system

Abstract

The system according to this embodiment aims to provide effective real-time feedback and individually adapted tactical guidance. [Solution] The system according to the embodiment comprises a visual display unit, an audio advice unit, an aerial photography unit, an analysis unit, and a coaching unit. The visual display unit visually displays game data. The audio advice unit provides audio advice based on the data displayed by the visual display unit. The aerial photography unit uses a drone to grasp the overall picture of the match. The analysis unit analyzes the data acquired by the aerial photography unit. The coaching unit provides coaching appropriate to the player based on the analysis results obtained 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 prior art, there are problems that real-time effective feedback and individually adapted tactical guidance are insufficient, and the environment supporting long-term technological improvement is limited.

[0005] The system according to the embodiment aims to provide real-time effective feedback and perform individually adapted tactical guidance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a visual display unit, an audio advice unit, an aerial photography unit, an analysis unit, and a coaching unit. The visual display unit visually displays game data. The audio advice unit provides audio advice based on the data displayed by the visual display unit. The aerial photography unit uses a drone to grasp the overall picture of the match. The analysis unit analyzes the data acquired by the aerial photography unit. The coaching unit provides coaching appropriate to the player based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide effective real-time feedback and individually adapted tactical guidance. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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 real-time coaching system according to an embodiment of the present invention is a system that combines AR goggles, earphones, and drone technology. This system visually displays game data and provides voice advice. Furthermore, it uses a drone to grasp the overall picture of the match, and the AI ​​proposes efficient positioning and movements. The AI ​​model analyzes the players' movements and provides instruction tailored to each individual player. This system provides effective real-time feedback, enables individually adapted tactical instruction, and creates an environment that supports long-term skill improvement. For example, a player wearing an AR device can play while visually checking game data. Tactical positioning and movement instructions are displayed in the player's field of view. This allows the player to intuitively receive feedback and respond immediately. Next, aerial photography is performed using a drone to analyze the scene from a tactical perspective. The drone grasps the overall picture of the match, and the AI ​​proposes efficient positioning and movements. For example, it can analyze the position and movement of players in real time and instruct them on optimal positioning and movement. Furthermore, personalized coaching is performed through AI analysis. The AI ​​model analyzes the players' movements in detail and provides instruction tailored to each individual player. For example, it analyzes the patterns of the players' movements and points out areas for improvement and points to strengthen. This allows players to practice efficiently and achieve their goals in a shorter amount of time. The system provides effective real-time feedback, enhancing players' ability to react quickly. It also enables individually tailored tactical instruction, contributing to overall skill improvement. Furthermore, regular personalized coaching fosters long-term skill development. In short, this real-time coaching system enhances players' ability to react quickly, enabling efficient practice and time optimization.

[0029] The real-time coaching system according to this embodiment comprises a visual display unit, an audio advice unit, an aerial photography unit, an analysis unit, and a coaching unit. The visual display unit visually displays game data. The visual display unit displays tactical positioning and movement instructions in the player's field of view, for example, using an AR device. The visual display unit provides optimal information to the player based on, for example, the screen resolution and the type of data to be displayed. The audio advice unit provides audio advice based on the data displayed by the visual display unit. The audio advice unit provides tactical advice and instructions to the player by voice, for example. The audio advice unit provides optimal feedback to the player based on, for example, the tone and type of advice. The aerial photography unit grasps the overall picture of the match using a drone. The aerial photography unit grasps the overall picture of the match based on, for example, the drone's flight pattern and the camera's field of view. The aerial photography unit optimizes the drone's flight pattern to capture important moments of the match without missing them. The analysis unit analyzes the data acquired by the aerial photography unit. The analysis unit, for example, analyzes the position and movement of players in real time and instructs them on optimal positioning and movement. The analysis unit proposes optimal positioning and movement for players based on the algorithm used and the accuracy of the analysis. The coaching unit provides appropriate coaching to players based on the analysis results obtained by the analysis unit. The coaching unit, for example, analyzes the patterns of players' movements and points out areas for improvement and points to strengthen. The coaching unit supports the long-term skill improvement of players by providing regular personalized coaching. As a result, the real-time coaching system according to this embodiment can enhance players' ability to respond immediately and achieve efficient practice and time optimization.

[0030] The visual display unit visually displays game data. For example, it can use an AR device to display tactical positioning and movement instructions within the player's field of view. The AR device is attached to the player's helmet or goggles and provides information in real time. This allows players to obtain necessary information without taking their eyes off the field during play. The visual display unit provides optimal information to players based on factors such as screen resolution and the type of data displayed. Using a high-resolution display allows for clear display of detailed tactical instructions and positioning. The types of data displayed include player positions, ball positions, and opposing team positions. By integrating and displaying this information, players can instantly grasp the situation and take appropriate action. Furthermore, the visual display unit can provide customized information according to the individual needs of each player. For example, it can provide individualized tactical instructions, such as highlighting defensive positioning for a specific player and highlighting offensive movements for another player. In this way, the visual display unit plays a crucial role in maximizing player performance.

[0031] The audio advice unit provides audio advice based on data displayed by the visual display unit. For example, the audio advice unit can verbally deliver tactical advice and instructions to players. The audio advice unit provides real-time audio feedback through earphones or headsets worn by players. This allows players to receive visual and audio information simultaneously, enabling them to act more quickly and accurately. The audio advice unit provides optimal feedback to players based on factors such as the tone and content of the advice. For example, it can give instructions in a strong tone during emergencies and advice in a calm tone during normal play, providing situation-appropriate audio feedback. Furthermore, the audio advice unit can provide personalized advice according to each player's individual playing style and personality. For example, it can provide detailed, specific instructions to one player and advice with many words of encouragement to another player, providing audio feedback tailored to individual needs. In this way, the audio advice unit provides important support for improving player performance.

[0032] The aerial photography team uses drones to grasp the overall picture of the match. For example, they understand the overall picture based on the drone's flight pattern and the camera's field of view. Using advanced flight control technology, the drone moves to the optimal position in accordance with the progress of the match, capturing important moments without missing a beat. The drone's camera transmits high-resolution video in real time, allowing for a detailed understanding of the overall picture of the match. For example, the aerial photography team optimizes the drone's flight pattern to capture crucial moments of the match without missing a beat. The drone automatically changes its flight pattern according to the progress of the match, tracking player movements and the ball's position. This allows for a real-time understanding of the overall picture of the match and the recording of important moments without missing a beat. Furthermore, by coordinating multiple drones, the aerial photography team can simultaneously acquire video footage from different viewpoints of the match. This allows for a multifaceted understanding of the match and more detailed analysis. The aerial photography team transmits this video data to the analysis team, which serves as a crucial source of information for real-time analysis of the match situation.

[0033] The analysis unit analyzes the data acquired by the aerial photography unit. For example, the analysis unit analyzes the positions and movements of players in real time and instructs them on optimal positioning and movement. The analysis unit uses advanced AI algorithms to analyze player movements and the progress of the match in real time. The AI ​​uses image recognition technology to analyze drone footage and accurately grasp the positions and movements of players. It also analyzes player movement patterns and match tactics to propose optimal positioning and movement. For example, the analysis unit proposes optimal positioning and movement for players based on the algorithms used and the accuracy of the analysis. The AI ​​can learn from past match data and player performance data to propose optimal tactics and positioning. Furthermore, the analysis unit can propose dynamic tactical changes according to the match situation based on data that is updated in real time. For example, it can change defensive positioning or adjust offensive tactics in response to the opposing team's movements. In this way, the analysis unit plays a crucial role in understanding the match situation in real time and proposing optimal tactics.

[0034] The coaching staff provides instruction tailored to each player based on the analysis results obtained by the analysis staff. For example, the coaching staff analyzes the players' movement patterns and points out areas for improvement and areas that need strengthening. The coaching staff provides personalized instruction based on each player's individual performance data. For example, they provide detailed technical improvement points to certain players and tactical advice to other players, tailoring instruction to individual needs. The coaching staff supports players' long-term skill improvement by providing regular personalized instruction. The coaching staff continuously monitors players' performance data and provides regular feedback. This allows players to understand their strengths and weaknesses and train effectively. Furthermore, the coaching staff also provides instruction that takes into account the players' mental aspects. For example, they aim to strengthen players' mental fortitude through mental training to alleviate pre-game anxiety and post-game reflection. In this way, the coaching staff supports not only the technical improvement of players but also the strengthening of their mental capabilities, achieving overall performance improvement.

