system

Abstract

The system according to this embodiment aims to analyze the gaze data of top athletes and provide personalized coaching and advice. [Solution] The system according to the embodiment comprises an eye-tracking unit, a recording unit, an analysis unit, and a provision unit. The eye-tracking unit tracks the gaze of an athlete wearing visual data storage glasses. The recording unit records the gaze data tracked by the eye-tracking unit. The analysis unit analyzes the data recorded by the recording unit. The provision unit provides coaching or advice based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the line of sight and awareness points of top players have not been fully grasped in detail, and coaching and advice based on them have not been sufficiently provided, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze the line-of-sight data of top players and provide individualized coaching and advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an eye-tracking unit, a recording unit, an analysis unit, and a provision unit. The eye-tracking unit tracks the gaze of an athlete wearing visual data storage glasses. The recording unit records the gaze data tracked by the eye-tracking unit. The analysis unit analyzes the data recorded by the recording unit. The provision unit provides coaching or advice based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the gaze data of top athletes and provide personalized coaching and advice. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The visual data storage system according to an embodiment of the present invention is a system that uses visual data storage glasses to collect data on where top athletes and instructors are looking, what they are focusing on, and what routines they are following. This data is then used, analyzed, and used to provide personalized coaching. The visual data storage system performs all data utilization, analysis, and coaching using AI. The visual data storage system can be applied not only to sports but also to hobbies such as Go and Shogi. First, top athletes wear the visual data storage glasses while playing, allowing their gaze and routines to be observed. These glasses digitize the athlete's gaze, points of focus, and routines. Specifically, the system consists of the following steps: For example, a top athlete wears the visual data storage glasses while playing. For example, the glasses record the athlete's gaze, points of focus, and routines in real time. For example, the recorded data is classified by sport and situation. For example, the classified data is analyzed by AI. For example, based on the analysis results, personalized coaching and advice are provided. For example, if a soccer player wears visual data-accumulating glasses while playing, data such as how the player tracks the ball and the timing of their passes will be collected. This data can be analyzed by AI to identify the player's playing style and weaknesses, and to suggest areas for improvement. Similarly, in hobbies such as Go or Shogi, wearing visual data-accumulating glasses can digitize the player's gaze, focus points, and routines, and AI can analyze this data to provide optimal strategies and advice. As a result, the visual data accumulation system can provide more effective coaching and advice to individual players, leading to improved performance.

[0029] The visual data storage system according to the embodiment comprises an eye-tracking unit, a recording unit, an analysis unit, and a data provision unit. The eye-tracking unit tracks the gaze of the athlete. The eye-tracking unit tracks the gaze using, for example, an infrared camera. The eye-tracking unit tracks the gaze using, for example, image processing technology. The eye-tracking unit collects data such as the position and movement patterns of the gaze. The recording unit records the gaze data tracked by the eye-tracking unit. The recording unit saves the gaze data in, for example, a digital format. The recording unit saves the gaze data to, for example, cloud storage. The recording unit saves the gaze data to, for example, a local device. The analysis unit analyzes the data recorded by the recording unit. The analysis unit analyzes the gaze data to identify the movement of the athlete's gaze. The analysis unit analyzes the gaze data to evaluate the degree of concentration of the athlete's gaze. The analysis unit analyzes the gaze data to extract patterns of the athlete's gaze. The data provision unit provides coaching and advice based on the data analyzed by the analysis unit. The providing unit can, for example, provide feedback to the player based on the analysis results. The providing unit can, for example, provide a training plan to the player based on the analysis results. The providing unit can, for example, provide tactical advice to the player based on the analysis results. In this way, the visual data storage system according to the embodiment can track, record, and analyze the player's gaze data and provide coaching and advice.

[0030] The eye-tracking unit tracks the athlete's gaze. For example, the eye-tracking unit uses an infrared camera to track the gaze. Infrared cameras can capture the athlete's eye movements with high precision and accurately track the gaze even in dark environments. Alternatively, the eye-tracking unit can use image processing technology to track the gaze. Image processing technology identifies the athlete's pupil position from the camera's footage and tracks their movement in real time. The eye-tracking unit collects data on the position and movement patterns of the gaze. The gaze position indicates the direction the athlete is looking, and the movement patterns show how the athlete is shifting their gaze. This allows the eye-tracking unit to record the athlete's gaze movements in detail, which can then be used for later analysis. Furthermore, the eye-tracking unit can also measure the speed and acceleration of the athlete's gaze, allowing for a more detailed understanding of the dynamics of their gaze movements. This enables the eye-tracking unit to collect comprehensive data on the athlete's gaze movements, contributing to improved athlete performance.

[0031] The recording unit records the eye-tracking data tracked by the eye-tracking unit. The recording unit saves the eye-tracking data in digital format, for example. Saving it in digital format makes it easy to search, analyze, and share. The recording unit saves the eye-tracking data to cloud storage, for example. Saving it to cloud storage makes it accessible from anywhere via the internet, allowing multiple coaches and analysts to use the data simultaneously. The recording unit saves the eye-tracking data to a local device, for example. Saving it to a local device ensures that the data is reliably saved even in environments with unstable internet connections. Furthermore, the recording unit regularly backs up the eye-tracking data to prevent data loss or corruption. This allows the recording unit to manage the eye-tracking data safely and efficiently, and utilize it for later analysis and feedback.

[0032] The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit analyzes eye-tracking data to identify the movements of the players' eyes. By identifying eye-tracking movements, it is possible to understand in detail how the players move their eyes. For example, the analysis unit analyzes eye-tracking data to evaluate the concentration level of the players' gaze. The concentration level of gaze indicates how long the players are looking at a particular object and serves as an indicator to evaluate the players' concentration and attention. For example, the analysis unit analyzes eye-tracking data to extract the players' gaze patterns. Gaze patterns show what kinds of eye movements the players are repeating, revealing the players' visual strategies and habits. Furthermore, the analysis unit can use AI to analyze eye-tracking data and automatically classify the players' gaze movements and patterns. The AI ​​learns from a large number of eye-tracking data samples and extracts the characteristics of the players' gaze with high accuracy. As a result, the analysis unit can analyze eye-tracking data quickly and accurately and provide information that helps improve players' performance.

[0033] The service provider offers coaching and advice based on data analyzed by the analysis department. For example, the service provider provides feedback to players based on the analysis results. This feedback includes specific information about the player's eye movements and concentration levels, providing guidance for players to improve their eye usage. For example, the service provider provides training plans to players based on the analysis results. These training plans include specific practice methods and drills to improve the player's eye movements, aiming to enhance the player's visual performance. For example, the service provider provides tactical advice to players based on the analysis results. This tactical advice includes specific instructions on how players should use their eyes during a match, aiming to improve the player's tactical decision-making abilities. Furthermore, the service provider enables players to intuitively understand their eye movements by visually displaying the analysis results. For example, heatmaps and graphs showing eye movements are used to visually indicate where the player is focusing their gaze. This allows the service provider to provide effective coaching and advice to players and support their performance improvement.

[0034] The visual data accumulating glasses include an awareness detection unit that detects points of attention. The awareness detection unit detects the player's points of attention. For example, the awareness detection unit detects the degree of gaze concentration. For example, the awareness detection unit detects the time spent fixating on a specific object. For example, the awareness detection unit measures how long the player's gaze remains on a specific point. This allows the awareness detection unit to detect the player's points of attention. Points of attention indicate, for example, objects or areas that the player is paying particular attention to. Detecting points of attention is important for understanding the player's playing style and tactical intentions. Some or all of the above processing in the awareness detection unit may be performed using, for example, AI, or not using AI. For example, the awareness detection unit can input gaze data into a generating AI and have the generating AI perform the detection of points of attention.

[0035] The visual data accumulating glasses include a routine recording unit that records the athlete's routine. The routine recording unit records the athlete's routine. For example, the routine recording unit records the athlete's movement patterns. For example, the routine recording unit records the athlete's repetitive movements. For example, the routine recording unit records the frequency of the athlete's specific movements. This allows the routine recording unit to record the athlete's routine. A routine, for example, represents the movements or procedures that an athlete repeatedly performs in a game or training. Recording routines is important for analyzing the athlete's performance and identifying areas for improvement. Some or all of the above-described processes in the routine recording unit may be performed using AI, for example, or without AI. For example, the routine recording unit can input movement data into a generating AI and have the generating AI record the routine.

