system

Abstract

We provide the system. [Solution] The system includes an information processing device for generating a virtual reality environment, and the virtual reality environment includes means for reproducing a scene intended for tactical training of movement, A means for detecting the actions of participants within a generated virtual reality environment using a detection device and acquiring information related to those actions, A means for immediately analyzing the information obtained above and generating tactical feedback based on the analysis results, Means including a display device or output device for notifying the user of the analysis results, A means of monitoring user behavior and providing virtual exercise scenarios using a humanoid robot for home use, 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern sports teams, there is a problem that it is difficult to grasp the situation in real time and respond quickly during tactical training and strategic changes during games. Due to this problem, it is particularly difficult to convey appropriate feedback to players without delay during games where the situation changes dynamically. Furthermore, it is required to efficiently conduct effective tactical simulations based on past games and continuously improve tactics.

Means for Solving the Problems

[0005] This invention provides a computing device for generating a virtual reality environment and means for reproducing scenarios for sports tactical training. It also includes means for detecting player movements within the generated virtual reality environment using sensors and collecting data related to those movements. This allows for real-time analysis of the collected data and the generation of tactical feedback based on the analysis results. Furthermore, by providing a display or output device for notifying the user of the analysis results, the system provides players and coaches with immediate, situation-appropriate feedback.

[0006] A "virtual reality environment" is a synthetic space created using computer technology that allows users to actually experience environments that do not exist in reality through sight, and sometimes hearing and touch.

[0007] A "calculation unit" is a device or system that receives numerical data or signals and processes them using predetermined algorithms or procedures.

[0008] "Tactical training" refers to practice or simulations conducted in a particular sport or activity to improve the skills and abilities necessary to effectively implement a target strategy.

[0009] A "scenario" is a plan or script that outlines a specific situation or series of events, and is used to predict and prepare for possible developments within that scenario.

[0010] A "sensor" is a device that detects changes in the physical environment and outputs them as data such as electrical signals.

[0011] "Data analysis" is the process of using collected data to find regularities and patterns within that data and derive meaningful information.

[0012] "Feedback" refers to information returned to the input side in order to re-evaluate the results and effects output by a system or process and to make improvements or adjustments as needed. [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] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

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

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 is a system for effectively training sports tactics using a virtual reality environment. This system combines virtual reality technology and AI analysis to provide real-time feedback on the player's movements, thereby supporting tactical improvement.

[0035] The server first retrieves sports match data and tactical scenarios from a database, and then uses this data to generate a virtual reality environment. The server then sends the generated environment to a terminal, where it is reproduced on the VR device used by the athlete. This allows users (athletes and coaches) to train in a virtual space different from the real world.

[0036] The device tracks the player's movements in real time through a VR device and collects motion data using sensors. The collected data is then sent back to the server and analyzed by an AI model. This analysis evaluates the player's current performance and tactical positioning, and generates specific feedback for improvement.

[0037] Users receive feedback from the server on their devices, which is displayed on the screen or announced as an audio alert. This allows users to quickly adjust their tactics as needed and improve their performance in training and matches.

[0038] As a concrete example, taking a soccer match as an illustration, the user (the team's coach) simulates the opposing team's formation in virtual reality and trains their own players on how to move in response to that situation. During the match, the server analyzes each player's movements in real time and provides feedback, such as adjusting the defensive line, as needed. This improves the ability to respond immediately to the dynamics of the match.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server retrieves sports match data and historical tactical scenarios from a database and uses this data to prepare the foundation for the virtual reality environment. The server then analyzes this data to determine the situations that should be recreated within the virtual reality environment.

[0042] Step 2:

[0043] The server sends information about the prepared virtual reality environment to the terminal. Based on this information, the terminal generates a virtual space that the athlete will experience using a VR device, allowing the user to begin an immersive training session.

[0044] Step 3:

[0045] The device uses sensors to collect motion data in order to track the player's movements in real time. The collected data is then transferred from the device to the server.

[0046] Step 4:

[0047] The server analyzes the motion data received from the terminal in real time. Using a generative AI model, it evaluates the performance and tactical effectiveness of the players' movements and generates data for feedback.

[0048] Step 5:

[0049] Feedback data generated from the server is sent to the terminal. The terminal notifies the user of this feedback through screen display or audio.

[0050] Step 6:

[0051] Users review the feedback received from their devices and modify their tactics as needed. Based on this information, they can respond in real time and maximize the effectiveness of their training.

[0052] (Example 1)

[0053] 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."

[0054] In athletics, tactical training should ideally be conducted in an environment closely resembling real-world match situations. However, replicating actual matches and providing rapid and accurate feedback to individual athletes is challenging. Traditional methods lack sufficient realistic environments for effective training and adequate real-time, detailed motion analysis.

[0055] 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.

[0056] In this invention, the server includes an information processing device, which includes means for generating a virtual environment for the purpose of tactical training of movement, means for detecting the movements of participants within the generated virtual environment using a sensing device and collecting information related to those movements, and means for analyzing the collected information in real time and generating tactical feedback based on the analysis results. This makes it possible to analyze the movements of participants in real time within the virtual environment and provide rapid feedback.

[0057] An "information processing device" is a general term for hardware and software that have the function of efficiently processing and analyzing data and generating a specified virtual environment.

[0058] A "virtual environment" is a realistic, three-dimensional simulated space created using computer technology for the purpose of tactical training in athletic competitions.

[0059] A "sensing device" is a device that includes sensor technology used to detect the movements and positions of participants with high precision and to collect information.

[0060] "Information" refers to a collection of data detected by sensing devices and used for analysis and feedback generation.

[0061] "Analysis" is the process of evaluating behavior and identifying areas for improvement based on collected information.

[0062] A "knowledge model" is an AI algorithm and its application technology designed for data analysis, used to evaluate things like movement efficiency and tactical positioning.

[0063] "Feedback" refers to information generated based on analysis results, including instructions and advice for improving tactics and athletic efficiency.

[0064] A "display device" is a device used to present analysis results and feedback to users visually or audibly.

[0065] This invention is an embodiment of a system that uses virtual reality technology and artificial intelligence technology to make tactical training for athletic competitions more effective. The following procedure and apparatus are required to carry out the invention.

[0066] First, the server is equipped with an information processing unit and retrieves athletic competition data and tactical scenarios from a database. Common database management systems used for this purpose include MySQL® and PostgreSQL. Based on the retrieved data, the server generates a virtual environment using a game engine such as Unity. Unity has advanced 3D modeling and simulation capabilities, making it suitable for recreating a realistic athletic environment.

[0067] The device uses a VR system to sense the user's movements in real time. Accelerometers and gyroscopes are used to accurately track the player's position and movement. This motion data is transmitted from the device to a server, where it is processed using high-performance computing resources.

[0068] The server employs an AI model using machine learning libraries such as TENSORFLOW® to analyze sensory data in real time. The AI ​​model utilizes player movement data to evaluate movement efficiency and tactical positioning. This enables feedback under situations equivalent to those in real matches.

[0069] Users receive feedback generated based on the analysis results on a VR device, perceiving it particularly through the display and audio output devices. This allows users to instantly identify areas for tactical improvement and operational efficiency, and adjust their movements as needed.

[0070] As a concrete example, considering soccer, a type of athletic sport, the server simulates a typical formation of the opposing team. The user, acting as a coach, provides appropriate tactical guidance to their own players within the virtual environment. By analyzing the players' movements in real time and providing feedback on appropriate positions and movements, more advanced tactical training becomes possible.

[0071] An example of a prompt to be input into the generating AI model is, "Perform a tactical simulation based on past match data in a virtual environment and analyze the players' movement data." This prompt allows the system to automate a series of steps to support professional and effective training.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server retrieves sports match data and tactical scenarios from a database management system. Inputs include digital logs and scenario files related to past matches. This data is retrieved from MySQL using SQL queries. The output is a structured dataset necessary for creating a virtual environment. Specifically, the server executes SQL commands and loads the retrieved data into memory.

[0075] Step 2:

[0076] The server generates a virtual environment using Unity based on the acquired data. The input is the structured dataset obtained in step 1. As part of the data processing, 3D models are placed and the environment is configured using the Unity API. The output is a virtual reality simulation in its initial state. Specifically, virtual models of players and the field are placed, and a match scenario is constructed along a timeline.

[0077] Step 3:

[0078] The server sends the generated virtual environment data to the terminal. The input is a virtual environment object created within Unity. Data processing involves data compression and serialization using network protocols. The output is virtual environment data that can be decompressed on the terminal. Specifically, the server sends data using HTTP or WebSocket.

[0079] Step 4:

[0080] The terminal uses a VR device to sense the user's movements in real time. Inputs are the user's movements and sensor data acquired from the VR device. For data processing, accelerometers and gyroscopes are used to collect and filter the motion data. Outputs are motion data packets for transmission to the server. Specifically, the terminal periodically captures data from the sensors.

[0081] Step 5:

[0082] The server receives motion data sent from the terminal and analyzes it using an AI model with TensorFlow. The input is motion data packets acquired from the terminal. As part of the data calculation, the AI ​​model evaluates motion efficiency and tactical positioning. The output is the evaluation result and feedback message. In terms of specific operation, the server executes the AI ​​model and generates results quickly.

[0083] Step 6:

[0084] The user receives feedback generated based on the analysis results through a VR device. The input is feedback messages provided by the server. The output is visual or audio feedback displayed in a way that the user can understand. Specifically, the device presents the feedback through a VR headset display or headphones.

[0085] (Application Example 1)

[0086] 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."

[0087] In modern sports training, tactical understanding and real-time improvement are complex. In particular, individual home training often lacks access to expert guidance, limiting opportunities for effective tactical learning. Furthermore, the challenge lies in precisely recreating sports scenarios and receiving instant feedback to achieve immediate tactical improvement.

[0088] 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.

[0089] In this invention, the server includes an information processing device for generating a virtual reality environment, the virtual reality environment includes means for reproducing a scene intended for tactical training of movement, means for sensing the actions of participants within the generated virtual reality environment with a detection device and acquiring information related to those actions, means for immediately analyzing the acquired information and generating tactical feedback based on the analysis results, and means for monitoring the user's actions and providing a virtual exercise scene using a home-use humanoid robot. This makes it possible for individual users to receive professional and real-time tactical training at home.

[0090] A "virtual reality environment" is a digitally recreated space that is different from the real world, using computer technology, and allows users to have experiences that are similar to reality.

[0091] An "information processing device" is a device used for collecting, processing, storing, and reproducing data, and is a concept that includes computers and programmable hardware.

[0092] A "situation designed for tactical training in sports" is a hypothetical scenario specifically designed to learn and improve sports and fitness techniques and methodologies.

[0093] A "detection device" is a device that converts physical phenomena into electrical signals, and it is a system that includes sensors.

[0094] "Behavioral information" refers to data that digitally maps a user's movements and location, and is fundamental information used for behavioral analysis.

[0095] "Instant analysis" refers to a method where data is processed and analyzed immediately upon collection, guaranteeing results without any time lag.

[0096] "Tactical feedback" refers to a method of providing users with information that specifically outlines areas for improvement and coaching methods regarding the tactical aspects of exercise.

[0097] A "household humanoid robot" is a humanoid automated machine designed primarily for use inside a home, which assists with tasks according to the user's instructions and actions.

[0098] In implementing this invention, the system first constructs a virtual reality environment and recreates a scenario in which the user performs tactical training for exercise. The server uses an information processing device for this purpose to send the virtual reality space to the VR set or head-mounted display that the user has as a home device. This allows the user to train in exercise scenarios that transcend physical limitations.

[0099] When a user begins an action, a detection device built into the terminal immediately senses the action and acquires information related to that action. For example, if a user is dribbling a soccer ball, sensors acquire the movement and speed of the user's feet as digital data. This data is sent to a server and immediately analyzed by an AI model. This analysis utilizes software such as TensorFlow.

[0100] The server utilizes a display or output device to generate and provide immediate feedback to the user based on the analysis results. This allows the user to receive instructions for tactical improvements to their movements. For example, by inputting a prompt such as, "Analyze the forehand motion data and list the points that need improvement," the generated AI model provides specialized feedback.

[0101] One concrete example is a scenario where a user practices basketball shooting at home using a home-use humanoid robot. In this case, the robot hands the user the ball and provides real-time advice on adjusting the shooting angle.

