system

Abstract

We provide the system. [Solution] A means for generating a virtual reality environment and providing training scenarios for athletes, A means of tracking the movements of players in real time within a generated virtual reality environment, A means equipped with an artificial intelligence module that analyzes data during a match and evaluates the movements of players or the opposing team, A means of providing immediate tactical feedback based on analysis results during a match, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Tactical training in sports competitions is extremely important for improving the proficiency of athletes and their quick judgment in real game situations. However, conventional training methods have problems that tactical feedback based on past game data tends to be delayed, and practical tactical training using virtual reality has not been sufficiently carried out. There is a demand for providing an environment where tactics can be corrected in real time and immediate feedback can be obtained.

Means for Solving the Problems

[0005] This invention provides a system for generating a virtual reality environment and offering training scenarios for athletes. It also includes an artificial intelligence module that tracks the athlete's movements in real time within the generated virtual reality environment and analyzes in-game data. This allows for the evaluation of the athlete's or opposing team's movements and provides immediate tactical feedback based on the analysis results during the match. Furthermore, the real-time data is stored in a database and used for future training, deepening the athlete's tactical understanding and enabling rapid tactical decision-making.

[0006] A "virtual reality environment" is a three-dimensional simulation space created using computer technology that allows users to experience a sense of immersion.

[0007] A "training scenario" refers to actions and procedures that simulate specific training content or situations performed by athletes in a virtual environment.

[0008] "Real-time" refers to a temporal characteristic where reactions and processing occur instantly, without any noticeable delay.

[0009] An "artificial intelligence module" is a part of a computer program that analyzes data and makes insights and decisions based on that analysis, and is constructed using machine learning and data modeling techniques.

[0010] "Tactical feedback" refers to specific and practical advice given to athletes and coaches to provide areas for improvement in their play and to suggest new strategies.

[0011] A "database" is a collection of information organized and structured to facilitate later searching and access, and is managed by specific software. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the 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.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0017] In the following embodiments, the 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a tactical training system for athletes that utilizes a virtual reality environment. This system is implemented through data communication between a server, a terminal, and a user. Its main functions and operation are described below.

[0034] First, the server manages the players' past match data and statistics, and uses this to generate a virtual reality environment. Within this environment, specific match scenarios and tactical patterns are set, and players can move freely within them. The virtual environment tracks the players' movements in real time, accumulating data on how they move around the field. Therefore, the server needs to have powerful computing capabilities and be able to process data in real time.

[0035] Next, the terminal uses a headset and motion sensors to capture the movements of the user, i.e., the player, and transmit this information to the server. The terminal functions as a direct user interface, updating the video and audio the player sees and hears in real time. The terminal also provides tactical feedback to the user during the match. This feedback is delivered through the terminal as visual displays and audio messages.

[0036] Furthermore, users can receive real-time analysis results from the AI ​​while actually moving around in the virtual reality environment. For example, if the AI ​​determines that a player is not covering a specific area during a match, the user will be advised to move to that area via their device. In this way, users can learn tactics more efficiently by having their movements in the virtual reality world analyzed in real time and receiving immediate suggestions for improvement.

[0037] Furthermore, this system stores players' training results in a database, which is used to refine and improve tactical plans for future training sessions and matches. This accumulated data can also be used for match analysis and team-wide tactical research, forming an important foundation for long-term player development and strategic planning.

[0038] In this way, the present invention is a system that enables tactical training integrating virtual reality and generative AI, and comprehensively supports performance improvement in sports competitions.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server retrieves players' past match data and performance records from a database. Based on this, it selects a training scenario suitable for each player and generates a virtual reality environment. The generated environment includes tactical patterns and match scenarios that the player should work on.

[0042] Step 2:

[0043] The user (athlete or coach) connects to the device and enters the virtual reality environment by wearing a headset and motion sensors. Once connected, the device tracks the user's movements in real time and immediately transmits this data to the server.

[0044] Step 3:

[0045] The server updates the virtual reality environment based on the user's movements and adjusts visual and audio information accordingly. It also collects player movements and analyzes them in real time using an AI data analysis module. This is to evaluate the accuracy of the players' positioning and movements.

[0046] Step 4:

[0047] The terminal provides tactical feedback to the user based on analysis results received from the server. This feedback is given as visual guides and audio messages indicating areas for improvement and the next actions to take.

[0048] Step 5:

[0049] Users adjust their gameplay in the virtual reality environment based on feedback from their devices and continuously strive to improve their tactics. They leverage real-time feedback to develop tactical decision-making skills and enhance their practical abilities.

[0050] Step 6:

[0051] The server saves user performance data to a database at the end of a virtual reality session and uses this data to create materials for the next training session or tactical planning. This provides strategic support that contributes to long-term performance improvement.

[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] Traditional sports training systems have faced challenges in tracking athletes' behavior in detail and in real time, and providing immediate tactical feedback based on information gathered during matches. Furthermore, effectively utilizing past performance and data, and adaptively adjusting the virtual environment, has been technically difficult. This results in a lack of sufficient support for effectively improving athlete performance and appropriately refining training tactics for subsequent sessions.

[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 means for generating a virtual reality environment and providing training settings for athletes; means for immediately tracking the behavior of participants within the generated virtual reality environment; means equipped with an artificial intelligence device that analyzes information during a match and evaluates the behavior of the players or opposing teams; means for adaptively modifying the virtual reality environment based on the athletes' past performance information; means for acquiring data in the virtual reality environment and performing real-time analysis of the athletes in response based on a generating AI model; means for performing evaluations using prompt sentences and immediately presenting tactical improvement proposals using artificial intelligence; and means for improving training tactics for the next session through the accumulation of training information. This enables real-time performance improvement of athletes and adaptive tactical improvements.

[0057] A "virtual reality environment" is a computer-aided, artificial environment that allows users to immerse themselves in a digital space different from reality.

[0058] An "athlete" is a person who participates in sports competitions and improves their physical and mental abilities through training and matches.

[0059] A "training setting" is a scenario or program designed to help athletes acquire and improve specific skills or tactics.

[0060] "Instant tracking" is a technology that instantly monitors user activity and behavior and processes that information in real time.

[0061] An "artificial intelligence device" is a computer program or system designed to analyze data and make decisions, and it is intended to mimic the workings of human intelligence.

[0062] A "generative AI model" is an algorithm or computer program that has the ability to self-learn based on large amounts of data and generate new information or results.

[0063] A "prompt statement" is an input statement given to a generative AI model to instruct it to perform a specific task.

[0064] "Accumulating training information" refers to the process of managing data so that athletes can save data from past training and competitions and use it for future growth and tactical improvement.

[0065] This invention is a system that utilizes a virtual reality environment designed to enhance the training of athletes. The embodiments of the system are described in detail below.

[0066] The server possesses powerful computing capabilities and generates a virtual reality environment. The server retrieves players' past match data and performance from a database and uses this data to set up virtual match scenarios and tactical patterns. Within this virtual environment, players can move freely, and the server tracks their movements in real time using data from motion sensors. The server also has the ability to analyze information during the match using a generated AI model and evaluate player movements. An example of a prompt message in this context might be, "Please suggest which areas of the field the player should focus on covering."

[0067] The terminal is equipped with hardware devices such as a headset and motion sensors to capture the player's movements. This device functions as a user interface, providing the player with visual and auditory stimuli. The terminal receives feedback from the server and communicates visual or audible instructions to the player in real time.

[0068] Users, or athletes, train within this virtual reality environment. Based on AI analysis results, they receive real-time feedback and can immediately adjust their tactical actions. For example, if the AI ​​determines that a particular area is not adequately covered, the athlete will be instructed to move to that area via their device, thereby improving the quality of their training.

[0069] These components allow the system to comprehensively support the improvement of player performance and, by accumulating training information in a database, contribute to long-term tactical improvement. This information is also used in planning tactics for future training sessions and matches, thus contributing to the improvement of the competitiveness of both players and the team.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server retrieves past match data and player statistics from the database. Using this data as input, the server executes an algorithm to generate a virtual reality environment, setting up virtual match scenarios and tactical patterns. During this process, data analysis and environment configuration are performed, and the result is output as a virtual reality environment. Specifically, it analyzes the players' past movement patterns and automatically constructs an environment suitable for training.

[0073] Step 2:

[0074] The device captures the user's movements in real time using a headset and motion sensors. Based on the sensor information as input, the device sends movement data to the server. This data records the player's position and movements with high precision and serves as output for further analysis on the server side. Specifically, raw data acquired from the sensors is sent to the server in real time and reflected in the virtual environment, so that the player's movements are instantly reflected in the environment.

[0075] Step 3:

[0076] The server inputs the received movement data into a generating AI model for analysis. Using information about the players' movements, the AI ​​generates specific tactical feedback. The output obtained from this analysis includes suggestions for tactical improvements and player performance indicators. The AI ​​performs evaluation using the prompt "Based on the input movement data, indicate the area the player should cover."

[0077] Step 4:

[0078] The terminal provides players with visual or audio feedback obtained from the server. Specifically, it displays real-time generated improvements on the interface and instructs the players. It manipulates input feedback data and presents it to players as output visual and audio guides. This allows players to immediately modify their tactics and optimize their movements within the virtual environment.

[0079] Step 5:

[0080] The server saves the results of all training sessions to a database. This process records tactical feedback and player responses, which are used as foundational information for future training plans. The saved data contributes to the long-term growth of players and the team, as well as strategic improvement. The input is the training results, and the output is the accumulated dataset. Specifically, the system automatically organizes and saves data after each session, preparing it for use in future training.

[0081] (Application Example 1)

[0082] 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."

[0083] In rehabilitation, it is essential that elderly individuals maintain motivation, safely engage in physical activity, and undergo effective training. However, traditional rehabilitation methods often struggle to maintain user motivation and ensure effective progress, sometimes making it difficult to continue rehabilitation. Therefore, there is a need to address these challenges by providing an environment where elderly individuals can train safely and enjoyably.

[0084] 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.

[0085] In this invention, the server includes means for generating a virtual reality environment and providing mobility training scenarios for the elderly, means for tracking the user's movements in real time within the generated virtual reality environment, and means equipped with an artificial intelligence module for analyzing data during rehabilitation and evaluating the user's physical movements. This enables the elderly to receive real-time feedback and undergo effective training in a safe and enjoyable manner.

[0086] A "virtual reality environment" is a computer-generated three-dimensional space, an artificial environment in which users can immerse themselves and interact.

[0087] A "mobility training scenario" is a series of virtual reality settings and exercises designed to facilitate movement tailored to specific rehabilitation objectives, allowing users to move their bodies.

[0088] "Means of real-time tracking" refers to technologies and devices that instantly detect and record a user's actions without any delay.

[0089] An "artificial intelligence module" is a computer program that analyzes acquired data to accurately evaluate the physical movements of elderly people, and it utilizes machine learning and data processing technologies.

[0090] "Feedback" is the process of providing real-time evaluations and guidance on actions performed by users, and communicating information to improve the effectiveness of training.

[0091] This invention realizes a rehabilitation training system for the elderly that utilizes a virtual reality environment.

[0092] The server possesses powerful computing capabilities, using NVIDIA GPUs for real-time data processing to generate a virtual reality environment. This virtual reality environment is designed to create mobility training scenarios that allow elderly users to safely move their bodies. Unity is selectively used to construct the virtual three-dimensional space.

[0093] The device tracks the user's movements in real time using a head-mounted display (e.g., Oculus Quest) and motion sensors (e.g., Kinect) worn by the user, and transmits that data to a server. The device also functions as an interface that provides audio and visual feedback, instantly displaying an evaluation of the user's actions.

[0094] Elderly users receive real-time feedback analyzed by a generative AI model during training in a virtual reality space, allowing them to immediately see areas for improvement and recommended actions. This enables users to progress through their training effectively.

[0095] As a concrete example, the system could monitor the user's walking pattern, and if their footing is unstable, the device could provide feedback such as, "Try walking slowly and with wider strides."

[0096] An example of a prompt to input into the generating AI model is: "When a woman in her 70s is walking while trying to maintain her balance, what feedback should be given if her foot movements are slightly unstable?"

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server generates a virtual reality space based on past rehabilitation data and user information. The input is rehabilitation information stored in a database, and the output is the virtual reality scenario the user will experience. The generated space simulates the actions the user would actually need to perform.