[0036] The awareness detection unit can detect a player's points of focus in real time. For example, the awareness detection unit can detect the degree of focus of a player's gaze in real time. For example, the awareness detection unit can measure in real time how long a player is fixated on a specific object. For example, the awareness detection unit can measure in real time how long a player's gaze remains on a specific point. This allows the awareness detection unit to detect a player's points of focus in real time. Real-time detection is important for immediately understanding a player's playing style and tactical intentions. Real-time detection means, for example, low latency and high data update frequency. Some or all of the above processing in the awareness detection unit may be performed using AI, for example, or without AI. For example, the awareness detection unit can input gaze data acquired in real time into a generating AI and have the generating AI perform real-time detection of points of focus.

[0037] The routine recording unit can record the athlete's movement patterns. For example, the routine recording unit can record the athlete's movement patterns in real time. For example, the routine recording unit can record the athlete's repetitive movements in real time. For example, the routine recording unit can record the frequency of a specific athlete movement in real time. This allows the routine recording unit to record the athlete's movement patterns in real time. Movement patterns represent, for example, the movements or procedures that an athlete repeatedly performs in a game or training. Recording movement patterns is important for analyzing an athlete's performance and identifying areas for improvement. Some or all of the above-described processes in the routine recording unit may be performed using, for example, AI, or not using AI. For example, the routine recording unit can input movement data acquired in real time into a generating AI and have the generating AI record movement patterns.

[0038] The analysis unit can analyze recorded gaze data, attention point data, and routine data to identify a player's playing style and weaknesses. For example, the analysis unit can analyze gaze data to identify the player's eye movements. For example, the analysis unit can analyze attention point data to evaluate the player's level of concentration. For example, the analysis unit can analyze routine data to extract the player's movement patterns. In this way, the analysis unit can analyze recorded gaze data, attention point data, and routine data to identify a player's playing style and weaknesses. Playing style indicates, for example, the player's movement characteristics and tactical tendencies. Weaknesses indicate, for example, the frequency of a player's mistakes or a decline in performance in specific situations. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input gaze data, attention point data, and routine data into a generating AI and have the generating AI perform the identification of playing style and weaknesses.

[0039] The service provider can provide optimal coaching and advice to each individual based on the analysis results. For example, the service provider can provide feedback to players based on the analysis results. For example, the service provider can provide training plans to players based on the analysis results. For example, the service provider can provide tactical advice to players based on the analysis results. In this way, the service provider can provide optimal coaching and advice to each individual based on the analysis results. Optimal coaching and advice may include, for example, feedback based on the individual characteristics of each player. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI perform the task of providing optimal coaching and advice.

[0040] The eye-tracking unit can correct the gaze data while tracking the athlete's head movements and posture. For example, when the athlete moves their head, the eye-tracking unit corrects the gaze data and records the exact position of the gaze. For example, when the athlete changes their posture, the eye-tracking unit corrects the gaze data to accurately reflect the direction of the gaze. For example, when tracking the athlete's gaze while they are moving, the eye-tracking unit corrects the head movements in real time to maintain the accuracy of the gaze data. This allows the eye-tracking unit to correct the gaze data while considering the athlete's head movements and posture. Head movements and posture are detected, for example, using an accelerometer or gyroscope. Some or all of the above processing in the eye-tracking unit may be performed using AI, for example, or without AI. For example, the eye-tracking unit can input head movement and posture data into a generating AI and have the generating AI perform the correction of the gaze data.

[0041] The eye-tracking unit can measure the focal length of the athlete's gaze in real time during eye-tracking and reflect this in the data. For example, when the athlete is looking at a distant object, the eye-tracking unit measures the focal length of the gaze and reflects this in the data. For example, when the athlete is looking at a nearby object, the eye-tracking unit measures the focal length of the gaze and reflects this in the data. For example, when the athlete moves their gaze, the eye-tracking unit measures the change in focal length in real time and reflects this in the data. In this way, the eye-tracking unit can measure the focal length of the athlete's gaze in real time and reflect this in the data. The focal length is measured, for example, using gaze depth measurement technology. Some or all of the above processing in the eye-tracking unit may be performed using, for example, AI, or not using AI. For example, the eye-tracking unit can input focal length data into a generating AI and have the generating AI perform focal length measurement and data reflection.

[0042] The eye-tracking unit can correct the gaze data by considering the environmental information surrounding the player during eye tracking. For example, if the player is playing in a bright place, the eye-tracking unit corrects the gaze data and records the accurate position of the gaze. For example, if the player is playing in a dark place, the eye-tracking unit corrects the gaze data to accurately reflect the direction of the gaze. For example, when tracking the gaze of a player while they are moving, the eye-tracking unit corrects the surrounding environmental information in real time to maintain the accuracy of the gaze data. This allows the eye-tracking unit to correct the gaze data by considering the environmental information surrounding the player. Surrounding environmental information includes, for example, lighting conditions and background movement. Some or all of the above processing in the eye-tracking unit may be performed using, for example, AI, or not using AI. For example, the eye-tracking unit can input environmental information data into a generating AI and have the generating AI perform the correction of the gaze data.

[0043] The eye-tracking unit can measure the speed of the player's eye movements during eye tracking and reflect this in the data. For example, if the player moves their eyes quickly, the eye-tracking unit measures the speed of the eye movements and reflects this in the data. For example, if the player moves their eyes slowly, the eye-tracking unit measures the speed of the eye movements and reflects this in the data. For example, if the player moves their eyes at a constant speed, the eye-tracking unit measures the speed of the eye movements and reflects this in the data. In this way, the eye-tracking unit can measure the speed of the player's eye movements and reflect this in the data. The speed of eye movements includes, for example, the speed of eye movement and the acceleration of the eye. Some or all of the above processing in the eye-tracking unit may be performed using, for example, AI, or not using AI. For example, the eye-tracking unit can input eye movement speed data to a generating AI and have the generating AI perform speed measurement and data reflection.

[0044] The recording unit can adjust the level of detail of the recording based on the importance of the data during recording. For example, the recording unit may record important data at a high level of detail so that it can be analyzed in detail later. For example, the recording unit may record normal data at a medium level of detail for efficient storage. For example, the recording unit may record unnecessary data at a low level of detail to save storage space. In this way, the recording unit can adjust the level of detail of the recording based on the importance of the data. The importance of the data includes, for example, the frequency of data use and the impact of the data. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit may input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the recording.

[0045] The recording unit can apply different recording algorithms depending on the type of data during recording. For example, the recording unit can apply a dedicated eye-tracking algorithm to eye-tracking data for accurate recording. For example, the recording unit can apply a dedicated consciousness detection algorithm to consciousness point data for accurate recording. For example, the recording unit can apply a dedicated behavior pattern algorithm to routine data for accurate recording. This allows the recording unit to apply different recording algorithms depending on the type of data. The recording algorithms include, for example, compression algorithms and database selection. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can input the data type to a generating AI and have the generating AI execute the application of the recording algorithm.

[0046] The recording unit can determine the priority of records based on the data submission date at the time of recording. For example, the recording unit may prioritize recording the most recent data to save the latest information. For example, the recording unit may prioritize recording historical data to use for long-term analysis. For example, the recording unit may prioritize recording data with an approaching submission deadline to save it efficiently. This allows the recording unit to determine the priority of records based on the data submission date. The submission date includes, for example, the date and time the data was generated and the data submission deadline. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit may input submission date data into a generating AI and have the generating AI perform the determination of record priorities.

[0047] The recording unit can adjust the order of recording based on the relevance of the data during recording. For example, the recording unit can prioritize recording highly relevant data for efficient storage. For example, the recording unit can postpone recording less relevant data to save storage space. For example, the recording unit can analyze the relevance of the data and record it in the optimal order. This allows the recording unit to adjust the order of recording based on the relevance of the data. The relevance of the data includes, for example, data interdependence and data co-occurrence frequency. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the recording order.

