system

Abstract

We provide the system. [Solution] A means of collecting professional match data and analyzing player movements and object trajectories, A means for recording a user's match information and transmitting that data to an information processing device, A means of analyzing professional match data and user match information to generate suggestions for match strategies suitable for the user, A method for allowing users to experience the proposed game plan using virtual environment technology, A means of providing immediate instruction and advice to users during practice, As a home training device, it provides a means to analyze the user's exercise style and create appropriate training guidelines, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot 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

[0006] "Professional match data" refers to information recorded in competitive matches regarding player movements, ball trajectories, and the flow of the game.

[0007] "Analysis" refers to the process of thoroughly analyzing data and extracting meaningful information.

[0008] "User match data" refers to video and performance information recorded during matches and practice sessions played by the user.

[0009] A "server" refers to a computer system that stores, analyzes, and provides data.

[0010] "Game management" refers to a series of decisions made by players during a game, including strategies, batting directions, and positioning.

[0011] "Virtual reality technology" refers to technology that provides computer-generated virtual environments and experiences.

[0012] "Real-time coaching" refers to the process of providing players with immediate feedback and advice during practice or matches. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

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

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

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

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

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

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

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

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0034] This invention relates to a learning system for tennis players that combines virtual reality technology and data analysis technology. The system provides strategic advice based on the user's matches and supports the improvement of practical skills.

[0035] First, the user uses a VR camera to record a match or practice session. This camera meticulously records the players' movements and the flow of the game. This data is temporarily stored on the user's device and later uploaded to a server.

[0036] Next, the server receives the user's uploaded match data. It also collects professional match data from online or stored databases. This allows the server to accumulate a large amount of match information.

[0037] The server analyzes the user's match data and professional match data to generate a match strategy optimized for each individual user. This process uses machine learning algorithms to identify the user's strengths and areas for improvement. For example, if a user frequently misses backhand shots during rallies, the server analyzes this information and suggests strategies for improvement.

[0038] Based on the analysis results, the server generates suggestions as content for a virtual reality (VR) system and provides them to the user. The user can wear VR goggles to experience the match in a virtual environment and put the suggested strategies into practice. This allows the user to learn strategic movements and shot selection as if they were actually playing the match.

[0039] Furthermore, during actual practice, the device provides users with real-time audio coaching. This coaching is based on strategic suggestions generated by the server. For example, the user might be given instructions such as, "On the next ball, try moving forward and attempting a net play." In this way, users can execute strategies in real time and hone their skills.

[0040] Thus, the system of the present invention becomes a powerful tool for technically skilled tennis players to learn personally optimized strategies and put them to effective use in actual matches.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users record their matches and practice sessions using a VR camera. This camera captures 360-degree video, allowing for detailed observation of player movements and the flow of the game. The recorded data is temporarily stored on the user's device.

[0044] Step 2:

[0045] The device uploads the saved recording data to the server via the internet. This data transfer is performed using a secure channel with the user's consent.

[0046] Step 3:

[0047] The server receives the user's uploaded match data. Furthermore, it retrieves data from an online professional match database, including player movements and match details.

[0048] Step 4:

[0049] The server compares the user's match data with professional match data and performs analysis. The analysis is performed to identify the characteristics of the user's playing style, their most successful shots, and areas for improvement.

[0050] Step 5:

[0051] Based on the analysis results, the server runs an algorithm that proposes an optimized game plan for the user. This proposal includes advice on appropriate batting trajectories and positioning.

[0052] Step 6:

[0053] The server constructs the proposed strategy as VR content and sends the data to the user's device. A match simulation in a virtual reality space is then set up.

[0054] Step 7:

[0055] Users wear VR goggles and experience the transmitted VR content. This allows them to actually try out suggested strategies and movements while playing matches in a virtual space.

[0056] Step 8:

[0057] The device provides users with real-time coaching through earphones during actual practice sessions. This includes specific action instructions based on strategic suggestions from the server.

[0058] (Example 1)

[0059] 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."

[0060] Traditional sports learning systems often rely heavily on on-site instruction and self-assessment, making it difficult to obtain objective and concrete improvement suggestions. In particular, the lack of tactical suggestions tailored to individual player characteristics makes it difficult to acquire effective strategies in actual competition. Furthermore, the underutilization of virtual training environments limits the flexibility of practice.

[0061] 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.

[0062] In this invention, the server includes means for collecting competition data and analyzing motion and object trajectories, means for recording the user's competition data and transmitting that data to an information processing device, and means for analyzing the competition data and the user's competition data to generate tactical suggestions suitable for the user. This enables individually optimized tactical suggestions based on objective data, allowing users to effectively improve their practical skills through strategic practice in a virtual environment.

[0063] "Competition data" refers to digital information that records actions and object movements during a competition.

[0064] "Movement" refers to actions or changes in position performed by a person or object.

[0065] "The trajectory of an object" refers to the path an object follows as it moves.

[0066] An "information processing device" refers to an electronic device used to collect, process, and analyze data.

[0067] "User" refers to an individual or organization that uses the system to record competition data and receives the analysis results.

[0068] "Virtual environment technology" refers to technology that uses computer technology to digitally reproduce environments that closely resemble reality.

[0069] "Tactical suggestions" refer to specific advice regarding effective actions and strategies in conducting a competition.

[0070] "Real-time guidance and advice" refers to advice and instructions provided in real time while the user is engaged in the activity.

[0071] This invention provides a system that combines a virtual environment and data analysis technology to help athletes improve their skills. The system mainly consists of three elements: users, servers, and terminals.

[0072] The user first operates a device that records the game or practice using a VR camera. In this case, the VR camera used is assumed to be a "360-degree camera" or similar. This camera records the user's movements and the ball's trajectory in detail and temporarily stores that data on the device.

[0073] The terminal is responsible for uploading the user's saved competition data to a server in the cloud. In this process, it is recommended to use an efficient communication protocol to ensure high-speed transfer while maintaining data integrity.

[0074] The server receives uploaded data and, in parallel, collects professional match data from sources such as "sports databases." Based on this rich dataset, it analyzes the user's performance data. The analysis uses "open-source machine learning frameworks" and other tools to generate tactics optimized for the user. These tactics are used to strengthen the user's strengths and identify areas for improvement.

[0075] The analysis results and generated tactical suggestions are provided to the user from the server. The user wears VR goggles and experiences and deepens their understanding of specific strategies in a virtual environment. Through this experience, the user can try out the movements and decision-making necessary for actual competition within the virtual environment.

[0076] Furthermore, the device provides users with real-time audio coaching during the session. This guidance is based on server analysis and helps users take the most appropriate action based on the situation.

[0077] For example, if data analysis reveals that a user struggles with backhand shots, the server will generate a virtual environment specifically designed to improve backhand technique based on this information. An example of a prompt for the generated AI model might be: "Please suggest strategies for the user to improve their backhand shots in a tennis match. Please also explain the effects of these strategies when implemented."

[0078] Through these means, users can objectively evaluate their own competitive skills and efficiently improve them.

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

[0080] Step 1:

[0081] The user records their actions during the game using a VR camera. The input consists of the user's movements and the ball's movement during the game. The output is 360-degree video data recorded by the VR camera. This process allows for detailed recording of the user's subtle movements and the ball's precise trajectory.

[0082] Step 2:

[0083] The device temporarily saves the recorded video data to local storage. The input is the video data acquired in step 1. The output is the saved data file. This file may be optimized using data compression techniques for later processing.

[0084] Step 3:

[0085] The user uploads data stored on their device to the server. The input is a video data file stored on the device. The output is the data transferred to the server, and this transfer is carried out efficiently using a communication protocol.

[0086] Step 4:

[0087] The server receives uploaded data and prepares it for analysis. The input is video data sent by the user. The output is a dataset formatted for data analysis. The server verifies the integrity of the data and converts it into a format that is easy for machine learning models to process.

[0088] Step 5:

[0089] The server collects professional match data from sources such as "sports databases." Input consists of requests and calls to external databases. Output is the collected professional match data. This data is used for comparative analysis with user data.

[0090] Step 6:

[0091] The server applies machine learning algorithms to compare and analyze the user's competitive data with professional data. The inputs are the user's competitive data and collected professional match data. The output is tactical suggestions optimized for the user, in which case the generating AI model operates based on prompt statements.

[0092] Step 7:

[0093] The server uses the generated tactical suggestions to create virtual reality content and provide it to the user. The input is tactical suggestion data obtained through analysis. The output is content data for the VR experience, which the user can then use to practice in the virtual environment.

[0094] Step 8:

[0095] The terminal provides real-time voice instructions during actual practice. Input consists of tactical suggestions delivered from the server and information about the user's current actions. Output is voice instructions provided to the user, allowing them to immediately apply tactics and improve their skills.

[0096] (Application Example 1)

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

[0098] Traditional tennis training methods are generally uniform, making it difficult to provide effective instruction tailored to the individual skills and strategies of each player. Furthermore, receiving real-time coaching and personalized training at home is challenging. Therefore, the challenge lies in efficiently providing personalized advice to improve daily practice.

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

[0100] This invention includes a server that collects professional match data and analyzes player movements and object trajectories; a server that records user match information and transmits that data to an information processing device; a server that analyzes professional match data and user match information to generate suggestions for match strategies suitable for the user; a server that allows the user to experience the suggested match strategies using virtual environment technology; a server that provides immediate instructional advice to the user during practice; and a server that analyzes the user's exercise style as a home practice device and creates appropriate practice guidelines. This enables the provision of practice programs optimized for individual players, allowing for immediate and effective self-improvement at home.

[0101] "Professional match data" refers to detailed information about movements and strategies collected from matches played by skilled players.

[0102] "Player movements" refer to the physical movements of a player's body during a match or practice.

[0103] "Object trajectory" is a concept that refers to information indicating the path of movement of a tennis ball or other object.

[0104] "User match information" refers to data obtained from the user's own matches and practice sessions, and includes their characteristics and tendencies.

[0105] An "information processing device" refers to a computer system used for data collection, analysis, and response generation.

[0106] "Virtual environment technology" is a technology that presents users with virtual simulations generated by a computer.

[0107] "Immediate instruction and advice" refers to specific instructional content provided in real time during practice.

[0108] "Home training equipment" refers to devices used to support individual sports training in a home environment.

[0109] "Practice guidelines" refer to instructional content that outlines the direction and procedures for practice aimed at improving the user's skills.

[0110] The system realizing this invention provides users with a means to efficiently practice sports in a home environment. Users can record their practice status in real time using home-use training equipment. The training equipment is equipped with cameras and sensors that record the user's movements and the trajectory of the ball, and transmit this information to a server. This information is processed immediately using an edge AI platform such as NVIDIA Jetson.

[0111] The server analyzes the received data using machine learning models written in Python (e.g., TENSORFLOW® or PyTorch) and generates optimal practice guidelines for the user. Based on the analyzed data, precise advice tailored to the user's characteristics is output. This allows users to receive professional-level instruction anonymously from the comfort of their homes.

[0112] The generated training guidelines are provided to users using virtual environment technology. For example, it is possible to try out the advice while experiencing a virtual match through VR goggles. Real-time voice guidance is also provided, allowing users to instantly hear advice such as, "Try moving forward and trying net play on the next ball."

[0113] An example of a prompt statement to use as input to a generative AI model is, "Please tell me how to improve the impact timing in my forehand shot, and provide specific advice." By using this prompt, the system can generate more specific and personalized advice.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The user records their practice sessions in real time using cameras and sensors installed in their home. The input consists of user movements and ball trajectory data, which are temporarily stored on the device. The output consists of recorded video data and motion data.

[0117] Step 2:

[0118] The terminal transmits recorded user practice data to the server via a communication network. Input consists of stored video and motion data, while output is a data reception completion notification on the server. Data compression may occur during data transfer.

[0119] Step 3:

[0120] The server performs data analysis using the received practice data. The input is the user's submitted practice data, and the output is the analysis results. The data analysis uses machine learning models (such as TensorFlow or PyTorch written in Python) to identify user characteristics and areas for improvement.

[0121] Step 4:

[0122] The server uses a generative AI model to generate practice programs and coaching advice tailored to the user. The input is analyzed data, and the output is specific practice guidelines and prompts. The generated prompts take the form of, "Please tell me how to improve my forehand shot, and suggest specific advice."