[0102] Overall, this invention makes the process of sports tactical training possible at home, providing personalized, real-time, and effective training for individual users.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server retrieves virtual data related to sports training scenarios from the database. This data includes the exercise scenarios the user will perform, and a generative AI model uses these prompts to design a virtual reality space. This prepares the virtual reality space for the server to send to the VR device.

[0106] Step 2:

[0107] The server transmits the generated virtual reality data to the terminal's VR device. This process maintains low latency data transfer, ensuring the user can instantly access the virtual environment. The user participates in the virtual reality environment by wearing the VR device.

[0108] Step 3:

[0109] When a user begins exercising, sensors attached to the device detect the user's movements. These include accelerometers and gyroscopes, and data from these sensors is acquired in real time. This data (e.g., speed and angle of movement) is aggregated by the device as information related to the action and sent to the server.

[0110] Step 4:

[0111] The server immediately analyzes the received motion data using an AI model. At this stage, it uses machine learning libraries such as TensorFlow to process the data based on the prompt "Analyze the forehand motion data and list areas for improvement." This extracts specific tactical improvements to the user's actions.

[0112] Step 5:

[0113] The generated feedback is sent from the server to the terminal. The terminal displays this feedback visually on the VR device or notifies the user as audio. This allows the user to understand the best way to improve their actions in real time and immediately take action.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention provides a system that highly personalizes sports tactical training and feedback by combining a virtual reality environment with an emotion engine. This system detects the player's movements and emotions and dynamically adjusts the training content and feedback based on that data.

[0116] First, the server imports past match data and predefined tactical scenarios, and uses this information as a foundation to generate a virtual reality environment. The generated environment is then transmitted to the terminal and presented to the user via their VR device. This allows the user to begin training in the virtual space.

[0117] The device is equipped with an emotion sensor that captures the user's emotional state in real time. This uses facial recognition technology and biosensor data to estimate emotions and understand the user's mental state. The obtained emotional data is sent to a server.

[0118] The server analyzes motion and emotion data transmitted from the terminal. The emotion engine uses this information to evaluate the user's psychological state during training and dynamically generates feedback accordingly. This includes applying relaxation techniques if the user is experiencing excessive stress, or sending encouraging feedback if the user is losing motivation.

[0119] As a concrete example, consider a basketball free throw practice session. If a user immediately becomes upset after missing, the server analyzes that emotion through an emotion engine. As a result, it reduces the user's mental burden and increases their chances of success by displaying advice with humor.

[0120] In this way, the system provides feedback that comprehensively considers the athlete's physical activity and emotions, helping them to acquire more effective sports tactics.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The server imports past match data and tactical scenarios, preparing the information necessary to generate a virtual reality environment. The server then analyzes this data to create virtual scenarios suitable for sports training.

[0124] Step 2:

[0125] The server sends the generated virtual reality environment to the terminal. The terminal provides the user with the virtual space through a VR device, and the user begins training within that environment.

[0126] Step 3:

[0127] The device collects the user's physical and psychological data using physical sensors to detect the player's movements and emotion sensors to estimate emotions from facial expressions and voice.

[0128] Step 4:

[0129] The server receives motion and emotion data transmitted from the terminal. The server analyzes this data in real time to evaluate the player's performance and emotional state.

[0130] Step 5:

[0131] The server uses an emotion engine to generate feedback that takes into account the user's emotional state. For example, if the user is stressed, it will generate feedback that promotes relaxation; if their motivation is low, it will generate encouraging feedback.

[0132] Step 6:

[0133] The server sends the generated feedback to the device. The device then provides the feedback to the user through methods such as screen display, audio notification, or vibration.

[0134] Step 7:

[0135] Users adjust their tactics based on feedback received from their devices to maximize the effectiveness of their training. They utilize the feedback to improve themselves and aim to enhance their performance.

[0136] (Example 2)

[0137] 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".

[0138] In recent years, there has been a growing demand for the development of efficient training methods for sports tactics. However, conventional methods primarily rely on feedback based solely on player movements, making it difficult to provide personalized training that takes emotional factors into account. Therefore, there is a need for a system that reflects the player's psychological state in real time and provides more effective tactical training.

[0139] 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.

[0140] In this invention, the server includes an information processing device and means for generating a virtual reality environment; means for detecting the user's movements within the generated virtual reality environment and collecting information related to those movements; means for analyzing the collected information in real time and generating tactical feedback based on the analysis results; and means for detecting the user's emotional state using an emotion analysis device and dynamically adjusting the feedback based on that emotional state. This enables highly personalized tactical training that takes into account both the player's movements and emotional state.

[0141] An "information processing device" is a computer device used to generate a virtual reality environment and analyze data on movement and emotions.

[0142] A "generated virtual reality environment" refers to a space that reproduces virtual scenarios and situations created for the purpose of tactical training in sports.

[0143] A "detection device" refers to a group of sensors that sense the user's physical movements and biosignals and collect related information.

[0144] An "emotion analysis device" refers to a system that estimates a user's emotional state from their facial expressions and biometric data, and evaluates it in real time.

[0145] "Information" refers to data necessary for training, including the user's movements and emotional state.

[0146] "Processing" refers to a series of algorithmic operations that analyze and evaluate the collected information.

[0147] "Feedback" refers to instructions, advice, or information provided to the user based on the analysis results.

[0148] "Dynamic adjustment" refers to the process of changing feedback and the environment on the fly according to the user's current state.

[0149] This invention is a system for sports tactical training that combines a virtual reality environment with emotion recognition technology. In this system, a server, terminal, and user work together to execute complex processes.

[0150] The server first uses an information processing device to collect past competition data and predefined tactical scenarios. This information is stored in a database and later used to generate the virtual reality environment. The virtual reality environment is realistically reproduced through a computer program, and a common example of a VR development platform used is Unity.

[0151] The generated virtual reality environment is transmitted to the terminal via the network. The terminal uses a VR headset to allow the user to experience this virtual space. Simultaneously, the terminal is equipped with multiple sensors that can detect the user's movements and emotional state in real time. Motion sensors and emotion analyzers utilize biosensors such as facial recognition technology and heart rate sensors.

[0152] The user puts on a VR headset and begins training in a virtual environment. During this training, the server uses motion and emotion data received from the device and analyzes it through a generative AI model. The feedback is dynamically adjusted based on the user's movements and emotional state and presented to the user as optimized instructions within the VR environment.

[0153] For example, if a user is practicing basketball free throws and misses a shot, and the user becomes upset, the server analyzes the user's emotions and sends the following prompt to the AI ​​model: "Suggest an appropriate encouraging message if the user loses motivation." This allows the system to provide timely and appropriate feedback, promoting the user's mental and technical growth.

[0154] This system allows athletes to receive advanced sports tactical training that comprehensively considers not only their physical activity but also their emotional state.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The server takes historical match data and predefined tactical scenarios from a database as input. Using this information, it generates a virtual reality environment. During this process, the data is processed by a virtual space generation algorithm and output as a 3D environment aligned with sports training scenarios. This virtual environment accurately reproduces tactical situations.

[0158] Step 2:

[0159] The server compresses the generated virtual environment data and sends it as output to the terminal over the network. After receiving this data, the terminal presents the virtual space to the user using a VR headset. At this point, the user puts on the headset and begins interactive tactical training.

[0160] Step 3:

[0161] The device captures the user's physical movements and emotional state as input using sensors. Data from the motion sensors provides information about limb movement and posture, while the emotion sensors provide facial expressions and heart rate data. This information is read in real time and transmitted to the server.

[0162] Step 4:

[0163] The server analyzes motion and emotion data transmitted from the terminal as input and performs data calculations using a generative AI model. The analysis results are output as information to determine the user's current psychological state. Based on this information, the server generates feedback, dynamically adjusting the feedback to match the user's emotional state.

[0164] Step 5:

[0165] The device receives feedback sent from the server as output and presents it to the user through the VR headset. Specific feedback includes advice to encourage success and messages to promote relaxation. Based on this feedback, the user continues training and improves their movements and mental state.

[0166] (Application Example 2)

[0167] 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".

[0168] Conventional sports training systems focus on the movements and techniques of players, but do not adequately consider their mental state or emotions. Furthermore, there is a problem in that consumers lack ways to fully experience the true comfort and performance of sports equipment when purchasing it. As a result, consumers may make incorrect decisions when choosing products. This invention aims to solve these problems and provide a system that offers personalized training and experience.

[0169] 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.

[0170] In this invention, the server includes a computing device for generating a virtual reality environment, means for the virtual reality environment to reproduce a scenario intended for sports tactical training, means for detecting the player's movements within the generated virtual reality environment using sensors and collecting data related to those movements, and means for an emotion recognition device that acquires emotion data and dynamically adjusts feedback based on the acquired emotion data. This enables the user to make more appropriate sports equipment selections and learn tactics while having an experience that is in line with their mental state and emotions.

[0171] A "virtual reality environment" is a computer-generated simulation space that users can experience intuitively.

[0172] A "processing unit" is a computer system or device used for data processing, and is used for generating virtual reality environments and analyzing data.

[0173] "Sports tactical training" refers to educational activities conducted with the aim of improving competitive strategies and techniques in a particular sport.

[0174] A "sensor" is a device that detects the physical environment and bodily movements and collects that information as digital data.

[0175] "Emotional data" refers to digital data that represents an individual's emotional state as numerical values ​​or information, and is acquired through facial recognition and biosensors.

[0176] "Feedback" refers to information and advice provided by a system based on the user's actions and state, with the aim of improving or supporting the user's behavior.

[0177] An "emotion recognition device" is a device or system that detects a user's emotional state and processes that information as digital data.

[0178] A "display device or output device" is a device that presents computer-generated information to a user visually or audibly.

[0179] To realize this invention, a virtual reality environment is constructed, and a system is used to analyze the user's movements and emotions in real time. A specific embodiment of this system is described below.

[0180] The server constructs a simulation space using computing devices to generate a virtual reality environment. This virtual environment can reproduce various scenarios for the purpose of sports tactical training. The generated virtual environment is provided to the user via a terminal.

[0181] Users wear VR headsets such as the Oculus Quest 2 and immerse themselves in a virtual environment. The device is equipped with sensors to detect the user's movements, and the data obtained from these sensors is sent to a server.

[0182] Furthermore, emotional data is acquired using a facial recognition camera or heart rate sensor. This emotional recognition device grasps the user's emotional state in real time and transmits it to the server.

[0183] The server analyzes both user movement data and emotional data. This analysis is performed using an emotion recognition algorithm in Python (for example, facial recognition using OpenCV). This evaluates the user's psychological state and physical reactions, and generates dynamic feedback tailored to each individual user.

[0184] The generated feedback is presented to the user through the terminal's display or output device. This feedback allows the user to receive tactical training more effectively or experience the comfort of the product in a way that suits their mental state.

[0185] As a concrete example, a user can try on specific running shoes in a VR experience booth at a sports goods store. The system records the user running in a fictional running environment and analyzes it along with emotional data. If the user shows discomfort or anxiety, feedback is displayed to suggest alternative shoes.

[0186] An example of a prompt for a generative AI model is: "A user is trying on running shoes in a VR environment. Based on facial recognition and heart rate data, please suggest new shoes that you think the user will like."

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The server uses computing power to generate a virtual reality environment and constructs scenarios based on sports tactical training. Past match data is used as input, and the virtual environment is configured based on this data. The output is a virtual reality environment accessible to the user. This environment serves as a platform for users to practice tactics.

[0190] Step 2:

[0191] The device presents the user with a virtual reality environment through a VR headset. By entering the virtual environment, the user experiences visual and auditory immersion. Inputs include video and audio data transmitted from the server. Output is the user's experience in the virtual space.

[0192] Step 3:

[0193] The device uses motion sensors to detect user movement and collects data on the user's physical actions. The input data is user movement information, and the output is motion data for analysis that is sent to the server. This provides detailed information about how the user is moving.

[0194] Step 4:

[0195] The device acquires user emotional data using emotion recognition devices, such as facial recognition cameras and heart rate sensors. Input includes camera images and sensor information. This data is sent to the server as digital data. The output is data that represents the user's emotional state in real time.

[0196] Step 5:

[0197] The server analyzes the received motion and emotion data. An emotion recognition algorithm (e.g., using OpenCV) running in Python or similar languages ​​is used to process the data. The input is motion and emotion data, and the output is the analysis results. These results include evaluations of the user's psychological state and performance.

[0198] Step 6:

[0199] The server generates feedback for the user based on the analysis results. This feedback is created using the system's generation AI model, resulting in personalized advice and motivational messages. The input is the analysis results, and the output is the feedback provided to the user.

