system

Abstract

We provide the system. [Solution] Means for acquiring video data, A means for analyzing the aforementioned video data and converting the motion characteristics into data, A means for comparing and analyzing professional competition data with the aforementioned movement characteristics, A means for generating a strategic model based on the results of comparative analysis, A means for generating a virtual reality environment using the aforementioned strategic model and presenting it to the user, A means of receiving user feedback and reflecting it in the strategic model again, 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional training methods for competitors, although the improvement of technical skills can be achieved to a certain extent, there is a problem that sufficient support cannot be provided for strategic planning and game operation skills in actual games. In particular, the opportunity to learn from the game operation of professional competitors is limited, and it is difficult to find a strategic approach suitable for the playing style of individual competitors. Therefore, an effective method for competitors to efficiently learn strategies and apply them in practice is required.

Means for Solving the Problems

[0005] This invention provides a system that acquires video data of athletes' matches and training sessions, analyzes this data to digitize the athletes' motor characteristics. Furthermore, it compares and analyzes this data with professional competition data to generate a strategic model optimized for each individual athlete. By presenting a virtual reality environment based on this strategic model to the user, athletes can receive real-time strategic guidance. In addition, feedback from the user can be received and reflected in the strategic model to achieve continuous improvement. In this way, it is possible to effectively improve the skills of individual athletes in managing matches in a way that suits their playing style.

[0006] "Video data" refers to digital data in image and video format that records the actions of athletes during matches or training.

[0007] "Motor characteristics" refer to data that numerically or qualitatively expresses features related to the physical movements of an athlete in actual competition, such as their movements, shot timing, and positioning.

[0008] "Professional competition data" refers to performance data recorded in matches involving professional-level athletes, and is used as a standard for strategies and techniques.

[0009] "Comparative analysis" is a process of comparing an athlete's athletic characteristics with professional competition data to clarify similarities and differences, and to identify the athlete's strengths and areas for improvement.

[0010] A "strategic model" is a theoretical framework or guideline generated based on a player's playing style to optimize game management and tactics.

[0011] A "virtual reality environment" is a digital space that simulates real-world match situations, allowing competitors to simulate matches, experience strategies, and learn from them.

[0012] "Feedback" refers to information that competitors provide to the system as input, based on insights and data gained from their experiences in the virtual reality environment and from actual matches.

[0013] "Game strategy" refers to a series of actions related to strategic movements and decisions during a competition, and is a planned approach by competitors to earn points. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention relates to a system for assisting athletes in learning how to play matches. This system is characterized by utilizing video data of athletes' matches and training sessions and providing a strategic approach based on the analysis results.

[0036] First, the user records video of their match or practice session. The recorded video data is uploaded to the system. Next, the server receives the video data, analyzes the video using a motion analysis algorithm, and extracts the user's movement characteristics.

[0037] The server identifies a player's strengths and areas for improvement by comparing their athletic characteristics with a professional competition database. Comparative analysis generates a customized strategy model for the user, drawing insights from professional gameplay. This strategy model is tailored to the user's own playing style and skill level.

[0038] The generated strategy model is presented to the user via a device. The device creates a virtual reality environment, which the user experiences using VR goggles. In the virtual reality environment, the user can learn strategies in real time through match simulations. Furthermore, the device provides voice instructions through earphones, offering appropriate advice and strategic suggestions based on the user's actions.

[0039] Users apply the strategies and insights they learn through the system to their own gameplay and then provide feedback to the system. The server receives this feedback, further improves the strategy model, and uses it to improve future training sessions.

[0040] For example, if a user wants to improve their serve success rate, the system will refer to data on professional serves and compare it to the user's serve. Based on the identified issues, the system will generate personalized improvement advice and present it as a usable strategy. In this way, users can learn from professionals while simultaneously achieving their own growth.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users record their matches and practice sessions with a camera and upload the video data to the system. Users can select specific matches or scenes and set the focus as needed.

[0044] Step 2:

[0045] The server analyzes the uploaded video data. It applies motion analysis algorithms to extract the user's movement characteristics, such as shot success rate, movement patterns, and positioning. This data is then structured for further analysis.

[0046] Step 3:

[0047] The server references a professional sports database and performs a comparative analysis of the user's athletic characteristics. This identifies the user's strengths and areas for improvement, and generates a detailed report based on the comparison results.

[0048] Step 4:

[0049] Based on the analysis results, the server generates a strategic model optimized for the user. This model is designed to serve as a guideline for gameplay and game improvement tailored to the user's play style.

[0050] Step 5:

[0051] The terminal receives a strategic model supplied from the server, adjusts it for the virtual reality environment, and generates VR content.

[0052] Step 6:

[0053] Users wear VR goggles and experience a virtual reality environment set up by their device. They progress through the simulation based on strategic instructions provided during the gameplay.

[0054] Step 7:

[0055] The device provides users with real-time audio feedback and instructions through earphones during practice, allowing them to make immediate corrections and improvements.

[0056] Step 8:

[0057] Users provide feedback to the system based on their experiences in virtual reality environments and actual matches. This feedback is used to improve future strategic models.

[0058] Step 9:

[0059] Based on user feedback, the server initiates a process to further improve its strategic model, thereby enhancing the overall effectiveness of the system.

[0060] (Example 1)

[0061] 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."

[0062] Traditional systems supporting athletes have made it difficult to obtain strategic advice based on individual athletic abilities. Furthermore, there has been a lack of means to provide real-time feedback tailored to specific competition scenarios, and the lack of training environments utilizing virtual reality has been a particular problem.

[0063] 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.

[0064] In this invention, the server includes means for analyzing video information, means for comparing and examining operational characteristics, and means for generating a strategic model. This enables the provision of strategic advice based on the user's operational characteristics. Furthermore, it enables real-time feedback and concrete training utilizing a virtual reality environment.

[0065] "Video information" refers to visual data that records the matches and training sessions of athletes.

[0066] A "device" is a hardware or software component used to perform a specific function.

[0067] "Motion characteristics" refer to quantified characteristic information about an athlete's physical movements, such as speed, angle, and positioning.

[0068] "Expert competition information" refers to data obtained from matches and training sessions of professional athletes.

[0069] "Comparing and examining" refers to the process of comparing extracted performance characteristics with expert competition data to identify key features and areas for improvement.

[0070] A "strategic model" is a data model created based on analysis results, which includes tactics that enable athletes to effectively play a match.

[0071] A "virtual reality space" is a computer-generated 3D environment in which competitors can experience realistic competition situations through simulation.

[0072] "Feedback" refers to evaluations and reaction information obtained after a competitor's actual play or simulation.

[0073] A "central device" refers to computing resources such as servers and cloud systems used for centralized data management.

[0074] An "information recording device" is a storage system for saving and managing digital data.

[0075] This invention relates to a system that provides athletes with strategic models by utilizing video information from individual matches and practice sessions to improve their athletic skills. Users film their matches and practice sessions with a video camera or smartphone and upload this video information to a server via a dedicated application. The server uses Python and the OpenCV library to analyze the video information and quantify motion characteristics such as speed, angle, and positioning.

[0076] Next, the server compares these quantified performance characteristics with expert competition data. Using machine learning algorithms powered by Scikit-learn, it identifies the user's strengths and areas for improvement. Based on the results of this comparison, the server leverages a generative AI model to create a strategy model and build a strategy customized to the user's play style and skill level.

[0077] This strategic model is provided to the user via a device. The device generates a virtual reality space using Unity and other virtual reality technologies, allowing the user to experience a realistic competition simulation through VR goggles. Furthermore, the device provides voice instructions through earphones, offering immediate guidance and advice based on the user's actions.

[0078] For example, if a user aims to improve their tennis serve, the system can compare their serve to that of professional players and provide customized advice on specific areas for improvement, such as "strengthen weight transfer during the serve."

[0079] An example of a prompt is as follows: "To improve your serve success rate in your next tennis match, compare your motion characteristics to those of a professional player and suggest areas for improvement and specific strategies."

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users record their matches or practice sessions using video cameras or smartphones. The recorded video data is uploaded to a server using a dedicated application. It is crucial that the video clearly captures the intended match scenes and actions. The input is video data, and the output is the video information stored on the server.

[0083] Step 2:

[0084] The server analyzes the received video information using Python and the OpenCV library. Specifically, it divides the video into frames and analyzes and quantifies motion characteristics such as speed, angle, and positioning. The input for this step is the video information stored on the server, and the output is the analyzed quantified data. Based on the analysis results, the user's motion characteristics are extracted in detail.

[0085] Step 3:

[0086] The server compares the analyzed performance characteristics with expert competition data. This comparison uses a machine learning algorithm based on Scikit-learn to identify similarities and differences in performance characteristics. The input for this step is the analyzed numerical data and expert competition data, and the output is the result of the comparison.

[0087] Step 4:

[0088] The server generates a strategy model using a generative AI model based on the results of the comparative analysis. This strategy model includes customized tactics tailored to the user's play style and skill level. The input for this step is the results of the comparative analysis, and the output is the generated strategy model.

[0089] Step 5:

[0090] The terminal constructs a virtual reality space based on the generated strategy model. Using tools like Unity, it creates a VR environment that users can experience using VR goggles. The input for this step is the generated strategy model, and the output is the construction of the virtual reality space.

[0091] Step 6:

[0092] The user learns strategies through simulations in a virtual reality space. The device provides voice instructions via earphones and sends real-time feedback that matches the user's actions. The inputs in this step are the virtual reality space and real-time user actions, and the outputs are instructions and feedback.

[0093] Step 7:

[0094] After completing the simulation, users put the strategies they learned into practice during actual training or matches and provide feedback to the server through the app. The server uses this feedback to further improve the strategy model and use it for future training. The input for this step is the user's feedback, and the output is the improved strategy model.

[0095] (Application Example 1)

[0096] 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."

[0097] Traditional methods for improving driving skills have made it difficult for drivers to receive real-time, specific instruction on efficient and safe driving techniques. This has resulted in a lack of smooth progress in improving driving skills. There is a need to provide drivers with an environment where they can receive customized training based on their own driving data.

[0098] 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.

[0099] In this invention, the server includes means for acquiring video information, means for analyzing the video information and digitizing motion characteristics, and means for comparing and analyzing the motion characteristics with expert motion information. This enables drivers to receive real-time guidance in a virtual environment based on their own driving data and learn a safe and effective driving style.

[0100] "Video information" refers to video data that records the driver's driving conditions.

[0101] "Driving characteristics" refer to digitized data that shows the driver's driving style and movement characteristics.

[0102] "Expert driving information" refers to information stored in a database that records the driving styles of professional drivers.

[0103] "Comparative analysis" is a process that compares the driver's motor characteristics with expert information to identify strengths and areas for improvement.

[0104] A "strategic model" is a training program created based on comparative analysis results with the aim of improving drivers' skills.

[0105] A "virtual environment" is a digital simulation environment built to allow drivers to train in real time based on strategic models.

[0106] "User feedback" refers to feedback information provided by drivers based on their training experience in a virtual environment.

[0107] A "data processing device" is a computer system used to centrally manage and analyze video information.

[0108] An "information repository" is a database where collected data is centrally managed.

[0109] The system for implementing the present invention functions by implementing various means, mainly involving servers, terminals, and users, in order to support the improvement of drivers' driving skills.

[0110] The server receives video information recorded by the user, analyzes it, and digitizes the motion characteristics. These digitized motion characteristics are compared and analyzed with expert motion data to generate a strategic model tailored to the user. Machine learning software such as Python and TENSORFLOW® are used for this comparison.

[0111] The terminal constructs a virtual environment based on the generated strategic model and presents it to the user. The user can experience this virtual environment using VR goggles. Within this virtual environment, real-time guidance is provided in response to the user's actions. The guidance content is provided as audio through earphones.

[0112] Users can further improve their driving skills based on insights gained through training in the virtual environment. This feedback is then returned to the server, further refining the strategic model.

[0113] For example, if a driver frequently applies sudden brakes while driving, the system will suggest ways to improve driving smoothness. The user can then practice smoother braking techniques within a virtual environment. Through this process, the driver can learn a safer and more efficient driving style.

[0114] An example of a prompt for a generative AI model is, "Please tell me how to improve the model if it frequently uses sudden braking."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user uploads video information acquired while driving to the server via a terminal. The input is the user's driving video, which the server receives and saves to data storage. The storage location is specified as a particular folder in the database.

[0118] Step 2:

[0119] The server takes uploaded video information as input and uses a video analysis algorithm to digitize the motion characteristics. The output is digitized data showing driving characteristics, and characteristic driving patterns (e.g., frequency of sudden braking) are extracted. A video processing library and a machine learning model are used for this analysis.

[0120] Step 3:

[0121] The server retrieves motion characteristics from a database to compare them with expert performance data. The input consists of extracted motion characteristics and expert baseline data. The output is the comparison result, identifying the user's strengths and weaknesses in driving performance.