[0123] Step 5:

[0124] The server sends generated training guidelines and advice to the terminal using virtual environment technology. The input is the generated training guidelines, and the output is a virtual match environment experienced by the user using VR goggles. The VR environment provides feedback to the user through sight and sound.

[0125] Step 6:

[0126] The device provides real-time voice guidance to the user based on the practice guidelines it receives. The input is the practice guidelines sent from the server, and the output is voice advice transmitted to the user. The voice guidance includes specific content such as, "Try coming forward and trying net play on the next ball."

[0127] 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.

[0128] This invention combines emotion recognition technology with a virtual reality system for tennis players to provide a personalized training experience. The system monitors the user's emotional state in real time and incorporates this information into strategic suggestions and coaching during training, thereby promoting more effective skill improvement.

[0129] First, users use a VR camera to record their matches or practice sessions in detail. This data is used to gain a detailed understanding of their performance. After recording, the data is uploaded to the server via the user's device.

[0130] The server receives the user's recorded data and analyzes it along with professional match data. Here, it identifies the emotional state from the user's facial expressions and body movements using player movements, ball image data, and emotion recognition technology. This emotion engine can utilize facial recognition technology and biometric sensors.

[0131] Once the analysis is complete, the server generates gameplay suggestions that take into account the user's emotional state. These suggestions are adjusted so that a more challenging playstyle is suggested if the user is relaxed, and a focus on fundamental strategies is placed if the user is stressed.

[0132] The proposed strategy is generated by the server as virtual reality content and sent to the user's device. The virtual reality experience here can simulate matches in various scenarios depending on the user's emotional state. For example, if the user is feeling fatigued, a strategy to help manage physical strength might be provided.

[0133] The user uses VR goggles to begin the virtual match experience. During this experience, the user's emotions are monitored in real time, and suggestions and coaching are automatically adjusted as needed. For example, if the user feels nervous, coaching such as "Take a deep breath and calm down" is provided through the device.

[0134] By utilizing this emotional data, the system can provide a more personalized training environment and effectively support the user's technical and psychological growth. Thus, this invention brings innovation not only to technical improvement but also to mental strengthening.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] Users record their matches or practice sessions using a VR camera. This camera captures the user's movements and facial expressions in high resolution, acquiring data necessary for emotion recognition. The recorded data is temporarily stored on the user's device.

[0138] Step 2:

[0139] The device securely transmits the user's recorded match data to the server via the internet. The transmitted data includes not only video information but also data on facial expressions and body movements.

[0140] Step 3:

[0141] The server collects not only the user's match data but also professional match data accessible online. It then prepares to analyze the user's performance by comparing the two.

[0142] Step 4:

[0143] The server uses a dedicated analysis algorithm to analyze the user's match data in detail. During this process, an emotion engine analyzes the user's facial expressions and body movements in real time to identify changes in their emotions during gameplay.

[0144] Step 5:

[0145] Based on the analysis results, the server generates gameplay suggestions that take the user's emotional state into account. For example, if the user is feeling anxious, the suggestions will be adjusted to prioritize strategies that promote relaxation.

[0146] Step 6:

[0147] The server builds the coordinated strategy as VR content and delivers this data to the user's device. The VR content includes video that simulates specific movements and course selections.

[0148] Step 7:

[0149] Users wear VR goggles and experience matches in a virtual environment. During the match, the user's emotional state is monitored in real time, and coaching tailored to their emotions is provided.

[0150] Step 8:

[0151] The device provides real-time coaching through the earphones whenever it detects a change in the user's emotions. For example, if the user feels pressured, it immediately offers advice on how to relax. This approach allows users to improve their overall skills, including their psychological abilities.

[0152] (Example 2)

[0153] 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".

[0154] Traditional training systems primarily aim to improve skills based on the user's physical performance, but they do not take into account mental state or emotional changes. Therefore, it is difficult for users to achieve maximum results when they are in a mentally unstable state, and there is a need for a system that promotes not only technical growth but also mental growth.

[0155] 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.

[0156] In this invention, the server includes means for collecting professional competition data and analyzing movement and object trajectories, means for identifying the user's emotional state and reflecting it in strategic suggestions, and means for automatically adjusting training content according to the emotional data. This makes it possible to provide a personalized training environment that is tailored to the user's emotional state.

[0157] "Professional competition data" refers to data from matches and training sessions conducted by skilled athletes, and is used as a standard for analyzing strategies and techniques.

[0158] "Motor movements" refer to the physical actions performed by athletes during matches or training, and are analyzed for performance evaluation and improvement.

[0159] "Object trajectory" refers to the path taken by a ball or other equipment during a game, and is used for technical analysis and improvement.

[0160] "User" refers to an individual who uses this system for training.

[0161] "Emotional state" refers to the psychological state and emotional expression of the user, and is a factor that influences performance and the quality of training.

[0162] A "strategic proposal" is a guide that takes into account the user's current skill level and emotional state to guide them toward the optimal actions in matches and training.

[0163] "Emotional data" refers to information about a user's emotions, obtained from their facial expressions, voice, and movements, and is used by the system to provide the user with the most suitable training.

[0164] A "personalized training environment" refers to providing a training setting designed to suit the individual needs and emotional state of each user.

[0165] This invention is a training system designed to help users improve their technical and mental skills in sports such as tennis. The system is configured as follows:

[0166] Users first record the competition using a VR camera. This camera features high resolution and audio recording capabilities, allowing for detailed recording of even the smallest details. The recorded data is temporarily stored on the user's device and then uploaded to a server via the internet.

[0167] The server is the primary component for analyzing the received data. This analysis includes facial recognition technology using OpenCV and techniques for identifying emotional states using biometric sensors. By comparing professional competition data with user data and performing motion analysis, the user's skill level is evaluated. Furthermore, a generative AI model is used to generate strategic suggestions tailored to the user's emotional state. This AI model uses prompts to plan its next training steps.

[0168] For example, based on its analysis, the server might suggest a play style that conserves energy by maintaining an open stance if the user is feeling nervous. This suggestion is then generated as content for the virtual space and sent to the user's terminal.

[0169] The user accepts the proposed strategy, puts on VR goggles, and begins training in the virtual space. Throughout this process, the system continues to monitor the user's emotions and provides further guidance and advice as needed.

[0170] An example of a prompt message is: "Generate an optimization strategy for a tennis player's playing style based on their emotional state. Provide specific instructions for both scenarios where the user is tense and relaxed."

[0171] In this way, the system can provide optimal training in real time based on the user's current emotional state, supporting efficient training tailored to individual needs.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user uses a VR camera to record the competition. In this step, the camera captures video and audio in high resolution. The input includes the user's overall movements and audio information, which is temporarily stored on the device as initial raw data. The output is a recorded file that serves as the basis for analysis.

[0175] Step 2:

[0176] The terminal uploads the recorded data to the server via the internet. Error checking is performed here to ensure the reliability of the data transfer. The input is the recorded data itself, and the output is the data collected on the server and ready for analysis. The terminal temporarily stores the data here.

[0177] Step 3:

[0178] The server analyzes the received video data. Specifically, it performs face recognition using OpenCV and emotion recognition using biometric sensors. The input is video data sent from the terminal, and the output is analyzed emotional state and behavioral evaluation data. This data is used to compare individual players' performance and emotional state to professional standards.

[0179] Step 4:

[0180] The server utilizes a generative AI model to generate optimal strategy suggestions based on the analysis results. This AI model takes emotional state and behavioral analysis data as input and generates prompt-based strategies. The output is a personalized training plan tailored to the user's emotions and skill level. Specifically, it determines what to reinforce compared to conventional data.

[0181] Step 5:

[0182] The server sends the generated strategy as virtual reality content to the user's terminal. The input is the generated strategy data, and the output is an interactive training scenario that can be experienced on the terminal. The server prepares to build a simulation environment tailored to the user.

[0183] Step 6:

[0184] The user uses VR goggles to begin training in virtual reality. During this phase, the device monitors the user's emotional state in real time and provides supplementary guidance and advice as needed. Input is the user's current performance and emotional data, and output is improved technique and mental framework. The device continuously processes information in real time and provides feedback to the user.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] Traditional training systems provide a uniform training program without considering the user's emotional state, making it difficult to provide training optimized for individual mental and physical conditions. Furthermore, there is a need to provide a more personalized and effective training experience by reflecting the user's emotional state. Additionally, there is a lack of real-time coaching, resulting in users not receiving appropriate feedback on the spot.

[0188] 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.

[0189] In this invention, the server includes means for collecting professional competition data and analyzing player movements and ball trajectories; means for recording individual user competition data and transmitting that data to an information processing device; means for analyzing professional competition data and user competition data to generate suggestions for competition strategies suitable for the user; and means for determining the user's emotional state and adjusting the competition experience based on the individual emotional state. This makes it possible to adjust the training content in a way that is most suitable for the user and to provide personalized feedback through real-time emotion recognition.

[0190] "Competition data" refers to information about the match content and actions of users and professional players. This data may include video and sensor information and is used for competition analysis.

[0191] "Analysis" is the process of thoroughly examining collected data and deriving useful information and patterns from the results.

[0192] "Recommendations" refer to suggestions regarding competitive strategies and training methods provided to users based on analyzed data.

[0193] "Emotional state" refers to the user's current psychological and emotional condition. This is determined based on indicators such as facial expressions, heart rate, and body movements.

[0194] "Virtual reality technology" refers to technology that uses computer simulations to allow users to experience virtual environments that closely resemble reality. This technology allows users to experience situations that are difficult to realize in the real world.

[0195] To realize this invention, the following processes are primarily required: collection and analysis of competition data, proposal generation, virtual reality experience, and emotion recognition. These processes are carried out through an appropriate combination of hardware and software.

[0196] First, the user uses a camera-equipped device to record their athletic activities. This captures the user's movements and the game environment in detail. The collected data is sent to the server via the user's device.

[0197] The server uses specialized analysis software to compare professional competition data with user-collected data and generate suggestions for competition strategies. The analysis includes facial recognition and motion recognition algorithms to determine the user's emotional state and physical condition.

[0198] After generating a proposal, the server uses virtual reality technology to send the proposal to the user's terminal. This allows the user to enter a virtual training environment through VR goggles and try out the proposed strategy as a real-world experience.

[0199] Furthermore, by utilizing emotion recognition technology, it is possible to provide immediate guidance and feedback in response to changes in the user's emotions. This feedback is provided as advice to alleviate the user's tension and stress, or as guidance to try new strategies.

[0200] For example, if a user's heart rate is elevated and they are feeling stressed, the system will immediately provide feedback such as, "Take a deep breath to calm down and relax." Generative AI models may be used in this process, and examples of prompts include, "Think of fitness advice for when the user is relaxed."

[0201] The above system can effectively support the technical and mental growth of users.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user records their athletic activities using a camera-equipped device. The input is video data including the user's movements and the game environment. The user saves this data to their device and verifies its accuracy.

[0205] Step 2:

[0206] The user's device sends the recorded video data to the server. The input is the video file recorded earlier, and the output is the upload of the data to the server. The device's role is to upload the data accurately without compromising its quality.

[0207] Step 3:

[0208] The server analyzes the received competition data and compares it to professional competition data. The input consists of video data from the user and professional reference data. The server then uses a motion recognition algorithm to identify similarities and areas for improvement in the movements, and generates a competition strategy suggestion as output.

[0209] Step 4:

[0210] The server constructs a virtual reality simulation based on the generated proposals and sends it to the user's terminal. The inputs are analysis results and strategic proposals, and the output is a virtual reality scenario. Here, VR content is dynamically generated, and the user's experience environment is prepared.

[0211] Step 5:

[0212] The user puts on VR goggles and begins training in the provided virtual reality environment. The input is a VR scenario received from the server, and the output is feedback and skill improvement through a realistic virtual experience. The user performs actual movements using motion sensors and receives virtual coaching that responds to them.

[0213] Step 6:

[0214] The server monitors the user's emotional state in real time and adjusts the feedback accordingly. Inputs are the user's facial expression data and biometric information, while outputs are emotion-based guidance and advice. This process utilizes a generative AI model to instantly deliver appropriate feedback to the user based on prompt text.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention relates to a learning system for tennis players that combines virtual reality technology and data analysis technology. The system provides strategic advice based on the user's matches and supports the improvement of practical skills.

[0232] First, the user uses a VR camera to record a match or practice session. This camera meticulously records the players' movements and the flow of the game. This data is temporarily stored on the user's device and later uploaded to a server.