[0200] Step 7:

[0201] The terminal presents the generated feedback to the user. The input is feedback messages sent from the server, and the user receives them visually or audibly as output. This feedback allows the user to receive real-time training assistance.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] This invention is a system for effectively training sports tactics using a virtual reality environment. This system combines virtual reality technology and AI analysis to provide real-time feedback on the player's movements, thereby supporting tactical improvement.

[0219] The server first retrieves sports match data and tactical scenarios from a database, and then uses this data to generate a virtual reality environment. The server then sends the generated environment to a terminal, where it is reproduced on the VR device used by the athlete. This allows users (athletes and coaches) to train in a virtual space different from the real world.

[0220] The device tracks the player's movements in real time through a VR device and collects motion data using sensors. The collected data is then sent back to the server and analyzed by an AI model. This analysis evaluates the player's current performance and tactical positioning, and generates specific feedback for improvement.

[0221] Users receive feedback from the server on their devices, which is displayed on the screen or announced as an audio alert. This allows users to quickly adjust their tactics as needed and improve their performance in training and matches.

[0222] As a concrete example, taking a soccer match as an illustration, the user (the team's coach) simulates the opposing team's formation in virtual reality and trains their own players on how to move in response to that situation. During the match, the server analyzes each player's movements in real time and provides feedback, such as adjusting the defensive line, as needed. This improves the ability to respond immediately to the dynamics of the match.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The server retrieves sports match data and historical tactical scenarios from a database and uses this data to prepare the foundation for the virtual reality environment. The server then analyzes this data to determine the situations that should be recreated within the virtual reality environment.

[0226] Step 2:

[0227] The server sends information about the prepared virtual reality environment to the terminal. Based on this information, the terminal generates a virtual space that the athlete will experience using a VR device, allowing the user to begin an immersive training session.

[0228] Step 3:

[0229] The device uses sensors to collect motion data in order to track the player's movements in real time. The collected data is then transferred from the device to the server.

[0230] Step 4:

[0231] The server analyzes the motion data received from the terminal in real time. Using a generative AI model, it evaluates the performance and tactical effectiveness of the players' movements and generates data for feedback.

[0232] Step 5:

[0233] Feedback data generated from the server is sent to the terminal. The terminal notifies the user of this feedback through screen display or audio.

[0234] Step 6:

[0235] Users review the feedback received from their devices and modify their tactics as needed. Based on this information, users can respond in real time and maximize the effectiveness of their training.

[0236] (Example 1)

[0237] 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."

[0238] In athletics, tactical training should ideally be conducted in an environment closely resembling real-world match situations. However, replicating actual matches and providing rapid and accurate feedback to individual athletes is challenging. Traditional methods lack sufficient realistic environments for effective training and adequate real-time, detailed motion analysis.

[0239] 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.

[0240] In this invention, the server includes an information processing device, which includes means for generating a virtual environment for the purpose of tactical training of movement, means for detecting the movements of participants within the generated virtual environment using a sensing device and collecting information related to those movements, and means for analyzing the collected information in real time and generating tactical feedback based on the analysis results. This makes it possible to analyze the movements of participants in real time within the virtual environment and provide rapid feedback.

[0241] An "information processing device" is a general term for hardware and software that have the function of efficiently processing and analyzing data and generating a specified virtual environment.

[0242] A "virtual environment" is a realistic, three-dimensional simulated space created using computer technology for the purpose of tactical training in athletic competitions.

[0243] A "sensing device" is a device that includes sensor technology used to detect the movements and positions of participants with high precision and to collect information.

[0244] "Information" refers to a collection of data detected by sensing devices and used for analysis and feedback generation.

[0245] "Analysis" is the process of evaluating behavior and identifying areas for improvement based on collected information.

[0246] A "knowledge model" is an AI algorithm and its application technology designed for data analysis, used to evaluate things like movement efficiency and tactical positioning.

[0247] "Feedback" refers to information generated based on analysis results, including instructions and advice for improving tactics and athletic efficiency.

[0248] A "display device" is a device used to present analysis results and feedback to users visually or audibly.

[0249] This invention is an embodiment of a system that uses virtual reality technology and artificial intelligence technology to make tactical training for athletic competitions more effective. The following procedure and apparatus are required to carry out the invention.

[0250] First, the server is equipped with an information processing unit and retrieves athletic competition data and tactical scenarios from a database. Common database management systems used for this purpose include MySQL and PostgreSQL. Based on the retrieved data, the server generates a virtual environment using a game engine such as Unity. Unity has advanced 3D modeling and simulation capabilities, making it suitable for recreating a realistic athletic environment.

[0251] The device uses a VR system to sense the user's movements in real time. Accelerometers and gyroscopes are used to accurately track the player's position and movement. This motion data is transmitted from the device to a server, where it is processed using high-performance computing resources.

[0252] The server employs an AI model using machine learning libraries such as TensorFlow to analyze sensory data in real time. The AI ​​model utilizes player movement data to evaluate movement efficiency and tactical positioning. This enables feedback under situations equivalent to those in real matches.

[0253] Users receive feedback generated based on the analysis results on a VR device, perceiving it particularly through the display and audio output devices. This allows users to instantly identify areas for tactical improvement and operational efficiency, and adjust their movements as needed.

[0254] As a concrete example, considering soccer, a type of athletic sport, the server simulates a typical formation of the opposing team. The user, acting as a coach, provides appropriate tactical guidance to their own players within the virtual environment. By analyzing the players' movements in real time and providing feedback on appropriate positions and movements, more advanced tactical training becomes possible.

[0255] An example of a prompt to be input into the generating AI model is, "Perform a tactical simulation based on past match data in a virtual environment and analyze the players' movement data." This prompt allows the system to automate a series of steps to support professional and effective training.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The server retrieves sports match data and tactical scenarios from a database management system. Inputs include digital logs and scenario files related to past matches. This data is retrieved from MySQL using SQL queries. The output is a structured dataset necessary for creating a virtual environment. Specifically, the server executes SQL commands and loads the retrieved data into memory.

[0259] Step 2:

[0260] The server generates a virtual environment using Unity based on the acquired data. The input is the structured dataset obtained in step 1. As part of the data processing, 3D models are placed and the environment is configured using the Unity API. The output is a virtual reality simulation in its initial state. Specifically, virtual models of players and the field are placed, and a match scenario is constructed along a timeline.

[0261] Step 3:

[0262] The server sends the generated virtual environment data to the terminal. The input is a virtual environment object created within Unity. Data processing involves data compression and serialization using network protocols. The output is virtual environment data that can be decompressed on the terminal. Specifically, the server sends data using HTTP or WebSocket.

[0263] Step 4:

[0264] The terminal uses a VR device to sense the user's movements in real time. Inputs are the user's movements and sensor data acquired from the VR device. For data processing, accelerometers and gyroscopes are used to collect and filter the motion data. Outputs are motion data packets for transmission to the server. Specifically, the terminal periodically captures data from the sensors.

[0265] Step 5:

[0266] The server receives motion data sent from the terminal and analyzes it using an AI model with TensorFlow. The input is motion data packets acquired from the terminal. As part of the data calculation, the AI ​​model evaluates motion efficiency and tactical positioning. The output is the evaluation result and feedback message. In terms of specific operation, the server executes the AI ​​model and generates results quickly.

[0267] Step 6:

[0268] The user receives feedback generated based on the analysis results through a VR device. The input is feedback messages provided by the server. The output is visual or audio feedback displayed in a way that the user can understand. Specifically, the device presents the feedback through a VR headset display or headphones.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] In modern sports training, tactical understanding and real-time improvement are complex. In particular, individual home training often lacks access to expert guidance, limiting opportunities for effective tactical learning. Furthermore, the challenge lies in precisely recreating sports scenarios and receiving instant feedback to achieve immediate tactical improvement.

[0272] 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.

[0273] In this invention, the server includes an information processing device for generating a virtual reality environment, the virtual reality environment includes means for reproducing a scene intended for tactical training of movement, means for sensing the actions of participants within the generated virtual reality environment with a detection device and acquiring information related to those actions, means for immediately analyzing the acquired information and generating tactical feedback based on the analysis results, and means for monitoring the user's actions and providing a virtual exercise scene using a home-use humanoid robot. This makes it possible for individual users to receive professional and real-time tactical training at home.

[0274] A "virtual reality environment" is a digitally recreated space that is different from the real world, using computer technology, and allows users to have experiences that are similar to reality.

[0275] An "information processing device" is a device used for collecting, processing, storing, and reproducing data, and is a concept that includes computers and programmable hardware.

[0276] A "situation designed for tactical training in sports" is a hypothetical scenario specifically designed to learn and improve sports and fitness techniques and methodologies.

[0277] A "detection device" is a device that converts physical phenomena into electrical signals, and it is a system that includes sensors.

[0278] "Behavioral information" refers to data that digitally maps a user's movements and location, and is fundamental information used for behavioral analysis.

[0279] "Instant analysis" refers to a method where data is processed and analyzed immediately upon collection, guaranteeing results without any time lag.

[0280] "Tactical feedback" refers to a method of providing users with information that specifically outlines areas for improvement and coaching methods regarding the tactical aspects of exercise.

[0281] A "household humanoid robot" is a humanoid automated machine designed primarily for use inside a home, which assists with tasks according to the user's instructions and actions.

[0282] In implementing this invention, the system first constructs a virtual reality environment and recreates a scenario in which the user performs tactical training for exercise. The server uses an information processing device for this purpose to send the virtual reality space to the VR set or head-mounted display that the user has as a home device. This allows the user to train in exercise scenarios that transcend physical limitations.

[0283] When a user begins an action, a detection device built into the terminal immediately senses the action and acquires information related to that action. For example, if a user is dribbling a soccer ball, sensors acquire the movement and speed of the user's feet as digital data. This data is sent to a server and immediately analyzed by an AI model. This analysis utilizes software such as TensorFlow.

[0284] Based on the analysis results, the server generates immediate feedback and utilizes a display device or an output device to provide it to the user. As a result, the user can receive instructions for tactical improvement of their movements. For example, by inputting a prompt sentence such as "Analyze the forehand movement data and list up the points to be improved", the generative AI model provides specialized feedback.

[0285] As a specific example, there is a scenario where a user practices shooting a basketball using a home-use humanoid robot at home. In this case, the robot gives advice on passing the ball or adjusting the shooting angle in real time.

[0286] Overall, this invention enables the realization of the sports tactical training process within a home environment and provides real-time and effective training specialized for individual users.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server acquires virtual data related to a sports training scenario from the database. This data includes the scenario of the movement performed by the user, and the generative AI model designs a virtual reality space using the prompt sentence. Thereby, the server prepares the virtual reality space to be transmitted to the VR device.

[0290] Step 2:

[0291] The server transmits the generated virtual reality space data to the VR device of the terminal. In this process, by performing data transfer with low latency, the state is maintained where the user can immediately access the virtual environment. The user participates in the virtual reality environment by wearing the VR device.

[0292] Step 3:

[0293] When a user begins exercising, sensors attached to the device detect the user's movements. These include accelerometers and gyroscopes, and data from these sensors is acquired in real time. This data (e.g., speed and angle of movement) is aggregated by the device as information related to the action and sent to the server.

[0294] Step 4:

[0295] The server immediately analyzes the received motion data using an AI model. At this stage, it uses machine learning libraries such as TensorFlow to process the data based on the prompt "Analyze the forehand motion data and list areas for improvement." This extracts specific tactical improvements to the user's actions.

[0296] Step 5:

[0297] The generated feedback is sent from the server to the terminal. The terminal displays this feedback visually on the VR device or notifies the user as audio. This allows the user to understand the best way to improve their actions in real time and immediately take action.

[0298] 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.

[0299] This invention provides a system that highly personalizes sports tactical training and feedback by combining a virtual reality environment with an emotion engine. This system detects the player's movements and emotions and dynamically adjusts the training content and feedback based on that data.

[0300] First, the server imports past match data and predefined tactical scenarios, and uses this information as a foundation to generate a virtual reality environment. The generated environment is then transmitted to the terminal and presented to the user via their VR device. This allows the user to begin training in the virtual space.

[0301] The device is equipped with an emotion sensor that captures the user's emotional state in real time. This uses facial recognition technology and biosensor data to estimate emotions and understand the user's mental state. The obtained emotional data is sent to a server.