[0122] Step 4:

[0123] The server generates a strategic model based on the comparison results. The input is the comparison results, and the output is a customized strategic model aimed at improving the user's driving skills. A generative AI model is used for this generation, and prompts are used to provide guidance for model creation.

[0124] Step 5:

[0125] The terminal constructs a virtual environment based on the strategic model received from the server. An interactive simulation is designed for the user to experience. The input is the strategic model, and the output is the virtual environment visualized via VR goggles.

[0126] Step 6:

[0127] Users learn strategies through driving simulations within a virtual environment. The virtual environment allows for real-time driving instruction. Inputs are the user's actual operations and strategic models, while output is the experience that facilitates improvement in driving skills. Audio instruction is provided via headphones.

[0128] Step 7:

[0129] Users return feedback to the server based on their training experience in a virtual environment. This feedback is recorded as information necessary for improving the strategic model. The input is the user's feedback information, and the output is the data used to improve the strategic model in the next iteration.

[0130] 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.

[0131] This invention is a system aimed at enabling athletes to effectively learn how to play a game, and in particular, by combining it with an emotion engine, it provides strategic support that takes into account the user's psychological state. The system collects and analyzes the user's video data and emotion data, generates a strategic model tailored to each individual athlete, and makes that model available to experience in a virtual reality environment.

[0132] First, users record themselves during matches or practice sessions and upload the video data. During this process, cameras and microphones are used to collect emotional data from facial expressions and voices. This data is then sent to the server.

[0133] Next, the server analyzes the received data to extract the user's motor characteristics and emotional state. Along with specific motor characteristics (shot success rate, movement patterns, etc.), psychological factors such as stress levels and concentration are also evaluated using an emotion engine. Based on this, a comparison is made with a professional competition database to identify the user's challenges and to construct a strategic model tailored to the user's psychological state.

[0134] After generating the strategy model, the terminal implements this model in a virtual reality environment. Users can experience this virtual reality environment using VR goggles. In this process, the system provides an interactive environment similar to an actual match, and by providing real-time feedback on the user's actions along with emotional data, more personalized instruction is possible.

[0135] For example, if the device's emotion engine detects that the user is feeling pressured, it may instantly change instructions and provide guidance to help the user play in a more relaxed state. This allows the user to learn a play style that suits their own psychological state.

[0136] Users send feedback to the system based on the insights they gain from their experiences in the virtual reality environment. The server then uses this feedback to optimize its strategic model, enabling more refined training sessions in subsequent sessions.

[0137] Thus, this system utilizes an emotion engine to take into account the psychological factors of athletes, enabling personalized strategic coaching and aiming to improve athletes' game management skills.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] Users set up cameras and microphones to record their matches and practice sessions, simultaneously recording video and audio. This captures not only motion data but also traces of the user's emotions (facial expressions and tone of voice).

[0141] Step 2:

[0142] The user uploads recorded video and audio data to the server through the system interface.

[0143] Step 3:

[0144] The server receives the user's video and audio data and extracts movement characteristics by applying motion analysis algorithms. Simultaneously, it uses an emotion engine to perform facial expression and voice analysis to identify the user's emotional state. This includes quantifying stress levels and concentration levels.

[0145] Step 4:

[0146] The server compares extracted motor skills and emotional data with a professional competition database to analyze the user's strengths and areas for improvement in their playing style. Based on this information, it generates a strategic model tailored to the user's characteristics and emotional state.

[0147] Step 5:

[0148] The device uses the generated strategy model to set up a virtual reality environment. This prepares the user to simulate actual match situations through VR goggles.

[0149] Step 6:

[0150] Users wear VR goggles and experience simulations provided in a virtual reality environment. In this environment, users experience scenes based on strategic models and receive various feedback during gameplay.

[0151] Step 7:

[0152] The device uses real-time data from the emotion engine to provide instructions and voice feedback tailored to the user's gameplay. For example, if the user's concentration is waning, it will send concise advice or motivational messages.

[0153] Step 8:

[0154] Users provide feedback to the system based on the insights they gain through their experience, sharing information such as which strategies were effective.

[0155] Step 9:

[0156] The server further optimizes its strategic model and emotional interface based on feedback provided by users. Continuing this cycle improves the overall training effect of the system and contributes to improving users' gameplay skills.

[0157] (Example 2)

[0158] 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".

[0159] Traditional training systems for athletes primarily rely on data on athletic characteristics and fail to adequately consider the athlete's psychological state. As a result, it is difficult to provide optimal strategic models that address individual athlete psychological pressure and changes in concentration levels. There is a need to solve these problems and provide support that enables athletes to maintain a calm and focused state even during competition.

[0160] 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.

[0161] In this invention, the server includes means for acquiring video and audio data, means for digitizing motor characteristics and psychological state, and means for generating a strategic model that takes psychological state into account based on the results of comparative analysis. This makes it possible to comprehensively evaluate the mental and physical characteristics of athletes and construct individualized strategies.

[0162] "Video data" refers to visual information collected using equipment such as cameras, which records the actions of athletes and the conditions of the environment.

[0163] "Audio data" refers to acoustic information collected using equipment such as microphones, and includes recordings of the athletes' voices and surrounding sounds.

[0164] "Motor characteristics" refer to the physical characteristics of an athlete expressed as numerical data, and specifically include things like shot success rate and movement patterns.

[0165] "Psychological state" refers to the mental condition of an athlete, and is a factor in which stress levels, concentration levels, and other aspects are evaluated by the emotional engine.

[0166] A "strategic model" is a plan or guideline generated by considering the athletic characteristics and psychological state of an athlete, and is a virtual blueprint for guiding optimal actions during competition.

[0167] A "virtual environment" refers to a computer-generated simulated space or situation in which users can receive training through experiences that mimic reality.

[0168] "Feedback" refers to the process by which users return information to the system based on their experiences within a virtual environment, and this information is used to optimize strategic models.

[0169] This invention is a system that enables athletes to receive personalized strategic guidance while taking their psychological state into consideration.

[0170] First, users collect video and audio data using cameras and microphones during matches and practice sessions. This data includes the user's movements and ambient sounds. In particular, elements such as facial expressions and tone of voice represent emotional data.

[0171] Next, the collected data is uploaded to a cloud-based server. This server uses a generative AI model to analyze the user's motor characteristics and psychological state. Motor characteristics refer to things like shot success rate and movement patterns, while psychological state is evaluated as stress level and concentration level.

[0172] Based on the analysis results, the server compares them with a professional competition database to identify the user's challenges. Furthermore, it generates a corresponding strategic model. This strategic model takes into account the user's psychological state and guides them toward the optimal actions during the competition.

[0173] The device uses this strategic model to build a virtual environment. In this virtual environment, users can wear VR goggles and engage in immersive training. The interactive environment provided by the device allows users to receive real-time feedback and instructions based on their actions and emotions.

[0174] For example, if a user feels pressured during a match, the system may detect this and provide guidance to help the user play in a relaxed state. This allows users to effectively learn a play style that suits their psychological state.

[0175] A concrete example of a prompt would be something like, "Identify the stress the user feels during a match and suggest a virtual reality scenario to induce a relaxed state." Based on this, the AI ​​model designs an appropriate strategy and provides it to the user.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] Users operate cameras and microphones to collect video and audio data during matches and practice sessions. This captures data on the user's movements, facial expressions, and voice. This input data serves as foundational information necessary for subsequent analysis.

[0179] Step 2:

[0180] The user uploads the collected video and audio data to the server through a dedicated application. The application converts the data to the appropriate format and sends it to the server via a secure communication channel. The output of this step is the raw data stored on the server.

[0181] Step 3:

[0182] The server analyzes uploaded video and audio data. Using a generative AI model, it extracts motor characteristics (e.g., shot success rate, movement patterns) from the data, while an emotion engine evaluates psychological state (e.g., stress level, concentration level). The input is the collected data, and the output is the motor characteristics and psychological evaluation results.

[0183] Step 4:

[0184] The server compares and analyzes user data against a professional competition database based on the analysis results. This analysis identifies the user's challenges and characteristics. Using prompts, the AI ​​model is instructed to "compare the user's movement characteristics with professional data and identify challenges." The output is the challenge identification result.

[0185] Step 5:

[0186] The server generates a strategic model that takes into account the identified problem and the user's psychological state. The generated AI model constructs the optimal strategy, which is then output as a plan to be reflected in the next virtual training session.

[0187] Step 6:

[0188] The device constructs a virtual environment based on a strategic model, allowing the user to immerse themselves in that environment using VR goggles. The device inputs strategic information into the VR system and outputs scenarios that allow for interactive conversation with the user.

[0189] Step 7:

[0190] Users train in a virtual environment and receive real-time feedback, allowing them to correct their behavior on the spot. The feedback input consists of user behavior and psychological data, while the output is appropriate guidance messages.

[0191] Step 8:

[0192] The user sends their experience in the virtual environment back to the server as feedback. The server receives this feedback and can further optimize the strategic model. The output is the optimized new strategic model.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0195] The goal is to address the challenge that athletes and individuals in their daily lives lack the means to effectively manage their emotions and improve their behavior while creating an optimal environment.

[0196] 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.

[0197] In this invention, the server includes means for acquiring dynamic data, means for digitizing biometric characteristics, means for comparing and analyzing expert behavioral data and biometric characteristics, and means for evaluating emotional states and adjusting environmental factors. This enables users to improve their behavior while experiencing an optimal environment tailored to their own emotions.

[0198] "Dynamic data" refers to information that reflects a user's physical and emotional state in real time.

[0199] "Biological characteristics" refer to data that characterizes an individual's physical behavior and physiological state.

[0200] "Expert behavioral data" refers to data on behaviors based on specialized skills and knowledge in a particular field.

[0201] Comparative analysis is the process of comparing different data to identify similarities and differences.

[0202] A "planning model" is a model designed to provide optimized guidelines for a user's specific goals and circumstances.

[0203] A "virtual environment" is a digital space created using computer technology that enables simulations of reality.

[0204] A "user" is an individual or group that operates this system and benefits from it.

[0205] "Response" refers to the reaction or feedback a user shows to a stimulus from a system.

[0206] A "centralized management system" is a device used to centrally process and store data.

[0207] An "information warehouse" is a system that systematically stores various types of data and manages them in a way that makes them accessible as needed.

[0208] "Emotional state" refers to the user's psychological health and emotional fluctuations.

[0209] "Environmental factors" refer to conditions, including physical or digital settings, surrounding the user.

[0210] The system that realizes this invention can acquire and analyze dynamic data, construct virtual environments, perform sentiment analysis, and adjust the environment. Specifically, it utilizes devices such as home robots and mobile terminals.

[0211] The server uses hardware equipped with cameras and microphones to acquire dynamic data that reflects the user's physical and emotional state in real time. This collects biometric data, which is then analyzed using software such as Emotion AI SDK and TensorFlow. The analyzed biometric data is then uploaded and stored in a centralized management system and compared and analyzed with a database of expert behaviors.

[0212] The device generates a planning model based on comparative analysis results and constructs a virtual environment using computer simulations and VR technology. Here, the user can experience an optimal environment tailored to their own behavior and emotional state. The device's interface processes user responses and determines the need for environmental adjustments.

[0213] The user aims to achieve an optimal lifestyle by recognizing changes in their emotions and behavior through guidance from the system. Based on their emotional state, the device controls environmental factors, such as playing music through the speaker or adjusting LED lighting.

[0214] For example, if the device detects that the user is experiencing stress, it will change the lighting to a warm color and play relaxation music to alleviate the stress. It will also utilize an interactive interface to encourage relaxation by saying things like, "You've been working hard lately, why don't you take a little break?"

[0215] Through the generative AI model, an example prompt statement such as "The system has detected that the user is experiencing stress. How would you guide the user to relax?" can be used.

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The server acquires dynamic data using a camera and microphone. The input consists of real-time video and audio data. This data is analyzed by the Emotion AI SDK, which evaluates and outputs biometric data such as stress levels and concentration levels.

[0219] Step 2:

[0220] The server uploads the analyzed biometric data to a centralized management system. The system receives biometric data as input and compares and analyzes it against a professional behavioral database. The output provides analysis results tailored to the user's characteristics.

[0221] Step 3:

[0222] The terminal generates a planning model based on the analysis results received from the server. The input is the analysis results, which are then used to generate the AI ​​model and output the optimal course of action for the user.

[0223] Step 4:

[0224] The device utilizes VR technology with a planning model to build a virtual environment. The input is the planning model, and the output is a virtual simulation environment that the user can experience.

[0225] Step 5:

[0226] The user acts within a virtual environment presented by the device. The device detects the user's actions and re-evaluates their emotional state. In this process, the user's behavioral data is the input, and their emotional state is the output.

[0227] Step 6:

[0228] The device adjusts environmental factors based on the re-evaluated emotional state. The user's emotional state is the input, and the output is environmental adjustment (e.g., changing the lighting or playing music). Specific actions include changing the lighting color to a warmer color or playing relaxing music through the speaker.