[0233] Next, the server receives the user's uploaded match data. It also collects professional match data from online or stored databases. This allows the server to accumulate a large amount of match information.

[0234] The server analyzes the user's match data and professional match data to generate a match strategy optimized for each individual user. This process uses machine learning algorithms to identify the user's strengths and areas for improvement. For example, if a user frequently misses backhand shots during rallies, the server analyzes this information and suggests strategies for improvement.

[0235] Based on the analysis results, the server generates suggestions as content for a virtual reality (VR) system and provides them to the user. The user can wear VR goggles to experience the match in a virtual environment and put the suggested strategies into practice. This allows the user to learn strategic movements and shot selection as if they were actually playing the match.

[0236] Furthermore, during actual practice, the device provides users with real-time audio coaching. This coaching is based on strategic suggestions generated by the server. For example, the user might be given instructions such as, "On the next ball, try moving forward and attempting a net play." In this way, users can execute strategies in real time and hone their skills.

[0237] Thus, the system of the present invention becomes a powerful tool for technically skilled tennis players to learn personally optimized strategies and put them to effective use in actual matches.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] Users record their matches and practice sessions using a VR camera. This camera captures 360-degree video, allowing for detailed observation of player movements and the flow of the game. The recorded data is temporarily stored on the user's device.

[0241] Step 2:

[0242] The device uploads the saved recording data to the server via the internet. This data transfer is performed using a secure channel with the user's consent.

[0243] Step 3:

[0244] The server receives the user's uploaded match data. Furthermore, it retrieves data from an online professional match database, including player movements and match details.

[0245] Step 4:

[0246] The server compares the user's match data with professional match data and performs analysis. The analysis is performed to identify the characteristics of the user's playing style, their most successful shots, and areas for improvement.

[0247] Step 5:

[0248] Based on the analysis results, the server runs an algorithm that proposes an optimized game plan for the user. This proposal includes advice on appropriate batting trajectories and positioning.

[0249] Step 6:

[0250] The server constructs the proposed strategy as VR content and sends the data to the user's device. A match simulation in a virtual reality space is then set up.

[0251] Step 7:

[0252] Users wear VR goggles and experience the transmitted VR content. This allows them to actually try out suggested strategies and movements while playing matches in a virtual space.

[0253] Step 8:

[0254] The device provides users with real-time coaching through earphones during actual practice sessions. This includes specific action instructions based on strategic suggestions from the server.

[0255] (Example 1)

[0256] 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."

[0257] Traditional sports learning systems often rely heavily on on-site instruction and self-assessment, making it difficult to obtain objective and concrete improvement suggestions. In particular, the lack of tactical suggestions tailored to individual player characteristics makes it difficult to acquire effective strategies in actual competition. Furthermore, the underutilization of virtual training environments limits the flexibility of practice.

[0258] 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.

[0259] In this invention, the server includes means for collecting competition data and analyzing motion and object trajectories, means for recording the user's competition data and transmitting that data to an information processing device, and means for analyzing the competition data and the user's competition data to generate tactical suggestions suitable for the user. This enables individually optimized tactical suggestions based on objective data, allowing users to effectively improve their practical skills through strategic practice in a virtual environment.

[0260] "Competition data" refers to digital information that records actions and object movements during a competition.

[0261] "Movement" refers to actions or changes in position performed by a person or object.

[0262] "The trajectory of an object" refers to the path an object follows as it moves.

[0263] An "information processing device" refers to an electronic device used to collect, process, and analyze data.

[0264] "User" refers to an individual or organization that uses the system to record competition data and receives the analysis results.

[0265] "Virtual environment technology" refers to technology that uses computer technology to digitally reproduce environments that closely resemble reality.

[0266] "Tactical suggestions" refer to specific advice regarding effective actions and strategies in conducting a competition.

[0267] "Real-time guidance and advice" refers to advice and instructions provided in real time while the user is engaged in the activity.

[0268] This invention provides a system that combines a virtual environment and data analysis technology to help athletes improve their skills. The system mainly consists of three elements: users, servers, and terminals.

[0269] The user first operates a device that records the game or practice using a VR camera. In this case, the VR camera used is assumed to be a "360-degree camera" or similar. This camera records the user's movements and the ball's trajectory in detail and temporarily stores that data on the device.

[0270] The terminal is responsible for uploading the user's saved competition data to a server in the cloud. In this process, it is recommended to use an efficient communication protocol to ensure high-speed transfer while maintaining data integrity.

[0271] The server receives uploaded data and, in parallel, collects professional match data from sources such as "sports databases." Based on this rich dataset, it analyzes the user's performance data. The analysis uses "open-source machine learning frameworks" and other tools to generate tactics optimized for the user. These tactics are used to strengthen the user's strengths and identify areas for improvement.

[0272] The analysis results and generated tactical suggestions are provided to the user from the server. The user wears VR goggles and experiences and deepens their understanding of specific strategies in a virtual environment. Through this experience, the user can try out the movements and decision-making necessary for actual competition within the virtual environment.

[0273] Furthermore, the device provides users with real-time audio coaching during the session. This guidance is based on server analysis and helps users take the most appropriate action based on the situation.

[0274] For example, if data analysis reveals that a user struggles with backhand shots, the server will generate a virtual environment specifically designed to improve backhand technique based on this information. An example of a prompt for the generated AI model might be: "Please suggest strategies for the user to improve their backhand shots in a tennis match. Please also explain the effects of these strategies when implemented."

[0275] Through these means, users can objectively evaluate their own competitive skills and efficiently improve them.

[0276] The flow of the specific process in Example 1 will be described using FIG. 11.

[0277] Step 1:

[0278] The user records the movements of the competition using a VR camera. The input is the user's movements and the movement of the ball during the competition. The output is 360-degree video data recorded by the VR camera. By this operation, it becomes possible to record in detail the user's fine movements and the exact trajectory of the ball.

[0279] Step 2:

[0280] The terminal temporarily stores the recorded video data in local storage. The input is the video data obtained in Step 1. The output is the stored data file. This file may be optimized using data compression technology for subsequent processing.

[0281] Step 3:

[0282] The user uploads the data stored in the terminal to the server. The input is the video data file stored in the terminal. The output is the data transferred to the server, and efficient transfer is performed by utilizing a communication protocol.

[0283] Step 4:

[0284] The server receives the uploaded data and prepares it for analysis. The input is the video data sent from the user. The output is a data set formatted for data analysis. The server checks the data integrity and converts the data into a form that is easy to process with a machine learning model.

[0285] Step 5:

[0286] The server collects professional match data from information sources such as "sports databases". The input is requests and calls to external databases. The output is the collected professional match data, which is used for comparative analysis with the user's data.

[0287] Step 6:

[0288] The server applies machine learning algorithms to compare and analyze the user's competitive data with professional data. The input is the user's competitive data and the collected professional match data. The output is a tactical proposal optimized for the user, and in this process, the generated AI model operates based on the prompt text.

[0289] Step 7:

[0290] The server uses the generated tactical proposal to create virtual reality content and provides it to the user. The input is the tactical proposal data obtained from the analysis. The output is content data for VR experience, which the user can use to practice in a virtual environment.

[0291] Step 8:

[0292] The terminal provides voice instructions in real-time during actual practice. The input is the tactical proposal distributed from the server and the user's current action information. The output is the voice instructions provided to the user, enabling the user to improve their skills while immediately applying the tactics.

[0293] (Application Example 1)

[0294] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0295] Traditional tennis training methods are generally uniform, making it difficult to provide effective instruction tailored to the individual skills and strategies of each player. Furthermore, receiving real-time coaching and personalized training at home is challenging. Therefore, the challenge lies in efficiently providing personalized advice to improve daily practice.

[0296] 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.

[0297] This invention includes a server that collects professional match data and analyzes player movements and object trajectories; a server that records user match information and transmits that data to an information processing device; a server that analyzes professional match data and user match information to generate suggestions for match strategies suitable for the user; a server that allows the user to experience the suggested match strategies using virtual environment technology; a server that provides immediate instructional advice to the user during practice; and a server that analyzes the user's exercise style as a home practice device and creates appropriate practice guidelines. This enables the provision of practice programs optimized for individual players, allowing for immediate and effective self-improvement at home.

[0298] "Professional match data" refers to detailed information about movements and strategies collected from matches played by skilled players.

[0299] "Player movements" refer to the physical movements of a player's body during a match or practice.

[0300] "Object trajectory" is a concept that refers to information indicating the path of movement of a tennis ball or other object.

[0301] "User match information" refers to data obtained from the user's own matches and practice sessions, and includes their characteristics and tendencies.

[0302] An "information processing device" refers to a computer system used for data collection, analysis, and response generation.

[0303] "Virtual environment technology" refers to the technology that presents users with virtual simulations generated by computers.

[0304] "Immediate guidance advice" means specific guidance content provided in real time during practice.

[0305] "Home exercise equipment" refers to devices used to assist individuals in sports practice in a home environment.

[0306] "Practice guidelines" refer to the guidance content indicating the direction and procedures of practice aimed at improving the user's skills.

[0307] The system for realizing this invention provides means for users to perform efficient sports practice in a home environment. Users can record their own practice situation in real time using home exercise equipment. The exercise equipment is equipped with cameras and sensors, which record the movements of users and the trajectories of balls and transmit them to the server. This information is immediately processed using an edge AI platform such as NVIDIA Jetson.

[0308] The server analyzes the received data using a machine learning model described in Python (for example, TensorFlow or PyTorch) and generates optimal practice guidelines for the user. Accurate advice according to the characteristics of the user is output from the analyzed data. As a result, users can receive professional-like guidance anonymously while staying at home.

[0309] The generated practice guidelines are provided to the user using virtual environment technology. As a specific example, it is possible to try advice while experiencing a virtual match through a VR headset. Real-time voice guidance is also provided, and for example, advice such as "Please try net play by going forward with the next ball" can be heard immediately.

[0310] An example of a prompt statement to use as input to a generative AI model is, "Please tell me how to improve the impact timing in my forehand shot, and provide specific advice." By using this prompt, the system can generate more specific and personalized advice.

[0311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0312] Step 1:

[0313] The user records their practice sessions in real time using cameras and sensors installed in their home. The input consists of user movements and ball trajectory data, which are temporarily stored on the device. The output consists of recorded video data and motion data.

[0314] Step 2:

[0315] The terminal transmits recorded user practice data to the server via a communication network. Input consists of stored video and motion data, while output is a data reception completion notification on the server. Data compression may occur during data transfer.

[0316] Step 3:

[0317] The server performs data analysis using the received practice data. The input is the user's submitted practice data, and the output is the analysis results. The data analysis uses machine learning models (such as TensorFlow or PyTorch written in Python) to identify user characteristics and areas for improvement.

[0318] Step 4:

[0319] The server uses a generative AI model to generate practice programs and coaching advice tailored to the user. The input is analyzed data, and the output is specific practice guidelines and prompts. The generated prompts take the form of, "Please tell me how to improve my forehand shot, and suggest specific advice."

[0320] Step 5:

[0321] The server sends generated training guidelines and advice to the terminal using virtual environment technology. The input is the generated training guidelines, and the output is a virtual match environment experienced by the user using VR goggles. The VR environment provides feedback to the user through sight and sound.

[0322] Step 6:

[0323] The device provides real-time voice guidance to the user based on the practice guidelines it receives. The input is the practice guidelines sent from the server, and the output is voice advice transmitted to the user. The voice guidance includes specific content such as, "Try coming forward and trying net play on the next ball."

[0324] 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.

[0325] This invention combines emotion recognition technology with a virtual reality system for tennis players to provide a personalized training experience. The system monitors the user's emotional state in real time and incorporates this information into strategic suggestions and coaching during training, thereby promoting more effective skill improvement.

[0326] First, users use a VR camera to record their matches or practice sessions in detail. This data is used to gain a detailed understanding of their performance. After recording, the data is uploaded to the server via the user's device.

[0327] The server receives the user's recorded data and analyzes it along with professional match data. Here, it identifies the emotional state from the user's facial expressions and body movements using player movements, ball image data, and emotion recognition technology. This emotion engine can utilize facial recognition technology and biometric sensors.

[0328] Once the analysis is complete, the server generates gameplay suggestions that take into account the user's emotional state. These suggestions are adjusted so that a more challenging playstyle is suggested if the user is relaxed, and a focus on fundamental strategies is emphasized if the user is stressed.

