback provided by the server. The output is the actions and skills that the user corrects or improves based on the feedback. This final step allows the user to continuously improve their skills.

[0436] (Application Example 1)

[0437] 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."

[0438] In modern times, effective training in sports and physical activities requires actual training grounds and instructors. However, frequent use of these resources is difficult for many people due to time constraints and financial burdens. Furthermore, it is difficult to receive feedback when training alone, making effective skill improvement challenging. To solve these problems, there is a need for methods that provide advanced virtual training experiences at home, enabling users to effectively improve their skills.

[0439] 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.

[0440] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user, motion capture means for capturing the user's actions in real time, data analysis and display means for analyzing the captured motion data and suggesting areas for improvement, and means for making suggestions for improving the user's skills using a generated AI model. As a result, the user can practice in a realistic virtual environment from the comfort of their home and receive instant feedback to improve their skills.

[0441] "Information processing means" refers to a device that has the function of generating a virtual environment corresponding to the training content selected by the user.

[0442] A "motion tracking means" is a device that has the function of recording and tracking the user's movements in real time.

[0443] "Data analysis and display means" refers to a device that analyzes captured user behavior data and visually presents the analysis results to the user.

[0444] A "generative AI model" is a computational model that uses machine learning techniques to generate improvement suggestions based on user behavior data.

[0445] "Means for providing suggestions for skill improvement" refers to a device that has the function of providing information and advice that contributes to improving the user's skills using AI models generated by the server.

[0446] This invention is a system that generates a virtual environment based on the sports training content selected by the user and provides feedback by capturing the user's movements in real time. The main components and their functions are described below.

[0447] The server generates a virtual environment based on the training content selected by the user through information processing. The virtual environment embodies scenarios for various sports such as soccer and volleyball, and allows the user to set the characteristics of the simulated opponent.

[0448] The terminal is equipped with motion capture capabilities that record the user's movements in real time. Furthermore, the terminal transmits the captured motion data to a server, where it is analyzed and displayed. The results of this analysis are provided to the user visually as suggestions for improvement and feedback.

[0449] The server uses a generative AI model to provide suggestions for improving the user's skills based on the analysis results. These suggestions are provided in a prompt format, based on the analysis of the user's actions by the generative AI model. For example, specific advice such as, "If you adjust the angle of your kick a little more inward, your shooting accuracy will improve," may be given. This allows the user to improve their skills through practice in a virtual environment.

[0450] An example of a prompt message would be: "User data: {Action data}. Please use this data to tell me how to improve my soccer free kick."

[0451] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0452] Step 1:

[0453] The user launches the training application on their device and selects the type of sport and scenario they wish to train. The input is the user's selection information, and the output is configuration data for the virtual environment based on this information. The device sends this data to the server.

[0454] Step 2:

[0455] The server uses information processing tools to generate a virtual environment corresponding to the selected sport based on the received configuration data. The input is the configured sport and scenario information, and the output is the scene data of the virtual environment. This data is sent to the terminal, and the virtual environment is displayed to the user.

[0456] Step 3:

[0457] The user starts an action within the virtual environment, and the terminal's action tracking system records the user's actions in real time. The input is the physical data of the user's actions, and the output is the recorded action data. This data is sent to the server.

[0458] Step 4:

[0459] The server analyzes the received motion data using data analysis and display means. The input is the user's motion data, and the output is detailed information about the analyzed motion. The analysis includes motion speed, angle, trajectory, etc.

[0460] Step 5:

[0461] The server uses a generative AI model to generate prompts for improving the user's skills based on the analysis results. The input is the analysis results, and the output is a feedback prompt that includes suggestions. For example, specific advice might be given such as, "If you angle your kick a little more inward, your shooting accuracy will improve."

[0462] Step 6:

[0463] The terminal displays feedback received from the server to the user. The input is a feedback prompt, and the output is a visual feedback presentation to the user. Based on this feedback, the user can adjust their actions and improve their skills.

[0464] 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.

[0465] This invention provides a virtual environment based on training content, enhancing the user's practice experience. Furthermore, this system incorporates an emotion engine that can recognize the user's emotional state in real time and improve the quality of feedback.

[0466] This system begins with the user selecting a sport or scenario using a terminal and starting training in a virtual environment. The terminal considers the characteristics of the opponent entered by the user and generates a realistic virtual environment via a motion capture device and VR goggles. While the user performs actions, the motion capture device collects data on the user's movements and sends it to a server. This motion data is then analyzed, and detailed feedback is provided.

[0467] Furthermore, the emotion engine analyzes the user's emotions in real time from their facial expressions and voice. Based on the data obtained from the emotion engine, the device can provide appropriate feedback according to the user's mental state. For example, if the user's motivation decreases during practice, the emotion engine will detect this, and the device will provide encouraging messages or adjust the training content.

[0468] As a concrete example, consider a scenario where a user is practicing tennis serve returns. Within the virtual environment, pre-set serve patterns are reproduced, and the user performs return actions in accordance with those patterns. The user's movements are measured by a motion capture device and analyzed in real time on a server. In addition, an emotion engine determines the user's level of concentration and tension from their facial expressions and provides necessary feedback on the device. This allows the user to obtain a highly personalized practice experience.