[0035] The voice advice unit can provide voice advice to players based on instructions displayed by the visual display unit. For example, the voice advice unit can provide specific voice advice to players based on tactical positioning and movement instructions displayed by the visual display unit. For example, the voice advice unit can convey detailed voice instructions in a way that complements information that players have visually confirmed. For example, the voice advice unit can provide specific advice regarding the next move based on information that players have visually confirmed. As a result, by providing voice advice based on instructions displayed by the visual display unit, players can receive feedback intuitively and respond immediately. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input instructions displayed by the visual display unit into a generating AI, and the generating AI can generate voice advice.

[0036] The analysis unit can analyze the players' positions and movements in real time and provide instructions for optimal positioning and movement. For example, the analysis unit can acquire and analyze players' position data and movement data in real time. For example, the analysis unit can provide instructions for optimal positioning and movement based on players' position data and movement data. For example, the analysis unit can analyze players' position data and movement data and provide specific instructions regarding the next movement. This improves the tactical movements of players by analyzing their positions and movements in real time and providing instructions for optimal positioning and movement. 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 players' position data and movement data into a generating AI, which can then provide instructions for optimal positioning and movement.

[0037] The coaching staff can analyze the movement patterns of the players and point out areas for improvement and areas that need strengthening. For example, the coaching staff can analyze the players' movement data and identify movement patterns. For example, the coaching staff can point out areas for improvement and areas that need strengthening based on the players' movement data. For example, the coaching staff can analyze the players' movement data and provide specific advice for the next practice session. In this way, by analyzing the players' movement patterns and pointing out areas for improvement and areas that need strengthening, the improvement of the players' skills is promoted. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input the players' movement data into a generating AI, and the generating AI can point out areas for improvement and areas that need strengthening.

[0038] The coaching staff can provide regular, personalized instruction to support the long-term skill improvement of athletes. For example, the coaching staff can regularly analyze the athletes' movement data and provide individualized instruction. For example, the coaching staff can provide instruction based on the athletes' movement data to support long-term skill improvement. For example, the coaching staff can analyze the athletes' movement data and provide specific advice for the next practice. As a result, long-term skill improvement of athletes can be expected through regular, personalized instruction. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input the athletes' movement data into a generating AI, and the generating AI can provide instruction to support long-term skill improvement.

[0039] The aerial photography department can use drones to grasp the overall picture of a match and analyze the scene from a tactical perspective. For example, the aerial photography department can use drones to capture the overall picture of a match from the air and analyze the scene from a tactical perspective. For example, the aerial photography department grasps the overall picture of a match based on the drone's flight pattern and the camera's field of view. For example, the aerial photography department optimizes the drone's flight pattern to capture important moments of the match without missing them. This allows them to grasp the overall picture of a match using drones and analyze the scene from a tactical perspective, enabling them to propose efficient positioning and movements. Some or all of the above processes in the aerial photography department may be performed using AI, for example, or not. For example, the aerial photography department can input the video data acquired by the drone into a generating AI, which can then analyze the scene from a tactical perspective.

[0040] The visual display unit can use the player's eye-tracking data to optimally position important information within the player's field of view. For example, the visual display unit acquires the player's eye-tracking data in real time and optimally positions important information within the player's field of view. For example, the visual display unit displays important tactical information in the direction the player is looking, minimizing eye movement. For example, if the player's gaze is concentrated on a particular area, the visual display unit highlights information related to that area. For example, the visual display unit analyzes the player's eye-tracking data in real time and dynamically adjusts the display position of information according to the movement of the gaze. This makes it possible to minimize the player's eye movement and provide information efficiently by optimally positioning important information using the player's eye-tracking data. Some or all of the above processing in the visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit inputs the player's eye-tracking data into a generating AI, which can then optimally position important information within the player's field of view.

[0041] The visual display unit can refer to a player's past play data and display information that highlights their individual weaknesses and strengths. For example, the visual display unit can analyze a player's past play data to identify their individual weaknesses and strengths. For example, based on a player's past play data, the visual display unit can highlight specific weaknesses and clearly indicate areas for improvement. For example, the visual display unit can highlight a player's strengths and provide messages to boost their confidence. For example, based on a player's past play data, the visual display unit can display specific advice for the next play. In this way, by referring to a player's past play data and displaying information that highlights their individual weaknesses and strengths, it is possible to increase a player's confidence and clarify areas for improvement. Some or all of the above processing in the visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit can input a player's past play data into a generating AI, which can then display information that highlights their individual weaknesses and strengths.

[0042] The visual display unit can acquire player location information in real time and display tactical information corresponding to that location. For example, the visual display unit can acquire player location information in real time and display tactical information corresponding to that location. For example, if a player is in a specific location, the visual display unit can display tactical information corresponding to that location. For example, based on the player's location information, the visual display unit can display specific instructions regarding the next move. For example, the visual display unit can analyze player location information in real time and dynamically display tactical information corresponding to that location. As a result, by acquiring player location information in real time and displaying tactical information corresponding to that location, the tactical movements of the players are improved. Some or all of the above processing in the visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit can input player location information into a generating AI, and the generating AI can display tactical information corresponding to the location.

[0043] The visual display unit can acquire the player's physical condition data and display advice tailored to their condition. For example, the visual display unit can acquire the player's physical condition data in real time and display advice tailored to their condition. For example, the visual display unit can display a message encouraging the player to take a break based on the player's physical condition data. For example, the visual display unit can display specific advice for the next play based on the player's physical condition data. For example, the visual display unit can analyze the player's physical condition data in real time and dynamically display advice tailored to their condition. This improves the player's health management by acquiring the player's physical condition data and displaying advice tailored to their condition. Some or all of the above processing in the visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit can input the player's physical condition data into a generating AI, and the generating AI can display advice tailored to their condition.

[0044] The voice advice unit can analyze a player's past voice feedback history and select the optimal advice method. For example, the voice advice unit analyzes a player's past voice feedback history to identify the optimal advice method. For example, the voice advice unit provides optimal advice based on a player's past voice feedback history. For example, the voice advice unit selects an effective advice method from a player's past voice feedback history. For example, the voice advice unit analyzes a player's past voice feedback history and reflects it in the next advice. In this way, by analyzing a player's past voice feedback history and selecting the optimal advice method, effective advice is provided to the player. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input a player's past voice feedback history into a generating AI, which can then select the optimal advice method.

[0045] The voice advice unit can apply different voice advice algorithms depending on the player's playing style. For example, the voice advice unit can analyze the player's playing style and select the optimal voice advice algorithm. For example, the voice advice unit can provide offensive advice depending on the player's playing style. For example, the voice advice unit can provide defensive advice depending on the player's playing style. For example, the voice advice unit can provide balanced advice depending on the player's playing style. In this way, by applying different voice advice algorithms depending on the player's playing style, the optimal advice is provided to the player. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input the player's playing style data into a generating AI, and the generating AI can apply the optimal voice advice algorithm.

[0046] The voice advice unit can acquire player location information in real time and provide voice advice tailored to that location. For example, the voice advice unit can acquire player location information in real time and provide voice advice tailored to that location. For example, if a player is in a specific location, the voice advice unit can provide voice advice tailored to that location. For example, based on the player's location information, the voice advice unit can provide specific voice advice regarding the next move. For example, the voice advice unit can analyze the player's location information in real time and dynamically provide voice advice tailored to that location. As a result, by acquiring player location information in real time and providing voice advice tailored to that location, the player's tactical movements are improved. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input player location information into a generating AI, and the generating AI can provide voice advice tailored to that location.

[0047] The voice advice unit can acquire the player's physical condition data and provide voice advice tailored to their physical condition. For example, the voice advice unit can acquire the player's physical condition data in real time and provide voice advice tailored to their physical condition. For example, the voice advice unit can provide voice advice encouraging rest based on the player's physical condition data. For example, the voice advice unit can provide specific voice advice for the next play based on the player's physical condition data. For example, the voice advice unit can analyze the player's physical condition data in real time and dynamically provide voice advice tailored to their physical condition. This improves the health management of players by acquiring their physical condition data and providing voice advice tailored to their physical condition. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input the player's physical condition data into a generating AI, and the generating AI can provide voice advice tailored to their physical condition.

[0048] The aerial photography unit can optimize the drone's flight pattern to capture crucial moments of the match without missing any. For example, the aerial photography unit can pre-set the drone's flight pattern to ensure that crucial moments of the match are captured. For example, the aerial photography unit can adjust the drone's flight pattern in real time to provide the optimal viewpoint according to the progress of the match. For example, the aerial photography unit can optimize the drone's flight pattern to ensure that crucial moments of the match are captured without missing any. This ensures that crucial moments of the match are captured by optimizing the drone's flight pattern. Some or all of the above processes in the aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input the drone's flight pattern data into a generating AI, which can then optimize the flight pattern.