[0048] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between gaze data and awareness point data. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between gaze data and routine data. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between awareness point data and routine data. In this way, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. The interrelationships between data include, for example, correlation analysis and identification of causal relationships. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input the interrelationships between data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0049] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can improve the accuracy of the analysis by considering attribute information such as the player's age and gender. For example, the analysis unit can improve the accuracy of the analysis by considering attribute information such as the player's experience and skill level. For example, the analysis unit can improve the accuracy of the analysis by considering attribute information such as the player's position and role. In this way, the analysis unit can perform analysis while considering the attribute information of the data submitter. Attribute information includes, for example, age, gender, and years of experience. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input attribute information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0050] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can perform analysis that reflects the characteristics of each region by considering the geographical distribution of the data. For example, the analysis unit can perform analysis that reflects the trends of each region by considering the geographical distribution of the data. For example, the analysis unit can perform analysis that reflects the challenges of each region by considering the geographical distribution of the data. In this way, the analysis unit can perform analysis while considering the geographical distribution of the data. Geographical distribution includes, for example, the data distribution and geographical characteristics of each region. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis. For example, the analysis unit can refer to relevant literature and perform an analysis that reflects the latest research results. For example, the analysis unit can refer to relevant literature and perform an analysis that reflects past research results. For example, the analysis unit can refer to relevant literature and perform an analysis that reflects knowledge in a specific field. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. Relevant literature includes, for example, academic papers and technical reports. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0052] The service provider can adjust the level of detail in coaching and advice based on the importance of the analysis results at the time of delivery. For example, the service provider can provide detailed coaching and advice for important analysis results. For example, the service provider can provide standard coaching and advice for ordinary analysis results. For example, the service provider can provide simplified coaching and advice for unnecessary analysis results. In this way, the service provider can adjust the level of detail in coaching and advice based on the importance of the analysis results. Level of detail includes, for example, the specificity of the information and the depth of the explanation. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0053] The service provider can apply different service algorithms depending on the category of the analysis results at the time of service provision. For example, the service provider can apply a service algorithm specifically for eye tracking to the analysis results of eye-tracking data. For example, the service provider can apply a service algorithm specifically for consciousness detection to the analysis results of consciousness point data. For example, the service provider can apply a service algorithm specifically for motion patterns to the analysis results of routine data. This allows the service provider to apply different service algorithms depending on the category of the analysis results. Service algorithms include, for example, personalized feedback and real-time service provision. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the category of the analysis results into a generating AI and have the generating AI execute the application of the service algorithm.

[0054] The service provider can determine the priority of coaching and advice based on the submission timing of analysis results at the time of delivery. For example, the service provider may prioritize coaching and advice based on the most recent analysis results. For example, the service provider may prioritize coaching and advice based on past analysis results. For example, the service provider may prioritize coaching and advice based on analysis results with approaching submission deadlines. This allows the service provider to determine the priority of coaching and advice based on the submission timing of analysis results. The priority may include, for example, importance and urgency. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider may input submission timing data into a generating AI and have the generating AI perform the priority determination.

[0055] The service provider can adjust the order of coaching and advice based on the relevance of the analysis results at the time of delivery. For example, the service provider can prioritize coaching and advice based on highly relevant analysis results. For example, the service provider can postpone coaching and advice based on less relevant analysis results. For example, the service provider can analyze the relevance of the analysis results and provide coaching and advice in the optimal order. This allows the service provider to adjust the order of coaching and advice based on the relevance of the analysis results. The order may include, for example, importance or relevance. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input relevance data into a generating AI and have the generating AI perform the order adjustment.

[0056] The consciousness detection unit can correct the consciousness point by considering the athlete's electroencephalogram (EEG) data when detecting consciousness. For example, the consciousness detection unit corrects the consciousness point by considering the EEG data when the athlete is concentrating. For example, the consciousness detection unit corrects the consciousness point by considering the EEG data when the athlete is relaxed. For example, the consciousness detection unit corrects the consciousness point by considering the EEG data when the athlete is tense. In this way, the consciousness detection unit can correct the consciousness point by considering the athlete's EEG data. The EEG data includes, for example, the frequency band and pattern of the brainwaves. Some or all of the above processing in the consciousness detection unit may be performed using, for example, AI, or without using AI. For example, the consciousness detection unit can input EEG data into a generating AI and have the generating AI perform the correction of the consciousness point.

[0057] The consciousness detection unit can correct the consciousness point by considering the athlete's heart rate data when detecting consciousness. For example, if the athlete's heart rate is high, the consciousness detection unit corrects the consciousness point by considering the heart rate data. For example, if the athlete's heart rate is low, the consciousness detection unit corrects the consciousness point by considering the heart rate data. For example, if the athlete's heart rate is fluctuating, the consciousness detection unit corrects the consciousness point by considering the heart rate data. In this way, the consciousness detection unit can correct the consciousness point by considering the athlete's heart rate data. Heart rate data includes, for example, heart rate fluctuations and heart rate patterns. Some or all of the above processing in the consciousness detection unit may be performed using, for example, AI, or without using AI. For example, the consciousness detection unit can input heart rate data to a generating AI and have the generating AI perform the correction of the consciousness point.

[0058] The routine recording unit can select the optimal recording method by referring to the athlete's past movement patterns when recording a routine. For example, the routine recording unit can refer to the athlete's past movement patterns and select the optimal recording method. For example, the routine recording unit can analyze the athlete's past movement patterns and select an efficient recording method. For example, the routine recording unit can select a detailed recording method based on the athlete's past movement patterns. In this way, the routine recording unit can refer to the athlete's past movement patterns and select the optimal recording method. Past movement patterns include, for example, past match data and practice data. Some or all of the above processing in the routine recording unit may be performed using, for example, AI, or without using AI. For example, the routine recording unit can input past movement pattern data into a generating AI and have the generating AI perform the selection of a recording method.

[0059] The routine recording unit can select the optimal recording method when recording a routine, taking into account the athlete's geographical location information. For example, when recording a routine performed by an athlete at a specific location, the routine recording unit can select the optimal recording method by taking into account geographical location information. For example, when recording a routine performed by an athlete while traveling, the routine recording unit can select the optimal recording method by taking into account geographical location information. For example, when recording a routine performed by an athlete at different locations, the routine recording unit can select the optimal recording method by taking into account geographical location information. This allows the routine recording unit to select the optimal recording method by taking into account the athlete's geographical location information. Geographical location information includes, for example, GPS data and location information services. Some or all of the above processing in the routine recording unit may be performed using, for example, AI, or without using AI. For example, the routine recording unit can input geographical location data into a generating AI and have the generating AI select the recording method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The visual data storage system can also include a biometric data acquisition unit that acquires the player's biometric data. The biometric data acquisition unit can acquire biometric data such as the player's heart rate, respiratory rate, and body temperature in real time. For example, if a player's heart rate suddenly increases during play, the biometric data acquisition unit can record that data and analyze it later. For example, if a player is relaxed, the biometric data acquisition unit can confirm that their respiratory rate is stable and record that data. As a result, the visual data storage system can acquire the player's biometric data and analyze it in combination with gaze data and awareness point data to provide more detailed coaching and advice.

[0062] The visual data storage system can further include a 3D motion recording unit that records the athlete's movements in 3D. The 3D motion recording unit, for example, captures the athlete's movements with multiple cameras and records them as a 3D model. The 3D motion recording unit can, for example, convert the athlete's movements into a 3D model in real time and analyze the details of the movements. The 3D motion recording unit can, for example, save the athlete's movements as a 3D model and play them back and analyze them later. As a result, the visual data storage system can record the athlete's movements in 3D and analyze them in combination with gaze data and awareness point data to provide more detailed coaching and advice.

[0063] The visual data storage system can also be equipped with an environmental recording unit to record the player's playing environment. The environmental recording unit can record, for example, the temperature, humidity, and lighting conditions of the place where the player is playing. If the player is playing outdoors, the environmental recording unit can record data such as wind speed and wind direction. If the player is playing indoors, the environmental recording unit can record data such as lighting brightness and sound level. As a result, the visual data storage system can record the player's playing environment and analyze it in combination with gaze data and awareness point data to provide more detailed coaching and advice.

[0064] The visual data storage system can further include a real-time feedback unit that provides real-time feedback on the player's movements. The real-time feedback unit can, for example, display eye-tracking data and awareness point data in real time while the player is playing. It can also, for example, provide real-time instructions to the player regarding areas for improvement during play. Furthermore, it can provide real-time coaching advice to the player during play. This allows the visual data storage system to provide real-time feedback on the player's movements, enabling immediate correction and improvement.

[0065] The visual data storage system can further include a motion analysis unit for analyzing the athlete's movements. The motion analysis unit can, for example, analyze the athlete's movements in detail and evaluate their efficiency. It can also, for example, analyze the athlete's movements and identify injury risks. Furthermore, it can analyze the athlete's movements and suggest specific areas for improvement to enhance performance. This allows the visual data storage system to analyze the athlete's movements in detail and combine this with gaze data and awareness point data to provide more effective coaching and advice.