[0329] The proposed strategy is generated by the server as virtual reality content and sent to the user's device. The virtual reality experience here can simulate matches in various scenarios depending on the user's emotional state. For example, if the user is feeling fatigued, a strategy to help manage physical strength might be provided.

[0330] The user uses VR goggles to begin the virtual match experience. During this experience, the user's emotions are monitored in real time, and suggestions and coaching are automatically adjusted as needed. For example, if the user feels nervous, coaching such as "Take a deep breath and calm down" is provided through the device.

[0331] By utilizing this emotional data, the system can provide a more personalized training environment and effectively support the user's technical and psychological growth. Thus, this invention brings innovation not only to technical improvement but also to mental strengthening.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] Users record their matches or practice sessions using a VR camera. This camera captures the user's movements and facial expressions in high resolution, acquiring data necessary for emotion recognition. The recorded data is temporarily stored on the user's device.

[0335] Step 2:

[0336] The device securely transmits the user's recorded match data to the server via the internet. The transmitted data includes not only video information but also data on facial expressions and body movements.

[0337] Step 3:

[0338] The server collects not only the user's match data but also professional match data accessible online. It then prepares to analyze the user's performance by comparing the two.

[0339] Step 4:

[0340] The server uses a dedicated analysis algorithm to analyze the user's match data in detail. During this process, an emotion engine analyzes the user's facial expressions and body movements in real time to identify changes in their emotions during gameplay.

[0341] Step 5:

[0342] Based on the analysis results, the server generates gameplay suggestions that take the user's emotional state into account. For example, if the user is feeling anxious, the suggestions will be adjusted to prioritize strategies that promote relaxation.

[0343] Step 6:

[0344] The server builds the coordinated strategy as VR content and delivers this data to the user's device. The VR content includes video that simulates specific movements and course selections.

[0345] Step 7:

[0346] Users wear VR goggles and experience matches in a virtual environment. During the match, the user's emotional state is monitored in real time, and coaching tailored to their emotions is provided.

[0347] Step 8:

[0348] The device provides real-time coaching through the earphones whenever it detects a change in the user's emotions. For example, if the user feels pressured, it immediately offers advice on how to relax. This approach allows users to improve their overall skills, including their psychological abilities.

[0349] (Example 2)

[0350] 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".

[0351] Traditional training systems primarily aim to improve skills based on the user's physical performance, but they do not take into account mental state or emotional changes. Therefore, it is difficult for users to achieve maximum results when they are in a mentally unstable state, and there is a need for a system that promotes not only technical growth but also mental growth.

[0352] 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.

[0353] In this invention, the server includes means for collecting professional competition data and analyzing movement and object trajectories, means for identifying the user's emotional state and reflecting it in strategic suggestions, and means for automatically adjusting training content according to the emotional data. This makes it possible to provide a personalized training environment that is tailored to the user's emotional state.

[0354] "Professional competition data" refers to data from matches and training sessions conducted by skilled athletes, and is used as a standard for analyzing strategies and techniques.

[0355] "Motor movements" refer to the physical actions performed by athletes during matches or training, and are analyzed for performance evaluation and improvement.

[0356] "Object trajectory" refers to the path taken by a ball or other equipment during a game, and is used for technical analysis and improvement.

[0357] "User" refers to an individual who uses this system for training.

[0358] "Emotional state" refers to the psychological state and emotional expression of the user, and is a factor that influences performance and the quality of training.

[0359] A "strategic proposal" is a guide that takes into account the user's current skill level and emotional state to guide them toward the optimal actions in matches and training.

[0360] "Emotional data" refers to information about a user's emotions, obtained from their facial expressions, voice, and movements, and is used by the system to provide the user with the most suitable training.

[0361] A "personalized training environment" refers to providing a training setting designed to suit the individual needs and emotional state of each user.

[0362] This invention is a training system designed to help users improve their technical and mental skills in sports such as tennis. The system is configured as follows:

[0363] Users first record the competition using a VR camera. This camera features high resolution and audio recording capabilities, allowing for detailed recording of even the smallest details. The recorded data is temporarily stored on the user's device and then uploaded to a server via the internet.

[0364] The server is the primary component for analyzing the received data. This analysis includes facial recognition technology using OpenCV and techniques for identifying emotional states using biometric sensors. By comparing professional competition data with user data and performing motion analysis, the user's skill level is evaluated. Furthermore, a generative AI model is used to generate strategic suggestions tailored to the user's emotional state. This AI model uses prompts to plan its next training steps.

[0365] For example, based on its analysis, the server might suggest a play style that conserves energy by maintaining an open stance if the user is feeling nervous. This suggestion is then generated as content for the virtual space and sent to the user's terminal.

[0366] The user accepts the proposed strategy, puts on VR goggles, and begins training in the virtual space. Throughout this process, the system continues to monitor the user's emotions and provides further guidance and advice as needed.

[0367] An example of a prompt message is: "Generate an optimization strategy for a tennis player's playing style based on their emotional state. Provide specific instructions for both scenarios where the user is tense and relaxed."

[0368] In this way, the system can provide optimal training in real time based on the user's current emotional state, supporting efficient training tailored to individual needs.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The user uses a VR camera to record the competition. In this step, the camera captures video and audio in high resolution. The input includes the user's overall movements and audio information, which is temporarily stored on the device as initial raw data. The output is a recorded file that serves as the basis for analysis.

[0372] Step 2:

[0373] The terminal uploads the recorded data to the server via the internet. Error checking is performed here to ensure the reliability of the data transfer. The input is the recorded data itself, and the output is the data collected on the server and ready for analysis. The terminal temporarily stores the data here.

[0374] Step 3:

[0375] The server analyzes the received video data. Specifically, it performs face recognition using OpenCV and emotion recognition using biometric sensors. The input is video data sent from the terminal, and the output is analyzed emotional state and behavioral evaluation data. This data is used to compare individual players' performance and emotional state to professional standards.

[0376] Step 4:

[0377] The server utilizes a generative AI model to generate optimal strategy suggestions based on the analysis results. This AI model takes emotional state and behavioral analysis data as input and generates prompt-based strategies. The output is a personalized training plan tailored to the user's emotions and skill level. Specifically, it determines what to reinforce compared to conventional data.

[0378] Step 5:

[0379] The server sends the generated strategy as virtual reality content to the user's terminal. The input is the generated strategy data, and the output is an interactive training scenario that can be experienced on the terminal. The server prepares to build a simulation environment tailored to the user.

[0380] Step 6:

[0381] The user uses VR goggles to begin training in virtual reality. During this phase, the device monitors the user's emotional state in real time and provides supplementary guidance and advice as needed. Input is the user's current performance and emotional data, and output is improved technique and mental framework. The device continuously processes information in real time and provides feedback to the user.

[0382] (Application Example 2)

[0383] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0384] Traditional training systems provide a uniform training program without considering the user's emotional state, making it difficult to provide training optimized for individual mental and physical conditions. Furthermore, there is a need to provide a more personalized and effective training experience by reflecting the user's emotional state. Additionally, there is a lack of real-time coaching, resulting in users not receiving appropriate feedback on the spot.

[0385] 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.

[0386] In this invention, the server includes means for collecting professional competition data and analyzing player movements and ball trajectories; means for recording individual user competition data and transmitting that data to an information processing device; means for analyzing professional competition data and user competition data to generate suggestions for competition strategies suitable for the user; and means for determining the user's emotional state and adjusting the competition experience based on the individual emotional state. This makes it possible to adjust the training content in a way that is most suitable for the user and to provide personalized feedback through real-time emotion recognition.

[0387] "Competition data" refers to information about the match content and actions of users and professional players. This data may include video and sensor information and is used for competition analysis.

[0388] "Analysis" is the process of thoroughly examining collected data and deriving useful information and patterns from the results.

[0389] "Recommendations" refer to suggestions regarding competitive strategies and training methods provided to users based on analyzed data.

[0390] "Emotional state" refers to the user's current psychological and emotional condition. This is determined based on indicators such as facial expressions, heart rate, and body movements.

[0391] "Virtual reality technology" refers to technology that uses computer simulations to allow users to experience virtual environments that closely resemble reality. This technology allows users to experience situations that are difficult to realize in the real world.

[0392] To realize this invention, the following processes are primarily required: collection and analysis of competition data, proposal generation, virtual reality experience, and emotion recognition. These processes are carried out through an appropriate combination of hardware and software.

[0393] First, the user uses a camera-equipped device to record their athletic activities. This captures the user's movements and the game environment in detail. The collected data is sent to the server via the user's device.

[0394] The server uses specialized analysis software to compare professional competition data with user-collected data and generate suggestions for competition strategies. The analysis includes facial recognition and motion recognition algorithms to determine the user's emotional state and physical condition.

[0395] After generating a proposal, the server uses virtual reality technology to send the proposal to the user's terminal. This allows the user to enter a virtual training environment through VR goggles and try out the proposed strategy as a real-world experience.

[0396] Furthermore, by utilizing emotion recognition technology, it is possible to provide immediate guidance and feedback in response to changes in the user's emotions. This feedback is provided as advice to alleviate the user's tension and stress, or as guidance to try new strategies.

[0397] For example, if a user's heart rate is elevated and they are feeling stressed, the system will immediately provide feedback such as, "Take a deep breath to calm down and relax." Generative AI models may be used in this process, and examples of prompts include, "Think of fitness advice for when the user is relaxed."

[0398] The above system can effectively support the technical and mental growth of users.

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The user records their athletic activities using a camera-equipped device. The input is video data including the user's movements and the game environment. The user saves this data to their device and verifies its accuracy.

[0402] Step 2:

[0403] The user's device sends the recorded video data to the server. The input is the video file recorded earlier, and the output is the upload of the data to the server. The device's role is to upload the data accurately without compromising its quality.

[0404] Step 3:

[0405] The server analyzes the received competition data and compares it to professional competition data. The input consists of video data from the user and professional reference data. The server then uses a motion recognition algorithm to identify similarities and areas for improvement in the movements, and generates a competition strategy suggestion as output.

[0406] Step 4:

[0407] The server constructs a virtual reality simulation based on the generated proposals and sends it to the user's terminal. The inputs are analysis results and strategic proposals, and the output is a virtual reality scenario. Here, VR content is dynamically generated, and the user's experience environment is prepared.

[0408] Step 5:

[0409] The user puts on VR goggles and begins training in the provided virtual reality environment. The input is a VR scenario received from the server, and the output is feedback and skill improvement through a realistic virtual experience. The user performs actual movements using motion sensors and receives virtual coaching that responds to them.

[0410] Step 6:

[0411] The server monitors the user's emotional state in real time and adjusts the feedback accordingly. Inputs are the user's facial expression data and biometric information, while outputs are emotion-based guidance and advice. This process utilizes a generative AI model to instantly deliver appropriate feedback to the user based on prompt text.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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".

[0428] This invention relates to a learning system for tennis players that combines virtual reality technology and data analysis technology. The system provides strategic advice based on the user's matches and supports the improvement of practical skills.

[0429] First, the user uses a VR camera to record a match or practice session. This camera meticulously records the players' movements and the flow of the game. This data is temporarily stored on the user's device and later uploaded to a server.

[0430] Next, the server receives the user's uploaded match data. It also collects professional match data from online or stored databases. This allows the server to accumulate a large amount of match information.

[0431] The server analyzes the user's match data and professional match data to generate a match strategy optimized for each individual user. This process uses machine learning algorithms to identify the user's strengths and areas for improvement. For example, if a user frequently misses backhand shots during rallies, the server analyzes this information and suggests strategies for improvement.

[0432] Based on the analysis results, the server generates suggestions as content for a virtual reality (VR) system and provides them to the user. The user can wear VR goggles to experience the match in a virtual environment and put the suggested strategies into practice. This allows the user to learn strategic movements and shot selection as if they were actually playing the match.

[0433] Furthermore, during actual practice, the device provides users with real-time audio coaching. This coaching is based on strategic suggestions generated by the server. For example, the user might be given instructions such as, "On the next ball, try moving forward and attempting a net play." In this way, users can execute strategies in real time and hone their skills.

[0434] Thus, the system of the present invention becomes a powerful tool for technically skilled tennis players to learn personally optimized strategies and put them to effective use in actual matches.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] Users record their matches and practice sessions using a VR camera. This camera captures 360-degree video, allowing for detailed observation of player movements and the flow of the game. The recorded data is temporarily stored on the user's device.