[0049] The aerial photography unit can automatically adjust the drone's camera settings to maintain optimal image quality. For example, the aerial photography unit can automatically adjust the drone's camera settings to maintain optimal image quality. For example, the aerial photography unit can adjust the drone's camera settings in real time to provide optimal image quality according to the progress of the match. For example, the aerial photography unit can optimize the drone's camera settings to capture important moments of the match without missing them. This ensures that optimal image quality is maintained by automatically adjusting the drone's camera settings. Some or all of the above processes in the aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input the drone's camera setting data into a generating AI, which can then automatically adjust the camera settings.

[0050] The aerial photography unit can change the drone's flight path in real time to provide the optimal viewpoint according to the progress of the match. For example, the aerial photography unit can change the drone's flight path in real time to provide the optimal viewpoint according to the progress of the match. For example, the aerial photography unit can pre-set the drone's flight path to ensure that important moments of the match are captured. For example, the aerial photography unit can optimize the drone's flight path to ensure that important moments of the match are captured without being missed. By changing the drone's flight path in real time, it is possible to provide the optimal viewpoint according to the progress of the match. Some or all of the above processing in the aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input the drone's flight path data into a generating AI, which can then change the flight path in real time.

[0051] The aerial photography unit can create an efficient flight plan by considering the drone's battery level. For example, the aerial photography unit can monitor the drone's battery level in real time and create an efficient flight plan. For example, the aerial photography unit can dynamically adjust the flight plan by considering the drone's battery level. For example, the aerial photography unit can optimize the drone's battery level to capture important moments without missing them. This allows for an efficient flight plan by considering the drone's battery level. Some or all of the above processes in the aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input drone battery level data into a generating AI, which can then create an efficient flight plan.

[0052] The analysis unit can refer to a player's past play data and apply individual analysis algorithms. For example, the analysis unit can analyze a player's past play data and select the optimal analysis algorithm. For example, the analysis unit can apply individual analysis algorithms based on a player's past play data. For example, the analysis unit can refer to a player's past play data and select the optimal analysis method. For example, the analysis unit can analyze a player's past play data and provide specific advice for the next play. This makes it possible to perform the optimal analysis for the player by referring to a player's past play data and applying individual analysis algorithms. 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 a player's past play data into a generating AI, and the generating AI can apply individual analysis algorithms.

[0053] The analysis unit can optimize the analysis algorithm in real time according to the progress of the match. For example, the analysis unit monitors the progress of the match in real time and selects the optimal analysis algorithm. For example, the analysis unit optimizes the analysis algorithm in real time according to the progress of the match. For example, the analysis unit provides specific advice for the next play based on the progress of the match. By optimizing the analysis algorithm in real time according to the progress of the match, optimal analysis tailored to the match situation becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match progress data into a generating AI, and the generating AI can optimize the analysis algorithm in real time.

[0054] The analysis unit can combine and use different analysis methods depending on the situation of the match. For example, the analysis unit monitors the situation of the match in real time and selects the optimal analysis method. For example, the analysis unit combines and uses different analysis methods depending on the situation of the match. For example, the analysis unit provides specific advice for the next play based on the situation of the match. In this way, by combining and using different analysis methods depending on the situation of the match, it becomes possible to perform optimal analysis according to the situation of the match. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input match situation data into a generating AI, and the generating AI can combine and use different analysis methods.

[0055] The analysis unit can provide an interface for visually displaying the analysis results in an easy-to-understand manner. For example, the analysis unit can provide an interface for visually displaying the analysis results in an easy-to-understand manner. For example, the analysis unit can display the analysis results in real time so that players can understand them intuitively. For example, the analysis unit can provide specific advice for the next play based on the analysis results. By providing an interface for visually displaying the analysis results in an easy-to-understand manner, players can understand them intuitively. 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 analysis result data into a generating AI, and the generating AI can provide an interface for visually displaying the results in an easy-to-understand manner.

[0056] The coaching staff can refer to a player's past coaching history and select the optimal coaching method. For example, the coaching staff can analyze a player's past coaching history to identify the optimal coaching method. For example, the coaching staff can select the optimal coaching method based on a player's past coaching history. For example, the coaching staff can refer to a player's past coaching history to select an effective coaching method. For example, the coaching staff can analyze a player's past coaching history and reflect it in the next coaching session. In this way, by referring to a player's past coaching history and selecting the optimal coaching method, effective coaching is provided for the player. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input a player's past coaching history data into a generating AI, and the generating AI can select the optimal coaching method.

[0057] The coaching staff can apply different coaching algorithms depending on the player's playing style. For example, the coaching staff can analyze the player's playing style and select the optimal coaching algorithm. For example, the coaching staff can provide offensive coaching depending on the player's playing style. For example, the coaching staff can provide defensive coaching depending on the player's playing style. For example, the coaching staff can provide balanced coaching depending on the player's playing style. In this way, by applying different coaching algorithms depending on the player's playing style, the optimal coaching is provided for the player. Some or all of the above processes in the coaching staff may be performed using AI, for example, or without AI. For example, the coaching staff can input player playing style data into a generating AI, and the generating AI can apply the optimal coaching algorithm.

[0058] The coaching staff can change the coaching content in real time and provide optimal coaching according to the progress of the match. For example, the coaching staff can change the coaching content in real time and provide optimal coaching according to the progress of the match. For example, the coaching staff can pre-set the coaching content and provide optimal coaching at crucial moments in the match. For example, the coaching staff can optimize the coaching content and provide coaching without missing crucial moments in the match. By changing the coaching content in real time, optimal coaching according to the progress of the match is provided. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input match progress data into a generating AI, and the generating AI can change the coaching content in real time.

[0059] The coaching staff can provide an interface to display the coaching content in a visually easy-to-understand manner. For example, the coaching staff can provide an interface to display the coaching content in a visually easy-to-understand manner. For example, the coaching staff can display the coaching content in real time so that players can understand it intuitively. For example, the coaching staff can provide an interface to display the coaching content in a visually easy-to-understand manner so that players can understand it intuitively. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input coaching content data into a generating AI and provide an interface for the generating AI to display it in a visually easy-to-understand manner.

[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 display unit can use player eye-tracking data to optimally position important information within the player's field of view. For example, it can acquire player eye-tracking data in real time and optimally position important information within the player's field of view. It can display important tactical information in the direction the player is looking, minimizing eye movement. If a player's gaze is concentrated on a specific area, it can highlight information related to that area. It can also analyze player eye-tracking data in real time and dynamically adjust the display position of information according to the movement of the player's eyes.

[0062] The analysis unit can refer to a player's past play data and apply individual analysis algorithms. For example, it can analyze a player's past play data and select the optimal analysis algorithm. It can apply individual analysis algorithms based on a player's past play data. It can refer to a player's past play data and select the optimal analysis method. It can also analyze a player's past play data and provide specific advice for the next play.

[0063] The aerial photography team optimizes drone flight patterns to capture crucial moments of a match without missing a beat. For example, they pre-set drone flight patterns to ensure key moments are captured. They also adjust drone flight patterns in real time to provide the optimal viewpoint according to the progress of the match. By optimizing drone flight patterns, they can capture crucial moments of a match without missing a beat.

[0064] The voice advice unit can analyze a player's past voice feedback history and select the optimal advice method. For example, it can analyze a player's past voice feedback history to identify the best advice method. Based on the player's past voice feedback history, it can provide optimal advice. It can select an effective advice method from a player's past voice feedback history. It can also analyze a player's past voice feedback history and reflect it in subsequent advice.

[0065] The analysis unit can combine different analysis methods depending on the match situation. For example, it can monitor the match situation in real time and select the optimal analysis method. It can also combine different analysis methods depending on the match situation and provide specific advice for the next play based on the match situation.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The visual display unit visually displays game data. For example, it uses an AR device to display tactical positioning and movement instructions within the player's field of view. Based on the screen resolution and the type of data to be displayed, it provides the player with the most relevant information. Step 2: The audio advice unit provides audio advice based on the data displayed by the visual display unit. For example, it provides tactical advice and instructions to players via voice. Based on the tone and type of advice, it provides the most appropriate feedback to the player. Step 3: The aerial photography team uses drones to grasp the overall picture of the match. For example, they grasp the overall picture of the match based on the drone's flight pattern and the camera's field of view. They optimize the drone's flight pattern to capture important moments of the match without missing any. Step 4: The analysis unit analyzes the data acquired by the aerial photography unit. For example, it analyzes the position and movement of the players in real time and instructs them on the optimal placement and movement. Based on the algorithm used and the accuracy of the analysis, it proposes the optimal placement and movement for the players. Step 5: The coaching staff provides appropriate coaching to the players based on the analysis results obtained by the analysis staff. For example, they analyze the players' movement patterns and point out areas for improvement and areas that need strengthening. They provide regular, personalized coaching to support the players' long-term skill development.