[0302] The server analyzes motion and emotion data transmitted from the terminal. The emotion engine uses this information to evaluate the user's psychological state during training and dynamically generates feedback accordingly. This includes applying relaxation techniques if the user is experiencing excessive stress, or sending encouraging feedback if the user is losing motivation.

[0303] As a concrete example, consider a basketball free throw practice session. If a user immediately becomes upset after missing, the server analyzes that emotion through an emotion engine. As a result, it reduces the user's mental burden and increases their chances of success by displaying advice with humor.

[0304] In this way, the system provides feedback that comprehensively considers the athlete's physical activity and emotions, helping them to acquire more effective sports tactics.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The server imports past game data and tactical scenarios and prepares the information necessary for generating a virtual reality environment. The server analyzes this data and creates virtual scenarios suitable for sports training.

[0308] Step 2:

[0309] The server sends the generated virtual reality environment to the terminal. The terminal provides the virtual space to the user through a VR device, and the user starts training within that environment.

[0310] Step 3:

[0311] The terminal uses physical sensors for detecting the player's movements and emotion sensors for estimating emotions from expressions and voices to collect the user's physical and psychological data.

[0312] Step 4:

[0313] The server receives the motion data and emotion data sent from the terminal. The server analyzes these data in real time and evaluates the player's performance and emotional state.

[0314] Step 5:

[0315] The server uses an emotion engine to generate feedback considering the user's emotional state. For example, it creates feedback to encourage relaxation when the user is feeling stressed, or feedback to motivate when the motivation is low.

[0316] Step 6:

[0317] The server sends the generated feedback to the terminal. The terminal provides the feedback to the user in ways such as screen display, voice notification, or vibration.

[0318] Step 7:

[0319] Users adjust their tactics based on feedback received from their devices to maximize the effectiveness of their training. They utilize the feedback to improve themselves and aim to enhance their performance.

[0320] (Example 2)

[0321] 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".

[0322] In recent years, there has been a growing demand for the development of efficient training methods for sports tactics. However, conventional methods primarily rely on feedback based solely on player movements, making it difficult to provide personalized training that takes emotional factors into account. Therefore, there is a need for a system that reflects the player's psychological state in real time and provides more effective tactical training.

[0323] 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.

[0324] In this invention, the server includes an information processing device and means for generating a virtual reality environment; means for detecting the user's movements within the generated virtual reality environment and collecting information related to those movements; means for analyzing the collected information in real time and generating tactical feedback based on the analysis results; and means for detecting the user's emotional state using an emotion analysis device and dynamically adjusting the feedback based on that emotional state. This enables highly personalized tactical training that takes into account both the player's movements and emotional state.

[0325] An "information processing device" is a computer device used to generate a virtual reality environment and analyze data on movement and emotions.

[0326] A "generated virtual reality environment" refers to a space that reproduces virtual scenarios and situations created for the purpose of tactical training in sports.

[0327] A "detection device" refers to a group of sensors that sense the user's physical movements and biosignals and collect related information.

[0328] An "emotion analysis device" refers to a system that estimates a user's emotional state from their facial expressions and biometric data, and evaluates it in real time.

[0329] "Information" refers to data necessary for training, including the user's movements and emotional state.

[0330] "Processing" refers to a series of algorithmic operations that analyze and evaluate the collected information.

[0331] "Feedback" refers to instructions, advice, or information provided to the user based on the analysis results.

[0332] "Dynamic adjustment" refers to the process of changing feedback and the environment on the fly according to the user's current state.

[0333] This invention is a system for sports tactical training that combines a virtual reality environment with emotion recognition technology. In this system, a server, terminal, and user work together to execute complex processes.

[0334] The server first uses an information processing device to collect past competition data and predefined tactical scenarios. This information is stored in a database and later used to generate the virtual reality environment. The virtual reality environment is realistically reproduced through a computer program, and a common example of a VR development platform used is Unity.

[0335] The generated virtual reality environment is transmitted to the terminal via the network. The terminal uses a VR headset to allow the user to experience this virtual space. Simultaneously, the terminal is equipped with multiple sensors that can detect the user's movements and emotional state in real time. Motion sensors and emotion analyzers utilize biosensors such as facial recognition technology and heart rate sensors.

[0336] The user puts on a VR headset and begins training in a virtual environment. During this training, the server uses motion and emotion data received from the device and analyzes it through a generative AI model. The feedback is dynamically adjusted based on the user's movements and emotional state and presented to the user as optimized instructions within the VR environment.

[0337] For example, if a user is practicing basketball free throws and misses a shot, and the user becomes upset, the server analyzes the user's emotions and sends the following prompt to the AI ​​model: "Suggest an appropriate encouraging message if the user loses motivation." This allows the system to provide timely and appropriate feedback, promoting the user's mental and technical growth.

[0338] This system allows athletes to receive advanced sports tactical training that comprehensively considers not only their physical activity but also their emotional state.

[0339] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0340] Step 1:

[0341] The server takes historical match data and predefined tactical scenarios from a database as input. Using this information, it generates a virtual reality environment. During this process, the data is processed by a virtual space generation algorithm and output as a 3D environment aligned with sports training scenarios. This virtual environment accurately reproduces tactical situations.

[0342] Step 2:

[0343] The server compresses the generated virtual environment data and sends it as output to the terminal over the network. After receiving this data, the terminal presents the virtual space to the user using a VR headset. At this point, the user puts on the headset and begins interactive tactical training.

[0344] Step 3:

[0345] The device captures the user's physical movements and emotional state as input using sensors. Data from the motion sensors provides information about limb movement and posture, while the emotion sensors provide facial expressions and heart rate data. This information is read in real time and transmitted to the server.

[0346] Step 4:

[0347] The server analyzes motion and emotion data transmitted from the terminal as input and performs data calculations using a generative AI model. The analysis results are output as information to determine the user's current psychological state. Based on this information, the server generates feedback, dynamically adjusting the feedback to match the user's emotional state.

[0348] Step 5:

[0349] The device receives feedback sent from the server as output and presents it to the user through the VR headset. Specific feedback includes advice to encourage success and messages to promote relaxation. Based on this feedback, the user continues training and improves their movements and mental state.

[0350] (Application Example 2)

[0351] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0352] Conventional sports training systems focus on the movements and techniques of players, but do not adequately consider their mental state or emotions. Furthermore, there is a problem in that consumers lack ways to fully experience the true comfort and performance of sports equipment when purchasing it. As a result, consumers may make incorrect decisions when choosing products. This invention aims to solve these problems and provide a system that offers personalized training and experience.

[0353] 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.

[0354] In this invention, the server includes a computing device for generating a virtual reality environment, means for the virtual reality environment to reproduce a scenario intended for sports tactical training, means for detecting the player's movements within the generated virtual reality environment using sensors and collecting data related to those movements, and means for an emotion recognition device that acquires emotion data and dynamically adjusts feedback based on the acquired emotion data. This enables the user to make more appropriate sports equipment selections and learn tactics while having an experience that is in line with their mental state and emotions.

[0355] A "virtual reality environment" is a computer-generated simulation space that users can experience intuitively.

[0356] A "processing unit" is a computer system or device used for data processing, and is used for generating virtual reality environments and analyzing data.

[0357] "Sports tactical training" refers to educational activities conducted with the aim of improving competitive strategies and techniques in a particular sport.

[0358] A "sensor" is a device that detects the physical environment and bodily movements and collects that information as digital data.

[0359] "Emotional data" refers to digital data that represents an individual's emotional state as numerical values ​​or information, and is acquired through facial recognition and biosensors.

[0360] "Feedback" refers to information and advice provided by a system based on the user's actions and state, with the aim of improving or supporting the user's behavior.

[0361] An "emotion recognition device" is a device or system that detects a user's emotional state and processes that information as digital data.

[0362] A "display device or output device" is a device that presents computer-generated information to a user visually or audibly.

[0363] To realize this invention, a virtual reality environment is constructed, and a system is used to analyze the user's movements and emotions in real time. A specific embodiment of this system is described below.

[0364] The server constructs a simulation space using a computing device to generate a virtual reality environment. This virtual environment can reproduce various scenarios for the purpose of sports tactical training. The generated virtual environment is provided to the user via a terminal.

[0365] Users wear VR headsets such as the Oculus Quest 2 and immerse themselves in a virtual environment. The device is equipped with sensors to detect the user's movements, and the data obtained from these sensors is sent to a server.

[0366] Furthermore, emotional data is acquired using a facial recognition camera or heart rate sensor. This emotional recognition device grasps the user's emotional state in real time and transmits it to the server.

[0367] The server analyzes both user movement data and emotional data. This analysis is performed using an emotion recognition algorithm in Python (for example, facial recognition using OpenCV). This evaluates the user's psychological state and physical reactions, and generates dynamic feedback tailored to each individual user.

[0368] The generated feedback is presented to the user through the terminal's display or output device. This feedback allows the user to receive tactical training more effectively or experience the comfort of the product in a way that suits their mental state.

[0369] As a concrete example, a user can try on specific running shoes in a VR experience booth at a sports goods store. The system records the user running in a fictional running environment and analyzes it along with emotional data. If the user shows discomfort or anxiety, feedback is displayed to suggest alternative shoes.

[0370] An example of a prompt for a generative AI model is: "A user is trying on running shoes in a VR environment. Based on facial recognition and heart rate data, please suggest new shoes that you think the user will like."

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server uses computing power to generate a virtual reality environment and constructs scenarios based on sports tactical training. Past match data is used as input, and the virtual environment is configured based on this data. The output is a virtual reality environment accessible to the user. This environment serves as a platform for users to practice tactics.

[0374] Step 2:

[0375] The device presents the user with a virtual reality environment through a VR headset. By entering the virtual environment, the user experiences visual and auditory immersion. Inputs include video and audio data transmitted from the server. Output is the user's experience in the virtual space.

[0376] Step 3:

[0377] The device uses motion sensors to detect user movement and collects data on the user's physical actions. The input data is user movement information, and the output is motion data for analysis that is sent to the server. This provides detailed information about how the user is moving.

[0378] Step 4:

[0379] The device acquires user emotional data using emotion recognition devices, such as facial recognition cameras and heart rate sensors. Input includes camera images and sensor information. This data is sent to the server as digital data. The output is data that represents the user's emotional state in real time.

[0380] Step 5:

[0381] The server analyzes the received motion and emotion data. An emotion recognition algorithm (e.g., using OpenCV) running in Python or similar languages ​​is used to process the data. The input is motion and emotion data, and the output is the analysis results. These results include evaluations of the user's psychological state and performance.

[0382] Step 6:

[0383] The server generates feedback for the user based on the analysis results. This feedback is created using the system's generation AI model, resulting in personalized advice and motivational messages. The input is the analysis results, and the output is the feedback provided to the user.

[0384] Step 7:

[0385] The terminal presents the generated feedback to the user. The input is feedback messages sent from the server, and the user receives them visually or audibly as output. This feedback allows the user to receive real-time training assistance.

[0386] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0387] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0388] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0389] [Third Embodiment]

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

[0391] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0392] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0393] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0394] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0395] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0396] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0397] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0398] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0399] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0400] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0401] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0402] This invention is a system for effectively training sports tactics using a virtual reality environment. This system combines virtual reality technology and AI analysis to provide real-time feedback on the player's movements, thereby supporting tactical improvement.

[0403] The server first retrieves sports match data and tactical scenarios from a database, and then uses this data to generate a virtual reality environment. The server then sends the generated environment to a terminal, where it is reproduced on the VR device used by the athlete. This allows users (athletes and coaches) to train in a virtual space different from the real world.

[0404] The device tracks the player's movements in real time through a VR device and collects motion data using sensors. The collected data is then sent back to the server and analyzed by an AI model. This analysis evaluates the player's current performance and tactical positioning, and generates specific feedback for improvement.

[0405] Users receive feedback from the server on their devices, which is displayed on the screen or announced as an audio alert. This allows users to quickly adjust their tactics as needed and improve their performance in training and matches.

[0406] As a concrete example, taking a soccer match as an illustration, the user (the team's coach) simulates the opposing team's formation in virtual reality and trains their own players on how to move in response to that situation. During the match, the server analyzes each player's movements in real time and provides feedback, such as adjusting the defensive line, as needed. This improves the ability to respond immediately to the dynamics of the match.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The server retrieves sports match data and historical tactical scenarios from a database and uses this data to prepare the foundation for the virtual reality environment. The server then analyzes this data to determine the situations that should be recreated within the virtual reality environment.