[0229] Step 7:

[0230] The device generates prompts through an AI model and provides appropriate guidance to the user. The input is a re-evaluated emotional state, and the output is a prompt. Specifically, the guidance might be in the form of, "We have detected that the user is feeling stressed. How would you guide the user to relax?"

[0231] 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.

[0232] 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.

[0233] 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.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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".

[0247] This invention relates to a system for assisting athletes in learning how to play matches. This system is characterized by utilizing video data of athletes' matches and training sessions and providing a strategic approach based on the analysis results.

[0248] First, the user records video of their match or practice session. The recorded video data is uploaded to the system. Next, the server receives the video data, analyzes the video using a motion analysis algorithm, and extracts the user's movement characteristics.

[0249] The server identifies a player's strengths and areas for improvement by comparing their athletic characteristics with a professional competition database. Comparative analysis generates a customized strategy model for the user, drawing insights from professional gameplay. This strategy model is tailored to the user's own playing style and skill level.

[0250] The generated strategy model is presented to the user via a device. The device creates a virtual reality environment, which the user experiences using VR goggles. In the virtual reality environment, the user can learn strategies in real time through match simulations. Furthermore, the device provides voice instructions through earphones, offering appropriate advice and strategic suggestions based on the user's actions.

[0251] Users apply the strategies and insights they learn through the system to their own gameplay and then provide feedback to the system. The server receives this feedback, further improves the strategy model, and uses it to improve future training sessions.

[0252] For example, if a user wants to improve their serve success rate, the system will refer to data on professional serves and compare it to the user's serve. Based on the identified issues, the system will generate personalized improvement advice and present it as a usable strategy. In this way, users can learn from professionals while simultaneously achieving their own growth.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] Users record their matches and practice sessions with a camera and upload the video data to the system. Users can select specific matches or scenes and set the focus as needed.

[0256] Step 2:

[0257] The server analyzes the uploaded video data. It applies motion analysis algorithms to extract the user's movement characteristics, such as shot success rate, movement patterns, and positioning. This data is then structured for further analysis.

[0258] Step 3:

[0259] The server references a professional sports database and performs a comparative analysis of the user's athletic characteristics. This identifies the user's strengths and areas for improvement, and generates a detailed report based on the comparison results.

[0260] Step 4:

[0261] Based on the analysis results, the server generates a strategic model optimized for the user. This model is designed to serve as a guideline for gameplay and game improvement tailored to the user's play style.

[0262] Step 5:

[0263] The terminal receives a strategic model supplied from the server, adjusts it for the virtual reality environment, and generates VR content.

[0264] Step 6:

[0265] Users wear VR goggles and experience a virtual reality environment set up by their device. They progress through the simulation based on strategic instructions provided during the gameplay.

[0266] Step 7:

[0267] The device provides users with real-time audio feedback and instructions through earphones during practice, allowing them to make immediate corrections and improvements.

[0268] Step 8:

[0269] Users provide feedback to the system based on their experiences in virtual reality environments and actual matches. This feedback is used to improve future strategic models.

[0270] Step 9:

[0271] Based on user feedback, the server initiates a process to further improve its strategic model, thereby enhancing the overall effectiveness of the system.

[0272] (Example 1)

[0273] 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."

[0274] Traditional systems supporting athletes have made it difficult to obtain strategic advice based on individual athletic abilities. Furthermore, there has been a lack of means to provide real-time feedback tailored to specific competition scenarios, and the lack of training environments utilizing virtual reality has been a particular problem.

[0275] 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.

[0276] In this invention, the server includes means for analyzing video information, means for comparing and examining operational characteristics, and means for generating a strategic model. This enables the provision of strategic advice based on the user's operational characteristics. Furthermore, it enables real-time feedback and concrete training utilizing a virtual reality environment.

[0277] "Video information" refers to visual data that records the matches and training sessions of athletes.

[0278] A "device" is a hardware or software component used to perform a specific function.

[0279] "Motion characteristics" refers to numerical characteristic information such as speed, angle, positioning, etc. related to the physical movements of athletes.

[0280] "Professional athlete competition information" refers to data obtained from professional athletes' matches and training.

[0281] "Comparative study" refers to the process of comparing the extracted motion characteristics with professional athlete competition information to identify features and areas for improvement.

[0282] "Strategic model" refers to a data model created based on the analysis results, including tactics for athletes to effectively conduct matches.

[0283] "Virtual reality space" refers to a computer-generated three-dimensional environment in which athletes can experience real competition situations through simulation.

[0284] "Feedback" refers to evaluation and reaction information obtained after an athlete's actual play or simulation.

[0285] "Central device" refers to computing resources such as servers and cloud systems used for centralized data management.

[0286] "Information recording device" refers to a storage system for storing and managing digital data.

[0287] This invention relates to a system that provides a strategic model by leveraging video information of individual matches and practices for athletes to improve their skills. The user shoots videos of their own matches and practices using a video camera or smartphone and uploads this video information to the server through a dedicated application. The server uses Python and the OpenCV library to analyze the video information and quantify motion characteristics such as speed, angle, and positioning.

[0288] Next, the server compares these quantified performance characteristics with expert competition data. Using machine learning algorithms powered by Scikit-learn, it identifies the user's strengths and areas for improvement. Based on the results of this comparison, the server leverages a generative AI model to create a strategy model and build a strategy customized to the user's play style and skill level.

[0289] This strategic model is provided to the user via a device. The device generates a virtual reality space using Unity and other virtual reality technologies, allowing the user to experience a realistic competition simulation through VR goggles. Furthermore, the device provides voice instructions through earphones, offering immediate guidance and advice based on the user's actions.

[0290] For example, if a user aims to improve their tennis serve, the system can compare their serve to that of professional players and provide customized advice on specific areas for improvement, such as "strengthen weight transfer during the serve."

[0291] An example of a prompt is as follows: "To improve your serve success rate in your next tennis match, compare your motion characteristics to those of a professional player and suggest areas for improvement and specific strategies."

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] Users record their matches or practice sessions using video cameras or smartphones. The recorded video data is uploaded to a server using a dedicated application. It is crucial that the video clearly captures the intended match scenes and actions. The input is video data, and the output is the video information stored on the server.

[0295] Step 2:

[0296] The server analyzes the received video information using Python and the OpenCV library. Specifically, it divides the video into frames and analyzes and quantifies motion characteristics such as speed, angle, and positioning. The input for this step is the video information stored on the server, and the output is the analyzed quantified data. Based on the analysis results, the user's motion characteristics are extracted in detail.

[0297] Step 3:

[0298] The server compares the analyzed performance characteristics with expert competition data. This comparison uses a machine learning algorithm based on Scikit-learn to identify similarities and differences in performance characteristics. The input for this step is the analyzed numerical data and expert competition data, and the output is the result of the comparison.

[0299] Step 4:

[0300] The server generates a strategy model using a generative AI model based on the results of the comparative analysis. This strategy model includes customized tactics tailored to the user's play style and skill level. The input for this step is the results of the comparative analysis, and the output is the generated strategy model.

[0301] Step 5:

[0302] The terminal constructs a virtual reality space based on the generated strategy model. Using tools like Unity, it creates a VR environment that users can experience using VR goggles. The input for this step is the generated strategy model, and the output is the construction of the virtual reality space.

[0303] Step 6:

[0304] The user learns strategies through simulations in the virtual reality space. The terminal provides voice instructions through earphones and sends real-time feedback according to the user's actions. The input of this step is the virtual reality space and the real-time user actions, and the output is instructions and feedback.

[0305] Step 7:

[0306] After the simulation, the user practices the strategies learned in actual practice or matches and feeds back the results to the server through the app. Based on this feedback, the server further improves the strategy model and uses it for the next training. The input of this step is the feedback from the user, and the output is the improved strategy model.

[0307] (Application Example 1)

[0308] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] In the conventional method for improving driving skills, it was difficult for drivers to receive specific guidance in real time to learn efficient and safe driving methods. As a result, there is a problem that the improvement of driving skills does not progress smoothly. There is a need to provide an environment where drivers can receive customized training based on their own driving data.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0311] In this invention, the server includes means for acquiring video information, means for analyzing the video information and digitizing the motion characteristics, and means for comparing and analyzing the motion characteristics with the operation information of experts. As a result, it becomes possible for the driver to receive guidance in real time in the virtual environment based on their own driving data and learn a safe and effective driving style.

[0312] "Video information" refers to video data that records the driver's driving conditions.

[0313] "Driving characteristics" refer to digitized data that shows the driver's driving style and movement characteristics.

[0314] "Expert driving information" refers to information stored in a database that records the driving styles of professional drivers.

[0315] "Comparative analysis" is a process that compares the driver's motor characteristics with expert information to identify strengths and areas for improvement.

[0316] A "strategic model" is a training program created based on comparative analysis results with the aim of improving drivers' skills.

[0317] A "virtual environment" is a digital simulation environment built to allow drivers to train in real time based on strategic models.

[0318] "User feedback" refers to feedback information provided by drivers based on their training experience in a virtual environment.

[0319] A "data processing device" is a computer system used to centrally manage and analyze video information.

[0320] An "information repository" is a database where collected data is centrally managed.

[0321] The system for implementing the present invention functions by implementing various means, mainly involving servers, terminals, and users, in order to support the improvement of drivers' driving skills.

[0322] The server receives video information recorded by the user, analyzes it, and digitizes the motion characteristics. These digitized motion characteristics are compared and analyzed with expert motion data to generate a strategic model suitable for the user. Machine learning software such as Python and TensorFlow are used for this comparison.

[0323] The terminal constructs a virtual environment based on the generated strategic model and presents it to the user. The user can experience this virtual environment using VR goggles. Within this virtual environment, real-time guidance is provided in response to the user's actions. The guidance content is provided as audio through earphones.

[0324] Users can further improve their driving skills based on insights gained through training in the virtual environment. This feedback is then returned to the server, further refining the strategic model.

[0325] For example, if a driver frequently applies sudden brakes while driving, the system will suggest ways to improve driving smoothness. The user can then practice smoother braking techniques within a virtual environment. Through this process, the driver can learn a safer and more efficient driving style.

[0326] An example of a prompt for a generative AI model is, "Please tell me how to improve the model if it frequently uses sudden braking."

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The user uploads video information acquired while driving to the server via a terminal. The input is the user's driving video, which the server receives and saves to data storage. The storage location is specified as a particular folder in the database.

[0330] Step 2:

[0331] The server takes uploaded video information as input and uses a video analysis algorithm to digitize the motion characteristics. The output is digitized data showing driving characteristics, and characteristic driving patterns (e.g., frequency of sudden braking) are extracted. A video processing library and a machine learning model are used for this analysis.

[0332] Step 3:

[0333] The server retrieves motion characteristics from a database to compare them with expert performance data. The input consists of extracted motion characteristics and expert baseline data. The output is the comparison result, identifying the user's strengths and weaknesses in driving performance.

[0334] Step 4:

[0335] The server generates a strategic model based on the comparison results. The input is the comparison results, and the output is a customized strategic model aimed at improving the user's driving skills. A generative AI model is used for this generation, and prompts are used to provide guidance for model creation.

[0336] Step 5:

[0337] The terminal constructs a virtual environment based on the strategic model received from the server. An interactive simulation is designed for the user to experience. The input is the strategic model, and the output is the virtual environment visualized via VR goggles.

[0338] Step 6:

[0339] Users learn strategies through driving simulations within a virtual environment. The virtual environment allows for real-time driving instruction. Inputs are the user's actual operations and strategic models, while output is the experience that facilitates improvement in driving skills. Audio instruction is provided via headphones.

[0340] Step 7:

[0341] Users return feedback to the server based on their training experience in a virtual environment. This feedback is recorded as information necessary for improving the strategic model. The input is the user's feedback information, and the output is the data used to improve the strategic model in the next iteration.

[0342] 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.

[0343] This invention is a system aimed at enabling athletes to effectively learn how to play a game, and in particular, by combining it with an emotion engine, it provides strategic support that takes into account the user's psychological state. The system collects and analyzes the user's video data and emotion data, generates a strategic model tailored to each individual athlete, and makes that model available to experience in a virtual reality environment.

[0344] First, users record themselves during matches or practice sessions and upload the video data. During this process, cameras and microphones are used to collect emotional data from facial expressions and voices. This data is then sent to the server.

[0345] Next, the server analyzes the received data to extract the user's motor characteristics and emotional state. Along with specific motor characteristics (shot success rate, movement patterns, etc.), psychological factors such as stress levels and concentration are also evaluated using an emotion engine. Based on this, a comparison is made with a professional competition database to identify the user's challenges and to construct a strategic model tailored to the user's psychological state.

[0346] After generating the strategy model, the terminal implements this model in a virtual reality environment. Users can experience this virtual reality environment using VR goggles. In this process, the system provides an interactive environment similar to an actual match, and by providing real-time feedback on the user's actions along with emotional data, more personalized instruction is possible.