[0068] (Example of form 2) The real-time coaching system according to an embodiment of the present invention is a system that combines AR goggles, earphones, and drone technology. This system visually displays game data and provides voice advice. Furthermore, it uses a drone to grasp the overall picture of the match, and the AI ​​proposes efficient positioning and movements. The AI ​​model analyzes the players' movements and provides instruction tailored to each individual player. This system provides effective real-time feedback, enables individually adapted tactical instruction, and creates an environment that supports long-term skill improvement. For example, a player wearing an AR device can play while visually checking game data. Tactical positioning and movement instructions are displayed in the player's field of view. This allows the player to intuitively receive feedback and respond immediately. Next, aerial photography is performed using a drone to analyze the scene from a tactical perspective. The drone grasps the overall picture of the match, and the AI ​​proposes efficient positioning and movements. For example, it can analyze the position and movement of players in real time and instruct them on optimal positioning and movement. Furthermore, personalized coaching is performed through AI analysis. The AI ​​model analyzes the players' movements in detail and provides instruction tailored to each individual player. For example, it analyzes the patterns of the players' movements and points out areas for improvement and points to strengthen. This allows players to practice efficiently and achieve their goals in a shorter amount of time. The system provides effective real-time feedback, enhancing players' ability to react quickly. It also enables individually tailored tactical instruction, contributing to overall skill improvement. Furthermore, regular personalized coaching fosters long-term skill development. In short, this real-time coaching system enhances players' ability to react quickly, enabling efficient practice and time optimization.

[0069] The real-time coaching system according to this embodiment comprises a visual display unit, an audio advice unit, an aerial photography unit, an analysis unit, and a coaching unit. The visual display unit visually displays game data. The visual display unit displays tactical positioning and movement instructions in the player's field of view, for example, using an AR device. The visual display unit provides optimal information to the player based on, for example, the screen resolution and the type of data to be displayed. The audio advice unit provides audio advice based on the data displayed by the visual display unit. The audio advice unit provides tactical advice and instructions to the player by voice, for example. The audio advice unit provides optimal feedback to the player based on, for example, the tone and type of advice. The aerial photography unit grasps the overall picture of the match using a drone. The aerial photography unit grasps the overall picture of the match based on, for example, the drone's flight pattern and the camera's field of view. The aerial photography unit optimizes the drone's flight pattern to capture important moments of the match without missing them. The analysis unit analyzes the data acquired by the aerial photography unit. The analysis unit, for example, analyzes the position and movement of players in real time and instructs them on optimal positioning and movement. The analysis unit proposes optimal positioning and movement for players based on the algorithm used and the accuracy of the analysis. The coaching unit provides appropriate coaching to players based on the analysis results obtained by the analysis unit. The coaching unit, for example, analyzes the patterns of players' movements and points out areas for improvement and points to strengthen. The coaching unit supports the long-term skill improvement of players by providing regular personalized coaching. As a result, the real-time coaching system according to this embodiment can enhance players' ability to respond immediately and achieve efficient practice and time optimization.

[0070] The visual display unit visually displays game data. For example, it can use an AR device to display tactical positioning and movement instructions within the player's field of view. The AR device is attached to the player's helmet or goggles and provides information in real time. This allows players to obtain necessary information without taking their eyes off the field during play. The visual display unit provides optimal information to players based on factors such as screen resolution and the type of data displayed. Using a high-resolution display allows for clear display of detailed tactical instructions and positioning. The types of data displayed include player positions, ball positions, and opposing team positions. By integrating and displaying this information, players can instantly grasp the situation and take appropriate action. Furthermore, the visual display unit can provide customized information according to the individual needs of each player. For example, it can provide individualized tactical instructions, such as highlighting defensive positioning for a specific player and highlighting offensive movements for another player. In this way, the visual display unit plays a crucial role in maximizing player performance.

[0071] The audio advice unit provides audio advice based on data displayed by the visual display unit. For example, the audio advice unit can verbally deliver tactical advice and instructions to players. The audio advice unit provides real-time audio feedback through earphones or headsets worn by players. This allows players to receive visual and audio information simultaneously, enabling them to act more quickly and accurately. The audio advice unit provides optimal feedback to players based on factors such as the tone and content of the advice. For example, it can give instructions in a strong tone during emergencies and advice in a calm tone during normal play, providing situation-appropriate audio feedback. Furthermore, the audio advice unit can provide personalized advice according to each player's individual playing style and personality. For example, it can provide detailed, specific instructions to one player and advice with many words of encouragement to another player, providing audio feedback tailored to individual needs. In this way, the audio advice unit provides important support for improving player performance.

[0072] The aerial photography team uses drones to grasp the overall picture of the match. For example, they understand the overall picture based on the drone's flight pattern and the camera's field of view. Using advanced flight control technology, the drone moves to the optimal position in accordance with the progress of the match, capturing important moments without missing a beat. The drone's camera transmits high-resolution video in real time, allowing for a detailed understanding of the overall picture of the match. For example, the aerial photography team optimizes the drone's flight pattern to capture crucial moments of the match without missing a beat. The drone automatically changes its flight pattern according to the progress of the match, tracking player movements and the ball's position. This allows for a real-time understanding of the overall picture of the match and the recording of important moments without missing a beat. Furthermore, by coordinating multiple drones, the aerial photography team can simultaneously acquire video footage from different viewpoints of the match. This allows for a multifaceted understanding of the match and more detailed analysis. The aerial photography team transmits this video data to the analysis team, which serves as a crucial source of information for real-time analysis of the match situation.

[0073] The analysis unit analyzes the data acquired by the aerial photography unit. For example, the analysis unit analyzes the positions and movements of players in real time and instructs them on optimal positioning and movement. The analysis unit uses advanced AI algorithms to analyze player movements and the progress of the match in real time. The AI ​​uses image recognition technology to analyze drone footage and accurately grasp the positions and movements of players. It also analyzes player movement patterns and match tactics to propose optimal positioning and movement. For example, the analysis unit proposes optimal positioning and movement for players based on the algorithms used and the accuracy of the analysis. The AI ​​can learn from past match data and player performance data to propose optimal tactics and positioning. Furthermore, the analysis unit can propose dynamic tactical changes according to the match situation based on data that is updated in real time. For example, it can change defensive positioning or adjust offensive tactics in response to the opposing team's movements. In this way, the analysis unit plays a crucial role in understanding the match situation in real time and proposing optimal tactics.

[0074] The coaching staff provides instruction tailored to each player based on the analysis results obtained by the analysis staff. For example, the coaching staff analyzes the players' movement patterns and points out areas for improvement and areas that need strengthening. The coaching staff provides personalized instruction based on each player's individual performance data. For example, they provide detailed technical improvement points to certain players and tactical advice to other players, tailoring instruction to individual needs. The coaching staff supports players' long-term skill improvement by providing regular personalized instruction. The coaching staff continuously monitors players' performance data and provides regular feedback. This allows players to understand their strengths and weaknesses and train effectively. Furthermore, the coaching staff also provides instruction that takes into account the players' mental aspects. For example, they aim to strengthen players' mental fortitude through mental training to alleviate pre-game anxiety and post-game reflection. In this way, the coaching staff supports not only the technical improvement of players but also the strengthening of their mental capabilities, achieving overall performance improvement.

[0075] The voice advice unit can provide voice advice to players based on instructions displayed by the visual display unit. For example, the voice advice unit can provide specific voice advice to players based on tactical positioning and movement instructions displayed by the visual display unit. For example, the voice advice unit can convey detailed voice instructions in a way that complements information that players have visually confirmed. For example, the voice advice unit can provide specific advice regarding the next move based on information that players have visually confirmed. As a result, by providing voice advice based on instructions displayed by the visual display unit, players can receive feedback intuitively and respond immediately. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input instructions displayed by the visual display unit into a generating AI, and the generating AI can generate voice advice.