[0438] Step 2:

[0439] The device uploads the saved recording data to the server via the internet. This data transfer is performed using a secure channel with the user's consent.

[0440] Step 3:

[0441] The server receives the user's uploaded match data. Furthermore, it retrieves data from an online professional match database, including player movements and match details.

[0442] Step 4:

[0443] The server compares the user's match data with professional match data and performs analysis. The analysis is performed to identify the characteristics of the user's playing style, their most successful shots, and areas for improvement.

[0444] Step 5:

[0445] Based on the analysis results, the server runs an algorithm that proposes an optimized game plan for the user. This proposal includes advice on appropriate batting trajectories and positioning.

[0446] Step 6:

[0447] The server constructs the proposed strategy as VR content and sends the data to the user's device. A match simulation in a virtual reality space is then set up.

[0448] Step 7:

[0449] Users wear VR goggles and experience the transmitted VR content. This allows them to actually try out suggested strategies and movements while playing matches in a virtual space.

[0450] Step 8:

[0451] The device provides users with real-time coaching through earphones during actual practice sessions. This includes specific action instructions based on strategic suggestions from the server.

[0452] (Example 1)

[0453] 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."

[0454] Traditional sports learning systems often rely heavily on on-site instruction and self-assessment, making it difficult to obtain objective and concrete improvement suggestions. In particular, the lack of tactical suggestions tailored to individual player characteristics makes it difficult to acquire effective strategies in actual competition. Furthermore, the underutilization of virtual training environments limits the flexibility of practice.

[0455] 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.

[0456] In this invention, the server includes means for collecting competition data and analyzing motion and object trajectories, means for recording the user's competition data and transmitting that data to an information processing device, and means for analyzing the competition data and the user's competition data to generate tactical suggestions suitable for the user. This enables individually optimized tactical suggestions based on objective data, allowing users to effectively improve their practical skills through strategic practice in a virtual environment.

[0457] "Competition data" refers to digital information that records actions and object movements during a competition.

[0458] "Movement" refers to actions or changes in position performed by a person or object.

[0459] "The trajectory of an object" refers to the path an object follows as it moves.

[0460] An "information processing device" refers to an electronic device used to collect, process, and analyze data.

[0461] "User" refers to an individual or organization that uses the system to record competition data and receives the analysis results.

[0462] "Virtual environment technology" refers to technology that uses computer technology to digitally reproduce environments that closely resemble reality.

[0463] "Tactical suggestions" refer to specific advice regarding effective actions and strategies in conducting a competition.

[0464] "Real-time guidance and advice" refers to advice and instructions provided in real time while the user is engaged in the activity.

[0465] This invention provides a system that combines a virtual environment and data analysis technology to help athletes improve their skills. The system mainly consists of three elements: users, servers, and terminals.

[0466] The user first operates a device that records the game or practice using a VR camera. In this case, the VR camera used is assumed to be a "360-degree camera" or similar. This camera records the user's movements and the ball's trajectory in detail and temporarily stores that data on the device.

[0467] The terminal is responsible for uploading the user's saved competition data to a server in the cloud. In this process, it is recommended to use an efficient communication protocol to ensure high-speed transfer while maintaining data integrity.

[0468] The server receives uploaded data and, in parallel, collects professional match data from sources such as "sports databases." Based on this rich dataset, it analyzes the user's performance data. The analysis uses "open-source machine learning frameworks" and other tools to generate tactics optimized for the user. These tactics are used to strengthen the user's strengths and identify areas for improvement.

[0469] The analysis results and generated tactical suggestions are provided to the user from the server. The user wears VR goggles and experiences and deepens their understanding of specific strategies in a virtual environment. Through this experience, the user can try out the movements and decision-making necessary for actual competition within the virtual environment.

[0470] Furthermore, the device provides users with real-time audio coaching during the session. This guidance is based on server analysis and helps users take the most appropriate action based on the situation.

[0471] For example, if data analysis reveals that a user struggles with backhand shots, the server will generate a virtual environment specifically designed to improve backhand technique based on this information. An example of a prompt for the generated AI model might be: "Please suggest strategies for the user to improve their backhand shots in a tennis match. Please also explain the effects of these strategies when implemented."

[0472] Through these means, users can objectively evaluate their own competitive skills and efficiently improve them.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The user records their actions during the game using a VR camera. The input consists of the user's movements and the ball's movement during the game. The output is 360-degree video data recorded by the VR camera. This process allows for detailed recording of the user's subtle movements and the ball's precise trajectory.

[0476] Step 2:

[0477] The device temporarily saves the recorded video data to local storage. The input is the video data acquired in step 1. The output is the saved data file. This file may be optimized using data compression techniques for later processing.

[0478] Step 3:

[0479] The user uploads data stored on their device to the server. The input is a video data file stored on the device. The output is the data transferred to the server, and this transfer is carried out efficiently using a communication protocol.

[0480] Step 4:

[0481] The server receives uploaded data and prepares it for analysis. The input is video data sent by the user. The output is a dataset formatted for data analysis. The server verifies the integrity of the data and converts it into a format that is easy for machine learning models to process.

[0482] Step 5:

[0483] The server collects professional match data from sources such as "sports databases." Input consists of requests and calls to external databases. Output is the collected professional match data. This data is used for comparative analysis with user data.

[0484] Step 6:

[0485] The server applies machine learning algorithms to compare and analyze the user's competitive data with professional data. The inputs are the user's competitive data and collected professional match data. The output is tactical suggestions optimized for the user, in which case the generated AI model operates based on prompt statements.

[0486] Step 7:

[0487] The server uses the generated tactical suggestions to create virtual reality content and provide it to the user. The input is tactical suggestion data obtained through analysis. The output is content data for the VR experience, which the user can then use to practice in the virtual environment.

[0488] Step 8:

[0489] The terminal provides real-time voice instructions during actual practice. Input consists of tactical suggestions delivered from the server and information about the user's current actions. Output is voice instructions provided to the user, allowing them to immediately apply tactics and improve their skills.

[0490] (Application Example 1)

[0491] 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."

[0492] Traditional tennis training methods are generally uniform, making it difficult to provide effective instruction tailored to the individual skills and strategies of each player. Furthermore, receiving real-time coaching and personalized training at home is challenging. Therefore, the challenge lies in efficiently providing personalized advice to improve daily practice.

[0493] 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.

[0494] This invention includes a server that collects professional match data and analyzes player movements and object trajectories; a server that records user match information and transmits that data to an information processing device; a server that analyzes professional match data and user match information to generate suggestions for match strategies suitable for the user; a server that allows the user to experience the suggested match strategies using virtual environment technology; a server that provides immediate instructional advice to the user during practice; and a server that analyzes the user's exercise style as a home practice device and creates appropriate practice guidelines. This enables the provision of practice programs optimized for individual players, allowing for immediate and effective self-improvement at home.

[0495] "Professional match data" refers to detailed information about movements and strategies collected from matches played by skilled players.

[0496] "Player movements" refer to the physical movements of a player's body during a match or practice.

[0497] "Object trajectory" is a concept that refers to information indicating the path of movement of a tennis ball or other object.

[0498] "User match information" refers to data obtained from the user's own matches and practice sessions, and includes their characteristics and tendencies.

[0499] An "information processing device" refers to a computer system used for data collection, analysis, and response generation.

[0500] "Virtual environment technology" is a technology that presents users with virtual simulations generated by a computer.

[0501] "Immediate instruction and advice" refers to specific instructional content provided in real time during practice.

[0502] "Home training equipment" refers to devices used to support individual sports training in a home environment.

[0503] "Practice guidelines" refer to instructional content that outlines the direction and procedures for practice aimed at improving the user's skills.

[0504] The system realizing this invention provides users with a means to efficiently practice sports in a home environment. Users can record their practice status in real time using home-use training equipment. The training equipment is equipped with cameras and sensors that record the user's movements and the trajectory of the ball, and transmit this information to a server. This information is processed immediately using an edge AI platform such as NVIDIA Jetson.

[0505] The server analyzes the received data using machine learning models written in Python (e.g., TensorFlow or PyTorch) and generates optimal practice guidelines for the user. Based on the analyzed data, precise advice tailored to the user's characteristics is output. This allows users to receive professional-level instruction anonymously from the comfort of their homes.

[0506] The generated training guidelines are provided to users using virtual environment technology. For example, it is possible to try out the advice while experiencing a virtual match through VR goggles. Real-time voice guidance is also provided, allowing users to instantly hear advice such as, "Try moving forward and trying net play on the next ball."

[0507] An example of a prompt statement to use as input to a generative AI model is, "Please tell me how to improve the impact timing in my forehand shot, and provide specific advice." By using this prompt, the system can generate more specific and personalized advice.

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The user records their practice sessions in real time using cameras and sensors installed in their home. The input consists of user movements and ball trajectory data, which are temporarily stored on the device. The output consists of recorded video data and motion data.

[0511] Step 2:

[0512] The terminal transmits recorded user practice data to the server via a communication network. Input consists of stored video and motion data, while output is a data reception completion notification on the server. Data compression may occur during data transfer.

[0513] Step 3:

[0514] The server performs data analysis using the received practice data. The input is the user's submitted practice data, and the output is the analysis results. The data analysis uses machine learning models (such as TensorFlow or PyTorch written in Python) to identify user characteristics and areas for improvement.

[0515] Step 4:

[0516] The server uses a generative AI model to generate practice programs and coaching advice tailored to the user. The input is analyzed data, and the output is specific practice guidelines and prompts. The generated prompts take the form of, "Please tell me how to improve my forehand shot, and suggest specific advice."

[0517] Step 5:

[0518] The server sends generated training guidelines and advice to the terminal using virtual environment technology. The input is the generated training guidelines, and the output is a virtual match environment experienced by the user using VR goggles. The VR environment provides feedback to the user through sight and sound.

[0519] Step 6:

[0520] The device provides real-time voice guidance to the user based on the practice guidelines it receives. The input is the practice guidelines sent from the server, and the output is voice advice transmitted to the user. The voice guidance includes specific content such as, "Try coming forward and trying net play on the next ball."

[0521] 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.

[0522] This invention combines emotion recognition technology with a virtual reality system for tennis players to provide a personalized training experience. The system monitors the user's emotional state in real time and incorporates this information into strategic suggestions and coaching during training, thereby promoting more effective skill improvement.

[0523] First, users use a VR camera to record their matches or practice sessions in detail. This data is used to gain a detailed understanding of their performance. After recording, the data is uploaded to the server via the user's device.

[0524] The server receives the user's recorded data and analyzes it along with professional match data. Here, it identifies the emotional state from the user's facial expressions and body movements using player movements, ball image data, and emotion recognition technology. This emotion engine can utilize facial recognition technology and biometric sensors.

[0525] Once the analysis is complete, the server generates gameplay suggestions that take into account the user's emotional state. These suggestions are adjusted so that a more challenging playstyle is suggested if the user is relaxed, and a focus on fundamental strategies is emphasized if the user is stressed.

[0526] The proposed strategy is generated by the server as virtual reality content and sent to the user's device. The virtual reality experience here can simulate matches in various scenarios depending on the user's emotional state. For example, if the user is feeling fatigued, a strategy to help manage physical strength might be provided.

[0527] The user uses VR goggles to begin the virtual match experience. During this experience, the user's emotions are monitored in real time, and suggestions and coaching are automatically adjusted as needed. For example, if the user feels nervous, coaching such as "Take a deep breath and calm down" is provided through the device.

[0528] By utilizing this emotional data, the system can provide a more personalized training environment and effectively support the user's technical and psychological growth. Thus, this invention brings innovation not only to technical improvement but also to mental strengthening.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] Users record their matches or practice sessions using a VR camera. This camera captures the user's movements and facial expressions in high resolution, acquiring data necessary for emotion recognition. The recorded data is temporarily stored on the user's device.

[0532] Step 2:

[0533] The device securely transmits the user's recorded match data to the server via the internet. The transmitted data includes not only video information but also data on facial expressions and body movements.

[0534] Step 3:

[0535] The server collects not only the user's match data but also professional match data accessible online. It then prepares to analyze the user's performance by comparing the two.

[0536] Step 4:

[0537] The server uses a dedicated analysis algorithm to analyze the user's match data in detail. During this process, an emotion engine analyzes the user's facial expressions and body movements in real time to identify changes in their emotions during gameplay.