[0469] This system makes individual sports training more effective and interactive, supporting the user's skill improvement.

[0470] The following describes the processing flow.

[0471] Step 1:

[0472] The user launches the application using their device and selects the sport and scenario they want to practice. The device then receives the user's selection and displays a settings screen for creating the virtual environment.

[0473] Step 2:

[0474] The user inputs the opponent's characteristics and various settings necessary for training. The terminal sends this information to the server and requests the creation of a virtual environment.

[0475] Step 3:

[0476] Based on the configuration information received by the server, a virtual environment is generated that reproduces the selected sports scenario. The virtual sports scene is then displayed in real time on the VR goggles connected to the terminal.

[0477] Step 4:

[0478] The user wears a motion capture device and begins performing actual movements within a virtual environment. The terminal acquires the user's movement data from the motion capture device and sends it to the server.

[0479] Step 5:

[0480] The server analyzes user action data in real time to evaluate the effectiveness of practice. Success rates and accuracy of actions are determined, and feedback is formulated accordingly.

[0481] Step 6:

[0482] The emotion engine recognizes and analyzes the user's emotions from their facial expressions and tone of voice. The device receives the emotion data, makes dynamic adjustments based on feedback from the server, and displays the results on the screen.

[0483] Step 7:

[0484] The device provides feedback information to the user and adjusts the training content and situation as needed. Based on the advice provided, the user works to improve their practice.

[0485] Step 8:

[0486] When a user finishes a practice session, the device saves the analysis results. This allows the user to refer to past records in subsequent practice sessions and track their progress.

[0487] (Example 2)

[0488] 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."

[0489] Conventional virtual training systems have faced challenges such as limited feedback on user actions and a lack of appropriate feedback that takes emotional states into account. This made it difficult to achieve sufficient results in improving individual users' skills and maintaining their motivation.

[0490] 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.

[0491] In this invention, the server includes information processing means for generating a simulation environment based on training content selected by the user; motion capture means for capturing the user's actions in real time; information analysis and presentation means for analyzing the captured motion information and presenting the information; emotion analysis means for analyzing the user's facial expressions and voice information and determining their emotional state; and feedback means for providing adaptive advice according to the user's mental state. This enables highly personalized feedback that responds to the user's actions and emotional state.

[0492] "Information processing means" refers to a function within the system that generates a virtual simulation environment based on the training content selected by the user.

[0493] "Motion tracking means" refers to a part of a system that senses the user's physical movements in real time and records them as digital information.

[0494] "Information analysis and presentation means" refers to technology for analyzing digital information obtained by motion capture means and providing the user with the analysis results.

[0495] "Emotional analysis methods" refer to technologies that analyze a user's facial expressions and voice data to estimate their psychological state and emotions.

[0496] "Feedback mechanism" refers to a system function that provides optimal guidance and advice to the user based on analyzed behavioral information and emotional state.

[0497] This invention provides a system that offers a virtual environment based on the training content selected by the user, thereby personalizing the user's practice experience. This system, primarily composed of a server and a terminal, operates as follows:

[0498] The user begins training by selecting the sport or scenario they want to practice via the device. The device then builds a simulation environment based on the user's input. The main hardware used here is VR goggles and motion capture devices. This allows the user to experience training in a realistic virtual environment.

[0499] The user begins moving within the virtual environment. A motion capture device tracks the user's movements in real time and transmits the movement information to the server via the terminal. The server analyzes this data using a generative AI model and generates feedback on the accuracy of the movements and areas for improvement.

[0500] Furthermore, the device incorporates an emotion analysis function that analyzes the user's facial expressions and voice data in real time. Based on this analysis, the device provides feedback tailored to the user's psychological state. For example, if the device determines that the user's concentration is declining, it displays an encouraging message to boost their motivation. This allows the user to engage in training in the most optimal state.

[0501] As a concrete example, when a user practices tennis service returns, various serve scenarios are reproduced on a virtual court. The user's return movements are recorded using motion capture and analyzed on a server. In addition, messages such as "Relax and try your next return" are displayed on the terminal according to the user's emotional state. In this way, the present invention effectively supports skill improvement by providing personalized training for the user.

[0502] An example of a prompt would be: "Simulate tennis serve return practice in a virtual environment. Analyze my motion capture data and provide real-time feedback. Also, take my emotional state into consideration and offer words of encouragement as needed."

[0503] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0504] Step 1:

[0505] The user uses a terminal to select the sport or scenario they wish to train in. The input is the user's selected sport or scenario, and the output is the selected data. This selected data is used as foundational information for the subsequent virtual environment construction.

[0506] Step 2:

[0507] The terminal activates VR goggles and motion capture devices based on the training content selected by the user, generating a simulation environment. The input is the selected data obtained in step 1, and the output is the constructed virtual environment. This process prepares the user to begin training visually and haptically.

[0508] Step 3:

[0509] The user begins training in a virtual environment, and a motion capture device tracks the user's movements in real time. The input is the user's physical movements, and the output is digitized motion data. This data is transmitted in real time to the server via the terminal.