[0076] The analysis unit can analyze the players' positions and movements in real time and provide instructions for optimal positioning and movement. For example, the analysis unit can acquire and analyze players' position data and movement data in real time. For example, the analysis unit can provide instructions for optimal positioning and movement based on players' position data and movement data. For example, the analysis unit can analyze players' position data and movement data and provide specific instructions regarding the next movement. This improves the tactical movements of players by analyzing their positions and movements in real time and providing instructions for optimal positioning and movement. 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 players' position data and movement data into a generating AI, which can then provide instructions for optimal positioning and movement.

[0077] The coaching staff can analyze the movement patterns of the players and point out areas for improvement and areas that need strengthening. For example, the coaching staff can analyze the players' movement data and identify movement patterns. For example, the coaching staff can point out areas for improvement and areas that need strengthening based on the players' movement data. For example, the coaching staff can analyze the players' movement data and provide specific advice for the next practice session. In this way, by analyzing the players' movement patterns and pointing out areas for improvement and areas that need strengthening, the improvement of the players' skills is promoted. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input the players' movement data into a generating AI, and the generating AI can point out areas for improvement and areas that need strengthening.

[0078] The coaching staff can provide regular, personalized instruction to support the long-term skill improvement of athletes. For example, the coaching staff can regularly analyze the athletes' movement data and provide individualized instruction. For example, the coaching staff can provide instruction based on the athletes' movement data to support long-term skill improvement. For example, the coaching staff can analyze the athletes' movement data and provide specific advice for the next practice. As a result, long-term skill improvement of athletes can be expected through regular, personalized instruction. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input the athletes' movement data into a generating AI, and the generating AI can provide instruction to support long-term skill improvement.

[0079] The aerial photography department can use drones to grasp the overall picture of a match and analyze the scene from a tactical perspective. For example, the aerial photography department can use drones to capture the overall picture of a match from the air and analyze the scene from a tactical perspective. For example, the aerial photography department grasps the overall picture of a match based on the drone's flight pattern and the camera's field of view. For example, the aerial photography department optimizes the drone's flight pattern to capture important moments of the match without missing them. This allows them to grasp the overall picture of a match using drones and analyze the scene from a tactical perspective, enabling them to propose efficient positioning and movements. Some or all of the above processes in the aerial photography department may be performed using AI, for example, or not. For example, the aerial photography department can input the video data acquired by the drone into a generating AI, which can then analyze the scene from a tactical perspective.

[0080] The visual display unit can estimate the player's emotions and adjust the content and timing of the data displayed based on the estimated emotions. For example, the visual display unit analyzes the player's facial expressions and biometric data to estimate emotions. For example, the visual display unit adjusts the content and timing of the data displayed based on the estimated emotions. For example, if the player is tense, the visual display unit can display a message to help them relax and display important information simply. For example, if the player is focused, the visual display unit can display detailed tactical information and provide specific instructions regarding the next move. For example, if the player is tired, the visual display unit can display a message encouraging them to rest and provide simple advice for the next play. This makes it possible to provide optimal information to the player by adjusting the content and timing of the data displayed based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit can input the player's facial expression data into a generating AI, which can then estimate the emotion and adjust the content and timing of the displayed data.

[0081] The visual display unit can use the player's eye-tracking data to optimally position important information within the player's field of view. For example, the visual display unit acquires the player's eye-tracking data in real time and optimally positions important information within the player's field of view. For example, the visual display unit displays important tactical information in the direction the player is looking, minimizing eye movement. For example, if the player's gaze is concentrated on a particular area, the visual display unit highlights information related to that area. For example, the visual display unit analyzes the player's eye-tracking data in real time and dynamically adjusts the display position of information according to the movement of the gaze. This makes it possible to minimize the player's eye movement and provide information efficiently by optimally positioning important information using the player's eye-tracking data. Some or all of the above processing in the visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit inputs the player's eye-tracking data into a generating AI, which can then optimally position important information within the player's field of view.

[0082] The visual display unit can refer to a player's past play data and display information that highlights their individual weaknesses and strengths. For example, the visual display unit can analyze a player's past play data to identify their individual weaknesses and strengths. For example, based on a player's past play data, the visual display unit can highlight specific weaknesses and clearly indicate areas for improvement. For example, the visual display unit can highlight a player's strengths and provide messages to boost their confidence. For example, based on a player's past play data, the visual display unit can display specific advice for the next play. In this way, by referring to a player's past play data and displaying information that highlights their individual weaknesses and strengths, it is possible to increase a player's confidence and clarify areas for improvement. Some or all of the above processing in the visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit can input a player's past play data into a generating AI, which can then display information that highlights their individual weaknesses and strengths.

[0083] The visual display unit can estimate the player's emotions and determine the priority of data to display based on the estimated emotions. For example, the visual display unit analyzes the player's facial expressions and biometric data to estimate emotions. For example, the visual display unit determines the priority of data to display based on the estimated emotions. For example, if the player is tense, the visual display unit prioritizes displaying messages to help them relax. For example, if the player is focused, the visual display unit prioritizes displaying detailed tactical information. For example, if the player is tired, the visual display unit prioritizes displaying messages encouraging them to rest. By prioritizing the data to display based on the player's emotions, the most important information for the player can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, 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 visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit can input the player's facial expression data into a generating AI, which can then estimate emotions and determine the priority of the data to display.

[0084] The visual display unit can acquire player location information in real time and display tactical information corresponding to that location. For example, the visual display unit can acquire player location information in real time and display tactical information corresponding to that location. For example, if a player is in a specific location, the visual display unit can display tactical information corresponding to that location. For example, based on the player's location information, the visual display unit can display specific instructions regarding the next move. For example, the visual display unit can analyze player location information in real time and dynamically display tactical information corresponding to that location. As a result, by acquiring player location information in real time and displaying tactical information corresponding to that location, the tactical movements of the players are improved. Some or all of the above processing in the visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit can input player location information into a generating AI, and the generating AI can display tactical information corresponding to the location.

[0085] The visual display unit can acquire the player's physical condition data and display advice tailored to their condition. For example, the visual display unit can acquire the player's physical condition data in real time and display advice tailored to their condition. For example, the visual display unit can display a message encouraging the player to take a break based on the player's physical condition data. For example, the visual display unit can display specific advice for the next play based on the player's physical condition data. For example, the visual display unit can analyze the player's physical condition data in real time and dynamically display advice tailored to their condition. This improves the player's health management by acquiring the player's physical condition data and displaying advice tailored to their condition. Some or all of the above processing in the visual display unit may be performed using AI, for example, or without AI. For example, the visual display unit can input the player's physical condition data into a generating AI, and the generating AI can display advice tailored to their condition.

[0086] The voice advice unit can estimate the player's emotions and adjust the tone and content of the voice advice based on the estimated emotions. For example, the voice advice unit analyzes the player's facial expressions and biometric data to estimate emotions. For example, the voice advice unit adjusts the tone and content of the voice advice based on the estimated emotions. For example, if the player is nervous, the voice advice unit provides advice in a calm tone. For example, if the player is focused, the voice advice unit provides advice that includes detailed tactical information. For example, if the player is tired, the voice advice unit provides advice that includes words of encouragement. In this way, by adjusting the tone and content of the voice advice based on the player's emotions, the optimal advice is provided to the player. 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 voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input the player's facial expression data into a generating AI, which can then estimate the player's emotions and adjust the tone and content of the voice advice accordingly.

[0087] The voice advice unit can analyze a player's past voice feedback history and select the optimal advice method. For example, the voice advice unit analyzes a player's past voice feedback history to identify the optimal advice method. For example, the voice advice unit provides optimal advice based on a player's past voice feedback history. For example, the voice advice unit selects an effective advice method from a player's past voice feedback history. For example, the voice advice unit analyzes a player's past voice feedback history and reflects it in the next advice. In this way, by analyzing a player's past voice feedback history and selecting the optimal advice method, effective advice is provided to the player. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input a player's past voice feedback history into a generating AI, which can then select the optimal advice method.

[0088] The voice advice unit can apply different voice advice algorithms depending on the player's playing style. For example, the voice advice unit can analyze the player's playing style and select the optimal voice advice algorithm. For example, the voice advice unit can provide offensive advice depending on the player's playing style. For example, the voice advice unit can provide defensive advice depending on the player's playing style. For example, the voice advice unit can provide balanced advice depending on the player's playing style. In this way, by applying different voice advice algorithms depending on the player's playing style, the optimal advice is provided to the player. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input the player's playing style data into a generating AI, and the generating AI can apply the optimal voice advice algorithm.