[0100] Step 2:

[0101] The device collects data from the user's head-mounted display and motion sensors, obtaining motion information in real time. Input is motion data from the sensors, and output is the transmission of motion information to the server. During this process, the user's movements are tracked and accurately recorded in 3D space.

[0102] Step 3:

[0103] The server analyzes the received behavioral information using a generating AI model and evaluates the user's current behavioral state. The input is behavioral data sent from the terminal, and the output is the evaluation result of that behavior. The AI ​​model generates suggestions for improvement and feedback as needed.

[0104] Step 4:

[0105] The terminal receives analysis results from the server and provides visual and auditory feedback to the user. The input is the evaluation results sent from the server, and the output is the feedback information presented to the user. For example, if the user loses their balance, specific instructions such as "Try walking slowly and with wider strides" will be given.

[0106] Step 5:

[0107] The user adjusts their actions based on feedback and continues training within the virtual environment. The input is feedback information, and the output is the adjusted actions. This allows the user to perform rehabilitation safely and efficiently.

[0108] 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.

[0109] This invention combines a tactical training system for athletes that utilizes a virtual reality environment and artificial intelligence with an emotion engine that recognizes user emotions. This system is implemented through communication between a server, a terminal, and the user. Its main functions and operation are described below.

[0110] First, the server manages the player's past match data and performance records, and sets up individual training scenarios based on this. Furthermore, the server implements an emotion engine, which processes data to recognize the user's emotional state. The emotion engine uses algorithms that recognize emotions from facial expressions, voice, and actions to assess the user's current psychological state.

[0111] Next, the device collects data in real time through the headset and motion sensors worn by the user. The device sends this data to a server and simultaneously adjusts the virtual reality environment based on instructions from the server. In particular, it provides a flexible training environment tailored to each athlete by adjusting the difficulty of the training, the content of the feedback, and the information presented according to the user's emotional state.

[0112] Furthermore, users receive emotion-based feedback and use that information to improve their gameplay in virtual reality. For example, if the emotion engine determines that a user is feeling anxious or stressed, adjustments will be made, such as sending messages to encourage relaxation through the device or switching to a session with a temporarily lowered difficulty level.

[0113] This entire system stores training results and emotion recognition data in a database, which can be used for long-term tactical evaluation and psychological support. The data can also be applied to the formulation of future training programs and the development of mental training plans for athletes. In this way, the present invention is a system that comprehensively supports performance improvement and psychological stability through adaptive training tailored to the individual circumstances of each athlete.

[0114] The following describes the processing flow.

[0115] Step 1:

[0116] The server retrieves past performance data and emotional records of players from a database. Based on this, it builds a training scenario tailored to each player and generates a virtual reality environment. The emotion engine also performs initial setup for recognizing the players' emotions.

[0117] Step 2:

[0118] The user (athlete or coach) connects to the terminal and wears a dedicated headset and motion sensors. This device captures the athlete's movements and voice in real time and prepares to send the data to the server.

[0119] Step 3:

[0120] The server tracks the player's movements within the virtual reality environment based on raw data transmitted from the terminal and analyzes the user's emotional state using an emotion engine. This involves a comprehensive emotional assessment combining facial expression analysis, voice tone analysis, heart rate, and other factors.

[0121] Step 4:

[0122] The device provides appropriate feedback to the user based on the analysis of movement and emotions received from the server. This feedback is provided as easily understandable visual and auditory information, dynamically adjusting the training content. For example, if the user is in a high-stress state, it may recommend simple skill practice.

[0123] Step 5:

[0124] Users adjust their actions within the virtual reality environment based on feedback. Real-time emotional feedback is used to maintain mental stability while playing and to train more efficiently.

[0125] Step 6:

[0126] At the end of each training session, the server saves user performance and emotional state data to a database, which is then used to improve future sessions. This data is used to continuously improve both the tactical proficiency and psychological state of the players.

[0127] (Example 2)

[0128] 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".

[0129] In modern sports training, strengthening not only an athlete's physical abilities but also their mental capabilities is crucial. However, traditional methods fail to adequately understand an athlete's psychological state and adjust training accordingly. Furthermore, the lack of feedback that takes an athlete's emotions into consideration has made it difficult to optimize individual performance.

[0130] 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.

[0131] In this invention, the server includes means for generating a virtual reality environment and providing a method for training an athlete, means for analyzing the athlete's emotional state using an emotion recognition engine, and means for adjusting the training scenario based on the emotional state. This enables training tailored to the athlete's physical and psychological characteristics.

[0132] A "virtual reality environment" is a digital space created using computer technology that allows users to immerse themselves in an environment different from physical reality.

[0133] An "athlete" refers to an individual who engages in sports or fitness activities, aiming for training or performance improvement.

[0134] A "training method" is a protocol of a set of activities or exercises designed to improve an athlete's performance.

[0135] An "intelligent module" is an artificial intelligence component that evaluates the movements of athletes and opponents through data analysis, and helps optimize training methods and match strategies.

[0136] An "emotion recognition engine" is an algorithm that analyzes a person's emotional state from their facial expressions, voice, and movements, and provides psychological feedback.

[0137] A "database" is a system for efficiently storing, managing, and retrieving structured information.

[0138] "Feedback" refers to the information and advice that athletes receive to improve their performance.

[0139] "Means of providing information through sound and visual means" refers to methods of giving instructions and feedback to an exerciser through audio messages and visual displays.

[0140] This invention is a training system for athletes that combines a virtual reality environment with artificial intelligence technology. The system consists of a server, terminals, and athletes.

[0141] The server uses an intelligent module to analyze the athlete's past performance data and provide individualized training methods. This module incorporates a generative AI model that determines the athlete's psychological status based on prompt messages. For example, it can understand a context in which the athlete is tense and provide measures to help them relax. It also features an emotion recognition engine that analyzes the athlete's emotions from their facial expressions, voice, and movements, and adjusts the scenario according to their emotional state.

[0142] The device collects movement and physiological data in real time through a headset and motion sensors worn by the user. This data is transmitted to a server using a high-speed and secure protocol, and feedback is received from the server to adjust the virtual reality environment and visual and auditory feedback.

[0143] The user (exerciser) engages in training in a virtual reality environment based on feedback provided through their device. For example, if the server determines that the exerciser is feeling stressed, a message such as "Calm down, breathe deeply, and continue practicing with self-confidence" will be displayed through the device.

[0144] The accumulated training results and emotion recognition data are stored in the server's database and used to optimize future training plans and for the long-term mental training of the athletes.

[0145] As an example of a prompt, the AI ​​model is provided with instructions such as, "Display a message encouraging relaxation to users who are showing signs of tension."

[0146] Overall, this system provides flexible and effective training tailored to the individual needs of athletes, comprehensively supporting performance improvement and psychological stability.

[0147] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0148] Step 1:

[0149] The terminal collects motion data, facial expression data, and voice data from the user in real time through a headset and motion sensors worn by the athlete. This data is fundamental information for real-time analysis of the athlete's movements and psychological state. The collected data is immediately converted into a digital format and transmitted to the server.

[0150] Step 2:

[0151] The server receives raw data from the terminal as input and first performs data preprocessing. This process involves noise reduction and signal standardization to prepare the data for analysis. The prepared data is then analyzed through an emotion recognition engine, and the emotional state of the person performing the action is extracted as output. Additionally, a generative AI model is used to create prompt messages that generate the optimal training scenario for that moment.

[0152] Step 3:

[0153] The server adjusts the training scenario within the virtual reality environment based on the extracted emotional state and generated prompt messages. During this process, the training content is modified using feedback from the generating AI model to reduce the psychological burden on the user. For example, if the server determines that the user is under stress, it generates instructions to lower the game difficulty. This result is then sent to the device as instructions.

[0154] Step 4:

[0155] The terminal updates the virtual reality environment in real time based on output from the server. Specifically, it provides the user with tailored feedback through visual information and audio messages. This allows the user to focus on training while addressing their tasks within the presented environment and receiving psychological support.

[0156] Step 5:

[0157] Users receive feedback from their devices and adjust their performance based on it. For example, they might receive feedback such as "Try to relax a bit," and then attempt to change their actual movements or mindset. These user responses are further collected by the device and used as data in the next cycle.

[0158] Step 6:

[0159] The server stores all collected data and output in a database after each training session. This data is used to improve future training sessions and track the psychological and physical growth of the athletes. This allows for the long-term development of training strategies best suited to each individual athlete.

[0160] (Application Example 2)

[0161] 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".

[0162] In modern society, athletes are required to acquire tactics more effectively and efficiently, while simultaneously managing their mental state. However, traditional training methods have made it difficult to grasp athletes' emotional states in real time and provide appropriate feedback based on that information. Therefore, there is a need to develop a system that reduces the psychological anxiety and stress that athletes experience, allowing them to perform at their best.

[0163] 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.

[0164] In this invention, the server includes means for generating a virtual reality environment and providing training scenarios for athletes, means for tracking the athlete's movements in real time within the generated virtual reality environment, and emotion analysis means for recognizing the user's emotional state using facial expressions, voice, and body movements. This makes it possible to grasp the athlete's emotional state in real time during training in virtual reality and provide tactical feedback accordingly immediately.

[0165] A "virtual reality environment" is a computer-generated, synthetic space that users can experience visually and aurally.

[0166] A "training scenario" refers to a specific training flow and set of tasks designed to improve the skills of athletes.

[0167] The "artificial intelligence module" is a program that uses machine learning and data analysis to evaluate performance from in-game data and support tactical decision-making.

[0168] "Emotional analysis means" refers to a technical device or software that evaluates a user's emotional state using data on facial expressions, voice, and body movements.

[0169] "Tactical feedback" is coaching information that takes into account a player's performance and emotional state, and points out areas for improvement in strategy and play during a match.

[0170] This invention provides a system that allows athletes to receive individualized training in a virtual reality environment. This system consists of a server, terminals, and users. The roles of each component and the overall operation of the system are described below.

[0171] The server manages players' past match data and performance records, and generates training scenarios tailored to each individual player based on this data. In this process, algorithms that recognize emotions from facial expressions, voice, and body movements are used as means of emotion analysis. Specifically, OpenCV is used for facial expression recognition, and Google's Speech-to-Text API is used for voice analysis, with the data stored in Firebase.

[0172] The device displays a virtual reality environment in real time via a headset and motion sensors worn by the user. Furthermore, it provides immediate feedback tailored to the user's emotional state, encouraging improvements in actions and tactics. This feedback is communicated to the user as both audio and visual information.

[0173] Users utilize feedback received from their devices to improve their gameplay within virtual reality. For example, if emotion analysis determines that a user is in a state of tension, the system will display a message encouraging relaxation and temporarily adjust the difficulty of the training to promote the user's psychological well-being.

[0174] For example, by using the prompt "The user appears to be stressed. Please suggest ways to help them relax," the AI ​​model can generate and provide practical advice such as "Take two deep breaths and listen to some light music." In this way, the system flexibly responds to the athlete's condition, enabling efficient training while maintaining psychological stability.

[0175] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0176] Step 1:

[0177] The server retrieves players' past match data and performance records from a database. The input data consists of player performance metrics and match results, which are used to generate training scenarios. Machine learning models are used as data analysis techniques to create individual training plans.

[0178] Step 2:

[0179] The device collects data in real time through a headset and motion sensors worn by the user. The input consists of user movement data and environmental information, which is then transmitted to a server. Based on the data from the sensors, a virtual reality environment is generated and presented to the user.

[0180] Step 3:

[0181] The server receives data sent from the user and analyzes facial expressions, voice, and actions using emotion analysis tools. The input data consists of camera footage and audio recordings, which are analyzed to identify the user's emotional state. The current emotional state is then evaluated through processing using OpenCV and the Google Speech-to-Text API.

[0182] Step 4:

[0183] Based on the user's emotional state, the server generates tactical feedback. Prompt statements are input into a generation AI model to construct additional advice and strategies. The generated feedback is a suggestion that takes recent performance data and emotional state into consideration.

[0184] Step 5:

[0185] The device provides the user with feedback sent from the server as audio and visual information. Output consists of messages regarding actionable instructions and relaxation methods. Actions include visual presentations on display devices and guidance from voice assistants.

[0186] Step 6:

[0187] Users modify and improve their gameplay in virtual reality based on feedback provided by their devices. Through implementing this feedback, they can observe changes in their emotional state and gain guidance for the next steps.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Second Embodiment]

[0192] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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".