[0410] Step 2:

[0411] The server sends information about the prepared virtual reality environment to the terminal. Based on this information, the terminal generates a virtual space that the athlete will experience using a VR device, allowing the user to begin an immersive training session.

[0412] Step 3:

[0413] The device uses sensors to collect motion data in order to track the player's movements in real time. The collected data is then transferred from the device to the server.

[0414] Step 4:

[0415] The server analyzes the motion data received from the terminal in real time. Using a generative AI model, it evaluates the performance and tactical effectiveness of the players' movements and generates data for feedback.

[0416] Step 5:

[0417] Feedback data generated from the server is sent to the terminal. The terminal notifies the user of this feedback through screen display or audio.

[0418] Step 6:

[0419] Users review the feedback received from their devices and modify their tactics as needed. Based on this information, users can respond in real time and maximize the effectiveness of their training.

[0420] (Example 1)

[0421] 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."

[0422] In athletics, tactical training should ideally be conducted in an environment closely resembling real-world match situations. However, replicating actual matches and providing rapid and accurate feedback to individual athletes is challenging. Traditional methods lack sufficient realistic environments for effective training and adequate real-time, detailed motion analysis.

[0423] 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.

[0424] In this invention, the server includes an information processing device, which includes means for generating a virtual environment for the purpose of tactical training of movement, means for detecting the movements of participants within the generated virtual environment using a sensing device and collecting information related to those movements, and means for analyzing the collected information in real time and generating tactical feedback based on the analysis results. This makes it possible to analyze the movements of participants in real time within the virtual environment and provide rapid feedback.

[0425] An "information processing device" is a general term for hardware and software that have the function of efficiently processing and analyzing data and generating a specified virtual environment.

[0426] A "virtual environment" is a realistic, three-dimensional simulated space created using computer technology for the purpose of tactical training in athletic competitions.

[0427] A "sensing device" is a device that includes sensor technology used to detect the movements and positions of participants with high precision and to collect information.

[0428] "Information" refers to a collection of data detected by sensing devices and used for analysis and feedback generation.

[0429] "Analysis" is the process of evaluating behavior and identifying areas for improvement based on collected information.

[0430] A "knowledge model" is an AI algorithm and its application technology designed for data analysis, used to evaluate things like movement efficiency and tactical positioning.

[0431] "Feedback" refers to information generated based on analysis results, including instructions and advice for improving tactics and athletic efficiency.

[0432] A "display device" is a device used to present analysis results and feedback to users visually or audibly.

[0433] This invention is an embodiment of a system that uses virtual reality technology and artificial intelligence technology to make tactical training for athletic competitions more effective. The following procedure and apparatus are required to carry out the invention.

[0434] First, the server is equipped with an information processing unit and retrieves athletic competition data and tactical scenarios from a database. Common database management systems used for this purpose include MySQL and PostgreSQL. Based on the retrieved data, the server generates a virtual environment using a game engine such as Unity. Unity has advanced 3D modeling and simulation capabilities, making it suitable for recreating a realistic athletic environment.

[0435] The device uses a VR system to sense the user's movements in real time. Accelerometers and gyroscopes are used to accurately track the player's position and movement. This motion data is transmitted from the device to a server, where it is processed using high-performance computing resources.

[0436] The server employs an AI model using machine learning libraries such as TensorFlow to analyze sensory data in real time. The AI ​​model utilizes player movement data to evaluate movement efficiency and tactical positioning. This enables feedback under situations equivalent to those in real matches.

[0437] Users receive feedback generated based on the analysis results on a VR device, perceiving it particularly through the display and audio output devices. This allows users to instantly identify areas for tactical improvement and operational efficiency, and adjust their movements as needed.

[0438] As a concrete example, considering soccer, a type of athletic sport, the server simulates a typical formation of the opposing team. The user, acting as a coach, provides appropriate tactical guidance to their own players within the virtual environment. By analyzing the players' movements in real time and providing feedback on appropriate positions and movements, more advanced tactical training becomes possible.

[0439] An example of a prompt to be input into the generating AI model is, "Perform a tactical simulation based on past match data in a virtual environment and analyze the players' movement data." This prompt allows the system to automate a series of steps to support professional and effective training.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The server retrieves sports match data and tactical scenarios from a database management system. Inputs include digital logs and scenario files related to past matches. This data is retrieved from MySQL using SQL queries. The output is a structured dataset necessary for creating a virtual environment. Specifically, the server executes SQL commands and loads the retrieved data into memory.

[0443] Step 2:

[0444] The server generates a virtual environment using Unity based on the acquired data. The input is the structured dataset obtained in step 1. As part of the data processing, 3D models are placed and the environment is configured using the Unity API. The output is a virtual reality simulation in its initial state. Specifically, virtual models of players and the field are placed, and a match scenario is constructed along a timeline.

[0445] Step 3:

[0446] The server sends the generated virtual environment data to the terminal. The input is a virtual environment object created within Unity. Data processing involves data compression and serialization using network protocols. The output is virtual environment data that can be decompressed on the terminal. Specifically, the server sends data using HTTP or WebSocket.

[0447] Step 4:

[0448] The terminal uses a VR device to sense the user's movements in real time. Inputs are the user's movements and sensor data acquired from the VR device. For data processing, accelerometers and gyroscopes are used to collect and filter the motion data. Outputs are motion data packets for transmission to the server. Specifically, the terminal periodically captures data from the sensors.

[0449] Step 5:

[0450] The server receives motion data sent from the terminal and analyzes it using an AI model with TensorFlow. The input is motion data packets acquired from the terminal. As part of the data calculation, the AI ​​model evaluates motion efficiency and tactical positioning. The output is the evaluation result and feedback message. In terms of specific operation, the server executes the AI ​​model and generates results quickly.

[0451] Step 6:

[0452] The user receives feedback generated based on the analysis results through a VR device. The input is feedback messages provided by the server. The output is visual or audio feedback displayed in a way that the user can understand. Specifically, the device presents the feedback through a VR headset display or headphones.

[0453] (Application Example 1)

[0454] 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."

[0455] In modern sports training, tactical understanding and real-time improvement are complex. In particular, individual home training often lacks access to expert guidance, limiting opportunities for effective tactical learning. Furthermore, the challenge lies in precisely recreating sports scenarios and receiving instant feedback to achieve immediate tactical improvement.

[0456] 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.

[0457] In this invention, the server includes an information processing device for generating a virtual reality environment, the virtual reality environment includes means for reproducing a scene intended for tactical training of movement, means for sensing the actions of participants within the generated virtual reality environment with a detection device and acquiring information related to those actions, means for immediately analyzing the acquired information and generating tactical feedback based on the analysis results, and means for monitoring the user's actions and providing a virtual exercise scene using a home-use humanoid robot. This makes it possible for individual users to receive professional and real-time tactical training at home.

[0458] A "virtual reality environment" is a digitally recreated space that is different from the real world, using computer technology, and allows users to have experiences that are similar to reality.

[0459] An "information processing device" is a device used for collecting, processing, storing, and reproducing data, and is a concept that includes computers and programmable hardware.

[0460] A "situation designed for tactical training in sports" is a hypothetical scenario specifically designed to learn and improve sports and fitness techniques and methodologies.

[0461] A "detection device" is a device that converts physical phenomena into electrical signals, and it is a system that includes sensors.

[0462] "Behavioral information" refers to data that digitally maps a user's movements and location, and is fundamental information used for behavioral analysis.

[0463] "Instant analysis" refers to a method where data is processed and analyzed immediately upon collection, guaranteeing results without any time lag.

[0464] "Tactical feedback" refers to a method of providing users with information that specifically outlines areas for improvement and coaching methods regarding the tactical aspects of exercise.

[0465] A "household humanoid robot" is a humanoid automated machine designed primarily for use inside a home, which assists with tasks according to the user's instructions and actions.

[0466] In implementing this invention, the system first constructs a virtual reality environment and recreates a scenario in which the user performs tactical training for exercise. The server uses an information processing device for this purpose to send the virtual reality space to the VR set or head-mounted display that the user has as a home device. This allows the user to train in exercise scenarios that transcend physical limitations.

[0467] When a user begins an action, a detection device built into the terminal immediately senses the action and acquires information related to that action. For example, if a user is dribbling a soccer ball, sensors acquire the movement and speed of the user's feet as digital data. This data is sent to a server and immediately analyzed by an AI model. This analysis utilizes software such as TensorFlow.

[0468] The server utilizes a display or output device to generate and provide immediate feedback to the user based on the analysis results. This allows the user to receive instructions for tactical improvements to their movements. For example, by inputting a prompt such as, "Analyze the forehand motion data and list the points that need improvement," the generated AI model provides specialized feedback.

[0469] One concrete example is a scenario where a user practices basketball shooting at home using a home-use humanoid robot. In this case, the robot hands the user the ball and provides real-time advice on adjusting the shooting angle.

[0470] Overall, this invention makes the process of sports tactical training possible at home, providing personalized, real-time, and effective training for individual users.

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] The server retrieves virtual data related to sports training scenarios from the database. This data includes the exercise scenarios the user will perform, and a generative AI model uses these prompts to design a virtual reality space. This prepares the virtual reality space for the server to send to the VR device.

[0474] Step 2:

[0475] The server transmits the generated virtual reality data to the terminal's VR device. This process maintains low latency data transfer, ensuring the user can instantly access the virtual environment. The user participates in the virtual reality environment by wearing the VR device.

[0476] Step 3:

[0477] When a user begins exercising, sensors attached to the device detect the user's movements. These include accelerometers and gyroscopes, and data from these sensors is acquired in real time. This data (e.g., speed and angle of movement) is aggregated by the device as information related to the action and sent to the server.

[0478] Step 4:

[0479] The server immediately analyzes the received motion data using an AI model. At this stage, it uses machine learning libraries such as TensorFlow to process the data based on the prompt "Analyze the forehand motion data and list areas for improvement." This extracts specific tactical improvements to the user's actions.

[0480] Step 5:

[0481] The generated feedback is sent from the server to the terminal. The terminal displays this feedback visually on the VR device or notifies the user as audio. This allows the user to understand the best way to improve their actions in real time and immediately take action.

[0482] 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.

[0483] This invention provides a system that highly personalizes sports tactical training and feedback by combining a virtual reality environment with an emotion engine. This system detects the player's movements and emotions and dynamically adjusts the training content and feedback based on that data.

[0484] First, the server imports past match data and predefined tactical scenarios, and uses this information as a foundation to generate a virtual reality environment. The generated environment is then transmitted to the terminal and presented to the user via their VR device. This allows the user to begin training in the virtual space.

[0485] The device is equipped with an emotion sensor that captures the user's emotional state in real time. This uses facial recognition technology and biosensor data to estimate emotions and understand the user's mental state. The obtained emotional data is sent to a server.

[0486] The server analyzes motion and emotion data transmitted from the terminal. The emotion engine uses this information to evaluate the user's psychological state during training and dynamically generates feedback accordingly. This includes applying relaxation techniques if the user is experiencing excessive stress, or sending encouraging feedback if the user is losing motivation.

[0487] As a concrete example, consider a basketball free throw practice session. If a user immediately becomes upset after missing, the server analyzes that emotion through an emotion engine. As a result, it reduces the user's mental burden and increases their chances of success by displaying advice with humor.

[0488] In this way, the system provides feedback that comprehensively considers the athlete's physical activity and emotions, helping them to acquire more effective sports tactics.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The server imports past match data and tactical scenarios, preparing the information necessary to generate a virtual reality environment. The server then analyzes this data to create virtual scenarios suitable for sports training.

[0492] Step 2:

[0493] The server sends the generated virtual reality environment to the terminal. The terminal provides the user with the virtual space through a VR device, and the user begins training within that environment.

[0494] Step 3:

[0495] The device collects the user's physical and psychological data using physical sensors to detect the player's movements and emotion sensors to estimate emotions from facial expressions and voice.

[0496] Step 4:

[0497] The server receives motion data and emotion data transmitted from the terminal. The server analyzes this data in real time to evaluate the player's performance and emotional state.

[0498] Step 5:

[0499] The server uses an emotion engine to generate feedback that takes into account the user's emotional state. For example, if the user is stressed, it will generate feedback that promotes relaxation; if their motivation is low, it will generate encouraging feedback.

[0500] Step 6:

[0501] The server sends the generated feedback to the device. The device then provides the feedback to the user through methods such as screen display, audio notification, or vibration.

[0502] Step 7:

[0503] Users adjust their tactics based on feedback received from their devices to maximize the effectiveness of their training. They utilize the feedback to improve themselves and aim to enhance their performance.