[0510] Step 4:

[0511] The server analyzes the received motion data using a generating AI model. The input is the motion data sent in step 3, and the output is the user's motion analysis results and feedback information on areas for improvement. This analysis provides a detailed evaluation of the user's actions.

[0512] Step 5:

[0513] As a means of emotion analysis, the device analyzes the user's facial expressions and voice in real time. The input is the user's facial expressions and voice data, and the output is information about the user's emotional state. This emotion analysis allows for an understanding of the user's psychological state.

[0514] Step 6:

[0515] The device provides personalized feedback to the user based on the behavioral and emotional analysis results from the server. The input is the analysis results obtained in steps 4 and 5, and the output is audio or visual feedback messages. For example, if the device determines that the user is not concentrating, a message such as "Relax and try the next action" will be displayed.

[0516] Through these steps, users can efficiently improve their skills while receiving feedback tailored to their individual circumstances.

[0517] (Application Example 2)

[0518] 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."

[0519] Training robot operators in factories presents a challenge: the lack of real-time feedback based on emotional states and motion data in typical training environments hinders the efficient improvement of operator skills.

[0520] 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.

[0521] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user; motion capture means for recording the user's movements in real time; information analysis and presentation means for analyzing the recorded movement information and providing feedback; emotion analysis means for analyzing the user's emotional state and reflecting it in the feedback; and visual presentation means for visually presenting the feedback on a display device. This enables the operator to receive detailed feedback based on their individual movements and emotional state.

[0522] "Information processing means" refers to a device or system that has the function of generating a virtual environment based on the training content selected by the user.

[0523] "Motion capture means" refers to a device or technology for recording a user's movements in real time and acquiring that data.

[0524] "Information analysis and presentation means" refers to a device or function for analyzing recorded operational information and presenting the results to the user as feedback.

[0525] "Emotional analysis means" refers to a technology or system for analyzing a user's emotional state and using that data for training feedback.

[0526] A "visual presentation means" is a display device or interface for visually presenting data-based feedback to a user.

[0527] This invention is a system for training robots in environments such as factories. The system uses information processing means to generate a virtual environment based on training content selected by the user. Motion capture devices worn by the user record movements in real time, and this data is transmitted to a server. The server analyzes the motion data using Xsens' MVN Animate. In addition, Affectiva's emotion analysis engine is used to evaluate the user's emotional state in real time from their facial expressions and voice, and the results are reflected in the training feedback.

[0528] The user's smart glasses visually display feedback information. For example, using Microsoft HoloLens, suggestions and guidance for improving operation are overlaid on the user's field of view. If the user's movements are analyzed as unstable while they are virtually controlling a robot, a message such as "Your movements are clumsy, try to operate calmly" will be displayed on the HoloLens, along with hints on how to operate it. This allows the user to receive immediate feedback tailored to their individual situation, enabling them to efficiently improve their skills.

[0529] Specific examples of prompts include phrases like, "Please consider strategies to adjust the training environment to help the user concentrate." This allows the generative AI model to provide the optimal training methods and strategies for the user.

[0530] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0531] Step 1:

[0532] The terminal receives input from the user and determines the training content and the characteristics of the opponent. This input consists of the training menu selected by the user and attribute data of the opponent. Based on this information, the terminal sends commands to the server to generate the virtual environment.

[0533] Step 2:

[0534] The server generates a virtual environment based on the received training data. Using Xsens' MVN Animate and related software, it prepares to record the user's actions in real time. The output is the generated virtual environment data, which is sent to the user's terminal.

[0535] Step 3:

[0536] The user puts on smart glasses and begins training in a virtual environment. This allows the user's motion data to be collected in real time by a motion capture device and sent to a server. The input is the user's motion data, and the output is the motion information sent to the server.

[0537] Step 4:

[0538] The system analyzes the operational data received by the server. Here, a generative AI model is used to evaluate the accuracy and stability of the operations. The data calculation results in analyzed operational information, which influences subsequent feedback. The output is the operational analysis result.

[0539] Step 5:

[0540] The emotion analysis system collects the user's facial expressions and voice data and analyzes their emotional state in real time. The emotion engine handles this, taking the user's facial expressions and voice data as input. The analyzed results are used in the next step. The output is the emotion analysis result.

[0541] Step 6:

[0542] The server integrates the results of motion analysis and emotion analysis to generate user-specific feedback. This feedback is presented to the user's smart glasses using visual means. This feedback includes suggestions for motion improvement and emotion-based advice. The input is the results of motion and emotion analysis, and the output is the feedback information presented to the user.

[0543] Step 7:

[0544] The feedback provided by the user is reviewed and used to improve subsequent actions and training. This allows the user to enhance their training effectiveness and further improve their skills.

[0545] 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.

[0546] 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.

[0547] 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.

[0548] [Fourth Embodiment]

[0549] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0550] 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.

[0551] 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).

[0552] 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.

[0553] 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.

[0554] 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).

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 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".

[0562] This invention is a system that virtually provides users with a sports training environment. This system is capable of recreating a match environment according to the selected sport and scenario. The user launches the application via a terminal and selects the type of sport they wish to train in from the main menu. The terminal displays a scenario setting screen based on the selected sport, allowing the user to input detailed information about the opponent's characteristics and the match environment.