[0066] The visual data storage system may further include a motion simulation unit that simulates the movements of athletes. The motion simulation unit can, for example, simulate the movements of athletes and evaluate their efficiency. It can also, for example, simulate the movements of athletes and identify the risk of injury. Furthermore, it can, for example, simulate the movements of athletes and suggest specific areas for improvement to enhance performance. This allows the visual data storage system to simulate the movements of athletes and combine this with gaze data and awareness point data to provide more effective coaching and advice.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The eye-tracking unit tracks the athlete's gaze. The eye-tracking unit tracks the gaze using, for example, an infrared camera or image processing technology, and collects data on the position and movement patterns of the gaze. Step 2: The recording unit records the eye-tracking data tracked by the eye-tracking unit. The recording unit saves the eye-tracking data in digital format, for example, and stores it in cloud storage or on a local device. Step 3: The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit analyzes the eye-tracking data to identify, evaluate, and extract the player's eye movements, concentration levels, and eye-tracking patterns. Step 4: The service provider provides coaching and advice based on the data analyzed by the analysis provider. For example, the service provider provides players with feedback, training plans, and tactical advice based on the analysis results.

[0069] (Example of form 2) The visual data storage system according to an embodiment of the present invention is a system that uses visual data storage glasses to collect data on where top athletes and instructors are looking, what they are focusing on, and what routines they are following. This data is then used, analyzed, and used to provide personalized coaching. The visual data storage system performs all data utilization, analysis, and coaching using AI. The visual data storage system can be applied not only to sports but also to hobbies such as Go and Shogi. First, top athletes wear the visual data storage glasses while playing, allowing their gaze and routines to be observed. These glasses digitize the athlete's gaze, points of focus, and routines. Specifically, the system consists of the following steps: For example, a top athlete wears the visual data storage glasses while playing. For example, the glasses record the athlete's gaze, points of focus, and routines in real time. For example, the recorded data is classified by sport and situation. For example, the classified data is analyzed by AI. For example, based on the analysis results, personalized coaching and advice are provided. For example, if a soccer player wears visual data-accumulating glasses while playing, data such as how the player tracks the ball and the timing of their passes will be collected. This data can be analyzed by AI to identify the player's playing style and weaknesses, and to suggest areas for improvement. Similarly, in hobbies such as Go or Shogi, wearing visual data-accumulating glasses can digitize the player's gaze, focus points, and routines, and AI can analyze this data to provide optimal strategies and advice. As a result, the visual data accumulation system can provide more effective coaching and advice to individual players, leading to improved performance.

[0070] The visual data storage system according to the embodiment comprises an eye-tracking unit, a recording unit, an analysis unit, and a data provision unit. The eye-tracking unit tracks the gaze of the athlete. The eye-tracking unit tracks the gaze using, for example, an infrared camera. The eye-tracking unit tracks the gaze using, for example, image processing technology. The eye-tracking unit collects data such as the position and movement patterns of the gaze. The recording unit records the gaze data tracked by the eye-tracking unit. The recording unit saves the gaze data in, for example, a digital format. The recording unit saves the gaze data to, for example, cloud storage. The recording unit saves the gaze data to, for example, a local device. The analysis unit analyzes the data recorded by the recording unit. The analysis unit analyzes the gaze data to identify the movement of the athlete's gaze. The analysis unit analyzes the gaze data to evaluate the degree of concentration of the athlete's gaze. The analysis unit analyzes the gaze data to extract patterns of the athlete's gaze. The data provision unit provides coaching and advice based on the data analyzed by the analysis unit. The providing unit can, for example, provide feedback to the player based on the analysis results. The providing unit can, for example, provide a training plan to the player based on the analysis results. The providing unit can, for example, provide tactical advice to the player based on the analysis results. In this way, the visual data storage system according to the embodiment can track, record, and analyze the player's gaze data and provide coaching and advice.

[0071] The eye-tracking unit tracks the athlete's gaze. For example, the eye-tracking unit uses an infrared camera to track the gaze. Infrared cameras can capture the athlete's eye movements with high precision and accurately track the gaze even in dark environments. Alternatively, the eye-tracking unit can use image processing technology to track the gaze. Image processing technology identifies the athlete's pupil position from the camera's footage and tracks their movement in real time. The eye-tracking unit collects data on the position and movement patterns of the gaze. The gaze position indicates the direction the athlete is looking, and the movement patterns show how the athlete is shifting their gaze. This allows the eye-tracking unit to record the athlete's gaze movements in detail, which can then be used for later analysis. Furthermore, the eye-tracking unit can also measure the speed and acceleration of the athlete's gaze, allowing for a more detailed understanding of the dynamics of their gaze movements. This enables the eye-tracking unit to collect comprehensive data on the athlete's gaze movements, contributing to improved athlete performance.

[0072] The recording unit records the eye-tracking data tracked by the eye-tracking unit. The recording unit saves the eye-tracking data in digital format, for example. Saving it in digital format makes it easy to search, analyze, and share. The recording unit saves the eye-tracking data to cloud storage, for example. Saving it to cloud storage makes it accessible from anywhere via the internet, allowing multiple coaches and analysts to use the data simultaneously. The recording unit saves the eye-tracking data to a local device, for example. Saving it to a local device ensures that the data is reliably saved even in environments with unstable internet connections. Furthermore, the recording unit regularly backs up the eye-tracking data to prevent data loss or corruption. This allows the recording unit to manage the eye-tracking data safely and efficiently, and utilize it for later analysis and feedback.

[0073] The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit analyzes eye-tracking data to identify the movements of the players' eyes. By identifying eye-tracking movements, it is possible to understand in detail how the players move their eyes. For example, the analysis unit analyzes eye-tracking data to evaluate the concentration level of the players' gaze. The concentration level of gaze indicates how long the players are looking at a particular object and serves as an indicator to evaluate the players' concentration and attention. For example, the analysis unit analyzes eye-tracking data to extract the players' gaze patterns. Gaze patterns show what kinds of eye movements the players are repeating, revealing the players' visual strategies and habits. Furthermore, the analysis unit can use AI to analyze eye-tracking data and automatically classify the players' gaze movements and patterns. The AI ​​learns from a large number of eye-tracking data samples and extracts the characteristics of the players' gaze with high accuracy. As a result, the analysis unit can analyze eye-tracking data quickly and accurately and provide information that helps improve players' performance.

[0074] The service provider offers coaching and advice based on data analyzed by the analysis department. For example, the service provider provides feedback to players based on the analysis results. This feedback includes specific information about the player's eye movements and concentration levels, providing guidance for players to improve their eye usage. For example, the service provider provides training plans to players based on the analysis results. These training plans include specific practice methods and drills to improve the player's eye movements, aiming to enhance the player's visual performance. For example, the service provider provides tactical advice to players based on the analysis results. This tactical advice includes specific instructions on how players should use their eyes during a match, aiming to improve the player's tactical decision-making abilities. Furthermore, the service provider enables players to intuitively understand their eye movements by visually displaying the analysis results. For example, heatmaps and graphs showing eye movements are used to visually indicate where the player is focusing their gaze. This allows the service provider to provide effective coaching and advice to players and support their performance improvement.

[0075] The visual data accumulating glasses include an awareness detection unit that detects points of attention. The awareness detection unit detects the player's points of attention. For example, the awareness detection unit detects the degree of gaze concentration. For example, the awareness detection unit detects the time spent fixating on a specific object. For example, the awareness detection unit measures how long the player's gaze remains on a specific point. This allows the awareness detection unit to detect the player's points of attention. Points of attention indicate, for example, objects or areas that the player is paying particular attention to. Detecting points of attention is important for understanding the player's playing style and tactical intentions. Some or all of the above processing in the awareness detection unit may be performed using, for example, AI, or not using AI. For example, the awareness detection unit can input gaze data into a generating AI and have the generating AI perform the detection of points of attention.

[0076] The visual data accumulating glasses include a routine recording unit that records the athlete's routine. The routine recording unit records the athlete's routine. For example, the routine recording unit records the athlete's movement patterns. For example, the routine recording unit records the athlete's repetitive movements. For example, the routine recording unit records the frequency of the athlete's specific movements. This allows the routine recording unit to record the athlete's routine. A routine, for example, represents the movements or procedures that an athlete repeatedly performs in a game or training. Recording routines is important for analyzing the athlete's performance and identifying areas for improvement. Some or all of the above-described processes in the routine recording unit may be performed using AI, for example, or without AI. For example, the routine recording unit can input movement data into a generating AI and have the generating AI record the routine.

[0077] The awareness detection unit can detect a player's points of focus in real time. For example, the awareness detection unit can detect the degree of focus of a player's gaze in real time. For example, the awareness detection unit can measure in real time how long a player is fixated on a specific object. For example, the awareness detection unit can measure in real time how long a player's gaze remains on a specific point. This allows the awareness detection unit to detect a player's points of focus in real time. Real-time detection is important for immediately understanding a player's playing style and tactical intentions. Real-time detection means, for example, low latency and high data update frequency. Some or all of the above processing in the awareness detection unit may be performed using AI, for example, or without AI. For example, the awareness detection unit can input gaze data acquired in real time into a generating AI and have the generating AI perform real-time detection of points of focus.