[0538] Step 5:

[0539] Based on the analysis results, the server generates gameplay suggestions that take the user's emotional state into account. For example, if the user is feeling anxious, the suggestions will be adjusted to prioritize strategies that promote relaxation.

[0540] Step 6:

[0541] The server builds the coordinated strategy as VR content and delivers this data to the user's device. The VR content includes video that simulates specific movements and course selections.

[0542] Step 7:

[0543] Users wear VR goggles and experience matches in a virtual environment. During the match, the user's emotional state is monitored in real time, and coaching is provided according to their emotions.

[0544] Step 8:

[0545] The device provides real-time coaching through the earphones whenever it detects a change in the user's emotions. For example, if the user feels pressured, it immediately offers advice on how to relax. This approach allows users to improve their overall skills, including their psychological abilities.

[0546] (Example 2)

[0547] 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."

[0548] Traditional training systems primarily aim to improve skills based on the user's physical performance, but they do not take into account mental state or emotional changes. Therefore, it is difficult for users to achieve maximum results when they are in a mentally unstable state, and there is a need for a system that promotes not only technical growth but also mental growth.

[0549] 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.

[0550] In this invention, the server includes means for collecting professional competition data and analyzing movement and object trajectories, means for identifying the user's emotional state and reflecting it in strategic suggestions, and means for automatically adjusting training content according to the emotional data. This makes it possible to provide a personalized training environment that is tailored to the user's emotional state.

[0551] "Professional competition data" refers to data from matches and training sessions conducted by skilled athletes, and is used as a standard for analyzing strategies and techniques.

[0552] "Motor movements" refer to the physical actions performed by athletes during matches or training, and are analyzed for performance evaluation and improvement.

[0553] "Object trajectory" refers to the path taken by a ball or other equipment during a game, and is used for technical analysis and improvement.

[0554] "User" refers to an individual who uses this system for training.

[0555] "Emotional state" refers to the psychological state and emotional expression of the user, and is a factor that influences performance and the quality of training.

[0556] A "strategic proposal" is a guide that takes into account the user's current skill level and emotional state to guide them toward the optimal actions in matches and training.

[0557] "Emotional data" refers to information about a user's emotions, obtained from their facial expressions, voice, and movements, and is used by the system to provide the user with the most suitable training.

[0558] A "personalized training environment" refers to providing a training setting designed to suit the individual needs and emotional state of each user.

[0559] This invention is a training system designed to help users improve their technical and mental skills in sports such as tennis. The system is configured as follows:

[0560] Users first record the competition using a VR camera. This camera features high resolution and audio recording capabilities, allowing for detailed recording of even the smallest details. The recorded data is temporarily stored on the user's device and then uploaded to a server via the internet.

[0561] The server is the primary component for analyzing the received data. This analysis includes facial recognition technology using OpenCV and techniques for identifying emotional states using biometric sensors. By comparing professional competition data with user data and performing motion analysis, the user's skill level is evaluated. Furthermore, a generative AI model is used to generate strategic suggestions tailored to the user's emotional state. This AI model uses prompts to plan its next training steps.

[0562] For example, based on its analysis, the server might suggest a play style that conserves energy by maintaining an open stance if the user is feeling nervous. This suggestion is then generated as content for the virtual space and sent to the user's terminal.

[0563] The user accepts the proposed strategy, puts on VR goggles, and begins training in the virtual space. Throughout this process, the system continues to monitor the user's emotions and provides further guidance and advice as needed.

[0564] An example of a prompt message is: "Generate an optimization strategy for a tennis player's playing style based on their emotional state. Provide specific instructions for both scenarios where the user is tense and relaxed."

[0565] In this way, the system can provide optimal training in real time based on the user's current emotional state, supporting efficient training tailored to individual needs.

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] The user uses a VR camera to record the competition. In this step, the camera captures video and audio in high resolution. The input includes the user's overall movements and audio information, which is temporarily stored on the device as initial raw data. The output is a recorded file that serves as the basis for analysis.

[0569] Step 2:

[0570] The terminal uploads the recorded data to the server via the internet. Error checking is performed here to ensure the reliability of the data transfer. The input is the recorded data itself, and the output is the data collected on the server and ready for analysis. The terminal temporarily stores the data here.

[0571] Step 3:

[0572] The server analyzes the received video data. Specifically, it performs face recognition using OpenCV and emotion recognition using biometric sensors. The input is video data sent from the terminal, and the output is analyzed emotional state and behavioral evaluation data. This data is used to compare individual players' performance and emotional state to professional standards.

[0573] Step 4:

[0574] The server utilizes a generative AI model to generate optimal strategy suggestions based on the analysis results. This AI model takes emotional state and behavioral analysis data as input and generates prompt-based strategies. The output is a personalized training plan tailored to the user's emotions and skill level. Specifically, it determines what to reinforce compared to conventional data.

[0575] Step 5:

[0576] The server sends the generated strategy as virtual reality content to the user's terminal. The input is the generated strategy data, and the output is an interactive training scenario that can be experienced on the terminal. The server prepares to build a simulation environment tailored to the user.

[0577] Step 6:

[0578] The user uses VR goggles to begin training in virtual reality. During this phase, the device monitors the user's emotional state in real time and provides supplementary guidance and advice as needed. Input is the user's current performance and emotional data, and output is improved technique and mental framework. The device continuously processes information in real time and provides feedback to the user.

[0579] (Application Example 2)

[0580] 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."

[0581] Traditional training systems provide a uniform training program without considering the user's emotional state, making it difficult to provide training optimized for individual mental and physical conditions. Furthermore, there is a need to provide a more personalized and effective training experience by reflecting the user's emotional state. Additionally, there is a lack of real-time coaching, resulting in users not receiving appropriate feedback on the spot.

[0582] 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.

[0583] In this invention, the server includes means for collecting professional competition data and analyzing player movements and ball trajectories; means for recording individual user competition data and transmitting that data to an information processing device; means for analyzing professional competition data and user competition data to generate suggestions for competition strategies suitable for the user; and means for determining the user's emotional state and adjusting the competition experience based on the individual emotional state. This makes it possible to adjust the training content in a way that is most suitable for the user and to provide personalized feedback through real-time emotion recognition.

[0584] "Competition data" refers to information about the match content and actions of users and professional players. This data may include video and sensor information and is used for competition analysis.

[0585] "Analysis" is the process of thoroughly examining collected data and deriving useful information or patterns from the results.

[0586] "Recommendations" refer to suggestions regarding competitive strategies and training methods provided to users based on analyzed data.

[0587] "Emotional state" refers to the user's current psychological and emotional condition. This is determined based on indicators such as facial expressions, heart rate, and body movements.

[0588] "Virtual reality technology" refers to technology that uses computer simulations to allow users to experience virtual environments that closely resemble reality. This technology allows users to experience situations that are difficult to realize in the real world.

[0589] To realize this invention, the following processes are primarily required: collection and analysis of competition data, proposal generation, virtual reality experience, and emotion recognition. These processes are carried out through an appropriate combination of hardware and software.

[0590] First, the user uses a camera-equipped device to record their athletic activities. This captures the user's movements and the game environment in detail. The collected data is sent to the server via the user's device.

[0591] The server uses specialized analysis software to compare professional competition data with user-collected data and generate suggestions for competition strategies. The analysis includes facial recognition and motion recognition algorithms to determine the user's emotional state and physical condition.

[0592] After generating a proposal, the server uses virtual reality technology to send the proposal to the user's terminal. This allows the user to enter a virtual training environment through VR goggles and try out the proposed strategy as a real-world experience.

[0593] Furthermore, by utilizing emotion recognition technology, it is possible to provide immediate guidance and feedback in response to changes in the user's emotions. This feedback is provided as advice to alleviate the user's tension and stress, or as guidance to try new strategies.

[0594] For example, if a user's heart rate is elevated and they are feeling stressed, the system will immediately provide feedback such as, "Take a deep breath to calm down and relax." Generative AI models may be used in this process, and examples of prompts include, "Think of fitness advice for when the user is relaxed."

[0595] The above system can effectively support the technical and mental growth of users.

[0596] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0597] Step 1:

[0598] The user records their athletic activities using a camera-equipped device. The input is video data including the user's movements and the game environment. The user saves this data to their device and verifies its accuracy.

[0599] Step 2:

[0600] The user's device sends the recorded video data to the server. The input is the video file recorded earlier, and the output is the upload of the data to the server. The device's role is to upload the data accurately without compromising its quality.

[0601] Step 3:

[0602] The server analyzes the received competition data and compares it to professional competition data. The input consists of video data from the user and professional reference data. The server then uses a motion recognition algorithm to identify similarities and areas for improvement in the movements, and generates a competition strategy suggestion as output.

[0603] Step 4:

[0604] The server constructs a virtual reality simulation based on the generated proposals and sends it to the user's terminal. The inputs are analysis results and strategic proposals, and the output is a virtual reality scenario. Here, VR content is dynamically generated, and the user's experience environment is prepared.

[0605] Step 5:

[0606] The user puts on VR goggles and begins training in the provided virtual reality environment. The input is a VR scenario received from the server, and the output is feedback and skill improvement through a realistic virtual experience. The user performs actual movements using motion sensors and receives virtual coaching that responds to them.

[0607] Step 6:

[0608] The server monitors the user's emotional state in real time and adjusts the feedback accordingly. Inputs are the user's facial expression data and biometric information, while outputs are emotion-based guidance and advice. This process utilizes a generative AI model to instantly deliver appropriate feedback to the user based on prompt text.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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".

[0626] This invention relates to a learning system for tennis players that combines virtual reality technology and data analysis technology. The system provides strategic advice based on the user's matches and supports the improvement of practical skills.

[0627] First, the user uses a VR camera to record a match or practice session. This camera meticulously records the players' movements and the flow of the game. This data is temporarily stored on the user's device and later uploaded to a server.

[0628] Next, the server receives the user's uploaded match data. It also collects professional match data from online or stored databases. This allows the server to accumulate a large amount of match information.

[0629] The server analyzes the user's match data and professional match data to generate a match strategy optimized for each individual user. This process uses machine learning algorithms to identify the user's strengths and areas for improvement. For example, if a user frequently misses backhand shots during rallies, the server analyzes this information and suggests strategies for improvement.

[0630] Based on the analysis results, the server generates suggestions as content for a virtual reality (VR) system and provides them to the user. The user can wear VR goggles to experience the match in a virtual environment and put the suggested strategies into practice. This allows the user to learn strategic movements and shot selection as if they were actually playing the match.

[0631] Furthermore, during actual practice, the device provides users with real-time audio coaching. This coaching is based on strategic suggestions generated by the server. For example, the user might be given instructions such as, "On the next ball, try moving forward and attempting a net play." In this way, users can execute strategies in real time and hone their skills.

[0632] Thus, the system of the present invention becomes a powerful tool for technically skilled tennis players to learn personally optimized strategies and put them to effective use in actual matches.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] Users record their matches and practice sessions using a VR camera. This camera captures 360-degree video, allowing for detailed observation of player movements and the flow of the game. The recorded data is temporarily stored on the user's device.

[0636] Step 2:

[0637] The device uploads the saved recording data to the server via the internet. This data transfer is performed using a secure channel with the user's consent.

[0638] Step 3:

[0639] The server receives the user's uploaded match data. Furthermore, it retrieves data from an online professional match database, including player movements and match details.

[0640] Step 4:

[0641] The server compares the user's match data with professional match data and performs analysis. The analysis is performed to identify the characteristics of the user's playing style, their most successful shots, and areas for improvement.

[0642] Step 5:

[0643] Based on the analysis results, the server runs an algorithm that proposes an optimized game plan for the user. This proposal includes advice on appropriate batting trajectories and positioning.

[0644] Step 6:

[0645] The server constructs the proposed strategy as VR content and sends the data to the user's device. A match simulation in a virtual reality space is then set up.

[0646] Step 7:

[0647] Users wear VR goggles and experience the transmitted VR content. This allows them to actually try out suggested strategies and movements while playing matches in a virtual space.

[0648] Step 8:

[0649] The device provides users with real-time coaching through earphones during actual practice sessions. This includes specific action instructions based on strategic suggestions from the server.

[0650] (Example 1)

[0651] 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".

[0652] Traditional sports learning systems often rely heavily on on-site instruction and self-assessment, making it difficult to obtain objective and concrete improvement suggestions. In particular, the lack of tactical suggestions tailored to individual player characteristics makes it difficult to acquire effective strategies in actual competition. Furthermore, the underutilization of virtual training environments limits the flexibility of practice.