[0204] This invention is a tactical training system for athletes that utilizes a virtual reality environment. This system is implemented through data communication between a server, a terminal, and a user. Its main functions and operation are described below.

[0205] First, the server manages the players' past match data and statistics, and uses this to generate a virtual reality environment. Within this environment, specific match scenarios and tactical patterns are set, and players can move freely within them. The virtual environment tracks the players' movements in real time, accumulating data on how they move around the field. Therefore, the server needs to have powerful computing capabilities and be able to process data in real time.

[0206] Next, the terminal uses a headset and motion sensors to capture the movements of the user, i.e., the player, and transmit this information to the server. The terminal functions as a direct user interface, updating the video and audio the player sees and hears in real time. The terminal also provides tactical feedback to the user during the match. This feedback is delivered through the terminal as visual displays and audio messages.

[0207] Furthermore, users can receive real-time analysis results from the AI ​​while actually moving around in the virtual reality environment. For example, if the AI ​​determines that a player is not covering a specific area during a match, the user will be advised to move to that area via their device. In this way, users can learn tactics more efficiently by having their movements in the virtual reality world analyzed in real time and receiving immediate suggestions for improvement.

[0208] Furthermore, this system stores players' training results in a database, which is used to refine and improve tactical plans for future training sessions and matches. This accumulated data can also be used for match analysis and team-wide tactical research, forming an important foundation for long-term player development and strategic planning.

[0209] In this way, the present invention is a system that enables tactical training integrating virtual reality and generative AI, and comprehensively supports performance improvement in sports competitions.

[0210] The following describes the processing flow.

[0211] Step 1:

[0212] The server retrieves players' past match data and performance records from a database. Based on this, it selects a training scenario suitable for each player and generates a virtual reality environment. The generated environment includes tactical patterns and match scenarios that the player should work on.

[0213] Step 2:

[0214] The user (athlete or coach) connects to the device and enters the virtual reality environment by wearing a headset and motion sensors. Once connected, the device tracks the user's movements in real time and immediately transmits this data to the server.

[0215] Step 3:

[0216] The server updates the virtual reality environment based on the user's movements and adjusts visual and audio information accordingly. It also collects player movements and analyzes them in real time using an AI data analysis module. This is to evaluate the accuracy of the players' positioning and movements.

[0217] Step 4:

[0218] The terminal provides tactical feedback to the user based on analysis results received from the server. This feedback is given as visual guides and audio messages indicating areas for improvement and the next actions to take.

[0219] Step 5:

[0220] Users adjust their gameplay in the virtual reality environment based on feedback from their devices and continuously strive to improve their tactics. They leverage real-time feedback to develop tactical decision-making skills and enhance their practical abilities.

[0221] Step 6:

[0222] The server saves user performance data to a database at the end of a virtual reality session and uses this data to create materials for the next training session or tactical planning. This provides strategic support that contributes to long-term performance improvement.

[0223] (Example 1)

[0224] 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."

[0225] Traditional sports training systems have faced challenges in tracking athletes' behavior in detail and in real time, and providing immediate tactical feedback based on information gathered during matches. Furthermore, effectively utilizing past performance and data, and adaptively adjusting the virtual environment, has been technically difficult. This results in a lack of sufficient support for effectively improving athlete performance and appropriately refining training tactics for subsequent sessions.

[0226] 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.

[0227] In this invention, the server includes means for generating a virtual reality environment and providing training settings for athletes; means for immediately tracking the behavior of participants within the generated virtual reality environment; means equipped with an artificial intelligence device that analyzes information during a match and evaluates the behavior of the players or opposing teams; means for adaptively modifying the virtual reality environment based on the athletes' past performance information; means for acquiring data in the virtual reality environment and performing real-time analysis of the athletes in response based on a generating AI model; means for performing evaluations using prompt sentences and immediately presenting tactical improvement proposals using artificial intelligence; and means for improving training tactics for the next session through the accumulation of training information. This enables real-time performance improvement of athletes and adaptive tactical improvements.

[0228] A "virtual reality environment" is a computer-aided, artificial environment that allows users to immerse themselves in a digital space different from reality.

[0229] An "athlete" is a person who participates in sports competitions and improves their physical and mental abilities through training and matches.

[0230] A "training setting" is a scenario or program designed to help athletes acquire and improve specific skills or tactics.

[0231] "Instant tracking" is a technology that instantly monitors user activity and behavior and processes that information in real time.

[0232] An "artificial intelligence device" is a computer program or system designed to analyze data and make decisions, and it is intended to mimic the workings of human intelligence.

[0233] A "generative AI model" is an algorithm or computer program that has the ability to self-learn based on large amounts of data and generate new information or results.

[0234] A "prompt statement" is an input statement given to a generative AI model to instruct it to perform a specific task.

[0235] "Accumulating training information" refers to the process of managing data so that athletes can save data from past training and competitions and use it for future growth and tactical improvement.

[0236] This invention is a system that utilizes a virtual reality environment designed to enhance the training of athletes. The embodiments of the system are described in detail below.

[0237] The server possesses powerful computing capabilities and generates a virtual reality environment. The server retrieves players' past match data and performance from a database and uses this data to set up virtual match scenarios and tactical patterns. Within this virtual environment, players can move freely, and the server tracks their movements in real time using data from motion sensors. The server also has the ability to analyze information during the match using a generated AI model and evaluate player movements. An example of a prompt message in this context might be, "Please suggest which areas of the field the player should focus on covering."

[0238] The terminal is equipped with hardware devices such as a headset and motion sensors to capture the player's movements. This device functions as a user interface, providing the player with visual and auditory stimuli. The terminal receives feedback from the server and communicates visual or audible instructions to the player in real time.

[0239] Users, or athletes, train within this virtual reality environment. Based on AI analysis results, they receive real-time feedback and can immediately adjust their tactical actions. For example, if the AI ​​determines that a particular area is not adequately covered, the athlete will be instructed to move to that area via their device, thereby improving the quality of their training.

[0240] These components allow the system to comprehensively support the improvement of player performance and, by accumulating training information in a database, contribute to long-term tactical improvement. This information is also used in planning tactics for future training sessions and matches, thus contributing to the improvement of the competitiveness of both players and the team.

[0241] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0242] Step 1:

[0243] The server retrieves past match data and player statistics from the database. Using this data as input, the server executes an algorithm to generate a virtual reality environment, setting up virtual match scenarios and tactical patterns. During this process, data analysis and environment configuration are performed, and the result is output as a virtual reality environment. Specifically, it analyzes the players' past movement patterns and automatically constructs an environment suitable for training.

[0244] Step 2:

[0245] The device captures the user's movements in real time using a headset and motion sensors. Based on the sensor information as input, the device sends movement data to the server. This data records the player's position and movements with high precision and serves as output for further analysis on the server side. Specifically, raw data acquired from the sensors is sent to the server in real time and reflected in the virtual environment, so that the player's movements are instantly reflected in the environment.

[0246] Step 3:

[0247] The server inputs the received movement data into a generating AI model for analysis. Using information about the players' movements, the AI ​​generates specific tactical feedback. The output obtained from this analysis includes suggestions for tactical improvements and player performance indicators. The AI ​​performs evaluation using the prompt "Based on the input movement data, indicate the area the player should cover."

[0248] Step 4:

[0249] The terminal provides players with visual or audio feedback obtained from the server. Specifically, it displays real-time generated improvements on the interface and instructs the players. It manipulates input feedback data and presents it to players as output visual and audio guides. This allows players to immediately modify their tactics and optimize their movements within the virtual environment.

[0250] Step 5:

[0251] The server saves the results of all training sessions to a database. This process records tactical feedback and player responses, which are used as foundational information for future training plans. The saved data contributes to the long-term growth of players and the team, as well as strategic improvement. The input is the training results, and the output is the accumulated dataset. Specifically, the system automatically organizes and saves data after each session, preparing it for use in future training.

[0252] (Application Example 1)

[0253] 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."

[0254] In rehabilitation, it is essential that elderly individuals maintain motivation, safely engage in physical activity, and undergo effective training. However, traditional rehabilitation methods often struggle to maintain user motivation and ensure effective progress, sometimes making it difficult to continue rehabilitation. Therefore, there is a need to address these challenges by providing an environment where elderly individuals can train safely and enjoyably.

[0255] 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.

[0256] In this invention, the server includes means for generating a virtual reality environment and providing mobility training scenarios for the elderly, means for tracking the user's movements in real time within the generated virtual reality environment, and means equipped with an artificial intelligence module for analyzing data during rehabilitation and evaluating the user's physical movements. This enables the elderly to receive real-time feedback and undergo effective training in a safe and enjoyable manner.

[0257] A "virtual reality environment" is a computer-generated three-dimensional space, an artificial environment in which users can immerse themselves and interact.

[0258] A "mobility training scenario" is a series of virtual reality settings and exercises designed to facilitate movement tailored to specific rehabilitation objectives, allowing users to move their bodies.

[0259] "Means of real-time tracking" refers to technologies and devices that instantly detect and record a user's actions without any delay.

[0260] An "artificial intelligence module" is a computer program that analyzes acquired data to accurately evaluate the physical movements of elderly people, and it utilizes machine learning and data processing technologies.

[0261] "Feedback" is the process of providing real-time evaluations and guidance on actions performed by users, and communicating information to improve the effectiveness of training.

[0262] This invention realizes a rehabilitation training system for the elderly that utilizes a virtual reality environment.

[0263] The server possesses powerful computing capabilities, using NVIDIA GPUs for real-time data processing to generate a virtual reality environment. This virtual reality environment is designed to create mobility training scenarios that allow elderly users to safely move their bodies. Unity is selectively used to construct the virtual three-dimensional space.

[0264] The device tracks the user's movements in real time using a head-mounted display (e.g., Oculus Quest) and motion sensors (e.g., Kinect) worn by the user, and transmits that data to a server. The device also functions as an interface that provides audio and visual feedback, instantly displaying an evaluation of the user's actions.

[0265] Elderly users receive real-time feedback analyzed by a generative AI model during training in a virtual reality space, allowing them to immediately see areas for improvement and recommended actions. This enables users to progress through their training effectively.

[0266] As a concrete example, the system could monitor the user's walking pattern, and if their footing is unstable, the device could provide feedback such as, "Try walking slowly and with wider strides."

[0267] An example of a prompt to input into the generating AI model is: "When a woman in her 70s is walking while trying to maintain her balance, what feedback should be given if her foot movements are slightly unstable?"

[0268] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0269] Step 1:

[0270] The server generates a virtual reality space based on past rehabilitation data and user information. The input is rehabilitation information stored in a database, and the output is the virtual reality scenario the user will experience. The generated space simulates the actions the user would actually need to perform.

[0271] Step 2:

[0272] The device collects data from the user's head-mounted display and motion sensors, obtaining motion information in real time. Input is motion data from the sensors, and output is the transmission of motion information to the server. During this process, the user's movements are tracked and accurately recorded in 3D space.

[0273] Step 3:

[0274] The server analyzes the received behavioral information using a generating AI model and evaluates the user's current behavioral state. The input is behavioral data sent from the terminal, and the output is the evaluation result of that behavior. The AI ​​model generates suggestions for improvement and feedback as needed.

[0275] Step 4:

[0276] The terminal receives analysis results from the server and provides visual and auditory feedback to the user. The input is the evaluation results sent from the server, and the output is the feedback information presented to the user. For example, if the user loses their balance, specific instructions such as "Try walking slowly and with wider strides" will be given.

[0277] Step 5:

[0278] The user adjusts their actions based on feedback and continues training within the virtual environment. The input is feedback information, and the output is the adjusted actions. This allows the user to perform rehabilitation safely and efficiently.

[0279] 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.

[0280] This invention combines a tactical training system for athletes that utilizes a virtual reality environment and artificial intelligence with an emotion engine that recognizes user emotions. This system is implemented through communication between a server, a terminal, and the user. Its main functions and operation are described below.

[0281] First, the server manages the past game data and performance records of the players, and sets individual training scenarios based on this. Furthermore, the server implements an emotion engine and processes data to recognize the user's emotional state. The emotion engine uses algorithms to recognize emotions from facial expressions, voices, and movements, and evaluates the user's current psychological state.