[0078] The routine recording unit can record the athlete's movement patterns. For example, the routine recording unit can record the athlete's movement patterns in real time. For example, the routine recording unit can record the athlete's repetitive movements in real time. For example, the routine recording unit can record the frequency of a specific athlete movement in real time. This allows the routine recording unit to record the athlete's movement patterns in real time. Movement patterns represent, for example, the movements or procedures that an athlete repeatedly performs in a game or training. Recording movement patterns is important for analyzing an athlete's performance and identifying areas for improvement. Some or all of the above-described processes in the routine recording unit may be performed using, for example, AI, or not using AI. For example, the routine recording unit can input movement data acquired in real time into a generating AI and have the generating AI record movement patterns.

[0079] The analysis unit can analyze recorded gaze data, attention point data, and routine data to identify a player's playing style and weaknesses. For example, the analysis unit can analyze gaze data to identify the player's eye movements. For example, the analysis unit can analyze attention point data to evaluate the player's level of concentration. For example, the analysis unit can analyze routine data to extract the player's movement patterns. In this way, the analysis unit can analyze recorded gaze data, attention point data, and routine data to identify a player's playing style and weaknesses. Playing style indicates, for example, the player's movement characteristics and tactical tendencies. Weaknesses indicate, for example, the frequency of a player's mistakes or a decline in performance in specific situations. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input gaze data, attention point data, and routine data into a generating AI and have the generating AI perform the identification of playing style and weaknesses.

[0080] The service provider can provide optimal coaching and advice to each individual based on the analysis results. For example, the service provider can provide feedback to players based on the analysis results. For example, the service provider can provide training plans to players based on the analysis results. For example, the service provider can provide tactical advice to players based on the analysis results. In this way, the service provider can provide optimal coaching and advice to each individual based on the analysis results. Optimal coaching and advice may include, for example, feedback based on the individual characteristics of each player. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI perform the task of providing optimal coaching and advice.

[0081] The eye-tracking unit can estimate the athlete's emotions and adjust the accuracy of eye-tracking based on the estimated emotions. For example, if the athlete is nervous, the eye-tracking unit can increase the accuracy of eye-tracking to capture even subtle eye movements. For example, if the athlete is relaxed, the eye-tracking unit can return the accuracy of eye-tracking to normal and record natural eye movements. For example, if the athlete is concentrating, the eye-tracking unit can optimize the accuracy of eye-tracking and prioritize recording important eye movements. In this way, the eye-tracking unit can adjust the accuracy of eye-tracking based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the eye-tracking unit may be performed using AI or not using AI. For example, the eye-tracking unit can input the athlete's emotion data into the generative AI and have the generative AI perform the adjustment of eye-tracking accuracy.

[0082] The eye-tracking unit can correct the gaze data while tracking the athlete's head movements and posture. For example, when the athlete moves their head, the eye-tracking unit corrects the gaze data and records the exact position of the gaze. For example, when the athlete changes their posture, the eye-tracking unit corrects the gaze data to accurately reflect the direction of the gaze. For example, when tracking the athlete's gaze while they are moving, the eye-tracking unit corrects the head movements in real time to maintain the accuracy of the gaze data. This allows the eye-tracking unit to correct the gaze data while considering the athlete's head movements and posture. Head movements and posture are detected, for example, using an accelerometer or gyroscope. Some or all of the above processing in the eye-tracking unit may be performed using AI, for example, or without AI. For example, the eye-tracking unit can input head movement and posture data into a generating AI and have the generating AI perform the correction of the gaze data.

[0083] The eye-tracking unit can measure the focal length of the athlete's gaze in real time during eye-tracking and reflect this in the data. For example, when the athlete is looking at a distant object, the eye-tracking unit measures the focal length of the gaze and reflects this in the data. For example, when the athlete is looking at a nearby object, the eye-tracking unit measures the focal length of the gaze and reflects this in the data. For example, when the athlete moves their gaze, the eye-tracking unit measures the change in focal length in real time and reflects this in the data. In this way, the eye-tracking unit can measure the focal length of the athlete's gaze in real time and reflect this in the data. The focal length is measured, for example, using gaze depth measurement technology. Some or all of the above processing in the eye-tracking unit may be performed using, for example, AI, or not using AI. For example, the eye-tracking unit can input focal length data into a generating AI and have the generating AI perform focal length measurement and data reflection.

[0084] The eye-tracking unit can estimate the athlete's emotions and determine the priority of eye-tracking based on the estimated emotions. For example, if the athlete is tense, the eye-tracking unit will prioritize tracking important eye movements. If the athlete is relaxed, the eye-tracking unit will prioritize tracking natural eye movements. If the athlete is concentrating, the eye-tracking unit will prioritize tracking specific eye movements. This allows the eye-tracking unit to determine the priority of eye-tracking based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the eye-tracking unit may be performed using AI or not. For example, the eye-tracking unit can input the athlete's emotion data into a generative AI and have the generative AI perform the determination of eye-tracking priorities.

[0085] The eye-tracking unit can correct the gaze data by considering the environmental information surrounding the player during eye tracking. For example, if the player is playing in a bright place, the eye-tracking unit corrects the gaze data and records the accurate position of the gaze. For example, if the player is playing in a dark place, the eye-tracking unit corrects the gaze data to accurately reflect the direction of the gaze. For example, when tracking the gaze of a player while they are moving, the eye-tracking unit corrects the surrounding environmental information in real time to maintain the accuracy of the gaze data. This allows the eye-tracking unit to correct the gaze data by considering the environmental information surrounding the player. Surrounding environmental information includes, for example, lighting conditions and background movement. Some or all of the above processing in the eye-tracking unit may be performed using, for example, AI, or not using AI. For example, the eye-tracking unit can input environmental information data into a generating AI and have the generating AI perform the correction of the gaze data.

[0086] The eye-tracking unit can measure the speed of the player's eye movements during eye tracking and reflect this in the data. For example, if the player moves their eyes quickly, the eye-tracking unit measures the speed of the eye movements and reflects this in the data. For example, if the player moves their eyes slowly, the eye-tracking unit measures the speed of the eye movements and reflects this in the data. For example, if the player moves their eyes at a constant speed, the eye-tracking unit measures the speed of the eye movements and reflects this in the data. In this way, the eye-tracking unit can measure the speed of the player's eye movements and reflect this in the data. The speed of eye movements includes, for example, the speed of eye movement and the acceleration of the eye. Some or all of the above processing in the eye-tracking unit may be performed using, for example, AI, or not using AI. For example, the eye-tracking unit can input eye movement speed data to a generating AI and have the generating AI perform speed measurement and data reflection.

[0087] The recording unit can estimate the player's emotions and adjust the format of the recorded data based on the estimated emotions. For example, if the player is nervous, the recording unit can save detailed data for later analysis. For example, if the player is relaxed, the recording unit can save normal data to record natural play. For example, if the player is focused, the recording unit can prioritize saving important data for later analysis. This allows the recording unit to adjust the format of the recorded data based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not using AI. For example, the recording unit can input the player's emotion data into a generative AI and have the generative AI perform the adjustment of the storage format.

[0088] The recording unit can adjust the level of detail of the recording based on the importance of the data during recording. For example, the recording unit may record important data at a high level of detail so that it can be analyzed in detail later. For example, the recording unit may record normal data at a medium level of detail for efficient storage. For example, the recording unit may record unnecessary data at a low level of detail to save storage space. In this way, the recording unit can adjust the level of detail of the recording based on the importance of the data. The importance of the data includes, for example, the frequency of data use and the impact of the data. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit may input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the recording.

[0089] The recording unit can apply different recording algorithms depending on the type of data during recording. For example, the recording unit can apply a dedicated eye-tracking algorithm to eye-tracking data for accurate recording. For example, the recording unit can apply a dedicated consciousness detection algorithm to consciousness point data for accurate recording. For example, the recording unit can apply a dedicated behavior pattern algorithm to routine data for accurate recording. This allows the recording unit to apply different recording algorithms depending on the type of data. The recording algorithms include, for example, compression algorithms and database selection. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can input the data type to a generating AI and have the generating AI execute the application of the recording algorithm.

[0090] The recording unit can estimate the athlete's emotions and determine the order in which to save the recorded data based on the estimated emotions. For example, if the athlete is nervous, the recording unit will prioritize saving important data. For example, if the athlete is relaxed, the recording unit will prioritize saving normal data. For example, if the athlete is focused, the recording unit will prioritize saving specific data. In this way, the recording unit can determine the order in which to save the recorded data based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the athlete's emotion data into a generative AI and have the generative AI perform the determination of the saving order.