[0347] For example, if the device's emotion engine detects that the user is feeling pressured, it may instantly change instructions and provide guidance to help the user play in a more relaxed state. This allows the user to learn a play style that suits their own psychological state.

[0348] Users send feedback to the system based on the insights they gain from their experiences in the virtual reality environment. The server then uses this feedback to optimize its strategic model, enabling more refined training sessions in subsequent sessions.

[0349] Thus, this system utilizes an emotion engine to take into account the psychological factors of athletes, enabling personalized strategic coaching and aiming to improve athletes' game management skills.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] Users set up cameras and microphones to record their matches and practice sessions, simultaneously recording video and audio. This captures not only motion data but also traces of the user's emotions (facial expressions and tone of voice).

[0353] Step 2:

[0354] The user uploads recorded video and audio data to the server through the system interface.

[0355] Step 3:

[0356] The server receives the user's video and audio data and extracts movement characteristics by applying motion analysis algorithms. Simultaneously, it uses an emotion engine to perform facial expression and voice analysis to identify the user's emotional state. This includes quantifying stress levels and concentration levels.

[0357] Step 4:

[0358] The server compares extracted motor skills and emotional data with a professional competition database to analyze the user's strengths and areas for improvement in their playing style. Based on this information, it generates a strategic model tailored to the user's characteristics and emotional state.

[0359] Step 5:

[0360] The device uses the generated strategy model to set up a virtual reality environment. This prepares the user to simulate actual match situations through VR goggles.

[0361] Step 6:

[0362] Users wear VR goggles and experience simulations provided in a virtual reality environment. In this environment, users experience scenes based on strategic models and receive various feedback during gameplay.

[0363] Step 7:

[0364] The device uses real-time data from the emotion engine to provide instructions and voice feedback tailored to the user's gameplay. For example, if the user's concentration is waning, it will send concise advice or motivational messages.

[0365] Step 8:

[0366] Users provide feedback to the system based on the insights they gain through their experience, sharing information such as which strategies were effective.

[0367] Step 9:

[0368] The server further optimizes its strategic model and emotional interface based on feedback provided by users. Continuing this cycle improves the overall training effect of the system and contributes to improving users' gameplay skills.

[0369] (Example 2)

[0370] 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".

[0371] Traditional training systems for athletes primarily rely on data on athletic characteristics and fail to adequately consider the athlete's psychological state. As a result, it is difficult to provide optimal strategic models that address individual athlete psychological pressure and changes in concentration levels. There is a need to solve these problems and provide support that enables athletes to maintain a calm and focused state even during competition.

[0372] 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.

[0373] In this invention, the server includes means for acquiring video and audio data, means for digitizing motor characteristics and psychological state, and means for generating a strategic model that takes psychological state into account based on the results of comparative analysis. This makes it possible to comprehensively evaluate the mental and physical characteristics of athletes and construct individualized strategies.

[0374] "Video data" refers to visual information collected using equipment such as cameras, which records the actions of athletes and the conditions of the environment.

[0375] "Audio data" refers to acoustic information collected using equipment such as microphones, and includes recordings of the athletes' voices and surrounding sounds.

[0376] "Motor characteristics" refer to the physical characteristics of an athlete expressed as numerical data, and specifically include things like shot success rate and movement patterns.

[0377] "Psychological state" refers to the mental condition of an athlete, and is a factor in which stress levels, concentration levels, and other aspects are evaluated by the emotional engine.

[0378] A "strategic model" is a plan or guideline generated by considering the athletic characteristics and psychological state of an athlete, and is a virtual blueprint for guiding optimal actions during competition.

[0379] A "virtual environment" refers to a computer-generated simulated space or situation in which users can receive training through experiences that mimic reality.

[0380] "Feedback" refers to the process by which users return information to the system based on their experiences within a virtual environment, and this information is used to optimize strategic models.

[0381] This invention is a system that enables athletes to receive personalized strategic guidance while taking their psychological state into consideration.

[0382] First, users collect video and audio data using cameras and microphones during matches and practice sessions. This data includes the user's movements and ambient sounds. In particular, elements such as facial expressions and tone of voice represent emotional data.

[0383] Next, the collected data is uploaded to a cloud-based server. This server uses a generative AI model to analyze the user's motor characteristics and psychological state. Motor characteristics refer to things like shot success rate and movement patterns, while psychological state is evaluated as stress level and concentration level.

[0384] Based on the analysis results, the server compares them with a professional competition database to identify the user's challenges. Furthermore, it generates a corresponding strategic model. This strategic model takes into account the user's psychological state and guides them toward the optimal actions during the competition.

[0385] The device uses this strategic model to build a virtual environment. In this virtual environment, users can wear VR goggles and engage in immersive training. The interactive environment provided by the device allows users to receive real-time feedback and instructions based on their actions and emotions.

[0386] For example, if a user feels pressured during a match, the system may detect this and provide guidance to help the user play in a relaxed state. This allows users to effectively learn a play style that suits their psychological state.

[0387] A concrete example of a prompt would be something like, "Identify the stress the user feels during a match and suggest a virtual reality scenario to induce a relaxed state." Based on this, the AI ​​model designs an appropriate strategy and provides it to the user.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] Users operate cameras and microphones to collect video and audio data during matches and practice sessions. This captures data on the user's movements, facial expressions, and voice. This input data serves as foundational information necessary for subsequent analysis.

[0391] Step 2:

[0392] The user uploads the collected video and audio data to the server through a dedicated application. The application converts the data to the appropriate format and sends it to the server via a secure communication channel. The output of this step is the raw data stored on the server.

[0393] Step 3:

[0394] The server analyzes uploaded video and audio data. Using a generative AI model, it extracts motor characteristics (e.g., shot success rate, movement patterns) from the data, while an emotion engine evaluates psychological state (e.g., stress level, concentration level). The input is the collected data, and the output is the motor characteristics and psychological evaluation results.

[0395] Step 4:

[0396] The server compares and analyzes user data against a professional competition database based on the analysis results. This analysis identifies the user's challenges and characteristics. Using prompts, the AI ​​model is instructed to "compare the user's movement characteristics with professional data and identify challenges." The output is the challenge identification result.

[0397] Step 5:

[0398] The server generates a strategic model that takes into account the identified problem and the user's psychological state. The generated AI model constructs the optimal strategy, which is then output as a plan to be reflected in the next virtual training session.

[0399] Step 6:

[0400] The device constructs a virtual environment based on a strategic model, allowing the user to immerse themselves in that environment using VR goggles. The device inputs strategic information into the VR system and outputs scenarios that allow for interactive conversation with the user.

[0401] Step 7:

[0402] Users train in a virtual environment and receive real-time feedback, allowing them to correct their behavior on the spot. The feedback input consists of user behavior and psychological data, while the output is appropriate guidance messages.

[0403] Step 8:

[0404] The user sends their experience in the virtual environment back to the server as feedback. The server receives this feedback and can further optimize the strategic model. The output is the optimized new strategic model.

[0405] (Application Example 2)

[0406] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0407] The goal is to address the challenge that athletes and individuals in their daily lives lack the means to effectively manage their emotions and improve their behavior while creating an optimal environment.

[0408] 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.

[0409] In this invention, the server includes means for acquiring dynamic data, means for digitizing biometric characteristics, means for comparing and analyzing expert behavioral data and biometric characteristics, and means for evaluating emotional states and adjusting environmental factors. This enables users to improve their behavior while experiencing an optimal environment tailored to their own emotions.

[0410] "Dynamic data" refers to information that reflects a user's physical and emotional state in real time.

[0411] "Biological characteristics" refer to data that characterizes an individual's physical behavior and physiological state.

[0412] "Expert behavioral data" refers to data on behaviors based on specialized skills and knowledge in a particular field.

[0413] Comparative analysis is the process of comparing different data to identify similarities and differences.

[0414] A "planning model" is a model designed to provide optimized guidelines for a user's specific goals and circumstances.

[0415] A "virtual environment" is a digital space created using computer technology that enables simulations of reality.

[0416] A "user" is an individual or group that operates this system and benefits from it.

[0417] "Response" refers to the reaction or feedback a user shows to a stimulus from a system.

[0418] A "centralized management system" is a device used to centrally process and store data.

[0419] An "information warehouse" is a system that systematically stores various types of data and manages them in a way that makes them accessible as needed.

[0420] "Emotional state" refers to the user's psychological health and emotional fluctuations.

[0421] "Environmental factors" refer to conditions, including physical or digital settings, surrounding the user.

[0422] The system that realizes this invention can acquire and analyze dynamic data, construct virtual environments, perform sentiment analysis, and adjust the environment. Specifically, it utilizes devices such as home robots and mobile terminals.

[0423] The server uses hardware equipped with cameras and microphones to acquire dynamic data that reflects the user's physical and emotional state in real time. This collects biometric data, which is then analyzed using software such as Emotion AI SDK and TensorFlow. The analyzed biometric data is then uploaded and stored in a centralized management system and compared and analyzed with a database of expert behaviors.

[0424] The device generates a planning model based on comparative analysis results and constructs a virtual environment using computer simulations and VR technology. Here, the user can experience an optimal environment tailored to their own behavior and emotional state. The device's interface processes user responses and determines the need for environmental adjustments.

[0425] The user aims to achieve an optimal lifestyle by recognizing changes in their emotions and behavior through guidance from the system. Based on their emotional state, the device controls environmental factors, such as playing music through the speaker or adjusting LED lighting.

[0426] For example, if the device detects that the user is experiencing stress, it will change the lighting to a warm color and play relaxation music to alleviate the stress. It will also utilize an interactive interface to encourage relaxation by saying things like, "You've been working hard lately, why don't you take a little break?"

[0427] Through the generative AI model, an example prompt statement such as "The system has detected that the user is experiencing stress. How would you guide the user to relax?" can be used.

[0428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0429] Step 1:

[0430] The server acquires dynamic data using a camera and microphone. The input consists of real-time video and audio data. This data is analyzed by the Emotion AI SDK, which evaluates and outputs biometric data such as stress levels and concentration levels.

[0431] Step 2:

[0432] The server uploads the analyzed biometric data to a centralized management system. The system receives biometric data as input and compares and analyzes it against a professional behavioral database. The output provides analysis results tailored to the user's characteristics.

[0433] Step 3:

[0434] The terminal generates a planning model based on the analysis results received from the server. The input is the analysis results, which are then used to generate the AI ​​model and output the optimal course of action for the user.

[0435] Step 4:

[0436] The device utilizes VR technology with a planning model to build a virtual environment. The input is the planning model, and the output is a virtual simulation environment that the user can experience.

[0437] Step 5:

[0438] The user acts within a virtual environment presented by the device. The device detects the user's actions and re-evaluates their emotional state. In this process, the user's behavioral data is the input, and their emotional state is the output.

[0439] Step 6:

[0440] The device adjusts environmental factors based on the re-evaluated emotional state. The user's emotional state is the input, and the output is environmental adjustment (e.g., changing the lighting or playing music). Specific actions include changing the lighting color to a warmer color or playing relaxing music through the speaker.

[0441] Step 7:

[0442] The device generates prompts through an AI model and provides appropriate guidance to the user. The input is a re-evaluated emotional state, and the output is a prompt. Specifically, the guidance might be in the form of, "We have detected that the user is feeling stressed. How would you guide the user to relax?"

[0443] 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.

[0444] 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.

[0445] 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.

[0446] [Third Embodiment]

[0447] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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".

[0459] This invention relates to a system for assisting athletes in learning how to play matches. This system is characterized by utilizing video data of athletes' matches and training sessions and providing a strategic approach based on the analysis results.

[0460] First, the user records video of their match or practice session. The recorded video data is uploaded to the system. Next, the server receives the video data, analyzes the video using a motion analysis algorithm, and extracts the user's movement characteristics.

[0461] The server identifies a player's strengths and areas for improvement by comparing their athletic characteristics with a professional competition database. Comparative analysis generates a customized strategy model for the user, drawing insights from professional gameplay. This strategy model is tailored to the user's own playing style and skill level.

[0462] The generated strategy model is presented to the user via a device. The device creates a virtual reality environment, which the user experiences using VR goggles. In the virtual reality environment, the user can learn strategies in real time through match simulations. Furthermore, the device provides voice instructions through earphones, offering appropriate advice and strategic suggestions based on the user's actions.

[0463] Users apply the strategies and insights they learn through the system to their own gameplay and then provide feedback to the system. The server receives this feedback, further improves the strategy model, and uses it to improve future training sessions.

[0464] For example, if a user wants to improve their serve success rate, the system will refer to data on professional serves and compare it to the user's serve. Based on the identified issues, the system will generate personalized improvement advice and present it as a usable strategy. In this way, users can learn from professionals while simultaneously achieving their own growth.

[0465] The following describes the processing flow.