[0653] 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.

[0654] In this invention, the server includes means for collecting competition data and analyzing motion and object trajectories, means for recording the user's competition data and transmitting that data to an information processing device, and means for analyzing the competition data and the user's competition data to generate tactical suggestions suitable for the user. This enables individually optimized tactical suggestions based on objective data, allowing users to effectively improve their practical skills through strategic practice in a virtual environment.

[0655] "Competition data" refers to digital information that records actions and object movements during a competition.

[0656] "Movement" refers to actions or changes in position performed by a person or object.

[0657] "The trajectory of an object" refers to the path an object follows as it moves.

[0658] An "information processing device" refers to an electronic device used to collect, process, and analyze data.

[0659] "User" refers to an individual or organization that uses the system to record competition data and receives the analysis results.

[0660] "Virtual environment technology" refers to technology that uses computer technology to digitally reproduce environments that closely resemble reality.

[0661] "Tactical suggestions" refer to specific advice regarding effective actions and strategies in conducting a competition.

[0662] "Real-time guidance and advice" refers to advice and instructions provided in real time while the user is engaged in the activity.

[0663] This invention provides a system that combines a virtual environment and data analysis technology to help athletes improve their skills. The system mainly consists of three elements: users, servers, and terminals.

[0664] The user first operates a device that records the game or practice using a VR camera. In this case, the VR camera used is assumed to be a "360-degree camera" or similar. This camera records the user's movements and the ball's trajectory in detail and temporarily stores that data on the device.

[0665] The terminal is responsible for uploading the user's saved competition data to a server in the cloud. In this process, it is recommended to use an efficient communication protocol to ensure high-speed transfer while maintaining data integrity.

[0666] The server receives uploaded data and, in parallel, collects professional match data from sources such as "sports databases." Based on this rich dataset, it analyzes the user's performance data. The analysis uses "open-source machine learning frameworks" and other tools to generate tactics optimized for the user. These tactics are used to strengthen the user's strengths and identify areas for improvement.

[0667] The analysis results and generated tactical suggestions are provided to the user from the server. The user wears VR goggles and experiences and deepens their understanding of specific strategies in a virtual environment. Through this experience, the user can try out the movements and decision-making necessary for actual competition within the virtual environment.

[0668] Furthermore, the device provides users with real-time audio coaching during the session. This guidance is based on server analysis and helps users take the most appropriate action based on the situation.

[0669] For example, if data analysis reveals that a user struggles with backhand shots, the server will generate a virtual environment specifically designed to improve backhand technique based on this information. An example of a prompt for the generated AI model might be: "Please suggest strategies for the user to improve their backhand shots in a tennis match. Please also explain the effects of these strategies when implemented."

[0670] Through these means, users can objectively evaluate their own competitive skills and efficiently improve them.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The user records their actions during the game using a VR camera. The input consists of the user's movements and the ball's movement during the game. The output is 360-degree video data recorded by the VR camera. This process allows for detailed recording of the user's subtle movements and the ball's precise trajectory.

[0674] Step 2:

[0675] The device temporarily saves the recorded video data to local storage. The input is the video data acquired in step 1. The output is the saved data file. This file may be optimized using data compression techniques for later processing.

[0676] Step 3:

[0677] The user uploads data stored on their device to the server. The input is a video data file stored on the device. The output is the data transferred to the server, and this transfer is carried out efficiently using a communication protocol.

[0678] Step 4:

[0679] The server receives uploaded data and prepares it for analysis. The input is video data sent by the user. The output is a dataset formatted for data analysis. The server verifies the integrity of the data and converts it into a format that is easy for machine learning models to process.

[0680] Step 5:

[0681] The server collects professional match data from sources such as "sports databases." Input consists of requests and calls to external databases. Output is the collected professional match data. This data is used for comparative analysis with user data.

[0682] Step 6:

[0683] The server applies machine learning algorithms to compare and analyze the user's competitive data with professional data. The inputs are the user's competitive data and collected professional match data. The output is tactical suggestions optimized for the user, in which case the generated AI model operates based on prompt statements.

[0684] Step 7:

[0685] The server uses the generated tactical suggestions to create virtual reality content and provide it to the user. The input is tactical suggestion data obtained through analysis. The output is content data for the VR experience, which the user can then use to practice in the virtual environment.

[0686] Step 8:

[0687] The terminal provides real-time voice instructions during actual practice. Input consists of tactical suggestions delivered from the server and information about the user's current actions. Output is voice instructions provided to the user, allowing them to immediately apply tactics and improve their skills.

[0688] (Application Example 1)

[0689] 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".

[0690] Traditional tennis training methods are generally uniform, making it difficult to provide effective instruction tailored to the individual skills and strategies of each player. Furthermore, receiving real-time coaching and personalized training at home is challenging. Therefore, the challenge lies in efficiently providing personalized advice to improve daily practice.

[0691] 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.

[0692] This invention includes a server that collects professional match data and analyzes player movements and object trajectories; a server that records user match information and transmits that data to an information processing device; a server that analyzes professional match data and user match information to generate suggestions for match strategies suitable for the user; a server that allows the user to experience the suggested match strategies using virtual environment technology; a server that provides immediate instructional advice to the user during practice; and a server that analyzes the user's exercise style as a home practice device and creates appropriate practice guidelines. This enables the provision of practice programs optimized for individual players, allowing for immediate and effective self-improvement at home.

[0693] "Professional match data" refers to detailed information about movements and strategies collected from matches played by skilled players.

[0694] "Player movements" refer to the physical movements of a player's body during a match or practice.

[0695] "Object trajectory" is a concept that refers to information indicating the path of movement of a tennis ball or other object.

[0696] "User match information" refers to data obtained from the user's own matches and practice sessions, and includes their characteristics and tendencies.

[0697] An "information processing device" refers to a computer system used for data collection, analysis, and response generation.

[0698] "Virtual environment technology" is a technology that presents users with virtual simulations generated by a computer.

[0699] "Immediate instruction and advice" refers to specific instructional content provided in real time during practice.

[0700] "Home training equipment" refers to devices used to support individual sports training in a home environment.

[0701] "Practice guidelines" refer to instructional content that outlines the direction and procedures for practice aimed at improving the user's skills.

[0702] The system realizing this invention provides users with a means to efficiently practice sports in a home environment. Users can record their practice status in real time using home-use training equipment. The training equipment is equipped with cameras and sensors that record the user's movements and the trajectory of the ball, and transmit this information to a server. This information is processed immediately using an edge AI platform such as NVIDIA Jetson.

[0703] The server analyzes the received data using machine learning models written in Python (e.g., TensorFlow or PyTorch) and generates optimal practice guidelines for the user. Based on the analyzed data, precise advice tailored to the user's characteristics is output. This allows users to receive professional-level instruction anonymously from the comfort of their homes.

[0704] The generated training guidelines are provided to users using virtual environment technology. For example, it is possible to try out the advice while experiencing a virtual match through VR goggles. Real-time voice guidance is also provided, allowing users to instantly hear advice such as, "Try moving forward and trying net play on the next ball."

[0705] An example of a prompt statement to use as input to a generative AI model is, "Please tell me how to improve the impact timing in my forehand shot, and provide specific advice." By using this prompt, the system can generate more specific and personalized advice.

[0706] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0707] Step 1:

[0708] The user records their practice sessions in real time using cameras and sensors installed in their home. The input consists of user movements and ball trajectory data, which are temporarily stored on the device. The output consists of recorded video data and motion data.

[0709] Step 2:

[0710] The terminal transmits recorded user practice data to the server via a communication network. Input consists of stored video and motion data, while output is a data reception completion notification on the server. Data compression may occur during data transfer.

[0711] Step 3:

[0712] The server performs data analysis using the received practice data. The input is the user's submitted practice data, and the output is the analysis results. The data analysis uses machine learning models (such as TensorFlow or PyTorch written in Python) to identify user characteristics and areas for improvement.

[0713] Step 4:

[0714] The server uses a generative AI model to generate practice programs and coaching advice tailored to the user. The input is analyzed data, and the output is specific practice guidelines and prompts. The generated prompts take the form of, "Please tell me how to improve my forehand shot, and suggest specific advice."

[0715] Step 5:

[0716] The server sends generated training guidelines and advice to the terminal using virtual environment technology. The input is the generated training guidelines, and the output is a virtual match environment experienced by the user using VR goggles. The VR environment provides feedback to the user through sight and sound.

[0717] Step 6:

[0718] The device provides real-time voice guidance to the user based on the practice guidelines it receives. The input is the practice guidelines sent from the server, and the output is voice advice transmitted to the user. The voice guidance includes specific content such as, "Try coming forward and trying net play on the next ball."

[0719] 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.

[0720] This invention combines emotion recognition technology with a virtual reality system for tennis players to provide a personalized training experience. The system monitors the user's emotional state in real time and incorporates this information into strategic suggestions and coaching during training, thereby promoting more effective skill improvement.

[0721] First, users use a VR camera to record their matches or practice sessions in detail. This data is used to gain a detailed understanding of their performance. After recording, the data is uploaded to the server via the user's device.

[0722] The server receives the user's recorded data and analyzes it along with professional match data. Here, it identifies the emotional state from the user's facial expressions and body movements using player movements, ball image data, and emotion recognition technology. This emotion engine can utilize facial recognition technology and biometric sensors.

[0723] Once the analysis is complete, the server generates gameplay suggestions that take into account the user's emotional state. These suggestions are adjusted so that a more challenging playstyle is suggested if the user is relaxed, and a focus on fundamental strategies is emphasized if the user is stressed.

[0724] The proposed strategy is generated by the server as virtual reality content and sent to the user's device. The virtual reality experience here can simulate matches in various scenarios depending on the user's emotional state. For example, if the user is feeling fatigued, a strategy to help manage physical strength might be provided.

[0725] The user uses VR goggles to begin the virtual match experience. During this experience, the user's emotions are monitored in real time, and suggestions and coaching are automatically adjusted as needed. For example, if the user feels nervous, coaching such as "Take a deep breath and calm down" is provided through the device.

[0726] By utilizing this emotional data, the system can provide a more personalized training environment and effectively support the user's technical and psychological growth. Thus, this invention brings innovation not only to technical improvement but also to mental strengthening.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] Users record their matches or practice sessions using a VR camera. This camera captures the user's movements and facial expressions in high resolution, acquiring data necessary for emotion recognition. The recorded data is temporarily stored on the user's device.

[0730] Step 2:

[0731] The device securely transmits the user's recorded match data to the server via the internet. The transmitted data includes not only video information but also data on facial expressions and body movements.

[0732] Step 3:

[0733] The server collects not only the user's match data but also professional match data accessible online. It then prepares to analyze the user's performance by comparing the two.

[0734] Step 4:

[0735] The server uses a dedicated analysis algorithm to analyze the user's match data in detail. During this process, an emotion engine analyzes the user's facial expressions and body movements in real time to identify changes in their emotions during gameplay.

[0736] Step 5:

[0737] Based on the analysis results, the server generates gameplay suggestions that take the user's emotional state into account. For example, if the user is feeling anxious, the suggestions will be adjusted to prioritize strategies that promote relaxation.

[0738] Step 6:

[0739] The server builds the coordinated strategy as VR content and delivers this data to the user's device. The VR content includes video that simulates specific movements and course selections.

[0740] Step 7:

[0741] Users wear VR goggles and experience matches in a virtual environment. During the match, the user's emotional state is monitored in real time, and coaching is provided according to their emotions.

[0742] Step 8:

[0743] The device provides real-time coaching through the earphones whenever it detects a change in the user's emotions. For example, if the user feels pressured, it immediately offers advice on how to relax. This approach allows users to improve their overall skills, including their psychological abilities.

[0744] (Example 2)

[0745] 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".

[0746] Traditional training systems primarily aim to improve skills based on the user's physical performance, but they do not take into account mental state or emotional changes. Therefore, it is difficult for users to achieve maximum results when they are in a mentally unstable state, and there is a need for a system that promotes not only technical growth but also mental growth.

[0747] 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.

[0748] In this invention, the server includes means for collecting professional competition data and analyzing movement and object trajectories, means for identifying the user's emotional state and reflecting it in strategic suggestions, and means for automatically adjusting training content according to the emotional data. This makes it possible to provide a personalized training environment that is tailored to the user's emotional state.