[0089] The voice advice unit can estimate the athlete's emotions and adjust the frequency of voice advice based on the estimated emotions. The voice advice unit estimates emotions by, for example, analyzing the athlete's facial expressions and biometric data. The voice advice unit adjusts the frequency of voice advice based on the estimated emotions. For example, if the athlete is nervous, the voice advice unit provides advice frequently to reassure them. For example, if the athlete is concentrating, the voice advice unit provides advice only when necessary, without interrupting their concentration. For example, if the athlete is tired, the voice advice unit provides advice frequently, including words of encouragement. By adjusting the frequency of voice advice based on the athlete's emotions, advice is provided at the optimal time for the athlete. Emotion estimation is achieved using an emotion estimation function, for example, 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 voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input the player's facial expression data into a generating AI, which can then estimate the player's emotions and adjust the frequency of the voice advice.

[0090] The voice advice unit can acquire player location information in real time and provide voice advice tailored to that location. For example, the voice advice unit can acquire player location information in real time and provide voice advice tailored to that location. For example, if a player is in a specific location, the voice advice unit can provide voice advice tailored to that location. For example, based on the player's location information, the voice advice unit can provide specific voice advice regarding the next move. For example, the voice advice unit can analyze the player's location information in real time and dynamically provide voice advice tailored to that location. As a result, by acquiring player location information in real time and providing voice advice tailored to that location, the player's tactical movements are improved. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input player location information into a generating AI, and the generating AI can provide voice advice tailored to that location.

[0091] The voice advice unit can acquire the player's physical condition data and provide voice advice tailored to their physical condition. For example, the voice advice unit can acquire the player's physical condition data in real time and provide voice advice tailored to their physical condition. For example, the voice advice unit can provide voice advice encouraging rest based on the player's physical condition data. For example, the voice advice unit can provide specific voice advice for the next play based on the player's physical condition data. For example, the voice advice unit can analyze the player's physical condition data in real time and dynamically provide voice advice tailored to their physical condition. This improves the health management of players by acquiring their physical condition data and providing voice advice tailored to their physical condition. Some or all of the above processing in the voice advice unit may be performed using AI, for example, or without AI. For example, the voice advice unit can input the player's physical condition data into a generating AI, and the generating AI can provide voice advice tailored to their physical condition.

[0092] The aerial photography unit can estimate the emotions of the athletes and adjust the timing and angle of aerial photography based on the estimated emotions. For example, the aerial photography unit analyzes the athletes' facial expressions and biometric data to estimate their emotions. For example, the aerial photography unit adjusts the timing and angle of aerial photography based on the estimated emotions. For example, if an aerial photography unit is tense, the aerial photography unit prioritizes capturing footage to help the athlete relax. For example, if an aerial photography unit is focused, the aerial photography unit prioritizes capturing footage containing detailed tactical information. For example, if an aerial photography unit is tired, the aerial photography unit prioritizes capturing footage encouraging the athlete to rest. By adjusting the timing and angle of aerial photography based on the athletes' emotions, the optimal footage for the athletes is provided. Emotion estimation is achieved using an emotion estimation function, for example, 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 aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input the players' facial expression data into a generating AI, which can then estimate their emotions and adjust the timing and angle of the aerial shots.

[0093] The aerial photography unit can optimize the drone's flight pattern to capture crucial moments of the match without missing any. For example, the aerial photography unit can pre-set the drone's flight pattern to ensure that crucial moments of the match are captured. For example, the aerial photography unit can adjust the drone's flight pattern in real time to provide the optimal viewpoint according to the progress of the match. For example, the aerial photography unit can optimize the drone's flight pattern to ensure that crucial moments of the match are captured without missing any. This ensures that crucial moments of the match are captured by optimizing the drone's flight pattern. Some or all of the above processes in the aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input the drone's flight pattern data into a generating AI, which can then optimize the flight pattern.

[0094] The aerial photography unit can automatically adjust the drone's camera settings to maintain optimal image quality. For example, the aerial photography unit can automatically adjust the drone's camera settings to maintain optimal image quality. For example, the aerial photography unit can adjust the drone's camera settings in real time to provide optimal image quality according to the progress of the match. For example, the aerial photography unit can optimize the drone's camera settings to capture important moments of the match without missing them. This ensures that optimal image quality is maintained by automatically adjusting the drone's camera settings. Some or all of the above processes in the aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input the drone's camera setting data into a generating AI, which can then automatically adjust the camera settings.

[0095] The aerial photography unit can estimate the emotions of the athletes and determine the priority of aerial photography based on the estimated emotions. The aerial photography unit can, for example, analyze the athletes' facial expressions and biometric data to estimate their emotions. The aerial photography unit can, for example, determine the priority of aerial photography based on the estimated emotions. For example, if the aerial photography unit is tense, the unit will prioritize shooting footage to help the aerial photographer relax. For example, if the aerial photography unit is focused, the unit will prioritize shooting footage containing detailed tactical information. For example, if the aerial photography unit is tired, the unit will prioritize shooting footage encouraging the aerial photographer to take a break. In this way, by determining the priority of aerial photography based on the emotions of the athletes, the optimal footage for the athletes can be provided. Emotion estimation is achieved using an emotion estimation function, for example, 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 aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input facial expression data of athletes into a generating AI, which can then estimate their emotions and determine the priority of aerial photography.

[0096] The aerial photography unit can change the drone's flight path in real time to provide the optimal viewpoint according to the progress of the match. For example, the aerial photography unit can change the drone's flight path in real time to provide the optimal viewpoint according to the progress of the match. For example, the aerial photography unit can pre-set the drone's flight path to ensure that important moments of the match are captured. For example, the aerial photography unit can optimize the drone's flight path to ensure that important moments of the match are captured without being missed. By changing the drone's flight path in real time, it is possible to provide the optimal viewpoint according to the progress of the match. Some or all of the above processing in the aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input the drone's flight path data into a generating AI, which can then change the flight path in real time.

[0097] The aerial photography unit can create an efficient flight plan by considering the drone's battery level. For example, the aerial photography unit can monitor the drone's battery level in real time and create an efficient flight plan. For example, the aerial photography unit can dynamically adjust the flight plan by considering the drone's battery level. For example, the aerial photography unit can optimize the drone's battery level to capture important moments without missing them. This allows for an efficient flight plan by considering the drone's battery level. Some or all of the above processes in the aerial photography unit may be performed using AI, for example, or without AI. For example, the aerial photography unit can input drone battery level data into a generating AI, which can then create an efficient flight plan.

[0098] The analysis unit can estimate the athlete's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit analyzes the athlete's facial expressions and biometric data to estimate emotions. For example, the analysis unit adjusts the display method of the analysis results based on the estimated emotions. For example, if the athlete is nervous, the analysis unit provides a simple and highly visible display method. For example, if the athlete is concentrating, the analysis unit displays detailed analysis results. For example, if the athlete is tired, the analysis unit highlights and displays only the important points. In this way, by adjusting the display method of the analysis results based on the athlete's emotions, it becomes possible to provide the athlete with the most optimal information. 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 analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the player's facial expression data into a generating AI, which can then estimate the emotion and adjust how the analysis results are displayed.

[0099] The analysis unit can refer to a player's past play data and apply individual analysis algorithms. For example, the analysis unit can analyze a player's past play data and select the optimal analysis algorithm. For example, the analysis unit can apply individual analysis algorithms based on a player's past play data. For example, the analysis unit can refer to a player's past play data and select the optimal analysis method. For example, the analysis unit can analyze a player's past play data and provide specific advice for the next play. This makes it possible to perform the optimal analysis for the player by referring to a player's past play data and applying individual analysis algorithms. 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 a player's past play data into a generating AI, and the generating AI can apply individual analysis algorithms.

[0100] The analysis unit can optimize the analysis algorithm in real time according to the progress of the match. For example, the analysis unit monitors the progress of the match in real time and selects the optimal analysis algorithm. For example, the analysis unit optimizes the analysis algorithm in real time according to the progress of the match. For example, the analysis unit provides specific advice for the next play based on the progress of the match. By optimizing the analysis algorithm in real time according to the progress of the match, optimal analysis tailored to the match situation becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match progress data into a generating AI, and the generating AI can optimize the analysis algorithm in real time.