[0282] Next, the terminal collects data in real time through the headset and motion sensors worn by the user. The terminal sends this data to the server and simultaneously adjusts the virtual reality environment based on the instructions from the server. In particular, by adjusting the training difficulty, the content of the feedback, and the information presented according to the user's emotional state, a flexible training environment suitable for each player is provided.

[0283] Furthermore, the user receives emotion-based feedback and uses this information to improve their play within the virtual reality. For example, if the emotion engine determines that the user is feeling anxious or tense, adjustments such as sending a message through the terminal to encourage relaxation or switching to a session with temporarily reduced difficulty are made.

[0284] The entire system accumulates the training results and emotion recognition data in a database, which can be used for long-term tactical evaluation and psychological support. The data is also applied to the formulation of subsequent training programs and the mental training plans of the players. Thus, the present invention is a system that comprehensively supports performance improvement and psychological stability through adaptive training according to the individual situations of the players.

[0285] The processing flow will be described below.

[0286] Step 1:

[0287] The server retrieves past performance data and emotional records of players from a database. Based on this, it builds a training scenario tailored to each player and generates a virtual reality environment. The emotion engine also performs initial setup for recognizing the players' emotions.

[0288] Step 2:

[0289] The user (athlete or coach) connects to the terminal and wears a dedicated headset and motion sensors. This device captures the athlete's movements and voice in real time and prepares to send the data to the server.

[0290] Step 3:

[0291] The server tracks the player's movements within the virtual reality environment based on raw data transmitted from the terminal and analyzes the user's emotional state using an emotion engine. This involves a comprehensive emotional assessment combining facial expression analysis, voice tone analysis, heart rate, and other factors.

[0292] Step 4:

[0293] The device provides appropriate feedback to the user based on the analysis of movement and emotions received from the server. This feedback is provided as easily understandable visual and auditory information, dynamically adjusting the training content. For example, if the user is in a high-stress state, it may recommend simple skill practice.

[0294] Step 5:

[0295] Users adjust their actions within the virtual reality environment based on feedback. Real-time emotional feedback is used to maintain mental stability while playing and to train more efficiently.

[0296] Step 6:

[0297] At the end of each training session, the server saves user performance and emotional state data to a database, which is then used to improve future sessions. This data is used to continuously improve both the tactical proficiency and psychological state of the players.

[0298] (Example 2)

[0299] 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".

[0300] In modern sports training, strengthening not only an athlete's physical abilities but also their mental capabilities is crucial. However, traditional methods fail to adequately understand an athlete's psychological state and adjust training accordingly. Furthermore, the lack of feedback that takes an athlete's emotions into consideration has made it difficult to optimize individual performance.

[0301] 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.

[0302] In this invention, the server includes means for generating a virtual reality environment and providing a method for training an athlete, means for analyzing the athlete's emotional state using an emotion recognition engine, and means for adjusting the training scenario based on the emotional state. This enables training tailored to the athlete's physical and psychological characteristics.

[0303] A "virtual reality environment" is a digital space created using computer technology that allows users to immerse themselves in an environment different from physical reality.

[0304] An "athlete" refers to an individual who engages in sports or fitness activities, aiming for training or performance improvement.

[0305] A "training method" is a protocol of a set of activities or exercises designed to improve an athlete's performance.

[0306] The "Intelligent Module" is a component based on artificial intelligence that evaluates the movements of athletes or opponents through data analysis and supports the optimization of training methods and game strategies.

[0307] The "Emotion Recognition Engine" is an algorithm that analyzes the emotional state from the expressions, voices, and movements of athletes and provides psychological feedback.

[0308] The "Database" is a system for efficiently storing, managing, and retrieving structured information.

[0309] "Feedback" is the information and advice that an athlete receives to improve their performance.

[0310] The "Means of Providing Information by Voice and Vision" is a method for giving instructions and feedback to athletes through voice messages and visual displays.

[0311] This invention is a training system for athletes that combines virtual reality environments and artificial intelligence technology. The system consists of a server, a terminal, and an athlete.

[0312] The server analyzes the past performance data of athletes using the intelligent module and provides individual training methods. This module incorporates a generative AI model that judges the psychological status of athletes based on prompt sentences. For example, it understands the context in which an athlete is nervous and provides countermeasures for relaxation. It also has an emotion recognition engine that analyzes emotions from the expressions, voices, and movements of athletes and makes scenario adjustments according to the emotional state.

[0313] The device collects movement and physiological data in real time through a headset and motion sensors worn by the user. This data is transmitted to a server using a high-speed and secure protocol, and feedback is received from the server to adjust the virtual reality environment and visual and auditory feedback.

[0314] The user (exerciser) engages in training in a virtual reality environment based on feedback provided through their device. For example, if the server determines that the exerciser is feeling stressed, a message such as "Calm down, breathe deeply, and continue practicing with self-confidence" will be displayed through the device.

[0315] The accumulated training results and emotion recognition data are stored in the server's database and used to optimize future training plans and for the long-term mental training of the athletes.

[0316] As an example of a prompt, the AI ​​model is provided with instructions such as, "Display a message encouraging relaxation to users who are showing signs of tension."

[0317] Overall, this system provides flexible and effective training tailored to the individual needs of athletes, comprehensively supporting performance improvement and psychological stability.

[0318] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0319] Step 1:

[0320] The terminal collects motion data, facial expression data, and voice data from the user in real time through a headset and motion sensors worn by the athlete. This data is fundamental information for real-time analysis of the athlete's movements and psychological state. The collected data is immediately converted into a digital format and transmitted to the server.

[0321] Step 2:

[0322] The server receives raw data from the terminal as input and first performs data preprocessing. This process involves noise reduction and signal standardization to prepare the data for analysis. The prepared data is then analyzed through an emotion recognition engine, and the emotional state of the person performing the action is extracted as output. Additionally, a generative AI model is used to create prompt messages that generate the optimal training scenario for that moment.

[0323] Step 3:

[0324] The server adjusts the training scenario within the virtual reality environment based on the extracted emotional state and generated prompt messages. During this process, the training content is modified using feedback from the generating AI model to reduce the psychological burden on the user. For example, if the server determines that the user is under stress, it generates instructions to lower the game difficulty. This result is then sent to the device as instructions.

[0325] Step 4:

[0326] The terminal updates the virtual reality environment in real time based on output from the server. Specifically, it provides the user with tailored feedback through visual information and audio messages. This allows the user to focus on training while addressing their tasks within the presented environment and receiving psychological support.

[0327] Step 5:

[0328] Users receive feedback from their devices and adjust their performance based on it. For example, they might receive feedback such as "Try to relax a bit," and then attempt to change their actual movements or mindset. These user responses are further collected by the device and used as data in the next cycle.

[0329] Step 6:

[0330] The server stores all collected data and output in a database after each training session. This data is used to improve future training sessions and track the psychological and physical growth of the athletes. This allows for the long-term development of training strategies best suited to each individual athlete.

[0331] (Application Example 2)

[0332] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0333] In modern society, athletes are required to acquire tactics more effectively and efficiently, while simultaneously managing their mental state. However, traditional training methods have made it difficult to grasp athletes' emotional states in real time and provide appropriate feedback based on that information. Therefore, there is a need to develop a system that reduces the psychological anxiety and stress that athletes experience, allowing them to perform at their best.

[0334] 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.

[0335] In this invention, the server includes means for generating a virtual reality environment and providing training scenarios for athletes, means for tracking the athlete's movements in real time within the generated virtual reality environment, and emotion analysis means for recognizing the user's emotional state using facial expressions, voice, and body movements. This makes it possible to grasp the athlete's emotional state in real time during training in virtual reality and provide tactical feedback accordingly immediately.

[0336] A "virtual reality environment" is a computer-generated, synthetic space that users can experience visually and aurally.

[0337] A "training scenario" refers to a specific training flow and set of tasks designed to improve the skills of athletes.

[0338] The "artificial intelligence module" is a program that uses machine learning and data analysis to evaluate performance from in-game data and support tactical decision-making.

[0339] "Emotional analysis means" refers to a technical device or software that evaluates a user's emotional state using data on facial expressions, voice, and body movements.

[0340] "Tactical feedback" is coaching information that takes into account a player's performance and emotional state, and points out areas for improvement in strategy and play during a match.

[0341] This invention provides a system that allows athletes to receive individualized training in a virtual reality environment. This system consists of a server, terminals, and users. The roles of each component and the overall operation of the system are described below.

[0342] The server manages players' past match data and performance records, and generates training scenarios tailored to each individual player based on this data. In this process, algorithms that recognize emotions from facial expressions, voice, and body movements are used as means of emotion analysis. Specifically, OpenCV is used for facial expression recognition, and the Google Speech-to-Text API is used for voice analysis, with the data stored in Firebase.

[0343] The device displays a virtual reality environment in real time via a headset and motion sensors worn by the user. Furthermore, it provides immediate feedback tailored to the user's emotional state, encouraging improvements in actions and tactics. This feedback is communicated to the user as both audio and visual information.

[0344] Users utilize feedback received from their devices to improve their gameplay within virtual reality. For example, if emotion analysis determines that a user is in a state of tension, the system will display a message encouraging relaxation and temporarily adjust the difficulty of the training to promote the user's psychological well-being.

[0345] For example, by using the prompt "The user appears to be stressed. Please suggest ways to help them relax," the AI ​​model can generate and provide practical advice such as "Take two deep breaths and listen to some light music." In this way, the system flexibly responds to the athlete's condition, enabling efficient training while maintaining psychological stability.

[0346] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0347] Step 1:

[0348] The server retrieves players' past match data and performance records from a database. The input data consists of player performance metrics and match results, which are used to generate training scenarios. Machine learning models are used as data analysis techniques to create individual training plans.

[0349] Step 2:

[0350] The device collects data in real time through a headset and motion sensors worn by the user. The input consists of user movement data and environmental information, which is then transmitted to a server. Based on the data from the sensors, a virtual reality environment is generated and presented to the user.

[0351] Step 3:

[0352] The server receives data sent from the user and analyzes facial expressions, voice, and actions using emotion analysis tools. The input data consists of camera footage and audio recordings, which are analyzed to identify the user's emotional state. The current emotional state is then evaluated through processing using OpenCV and the Google Speech-to-Text API.

[0353] Step 4:

[0354] Based on the user's emotional state, the server generates tactical feedback. Prompt statements are input into a generation AI model to construct additional advice and strategies. The generated feedback is a suggestion that takes recent performance data and emotional state into consideration.

[0355] Step 5:

[0356] The device provides the user with feedback sent from the server as audio and visual information. Output consists of messages regarding actionable instructions and relaxation methods. Actions include visual presentations on display devices and guidance from voice assistants.

[0357] Step 6:

[0358] Users modify and improve their gameplay in virtual reality based on feedback provided by their devices. Through implementing this feedback, they can observe changes in their emotional state and gain guidance for the next steps.

[0359] 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.

[0360] 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.

[0361] 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.

[0362] [Third Embodiment]

[0363] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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).

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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".

[0375] This invention is a tactical training system for athletes that utilizes a virtual reality environment. This system is implemented through data communication between a server, a terminal, and a user. Its main functions and operation are described below.

[0376] First, the server manages the players' past match data and statistics, and uses this to generate a virtual reality environment. Within this environment, specific match scenarios and tactical patterns are set, and players can move freely within them. The virtual environment tracks the players' movements in real time, accumulating data on how they move around the field. Therefore, the server needs to have powerful computing capabilities and be able to process data in real time.

[0377] Next, the terminal uses a headset and motion sensors to capture the movements of the user, i.e., the player, and transmit this information to the server. The terminal functions as a direct user interface, updating the video and audio the player sees and hears in real time. The terminal also provides tactical feedback to the user during the match. This feedback is delivered through the terminal as visual displays and audio messages.

[0378] Furthermore, users can receive real-time analysis results from the AI ​​while actually moving around in the virtual reality environment. For example, if the AI ​​determines that a player is not covering a specific area during a match, the user will be advised to move to that area via their device. In this way, users can learn tactics more efficiently by having their movements in the virtual reality world analyzed in real time and receiving immediate suggestions for improvement.

[0379] Furthermore, this system stores players' training results in a database, which is used to refine and improve tactical plans for future training sessions and matches. This accumulated data can also be used for match analysis and team-wide tactical research, forming an important foundation for long-term player development and strategic planning.

[0380] In this way, the present invention is a system that enables tactical training integrating virtual reality and generative AI, and comprehensively supports performance improvement in sports competitions.