[0091] The recording unit can determine the priority of records based on the data submission date at the time of recording. For example, the recording unit may prioritize recording the most recent data to save the latest information. For example, the recording unit may prioritize recording historical data to use for long-term analysis. For example, the recording unit may prioritize recording data with an approaching submission deadline to save it efficiently. This allows the recording unit to determine the priority of records based on the data submission date. The submission date includes, for example, the date and time the data was generated and the data submission deadline. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit may input submission date data into a generating AI and have the generating AI perform the determination of record priorities.

[0092] The recording unit can adjust the order of recording based on the relevance of the data during recording. For example, the recording unit can prioritize recording highly relevant data for efficient storage. For example, the recording unit can postpone recording less relevant data to save storage space. For example, the recording unit can analyze the relevance of the data and record it in the optimal order. This allows the recording unit to adjust the order of recording based on the relevance of the data. The relevance of the data includes, for example, data interdependence and data co-occurrence frequency. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the recording order.

[0093] The analysis unit can estimate the athlete's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the athlete is nervous, the analysis unit applies a detailed analysis algorithm and analyzes subtle data. For example, if the athlete is relaxed, the analysis unit applies a normal analysis algorithm and analyzes natural data. For example, if the athlete is focused, the analysis unit applies a specific analysis algorithm and analyzes important data. This allows the analysis unit to adjust the analysis algorithm based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The analysis algorithm includes, for example, machine learning algorithms and statistical analysis methods. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the athlete's emotion data into the generative AI and have the generative AI adjust the analysis algorithm.

[0094] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between gaze data and awareness point data. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between gaze data and routine data. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between awareness point data and routine data. In this way, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. The interrelationships between data include, for example, correlation analysis and identification of causal relationships. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input the interrelationships between data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0095] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can improve the accuracy of the analysis by considering attribute information such as the player's age and gender. For example, the analysis unit can improve the accuracy of the analysis by considering attribute information such as the player's experience and skill level. For example, the analysis unit can improve the accuracy of the analysis by considering attribute information such as the player's position and role. In this way, the analysis unit can perform analysis while considering the attribute information of the data submitter. Attribute information includes, for example, age, gender, and years of experience. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input attribute information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0096] The analysis unit can estimate the player's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the player is nervous, the analysis unit will prioritize displaying important analysis results. For example, if the player is relaxed, the analysis unit will prioritize displaying normal analysis results. For example, if the player is focused, the analysis unit will prioritize displaying specific analysis results. In this way, the analysis unit can adjust the display order of the analysis results based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The display order may include, for example, importance or relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the player's emotion data into a generative AI and have the generative AI perform the adjustment of the display order.

[0097] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can perform analysis that reflects the characteristics of each region by considering the geographical distribution of the data. For example, the analysis unit can perform analysis that reflects the trends of each region by considering the geographical distribution of the data. For example, the analysis unit can perform analysis that reflects the challenges of each region by considering the geographical distribution of the data. In this way, the analysis unit can perform analysis while considering the geographical distribution of the data. Geographical distribution includes, for example, the data distribution and geographical characteristics of each region. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0098] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis. For example, the analysis unit can refer to relevant literature and perform an analysis that reflects the latest research results. For example, the analysis unit can refer to relevant literature and perform an analysis that reflects past research results. For example, the analysis unit can refer to relevant literature and perform an analysis that reflects knowledge in a specific field. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. Relevant literature includes, for example, academic papers and technical reports. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0099] The service provider can estimate the athlete's emotions and adjust the way coaching and advice are expressed based on the estimated emotions. For example, if the athlete is nervous, the service provider will use gentle language for coaching and advice. For example, if the athlete is relaxed, the service provider will use detailed language for coaching and advice. For example, if the athlete is focused, the service provider will use specific language for coaching and advice. In this way, the service provider can adjust the way coaching and advice are expressed based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Expression methods include, for example, word choice and feedback format. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the athlete's emotion data into a generative AI and have the generative AI perform the adjustment of expression methods.

[0100] The service provider can adjust the level of detail in coaching and advice based on the importance of the analysis results at the time of delivery. For example, the service provider can provide detailed coaching and advice for important analysis results. For example, the service provider can provide standard coaching and advice for ordinary analysis results. For example, the service provider can provide simplified coaching and advice for unnecessary analysis results. In this way, the service provider can adjust the level of detail in coaching and advice based on the importance of the analysis results. Level of detail includes, for example, the specificity of the information and the depth of the explanation. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0101] The service provider can apply different service algorithms depending on the category of the analysis results at the time of service provision. For example, the service provider can apply a service algorithm specifically for eye tracking to the analysis results of eye-tracking data. For example, the service provider can apply a service algorithm specifically for consciousness detection to the analysis results of consciousness point data. For example, the service provider can apply a service algorithm specifically for motion patterns to the analysis results of routine data. This allows the service provider to apply different service algorithms depending on the category of the analysis results. Service algorithms include, for example, personalized feedback and real-time service provision. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the category of the analysis results into a generating AI and have the generating AI execute the application of the service algorithm.

[0102] The service provider can estimate the athlete's emotions and adjust the length of coaching and advice based on the estimated emotions. For example, if the athlete is nervous, the service provider will provide short, to-the-point coaching and advice. For example, if the athlete is relaxed, the service provider will provide longer coaching and advice that includes detailed explanations. For example, if the athlete is focused, the service provider will provide coaching and advice of a moderate length that includes specific content. In this way, the service provider can adjust the length of coaching and advice based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Length includes, for example, the time for feedback or the length of the text. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the athlete's emotion data into a generative AI and have the generative AI perform the length adjustment.

[0103] The service provider can determine the priority of coaching and advice based on the submission timing of analysis results at the time of delivery. For example, the service provider may prioritize coaching and advice based on the most recent analysis results. For example, the service provider may prioritize coaching and advice based on past analysis results. For example, the service provider may prioritize coaching and advice based on analysis results with approaching submission deadlines. This allows the service provider to determine the priority of coaching and advice based on the submission timing of analysis results. The priority may include, for example, importance and urgency. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider may input submission timing data into a generating AI and have the generating AI perform the priority determination.

[0104] The service provider can adjust the order of coaching and advice based on the relevance of the analysis results at the time of delivery. For example, the service provider can prioritize coaching and advice based on highly relevant analysis results. For example, the service provider can postpone coaching and advice based on less relevant analysis results. For example, the service provider can analyze the relevance of the analysis results and provide coaching and advice in the optimal order. This allows the service provider to adjust the order of coaching and advice based on the relevance of the analysis results. The order may include, for example, importance or relevance. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input relevance data into a generating AI and have the generating AI perform the order adjustment.

[0105] The consciousness detection unit can estimate the athlete's emotions and adjust the detection accuracy of consciousness points based on the estimated emotions. For example, if the athlete is tense, the consciousness detection unit increases the detection accuracy of consciousness points to capture even subtle movements of consciousness. For example, if the athlete is relaxed, the consciousness detection unit returns the detection accuracy of consciousness points to normal and records natural movements of consciousness. For example, if the athlete is focused, the consciousness detection unit optimizes the detection accuracy of consciousness points and prioritizes recording important movements of consciousness. In this way, the consciousness detection unit can adjust the detection accuracy of consciousness points based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Detection accuracy includes, for example, the error range and the reliability of detection. Some or all of the above processing in the consciousness detection unit may be performed using AI, for example, or without AI. For example, the consciousness detection unit can input the athlete's emotional data into a generating AI, which can then adjust the detection accuracy.

[0106] The consciousness detection unit can correct the consciousness point by considering the athlete's electroencephalogram (EEG) data when detecting consciousness. For example, the consciousness detection unit corrects the consciousness point by considering the EEG data when the athlete is concentrating. For example, the consciousness detection unit corrects the consciousness point by considering the EEG data when the athlete is relaxed. For example, the consciousness detection unit corrects the consciousness point by considering the EEG data when the athlete is tense. In this way, the consciousness detection unit can correct the consciousness point by considering the athlete's EEG data. The EEG data includes, for example, the frequency band and pattern of the brainwaves. Some or all of the above processing in the consciousness detection unit may be performed using, for example, AI, or without using AI. For example, the consciousness detection unit can input EEG data into a generating AI and have the generating AI perform the correction of the consciousness point.