[0563] The server generates a virtual environment using the input information and recreates the game situation in real time via the user's motion capture device and VR goggles. The user moves within this virtual environment, and the terminal records and analyzes their movements. The movement data is analyzed by the server, and feedback is provided to improve the user's performance. This feedback includes the success rate of the user's movements, areas for improvement, and suggestions for skill improvement in specific situations.

[0564] For example, if a user chooses to practice volleyball serve reception, the device generates a virtual gymnasium and simulates an opponent with set serve speed and trajectory. The user performs the action of receiving the serve in the VR environment, and motion capture data is collected. The server then analyzes the user's motion data in real time and displays feedback through the device. Based on this feedback, the user can adjust their movements and improve their skills. This system allows users to practice in a realistic, match-like environment even when practicing alone.

[0565] The following describes the processing flow.

[0566] Step 1:

[0567] The user launches the application using their device. The device displays the main menu and provides a screen where the user can select the type of sport they want to practice.

[0568] Step 2:

[0569] The user selects their desired sport and scenario. Based on the selection, the device displays a screen where the user can input details about the opponent's characteristics and scenario settings.

[0570] Step 3:

[0571] The terminal connects to the motion capture device and VR goggles, and is ready. The user puts on the equipment and inputs a ready command into the terminal.

[0572] Step 4:

[0573] The server receives user input data and generates a virtual environment. Based on the selected scenario, a specific match situation is recreated in the VR space.

[0574] Step 5:

[0575] The user begins acting according to a scenario set within the VR environment. The terminal acquires the user's movements in real time from a motion capture device and sends them to the server.

[0576] Step 6:

[0577] The server analyzes the operational data it receives and evaluates user performance. The analysis results generate data such as success rate, operational accuracy, and advice for improvement.

[0578] Step 7:

[0579] The device displays the analysis results and provides feedback to the user. Based on the displayed feedback, the user reflects on their actions and considers the next steps to take.

[0580] Step 8:

[0581] If the user chooses to save their practice results, the device will save the analysis results. This allows the user to refer to past data during their next practice session.

[0582] (Example 1)

[0583] 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".

[0584] Traditional sports training systems have made it difficult for users to experience realistic virtual environments effectively and flexibly. Furthermore, a lack of specific instruction tailored to individual skill improvement hinders users from efficiently enhancing their abilities. Additionally, conventional technologies have limitations in setting scenarios to meet user needs, sometimes preventing the provision of training optimized for individual users.

[0585] 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.

[0586] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user, motion capture means for recording the user's movements in real time, and information analysis and presentation means for analyzing the recorded movement information and displaying the results. This enables the user to experience effective training in the virtual environment in real time and receive specific feedback for improving their individual skills.

[0587] "Information processing means" refers to a device or method for generating an appropriate virtual environment based on the training content selected by the user.

[0588] "Motion capture means" refers to a device or method that records a user's physical movements in real time and acquires those movements as data.

[0589] "Information analysis and presentation means" refers to a device or method that analyzes recorded motion information and provides the results to the user visually or audibly.

[0590] A "generative model" is an algorithm or technology used to generate new scenarios or content based on specific information.

[0591] A "user interface" is a means by which a user interacts with a system and inputs or outputs information, and typically includes a display or input device.

[0592] This invention relates to a system that provides a virtual sports training environment to a user. The system mainly consists of three components: a server, a terminal, and a user.

[0593] The terminal launches the application via a user interface and displays a menu for the user to select the sport they wish to train in. The user then configures details such as the opponent's characteristics and the match environment based on their selection, and sends this information to the server.

[0594] The server generates a virtual match environment using information processing tools based on the information it receives. In this process, it utilizes a generation AI model to construct multiple scenarios and character movements. Specifically, graphics engines such as Unreal Engine and Unity are used to realize a highly accurate and realistic virtual environment. The generated environment is transmitted to the user's terminal via the user's motion capture device and VR goggles, allowing the user to experience the match in real time.

[0595] The user wears a motion capture device and trains in a virtual environment. This device accurately records the user's movements and transmits the data to a server. The server analyzes the movement information using information analysis and presentation tools and generates feedback on the user's performance. This feedback is provided to the user via a terminal and contributes to the user's skill improvement.

[0596] For example, if a user selects a volleyball practice scenario, the device creates a virtual gymnasium and generates a virtual opponent based on the set conditions. The user can then use the VR system to practice serve reception and receive appropriate advice and improvement suggestions from the server.

[0597] Example prompt: "Generate a volleyball practice environment. Focus on serve reception training, set the serve speed to 25 meters per second, and set the trajectory to random."

[0598] This system allows users to easily experience advanced virtual sports training tailored to their individual needs, either at home or in any location.

[0599] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0600] Step 1:

[0601] The user launches the training application on their device and selects their desired sport from the main menu. The input is the sport selected by the user. The output is the next interface screen based on the selected option. In this step, the application presents a sport scenario in the user interface according to the user's selection.