[0504] (Example 2)

[0505] 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."

[0506] In recent years, there has been a growing demand for the development of efficient training methods for sports tactics. However, conventional methods primarily rely on feedback based solely on player movements, making it difficult to provide personalized training that takes emotional factors into account. Therefore, there is a need for a system that reflects the player's psychological state in real time and provides more effective tactical training.

[0507] 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.

[0508] In this invention, the server includes an information processing device and means for generating a virtual reality environment; means for detecting the user's movements within the generated virtual reality environment and collecting information related to those movements; means for analyzing the collected information in real time and generating tactical feedback based on the analysis results; and means for detecting the user's emotional state using an emotion analysis device and dynamically adjusting the feedback based on that emotional state. This enables highly personalized tactical training that takes into account both the player's movements and emotional state.

[0509] An "information processing device" is a computer device used to generate a virtual reality environment and analyze data on movement and emotions.

[0510] A "generated virtual reality environment" refers to a space that reproduces virtual scenarios and situations created for the purpose of tactical training in sports.

[0511] A "detection device" refers to a group of sensors that sense the user's physical movements and biosignals and collect related information.

[0512] An "emotion analysis device" refers to a system that estimates a user's emotional state from their facial expressions and biometric data, and evaluates it in real time.

[0513] "Information" refers to data necessary for training, including the user's movements and emotional state.

[0514] "Processing" refers to a series of algorithmic operations that analyze and evaluate the collected information.

[0515] "Feedback" refers to instructions, advice, or information provided to the user based on the analysis results.

[0516] "Dynamic adjustment" refers to the process of changing feedback and the environment on the fly according to the user's current state.

[0517] This invention is a system for sports tactical training that combines a virtual reality environment with emotion recognition technology. In this system, a server, terminal, and user work together to execute complex processes.

[0518] The server first uses an information processing device to collect past competition data and predefined tactical scenarios. This information is stored in a database and later used to generate the virtual reality environment. The virtual reality environment is realistically reproduced through a computer program, and a common example of a VR development platform used is Unity.

[0519] The generated virtual reality environment is transmitted to the terminal via the network. The terminal uses a VR headset to allow the user to experience this virtual space. Simultaneously, the terminal is equipped with multiple sensors that can detect the user's movements and emotional state in real time. Motion sensors and emotion analyzers utilize biosensors such as facial recognition technology and heart rate sensors.

[0520] The user puts on a VR headset and begins training in a virtual environment. During this training, the server uses motion and emotion data received from the device and analyzes it through a generative AI model. The feedback is dynamically adjusted based on the user's movements and emotional state and presented to the user as optimized instructions within the VR environment.

[0521] For example, if a user is practicing basketball free throws and misses a shot, and the user becomes upset, the server analyzes the user's emotions and sends the following prompt to the AI ​​model: "Suggest an appropriate encouraging message if the user loses motivation." This allows the system to provide timely and appropriate feedback, promoting the user's mental and technical growth.

[0522] This system allows athletes to receive advanced sports tactical training that comprehensively considers not only their physical activity but also their emotional state.

[0523] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0524] Step 1:

[0525] The server takes historical match data and predefined tactical scenarios from a database as input. Using this information, it generates a virtual reality environment. During this process, the data is processed by a virtual space generation algorithm and output as a 3D environment aligned with sports training scenarios. This virtual environment accurately reproduces tactical situations.

[0526] Step 2:

[0527] The server compresses the generated virtual environment data and sends it as output to the terminal over the network. After receiving this data, the terminal presents the virtual space to the user using a VR headset. At this point, the user puts on the headset and begins interactive tactical training.

[0528] Step 3:

[0529] The device captures the user's physical movements and emotional state as input using sensors. Data from the motion sensors provides information about limb movement and posture, while the emotion sensors provide facial expressions and heart rate data. This information is read in real time and transmitted to the server.

[0530] Step 4:

[0531] The server analyzes motion and emotion data transmitted from the terminal as input and performs data calculations using a generative AI model. The analysis results are output as information to determine the user's current psychological state. Based on this information, the server generates feedback, dynamically adjusting the feedback to match the user's emotional state.

[0532] Step 5:

[0533] The device receives feedback sent from the server as output and presents it to the user through the VR headset. Specific feedback includes advice to encourage success and messages to promote relaxation. Based on this feedback, the user continues training and improves their movements and mental state.

[0534] (Application Example 2)

[0535] 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."

[0536] Conventional sports training systems focus on the movements and techniques of players, but do not adequately consider their mental state or emotions. Furthermore, there is a problem in that consumers lack ways to fully experience the true comfort and performance of sports equipment when purchasing it. As a result, consumers may make incorrect decisions when choosing products. This invention aims to solve these problems and provide a system that offers personalized training and experience.

[0537] 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.

[0538] In this invention, the server includes a computing device for generating a virtual reality environment, means for the virtual reality environment to reproduce a scenario intended for sports tactical training, means for detecting the player's movements within the generated virtual reality environment using sensors and collecting data related to those movements, and means for an emotion recognition device that acquires emotion data and dynamically adjusts feedback based on the acquired emotion data. This enables the user to make more appropriate sports equipment selections and learn tactics while having an experience that is in line with their mental state and emotions.

[0539] A "virtual reality environment" is a computer-generated simulation space that users can experience intuitively.

[0540] A "processing unit" is a computer system or device used for data processing, and is used for generating virtual reality environments and analyzing data.

[0541] "Sports tactical training" refers to educational activities conducted with the aim of improving competitive strategies and techniques in a particular sport.

[0542] A "sensor" is a device that detects the physical environment and bodily movements and collects that information as digital data.

[0543] "Emotional data" refers to digital data that represents an individual's emotional state as numerical values ​​or information, and is acquired through facial recognition and biosensors.

[0544] "Feedback" refers to information and advice provided by a system based on the user's actions and state, with the aim of improving or supporting the user's behavior.

[0545] An "emotion recognition device" is a device or system that detects a user's emotional state and processes that information as digital data.

[0546] A "display device or output device" is a device that presents computer-generated information to a user visually or audibly.

[0547] To realize this invention, a virtual reality environment is constructed, and a system is used to analyze the user's movements and emotions in real time. A specific embodiment of this system is described below.

[0548] The server constructs a simulation space using a computing device to generate a virtual reality environment. This virtual environment can reproduce various scenarios for the purpose of sports tactical training. The generated virtual environment is provided to the user via a terminal.

[0549] Users wear VR headsets such as the Oculus Quest 2 and immerse themselves in a virtual environment. The device is equipped with sensors to detect the user's movements, and the data obtained from these sensors is sent to a server.

[0550] Furthermore, emotional data is acquired using a facial recognition camera or heart rate sensor. This emotional recognition device grasps the user's emotional state in real time and transmits it to the server.

[0551] The server analyzes both user movement data and emotional data. This analysis is performed using an emotion recognition algorithm in Python (for example, facial recognition using OpenCV). This evaluates the user's psychological state and physical reactions, and generates dynamic feedback tailored to each individual user.

[0552] The generated feedback is presented to the user through the terminal's display or output device. This feedback allows the user to receive tactical training more effectively or experience the comfort of the product in a way that suits their mental state.

[0553] As a concrete example, a user can try on specific running shoes in a VR experience booth at a sports goods store. The system records the user running in a fictional running environment and analyzes it along with emotional data. If the user shows discomfort or anxiety, feedback is displayed to suggest alternative shoes.

[0554] An example of a prompt for a generative AI model is: "A user is trying on running shoes in a VR environment. Based on facial recognition and heart rate data, please suggest new shoes that you think the user will like."

[0555] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0556] Step 1:

[0557] The server uses computing power to generate a virtual reality environment and constructs scenarios based on sports tactical training. Past match data is used as input, and the virtual environment is configured based on this data. The output is a virtual reality environment accessible to the user. This environment serves as a platform for users to practice tactics.

[0558] Step 2:

[0559] The device presents the user with a virtual reality environment through a VR headset. By entering the virtual environment, the user experiences visual and auditory immersion. Inputs include video and audio data transmitted from the server. Output is the user's experience in the virtual space.

[0560] Step 3:

[0561] The device uses motion sensors to detect user movement and collects data on the user's physical actions. The input data is user movement information, and the output is motion data for analysis that is sent to the server. This provides detailed information about how the user is moving.

[0562] Step 4:

[0563] The device acquires user emotional data using emotion recognition devices, such as facial recognition cameras and heart rate sensors. Input includes camera images and sensor information. This data is sent to the server as digital data. The output is data that represents the user's emotional state in real time.

[0564] Step 5:

[0565] The server analyzes the received motion and emotion data. An emotion recognition algorithm (e.g., using OpenCV) running in Python or similar languages ​​is used to process the data. The input is motion and emotion data, and the output is the analysis results. These results include evaluations of the user's psychological state and performance.

[0566] Step 6:

[0567] The server generates feedback for the user based on the analysis results. This feedback is created using the system's generation AI model, resulting in personalized advice and motivational messages. The input is the analysis results, and the output is the feedback provided to the user.

[0568] Step 7:

[0569] The terminal presents the generated feedback to the user. The input is feedback messages sent from the server, and the user receives them visually or audibly as output. This feedback allows the user to receive real-time training assistance.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] [Fourth Embodiment]

[0574] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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".

[0587] This invention is a system for effectively training sports tactics using a virtual reality environment. This system combines virtual reality technology and AI analysis to provide real-time feedback on the player's movements, thereby supporting tactical improvement.

[0588] The server first retrieves sports match data and tactical scenarios from a database, and then uses this data to generate a virtual reality environment. The server then sends the generated environment to a terminal, where it is reproduced on the VR device used by the athlete. This allows users (athletes and coaches) to train in a virtual space different from the real world.

[0589] The device tracks the player's movements in real time through a VR device and collects motion data using sensors. The collected data is then sent back to the server and analyzed by an AI model. This analysis evaluates the player's current performance and tactical positioning, and generates specific feedback for improvement.

[0590] Users receive feedback from the server on their devices, which is displayed on the screen or announced as an audio alert. This allows users to quickly adjust their tactics as needed and improve their performance in training and matches.

[0591] As a concrete example, taking a soccer match as an illustration, the user (the team's coach) simulates the opposing team's formation in virtual reality and trains their own players on how to move in response to that situation. During the match, the server analyzes each player's movements in real time and provides feedback, such as adjusting the defensive line, as needed. This improves the ability to respond immediately to the dynamics of the match.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The server retrieves sports match data and historical tactical scenarios from a database and uses this data to prepare the foundation for the virtual reality environment. The server then analyzes this data to determine the situations that should be recreated within the virtual reality environment.

[0595] Step 2:

[0596] The server sends information about the prepared virtual reality environment to the terminal. Based on this information, the terminal generates a virtual space that the athlete will experience using a VR device, allowing the user to begin an immersive training session.

[0597] Step 3:

[0598] The device uses sensors to collect motion data in order to track the player's movements in real time. The collected data is then transferred from the device to the server.

[0599] Step 4:

[0600] The server analyzes the motion data received from the terminal in real time. Using a generative AI model, it evaluates the performance and tactical effectiveness of the players' movements and generates data for feedback.

[0601] Step 5:

[0602] Feedback data generated from the server is sent to the terminal. The terminal notifies the user of this feedback through screen display or audio.

[0603] Step 6:

[0604] Users review the feedback received from their devices and modify their tactics as needed. Based on this information, users can respond in real time and maximize the effectiveness of their training.

[0605] (Example 1)

[0606] 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".

[0607] In athletics, tactical training should ideally be conducted in an environment closely resembling real-world match situations. However, replicating actual matches and providing rapid and accurate feedback to individual athletes is challenging. Traditional methods lack sufficient realistic environments for effective training and adequate real-time, detailed motion analysis.

[0608] 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.

[0609] In this invention, the server includes an information processing device, which includes means for generating a virtual environment for the purpose of tactical training of movement, means for detecting the movements of participants within the generated virtual environment using a sensing device and collecting information related to those movements, and means for analyzing the collected information in real time and generating tactical feedback based on the analysis results. This makes it possible to analyze the movements of participants in real time within the virtual environment and provide rapid feedback.

[0610] An "information processing device" is a general term for hardware and software that have the function of efficiently processing and analyzing data and generating a specified virtual environment.

[0611] A "virtual environment" is a realistic, three-dimensional simulated space created using computer technology for the purpose of tactical training in athletic competitions.