[0101] The analysis unit can estimate the athlete's emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit analyzes the athlete's facial expressions and biometric data to estimate emotions. For example, the analysis unit determines the priority of analysis results based on the estimated emotions. For example, if the athlete is tense, the analysis unit prioritizes displaying analysis results that help them relax. For example, if the athlete is focused, the analysis unit prioritizes displaying detailed analysis results. For example, if the athlete is tired, the analysis unit prioritizes displaying only the most important points. In this way, by prioritizing analysis results based on the athlete's emotions, the most important information for the athlete can be provided preferentially. 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 analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the player's facial expression data into a generating AI, which can then estimate emotions and determine the priority of the analysis results.

[0102] The analysis unit can combine and use different analysis methods depending on the situation of the match. For example, the analysis unit monitors the situation of the match in real time and selects the optimal analysis method. For example, the analysis unit combines and uses different analysis methods depending on the situation of the match. For example, the analysis unit provides specific advice for the next play based on the situation of the match. In this way, by combining and using different analysis methods depending on the situation of the match, it becomes possible to perform optimal analysis according to the situation of the match. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input match situation data into a generating AI, and the generating AI can combine and use different analysis methods.

[0103] The analysis unit can provide an interface for visually displaying the analysis results in an easy-to-understand manner. For example, the analysis unit can provide an interface for visually displaying the analysis results in an easy-to-understand manner. For example, the analysis unit can display the analysis results in real time so that players can understand them intuitively. For example, the analysis unit can provide specific advice for the next play based on the analysis results. By providing an interface for visually displaying the analysis results in an easy-to-understand manner, players can understand them intuitively. 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 analysis result data into a generating AI, and the generating AI can provide an interface for visually displaying the results in an easy-to-understand manner.

[0104] The coaching staff can estimate the emotions of the players and adjust the coaching content based on the estimated emotions. For example, the coaching staff can analyze the players' facial expressions and biometric data to estimate their emotions. For example, the coaching staff can adjust the coaching content based on the estimated emotions. For example, if a player is nervous, the coaching staff can provide coaching to help them relax. For example, if a player is concentrating, the coaching staff can provide detailed coaching. For example, if a player is tired, the coaching staff can provide coaching that includes words of encouragement. In this way, by adjusting the coaching content based on the players' emotions, the optimal coaching is provided for the players. 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 coaching staff may be performed using AI or not using AI. For example, the coaching staff can input the players' facial expression data into a generative AI, which can estimate emotions and adjust the coaching content.

[0105] The coaching staff can refer to a player's past coaching history and select the optimal coaching method. For example, the coaching staff can analyze a player's past coaching history to identify the optimal coaching method. For example, the coaching staff can select the optimal coaching method based on a player's past coaching history. For example, the coaching staff can refer to a player's past coaching history to select an effective coaching method. For example, the coaching staff can analyze a player's past coaching history and reflect it in the next coaching session. In this way, by referring to a player's past coaching history and selecting the optimal coaching method, effective coaching is provided for the player. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input a player's past coaching history data into a generating AI, and the generating AI can select the optimal coaching method.

[0106] The coaching staff can apply different coaching algorithms depending on the player's playing style. For example, the coaching staff can analyze the player's playing style and select the optimal coaching algorithm. For example, the coaching staff can provide offensive coaching depending on the player's playing style. For example, the coaching staff can provide defensive coaching depending on the player's playing style. For example, the coaching staff can provide balanced coaching depending on the player's playing style. In this way, by applying different coaching algorithms depending on the player's playing style, the optimal coaching is provided for the player. Some or all of the above processes in the coaching staff may be performed using AI, for example, or without AI. For example, the coaching staff can input player playing style data into a generating AI, and the generating AI can apply the optimal coaching algorithm.

[0107] The coaching staff can estimate the emotions of the players and adjust the frequency of coaching based on the estimated emotions. For example, the coaching staff can analyze the players' facial expressions and biometric data to estimate their emotions. For example, the coaching staff can adjust the frequency of coaching based on the estimated emotions. For example, if a player is nervous, the coaching staff can provide frequent coaching to reassure them. For example, if a player is concentrating, the coaching staff can provide coaching only when necessary and not interrupt their concentration. For example, if a player is tired, the coaching staff can provide frequent coaching that includes words of encouragement. By adjusting the frequency of coaching based on the players' emotions, coaching is provided at the optimal time for the players. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input the players' facial expression data into a generating AI, which can then estimate their emotions and adjust the frequency of coaching accordingly.

[0108] The coaching staff can change the coaching content in real time and provide optimal coaching according to the progress of the match. For example, the coaching staff can change the coaching content in real time and provide optimal coaching according to the progress of the match. For example, the coaching staff can pre-set the coaching content and provide optimal coaching at crucial moments in the match. For example, the coaching staff can optimize the coaching content and provide coaching without missing crucial moments in the match. By changing the coaching content in real time, optimal coaching according to the progress of the match is provided. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input match progress data into a generating AI, and the generating AI can change the coaching content in real time.

[0109] The coaching staff can provide an interface to display the coaching content in a visually easy-to-understand manner. For example, the coaching staff can provide an interface to display the coaching content in a visually easy-to-understand manner. For example, the coaching staff can display the coaching content in real time so that players can understand it intuitively. For example, the coaching staff can provide an interface to display the coaching content in a visually easy-to-understand manner so that players can understand it intuitively. Some or all of the above processes in the coaching staff may be performed using AI, for example, or not using AI. For example, the coaching staff can input coaching content data into a generating AI and provide an interface for the generating AI to display it in a visually easy-to-understand manner.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The visual display unit can use player eye-tracking data to optimally position important information within the player's field of view. For example, it can acquire player eye-tracking data in real time and optimally position important information within the player's field of view. It can display important tactical information in the direction the player is looking, minimizing eye movement. If a player's gaze is concentrated on a specific area, it can highlight information related to that area. It can also analyze player eye-tracking data in real time and dynamically adjust the display position of information according to the movement of the player's eyes.

[0112] The voice advice unit can estimate the player's emotions and adjust the tone and content of the voice advice based on those estimated emotions. For example, it can analyze the player's facial expressions and biometric data to estimate their emotions. Based on the estimated emotions, it adjusts the tone and content of the voice advice. If the player is nervous, it provides advice in a calm tone. If the player is focused, it provides advice that includes detailed tactical information. If the player is tired, it can also provide advice that includes words of encouragement.

[0113] The analysis unit can refer to a player's past play data and apply individual analysis algorithms. For example, it can analyze a player's past play data and select the optimal analysis algorithm. It can apply individual analysis algorithms based on a player's past play data. It can refer to a player's past play data and select the optimal analysis method. It can also analyze a player's past play data and provide specific advice for the next play.

[0114] The coaching staff can estimate the emotions of the players and adjust the coaching content based on those estimates. For example, they can analyze the players' facial expressions and biometric data to estimate their emotions. Based on the estimated emotions, they can adjust the coaching content. If a player is tense, they can provide coaching to help them relax. If a player is focused, they can provide detailed coaching. If a player is tired, they can also provide coaching that includes words of encouragement.

[0115] The aerial photography team optimizes drone flight patterns to capture crucial moments of a match without missing a beat. For example, they pre-set drone flight patterns to ensure key moments are captured. They also adjust drone flight patterns in real time to provide the optimal viewpoint according to the progress of the match. By optimizing drone flight patterns, they can capture crucial moments of a match without missing a beat.

[0116] The visual display unit can estimate the player's emotions and determine the priority of the data to display based on the estimated emotions. For example, it can analyze the player's facial expressions and biometric data to estimate their emotions. Based on the estimated emotions, it determines the priority of the data to display. If the player is tense, it will prioritize displaying messages to help them relax. If the player is focused, it will prioritize displaying detailed tactical information. If the player is tired, it can also prioritize displaying messages encouraging them to take a break.

[0117] The voice advice unit can analyze a player's past voice feedback history and select the optimal advice method. For example, it can analyze a player's past voice feedback history to identify the best advice method. Based on the player's past voice feedback history, it can provide optimal advice. It can select an effective advice method from a player's past voice feedback history. It can also analyze a player's past voice feedback history and reflect it in subsequent advice.

[0118] The aerial photography team can estimate the emotions of the athletes and adjust the timing and angle of aerial shots based on those estimates. For example, they can analyze the athletes' facial expressions and biometric data to estimate their emotions. Based on these estimates, they adjust the timing and angle of aerial shots. If an athlete is tense, they prioritize filming footage that helps them relax. If an athlete is focused, they prioritize filming footage that includes detailed tactical information. If an athlete is tired, they can also prioritize filming footage that encourages them to take a break.