[0466] Step 1:

[0467] Users record their matches and practice sessions with a camera and upload the video data to the system. Users can select specific matches or scenes and set the focus as needed.

[0468] Step 2:

[0469] The server analyzes the uploaded video data. It applies motion analysis algorithms to extract the user's movement characteristics, such as shot success rate, movement patterns, and positioning. This data is then structured for further analysis.

[0470] Step 3:

[0471] The server references a professional sports database and performs a comparative analysis of the user's athletic characteristics. This identifies the user's strengths and areas for improvement, and generates a detailed report based on the comparison results.

[0472] Step 4:

[0473] Based on the analysis results, the server generates a strategic model optimized for the user. This model is designed to serve as a guideline for gameplay and game improvement tailored to the user's play style.

[0474] Step 5:

[0475] The terminal receives a strategic model supplied from the server, adjusts it for the virtual reality environment, and generates VR content.

[0476] Step 6:

[0477] Users wear VR goggles and experience a virtual reality environment set up by their device. They progress through the simulation based on strategic instructions provided during the gameplay.

[0478] Step 7:

[0479] The device provides users with real-time audio feedback and instructions through earphones during practice, allowing them to make immediate corrections and improvements.

[0480] Step 8:

[0481] Users provide feedback to the system based on their experiences in virtual reality environments and actual matches. This feedback is used to improve future strategic models.

[0482] Step 9:

[0483] Based on user feedback, the server initiates a process to further improve its strategic model, thereby enhancing the overall effectiveness of the system.

[0484] (Example 1)

[0485] 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."

[0486] Traditional systems supporting athletes have made it difficult to obtain strategic advice based on individual athletic abilities. Furthermore, there has been a lack of means to provide real-time feedback tailored to specific competition scenarios, and the lack of training environments utilizing virtual reality has been a particular problem.

[0487] 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.

[0488] In this invention, the server includes means for analyzing video information, means for comparing and examining operational characteristics, and means for generating a strategic model. This enables the provision of strategic advice based on the user's operational characteristics. Furthermore, it enables real-time feedback and concrete training utilizing a virtual reality environment.

[0489] "Video information" refers to visual data that records the matches and training sessions of athletes.

[0490] A "device" is a hardware or software component used to perform a specific function.

[0491] "Motion characteristics" refer to quantified characteristic information about an athlete's physical movements, such as speed, angle, and positioning.

[0492] "Expert competition information" refers to data obtained from matches and training sessions of professional athletes.

[0493] "Comparing and examining" refers to the process of comparing extracted performance characteristics with expert competition data to identify key features and areas for improvement.

[0494] A "strategic model" is a data model created based on analysis results, which includes tactics that enable athletes to effectively play a match.

[0495] A "virtual reality space" is a computer-generated three-dimensional environment in which competitors can experience realistic competition situations through simulation.

[0496] "Feedback" refers to evaluations and reaction information obtained after a competitor's actual play or simulation.

[0497] A "central device" refers to computing resources such as servers and cloud systems used for centralized data management.

[0498] An "information recording device" is a storage system for saving and managing digital data.

[0499] This invention relates to a system that provides athletes with strategic models by utilizing video information from individual matches and practice sessions to improve their athletic skills. Users film their matches and practice sessions with a video camera or smartphone and upload this video information to a server via a dedicated application. The server uses Python and the OpenCV library to analyze the video information and quantify motion characteristics such as speed, angle, and positioning.

[0500] Next, the server compares these quantified performance characteristics with expert competition data. Using machine learning algorithms powered by Scikit-learn, it identifies the user's strengths and areas for improvement. Based on the results of this comparison, the server leverages a generative AI model to create a strategy model and build a strategy customized to the user's play style and skill level.

[0501] This strategic model is provided to the user via a device. The device generates a virtual reality space using Unity and other virtual reality technologies, allowing the user to experience a realistic competition simulation through VR goggles. Furthermore, the device provides voice instructions through earphones, offering immediate guidance and advice based on the user's actions.

[0502] For example, if a user aims to improve their tennis serve, the system can compare their serve to that of professional players and provide customized advice on specific areas for improvement, such as "strengthen weight transfer during the serve."

[0503] An example of a prompt is as follows: "To improve your serve success rate in your next tennis match, compare your motion characteristics to those of a professional player and suggest areas for improvement and specific strategies."

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] Users record their matches or practice sessions using video cameras or smartphones. The recorded video data is uploaded to a server using a dedicated application. It is crucial that the video clearly captures the intended match scenes and actions. The input is video data, and the output is the video information stored on the server.

[0507] Step 2:

[0508] The server analyzes the received video information using Python and the OpenCV library. Specifically, it divides the video into frames and analyzes and quantifies motion characteristics such as speed, angle, and positioning. The input for this step is the video information stored on the server, and the output is the analyzed quantified data. Based on the analysis results, the user's motion characteristics are extracted in detail.

[0509] Step 3:

[0510] The server compares the analyzed performance characteristics with expert competition data. This comparison uses a machine learning algorithm based on Scikit-learn to identify similarities and differences in performance characteristics. The input for this step is the analyzed numerical data and expert competition data, and the output is the result of the comparison.

[0511] Step 4:

[0512] The server generates a strategy model using a generative AI model based on the results of the comparative analysis. This strategy model includes customized tactics tailored to the user's play style and skill level. The input for this step is the results of the comparative analysis, and the output is the generated strategy model.

[0513] Step 5:

[0514] The terminal constructs a virtual reality space based on the generated strategy model. Using tools like Unity, it creates a VR environment that users can experience using VR goggles. The input for this step is the generated strategy model, and the output is the construction of the virtual reality space.

[0515] Step 6:

[0516] The user learns strategies through simulations in a virtual reality space. The device provides voice instructions via earphones and sends real-time feedback that matches the user's actions. The inputs in this step are the virtual reality space and real-time user actions, and the outputs are instructions and feedback.

[0517] Step 7:

[0518] After completing the simulation, users put the strategies they learned into practice during actual training or matches and provide feedback to the server through the app. The server uses this feedback to further improve the strategy model and use it for future training. The input for this step is the user's feedback, and the output is the improved strategy model.

[0519] (Application Example 1)

[0520] 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."

[0521] Traditional methods for improving driving skills have made it difficult for drivers to receive real-time, specific instruction on efficient and safe driving techniques. This has resulted in a lack of smooth progress in improving driving skills. There is a need to provide drivers with an environment where they can receive customized training based on their own driving data.

[0522] 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.

[0523] In this invention, the server includes means for acquiring video information, means for analyzing the video information and digitizing motion characteristics, and means for comparing and analyzing the motion characteristics with expert motion information. This enables drivers to receive real-time guidance in a virtual environment based on their own driving data and learn a safe and effective driving style.

[0524] "Video information" refers to video data that records the driver's driving conditions.

[0525] "Driving characteristics" refer to digitized data that shows the characteristics of a driver's driving style and movements.

[0526] "Expert driving information" refers to information stored in a database that records the driving styles of professional drivers.

[0527] "Comparative analysis" is a process that compares the driver's motor characteristics with expert information to identify strengths and areas for improvement.

[0528] A "strategic model" is a training program created based on comparative analysis results with the aim of improving drivers' skills.

[0529] A "virtual environment" is a digital simulation environment built to allow drivers to train in real time based on strategic models.

[0530] "User feedback" refers to feedback information provided by drivers based on their training experience in a virtual environment.

[0531] A "data processing device" is a computer system used to centrally manage and analyze video information.

[0532] An "information repository" is a database where collected data is centrally managed.

[0533] The system for implementing the present invention functions by implementing various means, mainly involving servers, terminals, and users, in order to support the improvement of drivers' driving skills.

[0534] The server receives video information recorded by the user, analyzes it, and digitizes the motion characteristics. These digitized motion characteristics are compared and analyzed with expert motion data to generate a strategic model suitable for the user. Machine learning software such as Python and TensorFlow are used for this comparison.

[0535] The terminal constructs a virtual environment based on the generated strategic model and presents it to the user. The user can experience this virtual environment using VR goggles. Within this virtual environment, real-time guidance is provided in response to the user's actions. The guidance content is provided as audio through earphones.

[0536] Users can further improve their driving skills based on insights gained through training in the virtual environment. This feedback is then returned to the server, further refining the strategic model.

[0537] For example, if a driver frequently applies sudden brakes while driving, the system will suggest ways to improve driving smoothness. The user can then practice smoother braking techniques within a virtual environment. Through this process, the driver can learn a safer and more efficient driving style.

[0538] An example of a prompt for a generative AI model is, "Please tell me what improvements can be made if the sudden braking is used too frequently."

[0539] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0540] Step 1:

[0541] The user uploads video information acquired while driving to the server via a terminal. The input is the user's driving video, which the server receives and saves to data storage. The storage location is specified as a particular folder in the database.

[0542] Step 2:

[0543] The server takes uploaded video information as input and uses a video analysis algorithm to digitize the motion characteristics. The output is digitized data showing driving characteristics, and characteristic driving patterns (e.g., frequency of sudden braking) are extracted. A video processing library and a machine learning model are used for this analysis.

[0544] Step 3:

[0545] The server retrieves motion characteristics from a database to compare them with expert performance data. The input consists of extracted motion characteristics and expert baseline data. The output is the comparison result, identifying the user's strengths and weaknesses in driving performance.

[0546] Step 4:

[0547] The server generates a strategic model based on the comparison results. The input is the comparison results, and the output is a customized strategic model aimed at improving the user's driving skills. A generative AI model is used for this generation, and prompts are used to provide guidance for model creation.

[0548] Step 5:

[0549] The terminal constructs a virtual environment based on the strategic model received from the server. An interactive simulation is designed for the user to experience. The input is the strategic model, and the output is the virtual environment visualized via VR goggles.

[0550] Step 6:

[0551] Users learn strategies through driving simulations within a virtual environment. The virtual environment allows for real-time driving instruction. Inputs are the user's actual operations and strategic models, while output is the experience that facilitates improvement in driving skills. Audio instruction is provided via headphones.

[0552] Step 7:

[0553] Users return feedback to the server based on their training experience in a virtual environment. This feedback is recorded as information necessary for improving the strategic model. The input is the user's feedback information, and the output is the data used to improve the strategic model in the next iteration.

[0554] 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.

[0555] This invention is a system aimed at enabling athletes to effectively learn how to play a game, and in particular, by combining it with an emotion engine, it provides strategic support that takes into account the user's psychological state. The system collects and analyzes the user's video data and emotion data, generates a strategic model tailored to each individual athlete, and makes that model available to experience in a virtual reality environment.

[0556] First, users record themselves during matches or practice sessions and upload the video data. During this process, cameras and microphones are used to collect emotional data, such as facial expressions and voice. This data is then sent to the server.

[0557] Next, the server analyzes the received data to extract the user's motor characteristics and emotional state. Along with specific motor characteristics (shot success rate, movement patterns, etc.), psychological factors such as stress levels and concentration are also evaluated using an emotion engine. Based on this, a comparison is made with a professional competition database to identify the user's challenges and to construct a strategic model tailored to the user's psychological state.

[0558] After generating the strategy model, the terminal implements this model in a virtual reality environment. Users can experience this virtual reality environment using VR goggles. In this process, the system provides an interactive environment similar to an actual match, and by providing real-time feedback on the user's actions along with emotional data, more personalized instruction is possible.

[0559] For example, if the device's emotion engine detects that the user is feeling pressured, it may instantly change instructions and provide guidance to help the user play in a more relaxed state. This allows the user to learn a play style that suits their own psychological state.

[0560] Users send feedback to the system based on the insights they gain from their experiences in the virtual reality environment. The server then uses this feedback to optimize its strategic model, enabling more refined training sessions in subsequent sessions.

[0561] Thus, this system utilizes an emotion engine to take into account the psychological factors of athletes, enabling personalized strategic coaching and aiming to improve athletes' game management skills.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] Users set up cameras and microphones to record their matches and practice sessions, simultaneously recording video and audio. This captures not only motion data but also traces of the user's emotions (facial expressions and tone of voice).

[0565] Step 2:

[0566] The user uploads recorded video and audio data to the server through the system interface.

[0567] Step 3:

[0568] The server receives the user's video and audio data and extracts movement characteristics by applying motion analysis algorithms. Simultaneously, it uses an emotion engine to perform facial expression and voice analysis to identify the user's emotional state. This includes quantifying stress levels and concentration levels.

[0569] Step 4:

[0570] The server compares extracted motor skills and emotional data with a professional competition database to analyze the user's strengths and areas for improvement in their playing style. Based on this information, it generates a strategic model tailored to the user's characteristics and emotional state.

[0571] Step 5:

[0572] The terminal uses the generated strategy model to set up a virtual reality environment. This prepares the user to simulate actual match situations through VR goggles.

[0573] Step 6:

[0574] Users wear VR goggles and experience simulations provided in a virtual reality environment. In this environment, users experience scenes based on strategic models and receive various feedback during gameplay.