[0602] Step 2:

[0603] The terminal displays a scenario settings screen related to the sport selected by the user, allowing the user to input details about the opponent's characteristics and the match environment. The input consists of information about the opponent's characteristics and the match environment settings chosen by the user. The output is the confirmed settings data after reviewing the entered settings. This settings data is then sent to the server as information necessary for the subsequent virtual environment generation.

[0604] Step 3:

[0605] The server generates a virtual environment using information processing tools based on configuration data sent from the terminal. It utilizes a generation AI model to construct realistic match scenarios based on selected conditions. The input is the scenario data set by the user. The output is the constructed virtual environment. The server uses a graphics engine to create a virtual match scene and sends the results to the terminal.

[0606] Step 4:

[0607] The user wears VR goggles and a motion capture device and trains in a virtual environment provided by the server. The input is the user's physical movements within the virtual environment. The output is motion data recorded in real time. The motion capture device precisely captures the user's movements and collects motion data.

[0608] Step 5:

[0609] The server analyzes the acquired motion data using information analysis and presentation tools to generate feedback on the user's performance. The input is the user's recorded motion data. The output is feedback information obtained from the motion analysis. This feedback information is sent to the terminal as effective training advice and presented to the user.

[0610] Step 6:

[0611] The user reviews the feedback displayed on the device and takes action to improve their actions and skills. The input is the feedback provided by the server. The output is the actions and skills that the user corrects or improves based on the feedback. This final step allows the user to continuously improve their skills.

[0612] (Application Example 1)

[0613] 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".

[0614] In modern times, effective training in sports and physical activities requires actual training grounds and instructors. However, frequent use of these resources is difficult for many people due to time constraints and financial burdens. Furthermore, it is difficult to receive feedback when training alone, making effective skill improvement challenging. To solve these problems, there is a need for methods that provide advanced virtual training experiences at home, enabling users to effectively improve their skills.

[0615] 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.

[0616] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user, motion capture means for capturing the user's actions in real time, data analysis and display means for analyzing the captured motion data and suggesting areas for improvement, and means for making suggestions for improving the user's skills using a generated AI model. As a result, the user can practice in a realistic virtual environment from the comfort of their home and receive instant feedback to improve their skills.

[0617] "Information processing means" refers to a device that has the function of generating a virtual environment corresponding to the training content selected by the user.

[0618] A "motion tracking means" is a device that has the function of recording and tracking the user's movements in real time.

[0619] "Data analysis and display means" refers to a device that analyzes captured user behavior data and visually presents the analysis results to the user.

[0620] A "generative AI model" is a computational model that uses machine learning techniques to generate improvement suggestions based on user behavior data.

[0621] "Means for providing suggestions for skill improvement" refers to a device that has the function of providing information and advice that contributes to improving the user's skills using AI models generated by the server.

[0622] This invention is a system that generates a virtual environment based on the sports training content selected by the user and provides feedback by capturing the user's movements in real time. The main components and their functions are described below.

[0623] The server generates a virtual environment based on the training content selected by the user through information processing. The virtual environment embodies scenarios for various sports such as soccer and volleyball, and allows the user to set the characteristics of the simulated opponent.

[0624] The terminal is equipped with motion capture capabilities that record the user's movements in real time. Furthermore, the terminal transmits the captured motion data to a server, where it is analyzed and displayed. The results of this analysis are provided to the user visually as suggestions for improvement and feedback.

[0625] The server uses a generative AI model to provide suggestions for improving the user's skills based on the analysis results. These suggestions are provided in a prompt format, based on the analysis of the user's actions by the generative AI model. For example, specific advice such as, "If you adjust the angle of your kick a little more inward, your shooting accuracy will improve," may be given. This allows the user to improve their skills through practice in a virtual environment.

[0626] An example of a prompt message would be: "User data: {Action data}. Please use this data to tell me how to improve my soccer free kick."

[0627] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0628] Step 1:

[0629] The user launches the training application on their device and selects the type of sport and scenario they wish to train. The input is the user's selection information, and the output is configuration data for the virtual environment based on this information. The device sends this data to the server.

[0630] Step 2:

[0631] The server uses information processing tools to generate a virtual environment corresponding to the selected sport based on the received configuration data. The input is the configured sport and scenario information, and the output is the scene data of the virtual environment. This data is sent to the terminal, and the virtual environment is displayed to the user.

[0632] Step 3:

[0633] The user starts an action within the virtual environment, and the terminal's action tracking system records the user's actions in real time. The input is the physical data of the user's actions, and the output is the recorded action data. This data is sent to the server.

[0634] Step 4:

[0635] The server analyzes the received motion data using data analysis and display means. The input is the user's motion data, and the output is detailed information about the analyzed motion. The analysis includes motion speed, angle, trajectory, etc.

[0636] Step 5:

[0637] The server uses a generative AI model to generate prompts for improving the user's skills based on the analysis results. The input is the analysis results, and the output is a feedback prompt that includes suggestions. For example, specific advice might be given such as, "If you angle your kick a little more inward, your shooting accuracy will improve."

[0638] Step 6:

[0639] The terminal displays feedback received from the server to the user. The input is a feedback prompt, and the output is a visual feedback presentation to the user. Based on this feedback, the user can adjust their actions and improve their skills.