[0612] A "sensing device" is a device that includes sensor technology used to detect the movements and positions of participants with high precision and to collect information.

[0613] "Information" refers to a collection of data detected by sensing devices and used for analysis and feedback generation.

[0614] "Analysis" is the process of evaluating behavior and identifying areas for improvement based on collected information.

[0615] A "knowledge model" is an AI algorithm and its application technology designed for data analysis, used to evaluate things like movement efficiency and tactical positioning.

[0616] "Feedback" refers to information generated based on analysis results, including instructions and advice for improving tactics and athletic efficiency.

[0617] A "display device" is a device used to present analysis results and feedback to users visually or audibly.

[0618] This invention is an embodiment of a system that uses virtual reality technology and artificial intelligence technology to make tactical training for athletic competitions more effective. The following procedure and apparatus are required to carry out the invention.

[0619] First, the server is equipped with an information processing unit and retrieves athletic competition data and tactical scenarios from a database. Common database management systems used for this purpose include MySQL and PostgreSQL. Based on the retrieved data, the server generates a virtual environment using a game engine such as Unity. Unity has advanced 3D modeling and simulation capabilities, making it suitable for recreating a realistic athletic environment.

[0620] The device uses a VR system to sense the user's movements in real time. Accelerometers and gyroscopes are used to accurately track the player's position and movement. This motion data is transmitted from the device to a server, where it is processed using high-performance computing resources.

[0621] The server employs an AI model using machine learning libraries such as TensorFlow to analyze sensory data in real time. The AI ​​model utilizes player movement data to evaluate movement efficiency and tactical positioning. This enables feedback under situations equivalent to those in real matches.

[0622] Users receive feedback generated based on the analysis results on a VR device, perceiving it particularly through the display and audio output devices. This allows users to instantly identify areas for tactical improvement and operational efficiency, and adjust their movements as needed.

[0623] As a concrete example, considering soccer, a type of athletic sport, the server simulates a typical formation of the opposing team. The user, acting as a coach, provides appropriate tactical guidance to their own players within the virtual environment. By analyzing the players' movements in real time and providing feedback on appropriate positions and movements, more advanced tactical training becomes possible.

[0624] An example of a prompt to be input into the generating AI model is, "Perform a tactical simulation based on past match data in a virtual environment and analyze the players' movement data." This prompt allows the system to automate a series of steps to support professional and effective training.

[0625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0626] Step 1:

[0627] The server retrieves sports match data and tactical scenarios from a database management system. Inputs include digital logs and scenario files related to past matches. This data is retrieved from MySQL using SQL queries. The output is a structured dataset necessary for creating a virtual environment. Specifically, the server executes SQL commands and loads the retrieved data into memory.

[0628] Step 2:

[0629] The server generates a virtual environment using Unity based on the acquired data. The input is the structured dataset obtained in step 1. As part of the data processing, 3D models are placed and the environment is configured using the Unity API. The output is a virtual reality simulation in its initial state. Specifically, virtual models of players and the field are placed, and a match scenario is constructed along a timeline.

[0630] Step 3:

[0631] The server sends the generated virtual environment data to the terminal. The input is a virtual environment object created within Unity. Data processing involves data compression and serialization using network protocols. The output is virtual environment data that can be decompressed on the terminal. Specifically, the server sends data using HTTP or WebSocket.

[0632] Step 4:

[0633] The terminal uses a VR device to sense the user's movements in real time. Inputs are the user's movements and sensor data acquired from the VR device. For data processing, accelerometers and gyroscopes are used to collect and filter the motion data. Outputs are motion data packets for transmission to the server. Specifically, the terminal periodically captures data from the sensors.

[0634] Step 5:

[0635] The server receives motion data sent from the terminal and analyzes it using an AI model with TensorFlow. The input is motion data packets acquired from the terminal. As part of the data calculation, the AI ​​model evaluates motion efficiency and tactical positioning. The output is the evaluation result and feedback message. In terms of specific operation, the server executes the AI ​​model and generates results quickly.

[0636] Step 6:

[0637] The user receives feedback generated based on the analysis results through a VR device. The input is feedback messages provided by the server. The output is visual or audio feedback displayed in a way that the user can understand. Specifically, the device presents the feedback through a VR headset display or headphones.

[0638] (Application Example 1)

[0639] 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".

[0640] In modern sports training, tactical understanding and real-time improvement are complex. In particular, individual home training often lacks access to expert guidance, limiting opportunities for effective tactical learning. Furthermore, the challenge lies in precisely recreating sports scenarios and receiving instant feedback to achieve immediate tactical improvement.

[0641] 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.

[0642] In this invention, the server includes an information processing device for generating a virtual reality environment, the virtual reality environment includes means for reproducing a scene intended for tactical training of movement, means for sensing the actions of participants within the generated virtual reality environment with a detection device and acquiring information related to those actions, means for immediately analyzing the acquired information and generating tactical feedback based on the analysis results, and means for monitoring the user's actions and providing a virtual exercise scene using a home-use humanoid robot. This makes it possible for individual users to receive professional and real-time tactical training at home.

[0643] A "virtual reality environment" is a digitally recreated space that is different from the real world, using computer technology, and allows users to have experiences that are similar to reality.

[0644] An "information processing device" is a device used for collecting, processing, storing, and reproducing data, and is a concept that includes computers and programmable hardware.

[0645] A "situation designed for tactical training in sports" is a hypothetical scenario specifically designed to learn and improve sports and fitness techniques and methodologies.

[0646] A "detection device" is a device that converts physical phenomena into electrical signals, and it is a system that includes sensors.

[0647] "Behavioral information" refers to data that digitally maps a user's movements and location, and is fundamental information used for behavioral analysis.

[0648] "Instant analysis" refers to a method where data is processed and analyzed immediately upon collection, guaranteeing results without any time lag.

[0649] "Tactical feedback" refers to a method of providing users with information that specifically outlines areas for improvement and coaching methods regarding the tactical aspects of exercise.

[0650] A "household humanoid robot" is a humanoid automated machine designed primarily for use inside a home, which assists with tasks according to the user's instructions and actions.

[0651] In implementing this invention, the system first constructs a virtual reality environment and recreates a scenario in which the user performs tactical training for exercise. The server uses an information processing device for this purpose to send the virtual reality space to the VR set or head-mounted display that the user has as a home device. This allows the user to train in exercise scenarios that transcend physical limitations.

[0652] When a user begins an action, a detection device built into the terminal immediately senses the action and acquires information related to that action. For example, if a user is dribbling a soccer ball, sensors acquire the movement and speed of the user's feet as digital data. This data is sent to a server and immediately analyzed by an AI model. This analysis utilizes software such as TensorFlow.

[0653] The server utilizes a display or output device to generate and provide immediate feedback to the user based on the analysis results. This allows the user to receive instructions for tactical improvements to their movements. For example, by inputting a prompt such as, "Analyze the forehand motion data and list the points that need improvement," the generated AI model provides specialized feedback.

[0654] One concrete example is a scenario where a user practices basketball shooting at home using a home-use humanoid robot. In this case, the robot hands the user the ball and provides real-time advice on adjusting the shooting angle.

[0655] Overall, this invention makes the process of sports tactical training possible at home, providing personalized, real-time, and effective training for individual users.

[0656] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0657] Step 1:

[0658] The server retrieves virtual data related to sports training scenarios from the database. This data includes the exercise scenarios the user will perform, and a generative AI model uses these prompts to design a virtual reality space. This prepares the virtual reality space for the server to send to the VR device.

[0659] Step 2:

[0660] The server transmits the generated virtual reality data to the terminal's VR device. This process maintains low latency data transfer, ensuring the user can instantly access the virtual environment. The user participates in the virtual reality environment by wearing the VR device.

[0661] Step 3:

[0662] When a user begins exercising, sensors attached to the device detect the user's movements. These include accelerometers and gyroscopes, and data from these sensors is acquired in real time. This data (e.g., speed and angle of movement) is aggregated by the device as information related to the action and sent to the server.

[0663] Step 4:

[0664] The server immediately analyzes the received motion data using an AI model. At this stage, it uses machine learning libraries such as TensorFlow to process the data based on the prompt "Analyze the forehand motion data and list areas for improvement." This extracts specific tactical improvements to the user's actions.

[0665] Step 5:

[0666] The generated feedback is sent from the server to the terminal. The terminal displays this feedback visually on the VR device or notifies the user as audio. This allows the user to understand the best way to improve their actions in real time and immediately take action.

[0667] 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.

[0668] This invention provides a system that highly personalizes sports tactical training and feedback by combining a virtual reality environment with an emotion engine. This system detects the player's movements and emotions and dynamically adjusts the training content and feedback based on that data.

[0669] First, the server imports past match data and predefined tactical scenarios, and uses this information as a foundation to generate a virtual reality environment. The generated environment is then transmitted to the terminal and presented to the user via their VR device. This allows the user to begin training in the virtual space.

[0670] The device is equipped with an emotion sensor that captures the user's emotional state in real time. This uses facial recognition technology and biosensor data to estimate emotions and understand the user's mental state. The obtained emotional data is sent to a server.

[0671] The server analyzes motion and emotion data transmitted from the terminal. The emotion engine uses this information to evaluate the user's psychological state during training and dynamically generates feedback accordingly. This includes applying relaxation techniques if the user is experiencing excessive stress, or sending encouraging feedback if the user is losing motivation.

[0672] As a concrete example, consider a basketball free throw practice session. If a user immediately becomes upset after missing, the server analyzes that emotion through an emotion engine. As a result, it reduces the user's mental burden and increases their chances of success by displaying advice with humor.

[0673] In this way, the system provides feedback that comprehensively considers the athlete's physical activity and emotions, helping them to acquire more effective sports tactics.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] The server imports past match data and tactical scenarios, preparing the information necessary to generate a virtual reality environment. The server then analyzes this data to create virtual scenarios suitable for sports training.

[0677] Step 2:

[0678] The server sends the generated virtual reality environment to the terminal. The terminal provides the user with the virtual space through a VR device, and the user begins training within that environment.

[0679] Step 3:

[0680] The device collects the user's physical and psychological data using physical sensors to detect the player's movements and emotion sensors to estimate emotions from facial expressions and voice.

[0681] Step 4:

[0682] The server receives motion data and emotion data transmitted from the terminal. The server analyzes this data in real time to evaluate the player's performance and emotional state.

[0683] Step 5:

[0684] The server uses an emotion engine to generate feedback that takes into account the user's emotional state. For example, if the user is stressed, it will generate feedback that promotes relaxation; if their motivation is low, it will generate encouraging feedback.

[0685] Step 6:

[0686] The server sends the generated feedback to the device. The device then provides the feedback to the user through methods such as screen display, audio notification, or vibration.

[0687] Step 7:

[0688] Users adjust their tactics based on feedback received from their devices to maximize the effectiveness of their training. They utilize the feedback to improve themselves and aim to enhance their performance.

[0689] (Example 2)

[0690] 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".

[0691] In recent years, there has been a growing demand for the development of efficient training methods for sports tactics. However, conventional methods primarily rely on feedback based solely on player movements, making it difficult to provide personalized training that takes emotional factors into account. Therefore, there is a need for a system that reflects the player's psychological state in real time and provides more effective tactical training.

[0692] 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.

[0693] In this invention, the server includes an information processing device and means for generating a virtual reality environment; means for detecting the user's movements within the generated virtual reality environment and collecting information related to those movements; means for analyzing the collected information in real time and generating tactical feedback based on the analysis results; and means for detecting the user's emotional state using an emotion analysis device and dynamically adjusting the feedback based on that emotional state. This enables highly personalized tactical training that takes into account both the player's movements and emotional state.

[0694] An "information processing device" is a computer device used to generate a virtual reality environment and analyze data on movement and emotions.

[0695] A "generated virtual reality environment" refers to a space that reproduces virtual scenarios and situations created for the purpose of tactical training in sports.

[0696] A "detection device" refers to a group of sensors that sense the user's physical movements and biosignals and collect related information.

[0697] An "emotion analysis device" refers to a system that estimates a user's emotional state from their facial expressions and biometric data, and evaluates it in real time.

[0698] "Information" refers to data necessary for training, including the user's movements and emotional state.

[0699] "Processing" refers to a series of algorithmic operations that analyze and evaluate the collected information.

[0700] "Feedback" refers to instructions, advice, or information provided to the user based on the analysis results.

[0701] "Dynamic adjustment" refers to the process of changing feedback and the environment on the fly according to the user's current state.