[0381] The following describes the processing flow.

[0382] Step 1:

[0383] The server retrieves players' past match data and performance records from a database. Based on this, it selects a training scenario suitable for each player and generates a virtual reality environment. The generated environment includes tactical patterns and match scenarios that the player should work on.

[0384] Step 2:

[0385] The user (athlete or coach) connects to the device and enters the virtual reality environment by wearing a headset and motion sensors. Once connected, the device tracks the user's movements in real time and immediately transmits this data to the server.

[0386] Step 3:

[0387] The server updates the virtual reality environment based on the user's movements and adjusts visual and audio information accordingly. It also collects player movements and analyzes them in real time using an AI data analysis module. This is to evaluate the accuracy of the players' positioning and movements.

[0388] Step 4:

[0389] The terminal provides tactical feedback to the user based on analysis results received from the server. This feedback is given as visual guides and audio messages indicating areas for improvement and the next actions to take.

[0390] Step 5:

[0391] Users adjust their gameplay in the virtual reality environment based on feedback from their devices and continuously strive to improve their tactics. They leverage real-time feedback to develop tactical decision-making skills and enhance their practical abilities.

[0392] Step 6:

[0393] The server saves user performance data to a database at the end of a virtual reality session and uses this data to create materials for the next training session or tactical planning. This provides strategic support that contributes to long-term performance improvement.

[0394] (Example 1)

[0395] 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."

[0396] Traditional sports training systems have faced challenges in tracking athletes' behavior in detail and in real time, and providing immediate tactical feedback based on information gathered during matches. Furthermore, effectively utilizing past performance and data, and adaptively adjusting the virtual environment, has been technically difficult. This results in a lack of sufficient support for effectively improving athlete performance and appropriately refining training tactics for subsequent sessions.

[0397] 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.

[0398] In this invention, the server includes means for generating a virtual reality environment and providing training settings for athletes; means for immediately tracking the behavior of participants within the generated virtual reality environment; means equipped with an artificial intelligence device that analyzes information during a match and evaluates the behavior of the players or opposing teams; means for adaptively modifying the virtual reality environment based on the athletes' past performance information; means for acquiring data in the virtual reality environment and performing real-time analysis of the athletes in response based on a generating AI model; means for performing evaluations using prompt sentences and immediately presenting tactical improvement proposals using artificial intelligence; and means for improving training tactics for the next session through the accumulation of training information. This enables real-time performance improvement of athletes and adaptive tactical improvements.

[0399] A "virtual reality environment" is a computer-aided, artificial environment that allows users to immerse themselves in a digital space different from reality.

[0400] An "athlete" is a person who participates in sports competitions and improves their physical and mental abilities through training and matches.

[0401] A "training setting" is a scenario or program designed to help athletes acquire and improve specific skills or tactics.

[0402] "Instant tracking" is a technology that instantly monitors user activity and behavior and processes that information in real time.

[0403] An "artificial intelligence device" is a computer program or system designed to analyze data and make decisions, and it is intended to mimic the workings of human intelligence.

[0404] A "generative AI model" is an algorithm or computer program that has the ability to self-learn based on large amounts of data and generate new information or results.

[0405] A "prompt statement" is an input statement given to a generative AI model to instruct it to perform a specific task.

[0406] "Accumulating training information" refers to the process of managing data so that athletes can save data from past training and competitions and use it for future growth and tactical improvement.

[0407] This invention is a system that utilizes a virtual reality environment designed to enhance the training of athletes. The embodiments of the system are described in detail below.

[0408] The server possesses powerful computing capabilities and generates a virtual reality environment. The server retrieves players' past match data and performance from a database and uses this data to set up virtual match scenarios and tactical patterns. Within this virtual environment, players can move freely, and the server tracks their movements in real time using data from motion sensors. The server also has the ability to analyze information during the match using a generated AI model and evaluate player movements. An example of a prompt message in this context might be, "Please suggest which areas of the field the player should focus on covering."

[0409] The terminal is equipped with hardware devices such as a headset and motion sensors to capture the player's movements. This device functions as a user interface, providing the player with visual and auditory stimuli. The terminal receives feedback from the server and communicates visual or audible instructions to the player in real time.

[0410] Users, or athletes, train within this virtual reality environment. Based on AI analysis results, they receive real-time feedback and can immediately adjust their tactical actions. For example, if the AI ​​determines that a particular area is not adequately covered, the athlete will be instructed to move to that area via their device, thereby improving the quality of their training.

[0411] These components allow the system to comprehensively support the improvement of player performance and, by accumulating training information in a database, contribute to long-term tactical improvement. This information is also used in planning tactics for future training sessions and matches, thus contributing to the improvement of the competitiveness of both players and the team.

[0412] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0413] Step 1:

[0414] The server retrieves past match data and player statistics from the database. Using this data as input, the server executes an algorithm to generate a virtual reality environment, setting up virtual match scenarios and tactical patterns. During this process, data analysis and environment configuration are performed, and the result is output as a virtual reality environment. Specifically, it analyzes the players' past movement patterns and automatically constructs an environment suitable for training.

[0415] Step 2:

[0416] The device captures the user's movements in real time using a headset and motion sensors. Based on the sensor information as input, the device sends movement data to the server. This data records the player's position and movements with high precision and serves as output for further analysis on the server side. Specifically, raw data acquired from the sensors is sent to the server in real time and reflected in the virtual environment, so that the player's movements are instantly reflected in the environment.

[0417] Step 3:

[0418] The server inputs the received movement data into a generating AI model for analysis. Using information about the players' movements, the AI ​​generates specific tactical feedback. The output obtained from this analysis includes suggestions for tactical improvements and player performance indicators. The AI ​​performs evaluation using the prompt "Based on the input movement data, indicate the area the player should cover."

[0419] Step 4:

[0420] The terminal provides players with visual or audio feedback obtained from the server. Specifically, it displays real-time generated improvements on the interface and instructs the players. It manipulates input feedback data and presents it to players as output visual and audio guides. This allows players to immediately modify their tactics and optimize their movements within the virtual environment.

[0421] Step 5:

[0422] The server saves the results of all training sessions to a database. This process records tactical feedback and player responses, which are used as foundational information for future training plans. The saved data contributes to the long-term growth of players and the team, as well as strategic improvement. The input is the training results, and the output is the accumulated dataset. Specifically, the system automatically organizes and saves data after each session, preparing it for use in future training.

[0423] (Application Example 1)

[0424] 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."

[0425] In rehabilitation, it is essential that elderly individuals maintain motivation, safely engage in physical activity, and undergo effective training. However, traditional rehabilitation methods often struggle to maintain user motivation and ensure effective progress, sometimes making it difficult to continue rehabilitation. Therefore, there is a need to address these challenges by providing an environment where elderly individuals can train safely and enjoyably.

[0426] 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.

[0427] In this invention, the server includes means for generating a virtual reality environment and providing mobility training scenarios for the elderly, means for tracking the user's movements in real time within the generated virtual reality environment, and means equipped with an artificial intelligence module for analyzing data during rehabilitation and evaluating the user's physical movements. This enables the elderly to receive real-time feedback and undergo effective training in a safe and enjoyable manner.

[0428] A "virtual reality environment" is a computer-generated three-dimensional space, an artificial environment in which users can immerse themselves and interact.

[0429] A "mobility training scenario" is a series of virtual reality settings and exercises designed to facilitate movement tailored to specific rehabilitation objectives, allowing users to move their bodies.

[0430] "Means of real-time tracking" refers to technologies and devices that instantly detect and record a user's actions without any delay.

[0431] An "artificial intelligence module" is a computer program that analyzes acquired data to accurately evaluate the physical movements of elderly people, and it utilizes machine learning and data processing technologies.

[0432] "Feedback" is the process of providing real-time evaluations and guidance on actions performed by users, and communicating information to improve the effectiveness of training.

[0433] This invention realizes a rehabilitation training system for the elderly that utilizes a virtual reality environment.

[0434] The server possesses powerful computing capabilities, using NVIDIA GPUs for real-time data processing to generate a virtual reality environment. This virtual reality environment is designed to create mobility training scenarios that allow elderly users to safely move their bodies. Unity is selectively used to construct the virtual three-dimensional space.

[0435] The device tracks the user's movements in real time using a head-mounted display (e.g., Oculus Quest) and motion sensors (e.g., Kinect) worn by the user, and transmits that data to a server. The device also functions as an interface that provides audio and visual feedback, instantly displaying an evaluation of the user's actions.

[0436] Elderly users receive real-time feedback analyzed by a generative AI model during training in a virtual reality space, allowing them to immediately see areas for improvement and recommended actions. This enables users to progress through their training effectively.

[0437] As a concrete example, the system could monitor the user's walking pattern, and if their footing is unstable, the device could provide feedback such as, "Try walking slowly and with wider strides."

[0438] An example of a prompt to input into the generating AI model is: "When a woman in her 70s is walking while trying to maintain her balance, what feedback should be given if her foot movements are slightly unstable?"

[0439] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0440] Step 1:

[0441] The server generates a virtual reality space based on past rehabilitation data and user information. The input is rehabilitation information stored in a database, and the output is the virtual reality scenario the user will experience. The generated space simulates the actions the user would actually need to perform.

[0442] Step 2:

[0443] The device collects data from the user's head-mounted display and motion sensors, obtaining motion information in real time. Input is motion data from the sensors, and output is the transmission of motion information to the server. During this process, the user's movements are tracked and accurately recorded in 3D space.

[0444] Step 3:

[0445] The server analyzes the received behavioral information using a generating AI model and evaluates the user's current behavioral state. The input is behavioral data sent from the terminal, and the output is the evaluation result of that behavior. The AI ​​model generates suggestions for improvement and feedback as needed.

[0446] Step 4:

[0447] The terminal receives analysis results from the server and provides visual and auditory feedback to the user. The input is the evaluation results sent from the server, and the output is the feedback information presented to the user. For example, if the user loses their balance, specific instructions such as "Try walking slowly and with wider strides" will be given.

[0448] Step 5:

[0449] The user adjusts their actions based on feedback and continues training within the virtual environment. The input is feedback information, and the output is the adjusted actions. This allows the user to perform rehabilitation safely and efficiently.

[0450] 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.

[0451] This invention combines a tactical training system for athletes that utilizes a virtual reality environment and artificial intelligence with an emotion engine that recognizes user emotions. This system is implemented through communication between a server, a terminal, and the user. Its main functions and operation are described below.

[0452] First, the server manages the player's past match data and performance records, and sets up individual training scenarios based on this. Furthermore, the server implements an emotion engine, which processes data to recognize the user's emotional state. The emotion engine uses algorithms that recognize emotions from facial expressions, voice, and actions to assess the user's current psychological state.

[0453] Next, the device collects data in real time through the headset and motion sensors worn by the user. The device sends this data to a server and simultaneously adjusts the virtual reality environment based on instructions from the server. In particular, it provides a flexible training environment tailored to each athlete by adjusting the difficulty of the training, the content of the feedback, and the information presented according to the user's emotional state.

[0454] Furthermore, users receive emotion-based feedback and use that information to improve their gameplay in virtual reality. For example, if the emotion engine determines that a user is feeling anxious or stressed, adjustments will be made, such as sending messages to encourage relaxation through the device or switching to a session with a temporarily lowered difficulty level.

[0455] This entire system stores training results and emotion recognition data in a database, which can be used for long-term tactical evaluation and psychological support. The data can also be applied to the formulation of future training programs and the development of mental training plans for athletes. In this way, the present invention is a system that comprehensively supports performance improvement and psychological stability through adaptive training tailored to the individual circumstances of each athlete.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The server retrieves past performance data and emotional records of players from a database. Based on this, it builds a training scenario tailored to each player and generates a virtual reality environment. The emotion engine also performs initial setup for recognizing the players' emotions.

[0459] Step 2:

[0460] The user (athlete or coach) connects to the terminal and wears a dedicated headset and motion sensors. This device captures the athlete's movements and voice in real time and prepares to send the data to the server.

[0461] Step 3:

[0462] The server tracks the player's movements within the virtual reality environment based on raw data transmitted from the terminal and analyzes the user's emotional state using an emotion engine. This involves a comprehensive emotional assessment combining facial expression analysis, voice tone analysis, heart rate, and other factors.