[0640] 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.

[0641] This invention provides a virtual environment based on training content, enhancing the user's practice experience. Furthermore, this system incorporates an emotion engine that can recognize the user's emotional state in real time and improve the quality of feedback.

[0642] This system begins with the user selecting a sport or scenario using a terminal and starting training in a virtual environment. The terminal considers the characteristics of the opponent entered by the user and generates a realistic virtual environment via a motion capture device and VR goggles. While the user performs actions, the motion capture device collects data on the user's movements and sends it to a server. This motion data is then analyzed, and detailed feedback is provided.

[0643] Furthermore, the emotion engine analyzes the user's emotions in real time from their facial expressions and voice. Based on the data obtained from the emotion engine, the device can provide appropriate feedback according to the user's mental state. For example, if the user's motivation decreases during practice, the emotion engine will detect this, and the device will provide encouraging messages or adjust the training content.

[0644] As a concrete example, consider a scenario where a user is practicing tennis serve returns. Within the virtual environment, pre-set serve patterns are reproduced, and the user performs return actions in accordance with those patterns. The user's movements are measured by a motion capture device and analyzed in real time on a server. In addition, an emotion engine determines the user's level of concentration and tension from their facial expressions and provides necessary feedback on the device. This allows the user to obtain a highly personalized practice experience.

[0645] This system makes individual sports training more effective and interactive, supporting the user's skill improvement.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The user launches the application using their device and selects the sport and scenario they want to practice. The device then receives the user's selection and displays a settings screen for creating the virtual environment.

[0649] Step 2:

[0650] The user inputs the opponent's characteristics and various settings necessary for training. The terminal sends this information to the server and requests the creation of a virtual environment.

[0651] Step 3:

[0652] Based on the configuration information received by the server, a virtual environment is generated that reproduces the selected sports scenario. The virtual sports scene is then displayed in real time on the VR goggles connected to the terminal.

[0653] Step 4:

[0654] The user wears a motion capture device and begins performing actual movements within a virtual environment. The terminal acquires the user's movement data from the motion capture device and sends it to the server.

[0655] Step 5:

[0656] The server analyzes user action data in real time to evaluate the effectiveness of practice. Success rates and accuracy of actions are determined, and feedback is formulated accordingly.

[0657] Step 6:

[0658] The emotion engine recognizes and analyzes the user's emotions from their facial expressions and tone of voice. The device receives the emotion data, makes dynamic adjustments based on feedback from the server, and displays the results on the screen.

[0659] Step 7:

[0660] The device provides feedback information to the user and adjusts the training content and situation as needed. Based on the advice provided, the user works to improve their practice.

[0661] Step 8:

[0662] When a user finishes a practice session, the device saves the analysis results. This allows the user to refer to past records in subsequent practice sessions and track their progress.

[0663] (Example 2)

[0664] 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".

[0665] Conventional virtual training systems have faced challenges such as limited feedback on user actions and a lack of appropriate feedback that takes emotional states into account. This made it difficult to achieve sufficient results in improving individual users' skills and maintaining their motivation.

[0666] 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.

[0667] In this invention, the server includes information processing means for generating a simulation environment based on training content selected by the user; motion capture means for capturing the user's actions in real time; information analysis and presentation means for analyzing the captured motion information and presenting the information; emotion analysis means for analyzing the user's facial expressions and voice information and determining their emotional state; and feedback means for providing adaptive advice according to the user's mental state. This enables highly personalized feedback that responds to the user's actions and emotional state.

[0668] "Information processing means" refers to a function within the system that generates a virtual simulation environment based on the training content selected by the user.

[0669] "Motion tracking means" refers to a part of a system that senses the user's physical movements in real time and records them as digital information.

[0670] "Information analysis and presentation means" refers to technology for analyzing digital information obtained by motion capture means and providing the user with the analysis results.

[0671] "Emotional analysis methods" refer to technologies that analyze a user's facial expressions and voice data to estimate their psychological state and emotions.

[0672] "Feedback mechanism" refers to a system function that provides optimal guidance and advice to the user based on analyzed behavioral information and emotional state.

[0673] This invention provides a system that offers a virtual environment based on the training content selected by the user, thereby personalizing the user's practice experience. This system, primarily composed of a server and a terminal, operates as follows:

[0674] The user begins training by selecting the sport or scenario they want to practice via the device. The device then builds a simulation environment based on the user's input. The main hardware used here is VR goggles and motion capture devices. This allows the user to experience training in a realistic virtual environment.

[0675] The user begins moving within the virtual environment. A motion capture device tracks the user's movements in real time and transmits the movement information to the server via the terminal. The server analyzes this data using a generative AI model and generates feedback on the accuracy of the movements and areas for improvement.

[0676] Furthermore, the device incorporates an emotion analysis function that analyzes the user's facial expressions and voice data in real time. Based on this analysis, the device provides feedback tailored to the user's psychological state. For example, if the device determines that the user's concentration is declining, it displays an encouraging message to boost their motivation. This allows the user to engage in training in the most optimal state.