[0702] This invention is a system for sports tactical training that combines a virtual reality environment with emotion recognition technology. In this system, a server, terminal, and user work together to execute complex processes.

[0703] The server first uses an information processing device to collect past competition data and predefined tactical scenarios. This information is stored in a database and later used to generate the virtual reality environment. The virtual reality environment is realistically reproduced through a computer program, and a common example of a VR development platform used is Unity.

[0704] The generated virtual reality environment is transmitted to the terminal via the network. The terminal uses a VR headset to allow the user to experience this virtual space. Simultaneously, the terminal is equipped with multiple sensors that can detect the user's movements and emotional state in real time. Motion sensors and emotion analyzers utilize biosensors such as facial recognition technology and heart rate sensors.

[0705] The user puts on a VR headset and begins training in a virtual environment. During this training, the server uses motion and emotion data received from the device and analyzes it through a generative AI model. The feedback is dynamically adjusted based on the user's movements and emotional state and presented to the user as optimized instructions within the VR environment.

[0706] For example, if a user is practicing basketball free throws and misses a shot, and the user becomes upset, the server analyzes the user's emotions and sends the following prompt to the AI ​​model: "Suggest an appropriate encouraging message if the user loses motivation." This allows the system to provide timely and appropriate feedback, promoting the user's mental and technical growth.

[0707] This system allows athletes to receive advanced sports tactical training that comprehensively considers not only their physical activity but also their emotional state.

[0708] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0709] Step 1:

[0710] The server takes historical match data and predefined tactical scenarios from a database as input. Using this information, it generates a virtual reality environment. During this process, the data is processed by a virtual space generation algorithm and output as a 3D environment aligned with sports training scenarios. This virtual environment accurately reproduces tactical situations.

[0711] Step 2:

[0712] The server compresses the generated virtual environment data and sends it as output to the terminal over the network. After receiving this data, the terminal presents the virtual space to the user using a VR headset. At this point, the user puts on the headset and begins interactive tactical training.

[0713] Step 3:

[0714] The device captures the user's physical movements and emotional state as input using sensors. Data from the motion sensors provides information about limb movement and posture, while the emotion sensors provide facial expressions and heart rate data. This information is read in real time and transmitted to the server.

[0715] Step 4:

[0716] The server analyzes motion and emotion data transmitted from the terminal as input and performs data calculations using a generative AI model. The analysis results are output as information to determine the user's current psychological state. Based on this information, the server generates feedback, dynamically adjusting the feedback to match the user's emotional state.

[0717] Step 5:

[0718] The device receives feedback sent from the server as output and presents it to the user through the VR headset. Specific feedback includes advice to encourage success and messages to promote relaxation. Based on this feedback, the user continues training and improves their movements and mental state.

[0719] (Application Example 2)

[0720] 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".

[0721] Conventional sports training systems focus on the movements and techniques of players, but do not adequately consider their mental state or emotions. Furthermore, there is a problem in that consumers lack ways to fully experience the true comfort and performance of sports equipment when purchasing it. As a result, consumers may make incorrect decisions when choosing products. This invention aims to solve these problems and provide a system that offers personalized training and experience.

[0722] 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.

[0723] In this invention, the server includes a computing device for generating a virtual reality environment, means for the virtual reality environment to reproduce a scenario intended for sports tactical training, means for detecting the player's movements within the generated virtual reality environment using sensors and collecting data related to those movements, and means for an emotion recognition device that acquires emotion data and dynamically adjusts feedback based on the acquired emotion data. This enables the user to make more appropriate sports equipment selections and learn tactics while having an experience that is in line with their mental state and emotions.

[0724] A "virtual reality environment" is a computer-generated simulation space that users can experience intuitively.

[0725] A "processing unit" is a computer system or device used for data processing, and is used for generating virtual reality environments and analyzing data.

[0726] "Sports tactical training" refers to educational activities conducted with the aim of improving competitive strategies and techniques in a particular sport.

[0727] A "sensor" is a device that detects the physical environment and bodily movements and collects that information as digital data.

[0728] "Emotional data" refers to digital data that represents an individual's emotional state as numerical values ​​or information, and is acquired through facial recognition and biosensors.

[0729] "Feedback" refers to information and advice provided by a system based on the user's actions and state, with the aim of improving or supporting the user's behavior.

[0730] An "emotion recognition device" is a device or system that detects a user's emotional state and processes that information as digital data.

[0731] A "display device or output device" is a device that presents computer-generated information to a user visually or audibly.

[0732] To realize this invention, a virtual reality environment is constructed, and a system is used to analyze the user's movements and emotions in real time. A specific embodiment of this system is described below.

[0733] The server constructs a simulation space using a computing device to generate a virtual reality environment. This virtual environment can reproduce various scenarios for the purpose of sports tactical training. The generated virtual environment is provided to the user via a terminal.

[0734] Users wear VR headsets such as the Oculus Quest 2 and immerse themselves in a virtual environment. The device is equipped with sensors to detect the user's movements, and the data obtained from these sensors is sent to a server.

[0735] Furthermore, emotional data is acquired using a facial recognition camera or heart rate sensor. This emotional recognition device grasps the user's emotional state in real time and transmits it to the server.

[0736] The server analyzes both user movement data and emotional data. This analysis is performed using an emotion recognition algorithm in Python (for example, facial recognition using OpenCV). This evaluates the user's psychological state and physical reactions, and generates dynamic feedback tailored to each individual user.

[0737] The generated feedback is presented to the user through the terminal's display or output device. This feedback allows the user to receive tactical training more effectively or experience the comfort of the product in a way that suits their mental state.

[0738] As a concrete example, a user can try on specific running shoes in a VR experience booth at a sports goods store. The system records the user running in a fictional running environment and analyzes it along with emotional data. If the user shows discomfort or anxiety, feedback is displayed to suggest alternative shoes.

[0739] An example of a prompt for a generative AI model is: "A user is trying on running shoes in a VR environment. Based on facial recognition and heart rate data, please suggest new shoes that you think the user will like."

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

[0741] Step 1:

[0742] The server uses computing power to generate a virtual reality environment and constructs scenarios based on sports tactical training. Past match data is used as input, and the virtual environment is configured based on this data. The output is a virtual reality environment accessible to the user. This environment serves as a platform for users to practice tactics.

[0743] Step 2:

[0744] The device presents the user with a virtual reality environment through a VR headset. By entering the virtual environment, the user experiences visual and auditory immersion. Inputs include video and audio data transmitted from the server. Output is the user's experience in the virtual space.

[0745] Step 3:

[0746] The device uses motion sensors to detect user movement and collects data on the user's physical actions. The input data is user movement information, and the output is motion data for analysis that is sent to the server. This provides detailed information about how the user is moving.

[0747] Step 4:

[0748] The device acquires user emotional data using emotion recognition devices, such as facial recognition cameras and heart rate sensors. Input includes camera images and sensor information. This data is sent to the server as digital data. The output is data that represents the user's emotional state in real time.

[0749] Step 5:

[0750] The server analyzes the received motion and emotion data. An emotion recognition algorithm (e.g., using OpenCV) running in Python or similar languages ​​is used to process the data. The input is motion and emotion data, and the output is the analysis results. These results include evaluations of the user's psychological state and performance.

[0751] Step 6:

[0752] The server generates feedback for the user based on the analysis results. This feedback is created using the system's generation AI model, resulting in personalized advice and motivational messages. The input is the analysis results, and the output is the feedback provided to the user.

[0753] Step 7:

[0754] The terminal presents the generated feedback to the user. The input is feedback messages sent from the server, and the user receives them visually or audibly as output. This feedback allows the user to receive real-time training assistance.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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."

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] The following is further disclosed regarding the embodiments described above.

[0777] (Claim 1)

[0778] It comprises a computing device for generating a virtual reality environment, and the virtual reality environment is a means for reproducing a scenario intended for sports tactical training.

[0779] A means for detecting the player's movements within the generated virtual reality environment using sensors and collecting data related to those movements,

[0780] A means for analyzing the above-collected data in real time and generating tactical feedback based on the analysis results,

[0781] Means including a display device or output device for notifying the user of the analysis results,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, which retrieves data related to past matches from a recording device and dynamically adjusts the virtual reality environment based on said data.

[0785] (Claim 3)

[0786] The system according to claim 1, which improves the accuracy of sports tactical analysis by receiving user feedback and updating the system's learning model.

[0787] "Example 1"

[0788] (Claim 1)

[0789] The system includes an information processing device, the information processing device having means for generating a virtual environment for the purpose of tactical training in sports,

[0790] A means for detecting the movements of participants within the generated virtual environment using a sensing device and collecting information related to those movements,

[0791] A means for analyzing the above-collected information in real time and generating tactical feedback based on the analysis results,

[0792] Means including a display device or output device for notifying the user of the analysis results,

[0793] A means of analyzing a knowledge model using accumulated operational data from an information processing device to evaluate movement efficiency and tactical deployment,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, which retrieves data related to past matches from a storage device and dynamically adjusts the virtual environment based on said data.

[0797] (Claim 3)

[0798] The system according to claim 1, which improves the accuracy of motor tactical analysis by receiving feedback from users and updating the system's learning model.

[0799] "Application Example 1"

[0800] (Claim 1)

[0801] The system includes an information processing device for generating a virtual reality environment, and the virtual reality environment includes means for reproducing a scene intended for tactical training of movement,

[0802] A means for detecting the actions of participants within a generated virtual reality environment using a detection device and acquiring information related to those actions,

[0803] A means for immediately analyzing the information obtained above and generating tactical feedback based on the analysis results,

[0804] Means including a display device or output device for notifying the user of the analysis results,

[0805] A means of monitoring user behavior and providing virtual exercise scenarios using a humanoid robot for home use,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, which retrieves information about past competitions from a recording device and dynamically adapts the virtual reality environment based on said information.

[0809] (Claim 3)

[0810] The system according to claim 1, which improves the accuracy of analyzing motor tactics by receiving feedback from users and updating the system's learning algorithm.

[0811] "Example 2 of combining an emotion engine"

[0812] (Claim 1)

[0813] The system includes an information processing device, and the virtual reality environment is a means for recreating a situation intended for sports tactical training.

[0814] A means for detecting the user's movements within the generated virtual reality environment using a detection device and collecting information related to those movements,

[0815] A means for processing the above-collected information in real time and creating tactical feedback based on the processing results,

[0816] Means including a display medium or output medium for notifying the user of the processing results,

[0817] A means for detecting the user's emotional state using an emotion analysis device and dynamically adjusting feedback based on that emotional state,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, which retrieves information about past competitions from a storage device and dynamically adjusts the virtual reality environment based on said information.

[0821] (Claim 3)

[0822] The system according to claim 1, which improves the accuracy of tactical analysis by receiving feedback from users and updating the system's learning model.

[0823] "Application example 2 when combining with an emotional engine"

[0824] (Claim 1)

[0825] It comprises a computing device for generating a virtual reality environment, and the virtual reality environment is a means for reproducing a scenario intended for sports tactical training.

[0826] A means for detecting the player's movements within the generated virtual reality environment using sensors and collecting data related to those movements,

[0827] A means for analyzing the above-collected data in real time and generating tactical feedback based on the analysis results,

[0828] Means including a display device or output device for notifying the user of the analysis results,

[0829] The system includes an emotion recognition device that acquires emotion data, and means for dynamically adjusting feedback based on the acquired emotion data.

[0830] A means of providing an experience for determining the comfort of a product by presenting the generated feedback according to the user's mental state,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, which retrieves data related to past matches from a recording device and dynamically adjusts the virtual reality environment based on said data.

[0834] (Claim 3)

[0835] The system according to claim 1, which improves the accuracy of sports tactical analysis by receiving user feedback and updating the system's learning model. [Explanation of Symbols]

[0836] 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. The system includes an information processing device for generating a virtual reality environment, and the virtual reality environment includes means for reproducing a scene intended for tactical training of movement, A means for detecting the actions of participants within a generated virtual reality environment using a detection device and acquiring information related to those actions, A means for immediately analyzing the information obtained above and generating tactical feedback based on the analysis results, Means including a display device or output device for notifying the user of the analysis results, A means of monitoring user behavior and providing virtual exercise scenarios using a humanoid robot for home use, A system that includes this.

2. The system according to claim 1, which retrieves information about past competitions from a recording device and dynamically adapts the virtual reality environment based on said information.

3. The system according to claim 1, which improves the accuracy of analyzing motor tactics by receiving feedback from users and updating the system's learning algorithm.

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