[0575] Step 7:

[0576] The device uses real-time data from the emotion engine to provide instructions and voice feedback tailored to the user's gameplay. For example, if the user's concentration is waning, it will send concise advice or motivational messages.

[0577] Step 8:

[0578] Users provide feedback to the system based on the insights they gain through their experience, sharing information such as which strategies were effective.

[0579] Step 9:

[0580] The server further optimizes its strategic model and emotional interface based on feedback provided by users. Continuing this cycle improves the overall training effect of the system and contributes to improving users' gameplay skills.

[0581] (Example 2)

[0582] 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."

[0583] Traditional training systems for athletes primarily rely on data on athletic characteristics and fail to adequately consider the athlete's psychological state. As a result, it is difficult to provide optimal strategic models that address individual athlete psychological pressure and changes in concentration levels. There is a need to solve these problems and provide support that enables athletes to maintain a calm and focused state even during competition.

[0584] 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.

[0585] In this invention, the server includes means for acquiring video and audio data, means for digitizing motor characteristics and psychological state, and means for generating a strategic model that takes psychological state into account based on the results of comparative analysis. This makes it possible to comprehensively evaluate the mental and physical characteristics of athletes and construct individualized strategies.

[0586] "Video data" refers to visual information collected using equipment such as cameras, which records the actions of athletes and the conditions of the environment.

[0587] "Audio data" refers to acoustic information collected using equipment such as microphones, and includes recordings of the athletes' voices and surrounding sounds.

[0588] "Motor characteristics" refer to the physical characteristics of an athlete expressed as numerical data, and specifically include things like shot success rate and movement patterns.

[0589] "Psychological state" refers to the mental condition of an athlete, and is a factor in which stress levels, concentration levels, and other aspects are evaluated by the emotional engine.

[0590] A "strategic model" is a plan or guideline generated by considering the athletic characteristics and psychological state of an athlete, and serves as a virtual blueprint for guiding optimal actions during competition.

[0591] A "virtual environment" refers to a computer-generated simulated space or situation in which users can receive training through experiences that mimic reality.

[0592] "Feedback" refers to the process by which users return information to the system based on their experiences within a virtual environment, and this information is used to optimize strategic models.

[0593] This invention is a system that enables athletes to receive personalized strategic guidance while taking their psychological state into consideration.

[0594] First, users collect video and audio data using cameras and microphones during matches and practice sessions. This data includes the user's movements and ambient sounds. In particular, elements such as facial expressions and tone of voice represent emotional data.

[0595] Next, the collected data is uploaded to a cloud-based server. This server uses a generative AI model to analyze the user's motor characteristics and psychological state. Motor characteristics refer to things like shot success rate and movement patterns, while psychological state is evaluated as stress level and concentration level.

[0596] Based on the analysis results, the server compares them with a professional competition database to identify the user's challenges. Furthermore, it generates a corresponding strategic model. This strategic model takes into account the user's psychological state and guides them toward the optimal actions during the competition.

[0597] The device uses this strategic model to build a virtual environment. In this virtual environment, users can wear VR goggles and engage in immersive training. The interactive environment provided by the device allows users to receive real-time feedback and instructions based on their actions and emotions.

[0598] For example, if a user feels pressured during a match, the system may detect this and provide guidance to help the user play in a relaxed state. This allows users to effectively learn a play style that suits their psychological state.

[0599] A concrete example of a prompt would be something like, "Identify the stress the user feels during a match and suggest a virtual reality scenario to induce a relaxed state." Based on this, the AI ​​model designs an appropriate strategy and provides it to the user.

[0600] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0601] Step 1:

[0602] Users operate cameras and microphones to collect video and audio data during matches and practice sessions. This captures data on the user's movements, facial expressions, and voice. This input data serves as foundational information necessary for subsequent analysis.

[0603] Step 2:

[0604] The user uploads the collected video and audio data to the server through a dedicated application. The application converts the data to the appropriate format and sends it to the server via a secure communication channel. The output of this step is the raw data stored on the server.

[0605] Step 3:

[0606] The server analyzes uploaded video and audio data. Using a generative AI model, it extracts motor characteristics (e.g., shot success rate, movement patterns) from the data, while an emotion engine evaluates psychological state (e.g., stress level, concentration level). The input is the collected data, and the output is the motor characteristics and psychological evaluation results.

[0607] Step 4:

[0608] The server compares and analyzes user data against a professional competition database based on the analysis results. This analysis identifies the user's challenges and characteristics. Using prompts, the AI ​​model is instructed to "compare the user's movement characteristics with professional data and identify challenges." The output is the challenge identification result.

[0609] Step 5:

[0610] The server generates a strategic model that takes into account the identified problem and the user's psychological state. The generated AI model constructs the optimal strategy, which is then output as a plan to be reflected in the next virtual training session.

[0611] Step 6:

[0612] The device constructs a virtual environment based on a strategic model, allowing the user to immerse themselves in that environment using VR goggles. The device inputs strategic information into the VR system and outputs scenarios that allow for interactive conversation with the user.

[0613] Step 7:

[0614] Users train in a virtual environment and receive real-time feedback, allowing them to correct their behavior on the spot. The feedback input consists of user behavior and psychological data, while the output is appropriate guidance messages.

[0615] Step 8:

[0616] The user sends their experience in the virtual environment back to the server as feedback. The server receives this feedback and can further optimize the strategic model. The output is the optimized new strategic model.

[0617] (Application Example 2)

[0618] 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."

[0619] The goal is to address the challenge that athletes and individuals in their daily lives lack the means to effectively manage their emotions and improve their behavior while creating an optimal environment.

[0620] 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.

[0621] In this invention, the server includes means for acquiring dynamic data, means for digitizing biometric characteristics, means for comparing and analyzing expert behavioral data and biometric characteristics, and means for evaluating emotional states and adjusting environmental factors. This enables users to improve their behavior while experiencing an optimal environment tailored to their own emotions.

[0622] "Dynamic data" refers to information that reflects a user's physical and emotional state in real time.

[0623] "Biological characteristics" refer to data that characterizes an individual's physical behavior and physiological state.

[0624] "Expert behavioral data" refers to data on behaviors based on specialized skills and knowledge in a particular field.

[0625] Comparative analysis is the process of comparing different data to identify similarities and differences.

[0626] A "planning model" is a model designed to provide optimized guidelines for a user's specific goals and circumstances.

[0627] A "virtual environment" is a digital space created using computer technology that enables simulations of reality.

[0628] A "user" is an individual or group that operates this system and benefits from it.

[0629] "Response" refers to the reaction or feedback a user shows to a stimulus from a system.

[0630] A "centralized management system" is a device used to centrally process and store data.

[0631] An "information warehouse" is a system that systematically stores various types of data and manages them in a way that makes them accessible as needed.

[0632] "Emotional state" refers to the user's psychological health and emotional fluctuations.

[0633] "Environmental factors" refer to conditions, including physical or digital settings, surrounding the user.

[0634] The system that realizes this invention can acquire and analyze dynamic data, build virtual environments, perform sentiment analysis, and adjust the environment. Specifically, it utilizes devices such as home robots and mobile terminals.

[0635] The server uses hardware equipped with cameras and microphones to acquire dynamic data that reflects the user's physical and emotional state in real time. This collects biometric data, which is then analyzed using software such as Emotion AI SDK and TensorFlow. The analyzed biometric data is then uploaded and stored in a centralized management system and compared and analyzed with a database of expert behaviors.

[0636] The device generates a planning model based on comparative analysis results and constructs a virtual environment using computer simulations and VR technology. Here, the user can experience an optimal environment tailored to their own behavior and emotional state. The device's interface processes user responses and determines the need for environmental adjustments.

[0637] The user aims to achieve an optimal lifestyle by recognizing changes in their emotions and behavior through guidance from the system. Based on their emotional state, the device controls environmental factors, such as playing music through the speaker or adjusting LED lighting.

[0638] For example, if the device detects that the user is experiencing stress, it will change the lighting to a warm color and play relaxation music to alleviate the stress. It will also utilize an interactive interface to encourage relaxation by saying things like, "You've been working hard lately, why don't you take a little break?"

[0639] Through the generative AI model, an example prompt message can be used: "The system has detected that the user is experiencing stress. How would you guide the user to relax?"

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The server acquires dynamic data using a camera and microphone. The input consists of real-time video and audio data. This data is analyzed by the Emotion AI SDK, which evaluates and outputs biometric data such as stress levels and concentration levels.

[0643] Step 2:

[0644] The server uploads the analyzed biometric data to a centralized management system. The system receives biometric data as input and compares and analyzes it against a professional behavioral database. The output provides analysis results tailored to the user's characteristics.

[0645] Step 3:

[0646] The terminal generates a planning model based on the analysis results received from the server. The input is the analysis results, which are then used to generate the AI ​​model and output the optimal course of action for the user.

[0647] Step 4:

[0648] The device utilizes VR technology with a planning model to build a virtual environment. The input is the planning model, and the output is a virtual simulation environment that the user can experience.

[0649] Step 5:

[0650] The user acts within a virtual environment presented by the device. The device detects the user's actions and re-evaluates their emotional state. In this process, the user's behavioral data is the input, and their emotional state is the output.

[0651] Step 6:

[0652] The device adjusts environmental factors based on the re-evaluated emotional state. The user's emotional state is the input, and the output is environmental adjustment (e.g., changing the lighting or playing music). Specific actions include changing the lighting color to a warmer color or playing relaxing music through the speaker.

[0653] Step 7:

[0654] The device generates prompts through an AI model and provides appropriate guidance to the user. The input is a re-evaluated emotional state, and the output is a prompt. Specifically, the guidance might be in the form of, "We have detected that the user is feeling stressed. How would you guide the user to relax?"

[0655] 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.

[0656] 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.

[0657] 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.

[0658] [Fourth Embodiment]

[0659] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0660] 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.

[0661] 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).

[0662] 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.

[0663] 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.

[0664] 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).

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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".

[0672] This invention relates to a system for assisting athletes in learning how to play matches. This system is characterized by utilizing video data of athletes' matches and training sessions and providing a strategic approach based on the analysis results.

[0673] First, the user records video of their match or practice session. The recorded video data is uploaded to the system. Next, the server receives the video data, analyzes the video using a motion analysis algorithm, and extracts the user's movement characteristics.

[0674] The server identifies a player's strengths and areas for improvement by comparing their athletic characteristics with a professional competition database. Comparative analysis generates a customized strategy model for the user, drawing insights from professional gameplay. This strategy model is tailored to the user's own playing style and skill level.

[0675] The generated strategy model is presented to the user via a device. The device creates a virtual reality environment, which the user experiences using VR goggles. In the virtual reality environment, the user can learn strategies in real time through match simulations. Furthermore, the device provides voice instructions through earphones, offering appropriate advice and strategic suggestions based on the user's actions.

[0676] Users apply the strategies and insights they learn through the system to their own gameplay and then provide feedback to the system. The server receives this feedback, further improves the strategy model, and uses it to improve future training sessions.

[0677] For example, if a user wants to improve their serve success rate, the system will refer to data on professional serves and compare it to the user's serve. Based on the identified issues, the system will generate personalized improvement advice and present it as a usable strategy. In this way, users can learn from professionals while simultaneously achieving their own growth.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] Users record their matches and practice sessions with a camera and upload the video data to the system. Users can select specific matches or scenes and set the focus as needed.

[0681] Step 2:

[0682] The server analyzes the uploaded video data. It applies motion analysis algorithms to extract the user's movement characteristics, such as shot success rate, movement patterns, and positioning. This data is then structured for further analysis.

[0683] Step 3:

[0684] The server references a professional sports database and performs a comparative analysis of the user's athletic characteristics. This identifies the user's strengths and areas for improvement, and generates a detailed report based on the comparison results.

[0685] Step 4:

[0686] Based on the analysis results, the server generates a strategic model optimized for the user. This model is designed to serve as a guideline for gameplay and game improvement tailored to the user's play style.

[0687] Step 5:

[0688] The terminal receives a strategic model supplied from the server, adjusts it for the virtual reality environment, and generates VR content.

[0689] Step 6:

[0690] Users wear VR goggles and experience a virtual reality environment set up by their device. They progress through the simulation based on strategic instructions provided during the gameplay.

[0691] Step 7:

[0692] The device provides users with real-time audio feedback and instructions through earphones during practice, allowing them to make immediate corrections and improvements.

[0693] Step 8:

[0694] Users provide feedback to the system based on their experiences in virtual reality environments and actual matches. This feedback is used to improve future strategic models.

[0695] Step 9:

[0696] Based on user feedback, the server initiates a process to further improve its strategic model, thereby enhancing the overall effectiveness of the system.

[0697] (Example 1)

[0698] 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".