[0677] As a concrete example, when a user practices tennis service returns, various serve scenarios are reproduced on a virtual court. The user's return movements are recorded using motion capture and analyzed on a server. In addition, messages such as "Relax and try your next return" are displayed on the terminal according to the user's emotional state. In this way, the present invention effectively supports skill improvement by providing personalized training for the user.

[0678] An example of a prompt would be: "Simulate tennis serve return practice in a virtual environment. Analyze my motion capture data and provide real-time feedback. Also, take my emotional state into consideration and offer words of encouragement as needed."

[0679] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0680] Step 1:

[0681] The user uses a terminal to select the sport or scenario they wish to train in. The input is the user's selected sport or scenario, and the output is the selected data. This selected data is used as foundational information for the subsequent virtual environment construction.

[0682] Step 2:

[0683] The terminal activates VR goggles and motion capture devices based on the training content selected by the user, generating a simulation environment. The input is the selected data obtained in step 1, and the output is the constructed virtual environment. This process prepares the user to begin training visually and haptically.

[0684] Step 3:

[0685] The user begins training in a virtual environment, and a motion capture device tracks the user's movements in real time. The input is the user's physical movements, and the output is digitized motion data. This data is transmitted in real time to the server via the terminal.

[0686] Step 4:

[0687] The server analyzes the received motion data using a generating AI model. The input is the motion data sent in step 3, and the output is the user's motion analysis results and feedback information on areas for improvement. This analysis provides a detailed evaluation of the user's actions.

[0688] Step 5:

[0689] As a means of emotion analysis, the device analyzes the user's facial expressions and voice in real time. The input is the user's facial expressions and voice data, and the output is information about the user's emotional state. This emotion analysis allows for an understanding of the user's psychological state.

[0690] Step 6:

[0691] The device provides personalized feedback to the user based on the behavioral and emotional analysis results from the server. The input is the analysis results obtained in steps 4 and 5, and the output is audio or visual feedback messages. For example, if the device determines that the user is not concentrating, a message such as "Relax and try the next action" will be displayed.

[0692] Through these steps, users can efficiently improve their skills while receiving feedback tailored to their individual circumstances.

[0693] (Application Example 2)

[0694] 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".

[0695] Training robot operators in factories presents a challenge: the lack of real-time feedback based on emotional states and motion data in typical training environments hinders the efficient improvement of operator skills.

[0696] 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.

[0697] In this invention, the server includes information processing means for generating a virtual environment based on training content selected by the user; motion capture means for recording the user's movements in real time; information analysis and presentation means for analyzing the recorded movement information and providing feedback; emotion analysis means for analyzing the user's emotional state and reflecting it in the feedback; and visual presentation means for visually presenting the feedback on a display device. This enables the operator to receive detailed feedback based on their individual movements and emotional state.

[0698] "Information processing means" refers to a device or system that has the function of generating a virtual environment based on the training content selected by the user.

[0699] "Motion capture means" refers to a device or technology for recording a user's movements in real time and acquiring that data.

[0700] "Information analysis and presentation means" refers to a device or function for analyzing recorded operational information and presenting the results to the user as feedback.

[0701] "Emotional analysis means" refers to a technology or system for analyzing a user's emotional state and using that data for training feedback.

[0702] A "visual presentation means" is a display device or interface for visually presenting data-based feedback to a user.

[0703] This invention is a system for training robots in environments such as factories. The system uses information processing means to generate a virtual environment based on training content selected by the user. Motion capture devices worn by the user record movements in real time, and this data is transmitted to a server. The server analyzes the motion data using Xsens' MVN Animate. In addition, Affectiva's emotion analysis engine is used to evaluate the user's emotional state in real time from their facial expressions and voice, and the results are reflected in the training feedback.

[0704] The user's smart glasses visually display feedback information. For example, using Microsoft HoloLens, suggestions and guidance for improving operation are overlaid on the user's field of view. If the user's movements are analyzed as unstable while they are virtually controlling a robot, a message such as "Your movements are clumsy, try to operate calmly" will be displayed on the HoloLens, along with hints on how to operate it. This allows the user to receive immediate feedback tailored to their individual situation, enabling them to efficiently improve their skills.

[0705] Specific examples of prompts include phrases like, "Please consider strategies to adjust the training environment to help the user concentrate." This allows the generative AI model to provide the optimal training methods and strategies for the user.

[0706] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0707] Step 1:

[0708] The terminal receives input from the user and determines the training content and the characteristics of the opponent. This input consists of the training menu selected by the user and attribute data of the opponent. Based on this information, the terminal sends commands to the server to generate the virtual environment.

[0709] Step 2:

[0710] The server generates a virtual environment based on the received training data. Using Xsens' MVN Animate and related software, it prepares to record the user's actions in real time. The output is the generated virtual environment data, which is sent to the user's terminal.

[0711] Step 3:

[0712] The user puts on smart glasses and begins training in a virtual environment. This allows the user's motion data to be collected in real time by a motion capture device and sent to a server. The input is the user's motion data, and the output is the motion information sent to the server.

[0713] Step 4:

[0714] The system analyzes the operational data received by the server. Here, a generative AI model is used to evaluate the accuracy and stability of the operations. The data calculation results in analyzed operational information, which influences subsequent feedback. The output is the operational analysis result.