[0463] Step 4:

[0464] The device provides appropriate feedback to the user based on the analysis of movement and emotions received from the server. This feedback is provided as easily understandable visual and auditory information, dynamically adjusting the training content. For example, if the user is in a high-stress state, it may recommend simple skill practice.

[0465] Step 5:

[0466] Users adjust their actions within the virtual reality environment based on feedback. Real-time emotional feedback is used to maintain mental stability while playing and to train more efficiently.

[0467] Step 6:

[0468] At the end of each training session, the server saves user performance and emotional state data to a database, which is then used to improve future sessions. This data is used to continuously improve both the tactical proficiency and psychological state of the players.

[0469] (Example 2)

[0470] 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."

[0471] In modern sports training, strengthening not only an athlete's physical abilities but also their mental capabilities is crucial. However, traditional methods fail to adequately understand an athlete's psychological state and adjust training accordingly. Furthermore, the lack of feedback that takes an athlete's emotions into consideration has made it difficult to optimize individual performance.

[0472] 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.

[0473] In this invention, the server includes means for generating a virtual reality environment and providing a method for training an athlete, means for analyzing the athlete's emotional state using an emotion recognition engine, and means for adjusting the training scenario based on the emotional state. This enables training tailored to the athlete's physical and psychological characteristics.

[0474] A "virtual reality environment" is a digital space created using computer technology that allows users to immerse themselves in an environment different from physical reality.

[0475] An "athlete" refers to an individual who engages in sports or fitness activities, aiming for training or performance improvement.

[0476] A "training method" is a protocol of a set of activities or exercises designed to improve an athlete's performance.

[0477] An "intelligent module" is an artificial intelligence component that evaluates the movements of athletes and opponents through data analysis, and helps optimize training methods and match strategies.

[0478] An "emotion recognition engine" is an algorithm that analyzes a person's emotional state from their facial expressions, voice, and movements, and provides psychological feedback.

[0479] A "database" is a system for efficiently storing, managing, and retrieving structured information.

[0480] "Feedback" refers to the information and advice that athletes receive to improve their performance.

[0481] "Means of providing information through sound and visual means" refers to methods of giving instructions and feedback to an exerciser through audio messages and visual displays.

[0482] This invention is a training system for athletes that combines a virtual reality environment with artificial intelligence technology. The system consists of a server, terminals, and athletes.

[0483] The server uses an intelligent module to analyze the athlete's past performance data and provide individualized training methods. This module incorporates a generative AI model that determines the athlete's psychological status based on prompt messages. For example, it can understand a context in which the athlete is tense and provide measures to help them relax. It also features an emotion recognition engine that analyzes the athlete's emotions from their facial expressions, voice, and movements, and adjusts the scenario according to their emotional state.

[0484] The device collects movement and physiological data in real time through a headset and motion sensors worn by the user. This data is transmitted to a server using a high-speed and secure protocol, and feedback is received from the server to adjust the virtual reality environment and visual and auditory feedback.

[0485] The user (exerciser) engages in training in a virtual reality environment based on feedback provided through their device. For example, if the server determines that the exerciser is feeling stressed, a message such as "Calm down, breathe deeply, and continue practicing with self-confidence" will be displayed through the device.

[0486] The accumulated training results and emotion recognition data are stored in the server's database and used to optimize future training plans and for the long-term mental training of the athletes.

[0487] As an example of a prompt, the AI ​​model is provided with instructions such as, "Display a message encouraging relaxation to users who are showing signs of tension."

[0488] Overall, this system provides flexible and effective training tailored to the individual needs of athletes, comprehensively supporting performance improvement and psychological stability.

[0489] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0490] Step 1:

[0491] The terminal collects motion data, facial expression data, and voice data from the user in real time through a headset and motion sensors worn by the athlete. This data is fundamental information for real-time analysis of the athlete's movements and psychological state. The collected data is immediately converted into a digital format and transmitted to the server.

[0492] Step 2:

[0493] The server receives raw data from the terminal as input and first performs data preprocessing. This process involves noise reduction and signal standardization to prepare the data for analysis. The prepared data is then analyzed through an emotion recognition engine, and the emotional state of the person performing the action is extracted as output. Additionally, a generative AI model is used to create prompt messages that generate the optimal training scenario for that moment.

[0494] Step 3:

[0495] The server adjusts the training scenario within the virtual reality environment based on the extracted emotional state and generated prompt messages. During this process, the training content is modified using feedback from the generating AI model to reduce the psychological burden on the user. For example, if the server determines that the user is under stress, it generates instructions to lower the game difficulty. This result is then sent to the device as instructions.

[0496] Step 4:

[0497] The terminal updates the virtual reality environment in real time based on output from the server. Specifically, it provides the user with tailored feedback through visual information and audio messages. This allows the user to focus on training while addressing their tasks within the presented environment and receiving psychological support.

[0498] Step 5:

[0499] Users receive feedback from their devices and adjust their performance based on it. For example, they might receive feedback such as "Try to relax a bit," and then attempt to change their actual movements or mindset. These user responses are further collected by the device and used as data in the next cycle.

[0500] Step 6:

[0501] The server stores all collected data and output in a database after each training session. This data is used to improve future training sessions and track the psychological and physical growth of the athletes. This allows for the long-term development of training strategies best suited to each individual athlete.

[0502] (Application Example 2)

[0503] 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."

[0504] In modern society, athletes are required to acquire tactics more effectively and efficiently, while simultaneously managing their mental state. However, traditional training methods have made it difficult to grasp athletes' emotional states in real time and provide appropriate feedback based on that information. Therefore, there is a need to develop a system that reduces the psychological anxiety and stress that athletes experience, allowing them to perform at their best.

[0505] 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.

[0506] In this invention, the server includes means for generating a virtual reality environment and providing training scenarios for athletes, means for tracking the athlete's movements in real time within the generated virtual reality environment, and emotion analysis means for recognizing the user's emotional state using facial expressions, voice, and body movements. This makes it possible to grasp the athlete's emotional state in real time during training in virtual reality and provide tactical feedback accordingly immediately.

[0507] A "virtual reality environment" is a computer-generated, synthetic space that users can experience visually and aurally.

[0508] A "training scenario" refers to a specific training flow and set of tasks designed to improve the skills of athletes.

[0509] The "artificial intelligence module" is a program that uses machine learning and data analysis to evaluate performance from in-game data and support tactical decision-making.

[0510] "Emotional analysis means" refers to a technical device or software that evaluates a user's emotional state using data on facial expressions, voice, and body movements.

[0511] "Tactical feedback" is coaching information that takes into account a player's performance and emotional state, and points out areas for improvement in strategy and play during a match.

[0512] This invention provides a system that allows athletes to receive individualized training in a virtual reality environment. This system consists of a server, terminals, and users. The roles of each component and the overall operation of the system are described below.

[0513] The server manages players' past match data and performance records, and generates training scenarios tailored to each individual player based on this data. In this process, algorithms that recognize emotions from facial expressions, voice, and body movements are used as means of emotion analysis. Specifically, OpenCV is used for facial expression recognition, and the Google Speech-to-Text API is used for voice analysis, with the data stored in Firebase.

[0514] The device displays a virtual reality environment in real time via a headset and motion sensors worn by the user. Furthermore, it provides immediate feedback tailored to the user's emotional state, encouraging improvements in actions and tactics. This feedback is communicated to the user as both audio and visual information.

[0515] Users utilize feedback received from their devices to improve their gameplay within virtual reality. For example, if emotion analysis determines that a user is in a state of tension, the system will display a message encouraging relaxation and temporarily adjust the difficulty of the training to promote the user's psychological well-being.

[0516] For example, by using the prompt "The user appears to be stressed. Please suggest ways to help them relax," the AI ​​model can generate and provide practical advice such as "Take two deep breaths and listen to some light music." In this way, the system flexibly responds to the athlete's condition, enabling efficient training while maintaining psychological stability.

[0517] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0518] Step 1:

[0519] The server retrieves players' past match data and performance records from a database. The input data consists of player performance metrics and match results, which are used to generate training scenarios. Machine learning models are used as data analysis techniques to create individual training plans.

[0520] Step 2:

[0521] The device collects data in real time through a headset and motion sensors worn by the user. The input consists of user movement data and environmental information, which is then transmitted to a server. Based on the data from the sensors, a virtual reality environment is generated and presented to the user.

[0522] Step 3:

[0523] The server receives data sent from the user and analyzes facial expressions, voice, and actions using emotion analysis tools. The input data consists of camera footage and audio recordings, which are analyzed to identify the user's emotional state. The current emotional state is then evaluated through processing using OpenCV and the Google Speech-to-Text API.

[0524] Step 4:

[0525] Based on the user's emotional state, the server generates tactical feedback. Prompt statements are input into a generation AI model to construct additional advice and strategies. The generated feedback is a suggestion that takes recent performance data and emotional state into consideration.

[0526] Step 5:

[0527] The device provides the user with feedback sent from the server as audio and visual information. Output consists of messages regarding actionable instructions and relaxation methods. Actions include visual presentations on display devices and guidance from voice assistants.

[0528] Step 6:

[0529] Users modify and improve their gameplay in virtual reality based on feedback provided by their devices. Through implementing this feedback, they can observe changes in their emotional state and gain guidance for the next steps.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] [Fourth Embodiment]

[0534] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0535] 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.

[0536] 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).

[0537] 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.

[0538] 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.

[0539] 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).

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] 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".

[0547] This invention is a tactical training system for athletes that utilizes a virtual reality environment. This system is implemented through data communication between a server, a terminal, and a user. Its main functions and operation are described below.

[0548] First, the server manages the players' past match data and statistics, and uses this to generate a virtual reality environment. Within this environment, specific match scenarios and tactical patterns are set, and players can move freely within them. The virtual environment tracks the players' movements in real time, accumulating data on how they move around the field. Therefore, the server needs to have powerful computing capabilities and be able to process data in real time.

[0549] Next, the terminal uses a headset and motion sensors to capture the movements of the user, i.e., the player, and transmit this information to the server. The terminal functions as a direct user interface, updating the video and audio the player sees and hears in real time. The terminal also provides tactical feedback to the user during the match. This feedback is delivered through the terminal as visual displays and audio messages.

[0550] Furthermore, users can receive real-time analysis results from the AI ​​while actually moving around in the virtual reality environment. For example, if the AI ​​determines that a player is not covering a specific area during a match, the user will be advised to move to that area via their device. In this way, users can learn tactics more efficiently by having their movements in the virtual reality world analyzed in real time and receiving immediate suggestions for improvement.

[0551] Furthermore, this system stores players' training results in a database, which is used to refine and improve tactical plans for future training sessions and matches. This accumulated data can also be used for match analysis and team-wide tactical research, forming an important foundation for long-term player development and strategic planning.

[0552] In this way, the present invention is a system that enables tactical training integrating virtual reality and generative AI, and comprehensively supports performance improvement in sports competitions.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The server retrieves players' past match data and performance records from a database. Based on this, it selects a training scenario suitable for each player and generates a virtual reality environment. The generated environment includes tactical patterns and match scenarios that the player should work on.

[0556] Step 2:

[0557] The user (athlete or coach) connects to the device and enters the virtual reality environment by wearing a headset and motion sensors. Once connected, the device tracks the user's movements in real time and immediately transmits this data to the server.

[0558] Step 3:

[0559] The server updates the virtual reality environment based on the user's movements and adjusts visual and audio information accordingly. It also collects player movements and analyzes them in real time using an AI data analysis module. This is to evaluate the accuracy of the players' positioning and movements.

[0560] Step 4:

[0561] The terminal provides tactical feedback to the user based on analysis results received from the server. This feedback is given as visual guides and audio messages indicating areas for improvement and the next actions to take.

[0562] Step 5:

[0563] Users adjust their gameplay in the virtual reality environment based on feedback from their devices and continuously strive to improve their tactics. They leverage real-time feedback to develop tactical decision-making skills and enhance their practical abilities.

[0564] Step 6:

[0565] The server saves user performance data to a database at the end of a virtual reality session and uses this data to create materials for the next training session or tactical planning. This provides strategic support that contributes to long-term performance improvement.

[0566] (Example 1)

[0567] 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".

[0568] Traditional sports training systems have faced challenges in tracking athletes' behavior in detail and in real time, and providing immediate tactical feedback based on information gathered during matches. Furthermore, effectively utilizing past performance and data, and adaptively adjusting the virtual environment, has been technically difficult. This results in a lack of sufficient support for effectively improving athlete performance and appropriately refining training tactics for subsequent sessions.