[0699] Traditional systems supporting athletes have made it difficult to obtain strategic advice based on individual athletic abilities. Furthermore, there has been a lack of means to provide real-time feedback tailored to specific competition scenarios, and the lack of training environments utilizing virtual reality has been a particular problem.

[0700] 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.

[0701] In this invention, the server includes means for analyzing video information, means for comparing and examining operational characteristics, and means for generating a strategic model. This enables the provision of strategic advice based on the user's operational characteristics. Furthermore, it enables real-time feedback and concrete training utilizing a virtual reality environment.

[0702] "Video information" refers to visual data that records the matches and training sessions of athletes.

[0703] A "device" is a hardware or software component used to perform a specific function.

[0704] "Motion characteristics" refer to quantified characteristic information about an athlete's physical movements, such as speed, angle, and positioning.

[0705] "Expert competition information" refers to data obtained from matches and training sessions of professional athletes.

[0706] "Comparing and examining" refers to the process of comparing extracted performance characteristics with expert competition data to identify key features and areas for improvement.

[0707] A "strategic model" is a data model created based on analysis results, which includes tactics that enable athletes to effectively play a match.

[0708] A "virtual reality space" is a computer-generated three-dimensional environment in which competitors can experience realistic competition situations through simulation.

[0709] "Feedback" refers to evaluations and reaction information obtained after a competitor's actual play or simulation.

[0710] A "central device" refers to computing resources such as servers and cloud systems used for centralized data management.

[0711] An "information recording device" is a storage system for saving and managing digital data.

[0712] This invention relates to a system that provides athletes with strategic models by utilizing video information from individual matches and practice sessions to improve their athletic skills. Users film their matches and practice sessions with a video camera or smartphone and upload this video information to a server via a dedicated application. The server uses Python and the OpenCV library to analyze the video information and quantify motion characteristics such as speed, angle, and positioning.

[0713] Next, the server compares these quantified performance characteristics with expert competition data. Using machine learning algorithms powered by Scikit-learn, it identifies the user's strengths and areas for improvement. Based on the results of this comparison, the server leverages a generative AI model to create a strategy model and build a strategy customized to the user's play style and skill level.

[0714] This strategic model is provided to the user via a device. The device generates a virtual reality space using Unity and other virtual reality technologies, allowing the user to experience a realistic competition simulation through VR goggles. Furthermore, the device provides voice instructions through earphones, offering immediate guidance and advice based on the user's actions.

[0715] For example, if a user aims to improve their tennis serve, the system can compare their serve to that of professional players and provide customized advice on specific areas for improvement, such as "strengthen weight transfer during the serve."

[0716] An example of a prompt is as follows: "To improve your serve success rate in your next tennis match, compare your motion characteristics to those of a professional player and suggest areas for improvement and specific strategies."

[0717] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0718] Step 1:

[0719] Users record their matches or practice sessions using video cameras or smartphones. The recorded video data is uploaded to a server using a dedicated application. It is crucial that the video clearly captures the intended match scenes and actions. The input is video data, and the output is the video information stored on the server.

[0720] Step 2:

[0721] The server analyzes the received video information using Python and the OpenCV library. Specifically, it divides the video into frames and analyzes and quantifies motion characteristics such as speed, angle, and positioning. The input for this step is the video information stored on the server, and the output is the analyzed quantified data. Based on the analysis results, the user's motion characteristics are extracted in detail.

[0722] Step 3:

[0723] The server compares the analyzed performance characteristics with expert competition data. This comparison uses a machine learning algorithm based on Scikit-learn to identify similarities and differences in performance characteristics. The input for this step is the analyzed numerical data and expert competition data, and the output is the result of the comparison.

[0724] Step 4:

[0725] The server generates a strategy model using a generative AI model based on the results of the comparative analysis. This strategy model includes customized tactics tailored to the user's play style and skill level. The input for this step is the results of the comparative analysis, and the output is the generated strategy model.

[0726] Step 5:

[0727] The terminal constructs a virtual reality space based on the generated strategy model. Using tools like Unity, it creates a VR environment that users can experience using VR goggles. The input for this step is the generated strategy model, and the output is the construction of the virtual reality space.

[0728] Step 6:

[0729] The user learns strategies through simulations in a virtual reality space. The device provides voice instructions via earphones and sends real-time feedback that matches the user's actions. The inputs in this step are the virtual reality space and real-time user actions, and the outputs are instructions and feedback.

[0730] Step 7:

[0731] After completing the simulation, users put the strategies they learned into practice during actual training or matches and provide feedback to the server through the app. The server uses this feedback to further improve the strategy model and use it for future training. The input for this step is the user's feedback, and the output is the improved strategy model.

[0732] (Application Example 1)

[0733] 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".

[0734] Traditional methods for improving driving skills have made it difficult for drivers to receive real-time, specific instruction on efficient and safe driving techniques. This has resulted in a lack of smooth progress in improving driving skills. There is a need to provide drivers with an environment where they can receive customized training based on their own driving data.

[0735] 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.

[0736] In this invention, the server includes means for acquiring video information, means for analyzing the video information and digitizing motion characteristics, and means for comparing and analyzing the motion characteristics with expert motion information. This enables drivers to receive real-time guidance in a virtual environment based on their own driving data and learn a safe and effective driving style.

[0737] "Video information" refers to video data that records the driver's driving conditions.

[0738] "Driving characteristics" refer to digitized data that shows the characteristics of a driver's driving style and movements.

[0739] "Expert driving information" refers to information stored in a database that records the driving styles of professional drivers.

[0740] "Comparative analysis" is a process that compares the driver's motor characteristics with expert information to identify strengths and areas for improvement.

[0741] A "strategic model" is a training program created based on comparative analysis results with the aim of improving drivers' skills.

[0742] A "virtual environment" is a digital simulation environment built to allow drivers to train in real time based on strategic models.

[0743] "User feedback" refers to feedback information provided by drivers based on their training experience in a virtual environment.

[0744] A "data processing device" is a computer system used to centrally manage and analyze video information.

[0745] An "information repository" is a database where collected data is centrally managed.

[0746] The system for implementing the present invention functions by implementing various means, mainly involving servers, terminals, and users, in order to support the improvement of drivers' driving skills.

[0747] The server receives video information recorded by the user, analyzes it, and digitizes the motion characteristics. These digitized motion characteristics are compared and analyzed with expert motion data to generate a strategic model suitable for the user. Machine learning software such as Python and TensorFlow are used for this comparison.

[0748] The terminal constructs a virtual environment based on the generated strategic model and presents it to the user. The user can experience this virtual environment using VR goggles. Within this virtual environment, real-time guidance is provided in response to the user's actions. The guidance content is provided as audio through earphones.

[0749] Users can further improve their driving skills based on insights gained through training in the virtual environment. This feedback is then returned to the server, further refining the strategic model.

[0750] For example, if a driver frequently applies sudden brakes while driving, the system will suggest ways to improve driving smoothness. The user can then practice smoother braking techniques within a virtual environment. Through this process, the driver can learn a safer and more efficient driving style.

[0751] An example of a prompt for a generative AI model is, "Please tell me what improvements can be made if the sudden braking is used too frequently."

[0752] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0753] Step 1:

[0754] The user uploads video information acquired while driving to the server via a terminal. The input is the user's driving video, which the server receives and saves to data storage. The storage location is specified as a particular folder in the database.

[0755] Step 2:

[0756] The server takes uploaded video information as input and uses a video analysis algorithm to digitize the motion characteristics. The output is digitized data showing driving characteristics, and characteristic driving patterns (e.g., frequency of sudden braking) are extracted. A video processing library and a machine learning model are used for this analysis.

[0757] Step 3:

[0758] The server retrieves motion characteristics from a database to compare them with expert performance data. The input consists of extracted motion characteristics and expert baseline data. The output is the comparison result, identifying the user's strengths and weaknesses in driving performance.

[0759] Step 4:

[0760] The server generates a strategic model based on the comparison results. The input is the comparison results, and the output is a customized strategic model aimed at improving the user's driving skills. A generative AI model is used for this generation, and prompts are used to provide guidance for model creation.

[0761] Step 5:

[0762] The terminal constructs a virtual environment based on the strategic model received from the server. An interactive simulation is designed for the user to experience. The input is the strategic model, and the output is the virtual environment visualized via VR goggles.

[0763] Step 6:

[0764] Users learn strategies through driving simulations within a virtual environment. The virtual environment allows for real-time driving instruction. Inputs are the user's actual operations and strategic models, while output is the experience that facilitates improvement in driving skills. Audio instruction is provided via headphones.

[0765] Step 7:

[0766] Users return feedback to the server based on their training experience in a virtual environment. This feedback is recorded as information necessary for improving the strategic model. The input is the user's feedback information, and the output is the data used to improve the strategic model in the next iteration.

[0767] 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.

[0768] This invention is a system aimed at enabling athletes to effectively learn how to play a game, and in particular, by combining it with an emotion engine, it provides strategic support that takes into account the user's psychological state. The system collects and analyzes the user's video data and emotion data, generates a strategic model tailored to each individual athlete, and makes that model available to experience in a virtual reality environment.

[0769] First, users record themselves during matches or practice sessions and upload the video data. During this process, cameras and microphones are used to collect emotional data, such as facial expressions and voice. This data is then sent to the server.

[0770] Next, the server analyzes the received data to extract the user's motor characteristics and emotional state. Along with specific motor characteristics (shot success rate, movement patterns, etc.), psychological factors such as stress levels and concentration are also evaluated using an emotion engine. Based on this, a comparison is made with a professional competition database to identify the user's challenges and to construct a strategic model tailored to the user's psychological state.

[0771] After generating the strategy model, the terminal implements this model in a virtual reality environment. Users can experience this virtual reality environment using VR goggles. In this process, the system provides an interactive environment similar to an actual match, and by providing real-time feedback on the user's actions along with emotional data, more personalized instruction is possible.

[0772] For example, if the device's emotion engine detects that the user is feeling pressured, it may instantly change instructions and provide guidance to help the user play in a more relaxed state. This allows the user to learn a play style that suits their own psychological state.

[0773] Users send feedback to the system based on the insights they gain from their experiences in the virtual reality environment. The server then uses this feedback to optimize its strategic model, enabling more refined training sessions in subsequent sessions.

[0774] Thus, this system utilizes an emotion engine to take into account the psychological factors of athletes, enabling personalized strategic coaching and aiming to improve athletes' game management skills.

[0775] The following describes the processing flow.

[0776] Step 1:

[0777] Users set up cameras and microphones to record their matches and practice sessions, simultaneously recording video and audio. This captures not only motion data but also traces of the user's emotions (facial expressions and tone of voice).

[0778] Step 2:

[0779] The user uploads recorded video and audio data to the server through the system interface.

[0780] Step 3:

[0781] The server receives the user's video and audio data and extracts movement characteristics by applying motion analysis algorithms. Simultaneously, it uses an emotion engine to perform facial expression and voice analysis to identify the user's emotional state. This includes quantifying stress levels and concentration levels.

[0782] Step 4:

[0783] The server compares extracted motor skills and emotional data with a professional competition database to analyze the user's strengths and areas for improvement in their playing style. Based on this information, it generates a strategic model tailored to the user's characteristics and emotional state.

[0784] Step 5:

[0785] The terminal uses the generated strategy model to set up a virtual reality environment. This prepares the user to simulate actual match situations through VR goggles.

[0786] Step 6:

[0787] Users wear VR goggles and experience simulations provided in a virtual reality environment. In this environment, users experience scenes based on strategic models and receive various feedback during gameplay.

[0788] Step 7:

[0789] The device uses real-time data from the emotion engine to provide instructions and voice feedback tailored to the user's gameplay. For example, if the user's concentration is waning, it will send concise advice or motivational messages.

[0790] Step 8:

[0791] Users provide feedback to the system based on the insights they gain through their experience, sharing information such as which strategies were effective.

[0792] Step 9:

[0793] The server further optimizes its strategic model and emotional interface based on feedback provided by users. Continuing this cycle improves the overall training effect of the system and contributes to improving users' gameplay skills.

[0794] (Example 2)

[0795] 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".

[0796] Traditional training systems for athletes primarily rely on data on athletic characteristics and fail to adequately consider the athlete's psychological state. As a result, it is difficult to provide optimal strategic models that address individual athlete psychological pressure and changes in concentration levels. There is a need to solve these problems and provide support that enables athletes to maintain a calm and focused state even during competition.

[0797] 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.

[0798] In this invention, the server includes means for acquiring video and audio data, means for digitizing motor characteristics and psychological state, and means for generating a strategic model that takes psychological state into account based on the results of comparative analysis. This makes it possible to comprehensively evaluate the mental and physical characteristics of athletes and construct individualized strategies.

[0799] "Video data" refers to visual information collected using equipment such as cameras, which records the actions of athletes and the conditions of the environment.

[0800] "Audio data" refers to acoustic information collected using equipment such as microphones, and includes recordings of the athletes' voices and surrounding sounds.