[0119] The analysis unit can combine different analysis methods depending on the match situation. For example, it can monitor the match situation in real time and select the optimal analysis method. It can also combine different analysis methods depending on the match situation and provide specific advice for the next play based on the match situation.

[0120] The coaching staff can estimate the emotions of the players and adjust the frequency of coaching based on those estimates. For example, they can analyze the players' facial expressions and biometric data to estimate their emotions. Based on these estimates, they can adjust the frequency of coaching. If a player is nervous, they can provide frequent coaching to reassure them. If a player is focused, they can provide coaching only when necessary to avoid interrupting their concentration. If a player is tired, they can also provide frequent coaching that includes words of encouragement.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The visual display unit visually displays game data. For example, it uses an AR device to display tactical positioning and movement instructions within the player's field of view. Based on the screen resolution and the type of data to be displayed, it provides the player with the most relevant information. Step 2: The audio advice unit provides audio advice based on the data displayed by the visual display unit. For example, it provides tactical advice and instructions to players via voice. Based on the tone and type of advice, it provides the most appropriate feedback to the player. Step 3: The aerial photography team uses drones to grasp the overall picture of the match. For example, they grasp the overall picture of the match based on the drone's flight pattern and the camera's field of view. They optimize the drone's flight pattern to capture important moments of the match without missing any. Step 4: The analysis unit analyzes the data acquired by the aerial photography unit. For example, it analyzes the position and movement of the players in real time and instructs them on the optimal placement and movement. Based on the algorithm used and the accuracy of the analysis, it proposes the optimal placement and movement for the players. Step 5: The coaching staff provides appropriate coaching to the players based on the analysis results obtained by the analysis staff. For example, they analyze the players' movement patterns and point out areas for improvement and areas that need strengthening. They provide regular, personalized coaching to support the players' long-term skill development.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the visual display unit, audio advice unit, aerial photography unit, analysis unit, and coaching unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the visual display unit is implemented by the display 40A of the smart device 14 and displays tactical positioning and movement instructions in the player's field of view. The audio advice unit is implemented by the speaker 40B of the smart device 14 and provides tactical advice and instructions to the player by voice. The aerial photography unit uses a drone to grasp the overall picture of the match and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the position and movement of players in real time and instructs them on optimal positioning and movement. The coaching unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the movement patterns of players and points out areas for improvement and areas to strengthen. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the visual display unit, audio advice unit, aerial photography unit, analysis unit, and coaching unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the visual display unit is implemented by the display of the smart glasses 214 and displays tactical positioning and movement instructions in the player's field of view. The audio advice unit is implemented by the speaker 240 of the smart glasses 214 and provides tactical advice and instructions to the player by voice. The aerial photography unit uses a drone to grasp the overall picture of the match and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the position and movement of players in real time and instructs them on optimal positioning and movement. The coaching unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the movement patterns of players and points out areas for improvement and areas to strengthen. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the visual display unit, audio advice unit, aerial photography unit, analysis unit, and coaching unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the visual display unit is implemented by the display 343 of the headset terminal 314, displaying tactical positioning and movement instructions within the player's field of view. The audio advice unit is implemented by the speaker 240 of the headset terminal 314, providing tactical advice and instructions to the player via voice. The aerial photography unit uses a drone to capture the overall picture of the match, which is then analyzed by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the player's position and movement in real time and provides instructions for optimal positioning and movement. The coaching unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the player's movement patterns and points out areas for improvement and areas to strengthen. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the visual display unit, voice advice unit, aerial photography unit, analysis unit, and instruction unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the visual display unit is implemented by the display of the robot 414 and displays tactical positioning and movement instructions in the player's field of view. The voice advice unit is implemented by the speaker 240 of the robot 414 and provides tactical advice and instructions to the player by voice. The aerial photography unit uses a drone to grasp the overall picture of the match and analyzes it with the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the player's position and movement in real time and instructs the optimal positioning and movement. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the player's movement patterns and points out areas for improvement and areas to strengthen. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A visual display unit that displays game data visually, A voice advice unit provides voice advice based on the data displayed by the visual display unit, The aerial photography team uses drones to get an overview of the match, An analysis unit analyzes the data acquired by the aerial photography unit, The system includes a coaching unit that provides coaching suitable for the player based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned voice advice unit, Based on instructions displayed by the visual display unit, the system provides audio advice to the player. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It analyzes the players' positions and movements in real time and provides instructions for optimal positioning and movement. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned leadership, Analyze the movement patterns of the players and point out areas for improvement and areas that need strengthening. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned leadership, We provide regular, personalized coaching to support the long-term skill development of our players. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aerial photography unit is, Using drones to grasp the overall picture of the match and analyze the situation from a tactical perspective. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned visual display unit is The system estimates the players' emotions and adjusts the content and timing of the data displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned visual display unit is By using eye-tracking data from the players, important information is optimally placed within the players' field of vision. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned visual display unit is The system uses past player data to highlight individual weaknesses and strengths. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned visual display unit is The system estimates the players' emotions and determines the priority of the data to display based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned visual display unit is The system acquires player location information in real time and displays tactical information based on that location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned visual display unit is The system collects player health data and displays advice tailored to their physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned voice advice unit, The system estimates the player's emotions and adjusts the tone and content of the voice advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned voice advice unit, Analyze the player's past audio feedback history to select the most suitable advice method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned voice advice unit, Different voice advice algorithms are applied depending on the player's playing style. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned voice advice unit, The system estimates the player's emotions and adjusts the frequency of voice advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned voice advice unit, The system acquires the player's location information in real time and provides voice advice tailored to their location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned voice advice unit, The system collects player health data and provides voice advice tailored to their physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aerial photography unit is, The system estimates the emotions of the athletes and adjusts the timing and angle of aerial photography based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aerial photography unit is, Optimize the drone's flight patterns to capture crucial moments of the match without missing a beat. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aerial photography unit is, Automatically adjusts drone camera settings to maintain optimal image quality. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aerial photography unit is, The system estimates the emotions of the athletes and determines the priority of aerial photography based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aerial photography unit is, The drone's flight path can be changed in real time to provide the optimal viewpoint according to the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aerial photography unit is, Plan an efficient flight schedule, taking into account the drone's battery level. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, The system estimates the players' emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, Referencing the player's past performance data, we apply individual analysis algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, The analysis algorithm is optimized in real time according to the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, The system estimates the players' emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, Depending on the situation of the match, different analytical methods will be used in combination. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, Provides an interface for visually displaying analysis results in an easy-to-understand manner. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned leadership, The system estimates the players' emotions and adjusts the coaching content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned leadership, Refer to the player's past coaching history to select the most suitable coaching method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned leadership, Apply different coaching algorithms depending on the player's playing style. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned leadership, The system estimates the players' emotions and adjusts the frequency of coaching based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned leadership, The coaching content is changed in real time, providing optimal guidance according to the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned leadership, Provides an interface to display instructional content in a visually easy-to-understand manner. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. A visual display unit that displays game data visually, A voice advice unit provides voice advice based on the data displayed by the visual display unit, The aerial photography team uses drones to get an overview of the match, An analysis unit analyzes the data acquired by the aerial photography unit, The system includes a coaching unit that provides coaching suitable for the player based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned voice advice unit, Based on the instructions displayed by the aforementioned visual display unit, voice advice is provided to the player. The system according to feature 1.

3. The aforementioned analysis unit, It analyzes the players' positions and movements in real time and provides instructions for optimal positioning and movement. The system according to feature 1.

4. The aforementioned leadership, Analyze the movement patterns of the players and point out areas for improvement and areas that need strengthening. The system according to feature 1.

5. The aforementioned leadership, We provide regular, personalized coaching to support the long-term skill development of our players. The system according to feature 1.

6. The aerial photography unit is, Using drones to grasp the overall picture of the match and analyze the situation from a tactical perspective. The system according to feature 1.

7. The aforementioned visual display unit is The system estimates the players' emotions and adjusts the content and timing of the data displayed based on those estimated emotions. The system according to feature 1.

8. The aforementioned visual display unit is By using eye-tracking data from the players, important information is optimally placed within the players' field of vision. The system according to feature 1.

9. The aforementioned visual display unit is The system uses past player data to highlight individual weaknesses and strengths. The system according to feature 1.

10. The aforementioned visual display unit is The system estimates the players' emotions and determines the priority of the data to display based on the estimated emotions. The system according to feature 1.

Images