[0107] The consciousness detection unit can estimate the athlete's emotions and determine the priority of consciousness points based on the estimated emotions. For example, if the athlete is tense, the consciousness detection unit will prioritize detecting important consciousness points. For example, if the athlete is relaxed, the consciousness detection unit will prioritize detecting normal consciousness points. For example, if the athlete is focused, the consciousness detection unit will prioritize detecting specific consciousness points. This allows the consciousness detection unit to determine the priority of consciousness points based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Prioritization includes, for example, importance or urgency. Some or all of the above processing in the consciousness detection unit may be performed using AI, for example, or without AI. For example, the consciousness detection unit can input the athlete's emotion data into a generative AI and have the generative AI perform the priority determination.

[0108] The consciousness detection unit can correct the consciousness point by considering the athlete's heart rate data when detecting consciousness. For example, if the athlete's heart rate is high, the consciousness detection unit corrects the consciousness point by considering the heart rate data. For example, if the athlete's heart rate is low, the consciousness detection unit corrects the consciousness point by considering the heart rate data. For example, if the athlete's heart rate is fluctuating, the consciousness detection unit corrects the consciousness point by considering the heart rate data. In this way, the consciousness detection unit can correct the consciousness point by considering the athlete's heart rate data. Heart rate data includes, for example, heart rate fluctuations and heart rate patterns. Some or all of the above processing in the consciousness detection unit may be performed using, for example, AI, or without using AI. For example, the consciousness detection unit can input heart rate data to a generating AI and have the generating AI perform the correction of the consciousness point.

[0109] The routine recording unit can estimate the athlete's emotions and adjust the routine recording method based on the estimated emotions. For example, if the athlete is nervous, the routine recording unit may record a detailed routine for later analysis. For example, if the athlete is relaxed, the routine recording unit may record a normal routine and natural movements. For example, if the athlete is focused, the routine recording unit may prioritize recording important routines for later analysis. This allows the routine recording unit to adjust the routine recording method based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Recording methods include, for example, manual recording and automatic recording. Some or all of the above processing in the routine recording unit may be performed using AI, for example, or without AI. For example, the routine recording unit can input the athlete's emotion data into the generative AI and have the generative AI adjust the recording method.

[0110] The routine recording unit can select the optimal recording method by referring to the athlete's past movement patterns when recording a routine. For example, the routine recording unit can refer to the athlete's past movement patterns and select the optimal recording method. For example, the routine recording unit can analyze the athlete's past movement patterns and select an efficient recording method. For example, the routine recording unit can select a detailed recording method based on the athlete's past movement patterns. In this way, the routine recording unit can refer to the athlete's past movement patterns and select the optimal recording method. Past movement patterns include, for example, past match data and practice data. Some or all of the above processing in the routine recording unit may be performed using, for example, AI, or without using AI. For example, the routine recording unit can input past movement pattern data into a generating AI and have the generating AI perform the selection of a recording method.

[0111] The routine recording unit can estimate the athlete's emotions and determine the priority of routines based on the estimated emotions. For example, if the athlete is nervous, the routine recording unit will prioritize recording important routines. If the athlete is relaxed, the routine recording unit will prioritize recording normal routines. If the athlete is focused, the routine recording unit will prioritize recording specific routines. In this way, the routine recording unit can determine the priority of routines based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Prioritization includes, for example, importance and urgency. Some or all of the above processing in the routine recording unit may be performed using AI, for example, or not using AI. For example, the routine recording unit can input the athlete's emotion data into a generative AI and have the generative AI perform the priority determination.

[0112] The routine recording unit can select the optimal recording method when recording a routine, taking into account the athlete's geographical location information. For example, when recording a routine performed by an athlete at a specific location, the routine recording unit can select the optimal recording method by taking into account geographical location information. For example, when recording a routine performed by an athlete while traveling, the routine recording unit can select the optimal recording method by taking into account geographical location information. For example, when recording a routine performed by an athlete at different locations, the routine recording unit can select the optimal recording method by taking into account geographical location information. This allows the routine recording unit to select the optimal recording method by taking into account the athlete's geographical location information. Geographical location information includes, for example, GPS data and location information services. Some or all of the above processing in the routine recording unit may be performed using, for example, AI, or without using AI. For example, the routine recording unit can input geographical location data into a generating AI and have the generating AI select the recording method.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The visual data storage system can also include a biometric data acquisition unit that acquires the player's biometric data. The biometric data acquisition unit can acquire biometric data such as the player's heart rate, respiratory rate, and body temperature in real time. For example, if a player's heart rate suddenly increases during play, the biometric data acquisition unit can record that data and analyze it later. For example, if a player is relaxed, the biometric data acquisition unit can confirm that their respiratory rate is stable and record that data. As a result, the visual data storage system can acquire the player's biometric data and analyze it in combination with gaze data and awareness point data to provide more detailed coaching and advice.

[0115] The visual data storage system can further include an emotion adjustment unit that estimates the athlete's emotions and adjusts the coaching content based on the estimated emotions. For example, if the athlete is nervous, the emotion adjustment unit provides advice to help them relax. For example, if the athlete is concentrating, the emotion adjustment unit provides advice to help them maintain their concentration. For example, if the athlete is feeling down, the emotion adjustment unit provides advice to help them boost their motivation. In this way, the visual data storage system can provide coaching that is tailored to the athlete's emotions, thereby maximizing the athlete's performance.

[0116] The visual data storage system can further include a 3D motion recording unit that records the athlete's movements in 3D. The 3D motion recording unit, for example, captures the athlete's movements with multiple cameras and records them as a 3D model. The 3D motion recording unit can, for example, convert the athlete's movements into a 3D model in real time and analyze the details of the movements. The 3D motion recording unit can, for example, save the athlete's movements as a 3D model and play them back and analyze them later. As a result, the visual data storage system can record the athlete's movements in 3D and analyze them in combination with gaze data and awareness point data to provide more detailed coaching and advice.

[0117] The visual data storage system can also be equipped with an environmental recording unit to record the player's playing environment. The environmental recording unit can record, for example, the temperature, humidity, and lighting conditions of the place where the player is playing. If the player is playing outdoors, the environmental recording unit can record data such as wind speed and wind direction. If the player is playing indoors, the environmental recording unit can record data such as lighting brightness and sound level. As a result, the visual data storage system can record the player's playing environment and analyze it in combination with gaze data and awareness point data to provide more detailed coaching and advice.

[0118] The visual data storage system may further include an emotional training adjustment unit that estimates the athlete's emotions and adjusts the training plan based on those emotions. For example, if the athlete is tense, the emotional training adjustment unit suggests training to help them relax. For example, if the athlete is focused, the emotional training adjustment unit suggests training to help them maintain that focus. For example, if the athlete is tired, the emotional training adjustment unit suggests training to help them refresh. This allows the visual data storage system to provide a training plan tailored to the athlete's emotions, thereby maximizing the athlete's performance.

[0119] The visual data storage system can further include a real-time feedback unit that provides real-time feedback on the player's movements. The real-time feedback unit can, for example, display eye-tracking data and awareness point data in real time while the player is playing. It can also, for example, provide real-time instructions to the player regarding areas for improvement during play. Furthermore, it can provide real-time coaching advice to the player during play. This allows the visual data storage system to provide real-time feedback on the player's movements, enabling immediate correction and improvement.

[0120] The visual data storage system may further include an emotion feedback adjustment unit that estimates the athlete's emotions and adjusts the timing of feedback based on the estimated emotions. For example, if the athlete is tense, the emotion feedback adjustment unit may delay feedback to help them relax. For example, if the athlete is focused, the emotion feedback adjustment unit may provide immediate feedback to maintain their concentration. For example, if the athlete is tired, the emotion feedback adjustment unit may withhold feedback to encourage rest. In this way, the visual data storage system can provide feedback timing that is appropriate to the athlete's emotions, thereby maximizing the athlete's performance.

[0121] The visual data storage system can further include a motion analysis unit for analyzing the athlete's movements. The motion analysis unit can, for example, analyze the athlete's movements in detail and evaluate their efficiency. It can also, for example, analyze the athlete's movements and identify injury risks. Furthermore, it can analyze the athlete's movements and suggest specific areas for improvement to enhance performance. This allows the visual data storage system to analyze the athlete's movements in detail and combine this with gaze data and awareness point data to provide more effective coaching and advice.

[0122] The visual data storage system may further include an emotion-motion analysis adjustment unit that estimates the athlete's emotions and adjusts the accuracy of the motion analysis based on the estimated emotions. For example, if the athlete is tense, the emotion-motion analysis adjustment unit performs a detailed motion analysis to capture subtle differences in movement. For example, if the athlete is relaxed, the emotion-motion analysis adjustment unit performs a normal motion analysis to evaluate natural movements. For example, if the athlete is concentrating, the emotion-motion analysis adjustment unit performs an analysis that focuses on specific movements. In this way, the visual data storage system can provide motion analysis that is tailored to the athlete's emotions, thereby maximizing the athlete's performance.