[0801] "Motor characteristics" refer to the physical characteristics of an athlete expressed as numerical data, and specifically include things like shot success rate and movement patterns.

[0802] "Psychological state" refers to the mental condition of an athlete, and is a factor in which stress levels, concentration levels, and other aspects are evaluated by the emotional engine.

[0803] A "strategic model" is a plan or guideline generated by considering the athletic characteristics and psychological state of an athlete, and serves as a virtual blueprint for guiding optimal actions during competition.

[0804] A "virtual environment" refers to a computer-generated simulated space or situation in which users can receive training through experiences that mimic reality.

[0805] "Feedback" refers to the process by which users return information to the system based on their experiences within a virtual environment, and this information is used to optimize strategic models.

[0806] This invention is a system that enables athletes to receive personalized strategic guidance while taking their psychological state into consideration.

[0807] First, users collect video and audio data using cameras and microphones during matches and practice sessions. This data includes the user's movements and ambient sounds. In particular, elements such as facial expressions and tone of voice represent emotional data.

[0808] Next, the collected data is uploaded to a cloud-based server. This server uses a generative AI model to analyze the user's motor characteristics and psychological state. Motor characteristics refer to things like shot success rate and movement patterns, while psychological state is evaluated as stress level and concentration level.

[0809] Based on the analysis results, the server compares them with a professional competition database to identify the user's challenges. Furthermore, it generates a corresponding strategic model. This strategic model takes into account the user's psychological state and guides them toward the optimal actions during the competition.

[0810] The device uses this strategic model to build a virtual environment. In this virtual environment, users can wear VR goggles and engage in immersive training. The interactive environment provided by the device allows users to receive real-time feedback and instructions based on their actions and emotions.

[0811] For example, if a user feels pressured during a match, the system may detect this and provide guidance to help the user play in a relaxed state. This allows users to effectively learn a play style that suits their psychological state.

[0812] A concrete example of a prompt would be something like, "Identify the stress the user feels during a match and suggest a virtual reality scenario to induce a relaxed state." Based on this, the AI ​​model designs an appropriate strategy and provides it to the user.

[0813] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0814] Step 1:

[0815] Users operate cameras and microphones to collect video and audio data during matches and practice sessions. This captures data on the user's movements, facial expressions, and voice. This input data serves as foundational information necessary for subsequent analysis.

[0816] Step 2:

[0817] The user uploads the collected video and audio data to the server through a dedicated application. The application converts the data to the appropriate format and sends it to the server via a secure communication channel. The output of this step is the raw data stored on the server.

[0818] Step 3:

[0819] The server analyzes uploaded video and audio data. Using a generative AI model, it extracts motor characteristics (e.g., shot success rate, movement patterns) from the data, while an emotion engine evaluates psychological state (e.g., stress level, concentration level). The input is the collected data, and the output is the motor characteristics and psychological evaluation results.

[0820] Step 4:

[0821] The server compares and analyzes user data against a professional competition database based on the analysis results. This analysis identifies the user's challenges and characteristics. Using prompts, the AI ​​model is instructed to "compare the user's movement characteristics with professional data and identify challenges." The output is the challenge identification result.

[0822] Step 5:

[0823] The server generates a strategic model that takes into account the identified problem and the user's psychological state. The generated AI model constructs the optimal strategy, which is then output as a plan to be reflected in the next virtual training session.

[0824] Step 6:

[0825] The device constructs a virtual environment based on a strategic model, allowing the user to immerse themselves in that environment using VR goggles. The device inputs strategic information into the VR system and outputs scenarios that allow for interactive conversation with the user.

[0826] Step 7:

[0827] Users train in a virtual environment and receive real-time feedback, allowing them to correct their behavior on the spot. The feedback input consists of user behavior and psychological data, while the output is appropriate guidance messages.

[0828] Step 8:

[0829] The user sends their experience in the virtual environment back to the server as feedback. The server receives this feedback and can further optimize the strategic model. The output is the optimized new strategic model.

[0830] (Application Example 2)

[0831] 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".

[0832] The goal is to address the challenge that athletes and individuals in their daily lives lack the means to effectively manage their emotions and improve their behavior while creating an optimal environment.

[0833] 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.

[0834] In this invention, the server includes means for acquiring dynamic data, means for digitizing biometric characteristics, means for comparing and analyzing expert behavioral data and biometric characteristics, and means for evaluating emotional states and adjusting environmental factors. This enables users to improve their behavior while experiencing an optimal environment tailored to their own emotions.

[0835] "Dynamic data" refers to information that reflects a user's physical and emotional state in real time.

[0836] "Biological characteristics" refer to data that characterizes an individual's physical behavior and physiological state.

[0837] "Expert behavioral data" refers to data on behaviors based on specialized skills and knowledge in a particular field.

[0838] Comparative analysis is the process of comparing different data to identify similarities and differences.

[0839] A "planning model" is a model designed to provide optimized guidelines for a user's specific goals and circumstances.

[0840] A "virtual environment" is a digital space created using computer technology that enables simulations of reality.

[0841] A "user" is an individual or group that operates this system and benefits from it.

[0842] "Response" refers to the reaction or feedback a user shows to a stimulus from a system.

[0843] A "centralized management system" is a device used to centrally process and store data.

[0844] An "information warehouse" is a system that systematically stores various types of data and manages them in a way that makes them accessible as needed.

[0845] "Emotional state" refers to the user's psychological health and emotional fluctuations.

[0846] "Environmental factors" refer to conditions, including physical or digital settings, surrounding the user.

[0847] The system that realizes this invention can acquire and analyze dynamic data, build virtual environments, perform sentiment analysis, and adjust the environment. Specifically, it utilizes devices such as home robots and mobile terminals.

[0848] The server uses hardware equipped with cameras and microphones to acquire dynamic data that reflects the user's physical and emotional state in real time. This collects biometric data, which is then analyzed using software such as Emotion AI SDK and TensorFlow. The analyzed biometric data is then uploaded and stored in a centralized management system and compared and analyzed with a database of expert behaviors.

[0849] The device generates a planning model based on comparative analysis results and constructs a virtual environment using computer simulations and VR technology. Here, the user can experience an optimal environment tailored to their own behavior and emotional state. The device's interface processes user responses and determines the need for environmental adjustments.

[0850] The user aims to achieve an optimal lifestyle by recognizing changes in their emotions and behavior through guidance from the system. Based on their emotional state, the device controls environmental factors, such as playing music through the speaker or adjusting LED lighting.

[0851] For example, if the device detects that the user is experiencing stress, it will change the lighting to a warm color and play relaxation music to alleviate the stress. It will also utilize an interactive interface to encourage relaxation by saying things like, "You've been working hard lately, why don't you take a little break?"

[0852] Through the generative AI model, an example prompt message can be used: "The system has detected that the user is experiencing stress. How would you guide the user to relax?"

[0853] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0854] Step 1:

[0855] The server acquires dynamic data using a camera and microphone. The input consists of real-time video and audio data. This data is analyzed by the Emotion AI SDK, which evaluates and outputs biometric data such as stress levels and concentration levels.

[0856] Step 2:

[0857] The server uploads the analyzed biometric data to a centralized management system. The system receives biometric data as input and compares and analyzes it against a professional behavioral database. The output provides analysis results tailored to the user's characteristics.

[0858] Step 3:

[0859] The terminal generates a planning model based on the analysis results received from the server. The input is the analysis results, which are then used to generate the AI ​​model and output the optimal course of action for the user.

[0860] Step 4:

[0861] The device utilizes VR technology with a planning model to build a virtual environment. The input is the planning model, and the output is a virtual simulation environment that the user can experience.

[0862] Step 5:

[0863] The user acts within a virtual environment presented by the device. The device detects the user's actions and re-evaluates their emotional state. In this process, the user's behavioral data is the input, and their emotional state is the output.

[0864] Step 6:

[0865] The device adjusts environmental factors based on the re-evaluated emotional state. The user's emotional state is the input, and the output is environmental adjustment (e.g., changing the lighting or playing music). Specific actions include changing the lighting color to a warmer color or playing relaxing music through the speaker.

[0866] Step 7:

[0867] The device generates prompts through an AI model and provides appropriate guidance to the user. The input is a re-evaluated emotional state, and the output is a prompt. Specifically, the guidance might be in the form of, "We have detected that the user is feeling stressed. How would you guide the user to relax?"

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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."

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] The following is further disclosed regarding the embodiments described above.

[0890] (Claim 1)

[0891] Means for acquiring video data,

[0892] A means for analyzing the aforementioned video data and converting the motion characteristics into data,

[0893] A means for comparing and analyzing professional competition data with the aforementioned movement characteristics,

[0894] A means for generating a strategic model based on the results of comparative analysis,

[0895] A means for generating a virtual reality environment using the aforementioned strategic model and presenting it to the user,

[0896] A means of receiving user feedback and reflecting it in the strategic model again,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, in which the user receives instructions in real time based on their actions within a virtual reality environment.

[0900] (Claim 3)

[0901] The system according to claim 1, wherein the aforementioned video data is uploaded to a server and processed in a centrally managed database.

[0902] "Example 1"

[0903] (Claim 1)

[0904] A device for acquiring video information,

[0905] A device that analyzes the aforementioned video information and quantifies its operating characteristics,

[0906] A device for comparing and examining expert competition information and the aforementioned operating characteristics,

[0907] Based on the results of the comparative study described above, a device for creating a strategic model,

[0908] A device that generates a virtual reality space using the aforementioned strategic model and provides it to the user,

[0909] A device that collects user feedback and incorporates it back into the strategic model,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, in which the user receives immediate instructions based on their actions in a virtual reality space.

[0913] (Claim 3)

[0914] The system according to claim 1, wherein the aforementioned video information is uploaded to a central device and processed by a centrally managed information recording device.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] Means for acquiring video information,

[0918] A means for analyzing the aforementioned video information and digitizing its motion characteristics,

[0919] A means for comparing and analyzing the movement information of an expert with the aforementioned movement characteristics,

[0920] A means of creating a strategic model based on the results of comparative analysis,

[0921] A means of constructing a virtual environment using the aforementioned strategic model and presenting it to the user,

[0922] A means of receiving feedback from users and reflecting it in the strategic model again,

[0923] A means of having the aforementioned strategic model experienced in a virtual environment as training,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, in which the user receives real-time guidance based on their actions within a virtual environment.

[0927] (Claim 3)

[0928] The system according to claim 1, wherein the aforementioned video information is transferred to a data processing device and processed at a centrally managed information storage facility.

[0929] "Example 2 of combining an emotion engine"

[0930] (Claim 1)

[0931] Means for acquiring video data and audio data,

[0932] A means for analyzing the aforementioned data and converting motor characteristics and psychological state into data,

[0933] A method for evaluating psychological state and calculating stress levels and concentration levels,

[0934] A means for comparing and analyzing professional competition data with the aforementioned athletic characteristics and psychological state,

[0935] A means for generating a strategic model that takes psychological states into account, based on the results of comparative analysis,

[0936] A means for generating a virtual environment using the aforementioned strategic model and presenting it to the user,

[0937] A means of receiving feedback from users' experiences in a virtual environment and optimizing the strategic model,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, wherein the user receives adaptive instructions in real time based on behavioral and emotional data within a virtual environment.

[0941] (Claim 3)

[0942] The system according to claim 1, wherein the aforementioned data is uploaded to a server and processed by a centrally integrated information processing device.

[0943] "Application example 2 when combining with an emotional engine"

[0944] (Claim 1)

[0945] Means for obtaining dynamic data,

[0946] A means for analyzing the aforementioned dynamic data and converting biological characteristics into data,

[0947] A means for comparing and analyzing expert behavioral data with the aforementioned biological characteristics,

[0948] A means for generating a planning model based on the results of comparative analysis,

[0949] A means for generating a virtual environment using the aforementioned planning model and presenting it to the user,

[0950] A means of receiving user responses and reflecting them in the planning model again,

[0951] A means of evaluating emotional states and adjusting environmental factors,

[0952] A system that includes this.

[0953] (Claim 2)

[0954] The system according to claim 1, in which the user receives real-time guidance and adjusts the environment based on their actions within the virtual environment.

[0955] (Claim 3)

[0956] The system according to claim 1, wherein the dynamic data is uploaded to a centralized management device and processed in a centrally controlled information warehouse. [Explanation of Symbols]

[0957] 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. Means for acquiring video data, A means for analyzing the aforementioned video data and converting the motion characteristics into data, A means for comparing and analyzing professional competition data with the aforementioned movement characteristics, A means for generating a strategic model based on the results of comparative analysis, A means for generating a virtual reality environment using the aforementioned strategic model and presenting it to the user, A means of receiving user feedback and reflecting it in the strategic model again, A system that includes this.

2. The system according to claim 1, in which the user receives instructions in real time based on their actions within a virtual reality environment.

3. The system according to claim 1, wherein the aforementioned video data is uploaded to a server and processed in a centrally managed database.

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