[0569] 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.

[0570] In this invention, the server includes means for generating a virtual reality environment and providing training settings for athletes; means for immediately tracking the behavior of participants within the generated virtual reality environment; means equipped with an artificial intelligence device that analyzes information during a match and evaluates the behavior of the players or opposing teams; means for adaptively modifying the virtual reality environment based on the athletes' past performance information; means for acquiring data in the virtual reality environment and performing real-time analysis of the athletes in response based on a generating AI model; means for performing evaluations using prompt sentences and immediately presenting tactical improvement proposals using artificial intelligence; and means for improving training tactics for the next session through the accumulation of training information. This enables real-time performance improvement of athletes and adaptive tactical improvements.

[0571] A "virtual reality environment" is a computer-aided, artificial environment that allows users to immerse themselves in a digital space different from reality.

[0572] An "athlete" is a person who participates in sports competitions and improves their physical and mental abilities through training and matches.

[0573] A "training setting" is a scenario or program designed to help athletes acquire and improve specific skills or tactics.

[0574] "Instant tracking" is a technology that instantly monitors user activity and behavior and processes that information in real time.

[0575] An "artificial intelligence device" is a computer program or system designed to analyze data and make decisions, and it is intended to mimic the workings of human intelligence.

[0576] A "generative AI model" is an algorithm or computer program that has the ability to self-learn based on large amounts of data and generate new information or results.

[0577] A "prompt statement" is an input statement given to a generative AI model to instruct it to perform a specific task.

[0578] "Accumulating training information" refers to the process of managing data so that athletes can save data from past training and competitions and use it for future growth and tactical improvement.

[0579] This invention is a system that utilizes a virtual reality environment designed to enhance the training of athletes. The embodiments of the system are described in detail below.

[0580] The server possesses powerful computing capabilities and generates a virtual reality environment. The server retrieves players' past match data and performance from a database and uses this data to set up virtual match scenarios and tactical patterns. Within this virtual environment, players can move freely, and the server tracks their movements in real time using data from motion sensors. The server also has the ability to analyze information during the match using a generated AI model and evaluate player movements. An example of a prompt message in this context might be, "Please suggest which areas of the field the player should focus on covering."

[0581] The terminal is equipped with hardware devices such as a headset and motion sensors to capture the player's movements. This device functions as a user interface, providing the player with visual and auditory stimuli. The terminal receives feedback from the server and communicates visual or audible instructions to the player in real time.

[0582] Users, or athletes, train within this virtual reality environment. Based on AI analysis results, they receive real-time feedback and can immediately adjust their tactical actions. For example, if the AI ​​determines that a particular area is not adequately covered, the athlete will be instructed to move to that area via their device, thereby improving the quality of their training.

[0583] These components allow the system to comprehensively support the improvement of player performance and, by accumulating training information in a database, contribute to long-term tactical improvement. This information is also used in planning tactics for future training sessions and matches, thus contributing to the improvement of the competitiveness of both players and the team.

[0584] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0585] Step 1:

[0586] The server retrieves past match data and player statistics from the database. Using this data as input, the server executes an algorithm to generate a virtual reality environment, setting up virtual match scenarios and tactical patterns. During this process, data analysis and environment configuration are performed, and the result is output as a virtual reality environment. Specifically, it analyzes the players' past movement patterns and automatically constructs an environment suitable for training.

[0587] Step 2:

[0588] The device captures the user's movements in real time using a headset and motion sensors. Based on the sensor information as input, the device sends movement data to the server. This data records the player's position and movements with high precision and serves as output for further analysis on the server side. Specifically, raw data acquired from the sensors is sent to the server in real time and reflected in the virtual environment, so that the player's movements are instantly reflected in the environment.

[0589] Step 3:

[0590] The server inputs the received movement data into a generating AI model for analysis. Using information about the players' movements, the AI ​​generates specific tactical feedback. The output obtained from this analysis includes suggestions for tactical improvements and player performance indicators. The AI ​​performs evaluation using the prompt "Based on the input movement data, indicate the area the player should cover."

[0591] Step 4:

[0592] The terminal provides players with visual or audio feedback obtained from the server. Specifically, it displays real-time generated improvements on the interface and instructs the players. It manipulates input feedback data and presents it to players as output visual and audio guides. This allows players to immediately modify their tactics and optimize their movements within the virtual environment.

[0593] Step 5:

[0594] The server saves the results of all training sessions to a database. This process records tactical feedback and player responses, which are used as foundational information for future training plans. The saved data contributes to the long-term growth of players and the team, as well as strategic improvement. The input is the training results, and the output is the accumulated dataset. Specifically, the system automatically organizes and saves data after each session, preparing it for use in future training.

[0595] (Application Example 1)

[0596] 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".

[0597] In rehabilitation, it is essential that elderly individuals maintain motivation, safely engage in physical activity, and undergo effective training. However, traditional rehabilitation methods often struggle to maintain user motivation and ensure effective progress, sometimes making it difficult to continue rehabilitation. Therefore, there is a need to address these challenges by providing an environment where elderly individuals can train safely and enjoyably.

[0598] 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.

[0599] In this invention, the server includes means for generating a virtual reality environment and providing mobility training scenarios for the elderly, means for tracking the user's movements in real time within the generated virtual reality environment, and means equipped with an artificial intelligence module for analyzing data during rehabilitation and evaluating the user's physical movements. This enables the elderly to receive real-time feedback and undergo effective training in a safe and enjoyable manner.

[0600] A "virtual reality environment" is a computer-generated three-dimensional space, an artificial environment in which users can immerse themselves and interact.

[0601] A "mobility training scenario" is a series of virtual reality settings and exercises designed to facilitate movement tailored to specific rehabilitation objectives, allowing users to move their bodies.

[0602] "Means of real-time tracking" refers to technologies and devices that instantly detect and record a user's actions without any delay.

[0603] An "artificial intelligence module" is a computer program that analyzes acquired data to accurately evaluate the physical movements of elderly people, and it utilizes machine learning and data processing technologies.

[0604] "Feedback" is the process of providing real-time evaluations and guidance on actions performed by users, and communicating information to improve the effectiveness of training.

[0605] This invention realizes a rehabilitation training system for the elderly that utilizes a virtual reality environment.

[0606] The server possesses powerful computing capabilities, using NVIDIA GPUs for real-time data processing to generate a virtual reality environment. This virtual reality environment is designed to create mobility training scenarios that allow elderly users to safely move their bodies. Unity is selectively used to construct the virtual three-dimensional space.

[0607] The device tracks the user's movements in real time using a head-mounted display (e.g., Oculus Quest) and motion sensors (e.g., Kinect) worn by the user, and transmits that data to a server. The device also functions as an interface that provides audio and visual feedback, instantly displaying an evaluation of the user's actions.

[0608] Elderly users receive real-time feedback analyzed by a generative AI model during training in a virtual reality space, allowing them to immediately see areas for improvement and recommended actions. This enables users to progress through their training effectively.

[0609] As a concrete example, the system could monitor the user's walking pattern, and if their footing is unstable, the device could provide feedback such as, "Try walking slowly and with wider strides."

[0610] An example of a prompt to input into the generating AI model is: "When a woman in her 70s is walking while trying to maintain her balance, what feedback should be given if her foot movements are slightly unstable?"

[0611] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0612] Step 1:

[0613] The server generates a virtual reality space based on past rehabilitation data and user information. The input is rehabilitation information stored in a database, and the output is the virtual reality scenario the user will experience. The generated space simulates the actions the user would actually need to perform.

[0614] Step 2:

[0615] The device collects data from the user's head-mounted display and motion sensors, obtaining motion information in real time. Input is motion data from the sensors, and output is the transmission of motion information to the server. During this process, the user's movements are tracked and accurately recorded in 3D space.

[0616] Step 3:

[0617] The server analyzes the received behavioral information using a generating AI model and evaluates the user's current behavioral state. The input is behavioral data sent from the terminal, and the output is the evaluation result of that behavior. The AI ​​model generates suggestions for improvement and feedback as needed.

[0618] Step 4:

[0619] The terminal receives analysis results from the server and provides visual and auditory feedback to the user. The input is the evaluation results sent from the server, and the output is the feedback information presented to the user. For example, if the user loses their balance, specific instructions such as "Try walking slowly and with wider strides" will be given.

[0620] Step 5:

[0621] The user adjusts their actions based on feedback and continues training within the virtual environment. The input is feedback information, and the output is the adjusted actions. This allows the user to perform rehabilitation safely and efficiently.

[0622] 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.

[0623] This invention combines a tactical training system for athletes that utilizes a virtual reality environment and artificial intelligence with an emotion engine that recognizes user emotions. This system is implemented through communication between a server, a terminal, and the user. Its main functions and operation are described below.

[0624] First, the server manages the player's past match data and performance records, and sets up individual training scenarios based on this. Furthermore, the server implements an emotion engine, which processes data to recognize the user's emotional state. The emotion engine uses algorithms that recognize emotions from facial expressions, voice, and actions to assess the user's current psychological state.

[0625] Next, the device collects data in real time through the headset and motion sensors worn by the user. The device sends this data to a server and simultaneously adjusts the virtual reality environment based on instructions from the server. In particular, it provides a flexible training environment tailored to each athlete by adjusting the difficulty of the training, the content of the feedback, and the information presented according to the user's emotional state.

[0626] Furthermore, users receive emotion-based feedback and use that information to improve their gameplay in virtual reality. For example, if the emotion engine determines that a user is feeling anxious or stressed, adjustments will be made, such as sending messages to encourage relaxation through the device or switching to a session with a temporarily lowered difficulty level.

[0627] This entire system stores training results and emotion recognition data in a database, which can be used for long-term tactical evaluation and psychological support. The data can also be applied to the formulation of future training programs and the development of mental training plans for athletes. In this way, the present invention is a system that comprehensively supports performance improvement and psychological stability through adaptive training tailored to the individual circumstances of each athlete.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The server retrieves past performance data and emotional records of players from a database. Based on this, it builds a training scenario tailored to each player and generates a virtual reality environment. The emotion engine also performs initial setup for recognizing the players' emotions.

[0631] Step 2:

[0632] The user (athlete or coach) connects to the terminal and wears a dedicated headset and motion sensors. This device captures the athlete's movements and voice in real time and prepares to send the data to the server.

[0633] Step 3:

[0634] The server tracks the player's movements within the virtual reality environment based on raw data transmitted from the terminal and analyzes the user's emotional state using an emotion engine. This involves a comprehensive emotional assessment combining facial expression analysis, voice tone analysis, heart rate, and other factors.

[0635] Step 4:

[0636] The device provides appropriate feedback to the user based on the analysis of movement and emotions received from the server. This feedback is provided as easily understandable visual and auditory information, dynamically adjusting the training content. For example, if the user is in a high-stress state, it may recommend simple skill practice.

[0637] Step 5:

[0638] Users adjust their actions within the virtual reality environment based on feedback. Real-time emotional feedback is used to maintain mental stability while playing and to train more efficiently.

[0639] Step 6:

[0640] At the end of each training session, the server saves user performance and emotional state data to a database, which is then used to improve future sessions. This data is used to continuously improve both the tactical proficiency and psychological state of the players.

[0641] (Example 2)

[0642] 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".

[0643] In modern sports training, strengthening not only an athlete's physical abilities but also their mental capabilities is crucial. However, traditional methods fail to adequately understand an athlete's psychological state and adjust training accordingly. Furthermore, the lack of feedback that takes an athlete's emotions into consideration has made it difficult to optimize individual performance.

[0644] 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.

[0645] In this invention, the server includes means for generating a virtual reality environment and providing a method for training an athlete, means for analyzing the athlete's emotional state using an emotion recognition engine, and means for adjusting the training scenario based on the emotional state. This enables training tailored to the athlete's physical and psychological characteristics.

[0646] A "virtual reality environment" is a digital space created using computer technology that allows users to immerse themselves in an environment different from physical reality.

[0647] An "athlete" refers to an individual who engages in sports or fitness activities, aiming for training or performance improvement.

[0648] A "training method" is a protocol of a set of activities or exercises designed to improve an athlete's performance.

[0649] An "intelligent module" is an artificial intelligence component that evaluates the movements of athletes and opponents through data analysis, and helps optimize training methods and match strategies.