[0715] Step 5:

[0716] The emotion analysis system collects the user's facial expressions and voice data and analyzes their emotional state in real time. The emotion engine handles this, taking the user's facial expressions and voice data as input. The analyzed results are used in the next step. The output is the emotion analysis result.

[0717] Step 6:

[0718] The server integrates the results of motion analysis and emotion analysis to generate user-specific feedback. This feedback is presented to the user's smart glasses using visual means. This feedback includes suggestions for motion improvement and emotion-based advice. The input is the results of motion and emotion analysis, and the output is the feedback information presented to the user.

[0719] Step 7:

[0720] The feedback provided by the user is reviewed and used to improve subsequent actions and training. This allows the user to enhance their training effectiveness and further improve their skills.

[0721] 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.

[0722] 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.

[0723] 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 robot 414.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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."

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0742] The following is further disclosed regarding the embodiments described above.

[0743] (Claim 1)

[0744] A data processing means for generating a virtual environment based on the training content selected by the user,

[0745] A motion capture means for capturing user movements in real time,

[0746] A data analysis and display means for analyzing captured motion data and displaying feedback,

[0747] A system that includes this.

[0748] (Claim 2)

[0749] The system according to claim 1, wherein the virtual environment is an environment that includes multiple sports scenarios, and in each scenario, the user can set the characteristics of the opponent.

[0750] (Claim 3)

[0751] The system according to claim 1, further comprising a feedback means for providing suggestions for improving the user's skills based on the results of the analysis of the aforementioned operation data.

[0752] "Example 1"

[0753] (Claim 1)

[0754] Information processing means for generating a virtual environment based on training content selected by the user,

[0755] A motion capture means for recording user movements in real time,

[0756] Information analysis and presentation means for analyzing recorded motion information and displaying the results,

[0757] A means of constructing virtual environment scenarios using generative models,

[0758] A means of providing a training experience within a virtual environment through a user interface,

[0759] A system that includes this.

[0760] (Claim 2)

[0761] The system according to claim 1, wherein the virtual environment is an environment that includes multiple exercise scenarios, and in each scenario, the user can set the characteristics of the opponent.

[0762] (Claim 3)

[0763] The system according to claim 1, further comprising a feedback means for presenting suggestions for improving the user's skills based on the results of the analysis of the aforementioned operational information.

[0764] "Application Example 1"

[0765] (Claim 1)

[0766] Information processing means for generating a virtual environment based on training content selected by the user,

[0767] A motion capture means for capturing user actions in real time,

[0768] A data analysis and display means for analyzing captured motion data and suggesting areas for improvement,

[0769] A means of making suggestions for improving users' skills by utilizing a generated AI model based on the analysis results,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, wherein the virtual environment includes multiple training scenarios, and the user can set the characteristics of the simulated opponent in each scenario.

[0773] (Claim 3)

[0774] The system according to claim 1, comprising a feedback means that provides improvement suggestions in a specific training situation using the results of the analysis of the aforementioned motion data, and generates prompt sentences to adjust the user's behavior and improve skills.

[0775] "Example 2 of combining an emotion engine"

[0776] (Claim 1)

[0777] Information processing means for generating a simulation environment based on training content selected by the user,

[0778] A motion capture mechanism for capturing user actions in real time,

[0779] Information analysis and presentation means for analyzing captured motion information and presenting the information,

[0780] An emotion analysis means for analyzing the user's facial expressions and voice information to determine their emotional state,

[0781] A feedback mechanism to provide adaptive advice tailored to the user's mental state,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, wherein the simulation environment includes multiple competition scenarios, and the user can set the characteristics of the opponent in each scenario.

[0785] (Claim 3)

[0786] The system according to claim 1, further comprising a feedback means for presenting suggestions for improving the user's skills based on the analysis results of the motion information and the results of the emotion analysis means.

[0787] "Application example 2 of combining emotional engines"

[0788] (Claim 1)

[0789] Information processing means for generating a virtual environment based on training content selected by the user,

[0790] A motion capture means for recording user movements in real time,

[0791] Information analysis and presentation means for analyzing recorded motion information and providing feedback,

[0792] A means of analyzing the user's emotional state and reflecting it in feedback,

[0793] A visual presentation means for visually presenting feedback on a display device,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, wherein the virtual environment is an environment that includes multiple competition scenarios, and in each scenario, the user can set the characteristics of the opponent.

[0797] (Claim 3)

[0798] The system according to claim 1, further comprising a response means for presenting suggestions for improving the user's skills based on the aforementioned operational information and the results of the analysis of the user's emotional state. [Explanation of Symbols]

[0799] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data processing means for generating a virtual environment based on the training content selected by the user, A motion capture means for capturing user movements in real time, A data analysis and display means for analyzing captured motion data and displaying feedback, A system that includes this.

2. The system according to claim 1, wherein the virtual environment is an environment that includes multiple sports scenarios, and in each scenario, the user can set the characteristics of the opponent.

3. The system according to claim 1, further comprising a feedback means for providing suggestions for improving the user's skills based on the results of the analysis of the aforementioned operation data.

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