[0123] The visual data storage system may further include a motion simulation unit that simulates the movements of athletes. The motion simulation unit can, for example, simulate the movements of athletes and evaluate their efficiency. It can also, for example, simulate the movements of athletes and identify the risk of injury. Furthermore, it can, for example, simulate the movements of athletes and suggest specific areas for improvement to enhance performance. This allows the visual data storage system to simulate the movements of athletes and combine this with gaze data and awareness point data to provide more effective coaching and advice.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The eye-tracking unit tracks the athlete's gaze. The eye-tracking unit tracks the gaze using, for example, an infrared camera or image processing technology, and collects data on the position and movement patterns of the gaze. Step 2: The recording unit records the eye-tracking data tracked by the eye-tracking unit. The recording unit saves the eye-tracking data in digital format, for example, and stores it in cloud storage or on a local device. Step 3: The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit analyzes the eye-tracking data to identify, evaluate, and extract the player's eye movements, concentration levels, and eye-tracking patterns. Step 4: The service provider provides coaching and advice based on the data analyzed by the analysis provider. For example, the service provider provides players with feedback, training plans, and tactical advice based on the analysis results.

[0126] 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.

[0127] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] For example, the eye-tracking unit is implemented by the camera 42 of the smart device 14 and the specific processing unit 290 of the data processing device 12. For example, the recording unit stores the eye-tracking data in the storage 50 of the smart device 14 and the storage 32 of the data processing device 12. For example, the analysis unit analyzes the eye-tracking data using the specific processing unit 290 of the data processing device 12. For example, the providing unit provides coaching and advice based on the analysis results using the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. For example, the awareness detection unit detects the athlete's points of awareness using the camera 42 of the smart device 14 and the specific processing unit 290 of the data processing device 12. For example, the routine recording unit records the athlete's routine using the camera 42 of the smart device 14 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0135] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] 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.

[0137] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] For example, the eye-tracking unit is implemented by the camera 42 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. For example, the recording unit stores the eye-tracking data in the storage 50 of the smart glasses 214 and the storage 32 of the data processing device 12. For example, the analysis unit analyzes the eye-tracking data using the specific processing unit 290 of the data processing device 12. For example, the providing unit provides coaching and advice based on the analysis results using the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. For example, the awareness detection unit detects the athlete's points of awareness using the camera 42 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. For example, the routine recording unit records the athlete's routine using the camera 42 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0151] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] 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.

[0153] 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.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] For example, the eye-tracking unit is implemented by the camera 42 of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. For example, the recording unit stores the eye-tracking data in the storage 50 of the headset terminal 314 and the storage 32 of the data processing device 12. For example, the analysis unit analyzes the eye-tracking data using the specific processing unit 290 of the data processing device 12. For example, the providing unit provides coaching and advice based on the analysis results using the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. For example, the awareness detection unit detects the athlete's points of awareness using the camera 42 of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. For example, the routine recording unit records the athlete's routine using the camera 42 of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0165] 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.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0167] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] 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.

[0169] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] 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.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] For example, the eye-tracking unit is implemented by the camera 42 of the robot 414 and the specific processing unit 290 of the data processing device 12. For example, the recording unit stores the eye-tracking data in the storage 50 of the robot 414 and the storage 32 of the data processing device 12. For example, the analysis unit analyzes the eye-tracking data using the specific processing unit 290 of the data processing device 12. For example, the providing unit provides coaching and advice based on the analysis results using the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. For example, the awareness detection unit detects the athlete's points of awareness using the camera 42 of the robot 414 and the specific processing unit 290 of the data processing device 12. For example, the routine recording unit records the athlete's routine using the camera 42 of the robot 414 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0179] 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.

[0180] Figure 9 shows the 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.

[0181] 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.

[0182] 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.

[0183] 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, and motorcycles, 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 based, for example, 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.

[0184] 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."

[0185] 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.

[0186] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] 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 other things 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.

[0196] 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.

[0197] (Note 1) An eye-tracking unit that tracks the gaze of an athlete wearing visual data accumulating glasses, A recording unit that records the gaze data tracked by the aforementioned gaze tracking unit, An analysis unit that analyzes the data recorded by the recording unit, The system includes a providing unit that provides coaching or advice based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) Glasses that store visual data, It is equipped with a consciousness detection unit that detects points of consciousness. The system described in Appendix 1, characterized by the features described herein. (Note 3) Glasses that store visual data, It features a routine recording section for recording the athletes' routines. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned consciousness detection unit, Detects the player's points of focus in real time. The system described in Appendix 2, characterized by the features described herein. (Note 5) The routine recording unit is, Record the player's movement patterns. The system described in Appendix 3, characterized by the features described herein. (Note 6) The aforementioned analysis unit, By analyzing recorded eye-tracking data, awareness point data, and routine data, we identify the player's playing style and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Based on the analysis results, we provide coaching and advice tailored to each individual. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned eye-tracking unit, The system estimates the player's emotions and adjusts the accuracy of eye tracking based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned eye-tracking unit, During eye-tracking, the gaze data is corrected to take into account the athlete's head movements and posture. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned eye-tracking unit, During eye-tracking, the focal length of the athlete's gaze is measured in real time and reflected in the data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned eye-tracking unit, The system estimates the player's emotions and determines the priority of eye-tracking based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned eye-tracking unit, During eye-tracking, the gaze data is corrected by considering information about the environment surrounding the athlete. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned eye-tracking unit, During eye-tracking, the speed of the athlete's eye movements is measured and reflected in the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recording unit is, The system estimates the player's emotions and adjusts the format of the recorded data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recording unit is During recording, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recording unit is During recording, different recording algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The recording unit is, The system estimates the players' emotions and determines the order in which to save the recorded data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recording unit is, When recording, prioritize the records based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recording unit is, During recording, adjust the order of recordings based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The system estimates the players' emotions and adjusts the analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, The system estimates the players' emotions and adjusts the display order of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, When performing analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the athlete's emotions and adjusts the way coaching and advice are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, we will adjust the level of detail in the coaching and advice based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the results, different provisioning algorithms will be applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the athlete's emotions and adjusts the length of coaching and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the analysis results, we will prioritize coaching and advice based on when the results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the order of coaching and advice will be adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned consciousness detection unit, The system estimates the player's emotions and adjusts the accuracy of detecting their points of focus based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned consciousness detection unit, When detecting consciousness, the system adjusts the points of focus (attention) by taking into account the athlete's electroencephalogram (EEG) data. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned consciousness detection unit, The system estimates the players' emotions and determines the priority of their focus points based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned consciousness detection unit, When detecting consciousness, the system adjusts the points of focus (attention) by taking into account the athlete's heart rate data. The system described in Appendix 2, characterized by the features described herein. (Note 36) The routine recording unit is, The system estimates the player's emotions and adjusts the routine recording method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The routine recording unit is, When recording routines, the optimal recording method is selected by referring to the athlete's past movement patterns. The system described in Appendix 3, characterized by the features described herein. (Note 38) The routine recording unit is, The system estimates the player's emotions and determines the priority of routines based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The routine recording unit is, When recording routines, the optimal recording method is selected considering the athlete's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An eye-tracking unit that tracks the gaze of an athlete wearing visual data accumulating glasses, A recording unit that records the gaze data tracked by the aforementioned gaze tracking unit, An analysis unit that analyzes the data recorded by the recording unit, The system includes a providing unit that provides coaching or advice based on the data analyzed by the analysis unit. A system characterized by the following features.

2. Glasses that store visual data, It is equipped with a consciousness detection unit that detects points of consciousness. The system according to feature 1.

3. Glasses that store visual data, It features a routine recording section for recording the athletes' routines. The system according to feature 1.

4. The aforementioned consciousness detection unit, Detects the player's points of focus in real time. The system according to feature 2.

5. The routine recording unit is, Record the player's movement patterns. The system according to claim 3.

6. The aforementioned analysis unit, By analyzing recorded eye-tracking data, awareness point data, and routine data, we identify the player's playing style and weaknesses. The system according to feature 1.

7. The aforementioned supply unit is, Based on the analysis results, we provide coaching and advice tailored to each individual. The system according to feature 1.

8. The aforementioned eye-tracking unit, The system estimates the player's emotions and adjusts the accuracy of eye tracking based on the estimated emotions. The system according to feature 1.

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