[0650] An "emotion recognition engine" is an algorithm that analyzes a person's emotional state from their facial expressions, voice, and movements, and provides psychological feedback.

[0651] A "database" is a system for efficiently storing, managing, and retrieving structured information.

[0652] "Feedback" refers to the information and advice that athletes receive to improve their performance.

[0653] "Means of providing information through sound and visual means" refers to methods of giving instructions and feedback to an exerciser through audio messages and visual displays.

[0654] This invention is a training system for athletes that combines a virtual reality environment with artificial intelligence technology. The system consists of a server, terminals, and athletes.

[0655] The server uses an intelligent module to analyze the athlete's past performance data and provide individualized training methods. This module incorporates a generative AI model that determines the athlete's psychological status based on prompt messages. For example, it can understand a context in which the athlete is tense and provide measures to help them relax. It also features an emotion recognition engine that analyzes the athlete's emotions from their facial expressions, voice, and movements, and adjusts the scenario according to their emotional state.

[0656] The device collects movement and physiological data in real time through a headset and motion sensors worn by the user. This data is transmitted to a server using a high-speed and secure protocol, and feedback is received from the server to adjust the virtual reality environment and visual and auditory feedback.

[0657] The user (exerciser) engages in training in a virtual reality environment based on feedback provided through their device. For example, if the server determines that the exerciser is feeling stressed, a message such as "Calm down, breathe deeply, and continue practicing with self-confidence" will be displayed through the device.

[0658] The accumulated training results and emotion recognition data are stored in the server's database and used to optimize future training plans and for the long-term mental training of the athletes.

[0659] As an example of a prompt, the AI ​​model is provided with instructions such as, "Display a message encouraging relaxation to users who are showing signs of tension."

[0660] Overall, this system provides flexible and effective training tailored to the individual needs of athletes, comprehensively supporting performance improvement and psychological stability.

[0661] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0662] Step 1:

[0663] The terminal collects motion data, facial expression data, and voice data from the user in real time through a headset and motion sensors worn by the athlete. This data is fundamental information for real-time analysis of the athlete's movements and psychological state. The collected data is immediately converted into a digital format and transmitted to the server.

[0664] Step 2:

[0665] The server receives raw data from the terminal as input and first performs data preprocessing. This process involves noise reduction and signal standardization to prepare the data for analysis. The prepared data is then analyzed through an emotion recognition engine, and the emotional state of the person performing the action is extracted as output. Additionally, a generative AI model is used to create prompt messages that generate the optimal training scenario for that moment.

[0666] Step 3:

[0667] The server adjusts the training scenario within the virtual reality environment based on the extracted emotional state and generated prompt messages. During this process, the training content is modified using feedback from the generating AI model to reduce the psychological burden on the user. For example, if the server determines that the user is under stress, it generates instructions to lower the game difficulty. This result is then sent to the device as instructions.

[0668] Step 4:

[0669] The terminal updates the virtual reality environment in real time based on output from the server. Specifically, it provides the user with tailored feedback through visual information and audio messages. This allows the user to focus on training while addressing their tasks within the presented environment and receiving psychological support.

[0670] Step 5:

[0671] Users receive feedback from their devices and adjust their performance based on it. For example, they might receive feedback such as "Try to relax a bit," and then attempt to change their actual movements or mindset. These user responses are further collected by the device and used as data in the next cycle.

[0672] Step 6:

[0673] The server stores all collected data and output in a database after each training session. This data is used to improve future training sessions and track the psychological and physical growth of the athletes. This allows for the long-term development of training strategies best suited to each individual athlete.

[0674] (Application Example 2)

[0675] 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".

[0676] In modern society, athletes are required to acquire tactics more effectively and efficiently, while simultaneously managing their mental state. However, traditional training methods have made it difficult to grasp athletes' emotional states in real time and provide appropriate feedback based on that information. Therefore, there is a need to develop a system that reduces the psychological anxiety and stress that athletes experience, allowing them to perform at their best.

[0677] 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.

[0678] In this invention, the server includes means for generating a virtual reality environment and providing training scenarios for athletes, means for tracking the athlete's movements in real time within the generated virtual reality environment, and emotion analysis means for recognizing the user's emotional state using facial expressions, voice, and body movements. This makes it possible to grasp the athlete's emotional state in real time during training in virtual reality and provide tactical feedback accordingly immediately.

[0679] A "virtual reality environment" is a computer-generated, synthetic space that users can experience visually and aurally.

[0680] A "training scenario" refers to a specific training flow and set of tasks designed to improve the skills of athletes.

[0681] The "artificial intelligence module" is a program that uses machine learning and data analysis to evaluate performance from in-game data and support tactical decision-making.

[0682] "Emotional analysis means" refers to a technical device or software that evaluates a user's emotional state using data on facial expressions, voice, and body movements.

[0683] "Tactical feedback" is coaching information that takes into account a player's performance and emotional state, and points out areas for improvement in strategy and play during a match.

[0684] This invention provides a system that allows athletes to receive individualized training in a virtual reality environment. This system consists of a server, terminals, and users. The roles of each component and the overall operation of the system are described below.

[0685] The server manages players' past match data and performance records, and generates training scenarios tailored to each individual player based on this data. In this process, algorithms that recognize emotions from facial expressions, voice, and body movements are used as means of emotion analysis. Specifically, OpenCV is used for facial expression recognition, and the Google Speech-to-Text API is used for voice analysis, with the data stored in Firebase.

[0686] The device displays a virtual reality environment in real time via a headset and motion sensors worn by the user. Furthermore, it provides immediate feedback tailored to the user's emotional state, encouraging improvements in actions and tactics. This feedback is communicated to the user as both audio and visual information.

[0687] Users utilize feedback received from their devices to improve their gameplay within virtual reality. For example, if emotion analysis determines that a user is in a state of tension, the system will display a message encouraging relaxation and temporarily adjust the difficulty of the training to promote the user's psychological well-being.

[0688] For example, by using the prompt "The user appears to be stressed. Please suggest ways to help them relax," the AI ​​model can generate and provide practical advice such as "Take two deep breaths and listen to some light music." In this way, the system flexibly responds to the athlete's condition, enabling efficient training while maintaining psychological stability.

[0689] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0690] Step 1:

[0691] The server retrieves players' past match data and performance records from a database. The input data consists of player performance metrics and match results, which are used to generate training scenarios. Machine learning models are used as data analysis techniques to create individual training plans.

[0692] Step 2:

[0693] The device collects data in real time through a headset and motion sensors worn by the user. The input consists of user movement data and environmental information, which is then transmitted to a server. Based on the data from the sensors, a virtual reality environment is generated and presented to the user.

[0694] Step 3:

[0695] The server receives data sent from the user and analyzes facial expressions, voice, and actions using emotion analysis tools. The input data consists of camera footage and audio recordings, which are analyzed to identify the user's emotional state. The current emotional state is then evaluated through processing using OpenCV and the Google Speech-to-Text API.

[0696] Step 4:

[0697] Based on the user's emotional state, the server generates tactical feedback. Prompt statements are input into a generation AI model to construct additional advice and strategies. The generated feedback is a suggestion that takes recent performance data and emotional state into consideration.

[0698] Step 5:

[0699] The device provides the user with feedback sent from the server as audio and visual information. Output consists of messages regarding actionable instructions and relaxation methods. Actions include visual presentations on display devices and guidance from voice assistants.

[0700] Step 6:

[0701] Users modify and improve their gameplay in virtual reality based on feedback provided by their devices. Through implementing this feedback, they can observe changes in their emotional state and gain guidance for the next steps.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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."

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0723] The following is further disclosed regarding the embodiments described above.

[0724] (Claim 1)

[0725] A means for generating a virtual reality environment and providing training scenarios for athletes,

[0726] A means of tracking the movements of players in real time within a generated virtual reality environment,

[0727] A means equipped with an artificial intelligence module that analyzes data during a match and evaluates the movements of players or the opposing team,

[0728] A means of providing immediate tactical feedback based on analysis results during a match,

[0729] A system that includes this.

[0730] (Claim 2)

[0731] The system according to claim 1, comprising means for accumulating training results in a virtual reality environment in a database and reflecting them in future training.

[0732] (Claim 3)

[0733] The system according to claim 1, comprising means for providing real-time generated tactical feedback to a player or coach audibly and visually.

[0734] "Example 1"

[0735] (Claim 1)

[0736] A means for generating a virtual reality environment and providing training settings for athletes,

[0737] A means of instantly tracking participants' behavior within the generated virtual reality environment,

[0738] A means equipped with an artificial intelligence device that analyzes information during a match and evaluates the behavior of players or the opposing team,

[0739] A means of providing immediate tactical feedback based on analysis results during a match,

[0740] A means of adaptively modifying a virtual reality environment based on the past performance information of athletes,

[0741] A means for acquiring data in a virtual reality environment and performing real-time analysis of athletes in response to that data based on a generative AI model,

[0742] A means of using prompt statements to perform evaluations with artificial intelligence and immediately presenting tactical improvement proposals,

[0743] A means of improving next training tactics through the accumulation of training information,

[0744] A system that includes this.

[0745] (Claim 2)

[0746] The system according to claim 1, comprising means for accumulating training results in a virtual reality environment in an information management system and reflecting them in future training.

[0747] (Claim 3)

[0748] The system according to claim 1, comprising means for providing athletes or coaches with real-time generated tactical feedback audibly and visually.

[0749] "Application Example 1"

[0750] (Claim 1)

[0751] A means for generating a virtual reality environment and providing mobility training scenarios for the elderly,

[0752] A means of tracking the user's actions in real time within the generated virtual reality environment,

[0753] A means equipped with an artificial intelligence module that analyzes data during rehabilitation and evaluates the user's physical movements,

[0754] A means of providing immediate training feedback based on analysis results during rehabilitation,

[0755] A system that includes this.

[0756] (Claim 2)

[0757] The system according to claim 1, comprising means for accumulating training results in a virtual reality environment in an information base and reflecting them in future training.

[0758] (Claim 3)

[0759] The system according to claim 1, comprising means for providing real-time generated training feedback to a user or instructor audibly and visually.

[0760] "Example 2 of combining an emotion engine"

[0761] (Claim 1)

[0762] A means for generating a virtual reality environment and providing a method for training athletes,

[0763] A means of tracking the movements of an athlete in real time within a generated virtual reality environment,

[0764] A means equipped with an intelligent module that analyzes data during a match and evaluates the actions of athletes or opponents,

[0765] A means of providing immediate tactical feedback based on analysis results,

[0766] A means of analyzing the emotional state of a person using an emotion recognition engine,

[0767] A means of adjusting the training scenario based on emotional state,

[0768] Means for presenting the generated feedback to the person performing the action audibly and visually,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, comprising means for storing training results and emotion recognition data in a virtual reality environment in a database and reflecting them in subsequent training sessions.

[0772] (Claim 3)

[0773] The system according to claim 1, comprising means for providing real-time generated tactical feedback via audio and visual means.

[0774] "Application example 2 when combining with an emotional engine"

[0775] (Claim 1)

[0776] A means for generating a virtual reality environment and providing training scenarios for athletes,

[0777] A means of tracking the movements of players in real time within a generated virtual reality environment,

[0778] A means equipped with an artificial intelligence module that analyzes data during a match and evaluates the movements of players or the opposing team,

[0779] An emotion analysis method that recognizes the user's emotional state using facial expressions, voice, and body movements,

[0780] A means of providing immediate tactical feedback during a match based on analysis results and emotional state,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, comprising means for storing training results and emotion recognition data in a virtual reality environment in a database and reflecting them in future training.

[0784] (Claim 3)

[0785] The system according to claim 1, further comprising means for providing real-time generated tactical feedback to a player or coach audibly and visually, and for further enhancing adaptive feedback based on emotional state. [Explanation of Symbols]

[0786] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating a virtual reality environment and providing training scenarios for athletes, A means of tracking the movements of players in real time within a generated virtual reality environment, A means equipped with an artificial intelligence module that analyzes data during a match and evaluates the movements of players or the opposing team, A means of providing immediate tactical feedback based on analysis results during a match, A system that includes this.

2. The system according to claim 1, comprising means for accumulating training results in a virtual reality environment in a database and reflecting them in future training.

3. The system according to claim 1, comprising means for providing real-time generated tactical feedback to a player or coach audibly and visually.

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