[0749] "Professional competition data" refers to data from matches and training sessions conducted by skilled athletes, and is used as a standard for analyzing strategies and techniques.

[0750] "Motor movements" refer to the physical actions performed by athletes during matches or training, and are analyzed for performance evaluation and improvement.

[0751] "Object trajectory" refers to the path taken by a ball or other equipment during a game, and is used for technical analysis and improvement.

[0752] "User" refers to an individual who uses this system for training.

[0753] "Emotional state" refers to the psychological state and emotional expression of the user, and is a factor that influences performance and the quality of training.

[0754] A "strategic proposal" is a guide that takes into account the user's current skill level and emotional state to guide them toward the optimal actions in matches and training.

[0755] "Emotional data" refers to information about a user's emotions, obtained from their facial expressions, voice, and movements, and is used by the system to provide the user with the most suitable training.

[0756] A "personalized training environment" refers to providing a training setting designed to suit the individual needs and emotional state of each user.

[0757] This invention is a training system designed to help users improve their technical and mental skills in sports such as tennis. The system is configured as follows:

[0758] Users first record the competition using a VR camera. This camera features high resolution and audio recording capabilities, allowing for detailed recording of even the smallest details. The recorded data is temporarily stored on the user's device and then uploaded to a server via the internet.

[0759] The server is the primary component for analyzing the received data. This analysis includes facial recognition technology using OpenCV and techniques for identifying emotional states using biometric sensors. By comparing professional competition data with user data and performing motion analysis, the user's skill level is evaluated. Furthermore, a generative AI model is used to generate strategic suggestions tailored to the user's emotional state. This AI model uses prompts to plan its next training steps.

[0760] For example, based on its analysis, the server might suggest a play style that conserves energy by maintaining an open stance if the user is feeling nervous. This suggestion is then generated as content for the virtual space and sent to the user's terminal.

[0761] The user accepts the proposed strategy, puts on VR goggles, and begins training in the virtual space. Throughout this process, the system continues to monitor the user's emotions and provides further guidance and advice as needed.

[0762] An example of a prompt message is: "Generate an optimization strategy for a tennis player's playing style based on their emotional state. Provide specific instructions for both scenarios where the user is tense and relaxed."

[0763] In this way, the system can provide optimal training in real time based on the user's current emotional state, supporting efficient training tailored to individual needs.

[0764] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0765] Step 1:

[0766] The user uses a VR camera to record the competition. In this step, the camera captures video and audio in high resolution. The input includes the user's overall movements and audio information, which is temporarily stored on the device as initial raw data. The output is a recorded file that serves as the basis for analysis.

[0767] Step 2:

[0768] The terminal uploads the recorded data to the server via the internet. Error checking is performed here to ensure the reliability of the data transfer. The input is the recorded data itself, and the output is the data collected on the server and ready for analysis. The terminal temporarily stores the data here.

[0769] Step 3:

[0770] The server analyzes the received video data. Specifically, it performs face recognition using OpenCV and emotion recognition using biometric sensors. The input is video data sent from the terminal, and the output is analyzed emotional state and behavioral evaluation data. This data is used to compare individual players' performance and emotional state to professional standards.

[0771] Step 4:

[0772] The server utilizes a generative AI model to generate optimal strategy suggestions based on the analysis results. This AI model takes emotional state and behavioral analysis data as input and generates prompt-based strategies. The output is a personalized training plan tailored to the user's emotions and skill level. Specifically, it determines what to reinforce compared to conventional data.

[0773] Step 5:

[0774] The server sends the generated strategy as virtual reality content to the user's terminal. The input is the generated strategy data, and the output is an interactive training scenario that can be experienced on the terminal. The server prepares to build a simulation environment tailored to the user.

[0775] Step 6:

[0776] The user uses VR goggles to begin training in virtual reality. During this phase, the device monitors the user's emotional state in real time and provides supplementary guidance and advice as needed. Input is the user's current performance and emotional data, and output is improved technique and mental framework. The device continuously processes information in real time and provides feedback to the user.

[0777] (Application Example 2)

[0778] 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".

[0779] Traditional training systems provide a uniform training program without considering the user's emotional state, making it difficult to provide training optimized for individual mental and physical conditions. Furthermore, there is a need to provide a more personalized and effective training experience by reflecting the user's emotional state. Additionally, there is a lack of real-time coaching, resulting in users not receiving appropriate feedback on the spot.

[0780] 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.

[0781] In this invention, the server includes means for collecting professional competition data and analyzing player movements and ball trajectories; means for recording individual user competition data and transmitting that data to an information processing device; means for analyzing professional competition data and user competition data to generate suggestions for competition strategies suitable for the user; and means for determining the user's emotional state and adjusting the competition experience based on the individual emotional state. This makes it possible to adjust the training content in a way that is most suitable for the user and to provide personalized feedback through real-time emotion recognition.

[0782] "Competition data" refers to information about the match content and actions of users and professional players. This data may include video and sensor information and is used for competition analysis.

[0783] "Analysis" is the process of thoroughly examining collected data and deriving useful information or patterns from the results.

[0784] "Recommendations" refer to suggestions regarding competitive strategies and training methods provided to users based on analyzed data.

[0785] "Emotional state" refers to the user's current psychological and emotional condition. This is determined based on indicators such as facial expressions, heart rate, and body movements.

[0786] "Virtual reality technology" refers to technology that uses computer simulations to allow users to experience virtual environments that closely resemble reality. This technology allows users to experience situations that are difficult to realize in the real world.

[0787] To realize this invention, the following processes are primarily required: collection and analysis of competition data, proposal generation, virtual reality experience, and emotion recognition. These processes are carried out through an appropriate combination of hardware and software.

[0788] First, the user uses a camera-equipped device to record their athletic activities. This captures the user's movements and the game environment in detail. The collected data is sent to the server via the user's device.

[0789] The server uses specialized analysis software to compare professional competition data with user-collected data and generate suggestions for competition strategies. The analysis includes facial recognition and motion recognition algorithms to determine the user's emotional state and physical condition.

[0790] After generating a proposal, the server uses virtual reality technology to send the proposal to the user's terminal. This allows the user to enter a virtual training environment through VR goggles and try out the proposed strategy as a real-world experience.

[0791] Furthermore, by utilizing emotion recognition technology, it is possible to provide immediate guidance and feedback in response to changes in the user's emotions. This feedback is provided as advice to alleviate the user's tension and stress, or as guidance to try new strategies.

[0792] For example, if a user's heart rate is elevated and they are feeling stressed, the system will immediately provide feedback such as, "Take a deep breath to calm down and relax." Generative AI models may be used in this process, and examples of prompts include, "Think of fitness advice for when the user is relaxed."

[0793] The above system can effectively support the technical and mental growth of users.

[0794] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0795] Step 1:

[0796] The user records their athletic activities using a camera-equipped device. The input is video data including the user's movements and the game environment. The user saves this data to their device and verifies its accuracy.

[0797] Step 2:

[0798] The user's device sends the recorded video data to the server. The input is the video file recorded earlier, and the output is the upload of the data to the server. The device's role is to upload the data accurately without compromising its quality.

[0799] Step 3:

[0800] The server analyzes the received competition data and compares it to professional competition data. The input consists of video data from the user and professional reference data. The server then uses a motion recognition algorithm to identify similarities and areas for improvement in the movements, and generates a competition strategy suggestion as output.

[0801] Step 4:

[0802] The server constructs a virtual reality simulation based on the generated proposals and sends it to the user's terminal. The inputs are analysis results and strategic proposals, and the output is a virtual reality scenario. Here, VR content is dynamically generated, and the user's experience environment is prepared.

[0803] Step 5:

[0804] The user puts on VR goggles and begins training in the provided virtual reality environment. The input is a VR scenario received from the server, and the output is feedback and skill improvement through a realistic virtual experience. The user performs actual movements using motion sensors and receives virtual coaching that responds to them.

[0805] Step 6:

[0806] The server monitors the user's emotional state in real time and adjusts the feedback accordingly. Inputs are the user's facial expression data and biometric information, while outputs are emotion-based guidance and advice. This process utilizes a generative AI model to instantly deliver appropriate feedback to the user based on prompt text.

[0807] 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.

[0808] 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.

[0809] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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."

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] The following is further disclosed regarding the embodiments described above.

[0829] (Claim 1)

[0830] A means of collecting professional match data and analyzing player movements and ball trajectories,

[0831] A means of recording the user's match data and sending that data to a server,

[0832] A method for analyzing professional match data and user match data to generate suggestions for match strategies suited to the user,

[0833] A method for allowing users to experience the proposed game plan using virtual reality technology,

[0834] A means of providing real-time coaching advice to users during practice,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, comprising means for identifying successful plays and areas for improvement in the analysis of a user's match data.

[0838] (Claim 3)

[0839] The system according to claim 1, comprising means for visualizing proposed movements and ball selection in an experience using virtual reality technology.

[0840] "Example 1"

[0841] (Claim 1)

[0842] A means of collecting competition data and analyzing motion and object trajectories,

[0843] A means for recording user competition data and transmitting that data to an information processing device,

[0844] A means of analyzing competition data and user competition data to generate tactical suggestions suitable for the user,

[0845] A means of allowing users to experience the generated tactical proposals using virtual environment technology,

[0846] A means of providing real-time guidance and advice to users during implementation,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, comprising means for identifying successful actions and elements requiring improvement in the analysis of a user's competition data.

[0850] (Claim 3)

[0851] The system according to claim 1, comprising means for visualizing generated actions and selections in an experience using virtual environment technology.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] A means of collecting professional match data and analyzing player movements and object trajectories,

[0855] A means for recording a user's match information and transmitting that data to an information processing device,

[0856] A means of analyzing professional match data and user match information to generate suggestions for match strategies suitable for the user,

[0857] A method for allowing users to experience the proposed game plan using virtual environment technology,

[0858] A means of providing immediate instruction and advice to users during practice,

[0859] As a home training device, it provides a means to analyze the user's exercise style and create appropriate training guidelines,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, which has means for identifying successful plays and areas for improvement in the analysis of a user's match information.

[0863] (Claim 3)

[0864] The system according to claim 1, comprising means for visualizing proposed movements and ball selection in an experience using virtual environment technology.

[0865] "Example 2 of combining an emotion engine"

[0866] (Claim 1)

[0867] A means of collecting professional athletic data and analyzing movement patterns and object trajectories,

[0868] A means for recording user competition data and transmitting that data to an information processing device,

[0869] A means of analyzing professional competition data and user competition data to generate suggestions for competition strategies suitable for the user,

[0870] A means of allowing users to experience the proposed competition strategy using virtual space technology,

[0871] A means of providing real-time guidance and advice to users during training,

[0872] A means of identifying the emotional state of users and reflecting it in strategic proposals,

[0873] A means to automatically adjust training content according to emotional data,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, comprising means for identifying successful actions and areas requiring improvement in the analysis of a user's competition data.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising means for visualizing proposed actions and ball selection in an experience using virtual space technology.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] A means of collecting professional competition data and analyzing player movements and ball trajectories,

[0882] A means for recording individual users' competition data and transmitting that data to an information processing device,

[0883] A means of analyzing professional and user competition data to generate suggestions for competition strategies suitable for the user,

[0884] A means of allowing users to experience the proposed competitive strategy using virtual reality technology,

[0885] A means of providing real-time guidance to users during practice,

[0886] A means of determining the user's emotional state and adjusting the competitive experience based on that individual emotional state,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, comprising means for identifying successful plays and elements requiring improvement in the analysis of a user's competition data.

[0890] (Claim 3)

[0891] The system according to claim 1, comprising means for visualizing proposed actions and ball selections in an experience using virtual reality technology. [Explanation of Symbols]

[0892] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting professional match data and analyzing player movements and object trajectories, A means for recording a user's match information and transmitting that data to an information processing device, A means of analyzing professional match data and user match information to generate suggestions for match strategies suitable for the user, A method for allowing users to experience the proposed game plan using virtual environment technology, A means of providing immediate instruction and advice to users during practice, As a home training device, it provides a means to analyze the user's exercise style and create appropriate training guidelines, A system that includes this.

2. The system according to claim 1, which has means for identifying successful plays and areas for improvement in the analysis of a user's match information.

3. The system according to claim 1, comprising means for visualizing proposed movements and ball selection in an experience using virtual environment technology.

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